About me

Hello! I am Payal (hear pronunciation📢). I am a fourth-year PhD candidate in Computer Engineering at Northwestern University, part of the IDEAS Lab and advised by Dr. Qi Zhu.

My research is focused on human-centric applications that bridge computer science and health. Specifically, I develop machine learning techniques for real-world challenges involving audio, healthcare, and time-series sensor data. These applications are pervasive but often come with challenges in data analysis and model development. I aim to design robust algorithms that handle issues like data quality, sparsity, and resource constraints. A significant part of my work explores the trade-offs between personalization and generalization in algorithm design, with an emphasis on making technology more inclusive for underrepresented users (e.g., atypical speakers in voice technology, and skin-tone diversity in optical heart rate monitoring). In addition to model performance, I prioritize explainability and resource efficiency.

Before my PhD, I worked as an IC design engineer at Analog Devices Inc., developing formal verification methods for application-specific ICs. I hold a Masters by Research in Electrical Engineering from Indian Institute of Technology, Madras, where I designed a sensing and algorithms framework for cardiac wearables.

📧 You can reach me at payalmohapatra2026 at u dot northwestern dot edu. And here is a link to my CV (last updated February 2025).

If you prefer an audio-visual version of my research,

here is a 2-Minute overview (24, October 2024 : Lightning Talk at MIT, EECS Rising Stars.) and
a 5-Minute overview (8, April 2025 : Lightning Talk at Northwestern University, CoDEX.)

Ongoing Projects

Learning from Irregular and Heterogenous Modalities

Investigating methods to model the underlying process dynamics for irregular time-series, ranging from uniformly sampled to sparse or missing variates, to learn task-agnostic representations while modeling the inter-modality interactions.

Practitioner's Guide to Manufacturing Workplace Safety

Collaborating with Boeing and MxD to create a browsable repository of COTS sensors and wearable-data analytics to enhance workplace safety by quantifying risk factors. Assessing the validity of off-the-shelf pose analytics for extracting ergonomic risk metrics (RULA/REBA scores).

News

  • June 2025 - I will be interning with Mitsubishi Electric Labs (MERL), Boston, MA this summer as a Research Scientist. Reach out if you are here and want to collaborate (or just catch up over coffee ☕).
  • April 2025 — Selected as one of six speakers to present a Lightning Talk at CoDEX symposium, Northwestern University.
  • February 2025 - Submitted our paper on surface-EMG based silent-speech recognition using LLMs to ACL.
  • January 2025 - Submitted our paper on head-orientation based acoustic zones' localization to IMWUT.
  • January 2025 - Submitted our paper on phase-driven domain generalization for time series to TMLR.
  • December 2024🏃‍♀️ - Successfully passed my PhD Prospectus examination — 2/3 of the journey complete!
  • October 2024📰 - Our paper Wearable Network for Multi-Level Physical Fatigue Prediction in Manufacturing Workers is accepted in PNAS Nexus journal. Featured by tech news outlets like MSN, TechXplore, PopSci, News-Medical, Yahoo Tech, Northwestern Engineering.
  • August 2024🏆 - Excited to be selected as the EECS Rising Star 2024! Invited to the 2-day workshop hosted at MIT in October. Read more in this article by Northwestern.
  • June 2024 - Started my summer internship with Meta Reality Labs as a Research Scientist.
  • June 2024 - Our paper on Missingness-resilient Video-enhanced Multimodal Disfluency Detection is accepted and chosen for oral presentation at InterSpeech'24.
  • February 2024 - My internship work with Meta Reality Labs on efficient event detection on smart glasses—Non-verbal Hands-free Control for Smart Glasses using Teeth Clicks—is now live!
  • October 2023 - Corresponding with Meta Reality Labs, Audio Research group as a part-time student researcher.
  • July 2023 - Our paper on the Effect of Attention and Self-Supervised Speech Embeddings on Non-Semantic Speech Tasks has been accepted for ACM Multimedia 2023 Multimedia Grand Challenges Track.
  • June 2023 - I will be interning with Meta Reality Labs, Redmond, WA this summer as a Research Scientist. Reach out if you are here and want to collaborate (or just catch up over coffee ☕).
  • May 2023 - We are participating in the ACM Multimedia 2023 Computational Paralinguistics Challenge (ComParE).
  • February 2023 - Our paper on Efficient Stuttering Event Detection using Siamese Networks is accepted in ICASSP'23.
  • February 2023 - Secured third place in e-Prevention: Person Identification and Relapse Detection from Continuous Recordings of Biosignals Challenge in ICASSP'23. Invited to present a paper on methodology - Person Identification with Wearable Sensing using Missing Feature Encoding and Multi-Stage Modality Fusion.
  • December 2022 - Demonstrated final working prototype of fatigue prediction in workers with near-real-time visualization at the Boeing, Everett, Washington, factory floor.
  • August 2022 - Demonstrated initial working prototype of fatigue prediction in workers with near-real-time visualization at the John Deere, Knoxville, Tennessee, factory floor.
  • July 2022 - Presented Speech Disfluency Detection with Contextual Representation and Data Distillation at Intelligent Acoustic Systems and Applications co-located with MobiSys'22.
  • April 2022 - Presented poster on Speech Disfluency Detection under Data Constraints at CRA-WP grad cohort at New Orleans, Louisiana.

Services

  • Reviewer: ICASSP’25, ICLR’25, NeurIPS’24 (TSALM workshop), IMWUT’24, IROS’24, ICASSP’24
    • External Reviewer: ASP-DAC’24, EMSOFT’23, ICCPS’23, NSys’22
    • Book Reviews: Cambridge University Press early reader’s reviewing
  • Organize biweekly inter-laboratory Cyber-Physical Systems study group at Northwestern.
  • Active participant in the ML reading group at Northwestern, covering topics like XGBoost, MultiModal Learning, and Non-Stationary Transformers.
  • Mentoring:
    • 2024: Xiaoyuan Zhang, Talia-Ben Naim (MS students, Computer Engineering, Northwestern), Brooks Hu (Undergraduate, Computer Engineering, Northwestern), Mark Zhang (MS students, Mechanical Engineering, Northwestern)
    • 2023:
      • Yueyuan Sui (MS, Northwestern University → PhD, Northwestern University), Shamika Likhite (MS, Northwestern University → SWE, SpeechAce)
      • Kiva Joseph (Undergraduates, Computer Engineering, Northwestern)
      • Jonathan Li Chen, Ben Forbes, Justin Lau (Undergraduates, Mechanical Engineering, Northwestern)
    • 2022: Devashri Naik (MS, Northwestern University → PhD, University of Illinois at Chicago), Jinjin Cai (MS, Northwestern University → PhD, Purdue University) (MS students, Computer Engineering, Northwestern)