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.

Courses (Aug-Dec 2020)

    • IE 643: Deep Learning- Theory and Practice

    • IE 507: Modelling and Computation Lab along with Prof. Ashutosh Mahajan.

Courses (Past)

    • IE 643: Deep Learning - Theory and Practice (July-Nov 2019, July-Nov 2018)

    • IE 663: Advanced Topics in Deep Learning (Jan-Apr 2020)

    • IE 614: Linear Systems (Jan-Apr 2020, Jan-Apr 2019)

    • IE 507: Modeling & Computation Lab (July -Nov 2018, July-Nov 2019 with Prof. Veeraruna Kavitha).

    • IE 684: IEOR Lab (Jan-Apr 2019 with Prof. N. Hemachandra, Jan-Apr 2018 with Prof. Ashutosh Mahajan).

    • IE 712: Selected Applications of Stochastic Models (Jan-Apr 2018 with Prof. N. Hemachandra).

    • IE 613: Online Machine Learning (Jan-Apr 2018 with Prof. Manjesh Hanawal).



    • Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces.

      • Akash Saha, P. Balamurugan.

      • Accepted for Oral Presentation In Advances in Neural Information Processing Systems (NeurIPS), 2020.

        • Acceptance rate for oral presentation: 1.1% (105/9454)


    • 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]


    • Modeling Label Interactions in Multi-label Classification: A Multi-structure SVM Perspective. [talk video]



    • 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.



    • 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]


    • 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.


    • 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]


Oral Abstracts

    • 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

    • 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.

    • Efficient Variational Inference for Gaussian Process Structured Prediction.

Technical Reports and Preprints

    • Stochastic Variance Reduction Methods for Saddle-Point Problems.


    • 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.

Work Experience

    • Research Intern at IBM India Research Lab, Bangalore, during May-August, 2013. Mentor: Dr. Vikas C. Raykar.

    • Project Assistant under Dr. 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.


    • 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 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