Applied Scientist at Microsoft
PhD in Computer Science
and
MS in Statistics
University of Minnesota - Twin Cities
Embedded Files
Somya Sharma Chatterjee
Somya Sharma Chatterjee
(Formerly Somya Sharma)
Education
Education
I am an incoming applied scientist at Microsoft's AI Development and Acceleration Program. I completed my PhD in Computer Science at the University of Minnesota - Twin Cities, co-advised by Vipin Kumar and Snigdhansu (Ansu) Chatterjee. I received my MS in Statistics from the School of Statistics at the University of Minnesota - Twin Cities where I researched with Snigdhansu (Ansu) Chatterjee on precision agriculture and statistical machine learning. I received my BS in Statistics (Honors) from the University of Delhi.
My PhD research focuses on generative modeling and explainable AI for deep learning applications in physical and geosciences research. I also worked in the Research for Industry team at Microsoft Research and collaborated with the Global Soil Health Program on modeling soil health using causal structure learning and causally adaptive, large-scale deep learning models. My research addresses the challenges in Precision Agriculture, Wildfire Management, Hydrology, and Molecular Science.
Recognition
Recognition
My research has been presented at several highly selective, peer-reviewed ML conferences like NeurIPS, KDD, SDM, and IISA. Some of my research also won the Doctoral Dissertation Award at the University of Minnesota, the Best Paper Award, and the Doctoral Forum Award at SIAM Internation Conference on Data Mining, has received media coverage (Microsoft, MetroTransit, NOAA NWS news), and has been highlighted as spotlight talks at NeurIPS Workshop on Tackling Climate Change. My research has been awarded university research fellowship awards (e.g., Doctoral Dissertation Research Fellowship) and funded by NSF and Cisco Research. I contributed to the writing of an NSF grant (#2313174) that partially funded my doctoral research. I received my BS in Statistics from the University of Delhi where I was awarded the Award of Academic Excellence and Certificate of Merit.
News
News
Selected Experience
Selected Experience
Graduate Appointments
Research Assistant (2019 - Present)
Teaching Assistant (2019 - 2021)
Industrial Experience
Microsoft Research - Research Scientist Internship (2023)
Microsoft Research - Research Scientist Internship (2022-2023)
Allianz Life - Data Scientist Internship ( 2020, 2021)
Metro Transit - Data Scientist Internship (2020)
Research and Selected Publications
Research and Selected Publications
My research focuses on developing new data-driven technologies for physical sciences and geosciences. I have developed new methods in machine learning and statistics, and leveraged these approaches to accelerate scientific discovery in material discovery, wildfire management, precision agriculture and hydrology.
Prescribed Fire Modeling using Knowledge-Guided Machine Learning for Land Management (Best Paper Award)
Somya Sharma Chatterjee, Kelly Lindsay, Neel Chatterjee, Rohan Patil, Ilkay Altintas De Callafon, Michael Steinbach, Daniel Giron, Mai H Nguyen, Vipin Kumar
Proceedings of the 2024 SIAM International Conference on Data Mining (SDM)
Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization
Somya Sharma, Swati Sharma, Rafael Padilha, Emre Kiciman, Ranveer Chandra
NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning: Blending New and Existing Knowledge Systems
Uncertainty Quantification in Inverse Models in Hydrology
Somya Sharma Chatterjee, Rahul Ghosh, Arvind Renganathan, Xiang Li, Snigdhansu Chatterjee, John Nieber, Christopher Duffy, Vipin Kumar
29th Association for Computing Machinery SIGKDD conference on knowledge discovery and data mining - PhD Consortium
Knowledge Guided Representation Learning and Causal Structure Learning in Soil Science
Somya Sharma, Swati Sharma, Licheng Liu, Rishabh Tushir, Andy Neal, Robert Ness, John Crawford, Emre Kiciman, Ranveer Chandra
ArXiv
Entity Aware Modelling: A Survey
Rahul Ghosh, Haoyu Yang, Ankush Khandelwal, Erhu He, Arvind Renganathan, Somya Sharma, Xiaowei Jia, Vipin Kuma
ArXiv
Probabilistic Inverse Modeling: An Application in Hydrology
Somya Sharma, Rahul Ghosh, Arvind Renganathan, Xiang Li, Snigdhansu Chatterjee, John Nieber, Christopher Duffy, Vipin Kumar
Proceedings of the 2023 SIAM International Conference on Data Mining (SDM)
Causal Modeling of Soil Processes for Improved Generalization
Somya Sharma, Swati Sharma, Andy Neal, Sara Malvar, Eduardo Rodrigues, John Crawford, Emre Kiciman, Ranveer Chandra
NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning
Data-Driven Approaches to Improve Understanding of Process-based Models and Decision Making
Somya Sharma, Swati Sharma, Emre Kiciman, Ranveer Chandra
US Patent App 63/394,946
Machine Learning Methods for Multiscale Physics and Urban Engineering Problems
Somya Sharma, Marten Thompson, Debra Laefer, Michael Lawler, Kevin McIlhany, Olivier Pauluis, Dallas R Trinkle, Snigdhansu Chatterjee
Entropy 23 (11), 1546
Approximate Bayesian Computation for Physical Inverse Modeling
Neel Chatterjee, Somya Sharma, Sarah Swisher, Snigdhansu Chatterjee
NeurIPS 2021 Fourth Workshop on Machine Learning and the Physical Sciences
Winsorization for robust Bayesian neural networks
Somya Sharma, Snigdhansu Chatterjee
Entropy 23 (11), 1546
Corn Yield Prediction in US Midwest Using Artificial Neural Networks
Somya Sharma, Snigdhansu Chatterjee
Pacific-Asia Conference on Knowledge Discovery and Data Mining, Workshop on Smart & Precise Agriculture
Teaching
Teaching
Graduate Teaching Assistant at the Department of Computer Science and Engineering, UMN
Introduction to Data Mining (CSCI 5523) in Spring and Fall 2021
Introduction to Machine Learning (CSCI 5521) in Fall 2020
Student Instructor for "Run the World" Machine Learning camp for High Schoolers organized by the Minnesota Center for Financial and Actuarial Mathematics (MCFAM) and Institute of Research in Statistics and its Applications (IRSA)
Service
Service
Public and Other Service
1. Reviewer for Council of Graduate Students – Research and Career Fellowship Grants. 2021, 2022, 2023.
2. Co-founder of International Graduate Students Organization – UMANG at UMN. 2021.
3. Co-founder of Association for Gender Diversity in Science at UMN. 2022.
4. Student Mentor for new graduate students, School of Statistics. 2020, 2021.
5. Student Mentor for USSSTC Underrepresented Students in STEM Symposium. 2021, 2022.
Conference and Journal Review Service
1. Reviewer for Synergy of Scientific and Machine Learning Modelling (SynS & ML) Workshop at the International Conference on Machine Learning. 2023.
2. Session Chair for SIAM International Conference on Data Mining. 2023
Reviewer for Thirty-sixth Conference on Neural Information Processing Systems. 2022.
3. Reviewer for International Conference on Artificial Intelligence and Statistics. 2021, 2022, 2023.
4. Reviewer for Journal of the Indian Society of Agricultural Statistics. 2021, 2022.
5. Reviewer for Engineering Applications of Artificial Intelligence. 2022.
6. Reviewer for International Conference on Machine Learning. 2021.
7. Reviewer for Entropy. 2020, 2021.
8. Program Committee for AAAI Conference on Artificial Intelligence Fall Symposium Series. 2022.
9. Reviewer for AAAI Conference on Artificial Intelligence Fall Symposium Series. 2021.
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