Ali Yazdani is an Adjunct Professor at the University of Illinois Urbana-Champaign's Grainger College of Engineering, Department of Physics, and Director of the Princeton Center for Complex Materials at Princeton University. His research focuses on quantum condensed matter physics, leveraging scanning tunneling microscopy (STM) and spectroscopy to explore novel quantum phases in materials such as graphene, twisted bilayer graphene, and topological insulators. Key achievements include the first direct observation of Hofstadter's fractal energy spectrum in quantum materials (2025), studies on Majorana fermions in atomic chains, and investigations into strongly correlated Chern insulators. His work bridges theoretical predictions with experimental validation, emphasizing quantum materials' topological and correlated properties. Affiliations: Princeton University, Department of Physics; University of Illinois Urbana-Champaign, Grainger College of Engineering. Research Themes: Quantum fractals, topological insulators, superconductivity, Majorana fermions, moiré materials. Research Summary: Dr. Yazdani’s lab employs advanced STM techniques to visualize electronic wavefunctions and study correlated phases. Notable projects include: - Visualization of Hofstadter’s butterfly in twisted bilayer graphene. - Discovery of valley skyrmions in graphene quantum Hall ferromagnets. - Unconventional superconductivity in magic-angle graphene. - Development of methods to detect Majorana zero modes. Labs/Teams: Yazdani Lab at Princeton University focuses on quantum materials and topological phases, collaborating with theorists and experimentalists globally.
Trevor Campbell is an Associate Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver. He holds a Ph.D. in Machine Learning and Statistics from MIT and a B.A.Sc. in Aerospace Engineering from the University of Toronto. His research focuses on automated, scalable Bayesian inference algorithms, Bayesian nonparametrics, and streaming data analysis. Campbell is a core contributor to probabilistic programming tools like Pigeons.jl and has developed influential methods for coreset-based Bayesian inference. Education : Ph.D. in Machine Learning and Statistics (2016), MIT M.S. in Aeronautics and Astronautics (2013), MIT B.A.Sc. in Aerospace Engineering (2011), University of Toronto Research Interests : His work emphasizes scalable Bayesian computation, including variational inference, MCMC optimization, and coresets. He develops algorithms that balance statistical accuracy with computational efficiency, particularly for large-scale datasets. His recent work explores adaptive samplers (e.g., AutoStep, autoMALA) and theoretical guarantees for coreset methods. Applications span astrophysics, materials science, and network analysis. Awards & Grants : NSERC Discovery Grant (2025) Blackwell-Rosenbluth Award (2021) PIMS Early Career Award (2023) Google Perception Academic Funding (2020) Advising & Labs : He supervises a team of PhD and M.Sc. students at UBC, focusing on Bayesian methodology and computational tools. His lab collaborates with institutions like SFU and MIT on projects involving distributed sampling and probabilistic modeling. He co-organizes workshops on Bayesian computation and serves on editorial boards for Bayesian Analysis and TMLR.
Meredith Tamminga is an Associate Professor of Linguistics at the University of Pennsylvania, where she directs the Language Variation and Cognition Lab . Her work bridges sociolinguistics , psycholinguistics , and theoretical linguistics , focusing on how linguistic variability is represented in mental grammar and how social context influences speech perception and production. She co-leads the Philadelphia Signs Project , exploring sign language sociolinguistics with collaborators at Penn and Gallaudet University. PhD in Linguistics (2014), University of Pennsylvania BA in Linguistics (2009), McGill University Her research integrates experimental methods with naturalistic speech analysis to study individual and group-level language change. Keywords include phonetics-phonology mapping , intraspeaker variation , and quantitative modeling . Notable grants include NSF awards and SAS Dean’s Mentorship Award . Key affiliations include MindCORE , Penn’s hub for integrative mind sciences, and collaborative ties to the Phonetics Lab , Child Language Lab , and Cultural Evolution of Language Lab . Her lab emphasizes cross-departmental research and welcomes undergraduate RAs and PhD students.
