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
Zaid Harchaoui is an Adjunct Professor in the Department of Statistics at the University of Washington. His research focuses on machine learning, generative AI, and algorithmic optimization, with applications spanning ecology, neuroscience, and artificial intelligence. He explores learning under distributional shifts and develops tools for scalable generative models in language and vision domains. University: University of Washington Department: Statistics Research Focus: Learning from data with computational, inferential, and mathematical rigor; distributional shift adaptation; generative model scaling Email: zaid@uw.edu His recent work emphasizes generative AI applications in ecology and neuroscience, stochastic optimization for robustness, and algorithmic efficiency in large-scale learning. Key contributions include techniques for distributionally robust optimization, interpretable authorship obfuscation, and uncertainty quantification in behavior classification. Scientific awards and honors are not explicitly mentioned in the provided text. Collaborative efforts often intersect with nonlinear control algorithms, spectral analysis, and privacy-preserving machine learning frameworks.
Vivek Shenoy is the Eduardo D. Glandt President's Distinguished Professor at the University of Pennsylvania, with primary appointments in the Department of Materials Science and Engineering and secondary appointments in Bioengineering and Mechanical Engineering and Applied Mechanics. He leads the Multiscale Mechanobiology and Biomaterials Laboratory, which focuses on developing theoretical frameworks and numerical methods to understand complex biological and engineering systems across multiple length scales. Shenoy's research spans mechanobiology, chromatin organization, cell mechanics, and biomaterials. His work addresses the fundamental challenge of modeling how small-scale cellular phenomena couple with long-range tissue-level interactions across micrometers to centimeters. By integrating insights from soft matter physics, solid mechanics, chemistry, and applied mathematics, his group develops multiphysics continuum and mesoscale theories to elucidate mechanisms controlling both biological and engineering systems. His recent publications demonstrate an increasing focus on nuclear mechanics, chromatin organization, and the interplay between mechanical forces and gene regulation. Analysis of Shenoy's publication record reveals a strong interdisciplinary approach, with high-impact papers spanning biophysics, materials science, and cell biology. His work shows consistent evolution from fundamental mechanics of materials to complex biological systems, with recent emphasis on the mechanical regulation of chromatin architecture, cell migration dynamics in 3D environments, and mechanotransduction in development and disease. His publications appear regularly in top journals including Nature, Science, and their affiliated publications, demonstrating significant influence across multiple fields. Eduardo D. Glandt President's Distinguished Professor Multiple publications in Nature, Science, and PNAS Active research program with publications through 2025 Shenoy actively mentors students and postdocs through his laboratory, with numerous co-authored publications indicating strong mentorship. His research program appears to be well-funded through multiple grants supporting his work in mechanobiology and biomaterials. The Multiscale Mechanobiology and Biomaterials Laboratory maintains active collaborations across disciplines and institutions, reflecting the interdisciplinary nature of his research. The Multiscale Mechanobiology and Biomaterials Laboratory, housed within the Department of Materials Science and Engineering at the University of Pennsylvania, serves as the primary research hub for Shenoy's work. The lab maintains an active presence on social media (Twitter: @ShenoyLab) for updates on activities and publications. Their research approach combines theoretical modeling with experimental validation to address fundamental questions at the interface of mechanics, materials science, and biology.
Pieter van Goor is a Research Fellow at the Australian National University (ANU), affiliated with the School of Engineering and the Systems Theory and Robotics (STR) group. He holds a PhD in Control Theory (completed 2022) and dual bachelor's degrees (BEng/BSc, 2018). His research focuses on equivariant systems theory, state estimation, and robotics applications. Key contributions include equivariant observer design, Lie group-based control, and geometric data fusion. Education: Bachelor of Engineering (Research & Development) (Honours) in Mechatronics (ANU, 2018) Bachelor of Science in Mathematics (ANU, 2018) PhD in Control Theory (ANU, 2022) Research Interests: Equivariant systems theory, nonlinear control, robotics applications, state estimation on Lie groups, sensor fusion, and geometric control methods. His work emphasizes symmetry exploitation in filter design and observer construction for systems with inherent geometric structures. Grants & Collaborations: Active collaborations include work with Robert Mahony and institutions like the IEEE. Research spans theoretical frameworks (e.g., equivariant filters) and applied systems (e.g., ArduPilot autopilot, event cameras). Labs/Teams: Member of the Systems Theory and Robotics (STR) group at ANU, focusing on advanced control theory and robotics.
