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Chih-Hung ChenView profile

Professor

• Biomedical Technologies
• Microelectronics & VLSI
• Digital & Smart Systems
• +5 more

Dr. Chih-Hung (James) Chen is a Professor in the Department of Electrical & Computer Engineering at McMaster University. His research focuses on noise-related issues in semiconductor devices, low-noise circuit design for medical and communication applications, and thermal noise characterization in nano-scale transistors. He holds senior member status in IEEE and is a licensed Professional Engineer in Ontario. Education: Ph.D., McMaster University, 2002 M.A.Sc., Simon Fraser University, 1997 B.Sc., National Central University, Taiwan, 1991 Research interests include biomedical technologies, microelectronics & VLSI, and digital/smart systems. He has collaborated with companies like Sony Corporation, United Microelectronics Corporation, and Focus Microwaves. His work is supported by grants from the Canada Foundation for Innovation (CFI), NSERC, and the Ontario Innovation Trust (OIT). Notable achievements include serving on the International Advisory Committee of the International Conference on Noise and Fluctuations (2015) and as an editor for the Journal of Low Power Electronics and Applications since 2022. Teaching includes courses like Analysis and Design of RF ICs for Communications and Electronic Devices and Circuits 2. His research lab focuses on advancing noise measurement techniques and designing ultra-low-power analog circuits for emerging applications.

Bryon AragamView profile

Associate Professor

• Causality
• Statistical Machine Learning
• Probabilistic Modeling
• +6 more

Bryon Aragam is an Associate Professor of Econometrics and Statistics and Robert H. Topel Faculty Scholar at the University of Chicago Booth School of Business. His research focuses on the intersection of causality, statistical machine learning, and probabilistic modeling, with particular emphasis on applications to artificial intelligence systems including large language models like ChatGPT and generative models like DALL-E. Dr. Aragam completed his PhD in Statistics and a Masters in Applied Mathematics at UCLA, where he was an NSF graduate research fellow. Prior to joining the University of Chicago, he was a project scientist and postdoctoral researcher in the Machine Learning Department at Carnegie Mellon University. Research Focus: Causal structure learning in probabilistic generative models Key Areas: Causal machine learning, deep generative models, latent variable models, statistical learning theory Applications: AI interpretability, ethics, and fairness in artificial intelligence systems Teaching: Business Statistics, Econometrics and Statistics Colloquium His recent publications demonstrate a strong theoretical foundation combined with practical applications, particularly in understanding and improving AI systems. His work spans causal discovery, graphical models, deep learning, and latent variable modeling, with particular attention to the theoretical properties of these methods and their applications to real-world AI challenges. The research shows a progression toward increasingly complex problems in causal representation learning and AI interpretability. Scientific Awards: Robert H. Topel Faculty Scholar NSF Graduate Research Fellow Dr. Aragam's work has been published in top statistics and machine learning venues including the Annals of Statistics, Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), and the Journal of Machine Learning Research (JMLR). His research group publishes broadly across both statistical and machine learning communities, demonstrating the interdisciplinary nature of his work at the intersection of statistics, machine learning, and causal inference. As a data science consultant for technology and marketing firms, Dr. Aragam has applied his expertise to problems in survey design, customer retention, logistics, and ranking, bridging the gap between theoretical research and practical applications.

Stanislav HenclView profile

Professor

• Mathematical Analysis
• Sobolev Spaces
• Geometric Function Theory
• +3 more

Prof. Stanislav Hencl is a faculty member at the Department of Mathematical Analysis , Faculty of Mathematics and Physics , Charles University in Prague. His research focuses on mathematical analysis, particularly on Sobolev mappings, finite distortion theory, and their applications to nonlinear elasticity and geometric function theory. Research Interests : Sobolev spaces, geometric function theory, nonlinear elasticity, measure theory, and partial differential equations. Students : Supervised 18 PhD, master's, and bachelor's theses, including Jaromír Mielec (Sobolev mappings extensions), Ondřej Bouchala (noncompactness of Sobolev embeddings), and Anna Doležalová (nonlinear mapping classes). Key Publications : Monograph Lectures on mappings of finite distortion (Springer, 2014) and 15+ recent works on Sobolev homeomorphism properties, injectivity, and energy functionals. Contact : Office K255, Karlín campus, email Stanislav.Hencl@mff.cuni.cz and hencl@karlin.mff.cuni.cz .

