BEGIN:VCALENDAR VERSION:2.0 PRODID:talks.ox.ac.uk BEGIN:VEVENT SUMMARY:POSTPONED: Distribution-Free Nonparametric Inference Based on Opti mal Transport and Kernel Methods - Professor Bhaswar B. Bhattacharya (The Wharton School\, University of Pennsylvania) DTSTART;VALUE=DATE-TIME:20260305T153000Z DTEND;VALUE=DATE-TIME:20260305T163000Z UID:https://talks.ox.ac.uk/talks/id/10f16552-474b-4f42-92cc-510c53bfcc63/ DESCRIPTION:The Wilcoxon rank-sum (or Mann–Whitney) test is one of the m ost widely used tools for comparing two groups without making assumptions about the underlying data distribution. One of the reasons for its endurin g popularity is a remarkable result of Hodges and Lehmann (1956)\, which s hows that the asymptotic relative efficiency of Wilcoxon's test with respe ct to Student's t-test\, under location alternatives\, never falls below 0 .864\, despite the former being distribution-free in finite samples. Even more striking is the result of Chernoff and Savage (1958)\, which shows th at the efficiency of a Gaussian score transformed Wilcoxon's test\, agains t the t-test\, is lower bounded by 1. In other words\, the Gaussian score transformed Wilcoxon test uniformly dominates the t-test in terms of effic iency\, while also remaining distribution-free.\n\nIn this talk we will di scuss multivariate versions of these celebrated results\, by considering d istribution-free analogues of the Hotelling T²-test based on optimal tran sport. The proposed tests are consistent against a general class of altern atives and satisfy Hodges-Lehmann and Chernoff-Savage-type efficiency lowe r bounds over various natural families of multivariate distributions\, des pite being entirely agnostic to the underlying data generating mechanism. We will also discuss how optimal-transport-based multivariate ranks can be used to construct distribution-free analogues of the celebrated kernel tw o-sample test that enjoy a trifecta of desirable properties: universal con sistency\, efficient computation\, and nontrivial asymptotic efficiency. \ nSpeakers:\nProfessor Bhaswar B. Bhattacharya (The Wharton School\, Univer sity of Pennsylvania) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/10f16552-474b-4f42-92cc-510c53bfcc63/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:POSTPONED: Distribution-Free Nonparametric Inference Base d on Optimal Transport and Kernel Methods - Professor Bhaswar B. Bhattach arya (The Wharton School\, University of Pennsylvania) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:POSTPONED: Distribution-Free Nonparametric Inference Based on Opti mal Transport and Kernel Methods - Professor Bhaswar B. Bhattacharya (The Wharton School\, University of Pennsylvania) DTSTART;VALUE=DATE-TIME:20260305T153000Z DTEND;VALUE=DATE-TIME:20260305T163000Z UID:https://talks.ox.ac.uk/talks/id/10f16552-474b-4f42-92cc-510c53bfcc63/ DESCRIPTION:The Wilcoxon rank-sum (or Mann–Whitney) test is one of the m ost widely used tools for comparing two groups without making assumptions about the underlying data distribution. One of the reasons for its endurin g popularity is a remarkable result of Hodges and Lehmann (1956)\, which s hows that the asymptotic relative efficiency of Wilcoxon's test with respe ct to Student's t-test\, under location alternatives\, never falls below 0 .864\, despite the former being distribution-free in finite samples. Even more striking is the result of Chernoff and Savage (1958)\, which shows th at the efficiency of a Gaussian score transformed Wilcoxon's test\, agains t the t-test\, is lower bounded by 1. In other words\, the Gaussian score transformed Wilcoxon test uniformly dominates the t-test in terms of effic iency\, while also remaining distribution-free.\n\nIn this talk we will di scuss multivariate versions of these celebrated results\, by considering d istribution-free analogues of the Hotelling T²-test based on optimal tran sport. The proposed tests are consistent against a general class of altern atives and satisfy Hodges-Lehmann and Chernoff-Savage-type efficiency lowe r bounds over various natural families of multivariate distributions\, des pite being entirely agnostic to the underlying data generating mechanism. We will also discuss how optimal-transport-based multivariate ranks can be used to construct distribution-free analogues of the celebrated kernel tw o-sample test that enjoy a trifecta of desirable properties: universal con sistency\, efficient computation\, and nontrivial asymptotic efficiency. \ nSpeakers:\nProfessor Bhaswar B. Bhattacharya (The Wharton School\, Univer sity of Pennsylvania) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/10f16552-474b-4f42-92cc-510c53bfcc63/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:POSTPONED: Distribution-Free Nonparametric Inference Base d on Optimal Transport and Kernel Methods - Professor Bhaswar B. Bhattach arya (The Wharton School\, University of Pennsylvania) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:A Collectivist\, Economic Perspective on AI - Professor Michael I . Jordan (Inria Paris and University of California\, Berkeley) DTSTART;VALUE=DATE-TIME:20260129T153000Z DTEND;VALUE=DATE-TIME:20260129T163000Z UID:https://talks.ox.ac.uk/talks/id/9624b8ec-608c-4444-bbd0-9a02abd1842d/ DESCRIPTION:Information technology is in the midst of a revolution in whic h omnipresent data collection and machine learning are impacting the human world as never before. The word "intelligence" is being used as a North Star for the development of this technology\, with human cognition viewed as a baseline. This view neglects the fact that humans are social animals \, and that much of our intelligence is social and cultural in origin. Th us\, a broader framing is to consider the system level\, where the agents in the system\, be they computers or humans\, are active\, they are cooper ative\, and they wish to obtain value from their participation in learning -based systems. Agents may supply data and other resources to the system o nly if it is in their interest to do so\, and they may be honest and coope rative only if it is in their interest to do so. Critically\, intelligence inheres as much in the overall system as it does in individual agents. T his is a perspective that is familiar in economics\, although without the focus on learning algorithms. A key challenge is thus to bring (micro)eco nomic concepts into contact with foundational issues in the computing and statistical sciences. I'll discuss some concrete examples of problems and solutions at this tripartite interface.\nSpeakers:\nProfessor Michael I. Jordan (Inria Paris and University of California\, Berkeley) LOCATION:Large Lecture Theatre\, Department of Statistics TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/9624b8ec-608c-4444-bbd0-9a02abd1842d/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:A Collectivist\, Economic Perspective on AI - Professor Michael I. Jordan (Inria Paris and University of California\, Berkeley) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:A Collectivist\, Economic Perspective on AI - Professor Michael I . Jordan (Inria Paris and University of California\, Berkeley) DTSTART;VALUE=DATE-TIME:20260129T153000Z DTEND;VALUE=DATE-TIME:20260129T163000Z UID:https://talks.ox.ac.uk/talks/id/9624b8ec-608c-4444-bbd0-9a02abd1842d/ DESCRIPTION:Information technology is in the midst of a revolution in whic h omnipresent data collection and machine learning are impacting the human world as never before. The word "intelligence" is being used as a North Star for the development of this technology\, with human cognition viewed as a baseline. This view neglects the fact that humans are social animals \, and that much of our intelligence is social and cultural in origin. Th us\, a broader framing is to consider the system level\, where the agents in the system\, be they computers or humans\, are active\, they are cooper ative\, and they wish to obtain value from their participation in learning -based systems. Agents may supply data and other resources to the system o nly if it is in their interest to do so\, and they may be honest and coope rative only if it is in their interest to do so. Critically\, intelligence inheres as much in the overall system as it does in individual agents. T his is a perspective that is familiar in economics\, although without the focus on learning algorithms. A key challenge is thus to bring (micro)eco nomic concepts into contact with foundational issues in the computing and statistical sciences. I'll discuss some concrete examples of problems and solutions at this tripartite interface.\nSpeakers:\nProfessor Michael I. Jordan (Inria Paris and University of California\, Berkeley) LOCATION:Large Lecture Theatre\, Department of Statistics TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/9624b8ec-608c-4444-bbd0-9a02abd1842d/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:A Collectivist\, Economic Perspective on AI - Professor Michael I. Jordan (Inria Paris and University of California\, Berkeley) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Environmental consequences of an ascendant aerospace sector - Prof essor Sebastian Eastham (Imperial College London) DTSTART;VALUE=DATE-TIME:20251029T153000Z DTEND;VALUE=DATE-TIME:20251029T163000Z UID:https://talks.ox.ac.uk/talks/id/7da1d987-9dcb-41da-a3ab-e91a2436e866/ DESCRIPTION:The aerospace sector provides unique and irreplaceable service s but produces outsized environmental impacts which extend far beyond the carbon released by burning jet fuel. As the industry continues to grow exp onentially\, a robust and nuanced understanding of its full climate and ai r quality footprint is essential. This lecture will explore the challengin g methodological problems inherent in quantifying these impacts\, what we can do to improve our understanding\, and possible near-term strategies to achieve clean aviation. \n\nThrough a combination of atmospheric simula tion and analysis of observational data\, we will delve into four specific areas: using deep learning to enable automated detection of condensation trails (contrails)\; the challenge of predicting and addressing contrail c limate impacts\; the global air quality burden of cruise-altitude emission s\; and the nascent field of launch vehicle emissions. These are not just scientific challenges\, but opportunities for rigorous analysis to advance our understanding of the sector's overall environmental footprint.\n\nThi s talk will also discuss how these insights can be leveraged to inform ind ividual choices\, empowering people to reduce their personal environmental footprints\, while also highlighting the coordinated actions required to drive meaningful change across the entire sector. The goal is to show that while this is a profoundly challenging problem\, it is one where scientif ic rigor and innovation are making real progress.