Dr. Bisan Alsalibi is a Lecturer at the School of Information Technology, Monash University (Malaysia campus). She holds a PhD (2017) and MSc (2013) in computer science from Universiti Sains Malaysia, specializing in Artificial Intelligence. Previously, she served as Senior Lecturer at Taylor's University (2022–2023) and Teaching Fellow at Universiti Sains Malaysia (2018–2020). Education: PhD in Computer Vision and Optimization (2014–2017), Universiti Sains Malaysia MSc in Artificial Intelligence (2011–2013), Universiti Sains Malaysia Research focuses on AI-driven solutions in feature selection, bio-inspired optimization techniques, and computer vision applications. Her work bridges membrane computing and real-world problem-solving, including privacy-preserving recommender systems for social networks. She currently supervises PhD students exploring bio-inspired algorithms for complex challenges. Advising & Grants: Acknowledged PhD project areas: Bio-inspired optimization techniques and evolutionary machine learning
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.
Philip Evans serves as Professor in the Department of Wood Science at the University of British Columbia's Faculty of Forestry, holding the British Columbia Leadership Chair in Advanced Forest Products Manufacturing Technology. He maintains an additional affiliation as Honorary Professor in the Department of Materials Physics at The Australian National University, reflecting his international research impact in sustainable forest products. His academic foundation includes a First Class BSc (summa cum laude) and Ph.D. from the University of Wales Bangor, followed by postdoctoral research at Abertay University in Scotland. Career progression led him from Lecturer to Professor and Director of ANU’s Centre for Science & Engineering of Materials before his current UBC appointment. Professor Evans' research integrates molecular-scale wood modification using plasma technology with practical material development. His portfolio addresses critical industrial challenges through plasma-modified wood surfaces for light absorption, X-ray micro-CT/VR systems for wood microstructure analysis, sustainable alternatives to old-growth mahogany in musical instruments, aerospace panel enhancements, and AI-driven detection of illegal timber trade. This dual focus on fundamental science and translational applications drives innovation in replacing non-sustainable materials. His exceptional contributions are recognized by: DOW Chemical Company Distinguished Lecturer Fellow of International Academy of Wood Science Fellow of Institute of Materials, Minerals & Mining Commonwealth Forestry Association Medal (Americas) Japanese Ministry for Science & Technology Research Award Oxford Commonwealth Forestry Bureau Book Prize As director of the Wood Surface Science Lab, he actively collaborates with industry partners to solve complex technical problems in forest products manufacturing, while mentoring junior colleagues through his integrated research-teaching approach. Current projects emphasize sustainable material development and advanced analytical techniques for global forestry challenges.
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).
Anna Gottard is an Associate Professor of Statistics at the University of Florence, where she leads the Department of Statistics, Computer Science, and Applications. She directs the Florence Center for Data Science (FDS) and participates in the Technical Scientific Committee of the Tuscan Center for Big Data, Data Science, and AI (CBDAI). Her research focuses on multivariate statistical models, particularly graphical models, and extends to statistical machine learning, fair models, and directional data analysis. She is an Associate Editor for the Journal of the Royal Statistical Society Series A (JRSSA) and Statistical Methods & Applications (SMA). Her recent work includes Bayesian approaches for mixed graphical models, uncertainty-aware classification trees, and methodological advancements in latent uncertainty models. Her contributions span theoretical developments and applied research in interdisciplinary areas like biostatistics and sustainability. Her research interests emphasize bridging statistical theory with practical applications, including fairness in machine learning, interpretable models, and tree-based methodologies. She has actively contributed to open-source software, notably the Mix3Trees R package for mixed-effect tree models. Her work addresses challenges in variable selection, graphical model inference, and ethical AI practices. Current projects explore Bayesian frameworks for complex data structures and methodological improvements in graphical model interpretability. Anna has advised on interdisciplinary collaborations, such as studies on GDPR compliance in biobanking and epidemiological modeling of the SARS-CoV-2 pandemic in Tuscany. She collaborates with institutions like the CBDAI to advance data science applications in regional policy and healthcare. Her research trajectory reflects a commitment to both foundational statistical theory and real-world problem-solving across diverse domains.