Han Liu is a Professor in the Department of Computer Science at Northwestern University's McCormick School of Engineering. He directs the MAGICS (Modern Artificial General Intelligible and Computer Systems) Lab and the Center for Foundation Models and Generative AI at Northwestern, with prior roles as director of the Deep Reinforcement Learning Center at Tencent AI Lab and professor at Princeton and Johns Hopkins Universities. PhD in Machine Learning and Statistics from Carnegie Mellon University (2012), advised by John Lafferty and Larry Wasserman Han Liu's research focuses on integrating artificial intelligence with computer systems, particularly through foundation models and probabilistic graphical models. His work aims to revolutionize science, engineering, and business by deploying statistical machine learning methods in edge and cloud computing environments. Recent research trends include transformer-based models, modern Hopfield networks, genomic foundation models, and theoretical analysis of attention mechanisms. His 2025 publications explore topics like species differentiation with DNA embeddings, universal approximation capabilities of transformers, and metaverse spatial reasoning. Alfred P Sloan Fellowship in Mathematics IMS Tweedie New Researcher Award ASA Noether Young Scholar Award NSF CAREER Award Presidential Early Career Awards for Scientists and Engineers Han Liu serves as associate editor for the Journal of American Statistical Association, Electronic Journal of Statistics, Technometrics, and the Journal of Portfolio Management. He has directed research centers at Northwestern and contributed to major conferences as area chair (NeurIPS, ICML, ICLR).
Avi Turetsky is an Adjunct Professor at the Weatherhead School of Management (Case Western Reserve University) and a Research Fellow in private equity. Currently a Partner and Co-Head of the Quantitative Research Group (QRG) at Ares Management, he oversees original research, quantitative software development, and tools production teams. Previously served as Chief Operating Officer for The Riverside Company's Europe Fund and holds advisory roles at EDHEC and INSEAD . Research Focus: Turetsky’s work bridges private equity practice and quantitative finance, examining distributional patterns in investment outcomes, competency frameworks for financial professionals, and mathematical models for performance evaluation. His recent publications explore robust statistical methods in portfolio construction, direct alpha calculations for skill assessment, and sector-based value creation strategies in private equity-owned companies. Key Article Trends: His research spans 2016–2023, emphasizing quantitative methodologies in private equity, including skew analysis , value creation metrics , and competency clustering . Topics range from alliance changes in intercollegiate athletics to mathematical modeling of investment professional performance. Leadership & Teams: At Ares Management, Turetsky co-leads the QRG, managing cross-functional teams in Original Research , Quantitative Software Development , and Tools Production . He collaborates with investment teams to integrate quantitative strategies into decision-making processes.
Simon King is a Professor of Speech Processing at the University of Edinburgh , affiliated with the School of Philosophy, Psychology and Language Sciences . He serves as Director of the Centre for Speech Technology Research (CSTR) and teaches courses like Speech Processing and Speech Synthesis , while directing the MSc in Speech and Language Processing . Research Interests His research focuses on: Developing new acoustic models (e.g., Linear Dynamical Models, factorial-HMMs) for speech recognition Advancing unit selection and HMM-based speech synthesis Integrating articulatory measurement data for enhanced modeling Exploring perceptual measures in synthesis criteria Building multilingual speech systems to identify universal speech building blocks Publication Trends Simon's recent work emphasizes deep learning (DNNs, LSTMs) in speech synthesis, multilingual frameworks , and articulatory-acoustic feature integration . His studies often bridge grapheme-based modeling , perceptual error reduction , and noise-robust synthesis . Scientific Awards EPSRC Advanced Research Fellowship (2005-2009) Students & Collaborations He has supervised numerous PhD students including Rasmus Dall, Tom Merritt, and Srikanth Ronanki. Current research fellows like Mirjam Wester and Zhizheng Wu contribute to projects such as Natural Speech Technology (NST) and Simple4All .
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 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
Moe Z. Win is the Robert R. Taylor Professor at the Massachusetts Institute of Technology (MIT), specializing in wireless communications, optical communications, and space communications systems. His research bridges theoretical and applied domains, including quantum sensing, network localization, and signal processing. B.S.E.E., Texas A&M (1987) M.S.E.E. & Ph.D., University of Southern California (1989, 1998) Recent work focuses on quantum-enhanced positioning, machine learning for localization, and next-generation (xG) non-terrestrial networks. He leads research at the Quantum neXus Laboratory (QX Lab), Wireless Information & Network Sciences Lab, and Laboratory for Information and Decision Systems. His career spans the Jet Propulsion Laboratory (1987-1995) and AT&T Research Laboratories (1998-2002). Key methodologies include soft information fusion, variational quantum sensing, and robust beam tracking for terahertz communications.
