جستجوی استاد راهنمای دانشگاهی

Tianqi Chenمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning Systems
• Compiler Optimization
• +۶ مورد دیگر

Tianqi Chen is an Assistant Professor at the Machine Learning Department and Computer Science Department of Carnegie Mellon University (CMU), with a courtesy appointment as a Professor in the Electrical and Computer Engineering Department within the College of Engineering. His research focuses on scalable machine learning systems, compiler optimization, and efficient deep learning frameworks. He holds a PhD from the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Key contributions include the creation of XGBoost, Apache TVM, and MLC-LLM—widely adopted systems for machine learning and large language models. His work bridges algorithmic innovation with high-performance computing, emphasizing efficient deployment, quantization, and edge computing. Recent publications highlight advancements in LLM serving (e.g., WebLLM, Flashinfer), compiler-driven optimizations (e.g., TVM, Relax), and low-latency inference techniques (e.g., Magicdec, Tilus). These efforts address scalability, energy efficiency, and cross-platform compatibility in modern AI systems. Chen’s research has been applied to diverse domains, including music AI, browser-based inference, and microservice architectures for LLMs. His work underscores the importance of system-level thinking in advancing AI capabilities.

• Machine Learning
• Statistics
• Theoretical Machine Learning
• +۷ مورد دیگر

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 .

Benjamin Van Royمشاهده پروفایل

استاد

• Reinforcement Learning
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Benjamin Van Roy is a Professor at Stanford University since 1998, affiliated with the Departments of Electrical Engineering and Management Science and Engineering, and the Institute for Computational and Mathematical Engineering. He leads the Efficient Agent Team at Google DeepMind and previously held leadership roles at Morgan Stanley, Unica, and Enuvis. He holds SB, SM, and PhD degrees in Computer Science and Electrical Engineering from MIT, advised by John Tsitsiklis. His research focuses on reinforcement learning, alignment, and information theory, with contributions to machine learning foundations, optimization, and finance. He has authored over 150 publications, including influential works on Thompson Sampling, approximate dynamic programming, and exploration strategies. His honors include INFORMS and IEEE Fellowships and the INFORMS Lanchester Prize. Van Roy advises doctoral students across academia and industry, with graduates at top institutions and companies like Meta, Tesla, and Citadel. He teaches courses on reinforcement learning, stochastic control, and optimization. His open-source projects include Epistemic Neural Networks and the Neural Testbed for evaluating machine learning models. Key contributions include foundational work in reinforcement learning theory, scalable methods for recommendation systems, and applications in finance and resource allocation. His research bridges theoretical insights with practical applications, emphasizing alignment and safety of AI systems.

Jackie Chit Kit Cheungمشاهده پروفایل

دانشیار

• Natural Language Processing
• Natural Language Generation
• Automatic Summarization
• +۱۲ مورد دیگر

Jackie Chit Kit Cheung is an Associate Professor in the School of Computer Science at McGill University, where he co-directs the Reasoning and Learning Lab. He holds the Canada CIFAR AI Chair and serves as an Associate Scientific Co-Director at the Mila Quebec AI Institute. He is also a consulting researcher at Microsoft Research Montreal. Academic Background: Ph.D. in Computer Science, University of Toronto (2010–2014) M.Sc. in Computer Science, University of Toronto (2008–2010) B.Sc. (Honours) in Computer Science, minors in Linguistics and German, University of British Columbia (2004–2008) Research Interests: Jackie Cheung's research lies at the intersection of natural language processing, machine learning, and cognitive science. He focuses on natural language generation , automatic summarization , commonsense and pragmatic reasoning , and the evaluation of NLP systems . His work aims to build language models that reflect real-world structure and reasoning, with applications in health, education, and language revitalization. He is particularly interested in how implicit meaning is processed in context, a core concern in linguistic pragmatics. Publication Trends: His recent publications (2024–2025) show a strong emphasis on evaluation methodologies (e.g., COSMIC, ECBD), hallucination and factuality in language models, coreference and reasoning , and long-context modeling . He frequently collaborates across institutions and integrates insights from linguistics and psychology into NLP system design and analysis. Scientific Awards: Best Paper Award, ACL 2018 Outstanding Paper Award, NAACL 2018 Student Research Workshop SAC Award, ACL 2024 (for COSMIC) Best Poster Award, CMDO AI-Health Symposium 2024 Advising and Grants: He advises a large and diverse group of graduate students, including PhD and Master’s candidates, often in co-supervision with other faculty. His group has received support from major AI and health research initiatives, including CIFAR and Mila. He has trained alumni who have gone on to faculty and industry research positions. He has served in leadership roles in top NLP conferences, including as Senior Area Chair, Workshop Chair, and Program Chair of Canadian AI 2018. Labs and Teams: He co-directs the Reasoning and Learning Lab at McGill and is deeply involved with Mila – Quebec AI Institute . He founded the NLP Reading Group at McGill, which brings together researchers from computer science, linguistics, and information studies to discuss theoretical and applied NLP topics.

