Nirupam Gupta

E-mail: nirupam [at] umd . edu
Bio: CV
Links: Google Scholar Profile

Overview-

  1. Journal Papers
  2. Conference Papers
  3. Technical Reports

Nirupam Gupta

I am a Post Doctoral Fellow at Georgetown University, working with Dr. Nitin Vaidya.

I completed my PhD in Mechanical Engineering at University of Maryland - College Park in 2018, where I worked on Privacy and Security of Multi-Agent Collaboration under the guidance of Dr. Nikhil Chopra. I got my Bachelors in Electrical Engineering at Indian Institute of Technology Delhi in 2013.

My field of study is Distirbuted Control and Optimization. My research work is mainly focused on Privacy and Security in Distributed Multi-Agent Control or Optimization.

Journal Papers

  1. Privacy of Agents' Costs in Distributed Optimization
    with Nikhil Chopra
    In Preparation.
  2. Model-Based Encryption in Networked Control Systems
    with Nikhil Chopra
    In Preparation.
  3. Information-Theoretic Privacy in Distributed Average Consensus ( arXiv )
    with Jonathan Katz, Nikhil Chopra
    [Under Review] Automatica (2018, Elsevier).
  4. Content Modification Attacks in Bilateral Teleoperation Systems
    with Yimeng Dong, Nikhil Chopra
    [Under Review] Transactions on Control Systems Technology (2018, IEEE).
  5. Content modification attacks on consensus seeking multi-agent system with double-integrator dynamics
    with Yimeng Dong, Nikhil Chopra
    Chaos: An Interdisciplinary Journal of Nonlinear Science (2016, AIP).

Conference Papers

  1. Perfect Privacy in Distributed Average Consensus with Finite Real-Valued Inputs
    with Jonathan Katz, Nikhil Chopra
    [Under Review] American Control Conference (2019, IEEE).
  2. Model-Based Encryption: Privacy of States in Networked Control Systems (pdf)
    with Nikhil Chopra
    Allerton Conference at UIUC (2018, IEEE).
  3. Privacy in Distributed Average Consensus (pdf)
    with Jonathan Katz, Nikhil Chopra
    IFAC World Congress (2017, Elsevier).
  4. Robustness of distributive double-integrator consensus to loss of graph connectivity
    with Yimeng Dong, Nikhil Chopra
    American Control Conference (2017, IEEE).
  5. Confidentiality in distributed average information consensus
    with Nikhil Chopra
    Conference on Decision and Control (2016, IEEE).
  6. On content modification attacks in bilateral teleoperation systems
    with Yimeng Dong, Nikhil Chopra
    American Control Conference (2016, IEEE) .
  7. Stability analysis of a two-channel feedback networked control system
    with Nikhil Chopra
    Indian Control Conference (2016, IEEE).

Technical Reports

  1. One-Time Pad For Real Values
    Description: An equivalent of the well-known one-time pad encryption scheme for (bounded) real values using the concept of fractional part. (year: 2018)
  2. Distributed Computation of Continuous Functions Using Distributed Average Consensus
    Description: It is well-known that the arguments of sigmoidal functions -- superimposed for universal approximation of functions -- in neural networks are weighted linear combination of the inputs. Thus, this gives a natural extension for distributed average consensus techniques for computing any function asynchronously over a jointly-connected graph(network).
  3. Solving System of Linear Equations With Uncertain Coefficients
    Description: Proposed a convex relaxation (relies on solution of convex optimization problem) for determining the solution set of system of linear equations with coefficients belong to a bounded real-valued intervals. (year: 2017)
  4. Nearest Neighbor Based Heuristic Solution to Optimization Problemes with Boolean Constraints
    Description: Proposed a heuristic algorithm based on the nearest neighbor search approach for solving a non-convex robust linear programming with boolean constraints, and compared our proposed algorithm against the branch and bound algorithm. (year: 2015)
  5. Switched Autonomous Dynamic Systems
    Description: Studying design of switching controllers for system with switching dynamics and guarantee stability using multiple Lyapunov functions (MLFs). (year: 2016)