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Snehasis Mukhopadhyay, Computer Science Professor

Education

  • B.E. Electronics and Telecommunications Engineering, Jadavpur University, 1985
  • M.E. Systems Science and Automation, Indian Institute of Science, 1987
  • M.S. Electrical Engineering, Yale University, 1991
  • Ph.D. Electrical Engineering, Yale University, 1994

Courses Taught / Teaching

  • CSCI 54900 Intelligent Systems (Fall 2017)

Research

Research Areas

Database, Data Mining & Machine Learning (DDML) Research Group

Current Projects

(i) Machine Learning

Title: Fast Reinforcement Learning Using Multiple Models and State Decomposition

Source of support: National Science Foundation (NSF)

Collaboration: Yale University

Abstract: Intelligent behavior in both natural and man-made systems consists in being repeatedly successful in achieving some desired goals in diverse, observably different situations on the basis of past experience. Learning is central to such behavior, since in both cases, mechanisms have to exist which yield rapid improvement with minimum a priori information. In fact, organizing, coordinating, and executing diverse tasks such as manipulation of effectors, obstacle avoidance, path planning, scene analysis, tracking which are common to both classes of systems, involve learning. Ever since the time of Estes, mathematical learning theory has treated learning and performance as stochastic processes. Asymptotic properties of such processes have been used both descriptively and prescriptively in models of learning. This project deals with a class of such learning systems of great interest at present time. The slow speed of convergence of stochastic learning schemes is well recognized by the learning community. Equally well known is the fact that this limits the applicability of the schemes in many practical situations. The principal objective of this project is to address this important problem using two different methods: (a) the use of multiple identification models, and (b) decomposition of high dimensional state and action spaces. The project elaborates on the different ways in which (a) can be used to improve convergence. In (b) multiple agents with lower dimensional state spaces are used in place of high dimensional state and action spaces to overcome "the curse of dimensionality". Also, judicious infrequent communication between agents is also proposed to speed up convergence. An important off-shoot of this research is the possibility quantifying the trade-off between learning speed and the quality of the "learned solutions". The research described above will find application in situations where rapid learning is mandatory. One such area is the control of a fleet of Plug-in, Hybrid, Electric Vehicles (PHEVs). Given a fleet of vehicles, the objective reduces to a complex optimization problem of orchestrating switching between internal combustion engines and electric engines, under a variety of constraints.

(ii) Intelligent Systems

Title: A secure decision support system for coordination of adaptation planning among Food, Energy, and Water actors in the Pacific Northwest

Source of Funding: National Science Foundation (NSF)/United States Department of Agriculture (USDA)

Collaboration: Oregon State University

Abstract: Given the increasingly strong evidence for emerging climate change and economic trends, coordination of adaptation decisions for managing limited natural resources - such as water and arable land - in food, energy, and water (FEW) sectors, are expected to become increasingly critical. The goal of this project is to establish a novel, intelligent, secure, and human computation-based decision support system that will enable local and regional community actors to coordinate and co-identify robust adaptation decisions for natural resources management in FEW systems, when chronic and/or acute physical and socio-economic perturbations occur. While most studies investigate adaptation at global or regional scale, this study focuses on adaptation to climate-related and policy related perturbations in local FEW systems (where communities are most invested). The PIs will collaborate to investigate five specific interdisciplinary research objectives with pertinent research questions, one outreach objective, and one education objective. In summary, the approach will first develop formulation of interlinked adaptation decisions in interlinked FEW sub-systems, stakeholder-related parameters, and perturbation scenarios for the testbed site in Hermiston, Oregon. Next, novel mathematical and computational approaches for human computation-based Multidisciplinary Design Optimization methods and trust management models will be created, and tested for their effectiveness in enabling coordination of decisions and stakeholder participation in a secure design environment. A tightly integrated plan for research and education is enabled by the participation of undergraduate and high school underrepresented and minority students, along with stakeholder groups in various research tasks.