Tülay Adali is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). She has held this position since 1992 and was named a Distinguished University Professor in 2015 for her contributions to statistical signal processing and machine learning. Currently serving as Editor-in-Chief of the IEEE Signal Processing Magazine, she has also held leadership roles in IEEE committees and conferences. Her research focuses on statistical signal processing, machine learning, and their applications in medical imaging and data fusion. Dr. Adali earned her Ph.D. in Electrical Engineering from North Carolina State University in 1992. Her work integrates foundational signal processing techniques with biomedical applications, addressing challenges in neuroimaging analysis. She leads the Machine Learning for Signal Processing laboratory, supported by grants from NSF and NIH. Her lab develops algorithms for analyzing complex signals in medical contexts, emphasizing reproducibility and interdisciplinary collaboration. Recognition includes IEEE Fellow, AIMBE Fellow, AAIA Fellow, Humboldt Research Award, and NSF CAREER Award. She has authored numerous papers on fMRI analysis, independent component analysis, and multimodal data fusion. Her editorial leadership and service to technical communities reflect her commitment to advancing signal processing and education. Education: Ph.D. in Electrical Engineering, North Carolina State University (1992) Grants: NSF, NIH-funded projects on medical imaging and signal processing Awards: SPS Meritorious Service Award, SPIE Pioneer Award
Dean Eckles is an Associate Professor of Marketing at MIT Sloan School of Management and serves as an Associate Director of the MIT Institute for Data, Systems, and Society (IDSS). He is also affiliated with the MIT Schwarzman College of Computing through the Institute for Data, Systems & Society and its Statistics and Data Science Center. Additionally, he leads the analytics research area at the Initiative on the Digital Economy and organizes the annual Conference on Digital Experimentation (CODE@MIT). His educational background includes a BA in philosophy, BS and MS in cognitive science, MS in statistics, and PhD in communication, all from Stanford University. Prior to joining MIT, Eckles worked as a scientist at Facebook, where he contributed to areas including News Feed, messaging, advertising, tools for randomized experiments, and survey methods. He previously held research positions at Nokia and Yahoo. Eckles's research primarily focuses on social influence mediated by interactive technologies, examining how communication technologies mediate, amplify, and direct social influence. His work spans multiple specific areas including social interactions, contagion, and interventions in networks; experimental design and inference in networks; and methods for causal inference. His research often combines social science with advanced statistical methods. His notable publications include research on long ties in social networks and their relationship to economic prosperity, how network structure affects social contagions, and algorithmic transparency in social media platforms. His work has appeared in prestigious journals including PNAS and Nature Human Behaviour, and he has provided expert testimony before the US Senate on algorithmic ranking. Long ties, disruptive life events and economic prosperity (PNAS) Long ties accelerate noisy threshold-based contagions (Nature Human Behaviour) Algorithmic transparency and assessing effects of algorithmic ranking (Senate testimony) Eckles actively shares his research through social media platforms including Bluesky, Twitter, and Mastodon, as well as through his blog and contributions to the Gelman et al. blog. His work bridges academic research with practical applications in technology and policy.
Andreas Vlachos is a Professor of Natural Language Processing and Machine Learning at the Department of Computer Science and Technology, University of Cambridge, and holds the Dinesh Dhamija Fellowship at Fitzwilliam College. His research spans dialogue modeling, automated fact-checking, imitation learning, semantic parsing, biomedical text mining, and trustworthiness in AI systems. PhD in Computer Science, University of Cambridge (supervised by Ted Briscoe and Zoubin Ghahramani) Lecturer at University of Sheffield Postdoctoral roles at UCL, University of Cambridge (NLIP group, Stephen Clark), and University of Wisconsin-Madison (Mark Craven) Current research focuses on evaluating and mitigating biases in language models, advancing fact-checking methodologies, and improving model robustness through interpolation, reinforcement learning, and causal reasoning. His work integrates natural logic, knowledge graphs, and multimodal evidence for verification tasks. Recent publications address uncertainty quantification, temporal planning benchmarks, and ethical framing of NLP artifacts. Grants from ERC, EPSRC, Facebook, Google, and the Alan Turing Institute fund his research team. Collaborations include Sebastian Riedel, Stephen Clark, and Mark Craven. Key projects explore disinformation detection, long-form generation, and confidence calibration in AI systems.