Carol Ann Allredمشاهده پروفایل

دانشیار

• Vocal Performance
• Classical Music
• Opera
• +۶ مورد دیگر

Carol Ann Allred is an Associate Professor (Lecturer) at the University of Utah's School of Music, specializing in Voice. She holds a DMA and MA from Eastman School of Music, and a BM in Vocal Pedagogy from Brigham Young University. Her career spans performance and education, with roles at SUNY Fredonia, Seton Hill College, and Hampden-Sydney Chamber Music Festival. Doctor of Musical Arts (DMA), Vocal Performance - Eastman School of Music (1989) Master of Music (MA), Vocal Performance - Eastman School of Music Bachelor of Music (BM), Vocal Pedagogy - Brigham Young University Allred's research and creative works focus on vocal performance across diverse genres, including choral masterpieces like Verdi Requiem and Brahms Requiem, contemporary works such as Golijov's La Pasion Según San Marcos, and historical repertoires from Mozart and Handel. Her recent projects include international tours and cultural outreach initiatives. Her scholarly performances highlight trends in sacred choral music, operatic excerpts, and community engagement through LDS church services and interfaith concerts. She has contributed to recordings including Christmas CDs and collaborations with Salt Lake Vocal Artists. Scientific Awards : Multiple first prizes in prestigious competitions and a Faculty Recognition Award nomination Teaching : Offers private voice lessons and masterclasses; advises the Michie Undergraduate Vocal Quartet Community Leadership : Conducts multi-congregational choirs and organizes veteran-focused concerts Allred's professional activities include collaborations with orchestras such as the Pittsburgh Symphony and Utah Symphony, with a strong emphasis on vocal pedagogy, interfaith musical outreach, and family ensemble performances. She maintains active roles in judging competitions and mentoring students at both University of Utah and UVU as a substitute instructor.

Celestine Mendler-Dünnerمشاهده پروفایل

استاد

• Machine Learning in Social Context
• Algorithmic Collective Action
• Performative Prediction
• +۶ مورد دیگر

