Professor Hong Hao is a John Curtin Distinguished Professor at Curtin University, affiliated with the School of Civil and Mechanical Engineering and the Curtin Research Centre for Infrastructural Monitoring & Protection. His expertise spans Structural Dynamics, Earthquake Engineering, Blast and Impact Engineering, and Structural Health Monitoring. He holds prestigious roles like Fellow of ATSE, ISEAM, and ASCE, and has led organizations such as the International Association of Protective Structures and the Australian Earthquake Engineering Society. Education: BE (Tianjin University, 1982), MSc (UC Berkeley, 1985), PhD (UC Berkeley, 1989). Awards include the Tan Chin Tuan Fellowship and multiple Ko Medals. He has authored over 200 journal articles, with recent work focusing on blast-resistant materials, seismic fragility, and AI-driven structural health monitoring. His research emphasizes resilient infrastructure, including metaconcrete structures, corrosion-resistant materials, and sensor-based damage detection. Ongoing projects involve smart tunnel safety under BLEVE explosions and modular building systems.
Prof. Dr. Steffen Marburg is a Full Professor at the Chair of Acoustics of Mobile Systems within the TUM School of Engineering and Design at the Technical University of Munich. His research focuses on numerical methods in vibroacoustics, structural optimization, and acoustic modeling for applications in automotive, maritime, and musical instrument domains. Education: PhD from Technical University of Dresden (1998). Academic Career: Junior Professor at TU Dresden (2004), Chair of Technical Dynamics at University of the Federal Armed Forces Munich (2010), Full Professor at TUM (2015–present). Editorial Roles: Co-Editor-in-Chief of Journal of Theoretical and Computational Acoustics, Associate Editor of Journal of the Acoustical Society of America, Editor of Acoustics Australia and Mechanical Systems and Signal Processing. His research integrates computational acoustics, boundary element methods, and machine learning to address noise control and structural optimization challenges. Recent work explores acoustic metamaterials, viscothermal losses, and data-driven modeling. He has co-authored over 150 publications and led advancements in multifrequency solution methods and noise-insulating structures. Scientific awards include the Innovation Award of the Industrieclub Sachsen e.V. (1999). His editorial contributions and leadership in journals highlight his influence in computational acoustics and structural dynamics.
Monika Henzinger is Professor at the Institute of Science and Technology Austria (ISTA), heading the research group of Theory and Applications of Algorithms. She also serves as Vice President for Technology Transfer at ISTA since 2024. Previously, she held professorships at the University of Vienna (2009-2023) and EPFL, Switzerland (2005-2009), was Director of Research at Google (1999-2005), and served as Assistant Professor at Cornell University. Professor Henzinger's research centers on efficient algorithms and data structures with three main thrusts. First, she investigates dynamic settings where program inputs are repeatedly updated, seeking solutions faster than restarting computations. Second, she develops privacy-preserving algorithms that add minimal noise to protect input data while maintaining efficiency. Third, she translates theoretically optimal algorithms into practical implementations for dynamically changing inputs. Her work consistently addresses resource conservation in data processing, particularly computing time and memory space, while exploring the theoretical limits of possible savings. Henzinger's recent publications (2024-2025) reveal strong trends in dynamic algorithms, differential privacy, and graph theory. Her research consistently bridges theoretical computer science with practical applications, focusing on algorithms that adapt to changing inputs while preserving computational efficiency and data privacy. She has made significant contributions to problems like dynamic matching, minimum cut computation, and privacy-preserving data analysis across various domains. Professor Henzinger has received numerous prestigious awards and honors: Wittgenstein Award (2021) Two ERC Advanced Grants (2014, 2021) Carus Medal of the German Academy of Sciences Leopoldina (2019) SIGIR Test of Time Award Fellow of the Association of Computing Machinery (2016) Member of the Austrian Academy of Sciences (2017) CAREER Development Award of the National Science Foundation Best paper Award at the Symposium on Discrete Algorithms (2024) Professor Henzinger currently advises PhD students Bardiya Aryanfard, Antoine El-Hayek, and Roodabeh Safavi Hemami, along with postdocs Anamay Chaturvedi and Niklas Hahn. Her research is supported by multiple significant grants including an ERC Advanced Grant for 'Design and Evaluation of Modern, Fully Dynamic Data Structures,' the FWF Wittgenstein Prize, and the WEAVE Project on 'Static and dynamic hierarchical graph decompositions.' She also serves as Principal Investigator for the FWF project 'Fast algorithms for a reactive network layer,' providing substantial funding for her innovative work in algorithms and data structures. Professor Henzinger leads the Theory and Applications of Algorithms research group at ISTA, which focuses on developing practical algorithms for dynamic environments. Her team investigates resource conservation in data processing, specializing in dynamic algorithms that efficiently handle changing inputs, privacy-preserving algorithms that minimize noise while protecting data, and translating theoretical algorithms into practical implementations. The group maintains a strong presence in theoretical computer science through regular publications in top conferences and journals, and collaborates extensively with institutions worldwide to advance algorithmic research.
