Computing and Mathematical Sciences

2026 · 2025 · 2024 · 2023 · 2022 · 2021 · 2020 · 2019 · 2017

2026

• Feng, Berthy T. — Advancing Scientific Computational Imaging Through Data-Driven and Physics-Based Priors (Dissertation (Ph.D.))
• Gao, Angela Fang — Uncovering Hidden Structure in Data and the Physical World for Seismic Tomography and Beyond (Dissertation (Ph.D.))
• Jimenez Rodriguez, Ivan Dario — Constructive Learning for Agile Underactuated Control (Dissertation (Ph.D.))
• Moreno Ferreira, Elvira — Scalable Approximation through Structure: Spectral Methods for Polynomial Optimization and Adaptive Sampling in Kernel Quadrature (Dissertation (Ph.D.))
• Wang, Guanzhi — Building Foundation Agents with Internet Knowledge and Large Language Models (Dissertation (Ph.D.))
• Wu, Zihui — Learning to Sample in Computational Imaging: Measurement Acquisition and Posterior Estimation (Dissertation (Ph.D.))
• Zellinger, Michael J. — White Elephants and Cash Cows: Economically Wrangling the Zoo of AI Models (Dissertation (Ph.D.))

2025

• Batlle Franch, Pau — Optimization-Based Statistical Inference: Constrained Inverse Problems, Worst-Case Priors, and Kernel Regression (Dissertation (Ph.D.))
• Chen, Xiaoqiao — Active Acquisition Methods for Single Cell Genomics (Dissertation (Ph.D.))
• Christianson, Nicolas Henry — Machine Learning-Augmented Algorithms: Theory and Applications in Energy and Sustainability (Dissertation (Ph.D.))
• Li, Zongyi — Neural Operator for Scientific Computing (Dissertation (Ph.D.))
• Lin, Yiheng — Predictions and Policy Optimization in Online Decision Making (Dissertation (Ph.D.))
• Slote, Joseph Alfred — Discrete Harmonic Analysis and its Applications to Testing, Learning, and Complexity (Dissertation (Ph.D.))
• Trautner, Margaret Katherine — Operator Learning for Scientific Computing (Dissertation (Ph.D.))
• Zhao, Jiawei — Understanding and Improving Efficiency in Training of Deep Neural Networks (Dissertation (Ph.D.))

2024

• Huang, Hsin-Yuan (Robert) — Learning in the Quantum Universe (Dissertation (Ph.D.))
• Mazaheri, Bijan Henrik Socrates — Combining Sources and Leveraging Contexts (Dissertation (Ph.D.))
• Sun, Jennifer Jianing — AI for Scientists: Accelerating Discovery Through Knowledge, Data, and Learning (Dissertation (Ph.D.))
• Voloshin, Cameron — Guaranteed Policy Performance in Reinforcement Learning (Dissertation (Ph.D.))

2023

• Beery, Sara Meghan — Where the Wild Things Are: Computer Vision for Global-Scale Biodiversity Monitoring (Dissertation (Ph.D.))
• Cole, Elijah Henry John — Visual and Spatial Representation Learning with Applications in Ecology (Dissertation (Ph.D.))
• Dorobantu, Victor David — Geometry and Dynamical Systems in Machine Learning and Control (Dissertation (Ph.D.))
• Eldjarn Hjoerleifsson, Kristjan — Graph Modeling for Genomics and Epidemiology (Dissertation (Ph.D.))
• Levine, Matthew Emanuel — Machine Learning and Data Assimilation for Blending Incomplete Models and Noisy Data (Dissertation (Ph.D.))
• Li, Tongxin — Learning-Augmented Control and Decision-Making: Theory and Applications in Smart Grids (Dissertation (Ph.D.))
• Werner, Lucien Desloge — Uncertainty and Decentralization: Two Themes in an Energy Transformation (Dissertation (Ph.D.))

2022

• Goel, Gautam — Regret-Optimal Control (Dissertation (Ph.D.))
• Liang, Chen — Cascading Failures in Power Systems: Modeling, Characterization, and Mitigation (Dissertation (Ph.D.))
• Zhan, Eric — New Algorithms for Programmatic Deep Learning with Applications to Behavior Modeling (Dissertation (Ph.D.))

2021

• Azizan Ruhi, Navid — Large-Scale Intelligent Systems: From Network Dynamics to Optimization Algorithms (Dissertation (Ph.D.))
• Murray, Riley John — Applications of Convex Analysis to Signomial and Polynomial Nonnegativity Problems (Dissertation (Ph.D.))
• Song, Jialin — Learning to Optimize: from Theory to Practice (Dissertation (Ph.D.))
• Su, Yu — Optimizing Cloud AI Platforms: Resource Allocation and Market Design (Dissertation (Ph.D.))

2020

• Coladangelo, Andrea Wei — Quantum Correlations, Certifying Quantum Devices, and the Quest for Infinite Entanglement (Dissertation (Ph.D.))
• Cvitkovic, Michael William (Milan0 — Deep Learning in Unconventional Domains (Dissertation (Ph.D.))
• Dathathri, Sumanth — Scalable Synthesis and Verification: Towards Reliable Autonomy (Dissertation (Ph.D.))
• Le, Hoang Minh — New Frameworks for Structured Policy Learning (Dissertation (Ph.D.))

2019

• Guo, Linqi — Impact of Transmission Network Topology on Electrical Power Systems (Dissertation (Ph.D.))
• Pang, John Zhen Fu — Online Platforms in Networked Markets: Transparency, Anticipation and Demand Management (Dissertation (Ph.D.))

2017

• Cummings, Rachel Autumn Dixon — The Implications of Privacy-Aware Choice (Dissertation (Ph.D.))