Celestine Mendler-Dünner is a Principal Investigator at the ELLIS Institute in Tübingen, co-affiliated with the Max Planck Institute for Intelligent Systems and the Tübingen AI Center. She leads the Algorithms and Society research group, focusing on machine learning in social contexts and the role of prediction in digital economies. Her work bridges theoretical machine learning with practical societal impact, developing tools for safe, reliable, and equitable AI ecosystems. Her educational background includes a PhD from ETH Zurich in collaboration with IBM Research, followed by an SNSF postdoctoral fellowship at UC Berkeley hosted by Moritz Hardt. She was previously a group leader at the Max Planck Institute for Intelligent Systems before joining the ELLIS Institute. Mendler-Dünner's research spans several interconnected themes including performative prediction (where predictions change the behavior they aim to predict), algorithmic collective action (how participants can steer AI systems toward common goals), and the role of LLMs in social science research. Her work combines theoretical foundations with practical implementations, addressing challenges in interactive machine learning, optimization in dynamic environments, and context-specific evaluation of AI systems. She particularly examines how algorithmic predictions mediate services and platforms at societal scale, exploring concepts of economic power in digital markets. Her publication record shows a clear evolution from system-aware machine learning algorithms (including foundational work on IBM Snap ML) toward increasingly sociotechnical questions at the intersection of machine learning, economics, and policy. Recent work focuses on measuring performative power in digital economies, evaluating LLMs as risk scores, and developing frameworks for algorithmic collective action in recommender systems and labor markets. Among her notable recognitions are the ETH Medal for her dissertation, the IBM Research Division Award, the Fritz Kutter Award, and the IBM Eminence and Excellence Award. She is an ELLIS Scholar, a fellow of the Elisabeth-Schiemann-Kolleg, and affiliated with several prestigious research programs including the International Max Planck Research School for Intelligent Systems and the Max Planck ETH Center for Learning Systems. ETH Medal (dissertation award) IBM Research Division Award Fritz Kutter Award IBM Eminence and Excellence Award SNSF Early Postdoc Mobility Fellowship Mendler-Dünner actively mentors the next generation of researchers, advising PhD student Patrik Wolf and supervising research interns including Joachim Baumann, Haiqing Zhu, and Anna Badalyan, as well as Master's student Dorothee Sigg. She serves as core faculty for the International Max Planck Research School and associated faculty for the Max Planck ETH Center for Learning Systems. Her group has secured significant research funding through fellowships and institutional support, enabling work on projects like Powermeter (measuring search engine influence) and Snap ML (resource-efficient machine learning library with over 1 million PyPI downloads). She leads the Algorithms and Society research group, which examines machine learning as part of broader sociotechnical ecosystems. The group explores human-population interactions with algorithmic systems and incorporates these insights into learning system fundamentals. Current projects include investigating economic incentives in digital platforms, developing tools for systematic LLM evaluation in social science contexts, and creating frameworks for collective action in algorithmic systems. Mendler-Dünner also co-organizes the Algorithmic Collective Action workshop at NeurIPS 2025, demonstrating her leadership in emerging research directions at the AI-society interface.

Michael A Osborneمشاهده پروفایل

استاد

• Machine Learning
• Bayesian Optimization
• Gaussian Processes
• +۳ مورد دیگر

Michael A Osborne is Professor of Machine Learning at the University of Oxford and leads the Bayesian Exploration Lab . He serves as Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and co-directs the Oxford Martin AI Governance Initiative. His research focuses on Bayesian optimization, Gaussian processes, and probabilistic numerics with applications in quantum devices, battery modeling, and AI governance. Key Positions: Professor of Machine Learning, University of Oxford Official Fellow, Exeter College Co-founder of Mind Foundry Lead Researcher, Oxford Martin Programme on Technology and Employment Research Themes: Probabilistic modeling for quantum systems Uncertainty quantification in energy storage AI safety and societal impact analysis Automated experimental design Quantum device calibration Probabilistic numerical methods Technical Contributions: Bridging reality gap in quantum devices Efficient Bayesian quadrature techniques Personalized neurostimulation algorithms Automated measurement protocols Quantum-classical hybrid ML

Tapio Schneiderمشاهده پروفایل

استاد

• Atmospheric Dynamics
• Climate Modeling
• Geophysical Turbulence
• +۴ مورد دیگر

