A portrait of Hetvi Shastri

Hetvi Shastri

Ph.D. Candidate in Computer Science at University of Massachusetts Amherst

I am a Computer Science PhD student in the Manning College of Information & Computer Sciences at the University of Massachusetts Amherst. I work at LASS lab with Prof. Prashant Shenoy . Currently, I am working on Foundation model as a service for dynamic IoT environments. This reasearch involves topics such as distributed systems, multi-tenancy, privacy and sensing.

Prior to this, I graduated from the Indian Institute of Technology, Gandhinagar, with Btech in Electrical Engineering and a minor in Computer Science. I have worked on applied machine learning for sustainability tasks such as Non-Intrusive Load Monitoring (NILM) .

My research interests lie broadly in the areas such as Distributed Systems, IoT and Machine Learning.

Llm-driven auto configuration for transient iot device collaboration

Hetvi Shastri, Walid A. Hanafy, Li Wu, David Irwin, Mani Srivastava, and Prashant Shenoy

2025

Rethinking Collaboration Among Mobile Devices in IoT Environments

Hetvi Shastri, Walid A. Hanafy, Li Wu, David Irwin, Mani Srivastava, and Prashant Shenoy

ACM SenSys 2025, Poster

Trust or bust: A survey of threats in decentralized wireless networks

Hetvi Shastri, Akanksha Atrey, Andre Beck, and Nirupama Ravi

NDSS Symposium 2025, FutureG Workshop

Vastr-gan: Versatile apparel synthesised from text using a robust generative adversarial network

Hetvi Shastri, Dhruvi Lodhavia, Palak Purohit, Ronak Kaoshik, and Nipun Batra

ACM CODS-COMAD 2022

I do not know: Quantifying uncertainty in neural network based approaches for non-intrusive load monitoring

Hetvi Shastri, Vibhuti Bansal, Rohit Khoiwal, Haikoo Khandor, and Nipun Batra

BuildSys 2022

Neural network approaches and dataset parser for nilm toolkit

Hetvi Shastri and Nipun Batra

BuildSys 2021

News

May 2025

Presenting CollabIoT poster at Sensys 2025!!

April 2025

Working at Prof Mani Srivastava's lab, UCLA for a week!!

March 2025

CollabIoT poster accepted at Sensys 2025!!

Feb 2025

ConnCast Patent Accepted!!

Jan 2025

Paper at Nokia Bell Labs internship accepted at FutureG workshop, NDSS!!

June 2024

Starting internship at Nokia Bell Labs, New Jersey!!

Aug 2023

Awarded travel grant for attending ACM SIGCOMM 2023 at New York.

Jun 2023

Selected for the prestigious GHC Student Scholarship, enabling participation at the virtual Grace Hopper Celebration 2023 (GHC 23).

May 2023

Awarded the distinguished James Kurose Scholarship at UMass Amherst for exceptional achievements in Computer Science.

May 2023

Granted the esteemed David W. Stemple Scholarship at UMass Amherst in recognition of excellence in pursuing Ph.D. research in Systems.

Nov 2022

Presenting our paper on quantifying uncertainity in NILM at ACM Buildsys 2022.

Sep 2022

Paper on quantifying uncertainity in NILM accepted at ACM Buildsys 2022.

Sep 2022

Starting MS+Ph.D. in computer science at LASS lab, UMass AMherst advised by Prof. Prashant Shenoy.

Jan 2022

Presenting our paper on Generative Adversarial Networks for fashion clothing at CODs COMAD 2022.

Jan 2022

Started working as a Teaching Assistant for ML654 course, IITGn.

Nov 2021

Presenting our paper on Neural network models for NILM at ACM Buildsys 2021.

Nov 2021

Awarded Conference Grant by IIT Gandhinagar for presenting at 8th ACM Buildsys 2021.

Oct 2021

Our paper on Generative Adversarial Networks for fashion clothing accepted at CODs COMAD 2022.

Sep 2021

First paper on Neural network models for NILM accepted at ACM Buildsys 2021.

May 2021

Started working on Non-Intrusive Load Monitoring at Sustainability Lab, IITGn advised by Prof Nipun Batra.

May 2020

Started working on ultrasound imaging at MUSE Lab, IITGn advised by Prof Himanshu Shekhar.

Jul 2018

Started Btech in Electrical Engineering at Indian Institue of Technology Gandhinagar.

Research Projects

Project visual for Foundational Model as a Service

Foundational Model as a Service for cyber physical systems

CPS-IoT pipelines are rich consisting of multiple stages of tasks. Resource intensive to incorporate one model for each task hence we use foundation model as a service architecture.

Project visual for LLM-Driven Auto Configuration

LLM-Driven Auto Configuration for Transient IoT Device Collaboration

Developed CollabloT, a system that uses LLM-driven natural language processing to generate fine-grained access control policies from user intent, employs capability-based access control for secure authorization, and leverages lightweight proxies for hardware-independent enforcement. Implemented an auto-configuration pipeline to seamlessly integrate new devices at runtime.

Project visual for Quantifying Uncertainty in NILM

Quantifying Uncertainty in Neural Network Based Approaches for Non-Intrusive Load Monitoring (NILM)

Implemented and evaluated 14 diverse deep learning models on the REDD dataset, we refined uncertainty quantification through recalibration methods. Demonstrated the ability of models in accurately measuring uncertainty while improving conventional metrics by 10%.

Expertise

Languages

Python Tensorflow Pytorch Pandas Numpy Keras JAX MATLAB WebAssembly C C++ Verilog

Tools

Docker RestAPI RPC Git OpenWrt Wireshark dash.js AWS EC2 Autodesk Inventor Field II LTspice STM32CubeIDE Keil LATEX

Hardware

Jetson Nano Jetson Orin Raspberry Pi FPGA Arduino

Contact

Please feel free to reach out for collaborations or inquiries.