Dr. Samir H. Mushrif is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta . Prior to this role, he served as faculty at the School of Chemical and Biomedical Engineering at Nanyang Technological University (NTU), Singapore . He holds a PhD in Chemical Engineering from McGill University and completed postdoctoral research at the University of Delaware, USA . Education : PhD (Chemical Engineering, McGill University), Postdoc (University of Delaware) His research focuses on computational catalysis , molecular modeling , and reaction engineering for biomass conversion and CO2 reduction . He develops novel catalysts, solvents, and reactor systems using integrated quantum mechanical and classical molecular simulations , synergized with experimental data to enable sustainable energy and chemical production . Recent publications highlight trends in condensed phase chemistry for biomass reactions, machine learning applications in solvent configuration prediction, and mechanistic studies of lignin-carbohydrate complex deconstruction. His work bridges methane activation on metal oxides, hydrodeoxygenation of bio-oil compounds, and polymerization pathways in lignin structures. Scientific Awards include: NSERC Doctoral and Post-doctoral Fellowships Discovery International Award 2017 (Australian Research Council) NANYANG EDUCATION AWARD 2016 (Singapore) SCBE Teaching Excellence Awards (Silver 2015, Gold 2016) Bharat Gaurav (Pride of India) Award 2014 Dr. Mushrif's NSERC Discovery Grant (2018), CFI John R. Evans Leaders Fund Grant (2022), and AcRF Tier-2 Grant (Singapore, 2015) have advanced his work. Current PhD and Master's students include José Carlos Velasco Calderón , Arul Mozhi Devan Padmanathan , and Sagar Bathla , among others. The CARES Lab (Catalysis Research for Sustainability) under his leadership combines ab initio molecular dynamics , machine learning potentials , and Density Functional Theory to design materials for renewable energy . Collaborations span institutions in France , Canada , India , and the UK .
Steven L. Manly is a Professor of Physics at the University of Rochester within the College of Arts, Sciences and Engineering. He has been affiliated with the University of Rochester since 1998, following a decade at Yale University as both a postdoc and faculty member. Professor Manly received his BA in chemistry, mathematics, and physics from Pfeiffer College in 1982 and his PhD in experimental high-energy physics from Columbia University in 1989 under Charles Baltay. His research spans high energy, nuclear, and gravitational physics, with a current focus on neutrino physics across multiple major experiments. His primary research interests include neutrino interactions and oscillations, with significant contributions to the T2K experiment (for which he shared the 2016 Breakthrough Prize in Fundamental Physics), the MINERvA experiment at Fermilab, and the Deep Underground Neutrino Experiment (DUNE). His work aims to understand neutrino properties, measure oscillation parameters, and investigate potential connections to matter-antimatter asymmetry in the universe. The recent publications reflect a strong focus on neutrino cross-section measurements, detector calibration techniques, and data analysis methods for the T2K and DUNE experiments. His research group contributes significantly to advancing our understanding of neutrino properties and interactions through precision measurements. NY State Professor of the Year (2003) Mercer Brugler Distinguished Teaching Professor (2002-2005) American Association of Physics Teachers (AAPT) Award for Excellence in Undergraduate Teaching (2007) Breakthrough Prize in Fundamental Physics (2016, shared as member of T2K) Professor Manly has authored or co-authored numerous publications in leading physics journals, with recent work focusing on neutrino interaction measurements, detector development, and data analysis techniques. His research has involved collaborations with major international facilities including Fermilab, J-PARC in Japan, and Brookhaven National Laboratory. While specific grant information isn't detailed in the provided text, his participation in large-scale international collaborations suggests significant research funding support.
