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Achille Varzi is a Full Professor of Philosophy at Columbia University since 2006, specializing in analytic metaphysics, logic, and mereology . His academic journey includes an Assistant Professorship at Columbia (1995–1999) and research roles in Artificial Intelligence at Trento’s Institute for Scientific and Technological Research (1989–1995). He earned his PhD from the University of Toronto (1994) under Hans G. Herzberger and holds an honorary professorship at Trento. Research Focus: Leading authority in mereology , exploring part-whole relations through numerous publications, edited volumes, and a forthcoming book with A.J. Cotnoir. His work bridges logic, ontology, and philosophy of language. Public Engagement: Active as a public intellectual with media contributions and a philosophical TV program on Italian national television. Publications Trends: Recent works emphasize classical mereology , ontological neutrality , and vagueness . Earlier contributions include foundational texts on spatial reasoning, identity, and paradoxes in logic. Scientific Awards: Recognized with prestigious fellowships and prizes in Italy and Canada, including the David Savan Dissertation Prize for his groundbreaking PhD thesis. Editorial Leadership: Serves as editor of The Journal of Philosophy since 2001.

• Analytic Metaphysics
• Mereology
• Logic
• +5 more

Victor Vianu is a Professor of Computer Science and Engineering at the University of California, San Diego and holds the INRIA International Chair at INRIA-Saclay in Paris. He has maintained continuous faculty status at UC San Diego since 1983 while developing extensive international collaborations, particularly with French research institutions including INRIA, ENST-Paris, ENS-Paris, and the University of Paris. His academic credentials include a Ph.D. in Computer Science from the University of Southern California (1983) and undergraduate studies in Mathematics and Informatics at the University of Bucharest (1974-1977). Professor Vianu's research spans computational logic, database systems and theory, and automatic verification. His work uniquely bridges theoretical foundations with practical applications in XML processing, workflow systems, and data-driven applications. He has made seminal contributions to understanding the theoretical underpinnings of database query languages and their expressive power, particularly in the context of XML technologies and workflow systems. His research demonstrates a consistent trajectory from theoretical computer science to practical database systems applications. His publication record reveals significant contributions to database theory spanning over three decades, with particular emphasis on XML technologies, workflow systems, and formal methods for data-driven applications. His work shows a clear evolution from foundational theoretical work to practical applications in business processes and web technologies. INRIA International Chair (2013) Fellow of the American Association for the Advancement of Science (AAAS) (2013) ACM PODS Alberto O. Mendelzon Test-of-Time Award (2010) Fellow of the Association for Computing Machinery (ACM) (2006) Professor Vianu has held significant leadership roles including Editor-in-Chief of the prestigious Journal of the ACM, numerous program committee chair positions for major database conferences (PODS, ICDT, ASIAN), and General Chair for ACM SIGMOD conferences. He has served on the executive committees of SIGMOD (1998-2000) and PODS (1993-2004), and was a member of the ICDT Council (1997-2007), demonstrating sustained influence in the theoretical database community. His extensive invited talks at major conferences including College de France, ACM PODS, and International Conference on Database Theory highlight his international recognition.

• Computational Logic
• Database Systems
• Database Theory
• +4 more

YooJung Choi is an Assistant Professor at the School of Computing and Augmented Intelligence, Arizona State University. Her research focuses on probabilistic machine learning, trustworthy AI, and tractable probabilistic modeling. She holds a PhD in Computer Science from UCLA and has been recognized with awards like the Cisco Research Award and Simons-Berkeley Research Fellowship. Education: PhD in Computer Science (University of California, Los Angeles). Research interests include probabilistic reasoning, fairness, robustness, and interpretability. Her work bridges theoretical foundations and practical applications, with contributions to probabilistic circuits and optimal transport. Notable achievements include co-organizing TPM 2024 and presenting at venues like NeurIPS and AAAI. She actively promotes fairness in AI through frameworks that address label bias and discrimination patterns. Awards: Cisco Research Award, Simons-Berkeley Fellowship, AAAI 2023 New Faculty Highlights. Teaching: Courses on artificial intelligence, machine learning, and research supervision. Service: Tutorial leadership on probabilistic circuits, workshop organization, and academic talks globally.