Related Project Website: https://wrestore.iupui.edu/

 

Publications & Professional Activities

Research Publications

1. S. Mukhopadhyay and M.A.L. Thathachar, "Associative Learning 
of Boolean Functions'', IEEE Transactions on Systems, Man and 
Cybernetics, pp.1008--1015, Vol.19, No.5, September/October,1989. 
  
2. V. J. Lumelsky, S. Mukhopadhyay and K. Sun, "Dynamic Path Planning 
in Sensor-Based Terrain Acquisition'', IEEE Transactions on Robotics 
and Automation, pp.462--472, Vol.6, No.4, August, 1990. 
  
3. K. S. Narendra and S. Mukhopadhyay, "Associative Learning in 
Random Environments Using Neural Networks'', IEEE Transactions on 
Neural Networks, pp.20--31, Vol.2, No.1, January, 1991. 

4. K. S. Narendra and S. Mukhopadhyay, "Intelligent Control Using 
Neural Networks'', IEEE Control Systems Magazine, pp. 11--18, 
April, 1992. (Also in Neuro-Control Systems: Theory and Applications. 
A Selected Reprinted Volume., Eds. M. M. Gupta and D. H. Rao, IEEE Press, 
1994). 
  
5. S. Mukhopadhyay and K. S. Narendra, "Disturbance Rejection in 
Nonlinear Systems Using Neural Networks'', 
IEEE Transactions on 
Neural Networks, pp. 63--72, January, 1993. 
  
6. K. S. Narendra and S. Mukhopadhyay, "Adaptive Control of Nonlinear 
Multivariable Systems Using Neural Networks'', invited article, 
Neural Networks, vol. 7, no. 5, pp. 737--752, 1994. 
  
7. K. S. Narendra and S. Mukhopadhyay. 
"Intelligent Control Using Neural Networks''. Book chapter, 
in Intelligent Control Systems, Eds. M. M. 
Gupta and N. K. Sinha, IEEE Press, 1995. 
  
8. K. S. Narendra and S. Mukhopadhyay. "Adaptive Control Using 
Neural Networks and Approximate Models'', 
IEEE Transactions on 
Neural Networks, vol. 8, no. 3, pp. 475--485, May, 1997. 
  
9. J. Mostafa, S. Mukhopadhyay, W. Lam and M. Palakal. 
"A Multi-level Approach to Intelligent Information 
Filtering: Model, System, and Evaluation''. 
ACM Transactions on 
Information Systems, vol. 15, no. 4, pp. 368--399, October, 1997. 
  
10. R. Raje, S. Mukhopadhyay , M. Boyles, A. Papiez, 
N. Patel, M. Palakal, 
and J. Mostafa. "A Bidding Mechanism for Web-Based Agents Involved 
in Information Classification''. World Wide Web Journal, Special 
Issue on Distributed World Wide Web Processing: Applications and 
Techniques for Web Agents, pp. 155--165, 1998. 
  
11. R. R. Raje, S. Mukhopadhyay , M. Boyles, N. Patel, 
and J. Mostafa. "On Designing and Implementing a Collaborative System Using the 
Distributed-Object Model of Java-RMI'', Parallel and Distributed 
Computing Practices Journal -- Editor: Marcin Paprzycki -- Publisher: Nova Science 
Publishers, Inc., vol. 1, no. 4, pp. 3--14, 1998. Also appeared as 
a book chapter in Progress in Computer Research, Vol. 1, Pages: 123-134, 2001. 
  
12. R. Raje, M. Qiao, S. Mukhopadhyay , S. Peng , M. Palakal, 
& J. Mostafa. "Homogeneous Agent-Based Distributed Information Filtering". 
Cluster Computing, vol. 5, pp. 377-388, 2002. 
  
13. H. Wang, S. Mukhopadhyay, and S. Fang. 
"Feature Decomposition Architectures for Neural Networks: Algorithms, Error Bounds, and Applications'', 
International Journal of Neural Systems, vol. 12, no. 1, pp.69-81, 2002. 
  
14. J. Briceno, H. El-Mounayri and S. Mukhopadhyay. 
"Selecting an Artificial Neural Network for Efficient Modelling and 
Accurate Simulation of the Milling Process", International Journal of Machine 
Tools and Manufacture, pp. 663--674, 42(6), 2002. 
  