Liping Liu is a Professor in the Department of Management at The University of Akron's College of Business. He holds a Ph.D. in Business from the University of Kansas (1995), Master of Engineering in Systems Engineering (1991), and dual bachelor's degrees in Applied Mathematics (1986) and River Dynamics (1987). Ph.D., University of Kansas MS, Huazhong University of Science and Technology B.E., Wuhan University BS, Huazhong University of Science and Technology His research spans Artificial Intelligence , Electronic Business , Systems Analysis , Data Quality , and Belief Function Theory . He pioneered coarse utility theory and linear belief functions , now taught in top Ph.D. programs across multiple disciplines. Key trends in his publications include Belief Function Applications (2012-2024), Medical Data Systems (2003-2015), and Decision Theory (2004-2014). Recent works focus on Gamma Belief Functions (2024) and computational improvements in linear belief function operations (2019-2016). Scientific contributions recognized via: Microsoft Azure Educator Grant (2014-2016) Inclusion in Who's Who in America (2010-2013) and Who's Who in the World (2011-2013) As an editor and committee member for major conferences (INFORMS, AMCIS, Belief Functions conferences), he bridges academic research with practical systems implementation in e-business and healthcare domains.
Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.
Ji Zhu is the Susan A. Murphy Collegiate Professor of Statistics at the University of Michigan, Department of Statistics. He holds affiliations with the Michigan Institute for Data Science (MIDAS) and the Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP). His research focuses on statistical machine learning, network analysis, and health science applications. Education: B.Sc. in Physics (Peking University, 1996), M.Sc. and Ph.D. in Statistics (Stanford University, 2000 and 2003). Notable awards include the NSF CAREER Award (2008), Fellowships from the ASA (2013) and IMS (2015), and recognition as a Web of Science Highly Cited Researcher (2014–2020). Research interests span statistical methodologies for networks, survival analysis, and high-dimensional data. He co-authored influential papers on community detection, network cross-validation, and latent space models. Current editorial roles include Editor-in-Chief of the Annals of Applied Statistics and Action Editor for the Journal of Machine Learning Research. Advising: Supervised over 50 students and postdocs, many now in academia and industry. Notable former advisees include Tianxi Li (University of Minnesota), Yuan Zhang (Ohio State University), and Weijing Tang (Carnegie Mellon University). Labs/Teams: Active in interdisciplinary projects at MIDAS and MiCHAMP, focusing on healthcare analytics and predictive modeling for diseases like hepatitis and cardiovascular outcomes.
Kenneth McLaughlin is the Evelyn and John G. Phillips Distinguished Chair in Mathematics at Tulane University's School of Science & Engineering. He holds a Ph.D. and B.A. in Mathematics from New York University (1994 and 1989). Prior to Tulane, he served as faculty at the University of North Carolina, Chapel Hill, the University of Arizona, Universidade Federal de Brasília, and Colorado State University, where he also held leadership roles as Department Head and Chair. His research focuses on integrability, applying techniques across mathematics to study complex systems and phenomena. He has held visiting positions at institutions worldwide, including France, Italy, Brazil, Belgium, and the UK. McLaughlin’s research spans integrable systems, nonlinear dynamics, and asymptotic analysis. His work often involves the Riemann-Hilbert problem approach, orthogonal polynomials, and random matrix theory. Notable contributions include studies on soliton gases, the KdV equation, and universality in quantum operator dynamics. His recent articles explore topics such as asymptotic behavior of polynomials, soliton gas condensation, and hydrodynamic limits in integrable systems. McLaughlin’s academic career is marked by interdisciplinary collaboration and international research engagement.
