Vishaal Krishnan

Research interests: Optimization, Control, Statistical Physics, Machine Learning, Soft Matter [CV] [Google Scholar] E-mail: vkrishnan@g.harvard.edu

Bio

• Postdoctoral Fellow

  • Applied Mathematics, Harvard University (2022–present, Mentor: Prof. L. Mahadevan)
    

  • Vision Group, Lund University (2022–2024, Mentor: Prof. Marie Dacke)
    

  • Mechanical Engineering, UC Riverside (2020–2022, Mentor: Prof. Fabio Pasqualetti )

• Ph.D.: Mechanical and Aerospace Engineering, UC San Diego (2019, Advisor: Prof. Sonia Martínez)

• B.Tech, M.Tech: Mechanical Engineering, IIT Madras (2014)

Papers

2024

• Hamiltonian bridge: a physics-driven generative framework for pattern control
  V. Krishnan, S. Sinha and L. Mahadevan
  Under Review

• Distributed Online Optimization for Multi-Agent Optimal Transport
  V. Krishnan and S. Martínez
  Automatica

• Data-driven Feedback Linearization using the Koopman Generator
  D. Gadginmath, V. Krishnan and F. Pasqualetti
  IEEE Conference on Decision and Control

2023

• Optimal Control of Interacting Active Particles on Complex Landscapes
  S. Sinha, V. Krishnan and L. Mahadevan
  Under Review

• Imitation and Transfer Learning for LQG Control
  T. Guo, A. A. R. Al Makkah, V. Krishnan and F. Pasqualetti
  IEEE Control Systems Letters

2022

• Direct vs Indirect Methods for Behavior-based Attack Detection
  D. Gadginmath, V. Krishnan and F. Pasqualetti
  IEEE Conference on Decision and Control

• Behavioral Feedback for Optimal LQG Control
  A. A. R. Al Makkah, V. Krishnan, V. Katewa and F. Pasqualetti
  IEEE Conference on Decision and Control

• A Multiscale Analysis of Multi-Agent Coverage Control Algorithms
  V. Krishnan and S. Martínez
  Automatica

• Learning Lipschitz Feedback Policies from Expert Demonstrations
  A. A. R. Al Makkah, V. Krishnan and F. Pasqualetti
  IEEE Open Journal of Control Systems

2021

• On Direct vs Indirect Data-driven Predictive Control
  V. Krishnan and F. Pasqualetti
  IEEE Conference on Decision and Control

2020

• Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing
  V. Krishnan, A. A. R. Al Makdah and F. Pasqualetti
  NeurIPS

• Data-driven Attack Detection for Linear Systems
  V. Krishnan and F. Pasqualetti
  IEEE Control Systems Letters (presented at the IEEE Conference on Decision and Control)

• A Probabilistic Framework for Moving Horizon Estimation: Stability and Privacy Considerations
  V. Krishnan and S. Martínez
  IEEE Transactions on Automatic Control

2019

• On Observability and Stability of Moving-Horizon Estimation in a Distributional Framework
  V. Krishnan and S. Martínez
  American Control Conference

2018

• Distributed Control for Spatial Self-Organization of Multi-Agent Swarms
  V. Krishnan and S. Martínez
  SIAM Journal on Control and Optimization

• Distributed optimal transport for the deployment of swarms
  V. Krishnan and S. Martínez
  IEEE Conference on Decision and Control

• Identification of critical nodes in large-scale spatial networks
  V. Krishnan and S. Martínez
  IEEE Transactions on Control of Network Systems

2017

• Identification of critical node clusters for consensus in large-scale spatial networks
  V. Krishnan and S. Martínez
  20th IFAC World Congress

• Formation control and trajectory tracking of nonholonomic mobile robots
  A. Saradgi, V. Muralidharan, V. Krishnan, S. Menta and A. D. Mahindrakar
  IEEE Transactions on Control Systems Technology

2016

• Self-Organization in Multi-Agent Swarms via Distributed Computation of Diffeomorphisms
  V. Krishnan and S. Martínez
  22nd International Symposium on Mathematical Theory of Networks and Systems

Thesis

2019

• Large-Scale Multi-Agent Transport: Theory, Algorithms and Analysis
  V. Krishnan
  Ph.D. Thesis, UC San Diego