Abhimanyu Das

Email: {first four characters of my first name followed by dot followed by my last name}@gmail.com

I am a Research Scientist at Google. My research interests span topics in machine learning, data-mining and theoretical computer science. Prior to Google, I was a Researcher at Microsoft Research and at Yahoo! Labs.

I obtained a PhD in Computer Science from the University of Southern California under the supervision of David Kempe, and a B. Tech in Computer Science from IIT Delhi. My PhD research focused on algorithms for subset selection and sparse approximation. Prior to my PhD, I also spent several years in the industry (Cortina Systems and Mahi Networks), where I worked on software stacks for networking and embedded systems.

My work has received a Distinguished Paper Award at the International Conference on Machine Learning (ICML) 2011, a Best Paper Award at the ACM Conference on Web Search and Data Mining (WSDM) 2014, and a Best Paper Honorable Mention Award at the Web Conference (WWW) 2018.

Selected Publications (full list on DBLP):

  • Pranjal Awasthi, Abhimanyu Das, Rajat Sen, Ananda Theertha Suresh: "On the benefits of maximum likelihood estimation for Regression and Forecasting", ICLR 2022

  • Ashok Cutkosky, Chris Dann, Abhimanyu Das, Richard Zhang: "Leveraging Initial Hints for Free in Stochastic Linear Bandits", ALT 2022

  • Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi: "Beyond GNNs: An Efficient Architecture for Graph Problems", AAAI 2022

  • Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi: "A Convergence Analysis of Gradient Descent on Graph Neural Networks", NeurIPS 2021

  • Ayya Alieva, Ashok Cutkosky, Abhimanyu Das: "Robust Pure Exploration in Linear Bandits with Limited Budget", ICML 2021

  • Ashok Cutkosky, Chris Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit: "Dynamic Balancing for Model Selection in Bandits and Reinforcement Learning", ICML 2021

  • Atish Agarwala, Abhimanyu Das, Brendan Juba, Rina Panigrahy, Vatsal Sharan, Xin Wang, Richard Zhang: "One network fits all? Modular versus monolithic task formulations in neural networks", ICLR 2021

  • Abhimanyu Das, Sreenivas Gollapudi, Ravi Kumar, Rina Panigrahy: "On the Learnability of Random Deep Networks", ACM SODA 2020

  • Abhimanyu Das, Sreenivas Gollapudi, Anthony Kim, Debmalya Panigrahi, Chaitanya Swamy: "Minimizing Latency in Online Ride and Delivery Services", WWW 2018 (best paper honorable mention)

  • Flavio Chierichetti, Abhimanyu Das, Anirban Dasgupta, Ravi Kumar: “Approximate Modularity”, IEEE FOCS 2015

  • Abhimanyu Das, Anitha Kannan, “Discovering Topical Aspects in Microblogs”, COLING 2014

  • Abhimanyu Das, Sreenivas Gollapudi, Arindam Khan, Renato Paes Leme, “Identifying Conformity in Social Networks”, ACM COSN 2014

  • James Cook, Abhimanyu Das, Krishnaram Kenthapadi, Nina Mishra, “Ranking Twitter Discussion Groups”, ACM COSN 2014

  • Abhimanyu Das, Sreenivas Gollapudi, Kamesh Munagala, “Modeling Opinion Dynamics in Social Networks”, WSDM 2014.

  • Amr Ahmed, Abhimanyu Das, Alexander Smola, “Hierarchical multitask learning: scalable algorithms and applications in display advertising”, WSDM 2014 (best paper award).

  • Abhimanyu Das, Sreenivas Gollapudi, Rina Panigrahy, Mahyar Salek, “Debiasing Social Wisdom”, KDD 2013.

  • Amr Ahmed, Abhimanyu Das, Mohammed Aly, Tasos Anastasakos, Alex Smola, “Terascale Multi-task Feature Selection for Behavioral Targeting and Advertising”, CIKM 2012.

  • Neha Gupta, Abhimanyu Das, Sandeep Pandey, Vijay Narayanan, “Factoring Past Exposure in Display Advertising Targeting”, KDD 2012.

  • Abhimanyu Das, Anirban Dasgupta, Ravi Kumar, “Selecting Diverse Features using Spectral Regularization”, NIPS 2012

  • Abhimanyu Das, David Kempe: "Submodular meets Spectral – Greedy Algorithms for Subset Selection and Sparse Approximation ", ICML 2011 (distinguished paper award)

  • Abhimanyu Das, David Kempe: "Estimating the Average of a Lipschitz-Continuous Function from One Sample", ESA 2010

  • Abhimanyu Das, David Kempe: "Algorithms for Subset Selection in Linear Regression, ACM Symposium on Theory of Computing, STOC 2008