P Balamurugan
Assistant Professor
Industrial Engineering and Operations Research (IEOR)
Indian Institute of Technology, Bombay (IIT-B)
Powai, Mumbai, Maharashtra, India.
email: balamurugan.palaniappan@iitb.ac.in
Research Interests
My primary research theme is to develop efficient optimization methods and algorithms for various machine learning, data mining problems. This involves investigation of both the theoretical and practical aspects of optimization methodologies. The interplay between statistical, learning theoretic, algorithmic and probabilistic aspects of machine learning models also fascinate me. I am also interested in looking into applications of machine learning and data mining in relatively less explored directions. I am also looking into theoretical and practical aspects of Deep Learning tools.
Publications
2024
Learning Sparse Graphs for Functional Regression using Graph-induced Operator-valued Kernels
Akash Saha, P. Balamurugan.
In Transations of Machine Learning Research (TMLR), 2024. [pdf] [code]
Distributed Accelerated Gradient Methods with Restart under Quadratic Growth Condition
Chhavi Sharma, Vishnu Narayanan, P. Balamurugan.
In Journal of Global Optimization (JOGO), 2024. (Available online) [pdf] [code]
MCDDPM: Multichannel Conditional Denoising Diffusion Model for Unsupervised Anomaly Detection in Brain MRI
Vivek Kumar Trivedi, Bheeshm Sharma, P. Balamurugan.
To appear in 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI 2024).
Automatic Tweet Mention Recommendation in X for Reporting Civic Issues - Case Study based on Mumbai City, India
Aayush Patel, Chaitya Dobariya, Dayanand Ambawade, Dhawal Thakkar, P. Balamurugan
To appear in The 2024 IEEE International Smart Cities Conference (ISC2-2024 )
2023
PB-FELTuCS: Patch-Based Filtering for Enhanced Liver Tumor Classification and Segmentation
Bheeshm Sharma, P. Balamurugan.
In International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD), 2023. [pdf]
Switch and Conquer: Efficient Algorithms By Switching Stochastic Gradient Oracles For Decentralized Saddle Point Problems
Chhavi Sharma, Vishnu Narayanan, P. Balamurugan.
In IEEE Conference on Decision and Control (CDC), 2023. [pdf][code][full paper]
2020
Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces.
Akash Saha, P. Balamurugan.
In Advances in Neural Information Processing Systems (NeurIPS), 2020. (Accepted for Oral Presentation)
Acceptance rate for oral presentation: 1.1% (105/9454) [pdf][Supplement][code]
A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation. [Highlighted in Science Editors' Choice]
Sukriti Singh, Monika Pareek, Avtar Changotra, Sayan Banerjee, Bangaru Bhaskararao, P. Balamurugan, Raghavan B. Sunoj.
In Proceedings of the National Academy of Sciences (PNAS), 2020. [pdf] [Supplement Document] [code]
2019
Classifying Diabetic Retinopathy Images using Induced Deep Region of Interest Extraction.
Ashutosh Kushwaha, P. Balamurugan.
In International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2019. [pdf]
2018
Modeling Label Interactions in Multi-label Classification: A Multi-structure SVM Perspective. [talk video]
Anusha Kasinikota, P. Balamurugan, Shirish Shevade.
In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2018. [pdf] [long paper+supplement pdf]
2017
Robust Discriminative Clustering with Sparse Regularizers.
Nicolas Flammarion, P. Balamurugan, Francis Bach.
Journal of Machine Learning Research, 2017. [pdf]
2016
Stochastic Variance Reduction Methods for Saddle-Point Problems.
P. Balamurugan, Francis Bach.
In Advances in Neural Information Processing Systems (NIPS), 2016. [pdf] [code]
Gaussian Process Pseudo-Likelihood Models for Sequence Labeling.
P. K. Srijith, P. Balamurugan, Shirish Shevade.
In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD), 2016. [pdf]
ADMM for Training Sparse Structural SVMs with augmented L1 regularizers.
