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Mark SchmidtView profile

Professor

• Machine Learning
• Artificial Intelligence
• Mathematical Optimization
• +6 more

Mark Schmidt is a Professor in the Department of Computer Science at the University of British Columbia, Faculty of Science. He holds the Canada Research Chair in Large-Scale Machine Learning and is a Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute. His research spans multiple centers including CAIDA (Centre for Artificial Intelligence Decision-making and Action), the Data Science Institute, and the Machine Intelligence Learning Discovery (MILD) group. Dr. Schmidt's educational background includes a Ph.D. from UBC (2005-2010), an M.Sc. from the University of Alberta (2003-2005), and a B.Sc. from the University of Alberta (2000-2003). His academic career progressed from Postdoc positions at Simon Fraser University, Ecole Normale Superieure, and UBC to Assistant Professor (2014-2019), Associate Professor (2019-2024), and current Professor (2024-present) at UBC. His research focuses on machine learning optimization, with particular emphasis on improving numerical algorithms for large-scale machine learning applications. His work bridges theoretical optimization and practical applications across computer vision, natural language processing, and reinforcement learning. He has developed numerous optimization techniques including variants of stochastic gradient methods, coordinate descent algorithms, and natural gradient approaches. Analysis of his recent publications reveals a strong focus on optimization for over-parameterized models, particularly transformers and large language models. His work addresses critical challenges in step-size selection, convergence guarantees, and efficient implementation of optimization algorithms. His research has significant implications for training deep neural networks more effectively and understanding why certain optimization methods outperform others in practice. Among his notable awards are the Dorothy Killam Fellowship (2025), Arthur B. McDonald Fellowship (2024), Sloan Research Fellowship (2017), and multiple UBC teaching awards. He has also received the Lagrange Prize in Continuous Optimization and Best Paper Award at AISTATS 2021. Dr. Schmidt actively supervises numerous graduate students, with over 40 PhD and Master's students listed as current or alumni members of his research group. His laboratory maintains strong connections with industry partners, with many alumni securing positions at leading AI companies including Google, Amazon, Meta, and Microsoft.

• Machine Learning
• Statistics
• Theoretical Machine Learning
• +7 more

Fanny Yang is an Assistant Professor in the Computer Science Department at ETH Zurich . She previously held postdoctoral positions at Stanford University and a Junior Fellowship at the Institute for Theoretical Studies at ETH Zurich, advised by Nicolai Meinshausen . Her PhD was completed at the EECS Department of UC Berkeley , supervised by Martin Wainwright . Research Interests : Theoretical foundations of machine learning and statistics , particularly focusing on overparameterized models and high-dimensional data . Developing trustworthy ML models with emphasis on distributional robustness , domain generalization , and interpretability . Applications in medical diagnostics and treatment effect analysis , aiming to address reliability issues in real-world domains. Recent Work Trends : 2025 Articles : Theoretical analysis of semi-supervised multi-objective learning , test-time scaling with verifiers, and foundation models for efficient randomized experiments . 2024 Articles : Studies on robust mixture learning , privacy-preserving data synthesis via optimal transport , confounding quantification in causal inference , and semi-private learning frameworks. 2023 Articles : Investigations into active vs. passive learning in high dimensions, inductive bias in noisy interpolation , and semi-supervised novelty detection using model ensembles .

Minh Q. PhanView profile

Associate Professor

• System Identification
• Control Theory
• Robotics
• +4 more

Minh Q. Phan is an Associate Professor of Engineering at Dartmouth College's Thayer School of Engineering. His expertise spans system identification, iterative learning control, model predictive control, robotic swarm control, and intelligent control systems. He holds a BS from the University of California, Berkeley, and MS/M.Phil/PhD degrees from Columbia University in Mechanical Engineering. Dr. Phan has contributed to over 50 peer-reviewed publications and serves as an Associate Editor for the Journal of Guidance, Control, and Dynamics. His research focuses on advancing control theory applications in robotics, structural health monitoring, and sustainable construction materials. Key contributions include the development of OKID (Observer/Kalman Filter Identification) methods and bilinear system identification frameworks. Education History: Bachelor of Science in Mechanical Engineering, UC Berkeley, 1985 Master of Science in Mechanical Engineering, Columbia University, 1986 Master of Philosophy in Mechanical Engineering, Columbia University, 1988 Doctor of Philosophy in Mechanical Engineering, Columbia University, 1989 Research Interests: Advanced control methodologies for dynamic systems Model-based predictive control strategies Applications in robotics and aerospace engineering Structural health monitoring via system identification Machine learning for materials science Teaching Responsibilities include courses like ENGG 149 (Systems Identification), ENGS 145 (Modern Control Theory), and ENGG 148 (Structural Mechanics). His work bridges theoretical control systems with practical industrial applications, including automation in food processing and sustainable construction practices. Dr. Phan has collaborated on projects addressing viral epidemiology in Vietnam and coastal erosion mitigation strategies.

