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DTSTART;TZID=America/New_York:20260903T090000
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SUMMARY:Big Data Conference 2026
DESCRIPTION:Big Data Conference 2026 \nDates: Sep. 3–4\, 2026 \nLocation: Harvard University CMSA\, 20 Garden Street\, Cambridge MA & via Zoom \nThe Big Data Conference features speakers from the Harvard community as well as scholars from across the globe\, with talks focusing on computer science\, statistics\, math and physics\, and economics. \nRegister to attend in person \nRegister for Zoom Webinar \n  \nConfirmed Speakers \n\nSueYeon Chung\, Harvard\nBailey Flanigan\, MIT\nSergey Ovchinnikov\, MIT\nAriel Procaccia\, Harvard\nAdit Radhakrishnan\, MIT\nAndrew Sutherland\, MIT\nChris Wiggins\, Columbia\nRex Ying\, Yale\nWei Zhou\, Harvard\n\n  \nOrganizers \n\nMichael Desai\, Harvard\nMichael R. Douglas\, CMSA\nYannai Gonczarowski\, Harvard\nMelanie Weber\, Harvard\n\n  \n  \nThursday\, Sep. 3\, 2026 \n8:45–9:10 am\nBreakfast \n9:10–9:15 am\nIntroductions \n9:15–10:15 am\nRex Ying\, Yale \n10:15–10:30 am\nBreak \n10:30–11:30 am\nAdit Radhakrishnan\, MIT\nToward universal steering and monitoring of AI models\nAbstract: Artificial intelligence (AI) models contain much of human knowledge. Understanding the representation of this knowledge will lead to improvements in model capabilities and safeguards. Building on advances in feature learning\, we developed an approach for extracting linear representations of semantic notions or concepts in AI models. We showed how these representations enabled model steering\, through which we exposed vulnerabilities and improved model capabilities. We demonstrated that concept representations were transferable across languages and enabled multiconcept steering. Across hundreds of concepts\, we found that larger models were more steerable and that steering improved model capabilities beyond prompting. We showed that concept representations were more effective for monitoring misaligned content than for using judge models. Our results illustrate the power of internal representations for advancing AI safety and model capabilities. \n11:30 am–12:45 pm\nLunch \n12:45–1:45 pm\ntba \n1:45–2:00 pm\nBreak \n2:00–3:00 pm\nBailey Flanigan\, MIT\nAlgorithmic Tools for Trading Off Sortition Ideals\nAbstract: Citizens’ assemblies and other deliberative minipublics — representative groups of everyday people convened to deliberate on a policy issue and then make recommendations — are now used by governments around the world. Choosing who sits on these panels is the problem of sortition: randomly selecting a small group of citizens that represents the broader population. Sortition has been the subject of substantial computer science research in recent years\, and the resulting algorithms are now widely used in practice. This talk will describe the key challenges that arise in the practice of sortition\, and the algorithmic tools that have been developed to navigate them optimally. \n3:00–3:15 pm\nBreak \n3:15–4:15 pm\nChris Wiggins\, Columbia \n  \nFriday\, Sep. 4\, 2026 \n8:45–9:15 am\nBreakfast \n9:15–10:15 am\nAriel Procaccia\, Harvard\nNo Generation Without Representation\nAbstract: AI systems and democratic processes are confronting similar challenges around representation. I examine two related questions that cut across both domains. First\, how can AI enable democratic processes that handle vast spaces of opinions or statements while ensuring proportional representation of a population’s views? Second\, when AI systems themselves provide normative guidance\, whose viewpoints do they reflect\, and can we make this precise? Drawing on social choice theory\, I present formal frameworks and algorithms for both problems\, showing that meaningful representation guarantees are feasible and practical. \n10:15–10:30 am\nBreak \n10:30–11:30 am\nWei Zhou\, Harvard\nBiobank-scale genetic discovery: from association testing to global meta-analysis\nAbstract: Biobanks linking genomic data with electronic health records provide unprecedented opportunities for genetic discovery for complex human diseases\, but they also pose analytical challenges that extend well beyond sample size. Within a biobank\, association studies must account for population structure and relatedness\, highly unbalanced case–control ratios\, rare genetic variants\, longitudinal and censored outcomes\, and the computational demands of analyzing hundreds of thousands of individuals and millions of genetic variants. Across biobanks\, additional challenges arise from differences in ancestry\, phenotype definitions\, recruitment strategies and genetic effects.In this talk\, I will discuss statistical and computational methods developed to address these challenges at successive stages of biobank analysis. These include scalable generalized linear mixed models for binary traits\, survival mixed models for censored time-to-event outcomes\, and gene- and region-based tests that aggregate rare variants. I will describe statistical approximations and computational strategies that make these analyses feasible at biobank scale while maintaining calibration in the presence of relatedness and highly unbalanced phenotypes. I will then introduce the Global Biobank Meta-analysis Initiative (GBMI) and describe how genetic evidence can be combined across biobanks without sharing individual-level data. Examples from GBMI will illustrate how combining evidence across biobanks can increase statistical power through larger sample sizes and broaden genetic discovery through greater ancestral diversity. \n11:30 am–12:45 pm\nLunch \n12:45–1:45 pm\nSergey Ovchinnikov\, MIT \n1:45–2:00 pm\nBreak \n2:00–3:00 pm\nSueYeon Chung\, Harvard \n3:00–3:15 pm\nBreak \n3:15–4:15 pm\nAndrew Sutherland\, MIT \n\n 
URL:https://cmsa.fas.harvard.edu/event/bigdata_2026/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Big Data Conference,Conference,Event
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DTSTART;TZID=America/New_York:20260903T161500
DTEND;TZID=America/New_York:20260903T173000
DTSTAMP:20260804T134427Z
CREATED:20260803T204031Z
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SUMMARY:Fall CMSA Welcome Event
DESCRIPTION:Fall CMSA Welcome Event \nDate: September 3\, 2026 \nTime: 4:15 pm \nLocation: CMSA Common Room\, 20 Garden Street\, Cambridge MA \n  \nAll CMSA and Math affiliates are invited. \n 
URL:https://cmsa.fas.harvard.edu/event/welcome2026/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Event
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