I am a research associate at Notre Dame AI and SciML, with a PhD in CS and Biomech from Iowa State University. My research interests focus on machine learning applied to a variety of sequential data, e.g., time-series, spatiotemporal, NLP with applications spanning healthcare, biomechanics, sensors, and engineering.
Known for contributions to integrate AI and IMUs to understand motion.
LinkedIn
Developed anomaly detection algorithms to identify abnormal server behavior using throughput and latency metrics. Applied Gaussian models to visualize and detect anomalies in 2D datasets and extended the approach to high-dimensional data, achieving high detection accuracy through cross-validation. Optimized detection thresholds using precision-recall metrics, improving the accuracy of identifying true anomalies.
Developed and optimized collaborative filtering algorithms to predict movie ratings using a dataset of 1682 movies and 943 users. Implemented regularization techniques to improve model generalization and minimize overfitting. Enhanced user satisfaction by personalizing movie recommendations based on user preferences, improving engagement.
A program that read IMU signal to estimate the real-time orientation and reconstruction of the object’s orientation back to 3D space.
This project is an implementation of the classic Checker game with an AI opponent that utilizes the MinMax algorithm for decision making. The MinMax algorithm is a widely-used technique in game theory for determining the best move in a two-player, zero-sum game.
Developed real-time pose estimation algorithms using OpenPose to track dynamic gymnastics movements. Designed data structures for body points and trajectories with interactive visualization tools, ensuring accuracy and usability. Enhanced algorithms and user interface through testing and feedback from gymnasts and coaches
Developed a Java application integrated with SQL to analyze state legislators’ tweets during an election year. Designed and executed SQL queries to extract insights from tweet content, trends, and sentiment. Provided data-driven visualizations to understand the political discourse and its evolution over time.
Machine Learning with Membership Privacy using Adversarial Regularization
Comprehensive Privacy Analysis of Deep Learning:Passive and Active White-box Inference Attacksagainst Centralized and Federated Learning
Adversarial Example in Biomech Research
Cloud Computing in Healthcare and Privacy
Classify individual with Overweight by IMUs Signals
Situation Awareness in Falling Prediction and Prevention
AI Survey Over Applications of Patient with Stroke
Advanced Algorithm
Theory of Computation
Advanced Computation (class content is not allowed to upload)
Turing Machine (TM) and Prove Computability, Decidability, Countability, Slice and Parameterized of Languages, Enumeration, Universal TM, Prefix TM, Classes and Subset, Quantum Computing
University Research Excellent 2024
“The purpose of this award is to recognize the top 10% of graduate students for outstanding research accomplishments as documented in their theses and dissertations.”
[1] JY Liang, H Bian, W Zhang, CK Chang, LS Chou. (2024) Striding into Clarity: Wearable Sensor-Driven Estimation of Knee Adduction Moment, Unveiling the Black Box with Sequence-Based Neural Networks and Explainable Artificial Intelligence. AAAI 2024 Spring Symposium on Clinical Foundation Models
[2] JY Liang & LS Chou. (2024) Center of mass acceleration during walking: comparison between IMU and camera-based motion capture methodologies. Wearable Technologies
[3] JY Liang & LS Chou. Estimation of Knee Adduction Moment During Walking Using Wearable Sensor Data With the Application of Sequence-based Artificial Recurrent Neural Network. IX World Congress of Biomechanics 2022
[4] JY Liang & LS Chou. (2023) Assessment of Gait Balance Control Using Inertial Measurement Units — A Narrative Review. World Scientific Annual Review of Biomechanics, 2330006
[5] JY Liang , M Zhang, N Lamoureux, J Lansing, LS Chou, GJ Welk. (2023) Estimation of STEADI Performance Using Inertial Measurement Unit