Jan von Delft is a Professor (chair) at Ludwig-Maximilians-University (LMU) Munich, working in the Faculty of Physics within the Chair of Theoretical Solid State Physics. His research group consists of postdocs, PhD students, and master's students working on various aspects of strongly correlated electron systems, with physical space located at Theresienstr. 37 (Room A420) in Munich. von Delft's research focuses on correlated electron and spin systems, with particular interest in dynamical and transport properties, quantum impurity models, Hund metals, unconventional superconductors, quantum magnets, and quantum criticality. His methodological expertise includes many-body field theory, parquet formalism (FRG), DMFT, and tensor networks (NRG, DMRG, PEPS, XTRG, etc.). His work bridges theoretical concepts with computational approaches to understand complex quantum phenomena in condensed matter systems. He has developed a distinctive emphasis on real-frequency calculations and numerical methods for studying quantum critical phenomena. Analysis of von Delft's recent publications reveals a strong focus on developing and applying advanced computational methods to study strongly correlated electron systems. His group has made significant contributions to numerical renormalization group techniques, tensor network methods, and the parquet formalism for calculating real-frequency correlation functions. His research shows increasing sophistication in handling quantum criticality, particularly in heavy-fermion systems, and exploring unconventional superconductivity mechanisms. Notably, his group has developed specialized computational libraries like KeldyshQFT to make these advanced methods more accessible to the broader physics community. von Delft actively mentors a substantial research group consisting of one postdoc (Markus Scheb), eleven PhD students (Anxiang Ge, Sasha Kovalska, Mathias Pelz, Marc Ritter, Nepomuk Ritz, Changkai Zhang, Markus Frankenbacher, Felipe Picoli, Simone Fodera, Ming Huang), and two master's students (Ester Pages, Gianluca Grosso). His detailed Style Guide for scientific communication demonstrates his commitment to high-quality research presentation. The group appears well-funded with ongoing research activities spanning theoretical development, computational implementation, and physical interpretation of complex quantum phenomena.
Changxi Zheng is an Associate Professor in the Department of Computer Science at Columbia University's School of Engineering and Applied Science (SEAS). He directs Columbia's Computer Graphics Group (C2G2) within the Columbia Vision and Graphics Center (CVGC). After receiving his PhD from Cornell University, he joined the faculty of Computer Science Department at Columbia, where he has established himself as a leading researcher in computer graphics and scientific computing. Dr. Zheng's research spans multiple areas of applied computer science with a particular focus on computer graphics and scientific computing. His work centers around developing numerical models for simulating physical phenomena involving complex motions such as fluids, bubbles, and thin rods, along with their resulting acoustic waves. Leveraging computational insights from these models, he devises methods for improving tangible object creation, enabling novel human-computer interactions, and developing software tools for acoustic and photonic devices. His research has attracted significant public interest and media coverage, including projects like FontCode, AirCode, and Computational Metallophone Design. His recent publications reveal a strong interdisciplinary approach, bridging computer graphics, physics simulation, machine learning, and hardware design. His work demonstrates consistent innovation in computational methods for simulating physical phenomena and applying these techniques to practical problems in 3D printing, acoustic modeling, and interactive systems. The breadth of his research spans from fundamental physics-based simulations to practical applications in industry. Columbia SEAS Dean's Fellow (for advised students) NSF Graduate Research Fellow (for Ruilin Xu) Snap Research Fellow (for Rundi Wu) CKGSB Fellow (for Yun Fei) Adobe Research Fellow (for Gabriel Cirio) Marie Sklodowska-Curie Individual Fellow (for Rundi Wu) Best Paper Award at ACM International Conference on Multimedia (ACMMM), 2019 Dr. Zheng actively mentors a diverse group of students, including current PhD candidates and postdoctoral researchers. His research group has received support from various sources that enable their innovative work in computational graphics and physics-based simulation. He has supervised numerous successful students who have gone on to positions at leading technology companies including Adobe, Tencent, Facebook, and academic institutions. As director of Columbia's Computer Graphics Group (C2G2) within the Columbia Vision and Graphics Center (CVGC), Dr. Zheng leads a vibrant research team focused on advancing the state of the art in computer graphics, physics-based simulation, and their applications. The group maintains strong collaborations with industry partners and academic institutions worldwide, fostering an environment of innovation and practical application of theoretical concepts.
Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.
Murat Erdogdu is an Assistant Professor at the University of Toronto, jointly appointed in the Department of Computer Science and Department of Statistical Sciences . He is also a faculty member at the Vector Institute and holds the CIFAR Chair in Artificial Intelligence . PhD in Statistics, Stanford University (advised by Mohsen Bayati and Andrea Montanari) MSc in Computer Science, Stanford University BSc in Electrical Engineering and Mathematics, Bogazici University His research focuses on machine learning theory , high-dimensional statistics , and optimization . He has contributed to understanding gradient-based algorithms, sampling methods, and feature learning in structured data. Recent publications address problems in: Heavy-tailed sampling Mean-field Langevin dynamics Robust feature learning Minimax regression Stochastic optimization under infinite noise Scientific Awards: CIFAR Chair in Artificial Intelligence ICLR 2023 Spotlight NeurIPS 2019 & 2021 Spotlights He teaches graduate courses like STA 414/2104 (Statistical Methods for Machine Learning II) and STA 4273 (Modern Learning Theory) , emphasizing probabilistic modeling and theoretical analysis.
