Kaveh Razavi is an Assistant Professor at ETH Zurich in the Department of Information Technology and Electrical Engineering, supervising the Computer Security Group. He is also affiliated with the Department of Computer Science. Research focuses on commodity hardware and operating systems security Teaching: Computer Engineering (BSc 2022–2025), Hardware Security (MSc 2016–2025), Advanced Operating Systems (MSc 2016–2019) Research Interests: Razavi's work bridges computer systems and security, emphasizing microarchitectural vulnerabilities, hardware exploitation, and security verification. His group develops tools for analyzing hardware-software interactions and mitigating memory attacks like Rowhammer. Scientific Awards: Distinguished/Best Paper Awards at IEEE S&P, USENIX Security, MICRO Five Pwnies Awards for innovative research Dutch Veni Grant, ERC Starting Grant Jochen Liedtke Young Researcher Award Golden Owl Teaching Award (2022) ETH Medal, Intel Bounty Reward, BlackHat USA Recognition
Theo Damoulas is a Professor of Machine Learning at the University of Warwick with a joint appointment in the Department of Computer Science and Statistics. He is a Turing AI Fellow (2021-2026) through UK Research and Innovation, an ELLIS member, and a Visiting Professor at New York University's Center for Urban Science and Progress (CUSP). He founded and leads the Warwick Machine Learning Group and has directed major projects at The Alan Turing Institute including Project Odysseus and the London Air Quality project. Education includes: PhD in Probabilistic Multiple Kernel Learning (University of Glasgow, 2009) MSc in Informatics (Distinction, University of Edinburgh, 2004) MEng in Mechanical Engineering (1st Class, University of Manchester, 2003) His research focuses on probabilistic machine learning and Bayesian statistics, emphasizing the integration of structural priors, spatiotemporal dependencies, physical laws, and causal relationships. Key applications include Digital Twins, urban science, and computational sustainability. His work advances robust and scalable inference methodologies for complex real-world systems. Publications demonstrate strong emphasis on Bayesian methods, spatiotemporal modeling, and uncertainty quantification, with applications spanning battery modeling, urban mobility, federated learning, and causal inference. Recent work shows increased focus on physics-informed models, federated learning frameworks, and causal abstraction techniques. Major scientific awards: Turing AI Acceleration Fellowship (2021-2026) Best Paper Awards (Wilkes 2024, AISTATS 2022, IEEE ICMLA 2010) ACM SIGMOD Most Reproducible Paper (2017) Dissertation Award (Classification Society 2012) Teaching Excellence nominations (Warwick 2015-2017) He actively advises PhD students and secured significant grants including the £multi-million Turing AI Fellowship. Current doctoral researchers investigate federated learning, causal inference, and spatiotemporal modeling. He leads the Warwick Machine Learning Group, a cross-departmental team developing foundational ML methods for scientific and societal challenges.
Eric V. Mazumdar is an Assistant Professor at the California Institute of Technology (Caltech), jointly appointed in the departments of Computing and Mathematical Sciences and Economics. He holds a B.S. from MIT (2015) and a Ph.D. from UC Berkeley (2021), co-advised by Michael Jordan and Shankar Sastry. His research bridges machine learning and economics, focusing on deploying algorithms into societal systems through theoretical and practical lenses. Key areas include strategic classification, multi-agent reinforcement learning, and distributionally robust optimization, with applications in healthcare, online markets, and intelligent infrastructure. Education: B.S., Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2015 Ph.D., Electrical Engineering and Computer Science, University of California, Berkeley, 2021 Research Interests: Mazumdar’s work emphasizes understanding learning algorithms in strategic environments, including min-max optimization, game theory, and multi-agent systems. He explores how algorithms interact with human and algorithmic agents in dynamic systems, with practical applications in healthcare delivery, e-commerce, and autonomous systems testing. Awards: NSF CAREER Award (2023) Simons Institute Research Fellowship in Learning in Games Grants & Funding: Supported by NSF, DARPA, Amazon, and other organizations. His NSF CAREER grant focuses on strategic interactions in societal-scale systems. Teaching: Courses include Networks: Structure & Economics (CMS/CS/EE/IDS 144) and Topics in Learning and Games (CMS/Ec 248). At UC Berkeley, he contributed to courses like Data, Inference, and Decisions (DS 102). Students & Postdocs: Current students: Lauren Conger, Tinashe Handina, Yizhou Zhang Postdocs: Zaiwei Chen, Laixi Shi, Kishan Panaganti (co-advised with Adam Wierman)
Ali Feizmohammadi is an Assistant Professor, Teaching Stream (LTA) in the Department of Mathematics at the University of Toronto Mississauga, affiliated with the Mathematical and Computational Sciences division. His research focuses on inverse problems, partial differential equations, and geometric analysis. He holds a position emphasizing teaching excellence within the university's framework. His work addresses advanced mathematical challenges such as coefficient identification in subdiffusion equations, fractional Laplacian problems on Riemannian manifolds, and nonlinear elliptic equations on manifolds. Recent articles highlight contributions to the Calderón problem in various contexts, wave equation control, and spacetime finite element methods. No scientific awards or grants are explicitly listed in the provided information. He has not yet listed advisees in the available data. His research trends emphasize rigorous mathematical analysis of inverse problems in both classical and fractional PDE frameworks, with applications to geometric and control-theoretic questions. Dr. Feizmohammadi's work spans theoretical advancements in inverse problems, numerical methods for control systems, and the interplay between differential geometry and PDEs. His contributions address both fundamental theory and applied methodologies in mathematical physics and engineering.