Tapio Schneider is the Theodore Y. Wu Professor of Environmental Science and Engineering at the California Institute of Technology. His research focuses on atmospheric dynamics across Earth and other planets, climate modeling innovations, and geophysical turbulence analysis. He contributes to the Climate Modeling Alliance (CliMA) and develops advanced computational tools for climate prediction. Albert-Ludwigs-Universität Freiburg (Vordiplom, 1993) Princeton University (M.Sc. 1997, Ph.D. 2001) University of Washington, Seattle (Visiting Graduate Student, 1994-1995) His research spans climate dynamics , atmospheric turbulence , and AI-enhanced climate modeling , addressing challenges in cloud dynamics, extreme weather patterns, and planetary climate systems. Current work emphasizes hybrid machine learning-physical models and computational acceleration for high-resolution simulations. Recent publications highlight trends in AI integration for climate science, with applications in hydrology , cloud microphysics , ocean circulation , snowpack modeling , and climate tipping points . His team develops open-source tools like ClimateMachine for GPU-accelerated simulations. Scientific Recognition: Fellow, American Geophysical Union (2022) Rosenstiel Award (2019) World Economic Forum Young Scientist (2012) David and Lucile Packard Fellow (2005-2010) Alfred P. Sloan Research Fellow (2004-2006) Tapio leads climate dynamics research at Caltech, directs the Linde Center for Global Environmental Science (2011-2012), and serves as Editor for the Journal of Advances in Modeling Earth Systems . His group collaborates with NASA Jet Propulsion Laboratory (2016-2024) and Google Research (2022-present).

Alice Reaganمشاهده پروفایل

استاد

• Theatre Directing
• Performance Studies
• New Play Development
• +۷ مورد دیگر

Alice Reagan is Professor of Professional Practice and Director of Undergraduate Studies in the Department of Theatre at Barnard College, Columbia University. She teaches directing, performance, and seminars on Zora Neale Hurston, Black performance, and AIDS activism, while also directing professionally across the U.S. Education: B.A., Bates College M.A., Tisch School of the Arts, New York University M.F.A., Columbia University, School of the Arts Her research and artistic practice centers on theatre directing, adaptation, new play development, and ensemble-based work , with a strong commitment to feminist and socially engaged theatre. She founded New Plays at Barnard , commissioning works by women-identified playwrights. Her recent and upcoming productions include Evelyn Brown (A Diary) , Cherry Orchard , Measure for Measure , and The Thanksgiving Play . Her recent publications and public scholarship focus on the revival of María Irene Fornés' work and the ethics of white educators teaching Black diasporic material, especially her influential 2019 article Teaching While White . The articles reflect a consistent engagement with race, pedagogy, classical reinterpretation, and feminist performance . Selected Awards & Honors: Named to Kilroys List, 2023 Individual Artist Grant, NYSCA, 2023 Cherry Orchard Teaching Excellence Award, 2022 Princess Grace Award Drama League Directing Fellow Berkshire Theatre Award nominations (9) She advises student productions and leads the New Plays at Barnard program, shaping the next generation of theatre artists. She has received grants from the Shubert Foundation and NYSCA. Her professional affiliations include Stage Directors and Choreographers Society, New Georges, and Wingspace Design. She is deeply involved in lab programs such as the Lincoln Center Directors Lab and Mabou Mines/SUITE. She leads the New Plays at Barnard initiative and collaborates with professional ensembles like Portland Experimental Theatre Ensemble and Shakespeare & Company, bridging academic and professional theatre.

Jan Herbstمشاهده پروفایل

استاد

• Popular Music Studies
• Metal Music Studies
• Music Production
• +۷ مورد دیگر

Jan Herbst is a Professor of Music at the University of Huddersfield and Director of the Centre for Research in Music and its Technologies. His academic journey includes multiple doctorates (PhD, Dr. habil.) from Leuphana University Lüneburg and research expertise in popular music studies, music production, and systematic musicology. He leads AHRC-funded projects such as 'Heaviness in Metal Music Production' and 'Songwriting Camps in the 21st Century.' His work bridges practical music production and theoretical research, with over 80 publications including books like The Cambridge Companion to Metal Music and Heaviness in Metal Music Production . Herbst’s research focuses on metal music aesthetics, record production techniques, and the cultural dimensions of music technology. He has held roles at German and Swiss universities, including teaching guitar performance and music production. His editorial roles include the Cambridge Companions series and journals like Metal Music Studies . Current projects explore blockchain in music, gear acquisition syndrome, and extreme metal vocal techniques. His academic affiliations include the IASPM UK & I executive committee and editorial boards for major musicology publications. Herbst actively engages with industry through collaborations with producers and performers, maintaining a balance between academic rigor and practical music creation.