Tengyu Ma is an Assistant Professor of Computer Science at Stanford University. His research focuses on machine learning, deep learning, optimization, and theoretical computer science. He is particularly known for work on neural networks, reinforcement learning, and algorithmic guarantees in AI systems. His email is tengyuma@stanford.edu . Ma's research interests span foundational aspects of machine learning, including generalization theory, optimization algorithms, and the theoretical underpinnings of deep learning. He has contributed to areas such as self-play theorem provers, learning rate schedules, and robustness in low-light vision tasks. His work often bridges theoretical insights with practical algorithm design. His recent publications emphasize advancements in large language models (LLMs), theorem proving via self-play, and understanding training dynamics in deep networks. Despite prolific output, no specific scientific awards are explicitly mentioned in the provided texts. Ongoing work includes exploring in-context learning mechanisms, formal verification of AI systems, and efficient pretraining techniques. His research has implications for both theoretical understanding and real-world applications of AI.
Zongyi Li is a Research Fellow at Massachusetts Institute of Technology , hosted by Kaiming He. They are currently pursuing a Ph.D. in Computing and Mathematical Sciences at Caltech (2019-2025), mentored by Anima Anandkumar and Andrew Stuart. Ph.D. candidate: Computing and Mathematical Sciences, Caltech (2019-2025) B.Sc. in Computer Science and Mathematics with a Jazz minor from Washington University in St. Louis (2015-2019) They focus on Neural Operators for learning solution operators in Partial Differential Equations (PDEs) , particularly in fluid mechanics and earth science . Their work models physical simulations with chaotic behaviors and complex geometries, showing applications in weather forecasting , carbon storage , and aerodynamics simulation . Publications emphasize resolution-invariant models , chaotic systems , and zero-shot super-resolution capabilities. Their research combines Fourier analysis , graph networks , and physics-informed loss functions to achieve state-of-the-art performance in PDE solving with up to 1000x speedup over traditional solvers. Fellowships: Kortschak Scholarship PIMCO Fellowship Amazon AI4Science Fellowship Nvidia Fellowship MIT Novo Nordisk AI Fellowship Code & Open-Source: Co-developer of the NeuralOperator library Implementations for Fourier Neural Operators , Graph Neural Operators , and Tensorized Neural Operators Media Recognition: Quanta Magazine MIT Tech Review NVIDIA Features Towards Data Science
Christopher L. Tucci is a Professor of Digital Strategy & Innovation at Imperial College London's Business School and currently serves as Interim Vice Dean of Education. He is also the Founding Dean of NEOM's College of Business & Innovation in Saudi Arabia. Previously, he held the Chair in Corporate Strategy & Innovation at École Polytechnique Fédérale de Lausanne (EPFL), where he served as Dean of the College of Management from 2013 to 2018. His academic qualifications include a PhD in Management from MIT and multiple degrees from Stanford University: an AB in Music, BS in Mathematical Sciences, and MS in Computer Science and Computer Science. His research focuses on digital strategy, innovation management, open innovation, and business model innovation, with particular emphasis on how firms transition to new technologies and organizational forms. He has pioneered work on crowdsourcing, inter-networking, and AI-driven innovation. His award-winning article 'Crowdsourcing as a Solution to Distant Search' (co-authored with Allan Afuah) has received multiple accolades, including the AMR Decade Award in 2022. Professor Tucci teaches courses such as AI Ventures, Co-Creation in AI & Robotics, and Design Thinking. He actively contributes to academic leadership roles, including serving on the Academy of Management's Board of Governors and leading initiatives like Imperial's I-X (Artificial Intelligence and Digital Innovation hub). His work integrates technology, strategy, and organizational dynamics to address modern business challenges. Educations: PhD in Management, MIT (1992–1997) SM in Technology & Policy/EECS, MIT (1989–1991) BS in Mathematical Sciences, Stanford (1978–1982) AB in Music, Stanford (1979–1983) MS in Computer Science, Stanford (1982–1984) Labs/Teams: Co-Director of Imperial's I-X initiative, focusing on AI, data science, and digital innovation.
