Christina Lee Yu is an Assistant Professor at Cornell University in the School of Operations Research and Information Engineering (ORIE). She holds a PhD and MS in Electrical Engineering and Computer Science from MIT (2017, 2013) and a BS in Computer Science from Caltech (2011). Her research focuses on algorithm design, high-dimensional statistics, causal inference in networks, and reinforcement learning. She is also an Amazon Scholar and has received prestigious awards, including the NSF CAREER Award and Intel Rising Stars Award. Her work is supported by grants from the NSF and Air Force Office of Scientific Research. Education: PhD in EECS, MIT (2017) MS in EECS, MIT (2013) BS in Computer Science, Caltech (2011) Research Interests: Algorithm design and analysis Inference over networks and causal inference Sequential decision making under uncertainty Online learning and reinforcement learning High-dimensional statistics Awards and Honors: NSF CAREER Award (2024) ACM SIGMETRICS Rising Stars Award (2024) Intel® Rising Stars Award (2021) JPMorgan Faculty Research Award (2021) Simons Institute Research Fellow (2020) INFORMS Dantzig Dissertation Award Honorable Mention (2018) Grants and Funding: National Science Foundation (NSF) CAREER Grant Air Force Office of Scientific Research Grant Advising: PhD Students: Sean Sinclair, Tyler Sam, Xumei Xi Collaborators: Mayleen Cortez, Matthew Eichhorn Labs and Collaborations: Member of ORIE, Statistics, CAM, and CS graduate fields at Cornell Amazon Visiting Academic in Fulfillment Optimization (2025)
Christopher Walters is a Professor at the Kenneth C. Griffin Department of Economics, University of Chicago, and previously served as an Assistant Professor at UC Berkeley (2013-2025). He is a Research Associate at the National Bureau of Economic Research, Research Fellow at IZA, and Faculty Affiliate at MIT's School Effectiveness and Inequality Initiative (SEII). PhD in Economics, MIT (2013) B.A. in Economics and Philosophy, University of Virginia (2008) Walters specializes in Labor Economics and the Economics of Education , focusing on school choice, early childhood interventions, and program evaluation. His work combines applied econometric methods with discrete choice modeling to analyze educational investments and labor market outcomes. Walters' recent publications examine class size effects, teacher quality impacts, and school finance policies, reflecting his interest in improving educational equity through rigorous empirical analysis. Research Fellow at IZA He collaborates with institutions like J-PAL North America and MIT Blueprint Labs, contributing to evidence-based policy design in education and labor economics.
David Simmons-Duffin is a Professor of Theoretical Physics at the California Institute of Technology (Caltech), where he has held positions since 2016. He is part of the Division of Physics, Mathematics and Astronomy, contributing to the Physics Department. His career progression includes roles as Visiting Associate (2016–17), Assistant Professor (2017–20), and Associate Professor (2020–21) before becoming full Professor in 2021. Education: A.B. and A.M. from Harvard University (2006), CASM from the University of Cambridge (2007), and Ph.D. from Harvard University (2012). His research focuses on conformal field theory (CFT), bootstrap methods, quantum field theory, and AdS/CFT correspondence. Key areas include precision computations in strongly coupled systems, critical phenomena, and applications to holography and quantum gravity. Research highlights include advancing the conformal bootstrap program, analyzing CFT data in 3D Ising models, and exploring connections between CFTs and gravitational theories. His work often bridges theoretical frameworks with numerical methods, yielding insights into operator product expansions (OPE), spectral gaps, and causality constraints. Affiliations include the Institute for Quantum Information and Matter (IQIM) and other Caltech research centers. His contributions have shaped modern approaches to understanding universality in critical systems and the geometric aspects of quantum field theories. Notable collaborations involve high-precision calculations, bootstrap island techniques, and studies of thermal QFT and light-ray operators. His work emphasizes interdisciplinary methods, combining analytic tools with computational advancements to tackle complex theoretical problems.