Kimon Fountoulakisمشاهده پروفایل

دانشیار

• Machine Learning on Graphs
• Numerical Optimization
• Graph Algorithms
• +۲ مورد دیگر

Kimon Fountoulakis is an Associate Professor at the University of Waterloo. His research focuses on Machine Learning on Graphs and Numerical Optimization, with a strong emphasis on algorithmic methods for graph-structured data. He holds a Ph.D. from The University of Edinburgh (2015), an M.Sc. from The University of Edinburgh (2010), and a B.Sc. from Athens University of Economics and Business (2009). His work spans theoretical foundations and practical applications in graph algorithms, optimization, and machine learning. Research interests include graph neural networks, local graph clustering algorithms, and algorithmic reasoning. His contributions address challenges in graph representation learning, message-passing architectures, and scalable optimization methods. Notable themes in his publications include improving counting abilities of vision-language models, analyzing graph convolutions, and developing flow-based clustering techniques with statistical guarantees. His work often bridges theory and practice, with applications in network analysis, pandemic containment strategies, and high-performance computing. While no specific grants or awards are listed, his research demonstrates significant contributions to graph-based machine learning and optimization. He maintains a research group at the University of Waterloo, with a focus on developing open-source tools and frameworks for graph algorithms. His lab’s work emphasizes local graph clustering methods and their scalability in real-world networks.

Mark Yatskarمشاهده پروفایل

استادیار

• Natural Language Processing
• Computer Vision
• Fairness in Machine Learning
• +۳ مورد دیگر

Mark Yatskar is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research focuses on the intersection of natural language processing, computer vision, and fairness in machine learning. He earned his PhD from the University of Washington under advisors Luke Zettlemoyer and Ali Farhadi, and previously worked as a Young Investigator at the Allen Institute for Artificial Intelligence. Education: PhD in Computer Science, University of Washington (Advisor: Luke Zettlemoyer & Ali Farhadi) Research Interests: Yatskar's work explores how language can structure visual perception and mitigate human biases in machine learning systems. Key themes include: Natural language as a scaffold for visual intelligence Bias characterization and control in machine learning systems His lab currently investigates projects like language-guided bottlenecks, annotator cognitive heuristics, and gender bias amplification. Teaching: CIS 5300: Computational Linguistics (2021-2024) CIS 7000: Language and Vision (2020) CIS 6300: Efficient NLP (2023, 2025) Awards: Best Paper Award at EMNLP (Gender Bias Amplification Research) Advising & Grants: Yatskar advises a team of PhD/Master's students and actively seeks motivated researchers. His group has explored funding in areas like interpretable AI, multimodal reasoning, and dataset bias mitigation. Labs/Teams: Leads the Penn NLP & Vision Lab, focusing on projects like MolMo/PixMo open models, ViUniT visual unit tests, and bias mitigation frameworks.

• Early Music Performance
• Countertenor Voice Research
• Historical Solo Singing
• +۲ مورد دیگر