Steven CeronView profile

Assistant Professor

• Swarm robotics
• Multi-agent systems
• Swarm intelligence
• +5 more

Steven Ceron is an Assistant Professor in Robotics at the University of Michigan's College of Engineering. His research focuses on swarm robotics, multi-agent systems, and programmable self-organization of micro- and macro-scale robot swarms. He leads the Synergetic Adaptive Machinas (SAM) Lab, which develops reconfigurable robot swarms for biomedical applications and smart materials integration. Key research areas include microrobot fabrication, heterogeneous swarm coordination, and self-reconfigurable modular systems. His work envisions seamless integration of robot swarms into daily life through innovations in design, control, and scalability. Recent publications emphasize swarmalator dynamics, strain-based coordination in soft robots, and scalable fabrication methods. His lab explores both theoretical frameworks and practical implementations, bridging micro-scale and macro-scale robotics applications. Though no awards were explicitly listed, his contributions to novel fabrication techniques and modular robotics suggest ongoing recognition in the field. Advising and grant details are not provided here, but his lab's focus on biomedical and aerospace applications indicates active collaborative projects.

Lin WangView profile

Lecturer

• Machine Learning
• Audio-Visual Signal Processing
• Robotic Perception
• +3 more

Dr. Lin Wang is a Lecturer in Applied Data Science and Signal Processing at Queen Mary University of London (QMUL), affiliated with the School of Electronic Engineering and Computer Science. He leads the Machine Listening Lab and is a member of the Centre for Multimodal AI, Centre for Intelligent Sensing (CIS), and Institute of Coding (IoC). His research focuses on audio-visual signal processing, robotic perception, and machine learning, with applications in healthcare, drone-based sensing, and human activity recognition. Dr. Wang holds a PhD from Dalian University of Technology and has held postdoctoral positions at QMUL, the University of Sussex, and the Alexander von Humboldt Foundation in Germany. Education and Roles: PhD in Signal Processing, Dalian University of Technology (2010) Postdoc at Queen Mary University of London (2014–2017) Postdoc at University of Sussex (2017–2018) Alexander von Humboldt Fellow at University of Oldenburg (2011–2013) Fellow of the Higher Education Academy (UK) Research Interests: Audio-visual signal processing for drones and wearable devices Machine listening and robotic perception Machine learning for healthcare and environmental monitoring Human activity recognition using multimodal sensors Awards and Grants: Early Career Champion on AI&Data, UK Acoustics Network Outstanding Article Award, Frontiers in Computer Science (2022) EPSRC grant: Bioacoustic Monitoring Using Drones (£46,821, 2022–2023) Innovate UK grant: Music Source Separation (£48,144, 2024–2025) Teaching and Students: Dr. Wang teaches Applied Statistics , Website Design and Authoring , and Machine Learning for Visual Data Analysis . He supervises PhD students including Ashish Alex (speech separation), Michael Clayton (drone audition), and Dmitrii Mukhutdinov (audio-visual processing). Labs and Teams: He co-leads the Machine Listening Lab and is part of the Centre for Multimodal AI, focusing on interdisciplinary projects in robotics, acoustics, and AI.

