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StatDS Research/Reading Groups
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Group Name
(Alphabetized by Topic)		Stat&DS FacultyContactWebsiteMeetingsDescription
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ACTIVE
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Machine learning in causal inference (Causal)Sivaraman Balakrishnan, Eli Ben-Michael, Zach Branson, Edward Kennedy, Aaditya Ramdas, Cosma Shalizi, Larry WassermanJungHo Lee (junghol@andrew.cmu.edu), Patrick Kramer (pkramer@andrew.cmu.edu), Edward Kennedy (edward@stat.cmu.edu)https://www.cmu.edu/dietrich/statistics-datascience/research/causal-inference-working-group.htmlTuesday 4-5pm in Baker Hall 232MWe read & discuss papers, or our own current work, re: causal inference using machine learning (email Edward to be added to listserv)
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Science of Data Science (How People Learn/Do Data Science), Integrated Statistics Learning Environment (ISLE)Rebecca Nugent, Chris Genovese, Peter Freeman, Zach Branson, Gordon Weinberg, Ron Yurkosomewhat merged with TeachStat (below)http://www.stat.cmu.edu/isleBehavioral Data Science (or Science of Data Science) largely supported by the Integrated Statistics Learning Environment platform; we generally do a mix of projects and experiments on researching how students and practitioners analyze and work with data (ISLE tracks every single action, click, word, etc). Opportunities for both methodological and pedagogical research.
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Delphi (Infectious disease epidemiology)Will Townes, Valérie Ventura, Larry WassermanWill Townes (ftownes@andrew.cmu.edu) with Christy Melucci Cc:d cmelucci@andrew.cmu.eduhttp://delphi.cmu.edu/Announced via slack channelEpidemiological tracking and forecasting are critically needed for decision making by public health officials, commercial and non-commercial institutions, and the general public. The Delphi group at Carnegie Mellon University focuses on developing epi tracking and forecasting technology, their role in decision making, both public and private. Our long term vision is to make this technology as universally accepted and useful as weather forecasting is today.
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Computational Genetics (Genetics and genomics)Kathryn Roeder, Will Townes, Weijing Tang, Gonzalo Menaroeder@andrew.cmu.eduMeeting announcements shared on slack and emailStudents and postdocs working in statistical genetics and genomics present their work and/or related literature to the group, including the Computational Genomics group from Pitt. We meet biweekly, 2:00-3:00 pm Fridays in 129Q Baker Hall. Our first meeting this fall will be Sept 12. Email Roeder to get on the d-list.
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Networkshop (Networks)Brian Junker, Cosma Shalizi, Nynke Niezink, Weijing TangAnni HongSubscribe hereFriday 10am according to Prof. Niezink email. In person room TBD. Zoom link existsWe meet weekly to discuss new research, papers, interesting problems, and half-baked ideas related to the statistical analysis of network data. Some examples of topics are: graphons, sparse networks, and non iidly sampled network data. We are interested in theory, methods and applications, and regularly have guest presentations from people outside the department. We emphasize the "workshop" aspect, so early-stage ideas are also encouraged.
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Neurostats (Neuroscience)Rob Kass, Valérie VenturaRob KassNeuroStat websiteDiscusses applications of stats/ML in neuroscience; students typically present a relevant paper. Sometimes other speakers (students or faculty) come from other labs/advisors. Some talks lean more toward neuroscience with a focus on methods, while a lot are more purely statistical. We meet in 232M (Zoom option usually available) at 4:00pm on Tuesdays. Email kass@stat.cmu.edu to get on d-list
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STAMPS: STAtistical Methods for the Physical Sciences (Physical Sciences)Ann Lee, Mikael Kuusela, Larry Wasserman, Chad Schafer, Joel GreenhouseThea Sukianto (tsukiant@andrew.cmu.edu)http://stat.cmu.edu/stamps/ (Subscribe at https://lists.andrew.cmu.edu/mailman/listinfo/statistical-methods-for-physical-sciences and at https://lists.andrew.cmu.edu/mailman/listinfo/stamps-webinar )Fridays at 1:30pm in BH 232M, announced in advance via mailing list on the leftThe STAMPS Research Center develops foundational statistical methodology that addresses emerging open problems in fundamental physics, environmental and climate sciences. STAMPS has weekly local group meetings, monthly public webinars / hybrid talks and annual workshops. Subscribe to our mailing lists and consult our website and Google Calendars for the latest information on meeting times and locations. Notice that there are two separate mailing lists; you need to sign up on both to receive all the communications.
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useR (R software)N/Ahttps://www.meetup.com/Pittsburgh-useR-Group/A group for all levels of R programmers to share tips, training, insights, and applications. We're always looking for new presentation ideas, so if you'd like to present, let us know!
