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Department
Computer Science
College
Arts & Sciences
Start Year at LSUS
2020
Terminal Degree/Yr
PhD/2020
Office Location
TC 250
318-795-4283

Biography

Subhajit Chakrabarty holds a Ph.D. in Computer Science from the University of Massachusetts Lowell, MA, USA and an earlier Ph.D. in International Business from the Indian Institute of Foreign Trade, New Delhi, India. He is also an alumnus of INSEAD (France/Singapore).


He has interests and expertise ranging from data science, machine learning, deep learning, data mining, algorithms, time series, bioinformatics, neuroscience, econometrics, cybersecurity and market research. His recent research is on biomedical applications of deep learning.


Subhajit has a working experience of about 3 decades, mostly in Information Technology management. He worked in the insurance industry, in government (armed forces), and in corporate, prior to moving to full-time teaching and research. In the armed forces (Border Security Force and National Security Guard, India) he served as Deputy Commandant. He was Director, IT & IS in a company. In academia, he served as Associate Professor at two universities in India. He moved to the USA in 2016 to follow his passion for research in data science and machine learning.


Subhajit is in academia, full-time, as a computer science faculty member and program director of the graduate program at LSU Shreveport. He is keen to contribute to industry and society through consulting and guiding student research.

Degrees

Research Interests

Data science, Machine learning, Deep learning, Data mining, Algorithms, Time series, Bioinformatics, Neuroscience, Econometrics, Cybersecurity and Market research

Selected Publications

SELECTED PUBLICATIONS
Books
• “Introduction to Data Analytics”, 2022, Chakrabarty, Subhajit, ISBN 9789390457670, Dreamtech Press, New Delhi (university textbook for NGASCE, Narsee Monjee Institute of Management Studies, Mumbai)

Book Chapters and Journals
• “Clustering methods in business intelligence”, 2017, “Global Business Intelligence”, Edited J. Mark Munoz, New York, NY: Routledge Taylor & Francis, ISBN 9780367889814
• “Geoeconomics: a review of the research methodologies of trade alliances”, 2017, “Advances in Geoeconomics”, Edited J. Mark Munoz, New York, NY: Routledge Taylor & Francis, ISBN 9780367876630
• “Determinants and Relationships in Sectoral Trade – A Bilateral Model for Knitwear Clothing”, 2016, Thunderbird International Business Review; Subhajit Chakrabarty, Biswajit Nag, Pinaki Dasgupta and Sidharth K. Rastogi; doi: 10.1002/tie.21787

Conferences (peer-reviewed)
• Chakrabarty S, et al. Stacking ensemble with transformer network and transfer learning for stock volatility forecasting. Southeast Decision Sciences Institute 2022; 16 - 18 Feb 2022; Jacksonville, FL.
• Chakrabarty S, Levkowitz H. A New Algorithm using Independent Components for Classification and Prediction of High Dimensional Data. 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 3: IVAPP; 265-272; 2020 February; Valletta, Malta; .
• Chakrabarty S, Levkowitz H. A New Index for Measuring Inconsistencies in Independent Component Analysis Using Multi-sensor Data. Cooperative Design, Visualization, and Engineering. CDVE 2019. Lecture Notes in Computer Science, Volume 11792. Springer, Cham; 98-107; 2019 October; Mallorca, Spain; .
• Chakrabarty S, Levkowitz H. Denoising and Stability using Independent Component Analysis in High Dimensions – Visual Inspection Still Required. 2019 23rd International Conference Information Visualisation (IV); 181-185; 2019; Paris, France; .
• Chakrabarty, Subhajit and Martin, Fred. Role of Prior Experience on Student Performance in the Introductory Undergraduate CS Course. SIGCSE, 1075, ACM (2018). .
• Martin, Fred, et al. The Tablet Game: An Embedded Assessment for Measuring Students' Programming Skill in App Inventor. SIGCSE, 1095, ACM (2018). .

Teaching Assignments

Introduction to Programming, CSC 120 (Fall and Spring)
Database Implementation, CSC 425/625 (Summer)
Intro Machine Learning, CSC 467/667 (Fall)
Deep learning, CSC 469/669 (Spring)

Previously:
Intro Database, CSC 315
Data mining, CSC 468/668
Ethical hacking, CSC 440/640

Office Hours

Mon/Wed: 9.00 am to 11.30 am

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