\n\nBio: Sebastian Eastha m is the Associate Professor of Sustainable Aviation at Imperial College L ondon\, and a member of the Brahmal Vasudevan Institute for Sustainable Av iation. His research focuses on developing and applying quantitative model s to better understand the environmental effects of the aerospace sector a nd to evaluate strategies for a more sustainable future. With a background in atmospheric science and engineering\, his work provides insights into the complexities of the sector's total environmental footprint\, going bey ond simple carbon emissions to include factors such as contrails and nitro gen oxides. Prior to joining Imperial in 2024 he was Principal Research Sc ientist in MIT's Center for Global Change Science\, and Associate Director of the MIT Laboratory for Aviation and the Environment.\nSpeakers:\nProfe ssor Sebastian Eastham (Imperial College London) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/7da1d987-9dcb-41da-a3ab-e91a2436e866/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Environmental consequences of an ascendant aerospace sect or - Professor Sebastian Eastham (Imperial College London) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Why do polygenic scores translate poorly across human ancestries? Identifying the impacts of gene-gene interactions in humans - Professor Si mon Myers (University of Oxford) DTSTART;VALUE=DATE-TIME:20250916T093000 DTEND;VALUE=DATE-TIME:20250916T103000 UID:https://talks.ox.ac.uk/talks/id/3375b792-f263-4421-8284-b2b87b976608/ DESCRIPTION:For our next talk\, in the BDI/CHG (gen)omics Seminar series\, we will be hearing from Professor Simon Myers\, Professor of Mathematical Genomics\, Department of Statistics\, University of Oxford. We’re delig hted to host Simon in what promises to be a great talk!\n\nTalk title: Why do polygenic scores translate poorly across human ancestries? Identifying the impacts of gene-gene interactions in humans\nDate: Tuesday 16 Septemb er\nTime: 9:30 – 10:30 am\nLocation: BDI/OxPop Seminar room 0\n——— ———————————————————————— —————————————\nAll members of the University are welcome to join\, please let reception at BDI know you’re here for the seminar and sign-in. We hope you can join us!\n\nAs a reminder\, the (gen) omics seminar series runs every other Tuesday morning and is intended to i ncrease interaction between individuals working in genomics across Oxford. We encourage in-person attendance where possible. There is time for discu ssion over\, tea\, coffee and pastries after the talks.\n\nHybrid Option:\ nPlease note that these meetings are closed meetings and only open to memb ers of the University of Oxford to encourage sharing of new and unpublishe d data. Please respect our speakers and do not share the link with anyone outside of the university.\n\nTeams Link - \nMeeting ID: 392 824 466 617 2 \nPasscode: FZ9uq2ig \n\nSpeakers:\nProfessor Simon Myers (University of Oxford) LOCATION:Big Data Institute (Seminar room 0 )\, Old Road Campus OX3 7LF TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/3375b792-f263-4421-8284-b2b87b976608/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Why do polygenic scores translate poorly across human anc estries? Identifying the impacts of gene-gene interactions in humans - Pro fessor Simon Myers (University of Oxford) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Gradient-free stochastic optimization - Professor Alexandre Tsyba kov (CREST-ENSAE Paris) DTSTART;VALUE=DATE-TIME:20250606T150000 DTEND;VALUE=DATE-TIME:20250606T160000 UID:https://talks.ox.ac.uk/talks/id/d99d6716-143d-417a-b843-e703df35b129/ DESCRIPTION:This talk will deal with optimization problems in a statistica l learning setup where the learner has no access to unbiased estimators of the gradient of the objective function. It includes stochastic optimizati on with zero-order oracle\, continuum bandit and contextual continuum ban dit problems. I’ll give an overview of recent results on minimax optimal algorithms and fundamental limits for these problems. \nSpeakers:\nProfes sor Alexandre Tsybakov (CREST-ENSAE Paris) LOCATION:Mathematical Institute (L1\, Mathematical Institute\, Andrew Wile s Building)\, Woodstock Road OX2 6GG TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/d99d6716-143d-417a-b843-e703df35b129/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Gradient-free stochastic optimization - Professor Alexan dre Tsybakov (CREST-ENSAE Paris) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Gradient-free stochastic optimization - Professor Alexandre Tsyba kov (CREST-ENSAE Paris) DTSTART;VALUE=DATE-TIME:20250606T150000 DTEND;VALUE=DATE-TIME:20250606T160000 UID:https://talks.ox.ac.uk/talks/id/d99d6716-143d-417a-b843-e703df35b129/ DESCRIPTION:This talk will deal with optimization problems in a statistica l learning setup where the learner has no access to unbiased estimators of the gradient of the objective function. It includes stochastic optimizati on with zero-order oracle\, continuum bandit and contextual continuum ban dit problems. I’ll give an overview of recent results on minimax optimal algorithms and fundamental limits for these problems. \nSpeakers:\nProfes sor Alexandre Tsybakov (CREST-ENSAE Paris) LOCATION:Mathematical Institute (L1\, Mathematical Institute\, Andrew Wile s Building)\, Woodstock Road OX2 6GG TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/d99d6716-143d-417a-b843-e703df35b129/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Gradient-free stochastic optimization - Professor Alexan dre Tsybakov (CREST-ENSAE Paris) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Bayesian Probabilistic Subnational Population Projections - Profes sor Adrian Raftery (University of Washington\, Seattle) DTSTART;VALUE=DATE-TIME:20250616T153000 DTEND;VALUE=DATE-TIME:20250616T163000 UID:https://talks.ox.ac.uk/talks/id/dbab5507-5784-471d-9801-e8ff8b513e86/ DESCRIPTION:Population projections have until recently usually been done d eterministically using the cohort-component method\, yielding a single val ue for each projected future population quantity of interest. Starting in 2015\, the United Nations Population Division changed their approach\, ins tead adopted a fully statistical Bayesian probabilistic approach to projec t fertility\, mortality and population for all countries\, using methods d eveloped by our group. In 2024\, for the first time\, uncertainty about ne t international migration was also included. \n\nIn this approach\, the to tal fertility rate\, female and male life expectancies at birth\, and the net migration rate are projected using Bayesian hierarchical models estima ted via Markov chain Monte Carlo. These are then combined with a cohort-co mponent model\, yielding probabilistic projections for any future quantity of interest. The methodology is implemented in the bayesPop R package\, w hich has been used by the UN to produce the World Population Prospects sin ce 2015. We have recently extended the method to subnational population pr ojections. I will describe the method and some recent extensions\, and ill ustrate it with subnational demographic data from several countries. \nSpe akers:\nProfessor Adrian Raftery (University of Washington\, Seattle) LOCATION:Department of Earth Sciences (Seminar Room\, Department of Earth Sciences)\, South Parks Road OX1 3AN TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/dbab5507-5784-471d-9801-e8ff8b513e86/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Bayesian Probabilistic Subnational Population Projections - Professor Adrian Raftery (University of Washington\, Seattle) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Bayesian Probabilistic Subnational Population Projections - Profes sor Adrian Raftery (University of Washington\, Seattle) DTSTART;VALUE=DATE-TIME:20250616T153000 DTEND;VALUE=DATE-TIME:20250616T163000 UID:https://talks.ox.ac.uk/talks/id/dbab5507-5784-471d-9801-e8ff8b513e86/ DESCRIPTION:Population projections have until recently usually been done d eterministically using the cohort-component method\, yielding a single val ue for each projected future population quantity of interest. Starting in 2015\, the United Nations Population Division changed their approach\, ins tead adopted a fully statistical Bayesian probabilistic approach to projec t fertility\, mortality and population for all countries\, using methods d eveloped by our group. In 2024\, for the first time\, uncertainty about ne t international migration was also included. \n\nIn this approach\, the to tal fertility rate\, female and male life expectancies at birth\, and the net migration rate are projected using Bayesian hierarchical models estima ted via Markov chain Monte Carlo. These are then combined with a cohort-co mponent model\, yielding probabilistic projections for any future quantity of interest. The methodology is implemented in the bayesPop R package\, w hich has been used by the UN to produce the World Population Prospects sin ce 2015. We have recently extended the method to subnational population pr ojections. I will describe the method and some recent extensions\, and ill ustrate it with subnational demographic data from several countries. \nSpe akers:\nProfessor Adrian Raftery (University of Washington\, Seattle) LOCATION:Department of Earth Sciences (Seminar Room\, Department of Earth Sciences)\, South Parks Road OX1 3AN TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/dbab5507-5784-471d-9801-e8ff8b513e86/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Bayesian Probabilistic Subnational Population Projections - Professor Adrian Raftery (University of Washington\, Seattle) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Differentially private M-estimation via noisy optimization - Profe ssor Po-Ling Loh (University of Cambridge) DTSTART;VALUE=DATE-TIME:20250529T151500 DTEND;VALUE=DATE-TIME:20250529T163000 UID:https://talks.ox.ac.uk/talks/id/92f239ab-fced-44e0-ba81-f915ad50733c/ DESCRIPTION:We present a noisy composite gradient descent algorithm for di fferentially private statistical estimation in high dimensions. We begin b y providing general rates of convergence for the parameter error of succes sive iterates under assumptions of local restricted strong convexity and l ocal restricted smoothness. Our analysis is local\, in that it ensures a l inear rate of convergence when the initial iterate lies within a constant- radius region of the true parameter. At each iterate\, multivariate Gaussi an noise is added to the gradient in order to guarantee that the output sa tisfies Gaussian differential privacy. We then derive consequences of our theory for linear regression and mean estimation. Motivated by M-estimator s used in robust statistics\, we study loss functions which downweight the contribution of individual data points in such a way that the sensitivity of function gradients is guaranteed to be bounded\, even without the usua l assumption that our data lie in a bounded domain. We prove that the obje ctive functions thus obtained indeed satisfy the restricted convexity and restricted smoothness conditions required for our general theory. We will also discuss the benefits of acceleration in optimization procedures\, spe cifically a private version of the Frank-Wolfe algorithm\, and its consequ ences for statistical estimation.