Joseph P. Romano is a distinguished Professor of Statistics and Economics at Stanford University, where he has been on the faculty since 1986. He holds joint appointments in both the Department of Statistics and the Department of Economics, reflecting his interdisciplinary research that bridges statistical theory with economic applications. Romano has established himself as a leading scholar in mathematical statistics with significant contributions to econometrics, climate science, and multiple testing methodologies. Ph.D. in Statistics, University of California, Berkeley (1986) M.S. in Statistics, University of California, Berkeley (1983) A.B. in Statistics, Princeton University (1982), Summa Cum Laude Romano's research focuses on the theoretical foundations and practical applications of statistical methods, particularly in nonparametric statistics, bootstrap and resampling techniques, and multiple testing procedures. His work addresses the challenges of analyzing massive datasets with complex structures, such as those found in biotechnology, clinical trials, and econometrics. He has developed universal statistical tools applicable across diverse fields including climate science, genetics, finance, and education. His recent work emphasizes methods for multiple testing and multivariate inference driven by the availability of massive datasets, where he tackles issues like unknown dependence structures, heterogeneity, and high dimensionality. Analysis of Romano's recent publications reveals a consistent focus on developing robust statistical methodologies for complex data structures. His work spans theoretical advances in U-statistics with growing dimensions, practical applications in seroprevalence studies, and innovative approaches to ranking inference across various domains. The interdisciplinary nature of his research is evident in publications spanning economics journals, statistics journals, and even behavioral science preprints, demonstrating the broad applicability of his methodological contributions. 2021 LGBTQ+ Scientist of the Year, Out to Innovate Fellow, International Association of Applied Econometrics (2020) Fellow, Institute of Mathematical Statistics Presidential Young Investigator Award, National Science Foundation The Canadian Journal of Statistics Award Romano has mentored dozens of doctoral students throughout his career at Stanford, serving as dissertation advisor, co-advisor, and committee member for numerous PhD candidates in Statistics. His research has been consistently supported by National Science Foundation grants, including recent funding for computer-intensive inference with applications to social sciences (2020-2023) and randomization inference for contemporary statistical problems (2013-2016). He has served in various administrative roles at Stanford including Associate Chairman and Chair of Committee on Faculty Affairs. Beyond his academic pursuits, Romano is actively involved in the 500 Queer Scientists visibility campaign and maintains a balanced life with passions in music (having performed at Carnegie Hall), competitive tennis (ranked nationally in his age group), cooking, and architecture.
Carl Henrik Ek is a Professor of Statistical Learning at the Department of Computer Science and Technology (Computer Laboratory) at the University of Cambridge. He is also a fellow and Director of Studies at Pembroke College, and holds visiting positions at Karolinska Institute in Stockholm and the Royal Institute of Technology. He serves as co-Director for the UKRI AI Centre for Doctoral Training in Decision Making for Complex Systems, a collaboration between Cambridge and Manchester universities, and is involved with the Accelerate Program in the Computer Laboratory. Dr. Ek's educational background includes a MEng degree in Vehicle Engineering from the Royal Institute of Technology in Stockholm, followed by a PhD from Oxford Brookes University. During his PhD, he spent time at the University of Manchester and the University of Sheffield. His PhD supervisors were Professor Neil Lawrence and Professor Phil Torr, and his postdoctoral research was conducted at UC Berkeley with Professor Trevor Darrell and Professor Raquel Urtasun. Professor Ek's research focuses on statistical learning, particularly on developing data-efficient and interpretable machine learning methods. His work spans modeling and inference in machine learning, with special emphasis on Bayesian non-parametric methods and Gaussian processes. He explores how to specify assumptions that allow learning from small amounts of data, bridging theoretical foundations with practical applications in various domains. His recent publications demonstrate a strong trend toward applying machine learning to healthcare, drug discovery, and engineering design. There's significant work on Gaussian processes, reinforcement learning, and generative models, with applications ranging from medical diagnostics to structural engineering. His research shows an increasing interdisciplinary focus, connecting machine learning with fields like cardiology, pharmacology, and computational geometry. Professor Ek has received numerous teaching awards throughout his career: Pilkington Price for Teaching Excellence (2024) Teacher of the year in Computer Science at University of Bristol (2016) Docent in Machine Learning at Royal Institute of Technology (2016) Teacher of the year at Royal Institute of Technology, Sweden (2015) Teacher of the year from Student chapter in Industrial Economics at Royal Institute of Technology (2015) Teacher of the year in Computer Science at Royal Institute of Technology (2012) Professor Ek teaches Advanced Data Science, Advanced topics in machine learning, and Machine Learning and the Physical World. He has supervised PhD students throughout his career but is not currently accepting new PhD students for 2025/26 or 2026/27. His research is supported by various grants, including his role as co-Director of the UKRI AI Centre for Doctoral Training. He is an active member of the ml@cl research group at Cambridge and has previously been involved with research groups at University of Bristol and Royal Institute of Technology. His work connects with several interdisciplinary initiatives, particularly in healthcare AI and engineering applications of machine learning.