Margaret E. Roberts is a Professor in the Department of Political Science at the University of California, San Diego. She co-directs the China Data Lab at the 21st Century China Center and serves as an affiliate at the UC Institute on Global Conflict and Cooperation. Her academic appointments reflect her interdisciplinary approach combining political science, statistics, and computational methods. University of California, San Diego, Department of Political Science (Current) Co-director, China Data Lab at the 21st Century China Center Affiliate, UC Institute on Global Conflict and Cooperation Roberts earned her PhD in Government from Harvard University (2014), MS in Statistics from Stanford University (2009), and BA in International Relations and Economics from Stanford University (2009). Her educational background bridges political science, statistics, and computational methods, forming the foundation for her interdisciplinary research approach. Professor Roberts' research focuses on the intersection of political methodology and the politics of information, with specific expertise in automated content analysis and the politics of censorship and propaganda in China. Her work employs innovative methods including social media analysis, online experiments, and large-scale text analysis to understand how censorship and propaganda influence information access and political beliefs. She has made significant contributions to text-as-data methodologies, developing tools like the Structural Topic Model (stm) R package that have become widely used in social science research. Roberts' research portfolio demonstrates consistent focus on authoritarian information control, particularly in China, while expanding into broader applications of text analysis in political science. Her publications span top journals in political science, computer science, and interdisciplinary fields, reflecting the cross-disciplinary nature of her work. Goldsmith Book Award Best Book Award in the Human Rights Section Best Book Award in Information Technology and Politics Section Best Book Award of the last decade in the Political Communication Section of the American Political Science Association Chancellor's Associates Endowed Chair at UCSD Foreign Affairs Best Books of 2018 Professor Roberts has secured significant research funding supporting her work on Chinese censorship, propaganda, and text analysis methodologies. Her research has practical applications for understanding digital authoritarianism, content moderation, and the development of computational tools for social science research. She has mentored numerous students and collaborators, contributing to the next generation of scholars working at the intersection of political science and computational methods. Roberts also leads the China Data Lab, which serves as a hub for research on Chinese politics and society using digital methods.
Jan G. Voelkel is an Assistant Professor at the Jeb E. Brooks School of Public Policy and the Department of Sociology at Cornell University. His research explores how micro-level preferences for equality and unity translate into macro-level political decisions, focusing on democratic attitudes, partisan dynamics, and moral framing. Ph.D. and M.A. in Sociology, Stanford University M.S. in Social and Behavioral Sciences, Tilburg University B.S. in Social Sciences, University of Cologne Voelkel’s work spans political psychology, metascience, and social policy, with a focus on interventions to reduce anti-democratic attitudes, partisan animosity, and gender bias in political leadership. His recent articles emphasize large-scale collaborations, reproducibility, and cross-partisan empathy. Scientific awards include: New Investigator Award (Behavioral Science & Policy Association) Public Sociology Award (American Sociological Association) Open Science Innovator Award (Stanford) Centennial Teaching Assistant Award (Stanford)
Daniel A. Spielman is a Sterling Professor of Computer Science, Statistics & Data Science, and Mathematics at Yale University. He serves as the inaugural James A. Attwood Director of the Institute for Foundations of Data Science (FDS) and was previously co-Director of the Yale Institute for Network Science (YINS). He is also a member of TILOS, the NSF Institute for Learning-Enabled Optimization at Scale. His research interests span Spectral Graph Theory, Algebraic Graph Theory, Laplacian Matrices, Expander Graphs, and Random Walks on Graphs. Spielman has made fundamental contributions to understanding the mathematical foundations of data science, including work on smoothed analysis of algorithms, spectral sparsification, and solutions to the Kadison-Singer problem. Spielman's publications demonstrate a consistent focus on developing efficient algorithms with strong theoretical foundations. His work bridges pure mathematics, theoretical computer science, and practical applications in network analysis and machine learning. His research has evolved from foundational work on smoothed analysis to recent contributions in experimental design using discrepancy theory. His scientific honors include: Nevanlinna Prize for contributions to mathematical aspects of computer science Two Gödel Prizes (2008 and 2015) for outstanding papers in theoretical computer science MacArthur Fellowship ('Genius Grant') Simons Investigator award Membership in the National Academy of Sciences and American Academy of Arts and Sciences Spielman has advised numerous PhD students who have gone on to prominent positions in academia and industry. His teaching includes advanced courses in Spectral Graph Theory and Computation and Optimization. He has developed important software packages like Laplacians.jl for solving Laplacian linear equations and related problems.