Adrian WellerView profile

Research Professor

• Artificial Intelligence
• Machine Learning
• Explainability
• +9 more

Adrian Weller is a prominent researcher and academic at the University of Cambridge, serving as a Director of Research in Machine Learning within the Department of Engineering. He holds multiple significant leadership roles including Programme Director for Trust and Society at the Leverhulme Centre for the Future of Intelligence (CFI), and previously served as Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI. His work bridges theoretical machine learning research with practical applications and societal implications of artificial intelligence. Weller's research interests span a broad spectrum of AI and machine learning topics with a particular focus on ensuring beneficial societal outcomes. His work encompasses explainability, fairness, robustness, scalability, privacy, safety, and ethics in AI systems. He has made significant contributions to trustworthy machine learning, including developing frameworks for AI governance, certification, and human-AI collaboration. His research group actively investigates neuro-symbolic approaches, privacy-preserving techniques, and methods for improving the reliability and interpretability of AI systems. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying AI systems in real-world contexts, particularly focusing on certification frameworks, governance mechanisms, and human-centered approaches. His work spans theoretical advances in machine learning architectures while maintaining a strong connection to societal impact, with publications appearing in top venues across AI, machine learning, and interdisciplinary applications. Scientific Awards: MBE for services to digital innovation (2022 Queen's Birthday Honours) Turing AI Fellowship for Trustworthy Machine Learning Weller actively supervises a large group of PhD students and postdocs, with current students including Juyeon Heo, Yanzhi Chen, Katie Collins, Isaac Reid, Yichao Liang, Herbie Bradley, and Shoaib Siddiqui. His former students have gone on to positions at leading institutions including Google DeepMind, ETH Zurich, NYU, and MPI-IS Tübingen. He has served on numerous advisory boards including the Centre for Data Ethics and Innovation, UNESCO's expert group on AI ethics, and the World Economic Forum's Global Future Council on AI. His research has been supported through his Turing AI Fellowship and various collaborative projects focused on safe and ethical AI development. Weller leads a vibrant research group focused on trustworthy machine learning, which actively organizes workshops and conferences including ICML 2024 (where he served as Program Chair), multiple workshops on responsible AI, and events through the ELLIS network. His group collaborates extensively across disciplines, working with researchers in computer science, social sciences, law, and policy to address the multifaceted challenges of developing beneficial AI systems.

David Williamson ShafferView profile

Professor

• Learning Analytics
• Learning Sciences
• Quantitative Ethnography
• +4 more

David Williamson Shaffer is the Sears Bascom Professor of Learning Analytics and Vilas Distinguished Achievement Professor of Learning Sciences at the University of Wisconsin-Madison's Department of Educational Psychology. He is also a Data Philosopher at the Wisconsin Center for Education Research. His work focuses on merging statistical and qualitative methods to model complex human collaboration. Shaffer holds an AB in History and East Asian Studies from Harvard University (1987), and MS/PhD in Media Arts and Sciences from MIT (1996/1998). Before academia, he was a teacher, curriculum developer, and game designer. His research emphasizes Quantitative Ethnography —a methodology combining big data analysis with cultural interpretation. Key contributions include Epistemic Network Analysis (ENA) and over 250 publications, including influential books like How Computer Games Help Children Learn (2006) and Quantitative Ethnography (2017). Awardees of the EU Marie Curie Fellowship (2008) and Spencer Foundation Fellowship (2003), Shaffer's work has been presented globally at conferences like Learning Analytics & Knowledge and Computer Supported Collaborative Learning. His interdisciplinary approach bridges education, data science, and humanities, addressing challenges in authentic STEM experiences, collaborative learning analytics, and culturally responsive pedagogy.