Professor Peter Godfrey-Smith holds a half-time position in the School of History and Philosophy of Science at the University of Sydney, alongside his role at the CUNY Graduate Center. He earned his undergraduate degree from the University of Sydney and a PhD in Philosophy from UC San Diego. His research spans the philosophy of biology, animal cognition, and consciousness evolution, with notable books like Other Minds and Metazoa . Key awards include the Royal Society of NSW Medal (2017) and the Lakatos Award (2010) for his work Darwinian Populations and Natural Selection . His fieldwork on octopuses has garnered international attention, exploring their intelligence and implications for understanding consciousness. Peter has taught at Stanford, Harvard, and ANU, balancing academic roles with public engagement through media appearances and podcasts. Education: B.A. University of Sydney; PhD in Philosophy, UC San Diego. Research Interests: Evolution of consciousness, animal minds, cephalopod behavior, philosophy of biology, and interdisciplinary studies on cognitive evolution. Awards: Royal Society of NSW Medal (2017), Lakatos Award (2010), multiple fellowships and recognitions in philosophy and biology. Grants and Advising: While no specific grants are listed, his extensive publications reflect sustained research funding. Advises students in history and philosophy of science, though current names are not provided. Labs/Teams: Engages in fieldwork on octopuses, collaborating with marine biologists and philosophers globally. Active in interdisciplinary research groups studying animal cognition.
June Huh is a Mathematics Professor at Princeton University's Department of Mathematics. His research focuses on the interplay between algebraic geometry, combinatorics, and matroid theory, with notable contributions to Hodge theory, tropical geometry, and log-concavity phenomena. He is actively involved in collaborative projects such as the FRG initiative on matroids, graphs, and algebraic geometry. Key research interests include matroid polytopes, Chow rings, Lagrangian geometry, and combinatorial applications of Hodge-Riemann relations. His work bridges discrete and continuous mathematics, with implications for enumerative geometry and geometric combinatorics. Recent publications explore topics like volume polynomials, Bergman fans, and singular Hodge theory in combinatorial geometries. He has received funding for interdisciplinary research through grants like the FRG Collaborative Research program. His contributions highlight innovative methods in geometric and algebraic combinatorics.