• Probabilistic Machine Learning
• Tractable Probabilistic Modeling
• Algorithmic Fairness
• +6 more

Éric Tanter is a Full Professor in the Computer Science Department (DCC) at the University of Chile, where he also leads the PLEIAD Lab. He holds an Inria International Chair (2025–2029) and is an Associate Researcher at the Millenium Institute for Foundational Research on Data (IMFD). His academic leadership includes prior roles as Director and Deputy Director of the DCC, and Coordinator of the PhD Program in Computer Science. His research centers on programming languages and software engineering, with a strong focus on gradual typing, type systems, program verification, and secure programming. He explores both theoretical foundations and empirical practices, aiming to bridge the gap between static and dynamic language paradigms. His recent work emphasizes gradual verification, refinement types, and applications in proof assistants and security. The trends in his recent publications show a deep engagement with foundational aspects of gradual and dependent typing, often aiming to enhance the reliability and security of software systems. These works frequently involve formalization in proof assistants like Coq and explore applications in differential privacy, secure interoperability, and symbolic execution. Inria International Chair (2025–2030) Best Paper Awards at POPL 2019, OOPSLA 2018, ICFP 2018, MSR 2011, AOSD 2010, SBLP 2008, DAIS 2006 Most Influential/Most Notable Paper Awards at DLS, Programming, DLS Facebook Research Testing and Verification Award (2018) Google Faculty Research Awards (2015, 2016) Best Professor Award, University of Chile (2011) Éric Tanter has advised numerous PhD and Master’s students, many of whom have gone on to publish influential work in top venues. He has led multiple research projects funded by FONDECYT, ANID, INRIA, and other national and international agencies. His service includes editorial roles in journals such as the Journal of Functional Programming and Science of Computer Programming, and extensive participation in program committees of major conferences like POPL, ICFP, and OOPSLA. He leads the PLEIAD Lab at the University of Chile, a research group focused on programming languages, software engineering, and formal methods. The lab fosters collaboration with international institutions and emphasizes both theoretical rigor and practical impact.

• Programming Languages
• Gradual Typing
• Software Engineering
• +9 more

David Baqaee is a Professor of Economics at the University of California, Los Angeles (UCLA), affiliated with the Department of Economics in the College of Letters and Science. His research focuses on aggregation and heterogeneity in macroeconomics, particularly examining how resource misallocation from market power, nominal rigidities, and increasing returns to scale impacts aggregate productivity. He has contributed to prestigious journals such as Econometrica and the American Economic Review. Education: Ph.D. Economics, Harvard University; B.Sc. Mathematics and Economics, University of Canterbury His research interests span applied economic theory, industrial organization, macroeconomics, and network economics. Recent work explores trade wars, sanctions' long-term effects, energy import dependencies, and the interplay between monetary policy and supply-side dynamics. He has served as an associate editor for Econometrica and the Quarterly Journal of Economics. His publications reflect a focus on policy-relevant macroeconomic questions, including the consequences of geopolitical events, trade restrictions, and pandemic impacts on global supply chains. His methodologies emphasize disaggregated models and input-output networks to capture microeconomic foundations of aggregate phenomena.

• Applied Economic Theory
• Industrial Organization
• Macroeconomics
• +3 more

Prof. Christoph Benzmüller is a Full Professor at the University of Bamberg (Chair for AI Systems Engineering) and an adjunct professor at Freie Universität Berlin's Department of Mathematics and Computer Science. He is a leading researcher in automated reasoning, computational metaphysics, and formal logic systems. His work focuses on integrating higher-order logic into AI to achieve transparent and ethically grounded systems. Research Interests: His research spans automated theorem proving, formal ontologies, and normative reasoning in AI. Notably, he has formalized Gödel's ontological argument using computational methods and developed the Leo theorem provers for higher-order logic. He emphasizes the use of symbolic reasoning for ethical and legal AI frameworks. Grants & Projects: He leads projects like PetraKIP (AI portfolios for teacher education) and NFDIxCS (National Research Data Infrastructure). His work is funded by DFG, EPSRC, and the Volkswagen Foundation. He also collaborates with institutions globally, including Stanford and Cambridge. Awards: Recipient of the Central Teaching Award (FU Berlin) for his Computational Metaphysics course and a DFG Heisenberg Fellowship. His research on Gödel's argument gained international media attention. Education: Studied at Saarland University, where he earned his PhD (1999) and habilitation (2006).曾是专业长跑运动员，后转向学术研究。