Emmanuel J. Candès is the Barnum-Simons Chair in Mathematics and Statistics at Stanford University, with joint appointments in the Institute of Computational and Mathematical Engineering and as Professor of Statistics and Electrical Engineering (by courtesy). His research spans mathematical signal processing , high-dimensional statistics , and data science , focusing on compressive sensing, inverse problems, and applications to imaging sciences. 2021 IEEE Jack S. Kilby Signal Processing Medal 2020 Princess of Asturias Award for Technical and Scientific Research 2017 MacArthur Fellow His recent work on conformal prediction and uncertainty quantification has advanced machine learning reliability, particularly in high-dimensional settings. Publications include breakthroughs in medical imaging, gravitational wave detection, and AI validation frameworks. Key collaborations include Terence Tao (UCLA) and Justin Romberg (Georgia Tech) for IEEE Kilby Medal recognition. He serves as Co-chair of Stanford's Data Science Institute and previously as Statistics Department Chair (2016–2019).
John Baillieul is Distinguished Professor at Boston University with joint appointments in Mechanical Engineering, Systems Engineering and Electrical & Computer Engineering. He directs experimental laboratories for real-time control of lightweight robotic systems and applies nonlinear control theory to complex multi-body, networked, and bio-inspired systems. Education: Ph.D., Harvard University Research Interests: Baillieul’s work spans robotics, nonlinear control, and networked systems. Early contributions resolved motion-planning for kinematically redundant manipulators; current themes include neuromimetic learning, vision-based navigation, and resilience of infrastructure networks such as power grids. His group couples rigorous geometric control with real-time hardware to create lightweight, high-performance robots and to uncover fundamental information limits in feedback systems. Recent Publication Trends (2021-2025): Over the past five years his output has concentrated on three synergistic directions: (i) neuromimetic and Koopman-based data-driven methods for estimating and controlling nonlinear systems, (ii) vision-based guidance and sparse optical-flow primitives for agile autonomous flight, and (iii) network-theoretic decomposition and information-rate studies for resilient operation of power grids and collective dynamics. Honors & Awards: IEEE Fellow 2025 Roger W. Brockett Control Systems Award Former Editor-in-Chief, IEEE Transactions on Automatic Control Affiliations & Service: He is a member of Boston University’s Center for Information and Systems Engineering (CISE), has served as Editor-in-Chief of IEEE Transactions on Automatic Control, and remains active in editorial and organizational roles across the IEEE control systems community.
Joey W Huston is a Professor at the Department of Physics & Astronomy, Michigan State University, and a Visiting Professor at the Institute for Particle Physics Phenomenology, Durham University. He has over 600 publications with >30,000 citations, including 9 papers with >500 cites each. Positions: MSU Research Foundation Professor (1998-present), Visiting Professor at Durham (2003-present) Education: Ph.D. (1983) and B.S. (1976) from University of Rochester and Carnegie-Mellon University His research focuses on Quantum Chromodynamics (QCD) , Parton Distribution Functions , and Jet Physics . He contributes to Higgs Boson studies, Supersymmetry , and Dark Matter Searches via the ATLAS experiment at the LHC. Recent articles (2025) highlight advancements in Jet Flavour Tagging , Top-Quark Mass Measurement , and Exotic Higgs Decays , alongside computational innovations like Neural Simulation-Based Inference and Cloud Resource Optimization . Scientific Awards: APS Fellow (2025), Distinguished Visitor by Scottish Universities Physics Alliance (SUPA) Prof. Huston has co-spoken for the CTEQ collaboration and organized workshops at Les Houches, Kavli Institute, and Fermilab. He authored the Handbook on Perturbative QCD and is writing a book on QCD at the LHC for Oxford University Press.
