Subrata Das

About

Dr. Subrata Das, a prominent figure in the fields of artificial intelligence and data science, currently holds the position of Principal Data Scientist at Humana and serves as an adjunct faculty member at Northeastern University. His extensive career, spanning over 25 years, has encompassed significant roles in both academia and industry. Dr. Das obtained his Ph.D. from Heriot-Watt University in Scotland, marking the beginning of an illustrious journey in the industry.

In the realm of industry experience, Dr. Das has played pivotal roles, including that of Chief AI and Data Scientist at Machine Analytics and positions at Xerox and MIT Lincoln Lab. His tenure at Machine Analytics involved critical data science contracting in various sectors like healthcare, e-commerce, and legal, where he employed advanced machine learning techniques to tackle complex problems. During his time at Xerox European Research Center in Grenoble, France, as the manager of the document content laboratory, he led research and development in computer vision, machine translation, and deep linguistic processing. Additionally, his work at the MIT Lincoln Lab as a technology consultant further underscores his expertise in the field.

Dr. Das’s academic contributions are equally impressive. He held a postdoctoral position at Imperial College, University of London, where he delved into advanced research. His commitment to education is evident in his role at Northeastern University, where he teaches courses like CSYE7380 – Theory & Practical Applications in AI Generative Modeling, and INFO7610 – Natural Language Engineering Methods/Tools. His teaching blends theoretical knowledge with practical application, offering students a comprehensive learning experience.

A prolific author, Dr. Das has written several influential books, including “Computational Business Analytics” (CRC Press/Chapman and Hall), “High-Level Data Fusion” (Artech House), “Foundations of Decision-Making Agents” (World Scientific/Imperial College Press), “Deductive Databases and Logic Programming” (Addison-Wesley), and co-authored “Safe and Sound: AI in Hazardous Applications” (MIT Press). These works reflect his practical yet theoretically robust approach to big data analytics, regularly employing advanced techniques like CNN, ResNet, BERT/Transformer, and GAN in his solutions.

Connect with Dr. Das on LinkedIn to stay informed about his ongoing contributions to the rapidly evolving domains of data science and artificial intelligence.