• Automated Theorem Proving
• Computational Metaphysics
• Artificial Intelligence
• +5 more

Murat Arcak is a Professor of Electrical Engineering and Computer Sciences and Mechanical Engineering at the University of California, Berkeley, holding the Robert M. Saunders Endowed Chair in the College of Engineering. His research spans control theory, autonomous systems, and multi-agent systems with applications in transportation, energy, and biology. Dr. Arcak received his Ph.D. in Electrical Engineering from the University of California, Santa Barbara in 2000, following an M.S. from the same institution in 1997 and a B.S. from Bogazici University in Istanbul, Turkey in 1996. His research interests focus on developing scalable control design and verification methods for complex systems with many interconnected components, nonlinear dynamics, and learning capabilities. He has made significant contributions to control theory, particularly in areas like reachability analysis, dissipative systems, and compositional verification methods. His work bridges theoretical advances with practical applications in transportation systems, energy networks, and biological systems. A leading researcher in control systems, Dr. Arcak's recent publications demonstrate a strong focus on data-driven approaches for system verification, synthetic biology applications, and formal methods for traffic control. His research combines mathematical rigor with practical implementation, often developing novel theoretical frameworks that address real-world engineering challenges. CAREER Award from the National Science Foundation (2003) Donald P. Eckman Award from the American Automatic Control Council (2006) Control and Systems Theory Prize from SIAM (2007) Antonio Ruberti Young Researcher Prize from IEEE Control Systems Society (2014) Brockett-Willems Outstanding Paper Award (2021) IFAC Fellow (2020) IFAC Automatica Paper Prize (2020) CSS Transactions on Control of Network Systems Outstanding Paper Award (2017) Electrical Engineering Award for Outstanding Teaching (2014) CSS Antonio Ruberti Young Researcher Prize (2014) IEEE Fellow (2012) SIAM Activity Group Control and Systems Theory Prize (2007) Dr. Arcak has advised numerous graduate students and postdoctoral researchers, though specific names are not listed in the available information. His research has been supported by various grants from the National Science Foundation and other funding agencies, enabling his work on control theory and applications across multiple domains. He is affiliated with several research centers at UC Berkeley including the Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Deep Drive (BDD), the Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB), the Institute of Transportation Studies (ITS), and Partners for Advanced Transit and Highways (PATH).

• Control Theory
• Autonomous Systems
• Multi-Agent Systems
• +5 more

Naomi J. Halas is a University Professor at Rice University, holding appointments in the Department of Electrical and Computer Engineering, Biomedical Engineering, Chemistry, and Physics & Astronomy. She is the Stanley C. Moore Professor in Electrical and Computer Engineering and serves as Director of both the Smalley-Curl Institute and the Laboratory for Nanophotonics. As a University Professor, she holds Rice's highest faculty rank, a distinction awarded to only 10 individuals (and only the second woman) in Rice's 111-year history. Halas is a pioneering researcher in the field of plasmonics, having created the concept of the "tunable plasmon" and invented a family of nanoparticles with resonances spanning the visible and infrared regions of the spectrum. Her research spans fundamental studies of coupled plasmonic systems as well as applications in biomedicine, optoelectronics, machine learning-enabled chemical sensing of environmental toxins, and plasmon-based photocatalysis. She is the author of more than 400 refereed publications, has over 30 issued patents, has presented more than 600 invited talks, and has been cited more than 130,000 times. Her recent publications demonstrate a strong focus on practical applications of plasmonics, particularly in water purification, environmental toxin detection, and cancer treatment. Her work combines nanotechnology with machine learning approaches to create innovative solutions for pressing global challenges in healthcare, environmental sustainability, and energy. The interdisciplinary nature of her research is reflected in publications spanning journals from Nature Water and PNAS to ACS Catalysis. Benjamin Franklin Medal in Chemistry (2025) - For the creation and development of nanoshells for biomedical and chemical applications Mildred Dresselhaus Prize in Nanoscience and Nanomaterials (2024) American Physical Society Frank Isakson Prize for Optical Effects in Solids Willis E. Lamb Award Wood Prize of Optica National Security Science and Engineering Faculty Fellow (Vannevar Bush Fellow) of the U.S. Department of Defense Halas has co-founded two companies based on her research: Nanospectra Biosciences, developing photothermal therapies for prostate cancer (nearing FDA approval), and Syzygy Plasmonics, a deep decarbonization platform. She has advised numerous students who have gone on to successful careers in academia and industry. Her research has been supported by significant grants from NSF, DoD, and other funding agencies. She serves as an advisor to the Mathematical and Physical Sciences Directorate of the National Science Foundation. Halas leads the Laboratory for Nanophotonics at Rice University, where her team focuses on designing new optically active nanostructures, developing nanofabrication strategies, characterizing physical properties of these materials, and prototyping applications of technological and societal interest. Her group is dedicated to producing PhD research scientists with expanded skill sets who can develop solutions beyond traditional disciplinary boundaries.