Professor Marcin Ciszewski is a distinguished faculty member at the Stanisław Moniuszko Academy of Music in Gdańsk, where he serves at the Faculty of Vocal and Acting. A graduate of the same institution with distinction in 1999, he has established himself as one of Poland's leading countertenors and vocal pedagogues. His educational background includes studies under Professor Piotr Kusiewicz and additional training with renowned instructors including Kinga Chełmińska, Ryszard Minkiewicz, Kai Wessel, Gerhard Kahry, and Dariusz Paradowski. Graduated with distinction from the Academy of Music in Gdańsk (1999) Habilitated doctor of musical arts (2019) Professor at the Faculty of Vocal and Acting Professor Ciszewski specializes in early music performance and is recognized as one of the first Polish countertenors who made significant contributions to the development of vocal art in the authenticity movement in Poland. His research focuses on the history and physiology of countertenor voices, and he regularly conducts lectures and voice training for professional and academic groups. His artistic output spans several hundred concerts across Poland and Europe, featuring collaborations with renowned ensembles, orchestras, and conductors. He has given over a dozen world premieres of both early and contemporary works, including compositions specifically written for him by modern composers. His recordings include extensive work for Polish Radio and Television, the French music television channel Mezzo, and Radio Classique. Notable Achievements Member of the European artists' association Spedidam based in Paris Nominated for the "Fryderyk" music award in the Early Music category Honored with international titles "Outstanding Teacher" and "Excellent Vocal Teacher" Since 2006, Professor Ciszewski has maintained a successful artistic partnership with organist Ewa Bąk, performing at over one hundred national and international organ festivals. Their collaborations have resulted in numerous CD, radio, and television recordings, including world premieres of works by Polish composers such as Marian Sawa and Jacek Urbaniak.

Emma Brunskillمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Educational Technology
• Artificial Intelligence
• +۳ مورد دیگر

Emma Brunskill is an Associate Professor of Computer Science at Stanford University, with a courtesy appointment in Education. She holds a PhD in Computer Science from MIT (2009). Her research focuses on reinforcement learning, educational technology, and healthcare applications, aiming to develop AI systems that support human learning and decision-making. Notable projects include AI tutoring systems, policy evaluation methods, and behavior change interventions using large language models. Her work bridges theory and practice, addressing challenges in off-policy evaluation, fairness-aware decision making, and scalable educational tools. Brunskill has contributed to foundational research in reinforcement learning algorithms and their applications in real-world scenarios such as healthcare, education, and human-AI collaboration. She also leads initiatives to improve equity and efficiency in educational technologies through data-driven approaches. Brunskill's research has been supported by grants such as the NSF RI: Small grant for data-efficient reinforcement learning. She actively explores the ethical implications of AI systems, particularly in healthcare and education settings. Her recent work emphasizes leveraging large language models (LLMs) for personalized feedback and simulated training environments, as seen in studies like GPTCoach and LLM-based counselor upskilling.

John P. O'Dohertyمشاهده پروفایل

استاد

• Decision Neuroscience
• Reward-related Learning
• Computational Modeling
• +۳ مورد دیگر

John P. O'Doherty serves as the Fletcher Jones Professor of Decision Neuroscience within Caltech's Division of Humanities and Social Sciences, holding continuous faculty appointments since 2004 (Assistant Professor 2004-07, Associate Professor 2007-09, Professor 2009-present, Fletcher Jones Professor 2021-present). He previously directed the Caltech Brain Imaging Center (2013-17) and maintains affiliations with the T&C Chen Center for Social and Decision Neuroscience. His educational background includes a B.A. from University of Dublin, Trinity College (1996) and D.Phil. from University of Oxford (2000). His research focuses on computational and neural mechanisms of reward-based learning and decision-making , employing fMRI, intracranial recordings, and mathematical modeling to investigate how the brain solves complex decision problems through evolutionarily conserved algorithms. Key areas include Reinforcement learning systems (model-based/model-free arbitration) Observational and social learning mechanisms Neural representation of value, risk, and uncertainty Computational phenotyping of mental disorders Temporal dynamics of goal persistence Analysis of his 2023-2025 publications reveals dominant trends in computational psychiatry (problem gambling, autism traits), hierarchical decision-making, and neuroeconomic modeling of social behavior. His work consistently integrates cross-species computational frameworks with human neuroimaging to identify transdiagnostic mechanisms. While specific awards beyond his endowed professorship aren't detailed, his leadership as Brain Imaging Center Director and prolific high-impact publications demonstrate significant recognition. Current advising includes graduate researcher Sneha Aenugu on goal-persistence projects, with administrative support from Mary A. Martin (mmartin@caltech.edu). His active research program continues to pioneer computational approaches to understanding decision pathologies.