Gauthier GidelView profile

Associate Professor

• Deep Learning
• Adversarial Machine Learning
• Generative Modeling
• +5 more

Gauthier Gidel is an Associate Professor at the Department of Computer Science and Operations Research (DIRO) within the Faculty of Arts and Science at Université de Montréal, where he also holds the prestigious Canada CIFAR AI Chair position. He is a core faculty member of Mila, Quebec's AI research institute, and maintains active research collaborations with leading institutions. His academic journey includes a PhD in Computer Science under the supervision of Simon Lacoste-Julien, with internships at Sierra, ElementAI, and DeepMind during his doctoral studies. Dr. Gidel's research spans multiple critical areas in machine learning, with particular emphasis on generative modeling , adversarial machine learning , and variational inequalities for machine learning. His work explores the intersection of optimization theory and practical AI systems, focusing on challenges like LLM safety alignment, multi-agent cooperation, and robustness against adversarial attacks. He is particularly known for his contributions to understanding the theoretical foundations of generative adversarial networks through variational inequality frameworks. His recent publications reveal a strong trend toward addressing critical challenges in large language model safety and alignment, with numerous 2024-2025 papers focusing on adversarial robustness, safety evaluation methodologies, and alignment techniques for LLMs. Simultaneously, his foundational work continues in optimization theory, particularly in variational inequalities and performative prediction, demonstrating his dual focus on practical AI safety concerns and theoretical machine learning foundations. Canada CIFAR AI Chair Core member of Mila Organizer of popular NeurIPS workshops on smooth games Co-founder of the ICLR blog post track Dr. Gidel actively supervises an extensive research group with approximately 10 current graduate students and numerous alumni who have secured positions at leading institutions including Inria Lyon, Oxford, and industry research labs. His research is supported by multiple substantial grants from CRSNG, MITACS, and IVADO, including the prestigious CRSNG Discovery Grant program and MITACS Acceleration Québec projects focused on fraud detection in music streaming and conditional generation. His laboratory maintains strong connections with both academic and industry partners, fostering a collaborative environment focused on advancing AI safety and theoretical understanding.

Martin BerggrenView profile

Professor

• Computational Design Optimization
• Numerical Optimization Algorithms
• Gradient-Based Algorithms
• +8 more

Martin Berggren is a Professor at the Department of Computing Science , Umeå University , Sweden. His work focuses on Computational Design Optimization , combining computer simulations and numerical optimization to enhance engineering designs for devices like antennas, microwave components, and loudspeakers. Berggren is also active in mathematical modeling of physical phenomena, particularly wave propagation and fluid mechanics, with a strong emphasis on finite-element methods . His research addresses large-scale conceptual design problems using thousands to millions of design variables, relying on gradient-based algorithms and adjoint-based computations of design sensitivities—similar to back-propagation in deep learning. Key application areas include acoustic and electromagnetic devices, where he investigates damping mechanisms, boundary conditions, and material distribution. Other interests, though less active, involve flow control and unsteady fluid–structure interaction . Berggren collaborates extensively on projects such as Structured Regularization , Topology Optimization of Acoustic Black Holes , and Design of Microstrip-to-Waveguide Transitions . His publications span journals like Journal of Computational Physics , Pattern Analysis and Applications , and IEEE Transactions on Antennas and Propagation , often co-authored with researchers like Linus Hägg , Eddie Wadbro , and Disi Lin .