\n\nThis is based on joint work with Marc o Avella-Medina\, Casey Bradshaw\, Zheng Liu\, and Laurentiu Marchis.\nSpe akers:\nProfessor Po-Ling Loh (University of Cambridge) LOCATION:Mathematical Institute (L3\, Andrew Wiles Building)\, Woodstock R oad OX2 6GG TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/92f239ab-fced-44e0-ba81-f915ad50733c/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Differentially private M-estimation via noisy optimizatio n - Professor Po-Ling Loh (University of Cambridge) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Differentially private M-estimation via noisy optimization - Profe ssor Po-Ling Loh (University of Cambridge) DTSTART;VALUE=DATE-TIME:20250529T151500 DTEND;VALUE=DATE-TIME:20250529T163000 UID:https://talks.ox.ac.uk/talks/id/92f239ab-fced-44e0-ba81-f915ad50733c/ DESCRIPTION:We present a noisy composite gradient descent algorithm for di fferentially private statistical estimation in high dimensions. We begin b y providing general rates of convergence for the parameter error of succes sive iterates under assumptions of local restricted strong convexity and l ocal restricted smoothness. Our analysis is local\, in that it ensures a l inear rate of convergence when the initial iterate lies within a constant- radius region of the true parameter. At each iterate\, multivariate Gaussi an noise is added to the gradient in order to guarantee that the output sa tisfies Gaussian differential privacy. We then derive consequences of our theory for linear regression and mean estimation. Motivated by M-estimator s used in robust statistics\, we study loss functions which downweight the contribution of individual data points in such a way that the sensitivity of function gradients is guaranteed to be bounded\, even without the usua l assumption that our data lie in a bounded domain. We prove that the obje ctive functions thus obtained indeed satisfy the restricted convexity and restricted smoothness conditions required for our general theory. We will also discuss the benefits of acceleration in optimization procedures\, spe cifically a private version of the Frank-Wolfe algorithm\, and its consequ ences for statistical estimation.\n\nThis is based on joint work with Marc o Avella-Medina\, Casey Bradshaw\, Zheng Liu\, and Laurentiu Marchis.\nSpe akers:\nProfessor Po-Ling Loh (University of Cambridge) LOCATION:Mathematical Institute (L3\, Andrew Wiles Building)\, Woodstock R oad OX2 6GG TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/92f239ab-fced-44e0-ba81-f915ad50733c/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Differentially private M-estimation via noisy optimizatio n - Professor Po-Ling Loh (University of Cambridge) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Does AI help humans make better decisions? A statistical evaluatio n framework for experimental and observational studies. - Professor Kosuke Imai (Harvard University) DTSTART;VALUE=DATE-TIME:20250313T160000Z DTEND;VALUE=DATE-TIME:20250313T170000Z UID:https://talks.ox.ac.uk/talks/id/8a6ea51c-ec41-464e-a7b6-d9964ec5bde5/ DESCRIPTION:The use of Artificial Intelligence (AI)\, or more generally da ta-driven algorithms\, has become ubiquitous in today's society. Yet\, in many cases\, and especially when stakes are high\, humans still make final decisions. The critical question\, therefore\, is whether AI helps humans make better decisions compared to a human-alone or AI-alone system. We in troduce a new methodological framework to empirically answer this question with a minimal set of assumptions. We measure a decision maker's ability to make correct decisions using standard classification metrics based on t he baseline potential outcome. We consider a single-blinded and unconfound ed treatment assignment\, where the provision of AI-generated recommendati ons is assumed to be randomized across cases with humans making final deci sions. Under this study design\, we show how to compare the performance of three alternative decision-making systems --- human-alone\, human-with-AI \, and AI-alone. Importantly\, the AI-alone system includes any individual ized treatment assignment\, including those not used in the original study . We also show when AI recommendations should be provided to a human-decis ion maker\, and when one should follow such recommendations. We apply the proposed methodology to our own randomized controlled trial evaluating a p retrial risk assessment instrument. We find that the risk assessment recom mendations do not improve the classification accuracy of a judge's decisio n to impose cash bail. Furthermore\, we find that replacing a human judge with algorithms --- the risk assessment score and a large language model i n particular --- leads to a worse classification performance.\nSpeakers:\n Professor Kosuke Imai (Harvard University) LOCATION:Seminar Room\, Department of Earth Sciences TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/8a6ea51c-ec41-464e-a7b6-d9964ec5bde5/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Does AI help humans make better decisions? A statistical evaluation framework for experimental and observational studies. - Profess or Kosuke Imai (Harvard University) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Does AI help humans make better decisions? A statistical evaluatio n framework for experimental and observational studies. - Professor Kosuke Imai (Harvard University) DTSTART;VALUE=DATE-TIME:20250313T160000Z DTEND;VALUE=DATE-TIME:20250313T170000Z UID:https://talks.ox.ac.uk/talks/id/8a6ea51c-ec41-464e-a7b6-d9964ec5bde5/ DESCRIPTION:The use of Artificial Intelligence (AI)\, or more generally da ta-driven algorithms\, has become ubiquitous in today's society. Yet\, in many cases\, and especially when stakes are high\, humans still make final decisions. The critical question\, therefore\, is whether AI helps humans make better decisions compared to a human-alone or AI-alone system. We in troduce a new methodological framework to empirically answer this question with a minimal set of assumptions. We measure a decision maker's ability to make correct decisions using standard classification metrics based on t he baseline potential outcome. We consider a single-blinded and unconfound ed treatment assignment\, where the provision of AI-generated recommendati ons is assumed to be randomized across cases with humans making final deci sions. Under this study design\, we show how to compare the performance of three alternative decision-making systems --- human-alone\, human-with-AI \, and AI-alone. Importantly\, the AI-alone system includes any individual ized treatment assignment\, including those not used in the original study . We also show when AI recommendations should be provided to a human-decis ion maker\, and when one should follow such recommendations. We apply the proposed methodology to our own randomized controlled trial evaluating a p retrial risk assessment instrument. We find that the risk assessment recom mendations do not improve the classification accuracy of a judge's decisio n to impose cash bail. Furthermore\, we find that replacing a human judge with algorithms --- the risk assessment score and a large language model i n particular --- leads to a worse classification performance.\nSpeakers:\n Professor Kosuke Imai (Harvard University) LOCATION:Seminar Room\, Department of Earth Sciences TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/8a6ea51c-ec41-464e-a7b6-d9964ec5bde5/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Does AI help humans make better decisions? A statistical evaluation framework for experimental and observational studies. - Profess or Kosuke Imai (Harvard University) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:E-Values\, Anytime-Validity and Bayes - Professor Peter Grunwald ( Leiden University) DTSTART;VALUE=DATE-TIME:20250220T160000Z DTEND;VALUE=DATE-TIME:20250220T170000Z UID:https://talks.ox.ac.uk/talks/id/b2ad754a-3bf7-415d-b39e-864d382ddf55/ DESCRIPTION:E-values (wikipedia) are an alternative to p-values that effor tlessly deal with optional continuation: with e-value based tests and the corresponding anytime valid (AV) confidence intervals\, one can always gat her additional data\, while keeping statistically valid conclusions. Until 2019\, publications on e-values were few and far between: the concept did not even have a name. Then\, in the course of a few months\, four papers by different research groups\, (including ours - see below) appeared on ar Xiv that firmly established them as an important statistical concept. By n ow\, there are 100s of papers on e-values and there have been two internat ional workshops on the topic. Allowing for optional continuation is just o ne way in which e-values provide more flexibility than p-values – they also allow to set a type of significance/confidence level alpha after see ing the data\, which is a mortal sin in classical testing. In this talk I will introduce e-values\, e-processes and AV confidence intervals\, and di scuss how like Bayesian approaches\, they employ priors\, while\, unlike i n Bayesian approaches\, we obtain error guarantees even if these priors mi salign with the data. \n \nMain literature: \nG.\, De Heide\, Koolen. Safe Testing. Journal of the Royal Statistical Society Series B\, 2024 (first version appeared on arXiv 2019).\nG. Beyond Neyman-Pearson: e-values enab le hypothesis testing with a data-driven alpha. Proceedings National Acade my of Sciences of the USA (PNAS)\, 2024.