Furong HuangView profile

Associate Professor

• Machine Learning
• Trustworthy Machine Learning
• Generative AI
• +7 more

Furong Huang is an Associate Professor at the University of Maryland's Department of Computer Science, with affiliations at the Institute for Advanced Computer Studies, Center for Machine Learning, Maryland Robotics Center, and Applied Mathematics, Statistics, and Scientific Computation Program. Her research bridges trustworthy machine learning, sequential decision-making, and foundation models for robotics, emphasizing reliability, interpretability, and ethical standards. Research Interests: Trustworthy AI Generative AI Reinforcement Learning AI Security Algorithmic Fairness Foundation Models for Robotics Recent Publications span leading conferences (NeurIPS, ICML, ICLR, CVPR) and journals, focusing on: Robustness in Vision-Language Systems Trustworthy Generative AI Foundation Models for Sequential Decision-Making AI Security and Watermarking Scientific Awards MIT TR35 Innovator Under 35 (Asia Pacific 2022) Best Paper Award, AdvML Frontier Workshop, NeurIPS 2024 NSF NAIRR Pilot Awardee Microsoft Accelerate Foundation Models Research Award (2023) JP Morgan Faculty Research Awards (2019–2022) Advising and Grants : Her lab has graduated students to roles at OpenAI, Google, Meta, and Netflix. Research funded by DARPA, NSF, ONR, AFOSR, and industry partners like Microsoft, Adobe, and Capital One. Labs & Teams : Leads research groups focused on Trustworthy AI and Robotics at the University of Maryland, collaborating with the Maryland Robotics Center and Applied Mathematics Program.

Huan ZhangView profile

Assistant Professor

• Trustworthy Machine Learning
• Formal Verification of Machine Learning
• AI Safety
• +4 more

Huan Zhang serves as an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC), with affiliate appointments in the Department of Computer Science and the Coordinated Science Laboratory. His research focuses on building trustworthy AI systems with formal verification techniques to provide provable guarantees for safety-critical applications, particularly in machine learning and neural networks. Dr. Zhang received his Ph.D. in Computer Science from UCLA in 2020, advised by Professor Cho-Jui Hsieh. His academic journey includes an M.S. in Computer Engineering from UC Davis (2014) and a Bachelor of Engineering from Zhejiang University (2012). Prior to joining UIUC, he completed a postdoctoral fellowship at Carnegie Mellon University (2021-2023) with Professor Zico Kolter. Huan Zhang's research program centers on formal verification of machine learning systems, with particular emphasis on neural network verification, AI safety, robustness, and reliability. He pioneered the linear bound propagation-based verification framework that enables formal verification for networks with millions of neurons. His work spans five major research categories: formal verification of machine learning, training trustworthy ML models, machine learning safety and adversarial attacks, reinforcement learning safety, and optimization for scalable machine learning. His CROWN framework (NeurIPS 2018) established a foundational approach for neural network verification through efficient linear bound propagation. His recent publications demonstrate a strategic expansion from foundational verification techniques toward increasingly complex systems including large language models, vision-language models, and robotic control systems. The research trajectory shows a clear progression from theoretical frameworks to practical implementations with real-world applications, particularly in safety-critical domains. His work increasingly bridges formal methods with practical AI deployment requirements. Winner of International Verification of Neural Networks Competition (VNN-COMP) as team leader (2021-2024) Schmidt Futures AI2050 Early Career Fellowship ($300,000 research grant) Adversarial Machine Learning (AdvML) Rising Star Award (2021) IBM PhD Fellowship (2018) Dr. Zhang leads the development of α,β-CROWN, a neural network verifier that has won VNN-COMP 2021-2023, and auto_LiRPA, a PyTorch-based library for perturbation analysis on general computational graphs. He has mentored numerous graduate students from CMU, UCLA, UIUC, and Columbia University. His research is supported by significant funding including the Schmidt Futures fellowship and industry collaborations. He teaches courses including ECE 120, ECE 484, ECE 584, and ECE 598 HZ on topics ranging from computing fundamentals to safe autonomy and machine learning. Dr. Zhang maintains active research collaborations across multiple institutions and is affiliated with UIUC's Coordinated Science Laboratory. His work has significant implications for safety-critical AI applications in autonomous systems, healthcare, and other mission-critical domains where reliability guarantees are essential. He regularly gives guest lectures at institutions including Yale, Stony Brook, and the University of Nebraska Lincoln on formal verification techniques.

Robin JiaView profile

Assistant Professor

• Natural Language Processing (NLP)
• Deep Learning
• Artificial Intelligence
• +7 more

Robin Jia is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC) , where he leads the AI, Language, Learning, Generalization, and Robustness (Allegro) Lab . His research focuses on enhancing the reliability and robustness of large language models (LLMs) through mechanistic understanding, benchmarking under distribution shifts, and neurosymbolic integration. Key affiliations include collaborations with the USC Keck School of Medicine and contributions to legal frameworks like the EU's Digital Services Act. Robin's research spans multiple domains, including: Scientific analysis of LLM capabilities in in-context learning , data memorization , and numerical reasoning Advancements in robust NLP systems , emphasizing uncertainty estimation and calibration Development of methods combining LLMs with symbolic solvers for complex reasoning tasks Interdisciplinary applications in medicine and law , such as privacy-preserving synthetic data generation and medical misconception evaluation . His recent publications (2024-2025) address Fourier-based numerical embeddings (NeurIPS), neurosymbolic planning (NAACL), and multimodal benchmarking (COLM), with a strong emphasis on privacy , fairness , and transparency . Scientific awards include the Google Research Scholar Award (2023) , SoCalNLP Symposium Best Paper Awards , and ACL/EMNLP outstanding papers . He advises PhD students like Johnny Wei and Ameya Godbole, and has secured grants from the NSF , USC-Capital One , and USC-Amazon .

