team

Krishna Jagannathan

Professor

Ph.D.  degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT)

S.M.  degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT)

B. Tech.  in Electrical Engineering from IIT Madras in 2004

044-2257 4469

krishnaj@ee.iitm.ac.in

Personal Link

ESB 246D

  • Krishna Jagannathan obtained his B. Tech. in Electrical Engineering from IIT Madras in 2004, and the S.M. and Ph.D. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT) in 2006 and 2010 respectively. During 2010-2011, he was a visiting post-doctoral scholar in Computing and Mathematical Sciences at Caltech, and an off-campus post-doctoral fellow at MIT.
  • Since November 2011, he has been with the Department of Electrical Engineering, IIT Madras, where he is currently an associate professor. His research interests lie in the stochastic modeling and analysis of communication networks, network control, and queuing theory.

  • Stochastic modeling,
  • Multiarmed bandits,
  • Quantum Information,
  • Queuing theory

  • Best Paper Award, WiOpt 2013, Tsukuba, Japan, for Scheduling Strategies to Mitigate the Impact of Bursty Traffic in Wireless Networks paper.
  • Young Faculty Recognition Award for excellence in teaching and research, IIT Madras 2014.

    Previous Courses

  • January 2012: Stochastic Modeling and the theory of Queues.
  • July 2012: Probability Foundations
  • Networks: Models, Theory and Algorithms with Prof Andrew Thangaraj.
  • January 2013: Stochastic Modeling and the theory of Queues (EE 6001) .
  • July 2013: Probability Foundations (EE 5110) .
  • January 2014: Convex Optimization (EE 5121).
  • July 2014: Probability Foundations (EE 5110).
  • January 2015: Convex Optimization (EE 5121).
  • July 2015: Probability Foundations (EE 5110).
  • January 2016: Signals & Systems (EE 1101).
  • July 2016: Topics in Random Processes and Concentrations (EE 6112).
  • January 2017: Signals & Systems (EE 1101).
  • July 2017: Probability Foundations (EE 5110).
  • January 2018: Signals & Systems (EE 1101) .
  • July 2018: Communication Networks (EE 5150) [with Gaurav Raina].
  • January 2019: Convex Optimization (EE 5121) [with Rachel Kalpana].
  • July 2019: Probability Foundations (Undergraduate version) (EE3110) [with Avhishek Chatterjee] .
  • January 2020: Stochastic Modeling and the theory of Queues (EE 6150).

Journals

  • Constrained regret minimization for multi?criterion multi armed bandits; Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan, Machine Learning, 2023.
  • Statistically Robust, Risk-Averse Best Arm Identification in Multi-Armed Bandits; Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan, IEEE Transactions on Information Theory, Volume: 68, Issue: 8, 2022.
  • Low-complexity scheduling algorithms with constant queue length and throughput guarantees; Subrahmanya Swamy Peruru, Aravind Srinivasan, Radhakrishna Ganti, Krishna Jagannathan, Performance Evaluation, 2022.
  • The Classical Capacity of Additive Quantum Queue-Channels; Prabha Mandayam, Krishna Jagannathan, Avhishek Chatterjee; IEEE Journal on Selected Areas in Information Theory (special issue on Quantum Information Science), Volume: 1, Issue: 2, 2020.
  • Right buffer sizing matters: some dynamical and statistical studies on Compound TCP; Debayani Ghosh, Krishna Jagannathan, Gaurav Raina; Performance Evaluation, Volume: 139, 2020.
  • A Framework for End-to-End Deep Learning-Based Anomaly Detection in Transportation Networks; Neema Davis, Gaurav Raina, Krishna Jagannathan; Transportation Research - Interdisciplinary Perspectives, 2020.
  • Grids versus Graphs: Partitioning Space for Improved Taxi Demand-Supply Forecasts; Neema Davis, Gaurav Raina, Krishna Jagannathan; IEEE Transactions on Intelligent Transportation Systems, 2020.
  • Concentration bounds for empirical conditional value-at-risk: The unbounded case; Ravi Kumar Kolla, Prashanth L A, Sanjay P Bhat, Krishna Jagannathan; Operations Research Letters, Volume 47: Issue: 1, 2019.
  • Stability, convergence and Hopf bifurcation analyses of the classical car-following model; Gopal Krishna Kamath, Krishna Jagannathan, Gaurav Raina; Nonlinear Dynamics, Volume: 96, Issue: 1, 2019.
  • Taxi demand forecasting: A HEDGE based tessellation strategy for improved accuracy; Neema Davis, Gaurav Raina, Krishna Jagannathan; IEEE Transactions on Intelligent Transportation Systems, Volume: 19 Issue: 11, 2018.
  • Collaborative Learning of Stochastic Bandits over a Social Network; Ravi Kumar Kolla, Krishna Jagannathan, Aditya Gopalan; IEEE/ACM Transactions on Networking, Volume: 26 Issue: 4, 2018.
  • Efficient CSMA using Regional Free Energy Approximations; Peruru Subrahmanya Swamy, Venkata Pavan Kumar Bellam, Radha Krishna Ganti, Krishna Jagannathan; IEEE/ACM Transactions on Networking, Volume: 26 Issue: 4, 2018

