☵ Avradeep Bhowmik
I am currently an Applied Scientist at Amazon and part of the Sponsored Brands Advertising organisation since 2020. In my current team I use machine learning and auction theory to build data-driven models for various business products including smart bidding and bidding optimization, reserve pricing, seasonally adjusted auction pricing among others.
Prior to this, I was a Research Scientist at Criteo AI Labs where I worked on Machine Learning, Data Science and related areas. My projects involved gradient-boosted tree-based models for response modelling in online advertising, and using RNN's (LSTM's, GRU, etc) and language models (ELMo, BERT, etc.) for automated taxonomic product categorisation.
Before joining industry, I obtained a PhD degree from The University of Texas at Austin where my thesis focused on privacy-preserving learning from aggregated/obfuscated data. I was supervised by Prof. Joydeep Ghosh, and was affiliated with the Intelligent Data Exploration and Analysis Laboratory (IDEA Lab) and the Wireless Networking and Communications Group (WNCG) between 2013--2018.
I received my Master of Science (M.S.) degree from the Department of Electrical and Computer Engineering at UT Austin in 2016. Prior to graduate school, I received a Bachelor of Technology (B.Tech.) in Electrical Engineering from the Indian Institute of Technology, Bombay (IIT Bombay) in 2013. At IIT Bombay, I worked on my undergraduate thesis under the supervision of Prof. Vivek Borkar.
☵ Interests
I am broadly interested in machine learning, data mining, statistical inference and related fields. In my education and my professional life, I have had a specific focus on machine learning and data mining in two domains-- online advertising and healthcare-- that included techniques like gradient boosted decision trees for bid-optimisation, recommendation systems, data reconstruction algorithms, ranking and rank aggregation, and learning with obfuscated data. In parallel, I have worked on language models using state-of-the-art deep neural networks like RNN's--GRU's and LSTM's-- and Transformers (including a hobby project training a Shakespeare chatbot/text generator). In the past I have also worked on applications involving sparse modelling, image processing, language models, hierarchical learning and geometric maps for recommender systems, and submodular optimisation techniques applied to problems in operations research.
☵ Publications 
KD Doan, S Manchanda, F Wang, S Keerthi, A Bhowmik, CK Reddy, “Image Generation Via Minimizing Frechet Distance in Discriminator Feature Space”,
(Under Review)
S Badirli, X Liu, Z Xing, A Bhowmik, K Doan, SS Keerthi, “Gradient boosting neural networks: Grownet”,
(Under Review)
A Bhowmik, J. Ghosh, O. Koyejo, “An Aggregation Framework for Predictive Modelling with Non-Retention Constraints for Sensitive Data”,
(Under Review)
A Bhowmik, Z Xing, S. Rajan, “A General Framework for Learning Under Taxonomy”,
(Under Review)
A Bhowmik, M Chen, Z Xing, S. Rajan, “EstImAgg: A Learning Framework for Groupwise Aggregated Data”, In Proceedings of the 2019 SIAM International Conference on Data Mining (SDM), Calgary, Alberta, Canada, May 2-4, 2019
[Link]
A Bhowmik, J Ghosh, O Koyejo, “Frequency Domain Predictive Modelling with Aggregated Data”, In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017, Fort Lauderdale, Florida, April 20-22, 2017
[PDF] [Supplement]
A Bhowmik, J Ghosh, “LETOR Methods for Unsupervised Rank Aggregation”, In Proceedings of the 26th International World Wide Web Conference (WWW) 2017, Perth, Australia, April 3-7, 2017
[Link] [PDF]
A Bhowmik, J Ghosh, O Koyejo, “Sparse Parameter Recovery from Aggregated Data”, In Proceedings of the 33rd International Conference on Machine Learning (ICML) 2016, New York City, NY, USA, June 19-24, 2016
[PDF] [Supplement]
A Bhowmik, N Liu, E Zhong, B N Bhaskar, S Rajan, “Geometry Aware Mappings for High Dimensional Sparse Factors”,, In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, Cadiz, Spain, May 9-11, 2016
[PDF] [Supplement]
A Bhowmik, J Ghosh, O Koyejo, “Generalized Linear Models for Aggregated Data”, In Proceedingsof the 18th International Conference on Artificial Intelligence and Statistics, (AISTATS) 2015,San Diego, California, May 9-12, 2015, (Oral presentation)
[PDF]
A Bhowmik, V Borkar, D Garg, M Pallan, “Submodularity in the Team Formation Problem”,, In Proceedings of the 2014 SIAM International Conference on Data Mining (SDM), Philadelphia,Pennsylvania, April 24-26, 2014
[Link] [PDF] [Supplement]
☵ Work Experience 
February 2020 — present |
Applied Scientist
|
March 2019 — Dec 2019 |
Research Scientist
|
Sep 2013 — Dec 2018 |
Graduate Research Assistant
|
May 2017 — Aug 2017 |
Research Scientist Intern
|
Jun 2016 — Aug 2016 |
Intern Scientist
|
May 2015 — Aug 2015 |
Research Intern
|
May 2012 — Aug 2012 |
Visiting Scientist
|
☵ Contact 
Email: |
avradeep [dot] 1 [at] gmail [dot] com |
Phone: |
+1 (five one two) 300-4487 |
