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Michael MahoneyView profile

Professor

• Machine Learning
• Big Data
• Statistical Analysis
• +5 more

Michael Mahoney is a Professor in the Department of Statistics at the University of California, Berkeley. He holds roles as Vice President and Director of the Big Data Group at the International Computer Science Institute (ICSI), Group Lead for the Machine Learning and Analytics Group at Lawrence Berkeley National Laboratory (LBNL), and a core member of the RISELab within the Department of Electrical Engineering and Computer Sciences (EECS). He is also an Amazon Scholar. His research focuses on the applied mathematics of data, including randomized numerical linear algebra (RandNLA), optimization, and their applications in machine learning, climate science, genetics, and other domains. He teaches courses such as Linear Algebra for Data Science and has led initiatives like the FODA (Foundations of Data Analysis) Institute under the NSF TRIPODS program. His work emphasizes scalable algorithms, including contributions to RandBLAS/RandLAPACK frameworks and tools like SuperBench and SqueezeLLM. Mahoney collaborates with industry and academia, advancing methods for scientific machine learning and efficient AI infrastructure. Roles: Professor (UC Berkeley), Vice President (ICSI), Group Lead (LBNL), RISELab Member Education: Ph.D. in Computer Science (Yahoo! Research and Stanford University background) Research interests include algorithmic and statistical foundations of big data, with applications in internet analysis, climate modeling, and genomics. His work bridges theory and practice, developing scalable tools for high-dimensional data analysis. He advises numerous students and postdocs, contributing to projects like RandNLA, neural scaling laws, and physics-informed learning. His labs and teams focus on foundational methods for scientific machine learning and efficient AI systems.

Alvin CheungView profile

Associate Professor

• Data Management
• Programming Languages
• Software Systems
• +3 more

Alvin Cheung is an Associate Professor at the University of California at Berkeley, affiliated with the Department of Electrical Engineering and Computer Sciences (EECS). He leads the Data Systems and Foundations group, Programming Systems group, Sky Lab, and SLICE Lab, while also serving as a faculty affiliate at the Berkeley Institute for Data Science. His research focuses on integrating data management, programming languages, and software systems to develop tools for scalable data processing pipelines and improved data programming experiences. PhD students advised: Sahil Bhatia, Mick Kittivorawong, Jongseok Park Research keywords include Data Management , Programming Languages , Program Synthesis , Formal Verification , and Machine Learning . His recent work explores Verified Lifting techniques for database applications, stencil computations, and cloud systems, alongside novel programming paradigms for geospatial video analytics and speculative decoding. His publications from 2023-2025 demonstrate trends in LLM-driven code optimization , automated SQL equivalence , and neural code generation across domains like tensor operations and geospatial video systems. Notable scientific achievements include the Dahl-Nygaard Prize (2024) , VLDB Early Career Award (2023) , and CHI Best Paper Award (2021) .

Peter BartlettView profile

Professor

• Machine Learning
• Statistical Learning Theory
• Artificial Intelligence (AI)
• +5 more

Peter Bartlett is a Professor in the Department of Electrical Engineering and Computer Sciences and the Department of Statistics at the University of California, Berkeley. He also serves as Head of Google Research Australia and Director of the Foundations of Data Science Institute and the Collaboration on the Theoretical Foundations of Deep Learning. His research focuses on machine learning, statistical learning theory, and related areas such as pattern classification, adaptive control, and reinforcement learning. Bartlett earned a Ph.D. in Electrical Engineering from the University of Queensland, Australia (1992). He has held roles including Associate Director of the Simons Institute for the Theory of Computing and visiting positions at institutions like the University of Paris and the Australian National University. His contributions include co-authoring Neural Network Learning: Theoretical Foundations and pioneering work on AdaBoost, online learning, and high-order Langevin diffusion. He has been recognized with awards like the Malcolm McIntosh Prize (2001), ACM Fellowship (2018), and election to the Australian Academy of Science (2015). Bartlett advises numerous students and collaborates on grants related to theoretical foundations of machine learning. His leadership in research institutes and editorial roles at journals like Journal of Machine Learning Research underscores his impact on the field.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +3 more

