Hanchen David Wang

Welcome! My name is David. I am currently a PhD student at Vanderbilt University working with Prof. Meiyi Ma on eXplainable AI in Healthcare and Deep Learning research.

C.V.

GitHub

About Me

I am passionate about advancing the fields of eXplainable AI (XAI), healthcare, and machine learning. My work focuses on leveraging cutting-edge machine learning techniques to improve physical therapy, nursing simulations, and formal verification in AI. Through interdisciplinary collaborations and innovative approaches, I strive to create impactful solutions that bridge the gap between research and real-world applications.

Research Experience

Explainable AI for First-Person Video Segmentation in Nursing Simulations

Jul 2024 – Present
Collaborators: Daniel Levin, Gautam Biswas, Alyssa White, advised by Meiyi Ma

Developing explainable AI methods to analyze video segments from Tobii Glasses’ first-person perspective during nursing simulation training sessions.
Designed an unsupervised segmentation method optimized for processing long videos efficiently.
Focused on interpretable models to link gaze dynamics with task performance and learning outcomes.

IMU-Guided Segmentation and Sampling for Video Classification

Feb 2024 – Present
Collaborators: Meiyi Ma

Creating an IMU-guided method to enhance classification accuracy and efficiency in multimodal data.
Improved Temporal Segment Networks by incorporating motion-based insights for better frame selection.

Continual Multitask Learning

Jan 2024 – Oct 2024
Collaborators: Meiyi Ma

Developed a Continual Multitask Learning framework, addressing challenges in continual multitask learning without requiring replay buffers.
More details will be available soon.

Star-based Reachability Verification for Targeted and Robust XAI

Sep 2023 – May 2024
Collaborators: Meiyi Ma, Taylor Johnson, Diego Manzanas Lopez

Led the development of a framework designed to evaluate and test the comprehensiveness of state-of-the-art attribution methods.
Utilized Neural Network Verification (NNV) to analyze and validate the boundaries and trustworthiness of sampling-based attribution methods.
Designed experiments to showcase the method’s robustness in providing deterministic and targeted explainability for neural networks.
More details will be available soon.

EXACT: A Meta-Learning Framework for Precise Exercise Segmentation in Physical Therapy

May 2023 – June 2024
Collaborator: Meiyi Ma

Led the development of EXACT, a novel method for segmenting exercises within multivariate time series data using PyTorch.
Designed and implemented a deep learning model leveraging a U-Net architecture with temporal positional encoding to accurately identify and categorize exercise phases from complex sensor data.
Integrated dense labeling techniques with state-of-the-art neural network architectures, improving segmentation precision and reliability.
Conducted extensive experiments across various datasets, demonstrating the method’s adaptability and superiority over traditional segmentation techniques.
Developed a modular Python software framework to facilitate easy replication, experimentation with different architectures, and application to diverse time series segmentation tasks.

MicroXercise: A Micro-Level Comparative and Explainable System for Remote Physical Therapy

Aug 2022 – May 2023
Collaborators: Meiyi Ma, Pamela Wisniewski

Led the development of MicroXercise, a system that integrates Siamese Neural Networks with saliency maps to provide detailed feedback on physical therapy exercises.
Designed and implemented a Siamese Neural Network capable of determining input similarity and generating detailed attribution scores for interpretability.
Incorporated saliency map techniques and layer-wise relevance propagation into the architecture for explainability across modalities such as images and signals.
Built a comprehensive feedback system with visual and textual components to deliver actionable insights based on IMU sensory data.
Conducted a mixed-methods study involving semi-structured interviews, surveys, and quantitative analysis to evaluate system effectiveness and user perception.
Highlighted the system’s role in fostering self-awareness among users by serving as an observer during exercise sessions and encouraging the development of a mind-muscle connection.
Utilized Python and PyTorch for model development, with Swift and Unity for additional application integration.