David FinkelsteinView profile

Associate Professor

• Limnogeology
• Biogeochemistry
• Aqueous Geochemistry
• +7 more

David Finkelstein is an Associate Professor in the Department of Geosciences at Hobart and William Smith Colleges, where he has been a faculty member since 2013. He earned his Ph.D. from the University of Illinois at Urbana-Champaign and holds advanced degrees from the University of Massachusetts, Amherst. His academic affiliations include prior roles as a Research Assistant Professor and Lab Manager at the University of Massachusetts and as an Assistant Professor at the University of Tennessee. Ph.D., University of Illinois at Urbana-Champaign M.S., University of Massachusetts, Amherst B.S., University of Massachusetts, Amherst Dr. Finkelstein's research centers on limnogeology and biogeochemistry , with a focus on the chemical evolution of lake systems, stable isotope geochemistry, and microbial life in extreme environments. His work integrates aqueous geochemistry, organic molecular analysis, and microbiological methods to reconstruct paleoenvironments and climate change across geological timescales, including the Holocene, Pleistocene, Cretaceous, and Triassic periods. He has extensively studied lake sediments, wildfires, and the environmental impacts of the 2010 Gulf of Mexico oil spill. His recent publications show a strong trend in using biogeochemical proxies and isotopic signatures to understand past climate variability, human-environment interactions, and extreme environmental conditions. His research often involves multidisciplinary collaborations and field-based studies in diverse regions such as Russia, Iran, and Central America. Dr. Finkelstein is actively involved in scientific communities, with affiliations including: International Association of Limnogeology American Geophysical Union Geological Society of America Geochemical Society SEPM (Society for Sedimentary Geology) He has advised numerous research projects and mentored students in geoscience, though specific names are not listed. His teaching includes courses such as Stable Isotope Geochemistry, Limnology, and Environmental Geochemistry, reflecting his expertise in both theoretical and applied aspects of earth sciences. Dr. Finkelstein has not received any explicitly listed scientific awards in the provided text, but his publication record in high-impact journals demonstrates significant scholarly contributions. He does not appear to lead a named laboratory or research center, but his work is deeply embedded in field and laboratory-based geochemical analysis.

Daniela De SilvaView profile

Professor

• Partial Differential Equations
• Free Boundary Problems
• Harmonic Analysis
• +3 more

Daniela De Silva is the Olin Professor of Mathematics and Chair of the Department of Mathematics at Barnard College, Columbia University. She holds a B.A. from the University of Naples Federico II and a Ph.D. from the Massachusetts Institute of Technology (MIT). Her research focuses on partial differential equations (PDEs), particularly free boundary problems and geometric analysis. She teaches advanced courses such as Calculus and Analysis at Barnard and organizes the Geometry and Analysis seminar at Columbia University. Her work explores regularity theory for free boundaries, phase transitions, and harmonic analysis. Notably, her contributions include studies on energy-minimizing free boundaries and monotonicity formulas. De Silva has also contributed to nonlinear Schrödinger equations and has held academic positions at institutions like Johns Hopkins University and MIT before joining Barnard in 2007. Her research interests span theoretical and applied aspects of PDEs, with a focus on geometric implications and singularities. While her work has been featured in prestigious journals like Comm. on Pure and Applied Math , Duke Math. J. , and Indiana Univ. Math. J. , she remains active in academic outreach, including discussions on topics like the mathematics of melting ice and the significance of π in popular media.

Stéphanie RuphyView profile

Professor

• Philosophy of Science
• Scientific Pluralism
• Scientific Integrity
• +5 more

Stéphanie Ruphy is a Professor of Philosophy of Science at the École Normale Supérieure - PSL University in Paris, where she conducts research at the intersection of philosophy, science policy, and scientific integrity. She currently serves as Director of the French Office for Research Integrity (OFIS), demonstrating her leadership in promoting ethical research practices across France. Her educational background is uniquely interdisciplinary, holding a doctorate in astrophysics from Sorbonne University and a PhD in philosophy from Columbia University in New York. This dual expertise informs her distinctive approach to philosophical questions about science. Ruphy's research centers on scientific pluralism, scientific integrity, and the relationship between science and democracy. She has developed the concept of "foliated pluralism," which challenges traditional metaphysical interpretations of scientific diversity. Her work examines how citizen participation can enhance scientific objectivity and how research governance structures affect scientific progress. She explores the philosophical implications of computer simulations in scientific practice and investigates questions about natural kinds across different scientific domains. Her publications reveal a consistent focus on the philosophical foundations of scientific practice, with particular attention to the relationship between scientific methods and societal values. Ruphy has made significant contributions to understanding how scientific pluralism relates to metaphysical questions, how citizen science affects scientific objectivity, and how research integrity frameworks can be strengthened. Her work bridges theoretical philosophy with practical concerns about research governance. Among her notable honors: Member of Academia Europaea (2018) Member of European Academy of Sciences (EURASC) (2017) Appointed to French Council for Scientific Integrity (CoFIS) (2017) As an educator at the École Normale Supérieure, Ruphy mentors future researchers in understanding their responsibilities within knowledge societies. She has observed growing student interest in "involved" research connected to societal concerns. Her work on a research integrity oath for PhD students in France demonstrates her commitment to embedding ethical considerations in research training. She actively engages with policy questions about balancing scientific freedom with societal responsibilities. Ruphy leads significant initiatives in research integrity through her directorship of OFIS, where she organizes conferences and develops frameworks for maintaining trust in scientific practices. Her work connects academic philosophy with practical policy concerns, creating bridges between theoretical considerations and real-world research governance.