\n\nSpeakers:\nProfessor Peter Gru nwald (Leiden University) LOCATION:Seminar Room\, Department of Earth Sciences TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/b2ad754a-3bf7-415d-b39e-864d382ddf55/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:E-Values\, Anytime-Validity and Bayes - Professor Peter G runwald (Leiden University) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:The trajectories of complex disease - Professor Xavier Didelot (Un iversity of Warwick) DTSTART;VALUE=DATE-TIME:20250206T160000Z DTEND;VALUE=DATE-TIME:20250206T170000Z UID:https://talks.ox.ac.uk/talks/id/bc350677-d539-4c2d-b7ac-48f630eeb2fd/ DESCRIPTION:Genomic data are increasingly being used to understand infecti ous disease epidemiology. Isolates from a given outbreak are sequenced\, a nd the patterns of shared variation are used to infer phylogenetic trees. However these are not directly informative about who infected whom: a phyl ogenetic tree is not a transmission tree. A transmission tree can be infer red from a phylogeny while accounting for within-host genetic diversity by coloring the branches of a phylogeny according to which host those branch es were in. We show how this approach can be applied to partially sampled and ongoing outbreaks. This requires computing the correct probability of a partially observed transmission tree and we demonstrate how to do this f or a large class of epidemiological models. The resulting uncertainty on w ho infected whom can be high and we explore two solutions to this problem: the use of several genomes per host\, and the use of additional epidemiol ogical data.\nSpeakers:\nProfessor Xavier Didelot (University of Warwick) LOCATION:Seminar Room\, Department of Earth Sciences TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/bc350677-d539-4c2d-b7ac-48f630eeb2fd/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:The trajectories of complex disease - Professor Xavier Di delot (University of Warwick) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:The trajectories of complex disease - Professor Xavier Didelot (Un iversity of Warwick) DTSTART;VALUE=DATE-TIME:20250206T160000Z DTEND;VALUE=DATE-TIME:20250206T170000Z UID:https://talks.ox.ac.uk/talks/id/bc350677-d539-4c2d-b7ac-48f630eeb2fd/ DESCRIPTION:Genomic data are increasingly being used to understand infecti ous disease epidemiology. Isolates from a given outbreak are sequenced\, a nd the patterns of shared variation are used to infer phylogenetic trees. However these are not directly informative about who infected whom: a phyl ogenetic tree is not a transmission tree. A transmission tree can be infer red from a phylogeny while accounting for within-host genetic diversity by coloring the branches of a phylogeny according to which host those branch es were in. We show how this approach can be applied to partially sampled and ongoing outbreaks. This requires computing the correct probability of a partially observed transmission tree and we demonstrate how to do this f or a large class of epidemiological models. The resulting uncertainty on w ho infected whom can be high and we explore two solutions to this problem: the use of several genomes per host\, and the use of additional epidemiol ogical data.\nSpeakers:\nProfessor Xavier Didelot (University of Warwick) LOCATION:Seminar Room\, Department of Earth Sciences TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/bc350677-d539-4c2d-b7ac-48f630eeb2fd/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:The trajectories of complex disease - Professor Xavier Di delot (University of Warwick) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:The trajectories of complex disease - Professor Gil McVean (The El lison Institute of Technology\, Oxford) DTSTART;VALUE=DATE-TIME:20241024T153000 DTEND;VALUE=DATE-TIME:20241024T163000 UID:https://talks.ox.ac.uk/talks/id/2079adab-7381-434a-8432-5d8d056c6012/ DESCRIPTION:The analysis of longitudinal data from electronic health recor ds (EHRs) has the potential to improve clinical diagnosis and enable perso nalised medicine\, motivating efforts to identify disease commonalities an d subtypes from patient comorbidity information and other modalities. We h ave developed an age-dependent topic-modelling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR datasets and applied it to c. 300\,000 individuals f rom UK Biobank and >200\,000 individuals from the All of Us program. A sur prisingly small number of disease trajectories capture known and novel com binations of disorders that occur throughout life and identify disease sub types that occur in multiple topics\, with differential genetic risk profi les. Such stratification improves understanding of patient risk and hetero geneity\, leading to better identification of genetic risk\, characterisat ion of pathological pathways and the discovery of new therapeutic targets. \nSpeakers:\nProfessor Gil McVean (The Ellison Institute of Technology\, O xford) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/2079adab-7381-434a-8432-5d8d056c6012/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:The trajectories of complex disease - Professor Gil McVea n (The Ellison Institute of Technology\, Oxford) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:The trajectories of complex disease - Professor Gil McVean (The El lison Institute of Technology\, Oxford) DTSTART;VALUE=DATE-TIME:20241024T153000 DTEND;VALUE=DATE-TIME:20241024T163000 UID:https://talks.ox.ac.uk/talks/id/2079adab-7381-434a-8432-5d8d056c6012/ DESCRIPTION:The analysis of longitudinal data from electronic health recor ds (EHRs) has the potential to improve clinical diagnosis and enable perso nalised medicine\, motivating efforts to identify disease commonalities an d subtypes from patient comorbidity information and other modalities. We h ave developed an age-dependent topic-modelling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR datasets and applied it to c. 300\,000 individuals f rom UK Biobank and >200\,000 individuals from the All of Us program. A sur prisingly small number of disease trajectories capture known and novel com binations of disorders that occur throughout life and identify disease sub types that occur in multiple topics\, with differential genetic risk profi les. Such stratification improves understanding of patient risk and hetero geneity\, leading to better identification of genetic risk\, characterisat ion of pathological pathways and the discovery of new therapeutic targets. \nSpeakers:\nProfessor Gil McVean (The Ellison Institute of Technology\, O xford) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/2079adab-7381-434a-8432-5d8d056c6012/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:The trajectories of complex disease - Professor Gil McVea n (The Ellison Institute of Technology\, Oxford) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Approximate gradients for inference of partially-observed stochast ic processes - Professor Marc Suchard (David Geffen School of Medicine and Department of Biostatistics at UCLA) DTSTART;VALUE=DATE-TIME:20240507T153000 DTEND;VALUE=DATE-TIME:20240507T163000 UID:https://talks.ox.ac.uk/talks/id/cfef9751-30c0-4c9f-ad30-56ae6298180c/ DESCRIPTION:Bayesian computation remains onerous at scale for inference un der many discrete-valued stochastic process-based models\, while these mod els remain ubiquitous across biology and public health. In this talk\, we will explore how one can construct computationally efficient approximatio ns to the gradient of the data likelihood under continuous-time Markov cha in (CTMC) models with respect to their high-dimensional parameters. CTMCs underpin the most popular models for learning about how rapidly evolving pathogens change over time and space to give rise to human infection\, and the dimensionality of these problems are daunting. With these approximati ons in hand\, a new variant of Hamiltonian Monte Carlo (HMC) becomes tract able to explore the parameter posterior\, and we bound the approximation e rror using several small tricks from matrix analysis. This new sampling a pproach enables the introduction of a novel random-effects CTMC model that captures biological realism previously missing. Applied to the analysis of early SARS-CoV-2 genomes\, the random-effects remove bias in inference of the location and timing of the pathogen's split-over into humans\, whil e the approximate-gradient-based machinery is over an order of magnitude m ore time efficient than conventional sampling approaches.\nSpeakers:\nProf essor Marc Suchard (David Geffen School of Medicine and Department of Bios tatistics at UCLA) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/cfef9751-30c0-4c9f-ad30-56ae6298180c/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Approximate gradients for inference of partially-observed stochastic processes - Professor Marc Suchard (David Geffen School of Med icine and Department of Biostatistics at UCLA) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Approximate gradients for inference of partially-observed stochast ic processes - Professor Marc Suchard (David Geffen School of Medicine and Department of Biostatistics at UCLA) DTSTART;VALUE=DATE-TIME:20240507T153000 DTEND;VALUE=DATE-TIME:20240507T163000 UID:https://talks.ox.ac.uk/talks/id/cfef9751-30c0-4c9f-ad30-56ae6298180c/ DESCRIPTION:Bayesian computation remains onerous at scale for inference un der many discrete-valued stochastic process-based models\, while these mod els remain ubiquitous across biology and public health. In this talk\, we will explore how one can construct computationally efficient approximatio ns to the gradient of the data likelihood under continuous-time Markov cha in (CTMC) models with respect to their high-dimensional parameters. CTMCs underpin the most popular models for learning about how rapidly evolving pathogens change over time and space to give rise to human infection\, and the dimensionality of these problems are daunting. With these approximati ons in hand\, a new variant of Hamiltonian Monte Carlo (HMC) becomes tract able to explore the parameter posterior\, and we bound the approximation e rror using several small tricks from matrix analysis. This new sampling a pproach enables the introduction of a novel random-effects CTMC model that captures biological realism previously missing. Applied to the analysis of early SARS-CoV-2 genomes\, the random-effects remove bias in inference of the location and timing of the pathogen's split-over into humans\, whil e the approximate-gradient-based machinery is over an order of magnitude m ore time efficient than conventional sampling approaches.\nSpeakers:\nProf essor Marc Suchard (David Geffen School of Medicine and Department of Bios tatistics at UCLA) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/cfef9751-30c0-4c9f-ad30-56ae6298180c/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Approximate gradients for inference of partially-observed stochastic processes - Professor Marc Suchard (David Geffen School of Med icine and Department of Biostatistics at UCLA) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Tackling the hidden costs of computational science: GREENER princi ples for environmentally sustainable research - Dr Loïc Lannelongue (Hear t and Lung Research Institute\, University of Cambridge and the Cambridge- Baker Systems Genomics Initiative) DTSTART;VALUE=DATE-TIME:20240426T153000 DTEND;VALUE=DATE-TIME:20240426T163000 UID:https://talks.ox.ac.uk/talks/id/07d4447f-9324-4380-b700-dafdac60cfbc/ DESCRIPTION:From genetic studies and astrophysics simulations to statistic al modelling and AI\, scientific computing has enabled amazing discoveries and there is no doubt it will continue to do so. However\, the correspond ing environmental impact is a growing concern in light of the urgency of t he climate crisis\, so what can we all do about it? Tackling this issue an d making it easier for scientists to engage with sustainable computing is what motivated the Green Algorithms project. Through the prism of the GREE NER principles for environmentally sustainable science\, we will discuss w hat we learned along the way\, how to estimate the impact of our work and what levers scientists and institutions have to make their research more s ustainable. We will also debate what hurdles exist and what is still neede d moving forward.