I try to get my hands dirty on Machine Learning (a fancy name for statistics + computer programming), Natural Language Processing, Big Data Processing, and ML guided Artificial Intelligence.

My research objective is to develop mathematical and computational models for data that are simple, stands on comprehensible theoretical foundation and withstand the test of rigorous experimental evaluation. I put considerable effort behind any research work that I do to fulfil the above three criteria.

I enjoy writing non-trivial and efficient computer program for large data. In fact, thinking about efficient data structures, algorithms and their implementation on real data using a white box programming language is an intrinsic part of my thought process.

All Publications

A Deterministic-Probabilistic Approach to Neural Network Pruning by Banerjee S., Paik J. H. IEEE Transactions on Artificial Intelligence - (Accepted/In-Press)
A Positive-Unlabeled Learning Approach with Self-correcting Regularized Risk by Saha S., Hassan A. , Paik J. H. IEEE Transactions on Artificial Intelligence - (Accepted/In-Press)
A Stratified Seed Selection Algorithm for K-means Clustering on Big Data by Bajpai N., Paik J. H., Sarkar S. IEEE Transactions on Artificial Intelligence - (Accepted/In-Press)
A wrapper feature selection approach using Markov blankets by Hassan A., Paik J. H., Khare S. R. Pattern Recognition - (2025)
Balanced Seed Selection for K-means Clustering with Determinantal Point Process by Bajpai N., Paik J. H., Sarkar S. Pattern Recognition - (Accepted/In-Press)