Ph.D. student, Computer Science, University of Illinois Urbana-Champaign
I build wireless sensing systems with mmWave and FMCW radar and low-power backscatter tags, and develop the signal processing and machine learning that turn their signals into location, presence, and vital signs.
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Our team (with Ishan Bansal and David Alcantara) won 🥇 1st place at the ARAthon hackathon at AgWireless ’26 for our rural coverage planner, plus a signed copy of Dennis Roberson’s book as a special prize!
Shared our work on wireless backscatter soil sensing at AgWireless ’26, hosted by the ARA Wireless Living Lab at Iowa State University, and received the Best Poster Runner-Up award. Thanks to NSF for the travel support!
Presented our Nokia Bell Labs work “Generalizable Machine Learning-based Bistatic Ranging for Backscatter Tag Localization” at SPIE Defense + Security 2026 (Machine Learning from Challenging Data conference) in National Harbor, MD.
Started my wireless user research internship at Apple.
Our abstract on ML-based backscatter ranging from my Bell Labs internship project was accepted for presentation at SPIE.
Watch my online presentation of ChirpEye for MobiCom 2025.
Our poster “Passive FMCW Radar Detection and Profiling under Multipath and Mobility Conditions”, an extension of our MobiCom full paper, was accepted to MobiCom 2025.
Won the Outstanding Innovation Award (18/160) at the Nokia Bell Labs Global Student Program 2025.
Started my advanced sensing and device internship at Nokia Bell Labs.
Our paper “ChirpEye: Passive Sensing and Profiling of FMCW Radars with a Resource-constrained Tag” was accepted to MobiCom 2025.
Our team was selected as a finalist for the Qualcomm Innovation Fellowship.
Our paper with UVa, “Atlas: Ensuring Accuracy for Privacy-Preserving Federated IoT Applications”, was accepted to ICCPS 2025.
The paper from my internship at Abbott, “Case report: Potential physiological sources of the late response in epidural spinal recordings induced by spinal cord stimulation during intraoperative neuromonitoring”, was accepted to Clinical Neurophysiology Practice.
Selected for an N2Women Young Researcher Fellowship to organize an N2Women event at SenSys 2024.
Our paper “BSENSE: In-vehicle Child Detection and Vital Sign Monitoring with a Single mmWave Radar and Synthetic Reflectors” was accepted to SenSys 2024.
Our poster “Extended-Range Two-way Radar Backscatter Communication with Low-Power IoT Tags” was accepted to MobiCom 2024.
Volunteering as a student coordinator for CS 591 WN: Wireless Networking Seminar. Register if you are interested in wireless topics!
Our workshop with WKU, “Interactive Design with Autistic Children Using LLM and IoT for Personalized Training: The Good, The Bad and The Challenging”, was accepted at UbiComp 2024 and will take place in Melbourne, Australia.
Received my master’s degree in Systems and Information Engineering from UVa and transferred to UIUC.
Our fluid overload detection paper at CHIL 2023 was selected for an oral presentation (13.3%)!
Our paper on personalized state anxiety detection using linguistic indicators was accepted to EMBC 2023!
Our paper on fluid overload detection in ESKD patients was accepted to CHIL 2023!
Decided to continue my Ph.D. journey at the University of Illinois Urbana-Champaign.
Started my internship at Abbott as Scientist I.
I am a Ph.D. student in the Department of Computer Science at the University of Illinois Urbana-Champaign, advised by Prof. Elahé Soltanaghai in the iSENS group.
I have interned at Apple (wireless user research, 2026) and Nokia Bell Labs (advanced sensing, 2025). Before UIUC, I was an M.Eng. student (originally a Ph.D. student) at the University of Virginia Link Lab.
Earlier, I worked with Prof. Laura Barnes (UVa), Prof. Ang Li (UMD), Prof. Mehdi Boukhechba (UVa, now at Janssen R&D), Prof. Carl Yang (Emory), Prof. Pan Li (GaTech), Prof. José Luis Ambite (USC ISI), Prof. Tiffany Tang (WKU), and Prof. Pinata Winoto (WKU).
Can a low-power tag detect a nearby FMCW radar and work out its configuration with no prior knowledge of it?
A passive tag uses delay lines to turn GHz radar chirps into kHz beat tones, detecting radars with 99% accuracy up to 15 m and estimating their chirp slope and direction.
Can a single mmWave radar find a child left in a car, including in a rear-facing seat it cannot see directly?
Passive reflectors cover the radar’s blind spots, and a multi-task model detects child presence and estimates breathing rate, with over 97% detection accuracy and under 6 breaths per minute of error.
How can low-power backscatter tags be located accurately in multipath-rich industrial environments?
A machine learning framework performs time-difference-of-arrival ranging from pairs of channel impulse responses, halving positioning error compared with conventional methods in real industrial tests, and a calibration step adapts it to new sites with little data.
Optimized the accuracy of collaborative training on data from IoT edge devices while preserving privacy.
Monitoring social state anxiety with wearable sensors and webcams.
Multi-modal mobile sensing for better fluid control in end stage kidney disease (ESKD) patients: a 4-week study collecting time-series data from on-body physiological and behavioral sensors such as PPG and IMU.
A new unsupervised approach to graph learning that addresses limitations of graph autoencoders, graph structure learning, and infomax-based methods.
Full list on Google Scholar. * equal contribution.
Generalizable Machine Learning-based Bistatic Ranging for Backscatter Tag Localization
SPIE Defense + Security 2026 Paper
ChirpEye: Passive Sensing and Profiling of FMCW Radars with a Resource-constrained Tag
Atlas: Ensuring Accuracy for Privacy-Preserving Federated IoT Applications
BSENSE: In-vehicle Child Detection and Vital Sign Monitoring with a Single mmWave Radar and Synthetic Reflectors
Extended-Range Two-way Radar Backscatter Communication with Low-Power IoT Tags
Personalized State Anxiety Detection: An Empirical Study with Linguistic Biomarkers and Machine Learning Pipeline
SRDA: Mobile Sensing based Fluid Overload Detection for End Stage Kidney Disease Patients using Sensor Relation Dual Autoencoder
GNNs in IoT: A Survey
PFed-LDP: A Personalized Federated Differential Privacy Framework for IoT Sensing
Mobile Sensing in the COVID-19 Era
Dynamic Network Anomaly Modeling of Cell-Phone Call Detail Records for Infectious Disease Surveillance
Graph Auto-Encoder via Neighborhood Wasserstein Reconstruction
Using Ubiquitous Mobile Sensing and Temporal Sensor-Relation GNN to Predict Fluid Intake of End Stage Kidney Patients
Apple, Austin, TX
Wireless User Research Intern, Spring 2026
Nokia Bell Labs, NJ
Advanced Sensing & Device Intern, Summer 2025
Abbott, TX
Scientist I, Spring 2023
University of Virginia, School of Data Science, VA
Teaching Assistant, Spring 2021 to Fall 2022
Novartis, NJ
Data Strategy Team, Summer 2020
USC Information Sciences Institute, Marina del Rey, CA
Research Assistant, Fall 2019 and Fall 2020
Teaching Assistant, University of Virginia School of Data Science