Ian Frigaard is a Professor in the Department of Mechanical Engineering at the University of British Columbia (UBC), affiliated with the Faculty of Applied Science. He also holds an appointment in the Department of Mathematics. His research group operates in UBC's Complex Fluids Lab, focusing on interdisciplinary studies combining mathematical, experimental, and computational approaches. Education: B.Sc. (University of Wales) M.Sc. (University of Oxford) D.Phil. (University of Oxford) C.Math. (Certificate in Mathematics) Research Interests: Professor Frigaard specializes in non-Newtonian fluid mechanics, particularly the mechanics of visco-plastic (yield stress) fluids. His work addresses industrial challenges in petroleum engineering, including well cementing, leakage prevention, and abandonment techniques related to GHG emission control and environmental protection. Research methodologies span theoretical modeling, experimental validation, and computational simulations. Publication Trends: Recent work (2021–2023) emphasizes bubble dynamics in complex fluids, displacement flows in annular geometries, wellbore integrity modeling, and stochastic risk assessment for oil/gas operations. Publications frequently appear in top-tier journals like the Journal of Fluid Mechanics and Journal of Non-Newtonian Fluid Mechanics . Awards & Honors: CSME Fluid Mechanics Medal (2024) Stanley G. Mason Award, Canadian Society of Rheology (2022) Killam Research Prize, UBC (2019) Academic Leadership: Leads a research group of 10+ graduate students and postdocs. Provides summer internships and collaborates extensively with the petroleum industry. Research is supported by industrial partnerships and institutional grants. Facilities: Conducts experiments in UBC's Complex Fluids Lab, equipped for advanced rheological measurements and flow visualization.
Navid Azizan is the Alfred H. (1929) and Jean M. Hayes Career Development Assistant Professor at Massachusetts Institute of Technology (MIT), holding dual appointments in the Department of Mechanical Engineering (in Control, Instrumentation & Robotics) and the Schwarzman College of Computing's Institute for Data, Systems & Society (IDSS). He is also a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS), and a faculty member of the MIT Statistics and Data Science Center, the Center for Computational Science and Engineering, and the Operations Research Center. Dr. Azizan received his PhD in Computing and Mathematical Sciences from the California Institute of Technology (Caltech) in 2020, his MSc in Electrical Engineering from the University of Southern California in 2015, and his BSc in Electrical Engineering with a minor in Physics from Sharif University of Technology in 2013. Prior to joining MIT, he completed a postdoc at Stanford University's Autonomous Systems Laboratory and was a research scientist intern at Google DeepMind. His research spans the intersection of machine learning, systems and control, mathematical optimization, and network science. Dr. Azizan's work focuses on developing principled learning and optimization algorithms for reliable intelligent systems, with applications to autonomy and sociotechnical systems. His research has significant implications for creating trustworthy AI systems that can operate effectively in complex, uncertain environments. Dr. Azizan's recent publications demonstrate a strong focus on uncertainty quantification, reliable AI systems, constrained optimization, and control-oriented learning. His work bridges theoretical foundations with practical applications, particularly in autonomous systems where safety and reliability are paramount. His research group has made notable contributions to areas including neural network verification, multi-agent reinforcement learning, and adaptive inference techniques for large language models, with several papers featured on MIT News and selected for oral presentations at top conferences. Alfred H. (1929) and Jean M. Hayes Career Development Professorship (2025-present) Frank E. Perkins Award for Excellence in Graduate Advising (2025) List of Outstanding Academic Leaders in Data from the CDO Magazine (2024, 2023) Amazon Science Hub Research Award (2023) Outstanding UROP Faculty Mentor (2023) Esther and Harold E. Edgerton (1927) Career Development Chair (2022-2025) Information Theory and Applications (ITA) Gold Graduation Award (2020) Dr. Azizan has been recognized for his excellence in graduate advising, receiving the Frank E. Perkins Award for Excellence in Graduate Advising in 2025. During the pandemic, he founded and co-organized the 'Control meets Learning' virtual seminar series, connecting researchers across disciplines. His work has attracted significant research funding from industry partners including Google, Amazon, and MathWorks, supporting both fundamental research and practical applications in reliable intelligent systems. The Azizan Lab at MIT brings together researchers from mechanical engineering, computer science, and applied mathematics to tackle challenges at the intersection of learning and control. The lab emphasizes both theoretical foundations and practical implementations, with a particular focus on developing algorithms that provide guarantees of performance and safety. Current research directions include uncertainty quantification in AI systems, constrained optimization for neural networks, and control-oriented learning for autonomous systems, with applications spanning robotics, transportation, and complex sociotechnical systems.