A.T. Charlie Johnson serves as the Rebecca W. Bushnell Professor of Physics and Astronomy at the University of Pennsylvania's School of Arts & Sciences, where he has been a standing faculty member since 1994. His research program focuses on nanoscale systems and has established him as a leading figure in condensed matter physics, earning recognition from major scientific societies. His educational foundation includes: Ph.D. in Physics from Harvard University (1990) B.S. in Physics from Stanford University (1984) Professor Johnson's research centers on the development and application of atomic-layer nanomaterials, particularly graphene and transition metal dichalcogenides , for fundamental studies of transport phenomena and practical biosensor applications. His group employs advanced nanofabrication techniques at Penn's Singh Center for Nanotechnology to create devices that leverage biological molecules for chemical recognition in disease diagnosis, security screening, and environmental monitoring. This work bridges condensed matter physics with biomedical engineering , yielding innovative solutions for real-world detection challenges. Analysis of his 2023-2025 publications reveals three dominant research thrusts: (1) scalable graphene-based biosensor development for medical diagnostics, (2) exploration of quantum phenomena like Klein tunneling in novel nanoelectromechanical systems, and (3) interdisciplinary applications spanning oncology, planetary science, and fetal medicine. His work consistently emphasizes materials synthesis , device integration , and practical translation of nanoscale phenomena. His scientific contributions have been recognized with prestigious honors: Defense Science Study Group Fellow (2018-2019) Fellow of the American Association for the Advancement of Science (2017) Fellow of the American Physical Society (2011) Lindback Foundation Award for Distinguished Teaching (2003) David and Lucille Packard Foundation Fellowship (1994-1999) As an educator, Professor Johnson has mentored numerous graduate students and postdoctoral researchers, with notable alumni like Michael Biercuk (founder of Q-CTRL). His research has been supported through significant leadership roles including Director of the Nano/Bio Interface Center (2014-2017) and Packard Fellowship funding, enabling sustained innovation in nanotechnology. His group actively collaborates across disciplines to advance both fundamental understanding and practical applications of nanomaterials. Based at the Singh Center for Nanotechnology, Johnson leads a dynamic research team utilizing state-of-the-art facilities for nanofabrication and characterization. His laboratory maintains strong campus collaborations through secondary appointments in Electrical and Systems Engineering and Materials Science and Engineering, fostering an interdisciplinary environment for developing next-generation nanoscale devices.
Hakan Sandal-Wilson is an Assistant Professor of Gender, Peace and Security at the Department of Gender Studies, London School of Economics and Political Science. His research critically examines intersections of gender, sexuality, ethnicity, religion, and conflict in the Middle East, particularly focusing on Kurdish queer/LGBTIQ+ politics and diasporic movements. He is also invested in creative-critical interdisciplinary research methods, co-editing a volume on politically engaged methodologies. Previously, he served at the University of Cambridge as a Teaching Associate and Affiliated Lecturer, and as Campaigns Officer for Amnesty UK’s LGBTI Network. Education : - PhD, University of Cambridge Centre for Gender Studies - MA in Cultural Studies, Istanbul Bilgi University Research Interests : - Queer Studies in Conflict Zones - Kurdish Social Movements - Diasporic Feminist and Queer Activism - Ethnography and Creative Writing Methodologies Publications Trends : His work bridges theoretical inquiries with activism, emphasizing marginalized queer voices in conflict-affected regions. Recent projects include monographs on Kurdish queer politics and co-edited collections on creative-critical interventions for social justice. Awards & Grants : No awards explicitly mentioned, but his work has been translated into multiple languages and featured in outlets such as openDemocracy and Jadaliyya. Advising & Mentorship : No listed advisees, but directs LSE’s MSc Gender and MSc Gender (Research) programmes, teaching courses like GI402 (Gender, Knowledge & Research Practice) and GI413 (Gender, Race & Militarisation). Labs & Collaborations : Co-convened 'Creaction' interdisciplinary workshops at UCL, fostering global creative-critical social justice research.
Oleg Kozlovski is an Associate Professor at the University of Warwick. His research focuses on dynamical systems, ergodic theory, mathematical physics, and financial mathematics. He has held an EPSRC Advanced Fellowship (2001–2006) for research in Complex Dynamics and Fast Dynamo Theory. His work combines pure mathematical analysis with applications to economics and physics. Teaching responsibilities include MA132 Foundations. His research explores hyperbolicity, rigidity phenomena, and bifurcation theory in dynamical systems. Notable contributions include studies on unimodal maps' density in C^k topologies and rigidity of real polynomials. Grants include £1.9M EPSRC funding for foundational dynamical systems research. His work bridges pure mathematics with applied fields like economic modeling and nonlinear dynamics.