Thomas Hartman is a Professor of Physics in the College of Arts and Sciences at Cornell University. He received his A.B. in Physics from Princeton University in 2004 and his Ph.D. in Physics from Harvard University in 2010. His professional journey includes being a Member of the School of Natural Sciences at the Institute for Advanced Study (2010-2013), Research Associate at the Kavli Institute for Theoretical Physics, UCSB (2013-2014), Assistant Professor at Cornell University (2014-2020), Associate Professor at Cornell University (2020-2022), and Professor at Cornell University (2022-present). Hartman's research focuses on theoretical aspects of quantum gravity and quantum field theory, with particular emphasis on black hole information and strongly interacting quantum fields. His work explores four major interconnected areas: gauge/gravity duality (examining how quantum field theory degrees of freedom organize into fluctuating spacetime), black hole information paradox (investigating the relationship between classical black hole solutions and quantum statistical systems), new approaches to quantum field theory using dualities and entanglement dynamics, and the physics of de Sitter space with implications for early universe cosmology. His research employs techniques from string theory, holographic duality, general relativity, and quantum information theory. Analysis of Hartman's publication record reveals a strong focus on resolving fundamental questions in quantum gravity, particularly through the development of replica wormhole techniques that address the black hole information paradox. His work spans both highly mathematical approaches to quantum gravity and connections to potentially observable phenomena, with increasing emphasis on connections between quantum information science and gravitational physics in recent years. Member, School of Natural Sciences, Institute for Advanced Study, 2010-2013 Hartman has advised graduate students including Jeevan Chandra Namburi and Wan Zhen Chua, contributing to the next generation of theoretical physicists. His research group actively investigates the emergence of spacetime from quantum information principles and develops new mathematical frameworks for understanding quantum gravity. The group maintains strong connections with other leading institutions through collaborative projects and participates in major theoretical physics initiatives including Snowmass planning for future research directions in high energy physics. Hartman's research program represents a vital bridge between abstract theoretical concepts in quantum gravity and potential experimental tests, working to develop frameworks that could ultimately connect quantum gravity to observable phenomena in both high-energy physics and cosmological observations.
Andrés Buxó-Lugo serves as an Assistant Professor of Psychology at the University at Buffalo, where he directs the Language Processing and Computation Lab. His research investigates the cognitive mechanisms underlying language production, comprehension, and acquisition with a specialized focus on speech prosody—the rhythm, intonation, and intensity patterns in speech—and their role in human communication. His primary research interests include psycholinguistics, cognitive psychology, speech prosody, language production, language comprehension, language acquisition, and computational linguistics. He examines how listeners integrate diverse linguistic cues during speech processing, how individuals learn unfamiliar constructions like non-native pronunciations or novel prosodic patterns, and the cognitive basis of durational changes in speech. His work also explores how communicative context shapes prosodic production and how higher-level linguistic information aids prosodic structure parsing. Analysis of his 15 most recent publications (2019-2025) reveals consistent interdisciplinary work bridging cognitive science, linguistics, and computational modeling. Key trends include phonological representation studies, speech planning mechanisms, intonation adaptation across talkers, lexical representation structures, and the integration of input expectations in syntactic parsing. His research demonstrates significant methodological diversity, incorporating experimental paradigms, computational modeling, and acoustic analysis to unravel language processing complexities. As director of the Language Processing and Computation Lab at the University at Buffalo, Buxó-Lugo leads research initiatives focused on developing computational models of language processing while investigating the cognitive foundations of speech and prosody through empirical experimentation and theoretical innovation.
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).
