Aaditya Ramdas

 

Aaditya Ramdas
Tenure-track Assistant Professor
Department of Statistics and Data Science (75%)
Machine Learning Department (25%)
Carnegie Mellon University


132H Baker Hall
aramdas AT cmu FULLSTOP edu
[http://www.stat.cmu.edu/~aramdas]

How do I spend my research time?

I am motivated by broad practical issues in the application of statistics, data science, ML and AI to solve problems in science and technology, that I attempt to address using a theory-backed methodology lens. Most of my published work appears in the top proceedings in ML/AI (NeurIPS, ICML, COLT, ALT, AISTATS, UAI, AAAI, IJCAI, etc) and the top journals in statistics (AoS, AoAS, Biometrika, JRSSB, EJS, JCGS, PNAS, etc), with several one-off papers in the top IEEE, SIAM and probability journals.

I have various works in optimization, active learning, information theory, kernel methods, and several scientific collaborations (see the last few sections of my publication page). More details in my Curriculum Vitae.

How do I lead my life outside research?

I lead a very high energy and intense lifestyle, and I like the fact that an academic life provides a large amount of freedom and space to take the lead with new initiatives. I believe that it is the responsibility of the young faculty members (the “new guard”, who will be tomorrow's academic leaders), to innovate not just in research, but push the boundaries of what we want tomorrow's universities and academia to look like.

  • Teaching. I am a passionate teacher and enjoy developing novel classes. I have designed and taught new PhD courses on martingales, sequential analysis and concentration, statistical methods for reproducibility, and the ABCDE of statistical methods in ML. In Fall 2020, I designed and co-taught a new tri-department multidisciplinary undergraduate freshman seminar class on Voting, coinciding with the US elections. In Spring 2021, I am teaching a new tri-university class on game-theoretic statistical inference. I spend a lot of time designing the syllabi, homeworks, projects and course policies/structure, and one of my major inspirations is anonymous student feedback and the resulting faculty course evaluations. As a PhD student, I completed the Future Faculty Program offered by the Eberley Center for Teaching Excellence, and received a Graduate Teaching Award from the ML department (2014) and then the Alan J. Perlis Graduate Student Teaching Award (2015, one student in the School of CS). I have always involved myself in STEM outreach activities in various countries (USA, Oman, India) and to various audiences (primary, middle and high schools), including historically underrepresented communities (all-girls programs, lower income neighborhoods).

  • Communication. I enjoy communicating my research broadly and work hard at creating engaging talks and tutorials, many of which are linked here. I have given over 100 invited seminar talks in a variety of university departments (Stat, CS, EE, Math, OR) and industry research labs. It moves me to get positive feedback from people I have never met. I enjoy organizing cool workshops on diverse topics with fun colleagues, such as optimization, active learning, adaptive data analysis, nonparametrics, and learning theory. In summer 2021 (modulo Covid), Peter Grunwald and I will be co-organizing a week-long workshop on safe, anytime-valid inference. I was invited to be the primary instructor for a two-week Bocconi-Oxford-Imperial summer school in advanced statistics+probability at Lake Como, for which I chose the topic to be “Statistical inference using betting scores, e-values and martingales”, and Glenn Shafer has graciously accepted to be my co-instructor.

  • Service. I enjoy helping conference or journals experiment with new modern reviewing protocols. In the last few years, I have served as a meta-reviewer (or area chair or senior program committee member) for most major ML conferences, such as NeurIPS, ICML, COLT, ALT, AISTATS and UAI. These have each succesfully upgraded the peer-review experience and conference model in different ways to meet the increasing challenges of volume of papers and breadth of topics (OpenReview, GatherTown, randomized experiments, matching and bidding, etc). For 2021-22, I will serve as an associate editor for an exciting new journal with an interesting review process, the New England Journal of Statistics in Data Science. I also review dozens of papers per year for the top statistics journals (AoS, JRSSB, JASA, Biometrika, Stat. Sci., EJS, Bernoulli, etc). I am also one of the two arXiv stat.ML moderators, the highest load category within statistics.

  • Mentorship. I take my mentorship role very seriously, and I closely advise a talented group of postdocs, senior and junior PhD students, masters students and undergraduates in Stats and ML. I work with them not only on their research, but on a wide variety of other complementary skills such as writing and speaking. I have developed a variety of checklists for Stat-ML PhD students, which include topics like poster presentations, writing effective rebuttals, work-life balance, and so on, which seem important but are hard to get advice or mentorship on. I will continue to populate these over the next few years into a handbook involving other topics like navigating the academic job market.

