Raaz RSK Dwivedi

Coming soon! 

News

  • I am on the job market this year! Here are my statements — research (short / long), teaching, and diversity (upon request) — and CV

  • Upcoming presentations:

    • Jan 10 (talk): Presenting in the Statistics Seminar at UCLA!

    • Jan 13 (talk): Presenting in the Statistics and Operation Research Seminar at UNC-Chapel Hill!

    • Jan 18 (talk): Presenting in the Statistics and Data Science Seminar at Wharton, University of Pennsylvania!

    • Jan 20 (talk): Presenting in the Statistics Research Seminar at University of Chicago!

    • Jan 25 (talk): Presenting in the Operations, Information, and Technology Group at GSB, Stanford University!

    • Jan 30 (talk): Presenting in the Statistics Seminar at UW Madison!

    • Feb 2 (talk): Presenting in the Computer Science Seminar at UIUC!

    • Feb 6 (talk): Presenting in the Statistics and Data Science Seminar at Yale University!

    • Feb 7 (talk): Presenting in the Gatsby Computational Neuroscience Unit at University College London!

Recent presentations

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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