P. Balamurugan, Anusha Posinasetty, Shirish Shevade.
In SIAM International Conference on Data Mining (SDM), 2016. [pdf] [supplement pdf] [code]
2015
A Simple Label-switching Algorithm for Semi-supervised Structural SVMs.
P. Balamurugan, Shirish Shevade, S. Sundararajan.
In Neural Computation, 2015. [pdf]
2014
Scalable sequential alternating proximal methods for sparse structural SVMs and CRFs.
P. Balamurugan, Shirish Shevade, T. Ravindra Babu.
In Knowledge and Information Systems. Vol. 38(3), pages: 599-621., 2014. [pdf]
2013
Large-Scale Elastic Net Regularized Linear Classification SVMs and Logistic Regression.
P. Balamurugan.
In IEEE International Conference on Data Mining (ICDM), 2013. Acceptance Rate: 19.65% (159/809) [pdf]
Optimizing F-Measure With Non-Convex Loss and Sparse Linear Classifiers.
Punya Murthy Chinta, P. Balamurugan, Shirish Shevade, M. Narasimha Murty.
In International Joint Conference on Neural Networks (IJCNN), 2013. [pdf]
2012
Sequential Alternating Proximal Method for Scalable Sparse Structural SVMs.
P. Balamurugan, Shirish Shevade, T. Ravindra Babu. (One of the Best Papers invited for KAIS journal).
(Full Paper) In IEEE International Conference on Data Mining (ICDM), 2012. Acceptance Rate for full papers: 10.71% (81/756). [pdf]
Code for a fast version of sequential alternating proximal method along with other training methods available at [code link]
Efficient Algorithms for Linear Summed Error Structural SVMs.
P. Balamurugan, Shirish Shevade, T. Ravindra Babu.
In International Joint Conference on Neural Networks (IJCNN), 2012. [pdf]
2011
A Sequential Dual Method for Structural SVMs.
P. Balamurugan, Shirish Shevade, S. Sundararajan, S. Sathiya Keerthi.
In SIAM International Conference on Data Mining (SDM), 2011. [pdf] [code]
Oral Abstracts
2023
Leveraging Reinforcement Learning for Vehicle Routing Problems with Time Windows: Addressing Traffic uncertainties and optimizing depot departure times
Dhawal Manish Thakkar, P. Balamurugan.
Oral Abstract presentation at 56th annual convention of ORSI and 10th ICBAI Conference, 2023.
Customer Feedback driven Flight Sequence Scheduling
Dhawal Manish Thakkar, P. Balamurugan.
Oral Abstract presentation at 26th Air Transport Research Conference (ATRS) Conference, 2023.
2021
Customer Feedback Driven Optimization of Aircraft Flight Sequence Scheduling with Flight leg Priority Assignment and Propagated Delay Minimization
Dhawal Manish Thakkar, P. Balamurugan.
Oral Abstract presentation at 8th International Conference on Business Analytics and Intelligence (ICBAI) Conference, 2021.
2018
Business Process Flow Prediction using Machine Learning Algorithms
Dhawal Manish Thakkar, P. Balamurugan.
Oral Abstract presentation at Operations Research Society of India (ORSI) Conference , 2018.
Workshop Papers
Stochastic Gradient Methods with Compressed Communication for Decentralized Saddle Point Problems
Chhavi Sharma, Vishnu Narayanan, P. Balamurugan.
In International Workshop on Federated Learning: Recent Advances and New Challenges (FL-NeurIPS'22), 2022.
A simple and fast distributed accelerated gradient method.
Chhavi Sharma, Vishnu Narayanan, P. Balamurugan.
In Optimization for Machine Learning (OPT-ML) Workshop, 2019.
Distributed Accelerated Inexact Proximal Gradient Method via System of Coupled Ordinary Differential Equations.
Chhavi Sharma, Vishnu Narayanan, P. Balamurugan.