15. M. Palakal, S. Mukhopadhyay, J. Mostafa, R. Raje, S. 
Mishra, and M. N'Cho. "An Intelligent Biological Information Management 
System". Bioinformatics, 18:1283-1288, 2002. 
(same as conference paper number 26 under (c) below). 
  
  16. R. M. Pidaparti, S. Jayanti, M. J. Palakal, and S. Mukhopadhyay. 
"Structural Integrity Redesign Through Neural-Network Inverse Mapping", 
AIAA (American Institute of Aeronautics and Astronautics) Journal, 
vol. 41, no. 1, pp. 119--124. January, 2003.. 
  
17. M. Palakal, M. Stephens, S. Mukhopadhyay, R. Raje, and S. Rhodes. 
"Identification of Biological Relationships from text documents using 
efficient computational Methods", 
Journal of Bioinformatics and Computational 
Biology (JBCB), vol. 1, no. 2, pp. 1--34, 2003. 
(Expanded version of conference paper number 27 under (c) below). 
  
18. J. Mostafa, S. Mukhopadhyay and M. Palakal. 
"Simulation studies of different dimensions of users' interests and their impact on user modeling and information filtering" , vol. 6, no. 2, 
Information Retrieval, pp. 199--223, April 
2003. 
  
19. S. Mukhopadhyay, S. Peng, R. Raje, M. Palakal, and J. Mostafa. 
"Multi-Agent Information Classification Using Dynamic 
Acquaintance Lists", vol. 54, no. 10, Journal of the American Society 
for Information Science and Technology, pp. 966--975, 2003. 
  
20. S. Mukhopadhyay, C. Tang, J. Huang, and M. Palakal. 
"Genetic Sequence Classification and Its Application to Cross-Species 
Homology Detection", invited and peer-reviewed, 
The Journal of VLSI Signal Processing Systems, 
vol. 35, pp. 273-285, 2003. 
(Expanded Version of the paper number 28 under (c) Refereed Conference 
Proceedings). 
  
21. J. Varghese and S. Mukhopadhyay. 
"Automated Web Navigation Using Multi-agent Adaptive Dynamic Programming'', 
IEEE Transactions on Systems, Man, and 
Cybernetics -- Part A: Systems and Humans, vol. 33, no. 3, pp. 412--417, 
May 2003. 
  
22. R. Raje, D. Zhu, S. Mukhopadhyay, L.Tang, M. Palakal, and J. Mostafa. 
"COBioSIFTER -- A CORBA-based Distributed Multi-agent Biological 
Information Management System", Cluster Computing, Vol 7, No. 4, 
pp.373-389, October, 2004. 
  
23. V. Narayanasamy, S. Mukhopadhyay, M. Palakal, and D. Potter. 
"TransMiner: Mining Transitive Associations among Biological Objects 
from Text", Journal of Biomedical Science, vol. 11, no. 6, 
pp. 864--873, 2004. 
  
24. S. Mukhopadhyay, S. Peng, R. Raje, J. Mostafa, and M. Palakal. 
"Distributed Multi-Agent Information Filtering: A Comparative Study", 
accepted (to appear), Journal of the American Society 
for Information Science and Technology, 2005. 
  
25. M. Palakal, S. Mukhopadhyay, and M. Stephens. 
"Identification of Biological Relationships from Text Documents", 
invited, reviewed, and accepted (to appear), 
Book Chapter, In Medical Informatics: Advances in Knowledge 
Management and Data Mining in Biomedicine -- Editor: H. Chen, Kluwer 
Publishers, 2005 (Expected). 

Honors, Awards and Grants

  • Trustee Teaching Award, IUPUI, 2017
  • CAREER Award, National Science Foundation, 1996
  • NET (Network for Excellence in Teaching) Award, IUPUI, 1995
  • Khambhati Memorial Gold Medal, Indiana Institute of Science, 1987
  • National Merit Scholarship of the Government of India, 1979-1985