Dr. Ly Fie Sugianto is an Associate Professor in the Department of Accounting at Monash Business School, Monash University. Her research focuses on the integration of data analytics, artificial intelligence, and machine learning in accounting and business systems, with applications in the energy sector and organizational behavior. Monash Business School, Monash University Department of Accounting Specialization: Accounting Information Systems, Data Analytics, AI Her research interests span Accounting Information Systems , Agent-Based Simulation , Decision Support Systems , and Technology Adoption . She applies computational methods to study competitive dynamics in deregulated electricity markets and the impact of digital tools on employee well-being and organizational resilience. The recent publications reflect a strong trend in using AI and simulation to analyze complex socio-technical systems, particularly in energy markets and leadership dynamics. Keywords across her work include agent-based modeling , data analytics , servant leadership , and enterprise social media , indicating interdisciplinary research at the intersection of information systems, management, and public policy. Her scientific awards include competitive grants from the ARC (SPIRT/Linkage) , the Australia Indonesia Governance Research Partnership (AIGRP) , and the Sumitomo Foundation . ARC Grant: Dispatch Optimisation in the Australian National Electricity Market Sumitomo Foundation: Technology Use and Employee Well-Being AIGRP: Governance and MSME Resilience during Pandemic Dr. Sugianto has advised research projects and collaborated with industry partners such as Western Power , Ecogen Energy , and AEMO . She is currently accepting PhD students and leads externally funded research initiatives. Her work contributes to UN Sustainable Development Goals related to industry innovation and responsible consumption. She is affiliated with research teams focusing on intelligent decision support systems and digital transformation in business , with active collaborations in Australia and Indonesia.
Peter Selinger is a Professor in the Department of Mathematics and Statistics at Dalhousie University , with a cross-appointment in Computer Science. He specializes in mathematical methods in computer science, particularly quantum computing and combinatorial game theory . His work on quantum programming languages like Quipper and foundational research in category theory has garnered international recognition. Education : Ph.D. in Mathematics (University of Pennsylvania, 1997), undergraduate studies in Mathematics (Technische Universität Darmstadt). Research Interests span quantum computing, category theory, and combinatorial game theory. He has pioneered formalisms for quantum programming languages, developed categorical models for quantum mechanics, and analyzed game-theoretic structures in games like Hex. His recent work includes linear dependent type theory , quantum circuit synthesis , and combinatorial game classification . Publications demonstrate expertise in quantum programming languages, categorical semantics, and game theory. Key trends include Hamiltonian simulation , Clifford+T circuits , and monotone game realization . Scientific Honors include the Killam Professorship (2017–2022), Faculty of Science Award for Excellence in Teaching (2023), and fellowships from the Alfred P. Sloan Foundation and German National Scholarship Foundation . Students he has supervised include PhD graduates Xiaoning Bian , Francisco Rios , and Neil J. Ross , along with MSc students like Fahimeh Bayeh and Seth Greylyn . He has advised 16 postdoctoral researchers.