Julian AdamekView profile

Researcher

• Computational Cosmology
• Theoretical Cosmology
• Large-scale structure of the Universe
• +3 more

Julian Adamek is a computational cosmologist and lead developer of gevolution , a general-relativistic N-body code for cosmological simulations. His work focuses on modeling relativistic effects in cosmic structure formation to better understand gravity’s role on large scales and dark energy. Research Interests: Computational Cosmology, Theoretical Cosmology, Large-scale structure of the Universe, Relativistic N-body simulations. Technical Leadership: Lead developer of gevolution , a public cosmological simulation code available via GitHub. Recent publications span diverse applications of deep learning in geospatial analytics, environmental monitoring, and computer vision, including phenology modeling, biomass mapping, conflict assessment, and 3D reconstruction from point clouds. Key Trends: Integration of AI/ML for environmental tasks, cross-domain applications (cosmology, ecology, forestry), and satellite data processing. Technical Focus: Transformer networks, diffusion models, super-resolution imaging, and ensemble learning for uncertainty quantification. Julian collaborates with researchers in cosmology and geospatial science, though specific students or awards are not mentioned in the provided texts.

• Materials Science
• Condensed Matter Physics
• Computational Modeling
• +7 more

Alejandro Strachan is an Assistant Professor of Materials Engineering at Purdue University's College of Engineering. His research focuses on molecular modeling of advanced materials, with specific emphasis on atomistic and mesoscale simulations of condensed-phase chemistry, active materials, nanotechnology, and mechanical properties of structural materials. Ph.D. in Physics, University of Buenos Aires (1998) Postdoctoral Research, Caltech's Materials Process Simulation Center (1999-2002) Strachan's work integrates computational methods with machine learning to study material behavior under extreme conditions, including shock waves and high-pressure environments. His research spans energetic materials, phase transitions, and multiscale modeling frameworks. Recent publications highlight trends in combining quantum-accurate simulations with deep learning for non-equilibrium systems, FAIR data infrastructure for materials discovery, and multiscale reactive models for energetic composites. He also explores mechanochemistry, defect dynamics, and microstructure-property relationships. His computational simulations often address practical challenges in material stabilization, polymer interactions, and hotspot formation mechanisms. Strachan actively contributes to open science initiatives through platforms like nanoHUB and HUBzero.

Ulla MörtbergView profile

Professor

• Sustainable Development
• Environmental Science
• Geospatial Analysis
• +7 more

