Subrata Das

About Subrata Das

Subrata takes a practical and efficient - yet theoretically well-founded - approach to big data analytics, always keeping stakeholders in the loop and business goals in focus. He is both a visionary leader, setting strategic direction, and a hands-on contributor.He has applied cutting-edge techniques in artificial intelligence, machine and deep learning, reinforcement learning, natural language processing, generative AI, and image analysis to address complex industrial challenges. His work regularly incorporates advanced deep neural and Gen AI models as well as a variety of statistical methods including Bayesian inference, regression, and clustering.Subrata has conceived and led teams in the development of several commercial products and software packages in agile environments, using technologies such as Python, Java, R, MySQL, Keras/TensorFlow, Scikit-learn, and AWS. These data products have been successfully deployed across multiple industries, including finance, law, healthcare, eCommerce, and defense. Subrata has served as Chief Data Scientist at multiple companies, as AI Lab Manager at Xerox, and as a technology consultant at MIT Lincoln Laboratory. He spent two years in Grenoble, France, as Manager of the Document Content Laboratory at the Xerox European Research Center, guiding applied R&D in computer vision, machine translation, and deep linguistic processing. Subrata was one of five members of the high-profile Xerox task force Knowledge Work 2020, collaborating with colleagues from the Palo Alto Research Center (PARC) to explore the future of knowledge work.Earlier in his career, he served as Chief Scientist at Charles River Analytics in Cambridge, MA, leading data fusion and analytics projects funded by DARPA, NASA, and various branches of the U.S. Department of Defense. He also held a postdoctoral position at Imperial College London.Subrata currently teaches a graduate course in NLP and Gen AI at Northeastern University as a part-time lecturer and has previously taught at Villanova University.He is the author of several books, including Computational Business Analytics (Chapman & Hall/CRC Press), High-Level Data Fusion (Artech House), Foundations of Decision-Making Agents (World Scientific/Imperial College Press), and Deductive Databases and Logic Programming (Addison-Wesley). He also co-authored Safe and Sound: AI in Hazardous Applications, published by the MIT Press, with a foreword by Nobel laureate Herbert Simon.Subrata frequently delivers seminars and training programs in AI, data science, and analytics.

Experience

1. Principal Data Scientist

   Humana

   Apr 2023 — Present · Boston, MA, US

   Actively designing and developing AI solutions in areas such as customer segmentation, roster standardization, provider credentialing, prescription drug planning, and next best action - leveraging a range of Machine Learning, Deep Learning, Natural Language Processing, Reinforcement Learning, and Generative AI techniques. The deployed solutions and ongoing projects have delivered concrete, actionable intelligence to enhance operational efficiency, improve customer service, and drive membership growth, resulting in tens of millions of dollars in savings and increased revenue.

Education

• Indian Statistical Institute, Kolkata

  M.Tech, Computer Science

• Heriot-Watt University

  PhD, Artificial Intelligence and Databases

• University of Calcutta

  M.Sc, Mathematics

Skills

• Software Development
• Python
• Latex
• Artificial Intelligence
• High Performance Computing
• Image Processing
• System Architecture
• Databases
• Text Mining
• Business Analytics
• Data Fusion
• Deep Learning
• Hadoop
• Sql
• Big Data
• R
• Programming
• Optimization
• Scalability
• Information Retrieval
• Software Engineering
• Signal Processing
• Pattern Recognition
• Predictive Analytics
• Data Science
• Mapreduce
• Competency Management
• Management
• Java
• Information Extraction
• Algorithms
• Data Mining
• Cloud Computing
• Intelligent Agents
• Mathematical Logic
• Distributed Systems
• C++
• Predictive Modeling
• Text Analytics
• Statistical Modeling