Yue, Yisong

Committee Member — 90 thesises

• Johnston, Kadina Elizabeth — Acquiring Enzyme Sequence-Fitness Data at Scale Toward Predictive Methods for Enzyme Engineering (2024, Dissertation (Ph.D.))
• Chen, Xiaoqiao — Active Acquisition Methods for Single Cell Genomics (2025, Dissertation (Ph.D.))
• Hall, David Christopher — Advancing a Machine's Visual Awareness of People (2017, Dissertation (Ph.D.))
• Feng, Berthy T. — Advancing Scientific Computational Imaging Through Data-Driven and Physics-Based Priors (2026, Dissertation (Ph.D.))
• Sun, Jennifer Jianing — AI for Scientists: Accelerating Discovery Through Knowledge, Data, and Learning (2024, Dissertation (Ph.D.))
• Zheng, Hongkai — Algorithms and Representations for Learning and Inference with Diffusion Models (2027, Dissertation (Ph.D.))
• Kondapaneni, Neehar — Aligning and Comparing Vision Representations to Improve Understanding and Performance (2025, Dissertation (Ph.D.))
• Stevens, Benjamin Carter — Applications of Machine Learning to Finite Volume Methods (2022, Dissertation (Ph.D.))
• Yang, Jason — Artificial Intelligence Methods for Enzyme Engineering (2026, Dissertation (Ph.D.))
• Bagherian, Dawna Paria — Artificial Neural Networks for Nonlinear System Identification of Neuronal Microcircuits (2021, Dissertation (Ph.D.))
• Cheng, Richard — Assuring Safety under Uncertainty in Learning-Based Control Systems (2021, Dissertation (Ph.D.))
• Chalupka, Krzysztof — Automated Macro-scale Causal Hypothesis Formation Based on Micro-scale Observation (2017, Dissertation (Ph.D.))
• Qin, Yidan — Autonomous Temporal Understanding and State Estimation during Robot-Assisted Surgery (2022, Dissertation (Ph.D.))
• Talukder, Sabera — Beyond Text: The Rudiments of Next Generation Foundation Models (2026, Dissertation (Ph.D.))
• Zhang, Tony (Haoyu) — Biological Intelligence: from Behavior to Learning Theory (2022, Dissertation (Ph.D.))
• Appel, Ron — Boosting Boosting (2017, Dissertation (Ph.D.))
• Wang, Guanzhi — Building Foundation Agents with Internet Knowledge and Large Language Models (2026, Dissertation (Ph.D.))
• Liang, Chen — Cascading Failures in Power Systems: Modeling, Characterization, and Mitigation (2022, Dissertation (Ph.D.))
• Eyjolfsdottir, Eyrun-Arna — Computational Methods for Behavior Analysis (2017, Dissertation (Ph.D.))
• Jimenez Rodriguez, Ivan Dario — Constructive Learning for Agile Underactuated Control (2026, Dissertation (Ph.D.))
• Ho, Dimitar Mi — Control of Unknown Dynamical Systems: Robustness and Online Learning of Feedback Control (2024, Dissertation (Ph.D.))
• Wu, Zachary — Data-Driven Protein Engineering (2021, Dissertation (Ph.D.))
• Cvitkovic, Michael William (Milan0 — Deep Learning in Unconventional Domains (2020, Dissertation (Ph.D.))
• Rivière, Benjamin Pierre — Do Robots Dream of Random Trees? Monte Carlo Tree Search for Dynamical, Partially Observable, and Multi-Agent Systems (2024, Dissertation (Ph.D.))
• Cosner, Ryan Kazuo — Dynamic Safety Under Uncertainty: A Control Barrier Function Approach (2025, Dissertation (Ph.D.))
• Xu, Changhao — Electronic Skin in Robotics and Healthcare: Towards Multimodal Sensing and Intelligent Analysis (2024, Dissertation (Ph.D.))
• Tucker, Maegan Lindsay — Enabling Robust and User-Customized Bipedal Locomotion on Lower-Body Assistive Devices via Hybrid System Theory and Preference-Based Learning (2023, Dissertation (Ph.D.))
• Li, Francesca-Zhoufan — Evaluation of the Generalizability of Machine Learning-Assisted Protein Engineering Methods (2025, Dissertation (Ph.D.))
• Zheng, Stephan Tao — Exploiting Structure for Scalable and Robust Deep Learning (2018, Dissertation (Ph.D.))