M. Granger Morgan is the Hamerschlag University Professor of Engineering at Carnegie Mellon University , with appointments in the Department of Engineering and Public Policy , Department of Electrical and Computer Engineering , and H. John Heinz III College . He co-directs the NSF Center for Climate and Energy Decision Making and the Electricity Industry Center at CMU. Education: Ph.D., Applied Physics and Information Science, University of California, San Diego (1969) M.S., Astronomy and Space Science, Cornell University (1965) B.A., Physics, Harvard College (1963) His research spans science, technology, and public policy with focus areas in energy systems , climate change mitigation , electric grid resilience , and uncertainty characterization in policy analysis . Recent publications analyze hydrogen market barriers , carbon sequestration timelines , and interdependent energy infrastructure risks . Scientific leadership includes: Member, National Academy of Sciences Member, American Academy of Arts and Sciences Co-chair, NAS Report Review Committee Board member, International Risk Governance Council Foundation Advisory Board, E.ON Energy Research Center, RWTH Aachen DOE Electricity Advisory Committee member Former EPA Science Advisory Board Chair Fellow of AAAS, IEEE, and Society for Risk Analysis Contact: Office 5220 Wean Hall, Phone 412-268-2672, Email granger.morgan@andrew.cmu.edu
Andrew Spakowitz is a Professor of Chemical Engineering, Materials Science and Engineering, and by courtesy, Applied Physics and Chemistry at Stanford University. He currently serves as the Senior Associate Dean for Research and Faculty Affairs and holds the Tang Family Foundation Chair of the Department of Chemical Engineering. His academic career at Stanford spans from Assistant Professor (2006-2014) to Associate Professor (2014-2020) and now Professor since 2020. Dr. Spakowitz earned his PhD in 2004, MS in 2001 from the California Institute of Technology, and his BS in Chemical Engineering from the University of Wisconsin, Madison in 1999. He completed postdoctoral training in Molecular and Cell Biology and Biophysics at UC Berkeley from 2004-2006. His research focuses on theoretical and computational approaches to understanding biological processes and complex materials. The Spakowitz lab addresses fundamental chemical and physical phenomena through four main research themes: chromosomal organization and dynamics, protein self-assembly, polymer membranes, and charge transport in conducting polymers. His group employs diverse theoretical and computational methods including analytical theory of semiflexible polymers, polymer field theory, continuum elastic mechanics, Brownian dynamics simulation, equilibrium and dynamic Monte Carlo simulations, and reaction-diffusion modeling. Analysis of his recent publications reveals a strong emphasis on epigenetics and chromatin dynamics, with significant work on DNA methylation patterns, nucleosome clustering, and chromosome organization. His research also extends to polymer physics applications in biological systems, particularly in respiratory diseases, water purification membranes, and bacterial phage interactions with human mucus. Tang Family Foundation Chair of the Department of Chemical Engineering Professor Spakowitz mentors several graduate students and postdoctoral scholars in the Chemical Engineering and Materials Science departments. His lab members work on diverse projects spanning from chromatin dynamics to polymer membranes for water purification. He teaches multiple courses including CHEMENG 120B (Energy and Mass Transport), CHEMENG 340 (Molecular Thermodynamics), CHEMENG 466 (Polymer Physics), and CHEMENG 467 (Physics of Biomacromolecules). The Spakowitz lab operates from Clark S295 at Stanford University, conducting theoretical and computational research that bridges chemistry, physics, biology, and engineering disciplines to address complex problems across multiple length and time scales.
Illya V. Hicks is a Professor in the Computational and Applied Mathematics Department at Rice University. He holds a PhD from Rice University (2000) and a BS from Texas State University (1995). His research focuses on combinatorial optimization, integer programming, graph theory, and matroid theory, with applications in social networks, cancer treatment, and network design. He has advised numerous doctoral, post-doctoral, and masters students. Education: PhD and MA in Computational and Applied Mathematics, Rice University, 2000 BS in Mathematics, Texas State University, 1995 Research Interests: Utilizing graph decomposition techniques to solve NP-complete problems, including branch decompositions and matroid circuit problems. Applications include sensor network design, healthcare logistics, and algorithmic graph theory. Awards: Recognized with the 2015 Presidential Mentoring Award (Rice University), 2010 Forum Moving Spirit Award (INFORMS), and the 2005 Optimization Prize for Young Researchers. Grants and Projects: Includes NSF-funded research on branch decomposition techniques, submodular optimization, and healthcare service distribution. Active in promoting minority participation in operations research through travel grants and mentoring initiatives. Labs/Teams: Engaged in collaborative research on graph algorithms, combinatorial optimization, and interdisciplinary applications in healthcare and engineering.
