I am a Ph.D. student in Electrical Engineering at Arizona State University, working in the SINE lab under Professor Anna Scaglione. My research is focused on dynamic multi-agent systems, in particular on developing Byzantine fault-tolerant decentralized optimization algorithms. Most recently, I have been working on distributed ledger infrastructure in Blockchain based Transactive Energy Systems; particularly focusing on ensuring Byzantine fault-tolerance in transactions over ledgers for power systems state verification.


  • PhD in Electrical Engineering, 2017 - Present

    Arizona State University

  • BE in Electronics and Communication Engineering, 2017

    PES Institute of Technology



Kannada (Native), English (Bilingual), Sanskrit (basic writing and reading), Hindi (basic conversation)


MATLAB, Octave, Scilab, Xilinx ISE, Mentor Graphics, MultiSum, GNS3, Office, Neo4j.

Programming Languages

Python, HTML, LaTeX, AMPL, Java, C++, Assembly, Verilog, SQLite.



Graduate Research Associate

Arizona State University

Aug 2017 – Present Tempe
Advisor: Prof. Anna Scaglione

Project Intern

Indian Institute of Science

Jan 2017 – May 2017 Bangalore
Advisor: Prof. Chandra Murthy

Undergraduate Research Assistant

PES Institute of Technology

Aug 2016 – Dec 2016 Bangalore

Advisor: Prof. Chandar Subramanyam

Uncertainty and Detection Estimation Based Control of a Knee-Joint Orthosis


Summer Intern

ABB Global Industries and Services Private Limited (ABB GISPL)

May 2016 – Jul 2016 Bangalore
Worked with the ATMEL SAM D20 CortexTM-M0+ ARM micro-controller for an automation project involving IoT Smart Home devices.

Summer Research Fellow

Indian Academy of Sciences

May 2015 – Jul 2015 Bangalore

Advisor: Prof. S Sethu Selvi

Periocular Biometrics

  • Investigated the efficacy of periocular features of one’s face in biometric recognition tools.
  • Built a database of periocular images by extracting said regions from the face images on the Caltech-101 face database.
  • Employed Dense SIFT and Complex Dual Tree Discrete Wavelet Transform (CDTDWT) to extract relevant features and developed classification models to classify the images in the database.

Undergraduate Research Assistant

PES Institute of Technology

Dec 2014 – Aug 2016 Bangalore

Advisor: Prof. Nagamani A N Rao

Reversible Logic Circuits

  • Traditional computations are bound by what is known as the von Neumann-Landauer limit of k T ln(2) joules of energy dissipated per irreversible bit operation. Reversible Logic offers (is predicted to at least) the only way to improve the computational energy efficiency of computers in the coming decades.
  • Quantum Cost, the number of Garbage Outputs and Ancillary Inputs, and the computational Delay are some of the parameters that dictate the computational energy efficiency of Reversible circuits.
  • Worked on developing reversible counterparts for common use irreversible circuits. The goal was to develop reversible circuits for optimal Quantum costs and Ancillary input and Garbage output counts.
  • Developed a Radix-4 Multiplier that improved upon state-of-the-art designs by 80% and 50% in terms of Garbage output and Ancillary input counts, respectively.
  • In collaboration with fellow student Manish Nagaraj.

Member, Technical Staff

Ordell Ugo

Mar 2014 – Feb 2015 Bangalore
TV Channel Analytics

  • Analysis of TV watching habits of a household to recommend an ideal cost-efficient subscription plan.
  • Developed on Python and Java programming languages and implemented using a Raspberry Pi.