• London, Palma Alise den Nijs — Frameworks for High Dimensional Convex Optimization (2021, Dissertation (Ph.D.))
• Li, Qilin — From Cells to Functional Tissue Units: Scale-Aware Deep Learning for Biological Microscopy Segmentation (2026, Dissertation (Ph.D.))
• Liu, Yang — From Restoring Human Vision to Enhancing Computer Vision (2020, Dissertation (Ph.D.))
• Carilli, Maria Theresa Natalina — Genetic Interrogation of Expression Regulation (2026, Dissertation (Ph.D.))
• Dorobantu, Victor David — Geometry and Dynamical Systems in Machine Learning and Control (2023, Dissertation (Ph.D.))
• Korlakai Vinayak, Ramya — Graph Clustering: Algorithms, Analysis and Query Design (2018, Dissertation (Ph.D.))
• Wandelt, Sarah Kim — Grasp, Speech, and Internal Speech Representation in the Human Cortical Grasp Circuit (2023, Dissertation (Ph.D.))
• Voloshin, Cameron — Guaranteed Policy Performance in Reinforcement Learning (2024, Dissertation (Ph.D.))
• Shi, Xichen — Intelligent Control for Fixed-Wing eVTOL Aircraft (2021, Dissertation (Ph.D.))
• Folkestad, Carl A. A. — Koopman-based Learning and Control of Agile Robotic Systems (2022, Dissertation (Ph.D.))
• Azizan Ruhi, Navid — Large-Scale Intelligent Systems: From Network Dynamics to Optimization Algorithms (2021, Dissertation (Ph.D.))
• Csomay-Shanklin, Noel V. — Layered Control Architectures: Constructive Theory and Application to Legged Robots (2025, Dissertation (Ph.D.))
• Marino, Joseph Louis — Learned Feedback & Feedforward Perception & Control (2021, Dissertation (Ph.D.))
• Yeh, Christopher Tzong-Ran — Learning Decision-Focused Uncertainty Representations: Theory and Applications in Sustainability (2026, Dissertation (Ph.D.))
• Song, Jialin — Learning to Optimize: from Theory to Practice (2021, Dissertation (Ph.D.))
• Wu, Zihui — Learning to Sample in Computational Imaging: Measurement Acquisition and Posterior Estimation (2026, Dissertation (Ph.D.))
• Li, Tongxin — Learning-Augmented Control and Decision-Making: Theory and Applications in Smart Grids (2023, Dissertation (Ph.D.))
• Liu, Hao — Leveraging Structural Uncertainty for Decision Making: from Classical Methods to Foundation Model Agents (2026, Dissertation (Ph.D.))
• Levine, Matthew Emanuel — Machine Learning and Data Assimilation for Blending Incomplete Models and Noisy Data (2023, Dissertation (Ph.D.))
• Aceves, Aiden Joseph — Machine Learning and Modeling Methods for Protein Engineering (2022, Dissertation (Ph.D.))
• Maser, Michael Robert — Machine Learning Methods Inspired by Challenges in Total Synthesis (2022, Dissertation (Ph.D.))
• Hooper, Meredith Leigh — Machine-Learned Propulsion Strategies: From Adaptive Damage Compensation to Advanced Aeromobility (2025, Dissertation (Ph.D.))
• Chao, Daniel Shuteh — Measuring R(D()) for B → ‾D()τν_τ using Semileptonic Tags and Tau Decays to Hadrons (2018, Dissertation (Ph.D.))
• O'Connell, Michael Thomas — Methods for Robust Learning-Based Control (2023, Dissertation (Ph.D.))
• Cherry, Kevin Matthew — Molecular Pattern Recognition and Supervised Learning in DNA-Based Neural Networks (2024, Dissertation (Ph.D.))
• Guan, Charles — Neural Coding of Finger Movements in Human Posterior Parietal Cortex and Motor Cortex (2023, Dissertation (Ph.D.))
• Vafeidis, Panteleimon — Neural Network Models of Learning and Generalization (2025, Dissertation (Ph.D.))
• Yi, Sanghyun — Neurocomputational Understanding of Decision-Making in Novel Environments (2025, Dissertation (Ph.D.))
• Zhan, Eric — New Algorithms for Programmatic Deep Learning with Applications to Behavior Modeling (2022, Dissertation (Ph.D.))
• Le, Hoang Minh — New Frameworks for Structured Policy Learning (2020, Dissertation (Ph.D.))
