Deepayan received his M.S. and Ph.D. in Computational and Statistical Learning from Carnegie Mellon University in 2005, after a B.Tech. in Computer Science from IIT Kanpur in 2000. He subsequently worked at Yahoo! Research and Facebook, before joining the McCombs School of Business at the University of Texas, Austin as an Assistant Professor in 2014. He has authored about 40 peer-reviewed publications, 20 patents, 3 book chapters, and 1 book. He is a Siebel Scholar (class of 2002). his COLT 2010 paper received the best student paper award, and his R-MAT graph generator is the basis for the Graph500 supercomputer benchmark.
He works on a broad range of problems in Machine Learning and Data Mining, particularly focusing on mining large graphs and social networks, computational advertising, recommendation systems, and web search and information retrieval.
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|Deepayan Chakrabarti, Xueyu Mao, and Puramrita Sarkar. Estimating Mixed Memberships with Sharp Eigenvector Deviations. Journal of the American Statistical Association , forthcoming.|
|Deepayan Chakrabarti. Parameter-free Robust Optimization for the Maximum-Sharpe Portfolio Problem. European Journal of Operational Research, forthcoming.|
|Long Zhao, Deepayan Chakrabarti, and Kumar Muthuraman. 2019. Portfolio Construction by Mitigating Error Amplification: The Bounded-Noise Portfolio. Operations Research 67(4), 965-983.|
|Deepayan Chakrabarti, Stanislav Funiak, Jonathan Chang, and Sofus A. Macskassy. 2017. Joint Label Inference in Networks. Journal of Machine Learning 18(54-63), 1-39.|