\nSpeakers:\nDr Loïc Lannelongue (Heart and Lung Researc h Institute\, University of Cambridge and the Cambridge-Baker Systems Geno mics Initiative) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/07d4447f-9324-4380-b700-dafdac60cfbc/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Tackling the hidden costs of computational science: GREEN ER principles for environmentally sustainable research - Dr Loïc Lannelon gue (Heart and Lung Research Institute\, University of Cambridge and the C ambridge-Baker Systems Genomics Initiative) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Tackling the hidden costs of computational science: GREENER princi ples for environmentally sustainable research - Dr Loïc Lannelongue (Hear t and Lung Research Institute\, University of Cambridge and the Cambridge- Baker Systems Genomics Initiative) DTSTART;VALUE=DATE-TIME:20240426T153000 DTEND;VALUE=DATE-TIME:20240426T163000 UID:https://talks.ox.ac.uk/talks/id/07d4447f-9324-4380-b700-dafdac60cfbc/ DESCRIPTION:From genetic studies and astrophysics simulations to statistic al modelling and AI\, scientific computing has enabled amazing discoveries and there is no doubt it will continue to do so. However\, the correspond ing environmental impact is a growing concern in light of the urgency of t he climate crisis\, so what can we all do about it? Tackling this issue an d making it easier for scientists to engage with sustainable computing is what motivated the Green Algorithms project. Through the prism of the GREE NER principles for environmentally sustainable science\, we will discuss w hat we learned along the way\, how to estimate the impact of our work and what levers scientists and institutions have to make their research more s ustainable. We will also debate what hurdles exist and what is still neede d moving forward.\nSpeakers:\nDr Loïc Lannelongue (Heart and Lung Researc h Institute\, University of Cambridge and the Cambridge-Baker Systems Geno mics Initiative) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/07d4447f-9324-4380-b700-dafdac60cfbc/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Tackling the hidden costs of computational science: GREEN ER principles for environmentally sustainable research - Dr Loïc Lannelon gue (Heart and Lung Research Institute\, University of Cambridge and the C ambridge-Baker Systems Genomics Initiative) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Statistics\, nursing\, and social reform: Following in the footste ps of Florence Nightingale - Professor Barbara Engelhardt (Senior Investig ator at Gladstone Institutes and Professor at Stanford University in the D epartment of Biomedical Data Science) DTSTART;VALUE=DATE-TIME:20240223T153000Z DTEND;VALUE=DATE-TIME:20240223T163000Z UID:https://talks.ox.ac.uk/talks/id/fa010ec7-c6f7-46b3-b5af-26f6f05a5968/ DESCRIPTION:Decision-making tasks in healthcare settings use methods that make a number of assumptions that we know are violated in clinical data. F or example\, clinicians do not always act optimally\; clinicians are more or less aggressive in treating patients\; clinicians have biases\; and pat ients have (often unobserved) conditions that lead to differential respons e to interventions. In this talk\, and following in Florence Nightingale's path\, I will walk through a handful of these violated assumptions and di scuss statistical reinforcement learning and inverse reinforcement learnin g methods to address these violated assumptions. I will show on a number o f scenarios\, including sepsis treatment and electrolyte repletion\, that these methods that have more flexible assumptions than existing methods le ad to substantial improvements in decision-making tasks in clinical settin gs\, reducing bias and leading to improved clinical outcomes.\nSpeakers:\n Professor Barbara Engelhardt (Senior Investigator at Gladstone Institutes and Professor at Stanford University in the Department of Biomedical Data Science) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/fa010ec7-c6f7-46b3-b5af-26f6f05a5968/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Statistics\, nursing\, and social reform: Following in th e footsteps of Florence Nightingale - Professor Barbara Engelhardt (Senior Investigator at Gladstone Institutes and Professor at Stanford University in the Department of Biomedical Data Science) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Introducing the Forster-Warmuth Nonparametric Counterfactual Regre ssion - Professor Eric Tchetgen Tchetgen (University of Pennsylvania) DTSTART;VALUE=DATE-TIME:20231026T153000 DTEND;VALUE=DATE-TIME:20231026T163000 UID:https://talks.ox.ac.uk/talks/id/a6d1ed9f-bdf1-4965-b901-1f59e49bb34e/ DESCRIPTION:Series regression estimation is one of the most popular non-pa rametric regression techniques in practice. The most routinely used series estimator is based on ordinary least squares fitting\, which is known to be minimax rate optimal in various settings\, albeit under stringent restr ictions on the basis functions. In this work\, inspired by the recently de veloped Forster-Warmuth (FW) regression\, we propose an alternative nonpar ametric series estimator that can attain minimax estimation rates under st rictly weaker conditions imposed on the basis functions\, than virtually a ll existing series estimators in the literature. Another contribution of t his work generalizes the FW-regression to a so-called counterfactual regre ssion problem\, in which the response variable of interest may not be dire ctly observed (hence\, the name ``counterfactual'') on all sampled units. Although counterfactual regression is not entirely a new area of inquiry\, we propose the first-ever systematic study of this challenging problem fr om a unified pseudo-outcome perspective. In fact\, we provide what appears to be the first generic and constructive approach for generating the pseu do-outcome (to substitute for the unobserved response) which leads to the estimation of the counterfactual regression curve of interest with small b ias\, namely bias of second order. Several applications are used to illust rate the resulting FW counterfactual regression including a large class of nonparametric regression problems in missing data and causal inference li terature\, for which we establish conditions for minimax rate optimality. This is joint work with Yachong Yang and Arun kuchibhotla.\nSpeakers:\nPro fessor Eric Tchetgen Tchetgen (University of Pennsylvania) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/a6d1ed9f-bdf1-4965-b901-1f59e49bb34e/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Introducing the Forster-Warmuth Nonparametric Counterfact ual Regression - Professor Eric Tchetgen Tchetgen (University of Pennsylva nia) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Introducing the Forster-Warmuth Nonparametric Counterfactual Regre ssion - Professor Eric Tchetgen Tchetgen (University of Pennsylvania) DTSTART;VALUE=DATE-TIME:20231026T153000 DTEND;VALUE=DATE-TIME:20231026T163000 UID:https://talks.ox.ac.uk/talks/id/a6d1ed9f-bdf1-4965-b901-1f59e49bb34e/ DESCRIPTION:Series regression estimation is one of the most popular non-pa rametric regression techniques in practice. The most routinely used series estimator is based on ordinary least squares fitting\, which is known to be minimax rate optimal in various settings\, albeit under stringent restr ictions on the basis functions. In this work\, inspired by the recently de veloped Forster-Warmuth (FW) regression\, we propose an alternative nonpar ametric series estimator that can attain minimax estimation rates under st rictly weaker conditions imposed on the basis functions\, than virtually a ll existing series estimators in the literature. Another contribution of t his work generalizes the FW-regression to a so-called counterfactual regre ssion problem\, in which the response variable of interest may not be dire ctly observed (hence\, the name ``counterfactual'') on all sampled units. Although counterfactual regression is not entirely a new area of inquiry\, we propose the first-ever systematic study of this challenging problem fr om a unified pseudo-outcome perspective. In fact\, we provide what appears to be the first generic and constructive approach for generating the pseu do-outcome (to substitute for the unobserved response) which leads to the estimation of the counterfactual regression curve of interest with small b ias\, namely bias of second order. Several applications are used to illust rate the resulting FW counterfactual regression including a large class of nonparametric regression problems in missing data and causal inference li terature\, for which we establish conditions for minimax rate optimality. This is joint work with Yachong Yang and Arun kuchibhotla.