Liming Feng is an Associate Professor at the Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, and has served as Director of the Master of Science in Financial Engineering (MSFE) program since 2022. His academic career at the university spans from Assistant Professor (2006-2012) to his current role. He earned his Ph.D. in Industrial Engineering and Management Sciences from Northwestern University (2006), an M.S. in Mathematics from Northwestern University (2000), and a B.S. in Mathematics from Beijing Normal University (1997). Ph.D., Industrial Engineering and Management Sciences, Northwestern University, 2006 M.S., Mathematics, Northwestern University, 2000 B.S., Mathematics, Beijing Normal University, 1997 Feng’s research focuses on Financial Engineering, Stochastic Modeling, and Computational Methods. He has contributed extensively to quantitative finance, particularly in options pricing, portfolio optimization, and market impact models. His work leverages advanced numerical methods, Fourier transforms, and stochastic calculus to solve complex financial problems. The trends in his publications highlight expertise in Levy processes, jump diffusion models, and numerical algorithms for financial derivatives. He has developed innovative techniques for Bermudan options pricing, discretely monitored barrier options, and portfolio deleveraging strategies. His articles often intersect Operations Research with Financial Engineering, emphasizing computational efficiency and mathematical rigor. ISE Faculty Fellow (2025) INFORMS Financial Services Section Best Student Research Paper (2013) First runner-up of the 2012 Morgan Stanley Prize for Excellence in Financial Markets Feng has served on editorial boards for Operations Research Letters and Mathematical Finance . He has been recognized repeatedly for teaching excellence, including the Sharp Outstanding Teaching Award (2011, 2022) and multiple entries in the List of Teachers Ranked as Excellent by Their Students (2007-2024). He currently leads the MSFE program and contributes to curriculum development through courses like IE 522 (Statistical Methods in Finance) and IE 527 (MSFE Professional Development).
Gerhard Huisken is a Professor at the University of Tübingen and Director of the Mathematisches Forschungsinstitut Oberwolfach . His work spans Differential Geometry , Geometric Flows , and Mathematical Relativity . Education : Diploma (1982), PhD (1983), and Habilitation (1986) in Mathematics from Heidelberg University. His research focuses on geometric evolution equations, particularly mean curvature flow and inverse mean curvature flow , with applications to mathematical relativity and geometric inequalities . He has contributed to the understanding of singularities in curvature flows and developed surgical techniques for their analysis. His work on the Riemannian Penrose inequality and center of mass in isolated systems bridges geometry and physics. Selected publications highlight trends in geometric flows (mean curvature flow, Ricci flow), mathematical relativity (Penrose inequality, center of mass), and singularities in geometric PDEs. His collaborations with leading mathematicians like Simon Brendle and Tom Ilmanen reflect interdisciplinary impact. Scientific Awards and Honors : Fellow of the American Mathematical Society (2013) Clay Foundation Senior Fellowship (2013, 2007) Leibniz Preis from German Research Foundation (2003) Medal of the Australian Mathematical Society (1991) Member of the German Academy of Sciences Leopoldina (2004) He has held leadership roles including Dean of the Faculty of Mathematics at Tübingen University and directed major institutes like the Max Planck Institute for Gravitational Physics (2002-2013).
Ali Mostafazadeh is a Professor at the Department of Mathematics, College of Sciences, Koç University. His research spans Mathematical Physics, focusing on Quantum Mechanics, Scattering Theory, and PT-Symmetry. He has made significant contributions to understanding non-Hermitian Hamiltonians, electromagnetic wave propagation, and geometric scattering phenomena. Education: PhD in Physics (1994) from The University of Texas, BA in Physics and Mathematics (1989) from Boğaziçi University His work explores the intersection of mathematics and physics, particularly through spectral singularities, transfer matrix methods, and nonlinear optical systems. Recent publications highlight advancements in broadband directional invisibility, exact Born approximations, and time-dependent Hilbert spaces in quantum systems. 2011 Outstanding Success Award 2007 TÜBİTAK Science Award 2006 Werner-von-Siemens Excellence Award 2001 TÜBA Outstanding Young Scientists Award 2001 Parlar Foundation Research Incentive Award
