Angela Chen specializes in Human–AI Interaction, with broad training in Statistics, Artificial Intelligence, Robotics, and Human–Computer Interaction. She is passionate about creating technologies that enhance personal well-being.

Education

Carnegie Mellon University

Ph.D. Candidate in Robotics at the School of Computer Science

Dissertation: Designing LLM-Based Virtual Patients for Psychotherapy Training: Behavioral Simulation of Interpersonal Dynamics as Controllable Agentic AI Systems

Generative AI in Healthcare Fellow @ Carnegie Mellon University Center for Machine Learning and Health

Teaching assistant for Human-Robot Interaction‍ ‍

Reviewer for CHI, UIST, CSCW, NeurIPS, AAAI, CHIL, FAccT, HAI

University of Illinois at Urbana-Champaign

Master of Science in Statistics

Bachelor of Science in Statistics

Computational Genomics Fellow @ National Center for Supercomputing Applications

Recent Publications and Projects

  • Simulating Multi-party Interaction in Psychotherapy

    The First Known Multi-agent Framework for Training Couples Therapists

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  • Behaviorally-adaptive LLM-Based Virtual Patients for Psychotherapy Training

    Making the Consequences of a Trainee’s Choice Visible

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  • Empirical Modeling of Therapist–Client Dynamics Using LLM-Based Assessments

    Based on 2,000 Hours Clinical Psychotherapy Transcripts

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  • Foundation Models for Healthcare

    Adapting Frozen Univariate Time Series Foundation Models for Multivariate Healthcare Time Series

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  • Time Series Foundation Models

    Generalized Prompt Tuning: How to Use a Frozen Pre-Trained Univariate Time Series Foundation Model for Multivariate Time Series Prediction

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  • Trustworthy AI Design Specifications

    Evaluating a Machine Learning Model’s Trustworthiness via Data-driven Design Specifications

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  • Human-Robot Trust Design

    ”Do it my way!”: Impact of Customizations on Trust Perceptions in Human-Robot Collaboration

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  • Home Robot System Design

    AquaBot: Assistive Drinking Robot for the Mobility-impaired

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Her previous research explored genetic contributions to various traits using statistical modeling:

Chen, A., & Lipka, A. (2016). The use of targeted marker subsets to account for population structure and relatedness in genome-wide association studies of maize (Zea mays L.). G3: Genes, Genomes, Genetics, 6(8), 2365-2374.

Chen, A., Ge, W., Metcalf, W., Jakobsson, E., Mainzer, L., & Lipka, A. (2019). An assessment of true and false positive detection rates of stepwise epistatic model selection as a function of sample size and number of markers. Heredity, 122(5), 660-671.