• Plasmonics
• Nanophotonics
• Nanoengineering
• +5 more

Samuel V. Scarpino is a Professor at Northeastern University , leading as Director of AI + Life Sciences in the Institute for Experiential AI . He holds appointments in the Khoury College of Computer Sciences , Bouvé College of Health Sciences , and the Network Science Institute . Scarpino’s career spans roles at The Rockefeller Foundation, Dharma Platform, and co-founding Global.health , a Google-backed pathogen tracking initiative. Education: PhD in Biology (2013) from The University of Texas at Austin; Omidyar Fellow at the Santa Fe Institute (2013–2016). His research focuses on integrating AI , network science , and epidemiology to address global health challenges. Key areas include disease modeling , wastewater surveillance , and health equity . Recent work explores AI applications for H5N1 pandemic preparedness , scRNA-seq analysis , and social determinants of health . Scarpino’s scientific contributions include over 100 publications in Nature , Science , and PNAS , alongside fellowships from the ISI Foundation (2017), Santa Fe Institute (2020), and Vermont Complex Systems Institute (2021). He mentors PhD students like Wan He and leads interdisciplinary teams at Northeastern’s Roux Institute and Network Science Institute . Grants from the McGovern Foundation and Microsoft Research support his work on AI for public health.

• Artificial Intelligence
• Public Health
• Epidemiology
• +3 more

Jack Baker is the William Alden Campbell and Martha Campbell Professor of Engineering and Associate Dean for Faculty Affairs in the Stanford Doerr School of Sustainability at Stanford University. He is a Professor of Civil & Environmental Engineering with expertise in probabilistic and statistical tools for quantifying and managing disaster risk and resilience. His work has significantly influenced building codes, performance-based engineering guidelines, and catastrophe risk models. Dr. Baker's educational background includes: Ph.D. in Civil & Environmental Engineering from Stanford University (2005) M.A. in Statistics from Stanford University (2004) M.S. in Civil & Environmental Engineering from Stanford University (2002) B.A. in Mathematics/Physics from Whitman College (2000) His research focuses on disaster risk and resilience, particularly in earthquake engineering and seismic hazard analysis. Baker uses probabilistic and statistical approaches to analyze risk in spatially distributed systems, characterize earthquake ground motions, and simulate post-disaster recovery processes. His work bridges theoretical frameworks with practical applications in building codes and risk management strategies. He has made significant contributions to understanding the relationship between ground motion characteristics and structural response, while also expanding into climate-related hazards like atmospheric rivers and their compound effects. His recent publications demonstrate a growing focus on interdisciplinary research that connects engineering with socioeconomic factors in disaster contexts. There's a clear trend toward integrating machine learning techniques with traditional engineering approaches, particularly in modeling household displacement, economic recovery, and flood damage prediction. His work increasingly addresses the human dimension of disasters, examining how physical damage translates to social impacts and recovery timelines. Dr. Baker has received numerous prestigious awards recognizing his contributions to the field: William B. Joyner Lecture Award from the Seismological Society of America and Earthquake Engineering Research Institute (2023) PROSE Awards finalist for Seismic Hazard and Risk Analysis textbook (2022) Thorpe Medal from the European Council on Computing in Construction (2022) Walter L. Huber Civil Engineering Research Prize from the American Society of Civil Engineers (2018) CAREER Award from the National Science Foundation (2010) As an educator and mentor, Baker advises numerous doctoral and master's students while serving as Associate Dean for Faculty Affairs. His research group has secured significant funding for projects related to seismic risk, disaster recovery modeling, and infrastructure resilience. He has directed major initiatives like the Stanford Urban Resilience Initiative and co-founded the Haselton Baker Risk Group, demonstrating strong leadership in translating research into practical applications. Dr. Baker leads the Baker Research Group, which focuses on probabilistic approaches to disaster risk assessment and management. The group maintains active collaborations with government agencies, industry partners, and international research institutions to advance the state of knowledge in earthquake engineering and broader disaster resilience fields. Their work often involves developing innovative computational tools and frameworks that are made publicly available through GitHub repositories.