• Chen, Niangjun — Online Algorithms: From Prediction to Decision (2018, Dissertation (Ph.D.))
• Sui, Yanan — Online Learning for the Control of Human Standing via Spinal Cord Stimulation (2017, Dissertation (Ph.D.))
• Novoseller, Ellen Rachel — Online Learning from Human Feedback with Applications to Exoskeleton Gait Optimization (2021, Dissertation (Ph.D.))
• González Palacios, Carlos Roberto — Optimal Data Distributions in Machine Learning (2015, Dissertation (Ph.D.))
• Bernstein, Jeremy David — Optimisation & Generalisation in Networks of Neurons (2023, Dissertation (Ph.D.))
• Su, Yu — Optimizing Cloud AI Platforms: Resource Allocation and Market Design (2021, Dissertation (Ph.D.))
• Ren, Xiaoqi — Optimizing Resource Management in Cloud Analytics Services (2018, Dissertation (Ph.D.))
• Lupu, Elena Sorina — Perception-Driven Autonomy and Learning Control for Ground Vehicles (2025, Dissertation (Ph.D.))
• Lin, Yiheng — Predictions and Policy Optimization in Online Decision Making (2025, Dissertation (Ph.D.))
• Brown, David — Principles of Massively Parallel Sequencing for Engineering and Characterizing Gene Delivery (2022, Dissertation (Ph.D.))
• Yang, Kevin Kaichuang — Probabilistic Protein Engineering (2019, Dissertation (Ph.D.))
• Bruer, John Jacob — Recovering Structured Low-rank Operators Using Nuclear Norms (2017, Dissertation (Ph.D.))
• Yin, Lucy — Reducing Latencies in Earthquake Early Warning (2018, Dissertation (Ph.D.))
• Goel, Gautam — Regret-Optimal Control (2022, Dissertation (Ph.D.))
• Shi, Guanya — Reliable Learning and Control in Dynamic Environments: Towards Unified Theory and Learned Robotic Agility (2023, Dissertation (Ph.D.))
• Taylor, Andrew James — Robust Safety-Critical Control: A Lyapunov and Barrier Approach (2023, Dissertation (Ph.D.))
• Yu, Jing — Safe and Scalable Learning-Based Control: Theory and Application in Sustainable Energy Systems (2025, Dissertation (Ph.D.))
• Nakka, Yashwanth Kumar — Spacecraft Motion Planning and Control under Probabilistic Uncertainty for Coordinated Inspection and Safe Learning (2021, Dissertation (Ph.D.))
• Gordon, Spencer Lane — The Identification of Discrete Mixture Models (2023, Dissertation (Ph.D.))
• Cross, Logan Matthew — The Neural Mechanisms of Value Construction (2022, Dissertation (Ph.D.))
• Van Horn, Grant Richard — Towards a Visipedia: Combining Computer Vision and Communities of Experts (2019, Dissertation (Ph.D.))
• Pastor, Daniel — Towards Learning Robotic Dynamics: Application to Multirotor Takeoff and Landing (2021, Dissertation (Ph.D.))
• Tokpanov, Yury — Towards Next Generation of Optoelectronics: from Quantum Plasmonics and 2D Materials to Advanced Optimization Techniques of Nanophotonic Devices (2020, Dissertation (Ph.D.))
• Werner, Lucien Desloge — Uncertainty and Decentralization: Two Themes in an Energy Transformation (2023, Dissertation (Ph.D.))
• Gao, Angela Fang — Uncovering Hidden Structure in Data and the Physical World for Seismic Tomography and Beyond (2026, Dissertation (Ph.D.))
• Huang, Yujia — Understanding and Improving Reliability of Inference Dynamics in Deep Neural Networks (2024, Dissertation (Ph.D.))
• Yu, Changhua — Understanding Kinase-Substrate Interaction with Deep Learning and High-Throughput Scanning (2026, Dissertation (Ph.D.))
• Li, Kejun — User-Aligned and Robust Bipedal Locomotion (2026, Dissertation (Ph.D.))
• Ronchi, Matteo Ruggero — Vision for Social Robots: Human Perception and Pose Estimation (2020, Dissertation (Ph.D.))
• Cole, Elijah Henry John — Visual and Spatial Representation Learning with Applications in Ecology (2023, Dissertation (Ph.D.))
• Beery, Sara Meghan — Where the Wild Things Are: Computer Vision for Global-Scale Biodiversity Monitoring (2023, Dissertation (Ph.D.))