Hetvi Shastri
Ph.D. Candidate in Computer Science at University of Massachusetts Amherst
I am a Ph.D. student in the Manning College of Information & Computer Sciences at the University of Massachusetts Amherst, where I work in the LASS Lab under the guidance of Prof. Prashant Shenoy. My current research focuses on Foundation Models as a Service for dynamic IoT environments, exploring challenges in distributed systems and machine learning. Before joining UMass, I earned my B.Tech. in Electrical Engineering with a minor in Computer Science from the Indian Institute of Technology, Gandhinagar. During my undergraduate studies, I worked on applied machine learning for sustainability applications, including Non-Intrusive Load Monitoring (NILM) for energy disaggregation. Broadly, my research interests span Distributed Systems, Internet of Things (IoT), and Machine Learning, with a focus on building systems that are scalable, and intelligent systems.
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
ConnCast: Secure Connectivity Experience Sharing via Localized P2P Networks
Hetvi Shastri, Akanksha Atrey, Andre Beck, Nirupama Ravi
US Patent
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
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.
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.
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%.
Contact
Please feel free to reach out for collaborations or inquiries.