John M. Woodley is a distinguished Professor in the Department of Chemical and Biochemical Engineering at the Technical University of Denmark (DTU), where he leads research at the PROSYS - Process and Systems Engineering Centre and contributes to the DTU Microbes Initiative. With over 30 years of experience, he has established himself as a leading expert in biocatalysis and bioprocess engineering, with research spanning both theoretical and experimental work across multiple scales. His primary research interests focus on the interface of bioprocess engineering, process chemistry, and reaction engineering. Dr. Woodley's work encompasses multi-step biocatalysis (including systems biocatalysis and flow chemistry), downstream processing from biocatalytic reactors and fermentations (including ISPR), modeling tools for bioprocess assessment (thermodynamics, kinetics, process simulation, economic evaluation), and bio-oxidations (including oxygen supply methods). His enzymatic investigations particularly target alcohol oxidases, carbohydrate oxidases, cytochrome P450s, Baeyer-Villiger monooxygenases, and transaminases. His research portfolio demonstrates consistent innovation in sustainable chemical production, with particular emphasis on enzymatic synthesis of pharmaceuticals and chemicals from renewable resources. Analysis of his recent publications reveals a strong focus on overcoming industrial implementation challenges, particularly regarding enzyme stability in various reactor environments, optimization of multi-enzyme systems, and scale-up methodologies for biocatalytic processes. Dr. Woodley actively supervises multiple PhD students and leads several significant research projects, including 'P450-based biocatalytic processes for the pharmaceutical industry' (2025-2028), 'Integrated model for up- and downstream bioprocess intensification' (2024-2027), and 'ENFACE: A tool for prediction of enzyme stability at gas-liquid interfaces' (2024-2027). His work has resulted in an impressive publication record of 781 research outputs across various formats, including journal articles, book chapters, and conference proceedings. His research group operates within the Department of Chemical and Biochemical Engineering at DTU, utilizing advanced facilities for biocatalysis research, including specialized reactor systems for studying gas-liquid interfaces, computational modeling resources, and laboratories for enzyme characterization and bioprocess development. Through his leadership in the PROSYS center, he contributes to DTU's strategic focus on sustainable process technologies and systems engineering.
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.
Richard E. Turner is a Professor of Machine Learning at the University of Cambridge's Department of Engineering and Research Lead for AI for Weather Prediction at the Alan Turing Institute. He serves as Cambridge Lead for the EPSRC Probabilistic AI Hub and previously held roles including Visiting Researcher at Microsoft Research, Co-Director of the AI4ER CDT, and Course Director for the Machine Learning and Machine Intelligence MPhil program. Current research focuses on probabilistic machine learning fundamentals, environmental prediction (weather/climate), and spatio-temporal modeling combining deep learning with Bayesian methods Supervised 26 PhD students (13 graduated) and 7 research assistants/associates Secured over £30M in research funding from EPSRC, Microsoft, Toyota, Google, DeepMind, Amazon, and Improbable Featured in BBC Radio 5 Live's The Naked Scientist, BBC World Service's Click, and Wired Magazine His recent publications demonstrate expertise in diffusion models for PDE simulations, Gaussian Processes for environmental applications, and Bayesian methods for spatio-temporal forecasting. Key trends include climate modeling using ML, neural PDE solvers, and scalable probabilistic inference. Awards : Cambridge Students' Union Teaching Award for Lecturing; supervised Qualcomm Innovation Fellowship winner Collaborations : Microsoft Research (AI4Science), Alan Turing Institute, EPSRC Probabilistic AI Hub Turner leads the Turner Group within Cambridge's Machine Learning Group, focusing on uncertainty-aware ML for scientific applications. Current research assistants work on topics like meta-learning, Bayesian inference, and climate science applications.
Amir Ali Ahmadi is a Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE), with affiliations across multiple disciplines including PACM, Computer Science, Mechanical & Aerospace Engineering, Electrical Engineering, and the Center for Statistics and Machine Learning. He serves as Director of Princeton's Optimization and Quantitative Decision Science Certificate Program and has taken temporary roles at Citadel GQS (2021-2022) and Google Brain (2020-2021). His research bridges optimization theory , dynamical systems , and control theory , focusing on scalable algorithms for complex problems in robotics, autonomous systems, and machine learning. He has pioneered DSOS/SDSOS relaxations as alternatives to traditional sum-of-squares methods, enabling faster solutions through linear/second-order cone programming. Recent publications explore: Higher-order Newton methods for socially responsible investment Data-efficient learning of dynamical systems Computational complexity of local minima Robust-to-dynamics optimization frameworks Award highlights include: 2024 Egon Balas Prize in Optimization 2024 Princeton Engineering Council Teaching Award 2023 Distinguished Teaching Award (Princeton SEAS) 2019 NSF CAREER Award 2017 DARPA Young Faculty Award 2017 Sloan Fellowship in Computer Science He advises prominent researchers like Georgina Hall (Tucker Prize finalist) and Bachir El Khadir (Goldstine Fellow), and leads the Princeton Optimization Seminar and MURI project on Control-Oriented Learning on the Fly.
