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.
Python, HTML, LaTeX, AMPL, Java, C++, Assembly, Verilog, SQLite.
Advisor: Prof. S Sethu Selvi
Advisor: Prof. Nagamani A N Rao
Reversible Logic Circuits
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.
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.
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.
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.
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.