PhysiQ: Off-Site Quality Assessment of Exercises in Physical Therapy

Aug 2021 – May 2022
Collaborator: Meiyi Ma

Led the development of PhysiQ, a framework for continuous tracking and quantitative measurement of off-site exercise activity through passive sensory detection.
Designed a multi-task spatiotemporal Siamese Neural Network to evaluate both absolute exercise quality through classification and relative quality based on individual progress through similarity comparison.
Implemented PhysiQ to assess exercises based on three key metrics: range of motion, stability, and repetition.
Conducted data collection and annotation for 31 participants with varying exercise quality levels, enabling robust evaluation of the framework.
Demonstrated PhysiQ’s effectiveness with a detection accuracy of 89.67% for exercise quality levels and an average R-squared correlation of 0.949 in similarity comparison.
Highlighted the role of PhysiQ in addressing challenges posed by lack of supervision and self-correction during at-home physical therapy exercises.
Published a detailed overview of this work on Medium.

Publications

Wang, Hanchen David, Bae, Siwoo, Sun, Xutong, Thatigotla, Yashvitha, and Ma, Meiyi.
EXACT: A Meta-Learning Framework for Precise Exercise Segmentation in Physical Therapy.
International Conference on Cyber-Physical Systems (ICCPS), Nov. 2024, Under Review.
Wang, Hanchen David, Bae, Siwoo, Chen, Zirong, and Ma, Meiyi.
Learning with Preserving for Continual Multitask Learning.
International Conference on Learning Representations (ICLR), Oct. 2024, Under Review.
Cohn, Clayton, Davalos, Eduardo, Vatral, Caleb, Fonteles, Joyce, Wang, Hanchen David, Ma, Meiyi, and Biswas, Gautam.
Multimodal Methods for Analyzing Learning and Training Environments: A Systematic Literature Review.
ACM Computing Surveys, Aug. 2024, Under Review.

More on Publications

PROJECT EXPERIENCE (Explain in Details):

Database Management System: (Fall 2020)

Database Management System: undergrad course project equivalent to graduate level course on end-to-end implementation of a database at UC Irvine.

Categories: C/Cpp, Git

Single Sign On: (Winter 2020)

Single Sign On: internship At St. Jude Research Hospital, worked as Information Services

Categories: AngularCLI, TypeScript, Node.js

Online Movies Store Web Application: (Jun. 2019 – Aug. 2019)

Online Movies Store Web Application Project: undergrad course project at the University of California, Irvine. It is a 10-week project class that heavily targets the implementation of an application.

Categories: Java, MySQL, HTML, CSS, Maven

Stay Together: (Fall 2018)

Stay Together Project: my second year first hackathon working related to healthcare on how we can treat opioid-addicted patient better.

Categories: Swift, Storyboard

WORK EXPERIENCE:

Teaching Assistant (Aug. 2021 - May 2022)

@Vanderbilt University

Graded homeworks weekly for the students bodies and hosted weekly office hours to help students with the concepts, homework instructions, and debuggings.
Drew on the whiteboard to demonstrate many concepts including how operating system utilizes fork and the relationship between parent and child in processes.
Hosted additionally meeting with students to confront coding challenges and brainstormed with them to resolve problems in C programming

Undergraduate Grader (Sep. 2019 – Dec. 2019, Mar. 2020 – June 2020)

@UC Irvine

Designed and developed extensive test cases for command-line programs, Bash-like shell, dynamic memory allocator, and client/server network programming in C.
Collaborated with the professor and team of 2 graders to design C programming assignments which could be programmatically tested with the auto-grader.
Assisted students 1-on-1 with debugging programs and understanding test case failures.

Information Services Intern (Jun. 2019 – Aug. 2019)

@ St. Jude Research Hospital

Developed Single Sign-On project using Agile & Waterfall development cycles and researched Active Directory Federation Service applications for authentication.
Implemented idP-initiated SSO with Angular framework as frontend and Node.js as backend server to process SAML assertion from ADFS. Deployed application on Tomcat to test the Active Directory by using SecureAuth.
Optimized the application with MIT-Licensed NPM plugin for SAML 2.0, samlify, to establish connectivity with Active Directory over the Node.js.