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Keeping Them Honest: a Trustless Multi-Agent Algorithm to Validate Transactions Cleared on Blockchain using Physical Sensors

In recent years, many Blockchain based frameworks for transacting commodities on a congestible network have been proposed. In particular, as the number of controllable grid connected assets increases, there is a need for a decentralized, coupled economic and control mechanism to dynamically balance the entire electric grid. Blockchain based Transactive Energy (TE) systems have gained significant momentum as an approach to sustain the reliability and security of the power grid in order to support the flexibility of electricity demand. What is lacking in these designs, however, is a mechanism that physically verifies all the energy transactions, to keep the various inherently selfish players honest. In this paper, we introduce a secure peer-to-peer network mechanism for the physical validation of economic transactions cleared over a distributed ledger. The framework is textitsecure in the sense that selfish and malicious agents that are trying to inject false data into the network are prevented from adversely affecting the optimal functionality of the verification process by detecting and isolating them from the communication network. Preliminary simulations focusing on TE show the workings of this framework.

Detection and isolation of adversaries in decentralized optimization for non-strongly convex objectives

Decentralized optimization has found a significant utility in recent years, as a promising technique to overcome the curse of dimensionality when dealing with large-scale inference and decision problems in big data. While these algorithms are resilient to node and link failures, they however, are not inherently Byzantine fault-tolerant towards insider data injection attacks. This paper proposes a decentralized robust subgradient push (RSGP) algorithm for detection and isolation of malicious nodes in the network for optimization non-strongly convex objectives. In the attack considered in this work, the malicious nodes follow the algorithmic protocols, but can alter their local functions arbitrarily. However, we show that in sufficiently structured problems, the method proposed is effective in revealing their presence. The algorithm isolates detected nodes from the regular nodes, thereby mitigating the ill-effects of malicious nodes. We also provide performance measures for the proposed method.

A Case of Distributed Optimization in Adversarial Environment

In this paper, we consider the problem of solving a distributed (consensus-based) optimization problem in a network that contains regular and malicious nodes (agents). The regular nodes are performing a distributed iterative algorithm to solve their associated optimization problem, while the malicious nodes inject false data with a goal to steer the iterates to a point that serves their own interest. The problem consists of detecting and isolating the malicious agents, thus allowing the regular nodes to solve their optimization problem. We propose a method to dwarf data injection attacks on distributed optimization algorithms, which is based on the idea that the malicious nodes (individually or in collaboration) tend to give themselves away when broadcasting messages with the intention to drive the consensus value away from the optimal point for the regular nodes in the network. In particular, we provide a new gradient-based metric to detect the neighbors that are likely to be malicious. We also provide some simulation results demonstrating the performance of the proposed approach.

Network Inference and its Application to the Estimation of Crowd Dynamics from IoT Sensors

In this paper, we explore the application of system identification techniques to the inference of a model that characterizes crowd dynamics, inspired by the social force model proposed by Helbing and Molnar. We focus then on sensor observations of pedestrians’ actions considering that wearables, smart mobile phones and other IoT devices embedded in the environment give significant insights on their expected mobility patterns. Previous work using IoT sensors to uncover social interactions is not based on mathematical models, while most models used for tracking mobility ignore the strong coupling between the model-agents as well as their surroundings. Our aim is to bridge these approaches, by capturing in the data model the swarming behavior of the network, including social interactions.

Reversible Radix-4 booth multiplier for DSP applications

Power dissipation has become the major concern for circuit design and implementation. Reversible Logic is the best alternative to Irreversible Logic in terms of low power consumption. Circuits designed using reversible logic have a wide array of applications. The Quantum Cost, Garbage Outputs, Ancillary Inputs and Delay are some of the parameters of reversible circuits that can be used to determine their efficiency and compare them with existing works. Optimization of these parameters are highly essential. Garbage Outputs is an important parameter that must be considered. This paper presents a design for a Reversible Radix-4 Booth Multiplier that is optimized in Garbage Cost and Ancillary inputs. The design proposed is capable of both signed and unsigned multiplication. The optimization in Garbage Cost ensures lower heat dissipation. The Encoded Booth Algorithm or Radix-4 Booth Algorithm reduces the number of partial products generated in signed multiplication to half the number generated using a Radix-2 signed multiplier making it suitable for Digital Signal Processors. The design proposed is compared to existing multiplier circuits and the parameters are tabulated.