James A. Evans is a Professor at the University of Chicago, where he serves as Director of the Knowledge Lab and Faculty Director of the Masters Program in Computational Social Science. He is also an External Professor at the Santa Fe Institute. His research bridges computational methods with social theory to analyze collective cognition, innovation, and knowledge production across science, technology, and broader societal domains. Director, Knowledge Lab Faculty Director, Masters Program in Computational Social Science External Professor, Santa Fe Institute Evans’s research explores how social and technical institutions shape discovery processes, utilizing machine learning, network modeling, and large-scale data analysis. His work spans fields like computational social science, sociology of science, and data science, focusing on team dynamics, peer review, and the global structure of scholarship. His recent publications examine team size effects on innovation, discursive influence in academia, and the interplay between tradition and novelty in research strategies. Articles trend toward interdisciplinary approaches combining social theory, computational methods, and science policy. Evans supports novel observatories for human understanding through crowdsourcing, sensor networks, and semantic modeling. He has received funding from the National Science Foundation, National Institutes of Health, and Air Force Office of Scientific Research, with findings featured in major media outlets like Nature , Science , and The New York Times .
Aaron D. Ames is the Bren Professor of Mechanical and Civil Engineering, Control and Dynamical Systems, and Aerospace at the California Institute of Technology (Caltech). He serves as the Booth-Kresa Leadership Chair and Director of the Center for Autonomous Systems and Technologies since 2025. His academic journey includes a BS in Mechanical Engineering and BA in Mathematics from the University of St. Thomas (2001), followed by an MA in Mathematics and a PhD in Electrical Engineering and Computer Sciences from UC Berkeley (2006). Research Interests span theoretical and experimental work in hybrid systems, nonlinear control, and bipedal robotic locomotion. Specific focus areas include: Control Barrier Functions (CBFs) for safety-critical systems Powered prostheses and robotic assistive devices Legged locomotion for bipedal and quadrupedal robots Cyber-physical systems and autonomous control Model predictive control and real-time optimization Humanoid robotics and geometric safety filters Recent publications emphasize risk-aware control, safety verification, and locomotion on constrained environments, with applications to drones, exoskeletons, and quadrupedal robots. His work often integrates theoretical advancements with practical validation through the AMBER Lab. Scientific Awards & Honors : NSF CAREER Award (2010) Donald P. Eckman Award (2015) Leon O. Chua Award (2005) Bernard Friedman Prize (2006) Teaching includes graduate courses on nonlinear control, hybrid systems, and robotics at Caltech, alongside prior roles at Georgia Tech and Texas A&M. He leads the AMBER Lab (Bipedal Robotics) and has secured major grants from the National Science Foundation, NASA, and industry partners like Miso Robotics and SRI International.
Prof. Anya Belz is Full Professor of Computer Science at Dublin City University's School of Computing and Science Lead at ADAPT Research Centre. A leading NLP researcher with PhD-level expertise, she specializes in natural language generation, evaluation methodologies, and multimodal systems. Recipient of multiple best paper awards and NAACL Test of Time Award nomination. Research innovations include foundational work on statistical language generation (deployed in weather forecasting systems), comparative evaluation frameworks, vision-language integration, and reproducibility quantification. Current EPSRC-funded ReproHum project coordinates 20 global labs studying evaluation consistency. Achievements : Developed industry-deployed generation systems for accessibility applications Pioneered cross-modal alignment techniques for image description Authored 100+ publications spanning generation, evaluation, and reproducibility
Frede Blaabjerg is a Professor at Aalborg University (AAU Energy) , affiliated with the Faculty of Engineering and Science . Since 1998, he has pioneered power electronics research in applications such as wind turbines , photovoltaic (PV) systems , reliability engineering , and Power-2-X technologies. Education : PhD in Electrical Engineering (1995, Aalborg University) Honorary Degrees : Honoris Causa at University Politehnica Timisoara (2017) and Tallinn Technical University (2018) His research focuses on power electronics control , system optimization , and reliability for renewable energy and electric mobility . Recent work includes grid-forming converters , virtual synchronous generators , and smart EV charging systems. Key publication trends span 15+ years , with over 3,733 peer-reviewed articles and 900+ journal papers in power electronics , renewables , and energy storage . Notable book series: Control of Power Electronic Converters and Systems (4 volumes, Elsevier). Scientific Awards : 46 IEEE Prize Paper Awards 2020 IEEE Edison Medal 2019 Global Energy Prize 2014 IEEE William E. Newell Power Electronics Award Leadership Roles : Editor-in-Chief, IEEE Transactions on Power Electronics (2006–2012) Chairman, Danish Council for Research and Innovation Policy (2020–) President, IEEE Power Electronics Society (2019–2020)