Greg DurrettView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +7 more

Greg Durrett is an Associate Professor in the Department of Computer Science at University of Texas at Austin, leading the TAUR Lab (Text Analysis, Understanding, and Reasoning ). His research focuses on advancing Large Language Models (LLMs) for knowledge-intensive tasks in medical information processing scientific discovery legal reasoning . He received his B.S. in Computer Science and Mathematics from MIT (2010) and Ph.D. in Computer Science from UC Berkeley (2016). His work develops techniques to train LLMs with new capabilities augment models for reliability assess model outputs improve reasoning frameworks . His 15 most recent publications (2021-2025) span knowledge propagation in LLMs chain-of-thought reasoning code generation benchmarks multi-modal reasoning fact verification discourse analysis . Scientific honors include NSF CAREER Award (2024) NSF grants (2018, 2024) Bloomberg Data Science Grant (2017) Facebook Fellowship (2014) Best Paper Finalist (EMNLP 2013) . Teaching: CS388: Natural Language Processing (graduate) CS371N: NLP (undergraduate) High school NLP module .

Nancy Margaret ReidView profile

Professor

• Theoretical Statistics
• Likelihood Inference
• Design of Studies
• +3 more

Nancy Margaret Reid is a University Professor of Statistical Sciences at the University of Toronto, holding the Canada Research Chair in Statistical Theory and Applications. She has served as Scientific Director of the Canadian Statistical Sciences Institute (2015–2019) and led the Department of Statistical Sciences as Chair (1997–2002). Her research focuses on theoretical statistics, particularly likelihood inference and foundational aspects of statistical methodology. Reid earned her PhD from Stanford University (1979) under Rupert G. Miller, with Brad Efron and Vernon Johns on her committee. Reid's accolades include Fellowships from the Royal Society, Royal Society of Canada, and National Academy of Sciences, as well as the Guy Medal in Gold (2022) and David R. Cox Award (2023). She has authored influential books like *Theory of the Design of Experiments* and contributed to courses on mathematical statistics and likelihood inference. Active in academic service, she teaches graduate-level courses and has advised numerous students and postdocs in theoretical and applied statistical research.

• Uncertainty Quantification
• Scientific Machine Learning
• Stochastic Optimization
• +3 more

Hadi Meidani is a Clinical Associate Professor at the Carle Illinois College of Medicine , specifically within the Department of Biomedical and Translational Sciences at the University of Illinois at Urbana-Champaign . He teaches courses in Civil and Environmental Engineering, including topics like Systems Engineering & Economics , Machine Learning in CEE , and Uncertainty Quantification . Ph.D., Civil Engineering, University of Southern California (2012) M.S., Electrical Engineering, University of Southern California (2012) M.S., Structural Engineering, Sharif University of Technology (2005) B.S., Civil Engineering, K.N. Toosi University of Technology (2002) Dr. Meidani's research focuses on uncertainty quantification , scientific machine learning , and optimization under uncertainty for engineering systems. His work spans stochastic multiscale analysis , physics-informed machine learning , and model reduction techniques. His recent publications emphasize machine learning for infrastructure systems , graph neural networks , physics-informed models , and traffic assignment . Key trends include deep learning , multi-fidelity modeling , and neural operator transformers applied to metamaterial design , seismic reliability , and autonomous freight delivery .

Zhun DengView profile

Assistant Professor

• Machine Learning
• Statistics
• Theoretical Computer Science
• +4 more

Zhun Deng is a tenure-track Assistant Professor at the Department of Computer Science, University of North Carolina at Chapel Hill. His research bridges machine learning, statistics, and theoretical computer science, focusing on rigorous frameworks for responsible AI systems. He previously held postdoctoral positions at Columbia University and completed his Ph.D. at Harvard's Theory of Computation group under Cynthia Dwork. Ph.D. in Computer Science, Harvard University (2022) B.Sc. in Mathematics, Chu Kochen Honors College, Zhejiang University His research spans theoretical foundations of machine learning, including: Quantile-based risk control and conformal prediction Fairness guarantees in algorithmic decision-making Uncertainty quantification for LLMs Copyright frameworks for generative AI Physics-informed hybrid models Multi-agent reinforcement learning with constraints Recent work analyzes LLM alignment through distribution-free methods (ICML 2025), explores performativity challenges (ICML 2025), and develops calibration techniques (ICLR 2024). Collaborations include research interns from Stanford, MIT, and NYU. Students in his group include: Ruomeng Ding (Ph.D., UNC) Xiaowei Yin (Ph.D., UNC) Kaicheng Zhang (Ph.D., UNC)