Mohd Fikree Hassan is a Lecturer at the School of Information Technology, Monash University Malaysia, joining in June 2023. He holds a Ph.D. and Master's from the University of Malaya, and a B.Eng. in Electronics Engineering from Multimedia University. With over 14 years of academic experience, he is actively engaged in research, teaching, and supervision. B.Eng. in Electronics Engineering (Telecommunications), Multimedia University, 2004 M.Eng. in Engineering (Telecommunications), University of Malaya, 2015 Ph.D. in Signal and Systems, University of Malaya, 2018 His research focuses on image and signal processing , particularly in image enhancement, restoration, computer vision, and human color vision . His work contributes to improving image visibility, removing color casts, and developing algorithms for noisy or degraded images. He applies mathematical and computational techniques to solve real-world imaging challenges. The recent publication trends (2021–2025) show a strong focus on image restoration using variational methods (e.g., total variation, ℓ0 regularization), color enhancement in HSI space, and video analysis for sports applications. His work bridges theoretical optimization and practical computer vision systems. He actively contributes to the academic community through peer review for journals such as Neurocomputing , Journal of Imaging , and International Journal of Computational Intelligence Systems , as well as for IEEE conferences. Mohd Fikree is currently accepting PhD students and serves as an external examiner for academic programs. His consistent research output and editorial service reflect a growing impact in the field of image processing and computer vision. While no formal lab or team is mentioned in the text, his collaborations with researchers like R. Paramesran, T. Adam, and G. Krishnasamy suggest active research partnerships in signal and image processing.
Katrine Eldegard is a Professor at the Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management (MINA), Department of Ecology and Natural Resource Management (INA). She leads BatLab Norway and contributes extensively to national and international conservation science policy. Institution: Norwegian University of Life Sciences School: Faculty of Environmental Sciences and Natural Resource Management Department: Department of Ecology and Natural Resource Management Position: Professor Her research centers on understanding how human activities and land use affect natural ecosystems and species across taxa and spatial scales. She specializes in the behavioral, population, and community-level responses of mammals, birds, and insects to anthropogenic pressures such as energy infrastructure, transport networks, and forestry. A major focus is on bat ecology and conservation, pollination dynamics, and biodiversity monitoring in boreal and agricultural landscapes. Her recent publications reveal strong trends in climate change impacts on bat morphology and distribution, pollinator-plant interactions under environmental change, and the ecological consequences of infrastructure development. These works integrate field ecology with advanced modeling and policy-relevant assessments. She has played leading roles in key scientific committees: Chair, Mammal Committee, Norwegian Red List for Species (2021) Chair, Mammal Committee, Norwegian Alien Species List (2023) Member, Norwegian Scientific Committee for Food and Environment (VKM), CITES Expert Panel Norway’s representative, UNEP/Eurobats Advisory Committee Eldegard has supervised numerous research projects and collaborated with government agencies and private partners on applied ecology. She teaches courses including NATF200 Vern og forvaltning av norsk natur and the upcoming NATF300 Conservation Science. Her work is supported by extensive fieldwork, interdisciplinary collaboration, and integration of ecological theory with practical conservation. She leads BatLab Norway, a research group dedicated to advancing knowledge on bat ecology, behavior, and conservation through innovative methods including telemetry, acoustic monitoring, and landscape analysis.
Dr. Hyung Jin Chang is an Associate Professor at the School of Computer Science, University of Birmingham, and a Turing Fellow at the Alan Turing Institute. He holds a Ph.D. and B.S. from Seoul National University. His research focuses on human-centered visual learning, particularly in human-robot interaction, with expertise in computer vision, machine learning, and deep learning. He has been involved in organizing conferences like ECCV and ICCV workshops (e.g., VOTS Challenge, HANDS Workshop) and serves on program committees including AAAI and CVPR. His work spans areas like gaze estimation, domain adaptation, 3D pose estimation, and robotic perception for assistive technologies. Key achievements include receiving the Royal Society Research Grant (2019–2020) and Wellcome Trust funding. Notable contributions include frameworks for unsupervised domain adaptation, gaze estimation models (e.g., RT-Gene), and collaborative learning methods for hand-object reconstruction. He has led projects in medical robotics, personalized dressing assistance, and safety-critical systems like driver attention prediction. His 15 most recent articles (2024–2025) emphasize advancements in diffusion models, domain adaptation, 3D reconstruction, gaze-controllable systems, and generative AI for motion and interaction modeling. These reflect a trend toward integrating multimodal data (vision + language) and bridging theoretical foundations with applied robotics. Awards: Royal Society Grant, Wellcome Trust, Turing Fellowship Grants: Active in securing funding for robotics, vision, and healthcare applications He leads the Personal Robotics Lab and collaborates on projects like the VOTS Challenge for visual object tracking. His research bridges academia and real-world applications in healthcare robotics and human-technology interaction.
