Raaz RSK Dwivedi
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News
I will start as an Assistant Professor in the Operations Research and Information Engineering, Cornell Tech, Cornell University in Jan 2024!
Upcoming presentations:
Apr 27, 2:00–4:30 PM: International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain!
May 24-26: American Causal Inference Conference (ACIC), Austin, Texas!
Jun 19: Causal Inference Workshop, ACM Sigmetrics, Orlando, Florida!
Jun 26-27: International Conference on Monte Carlo Methods and Applications (MCM), Paris, France!
Jun 28-30: INFORMS Applied Probability Society (APS) Conference, Nancy, France!
Jul 24-28: CDT Summer School, Missenden Abbey, Buckinghamshire, UK!
Aug 5-10: Joint Statistical Meeting (JSM), Toronto, Canada!
Aug 21-23: TRIPODS Workshop, IDEAL/TTIC, Chicago, Illinois!
Oct 15-18: INFORMS Annual Meeting, Phoenix, Arizona!
Publications:
Apr 2023: ‘‘Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling’’, joint work with Susobhan Ghosh, Raphael Kim, Prasidh Chhabria, Predrag Klasjna, Peng Liao, Kelly Zhang, and Susan Murphy, up on arXiv!
Jan 2023: ‘‘Compress then test: Powerful kernel testing in near-linear time’’, joint work with Carles Domingo-Enrich and Lester Mackey, accepted in AISTATS 2023!
Nov 2022: Preliminary version of ‘‘Doubly robust nearest neighbors in factor models’’, joint work with Katherine Tian, Sabina Tomkins, Predrag Klasnja, Susan Murphy, and Devavrat Shah, up on arxiv!
Nov 2022: ‘‘Instability, computational efficiency, and statistical accuracy’’, joint work with Nhat Ho, Koulik Khamaru, Martin Wainwright, Michael Jordan, and Bin Yu, accepted with minor revision in the Journal of Machine Learning Research (JMLR)
Nov 2022: ‘‘Revisiting minimum description length complexity in overparameterized models’’, joint work with Chandan Singh, Bin Yu, and Martin Wainwright, accepted with minor revision in the Journal of Machine Learning Research (JMLR)
Oct 2022: ‘‘On counterfactual inference with unobserved confounding’’, joint work with Abhin Shah, Greg Wornell, and Devavrat Shah, accepted at NeurIPS 2022 Workshop on Causality for Real-world Impact
Awards:
Jul 2022: Teaching Award by Harvard University for the role of teaching fellow in the course on sequential decision making (STAT 234)
Jan 2022: Best Presentation Award for the talk on ‘‘Near-optimal Compression in Near-linear Time’’ at 27th Annual LIDS Student Conference, Machine Learning and Statistics Session at MIT
Jan 2022: Best Student Paper Award to joint work ‘‘Distribution compression in near-linear time’’ with Abhishek Shetty and Lester Mackey in Sections on Statistical Computing and Statistical Graphics by American Statistical Association (ASA); also accepted for presentation at ICLR 2022 and AABI 2022 ‘
Recent presentations
From HeartSteps to HeartBeats: Personalized Decison-making | animated slides |
Statistics and Data Science Seminar, Yale University, Feb 2022
Gatsby Computational Neuroscience Seminar, University College London, Feb 2022
Operations, Information, and Technology Seminar, GSB, Stanford University, Jan 2022
Statistics Seminar, UW Madison, Jan 2022
Computer Science Seminar, UIUC, Jan 2022
Statistics and Data Science Seminar, Wharton, University of Pennsylvania, Jan 2022
Statistics and Operation Research Seminar Seminar, UNC-Chapel Hill, Jan 2022
Statistics Seminar, UCLA, Jan 2022
ORIE Seminar, Cornell University, Dec 2022
ORIE Seminar, Cornell Tech, Dec 2022
ISL Colloquium, Stanford University, Nov 2022
Bliss Seminar, UC Berkeley, Nov 2022
On Counterfactual Inference in Sequential Experiments with Nearest Neighbors — slides | poster
On Counterfactual Inference in Sequential Experiments — video | slides | poster | preprint
Royal Statistical Society (RSS) International Conference, Aberdeen, Sep 2022
RL at Harvard workshop, Aug 2022