Jan Dreier is a Research Fellow at the Institute of Logic and Computation at Vienna University of Technology. His research centers on structural graph theory and algorithmic meta-theorems, particularly exploring the boundaries of tractability for model checking problems. Dreier's work bridges theoretical computer science and discrete mathematics, focusing on graph decompositions, parameterized complexity, and logical expressiveness. Key research themes include monadic stability in graph classes, applications of model theory to computer science, and efficient algorithms for logical queries on structured graphs. His publication record shows consistent focus on graph sparsity concepts and algorithmic applications of logic, with recent work expanding into approximation methods for counting queries. Research demonstrates sophisticated applications of combinatorial methods to fundamental problems in computational complexity.
Marek Pycia serves as Professor of Organizational Economics at the University of Zurich and Co-Director of the Zurich Center for Market Design. His research has driven tangible policy impacts including the U.S. kidney-transplant voucher program and school system redesigns, with publications in premier journals like American Economic Review and Econometrica . His academic foundation includes a Ph.D. in Economics from the Massachusetts Institute of Technology (MIT), complemented by prior faculty roles at UCLA, Pennsylvania State University, and Leon Koźmiński Academy of Entrepreneurship and Management, plus a visiting fellowship at Princeton University. Professor Pycia specializes in Market Design and Mechanism Design , where he bridges theoretical microeconomics with real-world applications in healthcare allocation and educational systems. His work emphasizes strategyproofness , simplicity , and efficiency in matching markets, influencing both academic frameworks and practical policy implementations globally. Recent publications (2024-2025) reveal a concentrated focus on simplifying mechanism design for discrete environments, advancing double auction theory, and optimizing information structures in trade. These works consistently apply game-theoretic principles to solve complex allocation problems across healthcare, education, and financial markets. Key recognition includes: Research Fellow at the Centre for Economic Policy Research (CEPR) Editorial board membership at Theoretical Economics , Journal of Economic Theory , and Games and Economic Behavior While specific grant details remain unmentioned, his editorial leadership and center directorship indicate active research funding. He supervises graduate work through the University of Zurich’s economics programs, though no named students appear in the source text. The Zurich Center for Market Design, which he co-directs, functions as an interdisciplinary hub connecting theoretical research with practitioners in healthcare, education, and public policy to implement novel market mechanisms.
Yvonne Rogers is a Professor of Interaction Design and Director of the UCL Interaction Centre (UCLIC), with a joint appointment as Deputy Head of the Department of Computer Science at University College London. She joined UCL in 2011 after holding professorships at the Open University (2006-2011) and Indiana University (2003-2006). Her pioneering work spans ubiquitous computing, interaction design, and human-computer interaction, with a current focus on human-centered AI. She co-authored the definitive 'Interaction Design' textbook (6 editions, 300,000+ copies) and serves as CTO of LetThink.com. Research interests center on designing technologies that enhance human cognition and daily activities: Ubiquitous Computing: Augmenting learning/work activities through pervasive technologies Human-AI Interaction: Developing AI systems that enhance human decision-making Health Technologies: Creating VR therapies and ADHD support systems Community Resilience: Designing tools for environmental monitoring and sustainable practices Publication analysis reveals three dominant trends: (1) VR applications for mental health (emotion regulation, ADHD support), (2) AI systems for cognitive augmentation, and (3) community-centered tools for healthcare/environmental challenges. Recent work demonstrates strong emphasis on vulnerable populations including children with ADHD, older adults, and postoperative patients. Major scientific recognition includes: International Member, National Academy of Engineering (2024) ACM SIGCHI Lifetime Research Award (2022) Royal Society Robin Milner Medal (2022) Royal Society Fellowship (2022) Microsoft Outstanding Collaborator Award MRC Suffrage and Science Award (2020) Triple Fellow status (ACM, BCS, CHI Academy) Advises PhD students including Leon Reicherts, Sheena Visram, and Tu Duong. Secured major grants including EPSRC Dream Fellowship on ageing/computing and led the Intel Collaborative Research Institute on Sustainable Connected Cities (2012-2018). Directs UCLIC research center and previously established the Pervasive Interaction Lab at Open University.
Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering , University of Washington, and a Senior Principal Researcher in Microsoft AI. His research spans convex optimization , convex geometry , graph algorithms , online algorithms , and differential privacy , with applications in machine learning and theoretical computer science.
Daniel Adelman is the Charles I. Clough, Jr. Professor of Operations Management at the University of Chicago Booth School of Business. He joined the faculty in 1997 after completing his PhD in industrial engineering and operations research at Georgia Tech. Adelman is a leading expert in Business Analytics and Management Analytics, helping companies deploy data and decision analysis to build world-class strategic and tactical management capabilities. Adelman received his PhD in industrial engineering and operations research in 1997, along with a bachelor's degree in industrial engineering and a master's degree in operations research, all from the School of Industrial and Systems Engineering at the Georgia Institute of Technology. Daniel Adelman's research focuses on applying analytical models to solve complex business problems across multiple industries. He has worked with firms from diverse sectors including internet services, chemical distribution, airlines, third party logistics, fiber-optics manufacturing, semiconductor manufacturing, oil, and healthcare. His research integrates real-world data with analytical models to bring structure and discipline to decision and control processes, enabling firms to achieve higher profits with lower risk. Adelman's recent work has concentrated heavily on healthcare analytics, where he leads the Healthcare Analytics Laboratory at Chicago Booth. This lab works with teams of doctoral and MBA students on projects with major healthcare institutions to optimize clinical, operational, and financial outcomes. His research spans foundational operations research including approximate dynamic programming, inventory theory/supply chain management, and revenue management/pricing optimization, as well as examining the linkage between operational performance metrics and financial performance of firms. Adelman's publications show a clear trend toward increasing focus on healthcare applications while maintaining strong theoretical foundations in operations research. His earlier work focused more on general operations management problems like inventory control and supply chain optimization, while his recent publications demonstrate a strategic shift toward healthcare analytics, particularly examining surgical team dynamics, hospital performance metrics, and resource allocation during public health emergencies like the COVID-19 pandemic. George B. Dantzig Prize (1998) for the best dissertation in operations research and management sciences that is innovative and relevant to practice Adelman regularly advises doctoral and MBA students through the Healthcare Analytics Laboratory at Chicago Booth. He has served as Associate Editor for Management Science, currently serves as Associate Editor for Manufacturing and Service Operations Management, and is the Area Editor for Operations and Supply Chain at Operations Research. His industry collaborations include significant projects with Akamai on internet pricing, with GE Global Research Labs on the electricity smart grid, with BP on gasoline supply contract portfolio optimization, and with Symantec on software release planning. Adelman leads the Healthcare Analytics Laboratory at Chicago Booth, which brings together interdisciplinary teams of doctoral and MBA students to work on a portfolio of projects with major healthcare institutions. The lab focuses on optimizing clinical, operational, and financial outcomes through advanced analytics and decision modeling.
Jim Bildner is an Adjunct Lecturer in Public Policy at Harvard Kennedy School and a Senior Research Fellow at the Hauser Institute for Civil Society and Center for Public Leadership. His academic work focuses on leveraging private capital and philanthropy to address complex societal challenges. AB, Dartmouth College MPA, Harvard University J.D., Case Western Reserve School of Law M.F.A., Lesley University His research spans systemic poverty, food insecurity, healthcare access, and public-private partnerships, with a particular interest in how foundations and social enterprises can sustain impact amid government disinvestment. He co-teaches courses on Public Problem Solving and Philanthropy at Harvard Kennedy School. Mr. Bildner has contributed to media outlets like NPR’s Morning Edition, The Boston Globe, and Inc. Magazine through op-eds and commentaries. His two maritime-themed publications reflect his lifelong passion for sailing. Dartmouth Alumni Award for service to the College and community (2008) He serves on over 20 nonprofit and civic boards, including The Kresge Foundation, WBUR, and Case Western Reserve University. With a background as a CEO, Deloitte consultant, and U.S. Senate aide, he brings multidisciplinary expertise to his academic roles.
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.