Ulla Mörtberg is a Professor and Docent at KTH Royal Institute of Technology's Digital Futures Faculty, specializing in Sustainable Development, Environmental Science and Engineering. She holds academic roles in the Department of Sustainable Development, Environmental Science and Engineering and is actively involved in interdisciplinary projects such as the EO-AI4GlobalChange initiative and the Embedding AI in geospatial policy tools for clean cooking adoption. Her work bridges geospatial technologies, environmental policy, and sustainable urban planning. Key research focuses include renewable energy planning (wind farms, bioenergy), urban ecosystem services, biodiversity conservation, and geodesign applications. She leads projects addressing climate solutions in Stockholm, such as integrating nature-based solutions into urban compaction strategies and evaluating green infrastructure impacts on urban heat. Her methods emphasize GIS-based decision support systems and multi-criteria analysis for balancing environmental, social, and economic objectives. Recent publications highlight advancements in ecosystem services assessment frameworks, wind energy sustainability, and urban biodiversity dynamics. Notable contributions include spatial optimization models for forest management and policy assessments of Brazil's Forest Code. Her work often involves global case studies but emphasizes regional applications in Sweden and the Stockholm region. Mörtberg collaborates extensively with municipal planners and policymakers, contributing to sustainable development goals through actionable research. Her projects often involve international partnerships and address global challenges such as deforestation in the Amazon and energy transition pathways in Europe.

Giuseppe MingioneView profile

Professor

• Calculus of Variations
• Elliptic Partial Differential Equations
• Parabolic Partial Differential Equations
• +3 more

Giuseppe Mingione is a Full Professor in the Department of Mathematical, Physical and Computer Sciences at the University of Parma, Italy. His research spans Calculus of Variations , Elliptic and Parabolic PDEs , and Nonlinear Potential Theory . He has delivered over 25 lecture series and 180+ invited talks globally. PhD in Mathematics, University of Naples Federico II (1999) Degree in Mathematics, University of Naples Federico II (1994) His work focuses on regularity theory for nonlinear PDEs, nonuniform ellipticity, and geometric analysis. Publications include breakthroughs in Schauder estimates, double phase problems, and nonlocal systems. He has received the Amerio Prize (2016) , Caccioppoli Prize (2010) , and Stampacchia Medal (2006) . Recent articles address nonuniform ellipticity, nonlocal PDEs, and gradient regularity. Awards include the Order of the Merit of the Italian Republic (2017) and Von Staudt Chair (2004) . He serves on editorial boards for international journals.

Xin PengView profile

Professor

• Software Analytics
• Intelligent Software Development
• Microservice
• +4 more

Xin Peng is a Professor and Deputy Dean at the School of Computer Science, Fudan University, China. He leads the CodeWisdom research team focusing on intelligent software engineering techniques for development, maintenance, and operation of software systems. His educational background includes a PhD in Computer Science (2001-2006) and Bachelor's degree in Computer Science (1997-2001), both from Fudan University. He progressed through the academic ranks from Assistant Professor (2006-2010) to Associate Professor (2010-2015) and finally to Professor (2015-present). Professor Peng's research interests span Software Analytics, Intelligent Software Development, Microservice systems, and AIOps. His work leverages AI technologies including deep learning and knowledge graphs to develop intelligent software engineering techniques. A significant portion of his recent work focuses on applying Large Language Models to various software engineering tasks, including vulnerability detection, API usage analysis, and test automation. His publication record shows a clear trend toward increasingly sophisticated applications of AI in software engineering, with recent work heavily featuring LLMs for tasks ranging from vulnerability patch porting to resource leak detection. The research spans multiple domains including microservice systems, automotive software, and Web of Things security. Best Paper Award of ICSM 2011 ACM SIGSOFT Distinguished Paper Award of ASE 2018 and 2021 IEEE TCSE Distinguished Paper Award of ICSME 2018, 2019, and 2020 IEEE Transactions on Software Engineering Best Paper award for 2018 Professor Peng serves in numerous leadership roles including Deputy Director of CCF Technical Committee on Software Engineering, Co-Editor-in-Chief of Journal of Software: Evolution and Process, and Associate Editor for ACM Transactions on Software Engineering and Methodology. He has been actively involved in program committees for major software engineering conferences including ICSE, ASE, ESEC/FSE, and ICSME. He leads the CodeWisdom research team at Fudan University, which has developed several benchmark systems including TrainTicket for microservice research. The team's work bridges academic research with industrial applications, particularly in microservice systems analysis and intelligent software development tools.