• Disaster Risk and Resilience
• Earthquake Engineering
• Seismic Hazard Analysis
• +4 more

Mohsen Heidari is an Assistant Professor in the Department of Computer Science at Indiana University, Bloomington. He is affiliated with the IU Quantum Science and Engineering Center (QSEc) and the NSF Center for Science of Information (CSoI). He previously held positions as a Visiting Assistant Professor at Purdue University and as a Postdoctoral Research Associate at CSoI. Ph.D. in Electrical Engineering (2019) and M.Sc. in Applied Mathematics (2017) from the University of Michigan His research focuses span quantum computing, theoretical machine learning, and information theory. Key themes include: Quantum algorithm design and sample complexity Fourier-based learning frameworks Quantum-classical duality in learning problems Information-theoretic approaches to biological systems Article trends show a strong emphasis on quantum-classical learning intersections (6/15 papers), Fourier analysis applications (5/15), and information-theoretic foundations (12/15). Notable venues include NeurIPS, IEEE Transactions, and ISIT. He directs research involving: Quantum Neural Network development Quantum measurement simulation Quantum data compression techniques Quantum algorithm implementation constraints

• Quantum Computing
• Quantum Algorithms
• Theoretical Machine Learning
• +3 more

Michael Molloy is a Professor in the Department of Computer Science at the University of Toronto, with a cross-appointment to the Department of Computer and Mathematical Sciences at the University of Toronto Scarborough (UTSC). He teaches courses in Discrete Mathematics and the Probabilistic Method, including CSC/MAT A67 and CSC2427/MAT1500 . Research Focus: Graph Theory, Probabilistic Methods, Random Graphs, Constraint Satisfaction Problems, and Markov Chain analysis. His work includes foundational contributions to graph coloring, such as adaptable/conflict coloring and correspondence coloring, and exploring phase transitions in random graphs. He has supervised numerous graduate students, including Lora Hrisch, Jurgen Aliaj, and Hamed Hatami, advancing combinatorial and algorithmic research. Recent publications analyze random graph processes, the freezing threshold for k-colorings, and the resolution complexity of constraint satisfaction problems. These studies intersect theoretical computer science, combinatorics, and probabilistic modeling, often revealing deep structural insights through rigorous mathematical proofs.

