Aaditya Ramdas - Core group

Post-proposal PhD Mentees and postdocs

Chirag Gupta (PhD student, 5th year MLD)

Sasha Podkopaev (PhD student, 5th year StatDS+MLD)

YJ Choe (PhD student, 5th year StatDS+MLD)

Shubhanshu Shekhar (postdoc 2021-23, StatDS)

Aditya Gangrade (postdoc 2022-23, StatDS)

Pre-proposal PhD Mentees

Hongjian Wang (MS student, 2nd year MLD)

Ben Chugg (PhD student, 1st year MLD)

Neil Xu (PhD student, 2nd year StatDS)

James Leiner (PhD student, 2nd year StatDS)

Ian Waudby-Smith (PhD student, 4th year StatDS)

Justin Whitehouse (PhD student, 4th year CSD, joint with Steven Wu)

Ojash Neopane (PhD student, 5th year MLD, joint with Aarti Singh)

Graduated PhD students and postdocs

Robin Dunn (PhD 2021, StatDS, joint with Larry Wasserman)

Boyan Duan (PhD 2021, StatDS, joint with Larry Wasserman)

Jaehyok Shin (PhD 2020, StatDS, joint with Alessandro Rinaldo)

Eugene Katsevich (postdoc 2019-20, StatDS, joint with Kathryn Roeder)

  • next position: Asst. Prof. at UPenn Wharton Statistics

Asaf Weinstein (postdoc 2018-19, StatDS, joint with Matan Gavish)

  • next position: Asst. Prof. at Hebrew Univ Statistics)

How do I spend my research time?

I take a theoretical and methodological perspective on fundamental questions in statistics, data science, ML and AI, when applied towards solving basic problems in science and technology. 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 and related areas (AoS, AoAS, Biometrika, JRSSB, EJS, JCGS, PNAS, IEEE, SIAM etc).

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.