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TeachStat (Pedagogy, Text Analysis)Alex Reinhart, Gordon Weinberg, David Brown (English), Rebecca NugentAlex Reinharthttps://www.cmu.edu/dietrich/statistics-datascience/research/statistical-pedagogy-educational-research.htmlThursdays 11am-noon, Baker 232MMeets weekly to discuss and conduct research on statistical pedagogy. Current projects include evaluating how students learn to write data analysis reports, studying the quantitative features of their writing, and exploring how large language models (LLMs) compare to human writing
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Statistics and Machine Learning Research Group (Machine Learning)Sivaraman Balakrishnan, Jing Lei, Aaditya Ramdas, Cosma Shalizi, Larry Wasserman, Gonzalo Mena, Weijing Tang, Ankit Pensia Ben Chugg (benchugg@cmu.edu) & Diego Martinez-Taboada (diegomar@andrew.cmu.edu)http://statml.cs.cmu.edu/Wednesdays 2-3:30pm, NSH 3305We are group of faculty and students in Statistics and Machine Learning broadly interested in research at the intersection of these two disciplines.
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Sports Content for Outreach, Research, and Education in Data Science (SCORE)Rebecca Nugent, Peter Freeman, Ron Yurko, and large national network of collaborators in academic, professional sports, and related media organizationshttps://scorenetwork.org/
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Carnegie Mellon Sports Analytics Center (CMSAC) Research Lab (Sports Analytics)Ron Yurko, Rebecca NugentRon Yurko (ryurko@andrew.cmu.edu)https://cmsac-research-lab.github.io/TBAFaculty and students developing novel, cutting-edge approaches in sports analytics across a range of problems, such as inferring player- and team-level effects with rich spatio-temporal data.
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Variational Analysis for Statistical Theory (VAST)Arun Kuchibhotla, Siva, Jing, Yandi, Mikael, EdArun Kuchibhotla (arunku@cmu.edu), Kenta Takatsu (ktakatsu@andrew.cmu.edu), & Woonyoung Chang (woonyouc@andrew.cmu.edu)Mondays 11-12:30, Baker Hall 229A (Until 9/22), Possible Porter Hall A19 (from 9/22, will be confirmed closer to this date)Variational analysis, derived as an extension of classical real and convex analysis, deals with non-smooth optimization problems, and perturbation analysis (i.e., how do the solutions change when the objective function or estimating function are changed 'slightly'?) The idea to use variational analysis for statistical theory is not new, but mostly forgotten I would say. Starting from Chernoff (1954, AoMS), several statisticians have used these tools to obtain asymptotic distribution results. Through this reading group, we learn more and derive reliable inference methods for irregular problems (including high-dimensional methods such as lasso or Dantzig selector). Reading list is available at https://docs.google.com/spreadsheets/d/1NACVjOpF9k9vm0B6bGipwi-qkMqnplGve_2CzGWpe6U/edit?usp=sharing_eil&ts=67e83b39.
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Optimal transportGonzalo, Larry, Siva, Mikael, AnkitSohehun Yi (soheuny@andrew.cmu.edu), Gonzalo Mena (gmena@andrew.cmu.edu)Thursdays 2-3pm. BH 129 Conference RoomOptimal transport, which provides a framework for measuring "distances" between probability distributions, has recently emerged as a prominent tool in data science. However, its statistical properties remain underexplored, along with many of its statistical applications. In an effort to bridge the field of statistics discipline and optimal transport, we discuss various aspects of optimal transport from statistician perspectives, both theoretical and applied. Contact Soheun (soheuny@andrew.cmu.edu) to join the mailing list.
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Center for Statistics and Applications in Forensic Evidence (Forensics)Robin Mejiahttp://forensicstats.org/A NIST Center of Excellece with Iowa State, UC Irvine, and University of Virginia, Duke, and West Virginia University, we work on statistical problems in forensic science and statistical training in forensics and the law.
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Astrostats (Astronomy)Peter Freeman, Larry Wasserman, Chris Genovese, Ann Lee, Chad SchaferA forum for presenting astro related research. So the typical meeting involves a research presentation, usually not accompanied by a paper. Most of the recent work has been by CMU and Pitt astro people. Group is currently merged with the STAMPS group (above).
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TopStats (Topology)Alessandro Rinaldo, Larry WassermanThe CMU Topological Statistics group is a research group at Carnegie Mellon University. The emphasis of our research is on statistical problems related to topological inference.
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History of Stats (History)Alex ReinhartFor students to read and discuss history, philosophy, and breakthroughs in statistics
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MIDAS: Models of Infectious Disease Agent Study (Epidemiology)Bill EddyWe study the statistical properties of infectious disease models, especially those related to agent-based models (AM). Our research group focuses on produces synthetic agents for use in AMs.
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Statistics Student Seminars (Misc.)(Student group)For students to practice presentating and keep updating other students on research.
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StatGen reading group (Genetics and genomics)(Student group)Tim BarryStudent (and postdoc) led reading group to discuss genetics and genomics papers covering a wide range of topics from methodology to the actual biology. Currently on hiatus for fall 2022.
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FEAT: Fairness, Ethics, Accountability, and Transparency (Fairness)Alexandra ChouldechovaNil-Jana AkpinarAs the name suggests, we are a group of CS/Public Policy/Stats students interested in works that span the FEAT space. FEAT is a new but quickly growing field involving problems at the intersection of computer science, economics, statistics, and public policy. There is a weekly reading group with paper presentations.
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