\nSpeakers:\nPro fessor Eric Tchetgen Tchetgen (University of Pennsylvania) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/a6d1ed9f-bdf1-4965-b901-1f59e49bb34e/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Introducing the Forster-Warmuth Nonparametric Counterfact ual Regression - Professor Eric Tchetgen Tchetgen (University of Pennsylva nia) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Understanding neural networks and quantification of their uncertai nty via exactly solvable models - Professor Lenka Lenka Zdeborová (École Polytechnique Fédérale de Lausanne) DTSTART;VALUE=DATE-TIME:20230505T153000 DTEND;VALUE=DATE-TIME:20230505T163000 UID:https://talks.ox.ac.uk/talks/id/cde53ef6-143e-4988-889f-7f05d1da1d88/ DESCRIPTION:The affinity between statistical physics and machine learning has a long history. Theoretical physics often proceeds in terms of solvabl e synthetic models\; I will describe the related line of work on solvable models of simple feed-forward neural networks. I will then discuss how thi s approach allows us to analyze uncertainty quantification in neural netwo rks\, a topic that gained urgency in the dawn of widely deployed artificia l intelligence. I will conclude with what I perceive as important specific open questions in the field. \nSpeakers:\nProfessor Lenka Lenka Zdeborov á (École Polytechnique Fédérale de Lausanne) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/cde53ef6-143e-4988-889f-7f05d1da1d88/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Understanding neural networks and quantification of their uncertainty via exactly solvable models - Professor Lenka Lenka Zdeborov á (École Polytechnique Fédérale de Lausanne) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Understanding neural networks and quantification of their uncertai nty via exactly solvable models - Professor Lenka Lenka Zdeborová (École Polytechnique Fédérale de Lausanne) DTSTART;VALUE=DATE-TIME:20230505T153000 DTEND;VALUE=DATE-TIME:20230505T163000 UID:https://talks.ox.ac.uk/talks/id/cde53ef6-143e-4988-889f-7f05d1da1d88/ DESCRIPTION:The affinity between statistical physics and machine learning has a long history. Theoretical physics often proceeds in terms of solvabl e synthetic models\; I will describe the related line of work on solvable models of simple feed-forward neural networks. I will then discuss how thi s approach allows us to analyze uncertainty quantification in neural netwo rks\, a topic that gained urgency in the dawn of widely deployed artificia l intelligence. I will conclude with what I perceive as important specific open questions in the field. \nSpeakers:\nProfessor Lenka Lenka Zdeborov á (École Polytechnique Fédérale de Lausanne) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/cde53ef6-143e-4988-889f-7f05d1da1d88/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Understanding neural networks and quantification of their uncertainty via exactly solvable models - Professor Lenka Lenka Zdeborov á (École Polytechnique Fédérale de Lausanne) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Causal learning from observational data - Professor Marloes Maathu is (ETH\, Zurich) DTSTART;VALUE=DATE-TIME:20230203T153000Z DTEND;VALUE=DATE-TIME:20230203T163000Z UID:https://talks.ox.ac.uk/talks/id/50442fbc-b3d6-4291-a2a3-28a9c16339bd/ DESCRIPTION:I will discuss a line of work on estimating causal effects fro m observational data. In the first part of the talk\, I will discuss ident ification and estimation of causal effects when the underlying causal grap h is known\, using adjustment. In the second part\, I will discuss what on e can do when the causal graph is unknown. Throughout\, examples will be u sed to illustrate the concepts and no background in causality is assumed. \nSpeakers:\nProfessor Marloes Maathuis (ETH\, Zurich) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/50442fbc-b3d6-4291-a2a3-28a9c16339bd/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Causal learning from observational data - Professor Marlo es Maathuis (ETH\, Zurich) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Causal learning from observational data - Professor Marloes Maathu is (ETH\, Zurich) DTSTART;VALUE=DATE-TIME:20230203T153000Z DTEND;VALUE=DATE-TIME:20230203T163000Z UID:https://talks.ox.ac.uk/talks/id/50442fbc-b3d6-4291-a2a3-28a9c16339bd/ DESCRIPTION:I will discuss a line of work on estimating causal effects fro m observational data. In the first part of the talk\, I will discuss ident ification and estimation of causal effects when the underlying causal grap h is known\, using adjustment. In the second part\, I will discuss what on e can do when the causal graph is unknown. Throughout\, examples will be u sed to illustrate the concepts and no background in causality is assumed. \nSpeakers:\nProfessor Marloes Maathuis (ETH\, Zurich) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/50442fbc-b3d6-4291-a2a3-28a9c16339bd/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Causal learning from observational data - Professor Marlo es Maathuis (ETH\, Zurich) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Compositional data analysis: A fresh approach - Professor David F irth (University of Warwick) DTSTART;VALUE=DATE-TIME:20230222T153000Z DTEND;VALUE=DATE-TIME:20230222T163000Z UID:https://talks.ox.ac.uk/talks/id/90bb5f33-8f8b-4095-baa5-2a413e5fbae5/ DESCRIPTION:The analysis of composition is a highly active area of applied multivariate statistics\, in fields that span physical and biological sci ence\, the social sciences and humanities. The composition of a multi-par t entity is a fixed-sum vector representing the relative amounts of each p art in the whole entity. Just a few examples are: in electoral politics\ , the vote shares for different political parties\; in geology\, the perce ntages of different minerals in rock samples\; in sociology and other beha vioural sciences\, the "time budgets" of individuals\, e.g.\, fractions of the day that are spent sleeping\, working\, sedentary\, physically active \, etc.\; in biology\, the relative prevalence of different microbes in th e human gut.\n\nThe statistical literature in this area is dominated by wo rk done in the 1980s by John Aitchison\, including the highly influential 1986 book "The Statistical Analysis of Compositional Data". The essence o f Aitchison's approach is to transform composition vectors to a set of con trasts among logarithms of the data\, and then work with the standard tool s of multivariate statistics such as multivariate normal distributions and linear models. In this talk I challenge that approach\, in terms of both its foundational principles and some well-known practical difficulties. A more flexible approach\, based on suitably targeted statistical models r ather than on data-transformation\, is advocated.\nSpeakers:\nProfessor Da vid Firth (University of Warwick) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/90bb5f33-8f8b-4095-baa5-2a413e5fbae5/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Compositional data analysis: A fresh approach - Professo r David Firth (University of Warwick) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Compositional data analysis: A fresh approach - Professor David F irth (University of Warwick) DTSTART;VALUE=DATE-TIME:20230222T153000Z DTEND;VALUE=DATE-TIME:20230222T163000Z UID:https://talks.ox.ac.uk/talks/id/90bb5f33-8f8b-4095-baa5-2a413e5fbae5/ DESCRIPTION:The analysis of composition is a highly active area of applied multivariate statistics\, in fields that span physical and biological sci ence\, the social sciences and humanities. The composition of a multi-par t entity is a fixed-sum vector representing the relative amounts of each p art in the whole entity. Just a few examples are: in electoral politics\ , the vote shares for different political parties\; in geology\, the perce ntages of different minerals in rock samples\; in sociology and other beha vioural sciences\, the "time budgets" of individuals\, e.g.\, fractions of the day that are spent sleeping\, working\, sedentary\, physically active \, etc.\; in biology\, the relative prevalence of different microbes in th e human gut.\n\nThe statistical literature in this area is dominated by wo rk done in the 1980s by John Aitchison\, including the highly influential 1986 book "The Statistical Analysis of Compositional Data". The essence o f Aitchison's approach is to transform composition vectors to a set of con trasts among logarithms of the data\, and then work with the standard tool s of multivariate statistics such as multivariate normal distributions and linear models. In this talk I challenge that approach\, in terms of both its foundational principles and some well-known practical difficulties. A more flexible approach\, based on suitably targeted statistical models r ather than on data-transformation\, is advocated.\nSpeakers:\nProfessor Da vid Firth (University of Warwick) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/90bb5f33-8f8b-4095-baa5-2a413e5fbae5/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Compositional data analysis: A fresh approach - Professo r David Firth (University of Warwick) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:From population to person: Counterfactual risk prediction - Profe ssor Ruth Keogh (London School of Hygiene & Tropical Medicine) DTSTART;VALUE=DATE-TIME:20221201T153000Z DTEND;VALUE=DATE-TIME:20221201T163000Z UID:https://talks.ox.ac.uk/talks/id/c2de1639-47e2-4530-a3cd-d7b7ac04aaad/ DESCRIPTION:Clinical risk prediction models enable predictions of a person ’s risk of an outcome (e.g. mortality) given their observed characterist ics. It is often of interest to use risk predictions to inform whether a p erson should initiate a particular treatment. However\, when standard clin ical prediction models are developed in a population in which patients fol low a mix of treatment strategies\, they are unsuitable for informing trea tment decisions. Counterfactual risk predictions aim to address this probl em - they are estimates of what a person’s risk would be if they were to follow a particular treatment strategy\, given their individual character istics that are also predictive of the outcome. \nCausal inference methods typically focus on estimating population average treatment effects. In th is talk I will discuss how causal inference methods can be used for indivi dual counterfactual risk prediction using longitudinal observational data on treatment use\, patient characteristics and a time-to-event outcome. An essential step in development and reporting of prediction models is to va lidate their performance. I will discuss the challenges of this\, and desc ribe some new methods for assessing the predictive performance of counterf actual risk prediction.\n\nIn a motivating example\, we are interested in counterfactual risk predictions for mortality in patients awaiting a liver transplant under the strategies of receiving or not receiving a transplan t. I will illustrate the methods using data from the US Scientific Registr y of Transplant Patients.\n\nSpeakers:\nProfessor Ruth Keogh (London Schoo l of Hygiene & Tropical Medicine) LOCATION:24-29 St Giles' (Large Lecture Theatre\, Department of Statistics )\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/c2de1639-47e2-4530-a3cd-d7b7ac04aaad/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:From population to person: Counterfactual risk prediction - Professor Ruth Keogh (London School of Hygiene & Tropical Medicine) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:WHG Lunchtime Lab Talks: Lygate and Myers Groups - Dr Sevasti Zerv ou (University of Oxford)\, Dr Tanveer Tabish (University of Oxford)\, Pro fessor Simon Myers (University of Oxford)\, Hrushikesh Loya (University of Oxford) DTSTART;VALUE=DATE-TIME:20221109T123000Z DTEND;VALUE=DATE-TIME:20221109T133000Z UID:https://talks.ox.ac.uk/talks/id/f6eb01a4-20e2-4fd6-b872-67d5ea97cab0/ DESCRIPTION:Lygate Group 12:30-13:00\nSpeaker 1: Dr Sevasti Zervou\nTitle: “A role for homoarginine in the heart: evidence from multi-omics studie s”\nSpeaker 2: Dr Tanveer Tabish\nTitle: “Nitric oxide releasing graph ene formulations for cardiovascular applications”\n\nMyers Group 13:00-1 3:30\nSpeaker 1: Hrushi Loya (DPhil student)\nTitle: "Finding ancient ghos t populations by building genome-wide genealogies"\nSpeaker 2: Professor S imon Myers (on behalf of DPhil student Lino Ferreira and ex-postdoc Sile H u) \nTitle: "Do interactions explain population differences between GWAS s ignals?"