Avisek Gupta

Research

My current research interests lie in:
- Weakly supervised machine learning approaches for efficient data aquisition and annotation, especially for scientific machine learning.
- Machine learning for combinatorial problems, which I'm exploring on coalition structure generation problems.
My doctoral thesis (available here) focused on center-based clustering methods, that operate in an unsupervised or weakly supervised manner.

Multiple Kernel Transfer Clustering (Github): In a source task a multiple kernel metric is learnt under weak supervision, which is transferred to a target task where a data set is clustered.

Entropy c-Means Clustering (Github): A multi-objective fuzzy clustering method to identify clusters at different levels of overlap.

The Last Leap and the Last Major Leap (Github): Methods to estimate the number of clusters in a data set.

Kernel k-Harmonic Means Clustering (Github): An extension of the k-Harmonic Means method incorporating kernel similarities.

Teaching

For first year Ph.D. in Computer Science, IAI, TCG CREST, Kolkata:
- Course Instructor for Deep Learning, from January to June 2024.
- Course Instructor for Elements of Probability and Statistics, from August to December 2023.
- Lectures for the course Deep Learning (Sequence Models), from January to June 2023.
- Course Instructor for Machine Learning, from August 2022 to January 2023.

Teaching Assistant

Computer Vision, from January to May 2020, for M.Tech. in Computer Science (Second Year), at ISI Kolkata.
Computing for Data Sciences, from July to December 2018, for Post-Graduate Diploma in Business Analytics (First Year), at ISI Kolkata.

Talks

2023 Sep 27: Panel Discussion on "AI - Curse or Boon? The ethical challenges of AI", at IEM Kolkata.
2023 June 9: Deep Learning I: Understanding Convolutional Neural Networks (CNNs), for the Eighth SSCVIPML, at ECSU, ISI Kolkata.
2023 Jan 21: Rudiments of Artifical Neural Networks, at the 2nd WSDL-2023, organised by ECSU, ISI Kolkata.
2023 Jan 27: Convolutional Neural Networks, at the 2nd WSDL-2023, organised by ECSU, ISI Kolkata.
2022 June 13: Machine Learning under Weak Supervision for Computer Vision and Computational Chemistry problems, for the India-Serbia Workshop on Selected Topics of Artificial Intelligence and Information Technology Techniques and Applications, at the Serbian Academy of Sciences and Arts, Belgrade, Serbia.
2021 May 7: Hands on Deep Learning using PyTorch (Virtual), in Big Data Analytics Using Soft Computing Tools (May 3 to May 8 2021), organised by the MCKV Institute of Engineering, India.
2019 Nov 22: Ensemble Learning , in A Short Course on Machine Learning for Practitioners (Nov 18 to Nov 23, 2019), organised by CAIML, ISI Kolkata.
2019 May 31 & Jun 04: Machine Learning Algorithms: Session 1 , Session 2(a) , Session 2(b) , in the Sixth SSCVGIP (May 30 to Jul 12, 2019), organised by ECSU, ISI Kolkata.
2018 Jun 12: Machine Learning in Python: Scikit-Learn , in the Fifth SSCVGIP (May 31 to Jul 13, 2018), organised by ECSU, ISI Kolkata.
2017 May 11: OpenGL graphics programming , for the Computer Graphics course of M.Tech in Computer Science, ISI Kolkata. (class materials)
2017 Feb 4: Data Clustering using R , at A Workshop on Big Data and R, organised by IEM Kolkata.

