I'm a PhD student in Computer Science at the University of Massachusetts Amherst, advised by Prof. VP Nguyen.
I develop multimodal wearable sensing systems to understand human engagement and behavior. My research brings together EEG, eye tracking, electrodermal activity, biosignal processing, human-subject studies, and machine learning.
August 2026–present
Benchmarking pretrained EEG foundation models on wearable datasets, with a focus on reference configurations, electrode-position shifts, and robustness when transferring from conventional scalp EEG to wearable sensors.
June 2026–present
Developing an ear-based fetal monitoring device and a biosignal hub for synchronized acquisition from ultrasound, an ECG belt, and ear-worn sensors. Investigating continuous blood pressure monitoring through pulse arrival time from in-ear PPG and chest ECG.
March 2026–present
Building a wearable EEG platform to investigate group synchrony as a proxy for classroom engagement, including the reliability and artifact sensitivity of frontal and over-the-ear channels.
A head-mounted platform integrating EEG, eye tracking, and forehead EDA. A 30-participant study collected 450 engagement ratings; evaluation on 15 held-out forehead-sensing participants achieved 75.0% balanced within-one-level prediction versus a 63.0% majority-rating baseline.
A 12-participant live-lecture study synchronizing scalp EEG, wearable EEG, eye tracking, and instructor speech. A multimodal CRNN using headband EEG, gaze, and the speech envelope achieved MAE 0.628 and 91.2% balanced within-one accuracy on held-out participants.
A unified benchmark spanning 20 public datasets, more than one million EMG segments, and nine pretrained foundation models. My contributions include standardized preprocessing and evaluation across transfer, cross-user generalization, few-shot adaptation, and EMG-to-text decoding.
June–August 2024
Research Scientist Intern · Cambridge, UK
Developed adaptive load balancing for stateful applications on the OmniBuds earable platform, and explored TinyML model partitioning and collaborative inference across earbuds.
June 2019–August 2021
Software Engineer
Distributed storage design for machine learning and high-performance computing workloads.
August 2018–June 2019
Associate Software Engineer
Logging pipelines for distributed storage.
May 2017–April 2018
Research project · Embedded activity recognition
Optimized an embedded human activity recognition system across computational complexity, energy consumption, and cost using compact features and machine learning models.
September 2023–June 2028 (expected)
University of Massachusetts Amherst
September 2021–August 2023
University of Massachusetts Amherst
July 2014–July 2018
SRM Institute of Science and Technology
Python, PyTorch, MNE, NumPy, Pandas, scikit-learn, TensorFlow, MATLAB / EEGLAB, C, C++, Go
Pupil Labs Neon, EmotiBit, Frenz EEG, OpenBCI Cyton, OmniBuds, XReal AR glasses
ESP32, MAX78000, Arduino, Docker, Kubernetes, Prometheus, Gluster, Ceph