Wenjie Li is a Professor at the Department of Computing, Hong Kong Polytechnic University, and holds a PhD from the Chinese University of Hong Kong (1997). His research spans Natural Language Processing, Artificial Intelligence, and Machine Learning , focusing on Large Language Models (LLMs) Multimodal Systems Dialogue and Recommender Systems Speculative Decoding and Inference Optimization Chain-of-Thought Reasoning Recent work emphasizes generative retrieval , personalized web agents , and error-resilient LLM frameworks . Key contributions include the JobFormer for skill-aware recommendations, STeCa for trajectory calibration, and TokenSkip for controllable reasoning compression. Publications reveal a trend toward enhancing multimodal alignment and safety mechanisms in aligned LLMs. Li actively collaborates with institutions like Queen's University , Tsinghua University , and Nanjing University , working on projects such as text-image interleaved retrieval and speculative decoding surveys . His 2024-2025 output includes 15+ papers at venues like ACL, CVPR, ICLR , and journals like IEEE Transactions on Neural Networks .

Peter L. BartlettView profile

Professor

• Machine Learning
• Statistical Learning Theory
• Pattern Classification
• +2 more

Peter L. Bartlett is a Professor at the University of California at Berkeley , affiliated with the Department of Electrical Engineering and Computer Sciences and the Department of Statistics. He also serves as ML Research Director at the Simons Institute for the Theory of Computing, Director of the Foundations of Data Science Institute, and Director of the Collaboration on the Theoretical Foundations of Deep Learning. Additionally, he holds a position as Principal Scientist at Google DeepMind. Research Interests : His work focuses on Machine Learning , Statistical Learning Theory , Pattern Classification , Adaptive Control , and Reinforcement Learning . He investigates theoretical aspects of learning algorithms, optimization dynamics, and generalization in overparameterized models. Article Trends : Recent publications emphasize scaling laws in linear regression , gradient descent optimization , benign overfitting , generative adversarial networks (GANs) , and implicit bias in neural training . Key sub-fields include overparameterized learning , minimax optimization , low-dimensional data analysis , and efficient decoding algorithms . Students and Postdocs : He has advised numerous students and postdocs across institutions, including Ishaq Aden-Ali , Spencer Frei , and Yeshwanth Cherapanamjeri , with former advisees like Yasin Abbasi-Yadkori (Adobe) and Alexander Rakhlin (Penn). Contact : Email peter@berkeley.edu , Address: 387 Soda Hall #1776, Berkeley, CA 94720-1776.

Sébastien RoldanView profile

Associate Professor

• Nineteenth-Century French Literature
• Literary Philosophy
• Romanticism
• +5 more

Sébastien Roldan serves as Associate Professor in the Department of Modern Languages and Literatures within the Faculty of Arts at the University of Winnipeg, a position held since Summer 2019. His scholarly authority spans nineteenth-century French literary movements—Romanticism, Realism, Naturalism, and Symbolism—with particular focus on the dialog between literature and philosophy in canonical works by Hugo, Balzac, Zola, Flaubert, Maupassant, and Daudet. Academic Credentials: PhD in Literary Studies (cotutelle), Université Paris Ouest-Nanterre and Université du Québec à Montréal (UQAM), 2013 Degree in Chemical Engineering, École polytechnique de Montréal Roldan's research pioneers intersections of literary geography and philosophical inquiry, examining how themes like suicide, the Seine River, time, nature, and terroir manifest across French and French-Canadian texts. His methodology combines close reading with spatial analysis, revealing how rivers function as narrative devices in Realist novels and how philosophical concepts permeate literary representation. This interdisciplinary approach bridges literary criticism with geography and philosophy to decode spatial and existential dimensions of nineteenth-century writing. Recent publications demonstrate sustained focus on three converging trajectories: the Seine's literary cartography (2018-2024), philosophical treatments of suicide in Naturalist works (2014-2024), and intertextual dialogues between Romantic and Naturalist authors (2020-2025). His scholarship consistently analyzes how geographical spaces shape narrative structures and how philosophical concepts like Schopenhauer's pessimism inform literary themes, particularly across Zola's Rougon-Macquart series. Roldan mentors undergraduate researchers in nineteenth-century French literature through course-based projects and the Atlas numérique de la Seine initiative. His collaborative role as Paris section lead for this Université Le Havre Normandie-funded digital humanities project provides students hands-on experience in geotagging literary references and building spatial databases for literary analysis. As Paris section lead for the Atlas numérique de la Seine project, Roldan directs a transnational effort mapping Seine River references in nineteenth-century French literature. This digital humanities initiative—managed by GéoSeine at Université Le Havre Normandie—exemplifies his commitment to innovative research methodologies that transform textual analysis through geographic information systems, creating new pathways for studying literary landscapes.