IMS Annual Meeting, June 2022
Causal Inference Reading Group, University of Cambridge, June 2022
American Causal Inference Conference (ACIC), Berkeley, May 2022
Econometrics Lunch, MIT, Mar 2022
Learning From Interventions Workshop, Simons Institute, Feb 2022
Near-optimal Compression in Near-linear Time — video | slides | COLT paper | ICLR paper | code
(Generalized) Kernel Thinning — video | slides | poster | COLT paper | ICLR paper | code
Selected Other Topics/talks
Non-asymptotic Guarantees for High-Dimensional Sampling — slides | JMLR paper (MALA) | JMLR paper (HMC) | JMLR paper (Vaidya/John walks)
Stable Discovery of Interpretable Subgroups in Causal Studies — slides | poster | Int. Stat. Review paper
Revisiting Minimum Description Length Complexity in Overparameterized Models — video | slides | poster | preprint
Detailed Bio
(See here for a bio in third person.)
Postdoc and Ph.D.: I am currently a FODSI postdoc fellow and fortunate to be advised by Prof. Susan Murphy in the Departments of Computer Science and Statistics at Harvard, and Prof. Devavrat Shah in the Laboratory of Information Decision and Systems (LIDS), Department of EECS at MIT. I finished my Ph.D. in Summer 2021 from the Department of EECS at the UC Berkeley where I was fortunate to be advised by Prof. Martin Wainwright and Prof. Bin Yu. My thesis committee members included Prof. David Aldous and Prof. Peter Bartlett. I was also fortunate to work with several collaborators, including Prof. Michael Jordan at UC Berkeley, Lester Mackey at Microsoft Research (MSR), and Prof. David Madigan at the Northeastern University. At MIT, I am also associated with IDSS, and at Berkeley, I was also associated with the Berkeley Laboratory for Information and System Sciences (BLISS), and the Berkeley Artificial Intelligence Research group (BAIR).
Research interests: I am interested in both theoretical and applied aspects of statistical machine learning and data science and I enjoy building statistically and computationally efficient strategies for data- and computation-driven personalized decision-making with several applications in healthcare. En route, I develop theory and methods spanning the areas of causal inference, reinforcement learning, Bayesian inference, optimization, and high-dimensional statistics.
Awards: My work on distribution compression won the best student paper award in ASA sections on statistical computing and statistical graphics. For teaching, I have received the Certificate of Distinction and Excellence in Teaching (Q Award) at Harvard university in 2022, and Outstanding Graduate Student Instructor Award at UC Berkeley in 2020. I also received the prestigious Berkeley Fellowship, the highest award for the incoming graduate students, in 2015. During my graduation at IIT Bombay, I was awarded the President of India Gold Medal, the highest honor to a graduating batch of students, the Institute Silver Medal for the highest GPA, and the Best B. Tech Project Award in the EE department.
Work experience: I spent the summer of 2019 as a research intern at Microsoft Research New England. I also spent the summer of 2017 as an intern at Mist systems, Cupertino (later acquired by Juniper Networks). Before joining UC Berkeley, I worked for a year at WorldQuant Research in Mumbai, India, as a Senior Quantitative Researcher. During my undergrad, I spent the summer of 2013 at Stanford University as an intern with Prof. Balaji Prabhakar, and the winter of 2012 at Ivy Mobility.
Pre-Ph.D. life: Before UC Berkeley, I graduated from the Indian Institute of Technology, Bombay (IIT Bombay), with a B. Tech. (Honors) in Electrical Engineering and Minors in Mathematics. At IIT Bombay, I was also fortunate to work with Prof. Vivek Borkar, Prof. Pradeep Nair, and Prof. Juzer Vasi.
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