Publications

(accepted) Arkaprabha Basu, Avisek Gupta, Swagatam Das, Sourav Raha, "Enhancing MRI-Based Classification of Alzheimer’s Disease with Explainable 3D Hybrid Compact Convolutional Transformers", in the 2024 International Conference on Pattern Recognition (ICPR-2024).
Arindam Majee, Avisek Gupta, Sourav Raha, Swagatam Das, "Enhancing MRI-Based Classification of Alzheimer's Disease with Explainable 3D Hybrid Compact Convolutional Transformers", Proceedings of the 2024 International Joint Conference on Neural Networks (IJCNN-2024). DOI:10.1109/IJCNN60899.2024.10650462.
Pierre-François Maillard, Avisek Gupta, "Classification of Return-Oriented Pro-gramming Gadgets: A Machine Learning approach", Journal of Computer Virology and Hacking Techniques, vol. 20, pp. 751–763, 2024. DOI:10.1007/s11416-024-00517-1.
Tuhin Kumar Biswas, Avisek Gupta, Narayan Changder, Redha Taguelmimt, Samir Aknine, Samiran Chattopadhyay and Animesh Dutta, "Coalition Formation for Task Allocation using Multiple Distance Metrics", AAAI-24 Student Abstract and Poster Program (AAAI-2024). DOI:10.1609/aaai.v38i21.30421.
Avisek Gupta and Swagatam Das, "Transfer Clustering Using a Multiple Kernel Metric Learned Under Multi-Instance Weak Supervision", IEEE Transactions On Emerging Topics In Computational Intelligence, 2021. DOI:10.1109/TETCI.2021.3110526. (Github)
Avisek Gupta and Swagatam Das, "Improved Efficient Model Selection for Sparse Hard and Fuzzy Center-Based Clustering", Information Sciences, vol. 590, pp. 29-44, 2022. DOI:10.1016/j.ins.2021.12.070. (Github)
Avisek Gupta, Shounak Datta, and Swagatam Das, "Fuzzy Clustering to Identify Clusters at Different Levels of Fuzziness: An Evolutionary Multiobjective Optimization Approach", IEEE Transactions on Cybernetics, vol. 51 (5), pp. 2601–2611, 2019. DOI:10.1109/TCYB.2019.2907002. (Github)
Avisek Gupta, Shounak Datta, Swagatam Das, "Fast automatic estimation of the number of clusters from the minimum inter-center distance for k-means clustering", Pattern Recognition Letters, vol. 116, pp. 72-79, 2018. DOI:10.1016/j.patrec.2018.09.003. (Github)
Avisek Gupta, Swagatam Das, "On the Unification of k-Harmonic Means and Fuzzy c-Means Clustering Problems under Kernelization", In the Proceedings of the Ninth International Conference on Advances in Pattern Recognition (ICAPR-2017), pp. 386-391, 2017. DOI:10.1109/ICAPR.2017.8593078. (Github)

Other Academic Responsibilities:

Program Committee Member , AI IMPACT Track, The 20th Pacific Rim International Conference on Artificial Intelligence (PRICAI-2023)
External Advisory Committee, for the 2nd Winter School on Deep Learning 2023 (WSDL-2023), organized by ECSU, ISI Kolkata.
Organizing Chair for the 1st Winter School on Deep Learning 2022 (WSDL-2022), organized by ECSU, ISI Kolkata.

Regular Peer Reviewer for:

IEEE Transactions on Neural Networks and Learning Systems
IEEE Transactions on Cybernetics
Pattern Recognition (Elsevier)
Information Sciences (Elsevier)
SN Computer Science (Springer)
Engineering Applications of Artificial Intelligence (Elsevier)
Pattern Analysis and Applications (Springer)
among others...

Tutorials

Resources

Machine Learning & Deep Learning

[Book] Zhang A., Lipton Z. C., Li M., and Smola A. J., Dive Into Deep Learning.
[Book] Bishop C. M. Pattern Recognition and Machine Learning.
[Book] Murphy K. P., Machine Learning: a Probabilistic Perspective.
[Book & Videos] Shalev-Shwartz S. and Ben-David S., Understanding Machine Learning: From Theory to Algorithms. Youtube playlist: link.
[Book] James G., Witten D., Hastie T., and Tibshirani R., Introduction to Statistical Learning.

Calculus

[Book] Apostol T. M., Calculus Volume 1 and Calculus Volume 2
[Book] Spivak M., Calculus
[Videos] Jim Fowler's Calculus One
[Videos] Jim Fowler's Caculus Two

Linear Algebra

[Book & Videos] Strang G., Introduction to Linear Algebra. Youtube playlist: link.
[Book] Golub G. H. and Van Loan C. F., Matrix Computations

Probability and Statistics

[Book & Videos] Blitzstein J. K. and Hwang J,. Introduction to Probability. Youtube playlist: Statistics 110.
[Book] Abramovich F. and Ritov Y., Statistical Theory: A Concise Introduction
[Book] Mukhopadhyay N., Introductory Statistical Inference
[Book] Wasserman L., All of Statistics

Optimization

[Book] Chong E. K. P. and Zak S. H., An Introduction to Optimizaion
[Book] Boyd S. and Vandenberghe L., Convex Optimization
[Slides] Courses by Lieven Vandenberghe
[Book] Nesterov Y., Lectures on Convex Optimization

Data Structures and Algorithms

[Book] Kleinberg J. and Tardos E., Algorithm Design
[Book] Cormen T. H., Leiserson C. E., Rivest R. L., and Stein C., Introduction to Algorithms.
[Book] Aho A. V., Ullman J. D., and Hopcroft J. D., Data Structures and Algorithms