Eric WongView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Explainable AI
• +5 more

Eric Wong is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania, affiliated with the ASSET Center and leading the Brachio Lab. His research focuses on robust and reliable machine learning, including model debugging, adversarial robustness, and explainable AI. He holds a PhD from Carnegie Mellon University (CMU), advised by J. Zico Kolter, and completed a postdoc with Aleksander Madry. His work bridges theory and practice, addressing challenges in model interpretability, safety, and scalability. Teaching includes CIS 5200 (Machine Learning), CIS 3333 (Mathematics for Machine Learning), and a specialized course on debugging ML pipelines. Notable contributions include the FIX Benchmark for interpretable features and defenses against LLM jailbreaking attacks. He received an Amazon Research Award in 2024 and has published extensively in top conferences like ICML, NeurIPS, and ICLR. Affiliations: University of Pennsylvania (CIS), ASSET Center, Brachio Lab Education: PhD in Machine Learning (CMU), Postdoc at MIT Key Projects: FIX Benchmark, DOLPHIN framework, SmoothLLM defense Awards: Amazon Research Award (2024) Lab Focus: Safe AI, model debugging, neurosymbolic learning

• Machine Learning
• Adversarial Robustness
• Computer Vision
• +4 more

Matthias Hein is a Professor at the Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Tübingen. His research focuses on Machine Learning , Adversarial Robustness , and Out-of-Distribution Detection , with applications in computer vision and medical imaging. He has received notable recognition including the Best Paper Honorable Mention Prize at ICLR 2021 and Outstanding Paper Award at CVPR 2021. His work includes developing benchmarks like RobustBench and Spurious ImageNet , and frameworks such as Sparse-RS and DIG-IN . His recent publications emphasize adversarial robustness across multiple domains (vision, text), counterfactual explanations for classifiers, and improved OOD detection methods . Collaborators include prominent researchers like Francesco Croce, Julian Bitterwolf, and Alexander Meinke. Scientific Awards : Best Paper Honorable Mention (ICLR 2021) CVPR 2021 Outstanding Paper Award Key Research Areas : Adversarial Robustness Vision-Language Models Medical Imaging AI Neural Network Calibration

Oana CocarascuView profile

Senior Lecturer

• Argument mining
• Explainable AI
• Machine learning
• +5 more

Dr. Oana Cocarascu is a Senior Lecturer in Artificial Intelligence at the Department of Informatics, Faculty of Natural, Mathematical & Engineering Sciences, King's College London. She holds a PhD and MEng in Computing (Artificial Intelligence) from Imperial College London and conducts applied research focusing on how artificial intelligence can be deployed to support real-world applications, with machine learning and natural language processing as core components of her work. Her research interests span argument mining, explainable AI, machine learning, natural language processing, and symbolic reasoning. She is particularly focused on developing AI systems that can provide transparent, accountable, and ethical decision-making processes. Her work addresses critical challenges in bias mitigation, fairness metrics, fact verification systems, and argumentation-based explanations for complex AI decisions. Analysis of her recent publications (2023-2025) reveals a strong focus on fairness in AI systems, with particular attention to individual fairness metrics and nuanced evaluation frameworks. She has made significant contributions to fact verification systems, especially in multimodal contexts involving charts and tabular data. Her work increasingly integrates argumentation theory with natural language processing to create explainable AI systems that can justify their decisions through structured reasoning. Dr. Cocarascu leads an EPSRC-funded project titled 'A framework for evaluating and explaining the robustness of NLP models' (2024-2027) and is actively involved in the Natural Language Processing Group at KCL. Her research fingerprint shows strong activity in argumentation (100%), decision-making (74%), artificial intelligence (50%), explainable AI (39%), and bias mitigation (35%). She has contributed to numerous high-impact publications in top venues including ACL, EMNLP, AAAI, and the Journal of Artificial Intelligence Research, with a particular focus on making AI systems more transparent, accountable, and aligned with human values. Her work intersects with UN Sustainable Development Goals, particularly those related to reducing inequality and building resilient infrastructure.