In NeurIPS workshop on Beyond First Order Methods for ML, 2019.
Robust Discriminative Clustering with Sparse Regularizers.
Nicolas Flammarion, P. Balamurugan, Francis Bach.
In NIPS workshop on Feature Extraction: Modern Questions and Challenges, 2015.
Efficient Variational Inference for Gaussian Process Structured Prediction.
P. K. Srijith, P. Balamurugan, Shirish Shevade.
In NIPS workshop on Advances in Variational Inference, 2014.
Technical Reports and Preprints
Stochastic Gradient Methods with Compressed Communication for Decentralized Saddle Point Problems
Chhavi Sharma, Vishnu Naryanan, P. Balamurugan.
Preprint available in arxiv: https://arxiv.org/abs/2205.14452
Robust Discriminative Clustering with Sparse Regularizers.
Nicolas Flammarion, P. Balamurugan, Francis Bach.
Technical report, 2016. [arXiv link]
Stochastic Variance Reduction Methods for Saddle-Point Problems.
P. Balamurugan, Francis Bach.
Technical report, HAL-01319293, 2016. [pdf]
An Empirical Evaluation of Sequence-tagging Trainers.
P. Balamurugan, Shirish Shevade, S. Sundararajan, S. Sathiya Keerthi, 2013. [arXiv link]
Talks
Lecture on Generative Adversarial Networks in FDP on Generative AI Models For Engineering Applications organized by CSE Department, Thiagarajar College of Engineering, during July 2024.
Lecture on Recurrent Neural Networks and Transformers in CEP for ISRO professionals, CSRE, IIT Bombay during March 2024.
Lecture on Gradient methods in optimization in Winter School on Optimization organized by ACM-IIT Goa, during December 2022. [Github link]
e-Lecture on Mathematical Foundations of Deep Learning (Linear Algebra review) and Variational Auto-encoders in FDP on Foundations of Deep Learning using Python organized by CSE Department, Thiagarajar College of Engineering, during August 2021.
e-Lecture on Deep learning Based Models for Translation and Language Modelling in the FDP on Deep Learning on Natural Language Processing organized by CSE Department, Jaypee Institute of Information Technology, during June 2021.
e-Lecture on Optimization for Deep Learning in the FDP organized by CSE Department, Techno Main Salt Lake, during January 2021.
e-Lecture on Variational Auto-encoders (VAEs) in the FDP on Deep Learning for NLP, organized by CSE Department, NIT Andhra Pradesh during November 2020.
e-Lecture on Detection of Diabetic Retinopathy in the FDP on Data Analytics, organized by CSE Department, NIT Andhra Pradesh during June 2020.
Basics of convex, non-convex Optimization and Opimization algorithms for Deep Neural Networks Lecture at TEQIP III sponsored workshop on AI for Engineering Research, held at Thiagarajar College of Engineering, during November 2019.
Optimization algorithms for Deep Neural Networks Lecture at CEP, CSRE, IITB during November 2019.
Deep Neural Networks - A Dynamical Systems Perspective Talk at Advanced Machine Learning Workshop, MS Ramaiah University, Bengaluru, during August 2019.
Classifying bio-medical images using induced deep Region of Interest extraction - Case studies on Retinal and Cardiac images Talk at IEOR Day during March 2019.
Talk on Deep Learning Fundamentals in TEQIP sponsored Machine Learning workshop at NIT Andhra Pradesh during March 2019.
Talk on Advanced Neural Networks for Computer Vision in TEQIP sponsored Workshop on Visual Computing at Thiagarajar College of Engineering during February 2019.
Enthuse 2.0 talk for Undergraduate students at IITB to motivate them towards research in Machine Learning, during January 2019.
Talk on Machine Learning Possibilities for Oil and Gas Industry at BHGE, Bengaluru during November 2018.
Talk on Fundamentals of Deep Learning in TEQIP sponsored Faculty Development Program at Thiagarajar College of Engineering during October 2018.