\nSpeakers:\nDr Sevasti Zervou (University of Oxford)\, Dr Tanveer Tabish (University of Oxford)\, Professor Simon Myers (University of Oxfo rd)\, Hrushikesh Loya (University of Oxford) LOCATION:Wellcome Trust Centre for Human Genetics (In person: Rooms A&B / Remotely: via Teams. To log in via Teams please put yourself on mute and u se the following link: https://teams.microsoft.com/dl/launcher/launcher.ht ml?url=%2F_%23%2Fl%2Fmeetup-join%2F19%3Ameeting_M2Q3YzRiNjgtMzhkZS00YWIxLW I0ODgtNzNmMzUzMDcwZTg1%40thread.v2%2F0%3Fcontext%3D%257b%2522Tid%2522%253a %2522cc95de1b-97f5-4f93-b4ba-fe68b852cf91%2522%252c%2522Oid%2522%253a%2522 faee8eaf-f9bf-478e-9151-5bbfc3b39a0f%2522%257d%26anon%3Dtrue&type=meetup-j oin&deeplinkId=b3a4a919-01db-4fdd-8df4-b4fdd1088b85&directDl=true&msLaunch =true&enableMobilePage=true&suppressPrompt=true)\, Headington OX3 7BN TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/f6eb01a4-20e2-4fd6-b872-67d5ea97cab0/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:WHG Lunchtime Lab Talks: Lygate and Myers Groups - Dr Sev asti Zervou (University of Oxford)\, Dr Tanveer Tabish (University of Oxfo rd)\, Professor Simon Myers (University of Oxford)\, Hrushikesh Loya (Univ ersity of Oxford) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:WHG Lunchtime Lab Talks: Lygate and Myers Groups - Dr Sevasti Zerv ou (University of Oxford)\, Dr Tanveer Tabish (University of Oxford)\, Pro fessor Simon Myers (University of Oxford)\, Hrushikesh Loya (University of Oxford) DTSTART;VALUE=DATE-TIME:20221109T123000Z DTEND;VALUE=DATE-TIME:20221109T133000Z UID:https://talks.ox.ac.uk/talks/id/f6eb01a4-20e2-4fd6-b872-67d5ea97cab0/ DESCRIPTION:Lygate Group 12:30-13:00\nSpeaker 1: Dr Sevasti Zervou\nTitle: “A role for homoarginine in the heart: evidence from multi-omics studie s”\nSpeaker 2: Dr Tanveer Tabish\nTitle: “Nitric oxide releasing graph ene formulations for cardiovascular applications”\n\nMyers Group 13:00-1 3:30\nSpeaker 1: Hrushi Loya (DPhil student)\nTitle: "Finding ancient ghos t populations by building genome-wide genealogies"\nSpeaker 2: Professor S imon Myers (on behalf of DPhil student Lino Ferreira and ex-postdoc Sile H u) \nTitle: "Do interactions explain population differences between GWAS s ignals?"\nSpeakers:\nDr Sevasti Zervou (University of Oxford)\, Dr Tanveer Tabish (University of Oxford)\, Professor Simon Myers (University of Oxfo rd)\, Hrushikesh Loya (University of Oxford) LOCATION:Wellcome Trust Centre for Human Genetics (In person: Rooms A&B / Remotely: via Teams. To log in via Teams please put yourself on mute and u se the following link: https://teams.microsoft.com/dl/launcher/launcher.ht ml?url=%2F_%23%2Fl%2Fmeetup-join%2F19%3Ameeting_M2Q3YzRiNjgtMzhkZS00YWIxLW I0ODgtNzNmMzUzMDcwZTg1%40thread.v2%2F0%3Fcontext%3D%257b%2522Tid%2522%253a %2522cc95de1b-97f5-4f93-b4ba-fe68b852cf91%2522%252c%2522Oid%2522%253a%2522 faee8eaf-f9bf-478e-9151-5bbfc3b39a0f%2522%257d%26anon%3Dtrue&type=meetup-j oin&deeplinkId=b3a4a919-01db-4fdd-8df4-b4fdd1088b85&directDl=true&msLaunch =true&enableMobilePage=true&suppressPrompt=true)\, Headington OX3 7BN TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/f6eb01a4-20e2-4fd6-b872-67d5ea97cab0/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:WHG Lunchtime Lab Talks: Lygate and Myers Groups - Dr Sev asti Zervou (University of Oxford)\, Dr Tanveer Tabish (University of Oxfo rd)\, Professor Simon Myers (University of Oxford)\, Hrushikesh Loya (Univ ersity of Oxford) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:A path to personalised disease prevention: using genomics to predi ct risk for common diseases - Peter Donnelly (CEO of Genomics PLC and Prof essor of Statistical Science at University of Oxford) DTSTART;VALUE=DATE-TIME:20221021T153000 DTEND;VALUE=DATE-TIME:20221021T163000 UID:https://talks.ox.ac.uk/talks/id/f637bdfd-d154-49c7-bcf9-607a85794557/ DESCRIPTION:It has long been known that genetics is a major risk factor fo r all the common chronic human diseases\, such as heart disease\, diabetes \, osteoporosis\, and the mental health and auto-immune disorders\, and fo r the common cancers\, such as breast\, prostate\, and bowel cancer. For m any of these diseases\, or in some cases for important subsets of individu als\, it is the single most important predictor of disease risk. We now kn ow\, from 20 years of human genetics studies\, that this risk is polygenic : for any given disease\, it is due to the cumulative effects of a very la rge number of different genetic variants in our DNA\, each of individually small impact. Polygenic risk scores (PRSs) provide a single measure whic h quantifies the overall impact of these variants. For many diseases the u nderlying genetic studies are now large enough to provide good information about which variants matter for a particular disease\, and their effect s izes\, and in parallel we now have large prospective population cohorts\, such as UK Biobank\, in which to assess and validate predictive power. \n\ nThe talk will describe our work in Genomics plc on PRSs and their use\, t ypically in combination with non-genetic risk factors\, to provide a new l evel of risk prediction for common diseases\, and the path to their adopti on in healthcare systems to empower a new generation of risk-stratified\, personalised\, disease prevention. The identification of individuals at in creased risk of disease who are currently invisible to health systems will allow those systems to get more of the right individuals into the appropr iate screening\, prevention\, and treatment pathways. For the individual\, this can help to prevent disease entirely or to catch it early\, when out comes are much better\; for the health system\, it means existing preventi on and screening resources are used more efficiently by deploying them on higher-risk individuals\; and from a population health point of view\, by moving healthcare towards prevention\, lives and resources are saved over time.\n\nSpeakers:\nPeter Donnelly (CEO of Genomics PLC and Professor of S tatistical Science at University of Oxford) LOCATION:24-29 St Giles' (Large Lecture Theatre)\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/f637bdfd-d154-49c7-bcf9-607a85794557/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:A path to personalised disease prevention: using genomics to predict risk for common diseases - Peter Donnelly (CEO of Genomics PLC and Professor of Statistical Science at University of Oxford) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:A path to personalised disease prevention: using genomics to predi ct risk for common diseases - Peter Donnelly (CEO of Genomics PLC and Prof essor of Statistical Science at University of Oxford) DTSTART;VALUE=DATE-TIME:20221021T153000 DTEND;VALUE=DATE-TIME:20221021T163000 UID:https://talks.ox.ac.uk/talks/id/f637bdfd-d154-49c7-bcf9-607a85794557/ DESCRIPTION:It has long been known that genetics is a major risk factor fo r all the common chronic human diseases\, such as heart disease\, diabetes \, osteoporosis\, and the mental health and auto-immune disorders\, and fo r the common cancers\, such as breast\, prostate\, and bowel cancer. For m any of these diseases\, or in some cases for important subsets of individu als\, it is the single most important predictor of disease risk. We now kn ow\, from 20 years of human genetics studies\, that this risk is polygenic : for any given disease\, it is due to the cumulative effects of a very la rge number of different genetic variants in our DNA\, each of individually small impact. Polygenic risk scores (PRSs) provide a single measure whic h quantifies the overall impact of these variants. For many diseases the u nderlying genetic studies are now large enough to provide good information about which variants matter for a particular disease\, and their effect s izes\, and in parallel we now have large prospective population cohorts\, such as UK Biobank\, in which to assess and validate predictive power. \n\ nThe talk will describe our work in Genomics plc on PRSs and their use\, t ypically in combination with non-genetic risk factors\, to provide a new l evel of risk prediction for common diseases\, and the path to their adopti on in healthcare systems to empower a new generation of risk-stratified\, personalised\, disease prevention. The identification of individuals at in creased risk of disease who are currently invisible to health systems will allow those systems to get more of the right individuals into the appropr iate screening\, prevention\, and treatment pathways. For the individual\, this can help to prevent disease entirely or to catch it early\, when out comes are much better\; for the health system\, it means existing preventi on and screening resources are used more efficiently by deploying them on higher-risk individuals\; and from a population health point of view\, by moving healthcare towards prevention\, lives and resources are saved over time.