Conference Proceedings

  • K Nithin Varma, Krishna Jagannathan, An Erasure Queue-Channel With Feedback: Optimal Transmission Control to Maximize Capacity, Information Theory Workshop (ITW) 2023, Saint Malo, France.
  • Jaswanthi Mandalapu, Krishna Jagannathan, The Classical Capacity of Quantum Jackson Networks with Waiting Time-Dependent Erasures, Information Theory Workshop (ITW) 2022, IIT Bombay, India.
  • Vincent Y F Tan, Prashanth L A, Krishna Jagannathan, A Survey of Risk-Aware Multi-Armed Bandits, International Joint Conference on Artificial Intelligence (IJCAI) 2022, Vienna, Austria.
  • Amit Anand Jha, Nazal Mohamed, Krishna Jagannathan, Collaborative Best Arm Identification in Multi-armed Bandits, COMSNETS 2022, Bengaluru, India.
  • Sushmitha Shree S, Kishore GV, Avhishek Chatterjee, Krishna Jagannathan, Stochastic Bounded Confidence Opinion Dynamics: How Far Apart Do Opinions Drift COMSNETS 2022, Bengaluru, India.
  • Pawan Poojary, Sharayu Moharir, Krishna Jagannathan, A Coupon Collector based Approximation for LRU cache hits under Zipf requests, WiOpt 2021.
  • Jaswanthi Mandalapu, Krishna Jagannathan, The Capacity of Photonic Erasure Channels with Detector Dead Times, National Conference on Communications (NCC) 2021.
  • Kumar Ashutosh, Jayakrishnan Nair, Anmol Kagrecha, Krishna Jagannathan, Bandit algorithms: Letting go of logarithmic regret for statistical robustness, AISTATS 2021.
  • Prashanth L A, Krishna Jagannathan, Ravi Kolla, Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions, International Conference on Machine Learning (ICML) 2020.
  • Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan, Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards, NeurIPS 2019, Vancouver, Canada.

  • Editor, IEEE/ACM Transactions on Networking (2017 - 2021).
  • Editor, Performance Evaluation (2017 - 2022).
  • Member of the Steering Committee, WiOpt series of conferences.
  • TPC co-chair, COMSNETS 2021 (with Aruna Balasubramanian, Stony Brook, and Salil Kanhere, UNSW).
  • TPC co-chair, SPCOM 2020 (with Nikhil Karamchandani, IITB and Prasanta Ghosh, IISc).
  • Local Arrangements Chiar, MobiHoc 2017, IIT Madras
  • Workshop co-chair (with Gaurav Raina), Intelligent Transportation Systems Workshop, Bangalore, January 2016.
  • TPC co-chair, WiOpt 2015 (Mumbai), with Francois Baccelli (UT Austin) and Atilla Eryilmaz (Ohio State University).
  • Workshop co-chair (with Gaurav Raina), Intelligent Transportation Systems Workshop, Bangalore, January 2015.
  • Technical program committees (TPC) and reviewer of international conferences: Often serve and review for AISTATS, NeurIPS, IFIP Performance, MOBIHOC, COMSNETS, WiOpt NCC, SPCOM etc.
  • Workshop Chair for MobiHoc 2013 (Bangalore), along with Aditya Gopalan.
  • Publications chair for WiOpt 2014 (Tunisia).
  • Routinely review for several IEEE journals such as Transactions on Networking, Information Theory, Wireless Communications, Automatic Control, Wireless communication letters, signal processing letters, etc.
  • IEEE Member, ACM Member
  • Making an effective technical presentation: slides(Contains some borrowed ideas from Dimitri Bertsekas, Benam Aazhang, Uday Khankhoje).
  • JTG Summer School 2019 recordings Lectures by
  • Prof Michelle Effros (Caltech) on Multi-terminal Information Theory &
  • Dr Praneeth Netrapalli (presently Google, India) on Optimization Algorithms.
  • Mini workshop on Modern Optimization (Feb 17 - 19, 2020) recordings Featuring lectures by
  • Dr Praneeth Netrapalli (Google, India): Optimization Algorithms &
  • Prof Aditya Gopalan (ECE, IISc): Online Convex Optimization.
  • Prof L A Prashanth (CSE, IIT Madras): Zeroth Order Stochastic Optimization.
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