• Graph Theory
• Probabilistic Method
• Random Graphs
• +2 more

Jim Crutchfield is a Distinguished Professor of Physics at the University of California, Davis, where he also serves as Director of the Complexity Sciences Center. He holds additional affiliations as President and Scientific Director of the Art & Science Laboratory in Santa Fe, External Faculty at the Santa Fe Institute, General Member of the Telluride Science Research Center, and Visiting Scholar at the Redwood Center for Theoretical Neuroscience. His work bridges physics, computation, and complex systems. Education: B.A. summa cum laude in Physics and Mathematics, University of California, Santa Cruz (1979) Ph.D. in Physics, University of California, Santa Cruz (1983) Crutchfield's research centers on computational mechanics , a framework he pioneered to quantify how natural systems store, process, and transmit information. His interests span nonlinear dynamics, evolutionary dynamics, information engines, quantum computation, and pattern discovery. He explores how structure emerges in complex systems, from cellular automata to biological evolution and neural networks. His recent work focuses on thermodynamic computing, causal inference, and the physics of intelligence. His publications reveal a consistent focus on the interplay between information, energy, and computation in physical systems. Themes include the thermodynamics of information engines, causal architecture in time series, emergent organization, and intrinsic computation in quantum and classical domains. These works span disciplines such as physics, computer science, biology, and cognitive science. Scientific Recognition: Postdoctoral Fellow, Miller Institute for Basic Research in Science IBM Postdoctoral Fellow, Condensed Matter Physics Distinguished Visiting Research Professor, Beckman Institute Bernard Osher Fellow, San Francisco Exploratorium NSF Graduate Fellow UCB Chancellor’s Fellow Crutchfield has advised over two dozen PhD students in physics, computer science, and mathematics, contributing significantly to the next generation of complexity scientists. He has led major interdisciplinary initiatives, including NSF-funded museum exhibits and workshops on network dynamics, collective cognition, and evolutionary dynamics. He has also been active in public discourse through talks, films, and publications on the philosophy of complexity. He leads research groups exploring the dynamics of learning, pattern discovery, and distributed intelligence, often in collaboration with institutions like the Santa Fe Institute and Caltech. His work continues to shape the theoretical foundations of complex systems science.

• Computational Mechanics
• Dynamics of Learning
• Evolutionary Dynamics
• +12 more

Emily J. King is a tenured Associate Professor in the Department of Mathematics at Colorado State University (CSU), College of Natural Sciences. She previously held a faculty position at the University of Bremen and has been actively contributing to the mathematical community through research, mentorship, and academic leadership. Her primary research interests include Frame Theory , Harmonic Analysis , Algebraic and Geometric Combinatorics , and Data Science , with applications in signal and image processing, Earth science, and artificial intelligence. She integrates deep mathematical theory with practical data analysis challenges. Her recent scholarly output reflects a strong focus on equiangular tight frames, combinatorial structures in frames, mathematical models for attention mechanisms, and applications to satellite imagery and cloud processes. Her work often bridges pure and applied mathematics, with a growing emphasis on interpretable AI and data science foundations. Dr. King has supervised several doctoral and master’s students, including Lander ver Hoef, Sören Schulze, Harley Meade, and Kristina Moen. She is a co-PI on an NSF grant focused on cloud processes and has been recognized for mentoring excellence, as evidenced by her student Emma Slack receiving the inaugural Outstanding Undergraduate in Mathematics award. NSF Grant Co-PI (2024) Outstanding Undergraduate in Mathematics award (mentored student, 2023) She is a founding co-organizer of the international Codes and Expansions (CodEx) Seminar and has organized sessions at major conferences such as SIAM AG and the Joint Mathematics Meetings. She frequently delivers invited talks at universities and research institutes worldwide, including upcoming presentations at the Air Force Institute of Technology, SIAM AG25, and TU Clausthal. Dr. King’s academic lineage includes John Benedetto as her mathematical advisor and Chandler Davis as her mathematical grandfather. She is actively involved in interdisciplinary research, particularly in marine data science, having co-spoken for the Helmholtz School for Marine Data Science (MarDATA).

• Frame Theory
• Harmonic Analysis
• Algebraic Combinatorics
• +7 more

Dr. Ana Lucic is an Assistant Professor at the University of Amsterdam , holding a joint appointment between the Institute for Logic, Language, and Computation (ILLC) and the Informatics Institute . Her research focuses on interpretable machine learning for applications in science and society, with emphasis on Earth system modeling and AI for environmental forecasting . PhD in Explainable Machine Learning (University of Amsterdam, 2022) MSc/BSc in Mathematics (McMaster University, Canada) Former researcher at Microsoft Research AI for Science and Partnership on AI Her recent work includes Aurora , a foundation model for Earth system forecasting published in Nature , and Clifford-Steerable CNNs at ICML 2024. Ana actively mentors PhD students and leads projects in mechanistic interpretability and geospatial machine learning . Scientific awards include top placements in ML competitions. She contributes to open science through reproducibility initiatives and collaborates with AI for climate consortia.

• Machine Learning
• Artificial Intelligence
• Interpretable Machine Learning
• +6 more