Talk on Multi-structure SVM at JTG Summer School, IITB during July 2018.
Lectures on Basics of Convex Optimization and Proximal Methods in a TEQIP sponsored workshop on Role of Optimization in Engineering Applications organized by the Department of Electronics and Communication Engineering, VNIT Nagpur, during December 28-29, 2017.
Post-doctoral Research
During January 2015-December 2016, I was a post doctoral researcher at SIERRA Project Team, INRIA-Ecole Normale Superieure mentored by Professor Francis Bach.
During January-September 2017, I was a post-doc in the Signal, Statistique et Apprentissage (S2A) Group, Telecom-ParisTech, Paris, where I was mentored by Professor Stéphan Clémençon and Professor Florence d’Alché-Buc.
Education Details
I was a PhD student at Intelligent Systems Lab, headed by Prof. Shirish K. Shevade, in the department of Computer Science and Automation, Indian Institute of Science, Bangalore, India.
My PhD thesis focused on developing fast optimization methods for structured prediction and sparse classification problems.
Thesis Review Committee: Professor Inderjit S. Dhillon, University of Texas, Austin, USA and Professor Ashish Ghosh, Indian Statistical Institute, Kolkata, India.
I completed my Masters in Engineering at Computer Science and Automation, Indian Institute of Science, Bangalore, India.
I had my Bachelors degree in Information Technology from Thiagarajar College of Engineering, Madurai, India.
Work Experience
Research Intern at IBM India Research Lab, Bangalore, during May-August, 2013. Mentor: Dr. Vikas C. Raykar.
Project Assistant under Prof. Shirish Shevade during August 2009 - December 2009. Worked on "Efficient Algorithms for Structured Prediction".
Assistant Systems Engineer at Tata Consultancy Services during 2004 - 2007.
Teaching Assistance
Teaching Assistant for Big Data course. (February-May 2017)
Teaching Assistant for Introduction to Machine Learning course by Prof. Florence d’Alché-Buc. (February-May 2017)
Teaching Assistant for Probability and Statistics course by Dr. Indrajit Bhattacharya. (August-December 2011)
M.E. Thesis
L1-Norm Structural Classification SVMs.
Awards
Excellence in Teaching Award at Industrial Engineering and Operations Research Department, IIT Bombay, for the year 2024.
Recipient of the Alumni Medal from Computer Science and Automation Department, Indian Institute of Science, for the Best PhD Thesis in 2015.
A co-recipient of IBM Best PhD Thesis Award among theses from Computer Science and Automation Department, Indian Institute of Science, for the year 2015.
IBM PhD Fellowship awards for the years 2012 and 2013.
Travel awards from IBM, Infosys India, Indian Institute of Science (Sarukkai Jagannathan award) and IEEE ICDM.
Review Responsibility
A reviewer for ICML 2022, Sankhya Journal, AAAI 2022, Joint International Conference on Data Science and Management of Data (CODS-COMAD) 2022, Asian Conference on Machine Learning (ACML) Conference Track and Journal Track 2021, Asian Conference on Machine Learning (ACML) 2020, Mathematics of Operations Research (MOR) Journal, SIAM Journal on Optimization (SIOPT), CISP-BMEI, IEEE Signal Processing Letters, Indian Workshop on Machine Learning (iWML) 2018, US-Israel BSF Research Proposal, NIPS 2016, Neuro Computing Journal, Neural Networks Journal, Science China Mathematics Journal, and Sadhana Academy Proceedings in Engineering Sciences.
Other Activities
A volunteer for CSA Undergraduate Summer School, 2014.
An organizer for CSA Undergraduate Summer School, 2013.
Designed a movie-rating recommender system for CSA Open Days, 2013. (Joint work with Anusha Posinasetty, with inputs from P. K. Srijith and Divya Padmanabhan).
Gave a talk on "Basics of Classification and Support Vector Machines" during CSA Undergraduate Summer School, 2012.
Scribbling some miscellany