Kangwook LeeView profile

Associate Professor

• Large Language Models
• LLM Agents
• Deep Learning
• +4 more

Kangwook Lee serves as an Associate Professor in the Electrical and Computer Engineering Department with a courtesy appointment in Computer Sciences at the University of Wisconsin-Madison, where he also holds a Discovery Fellowship. He concurrently leads deep learning research initiatives at KRAFTON, bridging academic and industry innovation in artificial intelligence. His academic foundation includes a PhD in Electrical Engineering and Computer Sciences from UC Berkeley (2016), preceded by research assistant and postdoctoral positions at KAIST's Information and Electronics Research Institute. Hailing from Seoul, South Korea, Lee maintains active research operations through his laboratory in Madison's Discovery Building. Lee's research program centers on Large Language Models and LLM agents, with rigorous theoretical and empirical investigations into their operational mechanisms and improvement pathways. His work spans in-context learning dynamics, agent-based social simulations, multi-domain reward modeling, and efficient inference techniques, emphasizing both fundamental understanding and practical enhancement of AI capabilities. Recent publications reveal a concentrated effort on overcoming length generalization barriers, enabling compositional reasoning with rare concepts, and developing robust feature selection frameworks. His 2025 publications demonstrate significant advancements across LLM architecture, evaluation methodologies, and application domains. Key trends include the emergence of task vector representations in in-context learning, development of superposition techniques for multi-task processing, and innovative approaches to speculative decoding for multimodal systems. These works collectively advance the field toward more efficient, generalizable, and interpretable language models. Lee's research excellence is recognized through prestigious accolades: NSF CAREER Award (premier early-career grant) IEEE Joint Communications Society/Information Theory Society Paper Award Amazon Research Award KSEA Young Investigator Grant Award As principal investigator of the Lee Lab, he directs a dynamic research group focused on cutting-edge AI challenges. His work integrates theoretical analysis with empirical validation to address fundamental limitations in modern language models, while industry collaborations through KRAFTON ensure real-world impact. Current projects emphasize agent-based social dynamics modeling, efficient inference architectures, and robustness frameworks for diverse deployment scenarios.

Yang LiView profile

Associate Professor

• Statistical Machine Learning
• Bayesian Nonparametrics
• Scalable Optimization
• +5 more

Yang Li serves as Associate Professor of Marketing and Associate Dean for the MBA Program at Cheung Kong Graduate School of Business (CKGSB). Holding a PhD in Marketing from Columbia Business School alongside dual master's and bachelor's degrees from Columbia and Peking University respectively, he bridges advanced statistical methodologies with practical business applications. His research centers on statistical machine learning and Bayesian nonparametrics applied to consumer behavior analysis, with specialization in online personalization, text mining, and choice modeling. Recent work demonstrates significant focus on fragmented attention economies, ethical AI frameworks, and NFT network dynamics, reflecting contemporary digital market challenges. Management Science Marketing Science Journal of Marketing Research Journal of Consumer Research Harvard Business Review Professor Li's publications reveal evolving expertise from foundational pricing elasticity studies toward cutting-edge AI applications in consumer contexts. His work increasingly integrates generative models and graph neural networks to decode complex consumer collection behaviors and digital ecosystem dynamics. Scientific recognition includes being a Finalist for the 2021 Paul E. Green Best Paper Award. Industry impact is demonstrated through executive education programs and strategic consultancies with Tencent, Haier, and Tmall. As Associate Dean for MBA Programs, he oversees curriculum development while maintaining active corporate governance roles on boards of publicly traded companies across China and Hong Kong, directly applying his research insights to strategic decision-making in digital transformation initiatives.