\n\nSpeakers:\nPeter Donnelly (CEO of Genomics PLC and Professor of S tatistical Science at University of Oxford) LOCATION:24-29 St Giles' (Large Lecture Theatre)\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/f637bdfd-d154-49c7-bcf9-607a85794557/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:A path to personalised disease prevention: using genomics to predict risk for common diseases - Peter Donnelly (CEO of Genomics PLC and Professor of Statistical Science at University of Oxford) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Ethics from the perspective of an applied statistician - Professo r Denise Lievesley (Honorary Fellow of Green Templeton College\, Universit y of Oxford) DTSTART;VALUE=DATE-TIME:20220210T153000Z DTEND;VALUE=DATE-TIME:20220210T163000Z UID:https://talks.ox.ac.uk/talks/id/f8f900a2-65e4-458f-ab71-760d0a6f5d84/ DESCRIPTION:Statisticians work in a wide variety of different political an d cultural environments which influence their autonomy and their status\, which in turn impact on the ethical frameworks they employ. The need for a UN-led fundamental set of principles governing official statistics becam e apparent at the end of the 1980s when countries in Central Europe began to change from centrally planned economies to market-oriented democracies. It was essential to ensure that national statistical systems in such coun tries would be able to produce appropriate and reliable data that adhered to certain professional and scientific standards. Alongside the UN initia tive\, a number of professional statistical societies adopted codes of con duct. \n\nDo such sets of principles and ethical codes remain relevant ove r time? Or do changes in the way statistics are compiled and used mean th at we need to review and adapt them? For example as combining data source s becomes more prevalent\, record linkage\, in particular\, poses privac y and ethical challenges. Similarly obtaining informed consent from units for access to and linkage of their data from non-survey sources continues to be challenging. Denise will particularly draw on her earlier role as a statistician in the United Nations\, working with some 200 countries\, to discuss some of the ethical issues she encountered then and how these mig ht change over time. \nSpeakers:\nProfessor Denise Lievesley (Honorary Fe llow of Green Templeton College\, University of Oxford) LOCATION:24-29 St Giles' (Large Lecture Theatre)\, 24-29 St Giles' OX1 3LB TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/f8f900a2-65e4-458f-ab71-760d0a6f5d84/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Ethics from the perspective of an applied statistician - Professor Denise Lievesley (Honorary Fellow of Green Templeton College\, University of Oxford) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY: Statistics and the fight against modern slavery - Professor Sir B ernard Silverman (University of Nottingham and University of Oxford) DTSTART;VALUE=DATE-TIME:20220304T150000Z DTEND;VALUE=DATE-TIME:20220304T170000Z UID:https://talks.ox.ac.uk/talks/id/70fdc9e9-86c3-4da3-9e6d-39c89878e917/ DESCRIPTION:Modern slavery takes many forms and has only in recent decades come to wide public and political consciousness. The Modern Slavery Act 2015 consolidated a number of relevant offences\, and introduced a maximum penalty of life imprisonment. The numbers of victims known to the author ities\, and of prosecutions\, have considerably increased in recent years\ , but modern slavery persists\, both in the UK and worldwide.\n\nA number of different statistical approaches have been used in the fight against m odern slavery and other hidden crimes. I will survey and explore some of these\, and consider their role leading up to the Modern Slavery Act and i n subsequent developments.\n\nOverall I hope that the lecture will provide the opportunity to reflect more widely on what is the best appropriate st atistical contribution to what is an important and disturbing aspect of pu blic policy. In particular there are broader questions about presenting s tatistical evidence in an area where there is so much uncertainty. Should we follow George Box’s dictum that “all models are wrong\, but some a re useful”? There are some (imperfect) analogies with Florence Nighting ale’s own work and campaign leading to the Public Health Act 1875\, whic h saved the lives of millions.\nSpeakers:\nProfessor Sir Bernard Silverman (University of Nottingham and University of Oxford) LOCATION:Venue to be announced TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/70fdc9e9-86c3-4da3-9e6d-39c89878e917/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk: Statistics and the fight against modern slavery - Profes sor Sir Bernard Silverman (University of Nottingham and University of Oxfo rd) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Weldon Memorial Prize Lecture - Cracking the code of sexual reprod uction and speciation - Professor Simon Myers (University of Oxford) DTSTART;VALUE=DATE-TIME:20220523T160000 DTEND;VALUE=DATE-TIME:20220523T171500 UID:https://talks.ox.ac.uk/talks/id/719105f9-f591-4510-a70e-00f72ad8520a/ DESCRIPTION:In mammals\, hybrids are often infertile\, and this is thought to be a key driver of species formation – but how does this happen? In mammals\, only a single speciation gene has so far been identified\, in mi ce. In this talk I will talk about how this sterility is deeply connected to recombination\, which shuffles mutations into new combinations\, and to the pairing of chromosomes\, essential to ensure one of each pair is pass ed on to offspring\, two fundamental processes which are very incompletely understood. I will describe our collaborative work that has revealed how specific DNA sequences\, which differ between all species so far examined\ , “code” for possible recombination sites\, and identified key genes i nvolved. Remarkably\, simply modifying this code fully reverses hybrid ste rility\, in several cases.\n\nEvolution of this code occurs extraordinaril y rapidly both in cis and in trans\, driving diverse phenomena: it explain s hybrid sterility in mice\, the origins of many human diseases\, and why different human populations have differing recombination landscapes. The s tudy of hybrid mice has led to an improved understanding of an enduring bi ological mystery: how do homologous chromosomes precisely align\, finding their exact partners among billions of genetic bases of DNA within the cel l? We speculate that this explains why\, in most studied species\, recombi nation occurs in only a tiny fraction of the genome\, in “hotspots”\, and discuss a range of evolutionary implications.\n\nProfessor Simon Myers is a member of the Department of Statistics\, the Wellcome Trust Centre f or Human Genetics and St John’s College\, in Oxford. He has spent much o f his career in Oxford\, as well as several years spent at Harvard and the Broad Institute of MIT/Harvard. Although his original degree is in mathem atics\, his labs’ research has gradually broadened to combine the develo pment of novel statistical techniques to investigate genetic data with exp erimental work. His research is diverse but centers around the study of ge netic variation\, its drivers and its impacts\, for example revealing the timing and impacts on our DNA of many human migration events such as the M ongol empire\, and Anglo-Saxon migrations. Another key research strand is the study of meiosis and its key defining features\, namely recombination and the pairing of homologous chromosomes\, and their connections to speci ation in mammals. This has discovered a number of the key genes involved i n the earliest steps of these processes\, including some of the most rapid ly evolving genes in the human genome. For his work\, Professor Myers has been awarded the Genetics Society Balfour prize\, and the Royal Society Fr ancis Crick medal and lecture.\nSpeakers:\nProfessor Simon Myers (Universi ty of Oxford) LOCATION:Oxford Martin School Lecture Theatre\, 34 Broad Street\, Oxford\, OX1 3BD TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/719105f9-f591-4510-a70e-00f72ad8520a/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Weldon Memorial Prize Lecture - Cracking the code of sexu al reproduction and speciation - Professor Simon Myers (University of Oxfo rd) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Population History Genetics Disease Day *CANCELLED* - Professor Si mon Myers (University of Oxford)\, Dr Anjali Hinch (University of Oxford) DTSTART;VALUE=DATE-TIME:20191101T094500Z DTEND;VALUE=DATE-TIME:20191101T123000Z UID:https://talks.ox.ac.uk/talks/id/71157781-0ff7-4038-afad-571f9ed588f7/ DESCRIPTION:Professor Simon Myers\n"Building the trees relating thousands of people reveals population-specific directional selection\, across many polygenic diseases and normal traits"\n\nDr Anjali Hinch\n“Cut\, copy\, paste: making crossovers in mammalian meiosis”\n\nDr Clare Bycroft\n“U sing DNA to learn about the demographic history of human populations: the case of the Iberian Peninsula"\nSpeakers:\nProfessor Simon Myers (Universi ty of Oxford)\, Dr Anjali Hinch (University of Oxford) LOCATION:Wellcome Trust Centre for Human Genetics (Rooms A&B)\, Headington OX3 7BN TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/71157781-0ff7-4038-afad-571f9ed588f7/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Population History Genetics Disease Day *CANCELLED* - Pro fessor Simon Myers (University of Oxford)\, Dr Anjali Hinch (University of Oxford) TRIGGER:-PT1H END:VALARM END:VEVENT BEGIN:VEVENT SUMMARY:Genes\, Cells\, and Schizophrenia - Professor Steven McCarroll (Ha rvard Medical School ) DTSTART;VALUE=DATE-TIME:20160115T120000Z DTEND;VALUE=DATE-TIME:20160115T130000Z UID:https://talks.ox.ac.uk/talks/id/e4231fd6-28f9-45cf-8213-feafa15e7828/ DESCRIPTION:\nStatus: This talk is in preparation - details may change\nGe nes\, cells\, and schizophrenia\n\nTo understand the biological and geneti c basis of common\, complex disease\, two challenges today are (i) to iden tify the functional alleles and biological mechanisms underlying genetic a ssociations with disease risk\; and (ii) to understand how these genes and alleles affect the biology of specific cell populations in complex tissue s. Our lab’s work focuses on these challenges. I will talk on Friday a bout two such projects in our work to understand the biological basis of n europsychiatric disorders.\n\nThe strongest genetic influence on schizophr enia at a population level involves the disorder’s association with comm on SNPs in the major histocompatibility complex (MHC) locus on chromosome 6. Though the MHC association is the earliest and strongest genetic signa l in schizophrenia\, it has been seen as presenting an intractable fine-ma pping problem because the complex association signal does not track with p atterns of linkage disequilibrium around any known variants. I will descr ibe our work to understand this genetic signal and the functional alleles underlying it. The work has led us to surprising insights about both gene tic architecture and a neuro-immune mechanism in schizophrenia. \n\nUltim ately we must understand this and other genetic effects in terms of how ge nome variation shapes the biology of specific cell populations within comp lex tissues. A challenge in studying the brain and other complex tissues is that they contain tens to hundreds of cell types and cell states\, each of which utilizes the genome in distinct ways. I will describe our lab ’s work to develop Drop-seq\, a new technology for simultaneously analyz ing genome-wide gene expression in tens of thousands of individual cells. I will discuss ways that we are applying Drop-seq to better understand ho w diverse cell populations utilize their genomes and are affected by genet ic variation.\n\nSpeakers:\nProfessor Steven McCarroll (Harvard Medical Sc hool ) LOCATION:Seminar Room A TZID:Europe/London URL:https://talks.ox.ac.uk/talks/id/e4231fd6-28f9-45cf-8213-feafa15e7828/ BEGIN:VALARM ACTION:display DESCRIPTION:Talk:Genes\, Cells\, and Schizophrenia - Professor Steven McCa rroll (Harvard Medical School ) TRIGGER:-PT1H END:VALARM END:VEVENT END:VCALENDAR