Sense · Infer user and environmental states
Wearable Multimodal Sensing
Building compact multimodal wearables with sensing modalities such as Electrical Impedance Tomography (EIT) and IMUs to infer user and environmental states.
About
Embodied AI through Wearable Interactive Systems
I am a graduate student in Computer Science at Northwestern University (MU Collective), advised by Prof. Jessica Hullman. My earlier projects built wearable sensing and stimulation; at MU Collective I design interactive systems that provide information with high complementarity for human-AI joint decision making.
My papers have appeared at ACM CHI, UIST, and CVPR, and I have given invited technical demos at Qualcomm and Google.
Before Northwestern, I was a research assistant with Prof. Pedro Lopes at the University of Chicago (HC-Integration Lab), with Prof. Karan Ahuja at Northwestern (SPICE Lab), and with Prof. Teng Han at ISCAS.
News
- Aug 2026 Awarded the TGS Conference Travel Grant from Northwestern University!
- Aug 2026 Our paper "EITWatch" was accepted to UIST 2026, and we will present an interactive demo at the conference!
- Mar 2026 Our paper "MARIO" was accepted to Findings of CVPR 2026!
- Sep 2025 Started my graduate studies at Northwestern University!
- May 2025 Graduated from UPenn with M.S.E. in Robotics.
- Jan 2025 Our paper "Seeing with the Hands" was accepted to CHI 2025, and we presented an interactive demo at the conference!
Research
My research explores Embodied AI through Wearable Interactive Systems that provide context-aware, complementary assistance.
- Build multimodal wearables that sense the wearer and their surroundings.
- Develop and adapt models that decide when and how to assist based on sensed states.
- Design on-body haptics that translate model decisions into physical interventions.
Decide · Determine when and how to assist
Human-AI Complementarity
Developing and adapting models that decide when and how to assist based on sensed states, with the goal of complementing human capabilities under uncertainty.
Intervene · Deliver physical assistance
On-Body Haptics
Designing compact on-body interfaces that deliver assistance through haptic feedback to support human perception and action.
Publications
* denotes equal contribution
Wrist Electrical Impedance Tomography (EIT) can sense hand gestures from muscle- and tendon-driven impedance changes without cameras, but prior wrist-EIT systems wrap electrodes beyond the watch-back contact patch and rely on separate analog front ends. We present EITWatch, the first wrist-EIT system built around smartwatch case-back geometry. Eight planar electrodes in a compact ring read impedance from the skin patch the watch already occupies; because a planar array cannot encircle the wrist, EITWatch uses multi-depth scanning to probe tissue along multiple current paths. A user study shows that this watch-back form factor can support both macro- and micro-gesture recognition, pointing toward unobtrusive EIT input on everyday wearables.
Inertial odometry using only IMUs offers a lightweight approach to human motion tracking for augmented reality and wearables, but learning-based methods often drift because they lack explicit models of human motion dynamics. We propose MARIO, which grounds inertial odometry in human kinematics through a learned pose prior inferred from IMU data, promoting physically consistent motion during state propagation. We further develop a sensor-fusion framework that incorporates auxiliary signals from lightweight sensors already available on commercial AR glasses, including magnetometers, barometers, and secondary IMUs. Evaluated on a large-scale motion dataset, MARIO substantially reduces positional drift compared with strong IO baselines and improves robustness across diverse motion conditions, pointing toward lightweight, camera-free human tracking that unifies kinematic priors with multimodal sensing.
Sensory substitution devices enable users to perceive visual information through other modalities, such as touch. However, most existing devices place the camera at the user's eyes (head-mounted), which limits the ability to coordinate manual interactions. We propose "seeing and feeling" from the hand's perspective to enhance the flexibility and expressivity of sensory substitution. To this end, we engineered a back-of-the-hand electrotactile display that renders tactile images from a wrist-mounted camera, allowing users to feel objects while reaching and hovering. In a user study with sighted and blind or low-vision participants, we compared our hand-centered perspective against traditional head-mounted views in manipulation tasks (e.g., handling bottles, soldering). Results indicate that while both perspectives yield comparable performance, participants preferred the flexibility of the hand's perspective, which supported more ergonomic object manipulation strategies.
Electrotactile stimulation offers a promising path for high-resolution wearable haptics but has traditionally struggled with integration into soft, everyday textiles. We present TacTex, a textile interface that seamlessly integrates high-density haptic feedback and touch sensing. By employing a novel multi-layer woven structure that separates conductive electrodes with non-conductive yarns, TacTex achieves a sensing and actuation resolution of 512 × 512 with electrode spacing as small as 2mm. The system includes a custom driving board that enables precise spatial and temporal control of electrical stimuli while simultaneously monitoring voltage changes for touch tracking. Our technical evaluation and user studies demonstrate that TacTex can render a wide range of haptic effects, including complex static and dynamic patterns, effectively bringing high-fidelity haptic feedback to everyday clothing.
Teaching & Service
Reviewer
Serving as a reviewer for IMWUT (ubiquitous computing) and IC2S2 (computational social science).
Teaching Assistant: ESE5190 Smart Devices
Graduate course at University of Pennsylvania covering embedded systems, IoT, and smart device development.
Code Instructor
Teaching Python & Arduino programming fundamentals to middle school students at Fife-Penn CS Academy.
Student Representative
Represented students at the Global Youth Forum 2021, organized by the International Labour Organization.
More
Fun projects
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Fun facts
- Blogger HCI and academic life, more than 5,000 followers.
- Saxophone player Paid player. More than 10 years of practice.
- Truck driver Drove from Chicago to NYC overnight without resting.
- Professional Gamer Fruit Ninja top 20 worldwide; NBA 2K top 500 nationwide.
Contact
