Senior Research Scientist @Netflix
(Search and Recommendation Algorithms) 

Adjunct Faculty at Data Science Institute @UDel

My  research focus on developing recommender systems and search algorithms for large scale platforms [Video] [Video] [Video]. PhD research focused on Machine Learning applications in healthcare to provide personalized medical care. [Video]

Organizer:

1.  Organized a workshop on Personalization and Recommendations in Search (PaRiS) three years [Website] at SIGIR 2024, TheWebConf 2023, WSDM 2022.

2. Organized a tutorial on Deep Search Relevance Ranking in Practice [Website] at KDD 2022. 

Program Committee:

PC Member of RecSys, WSDM, TheWebConf; Reviewer of several conf and journals including BMC Medical Informatics and AMIA

Publications and Talks: [For complete list click here]

1) M. Bhattacharya*, Vito Ostuni*, S. Lamkhede. Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn). In Proc. of  RecSys. 

2) Sejoon Oh, Moumita Bhattacharya, Yesu Feng, Sudarshan Lamkhede. IntentRec: Predicting User Session Intent with Hierarchical Multi-Task Learning. arXiv link.

3) M. Bhattacharya, S. Lamkhede. Augmenting Netflix Search with In-Session Adapted Recommendations (link). In Proc. of RecSys 2022.

4) M. Bhattacharya, A. Barapatre. Query as Context for Item-to-Item Recommendation. In Proc. of  RecSys 2020.

5) R. Louca*, M. Bhattacharya*, D. Hu, L. Hong. Joint Optimization of Profit and Relevance for Recommendation Systems in E-commerce. In Proc. of  RecSys.

6) M. Bhattacharya, C. Jurkovitz and H. Shatkay. Co-occurrence of Medical Conditions: Exposing Patterns by Topic-Modeling of SNOMED Codes. Journal of Biomedical Informatics. 2018; 82: 31-40 .

7) M. Bhattacharya, C. Jurkovitz and H. Shatkay. Chronic Kidney Disease Stratification using Office Visit Records: Handling Data Imbalance via Hierarchical Meta-Classification. Journal of BMC Medical Informatics and Decision Making. 2018; 18 (Suppl 4), 35-44

8) M. Bhattacharya, D. Lu, P. Lingamaneni, et al. Identifying Ventricular Arrhythmia Cases and their Predictors by Applying Machine Learning Methods to Electronic Health Records of Hypertrophic Cardiomyopathy Patients. The American Journal of Cardiology. 2019; 123(10), 1681-1689

Honors and Awards: [For complete list click here]

• Hosted the 7th Heidelberg Laureate Forum opening ceremony in Heidelberg, Germany (Video)

• Heidelberg Laureate Forum Finalist (2018)– Selected to be among the top 100 young computer scientists worldwide participating in the 6th Heidelberg Laureate Forum, following a rigorous selection process by the scientific committee of the HLF Foundation, and ORAU and NSF (Article)

• Frank A. Pehrson Graduate Student Award for Outstanding Computer Science Research (2018) – Monetary award given annually to a CISC PhD student in recognition of his/her scientific research and potential for future success.  

• Doctoral Fellowship Award (2017-18)  - Awarded by the University of Delaware through a competitive selection process from candidates nominated by individual departments

• Lauri Pfeffer Shinn Memorial Award (2017) – Monetary award given annually to a female student in recognition of her contribution to the department, her academic honors and achievements

Industry Experience: [For a complete list click here]

• Currently working as a Senior Research Scientist at Netflix Research

• Before this worked as a senior data scientist at Etsy, a two sided marketplace 

• Also have 3 years of software engineering experience in  large corporations 

Article about my PhD research project in Science Daily etc.:

• Science Daily:Big data, better care for chronic kidney disease patients

• CKD News: Analysis of Merged Electronic Health Records Aims to Improve CKD Patient Care

• Big Data, Better Care for Chronic Kidney Disease Patients - UD computer scientists team with clinicians to improve treatment and clinical outcomes

• UDaily:Big data, better care for chronic kidney disease patients