Michele DiBenedetto is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Princeton University, associated with the High Meadows Environmental Institute (HMEI). Her research focuses on environmental fluid mechanics, ocean waves, and turbulent flows, with applications in contaminant transport, biomonitoring, and ocean sensing. She holds a PhD from Stanford University (2019) and moved to Princeton in 2025 after serving as an Assistant Professor at the University of Washington (UW). Her work integrates laboratory experiments, mathematical modeling, and field observations to study interdisciplinary challenges like plastic pollution, renewable energy, and air-sea interactions. Key achievements include an NSF CAREER Award (2023) and NOAA Sea Grant funding (2024). She advises a team of graduate and undergraduate students, including Carlos, Julio, Andrew, and Ethan, whose research contributes to understanding particle dynamics in turbulent systems. Notable projects include investigating buoyant particles in wind-driven ocean boundary layers and developing methods to track particle orientation using collimated light. Her lab’s move to Princeton in 2025 marks a strategic shift to enhance collaborations in environmental fluid mechanics. Publications span experimental fluid mechanics and environmental applications, emphasizing ocean transport and marine biology.
Stefanie Jegelka is an Associate Professor (currently on leave) at the Massachusetts Institute of Technology's Department of Electrical Engineering and Computer Science, and a Humboldt Professor at Technical University of Munich. At MIT, she is a member of CSAIL (Computer Science and Artificial Intelligence Laboratory), IDSS (Institute for Data, Systems, and Society), the Center for Statistics and Machine Learning, and is affiliated with the Operations Research Center. Her educational background includes a PhD from ETH Zurich and the Max Planck Institute for Intelligent Systems, followed by postdoctoral research at UC Berkeley's AMPlab and computer vision group. Her research program focuses on algorithmic machine learning, with particular emphasis on exploiting mathematical structure for discrete and combinatorial machine learning problems, robustness in learning systems, and developing methods for scaling machine learning algorithms to large datasets. She has made significant theoretical contributions to submodular optimization and its applications in machine learning. Jegelka's publication record demonstrates a consistent focus on the intersection of discrete mathematics and machine learning. Her work spans theoretical foundations of optimization with discrete structures, applications in computer vision, and practical algorithms for submodular function optimization. Her research has evolved from foundational work on submodular functions to broader applications in deep learning and robust machine learning systems, showing increasing impact through numerous workshop best paper awards and high-impact conference publications. NSF CAREER Award Google Research Award German Pattern Recognition Award (Mustererkennngspreis) ICML Best Paper Award Sloan Research Fellowship DARPA Young Faculty Award NSF BIGDATA Award ONR MURI NSF AI Institute for Optimization Professor Jegelka has advised several successful students including Keyulu (recipient of MIT's George M. Sprowls Ph.D. Thesis Award), Derek (NSF Fellowship recipient), Ching-Yao (IBM Fellowship recipient), and Nisha (now Assistant Professor at Georgia Tech). Her research has been generously supported by multiple NSF grants, DARPA awards, and industry funding from Google, Two Sigma, and Adobe. She has also organized multiple workshops and tutorials on discrete optimization and submodularity in machine learning. At MIT, Jegelka is affiliated with the Center for Statistics and Machine Learning and collaborates with researchers across CSAIL. Her work bridges theoretical computer science, optimization, and practical machine learning applications, with recent focus on high-dimensional learning dynamics and in-context learning as evidenced by her group's multiple papers at leading conferences like ICLR.