  • Outside of work. I enjoy bursting my bubble via immersive travel; I have traveled to over 55 countries on all 6 habitable continents mostly via long, low-grade backpacking trips involving lots of walking, hostels and trains. I love the outdoors and wilderness; I am an advanced open-water certified scuba diver, and have completed multiple month-long courses in mountaineering and climbing, as well as first aid certifications. I try my best to lead a trash-free life; since Jan 1, 2016, I rely primarily on reusable, compostable and recyclable materials, and personally allowed myself a budget of about one big garbage bag of landfill trash per year. In 2013, I completed an Ironman triathlon in Louisville, but had a bad accident in 2014, and after recovery in 2015, I used multiple long-distance bicycle rides in California and Zambia as a way to fight back. Yoga is my moving-meditation, with running, swimming and biking being my other weekly mind-cleansers.

Given the huge freedom awarded to academics and the scope for creativity in all spheres of an academic life, the success of the scientific enterprise depends on the deep involvement of its members outside of pure research, leading me to strongly believe in the holistic development and enthusiastic contributions of faculty members in all aspects of helping departments, universities and disciplines flourish.

Selected recent papers

  • Estimating means of bounded random variables by betting
    I. Waudby-Smith, A. Ramdas       arxiv  

  • Dimension-agnostic inference
    I. Kim, A. Ramdas       arxiv

  • Admissible anytime-valid sequential inference must rely on nonnegative martingales
    A. Ramdas, J. Ruf, M. Larsson, W. Koolen       arxiv

  • False discovery rate control with e-values
    R. Wang, A. Ramdas       (JRSSB, minor revision)   arxiv

  • Testing exchangeability: fork-convexity, supermartingales, and e-processes
    A. Ramdas, J. Ruf, M. Larsson, W. Koolen       Intl J of Approximatte Reasoning, 2021   arxiv   proc

  • On the power of conditional independence testing under model-X
    E. Katsevich, A. Ramdas       arxiv

  • Fast and powerful conditional randomization testing via distillation
    M. Liu, E. Katsevich, L. Janson, A. Ramdas       Biometrika, 2021   arxiv   code

  • Time-uniform, nonparametric, nonasymptotic confidence sequences
    S. Howard, A. Ramdas, J. Sekhon, J. McAuliffe       Annals of Stat., 2021   arxiv   proc   code   talk1   tutorial

  • Time-uniform Chernoff bounds via nonnegative supermartingales
    S. Howard, A. Ramdas, J. Sekhon, J. McAuliffe       Prob. Surveys, 2020   arxiv   proc

  • Universal inference
    L. Wasserman, A. Ramdas, S. Balakrishnan       PNAS, 2020   arxiv   talk   proc

  • Are sample means in multi-armed bandits positively or negatively biased?
    J. Shin, A. Ramdas, A. Rinaldo       NeurIPS, 2019   arxiv   poster   proc

  • A unified treatment of multiple testing with prior knowledge using the p-filter
    A. Ramdas, R. F. Barber, M. Wainwright, M. Jordan       Annals of Stat., 2019   arxiv   code   proc

  • Classification accuracy as a proxy for two sample testing
    I. Kim*, A. Ramdas*, A. Singh, L. Wasserman       Annals of Stat., 2021   arxiv   proc

  • Predictive inference with the jackknife+
    R. Barber, E. Candes, A. Ramdas, R. Tibshirani       Annals of Stat., 2020   arxiv   code

  • Simultaneous high-probability bounds on the FDP in structured, regression and online settings
    E. Katsevich, A. Ramdas       Annals of Stat., 2020   arxiv   code   proc

Biography

Aaditya Ramdas is an assistant professor at Carnegie Mellon University, in the Departments of Statistics and Machine Learning. He was one of the inaugural inductees of the COPSS Leadership Academy, and a recipient of the Bernoulli New Researcher Award. His work is supported by an NSF CAREER Award, an Adobe Faculty Research Award, an ARL Grant on Safe Reinforcement Learning, the Block Center Grant for Technology and Society, amongst several others.

Aaditya's main theoretical and methodological research interests include selective and simultaneous inference (interactive, structured, post-hoc control of false decision rates, etc), sequential uncertainty quantification (confidence sequences, always-valid p-values, bias in bandits, etc), and distribution-free black-box predictive inference (conformal prediction, calibration, etc). His areas of applied interest include neuroscience, genetics and voting and his group's work has received multiple best paper awards.

He is one of the organizers of the amazing and diverse StatML Group at CMU.

Outside of work, here are some easy topics for conversation: travel/outdoors (hiking, scuba, etc.), trash-free living, completing the Ironman triathlon and long-distance bicycle rides.

Curriculum Vitae