Biography

Frank (Sicong) Chen is a postdoctoral researcher in the HumanX Cluster at Dartmouth College, where he works on human-centered wearable and mobile systems that sense, interpret, and respond to physiological and behavioral states in real time. His research examines how everyday devices can move beyond passive monitoring to become intelligent systems that detect risk, uncertainty, and vulnerability, and intervene in ways that meaningfully support human well-being.

His work integrates wearable sensing, behavioral biometrics, and multimodal machine learning to study signals related to movement, interaction patterns, and biosignals. By modeling how these signals evolve over time, he develops closed-loop systems that can adapt their behavior based on user state, confidence, and context. This perspective underlies his research on continuous authentication, stress and mental health monitoring, and adaptive intervention systems.

Frank earned his Ph.D. in Computer and Information Science and Engineering from Syracuse University, where his doctoral research focused on robust continuous authentication and real-world behavioral biometric systems. By bridging biometric security and digital health, his work aims to enable technologies that are both reliable and empathetic—systems that recognize who users are, infer how they are doing, and provide timely, personalized support.

Education

Ph.D., Computer and Information Science and Engineering, Syracuse University
Advisor: Prof. Vir V. Phoha
View Full CV →

Research Interests

Wearables

Wearable and Mobile Sensing for Human State Inference

Designing non-invasive wearable and mobile sensing systems to infer cognitive, emotional, and behavioral states in everyday settings. Integrating multimodal sensing and machine learning to enable continuous monitoring, early risk detection, and context-aware support across health, well-being, and daily human–technology interaction.

Systems

Closed-Loop Intelligent Interventions

Building end-to-end systems that connect real-time physiological and behavioral sensing with adaptive decision-making, enabling interventions to be delivered based on user state, context, and model confidence.

Security

Multimodal Continuous Authentication on Mobile and Wearable Devices

Developing continuous authentication frameworks that leverage multimodal behavioral and contextual signals on mobile and wearable devices to achieve robustness to real-world variability, uncertainty-aware modeling, and secure deployment beyond single-session or static authentication.

Publications

SSPRA: A robust approach to continuous authentication amidst real-world adversarial challenges
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IEEE Transactions on Biometrics, Behavior, and Identity Science, 2024
Frank Sicong Chen; Jingyu Xin; Vir V. Phoha
DSTER: A dual-stream transformer-based emotion recognition model through keystrokes dynamics
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IEEE International Joint Conference on Biometrics (IJCB), 2024 Best-Reviewed Paper
Frank Sicong Chen; Shruti Rao; Brijesh Tiwari; Vir V. Phoha
Gaitpoint: A gait recognition network based on point cloud analysis
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IEEE International Conference on Image Processing (ICIP), 2022
Jiajing Chen; Huantao Ren; Frank Sicong Chen; Senem Velipasalar; Vir V. Phoha

Projects & Systems

CalmNight: Closed-Loop Nightmare Detection and Intervention
Sleep & Mental Health
AI-powered wearable system for real-time nightmare detection and personalized, non-disruptive intervention during sleep.
Memorii: AI-Based Wearable Assistant for Dementia Care
Assistive AI & Dementia Care
An AI-powered wearable assistant supporting people with dementia and reducing caregiver burden.
BOAN: Brain Organoid–Based Biological Computing Platform
Biological Computing & Neurotechnology
An integrated software–hardware platform that supports living brain organoids as biological computing substrates, enabling sensing, interaction, and learning from real-world environmental signals.
SSPRA: Multimodal Continuous Authentication Framework
Security & Biometrics
A robust multimodal continuous authentication framework that models uncertainty and transitional states to enable secure, real-world deployment on mobile and wearable devices.
WearStreM: Wearable-Based Continuous Stress Monitoring Framework
Stress Monitoring
A wearable sensing and machine learning framework for continuous stress detection under real-world conditions with incomplete and noisy multimodal data.

Honors & Awards

Best-Reviewed Paper Selection and Journal Invitation, IEEE IJCB, 2024.
Doctoral Consortium Travel Scholarship, IEEE IJCB, 2024.
Graduate Dean’s Award for Excellence in Research and Creative Work, Syracuse University, 2025.
Engineering and Computer Science Research Day Award (First Place), Syracuse University, 2025.
University-wide Outstanding Teaching Assistant Award, Syracuse University, 2023.

Professional Service

Editorial & Journal Reviewing
Program Committee Member, IEEE Transactions on Computational Social Systems
Reviewer, ACM Digital Threats: Research and Practice
Conference Program Committees
Program Committee Member, IEEE FG 2026
Program Committee Member, AAAI 2026
Program Committee Member, IEEE CogMI 2025
Conference Reviewing
Reviewer, CAIP 2025
Reviewer, InterID Workshop (FG 2025)

Teaching Experience

Graduate and undergraduate instruction in machine learning for security, biometrics, software implementation, and discrete mathematics. Delivered independent lectures, developed instructional modules, and mentored student research projects, including projects leading to peer-reviewed publications. Emphasis on systems thinking and applied AI.

For a complete list of courses and responsibilities, see CV.