جست‌وجوی استاد و پژوهشگر

Chicheng Zhangمشاهده پروفایل

استادیار

• Interactive Machine Learning
• Reinforcement Learning
• Contextual Bandits
• +۱۵ مورد دیگر

Chicheng Zhang is an Assistant Professor in the Computer Science Department at the University of Arizona, where he conducts research in the theory and applications of interactive machine learning. He earned his Ph.D. in Computer Science from the University of California, San Diego (UCSD) in 2017 under the supervision of Professor Kamalika Chaudhuri, and was previously an undergraduate student at Peking University working with Professor Liwei Wang. From 2017 to 2019, he was a postdoctoral researcher at the Machine Learning Group at Microsoft Research NYC. His research lies at the intersection of learning theory and practical algorithm design, focusing on interactive machine learning paradigms such as reinforcement learning, contextual bandits, active learning, and imitation learning. He aims to develop algorithms that are data-efficient, computationally tractable, and robust, with applications in healthcare, wireless communication, and fair AI systems. His work emphasizes principled algorithm design with theoretical guarantees and empirical validation. The most recent publications reflect a strong trend in developing efficient, theoretically grounded methods for sequential decision-making and interactive learning. Key themes include sample efficiency, robustness to noise, fairness in algorithmic decisions, and application-driven research in domains like oral cancer detection and mmWave network optimization. His work frequently bridges theoretical analysis with real-world deployment considerations. While no scientific awards are mentioned in the provided text, Dr. Zhang actively mentors prospective PhD students and encourages collaboration. He has contributed to interdisciplinary projects involving fairness-aware bandit algorithms for network coexistence, interpretable classifiers for cancer detection, and LLM-based initialization for reinforcement learning. His lab focuses on developing intelligent agents that actively learn from environments and human experts. He can be reached at chichengz@arizona.edu .

Ana Serranoمشاهده پروفایل

دانشیار

• Visual Computing
• Computational Imaging
• Material Appearance Perception
• +۷ مورد دیگر

Ana Serrano is an Associate Professor at Universidad de Zaragoza, Spain, where she is affiliated with the Graphics & Imaging Lab in the EINA (Edificio Ada Byron) school. She earned her PhD at the same institution under the supervision of Prof. Diego Gutierrez and Prof. Belen Masia, and completed a postdoctoral fellowship at the Max-Planck-Institute for Informatics under Prof. Karol Myszkowski. Her research focuses on visual computing , particularly in computational imaging , material appearance perception and editing , and virtual reality . She is especially interested in developing perceptually-driven methods that leverage knowledge of the human perceptual system to enhance user experiences and assist content creation in immersive environments. Her recent publications (2023–2025) span top-tier venues such as SIGGRAPH, CVPR, IEEE TVCG, and Eurographics. These works explore topics like saliency prediction in 3D and 360° video, crossmodal perception in VR, gloss modeling, radiance fields, and perceptual evaluation of immersive content. The research demonstrates a strong integration of machine learning, human perception, and computer graphics to solve real-world problems in visual computing. She has received several prestigious awards, including: Eurographics 2023 Young Researcher Award VGTC VR 2024 Significant New Researcher Award Eurographics 2020 PhD Award Adobe Research Fellowship (honorable mention, 2017) NVIDIA Graduate Fellowship (2018) Ana Serrano actively supervises PhD and Master’s students and has taught courses such as Virtual Reality, Computational Imaging, and Deep Learning applications. She serves as an Associate Editor for Computer Graphics Forum , ACM Transactions on Applied Perception , and Computers and Graphics , and has held leadership roles in major conferences including Eurographics (Tutorials co-chair, 2023), ACM SAP (Program co-chair, 2022), and CEIG (Program co-chair, 2022). Her professional service includes extensive program committee and reviewer roles for SIGGRAPH, IEEE VR, ISMAR, and others. She leads a vibrant research group focused on human perception in virtual environments, with current projects on computational models of attention and perception, integrated with physiological signals. Her lab, the Graphics & Imaging Lab, fosters interdisciplinary collaboration and innovation in visual computing.

• Aeronautics
• Mechanical Engineering
• Fluid Dynamics
• +۵ مورد دیگر

Marco Raiola is an Associate Professor at the Department of Aerospace Engineering , Universidad Carlos III de Madrid (UC3M). His research focuses on fluid dynamics, turbulence, and aerodynamics, with applications in flow diagnostics, heat transfer, and control systems. Research Interests: Turbulent flows, data-driven modeling, particle image velocimetry (PIV), convective heat transfer, and bio-inspired aerodynamics. Projects: Principal researcher in INFLUENTIA-CM-UC3M (2024-2026) and Diagnóstico del ruido de chorro (2022-2025). Collaborator in EU-funded initiatives like HumanIC and ODE4HERA . Contact: Email mraiola@ing.uc3m.es | ORCID: 0000-0003-2744-6347

Martin Müllerمشاهده پروفایل

استاد

• Artificial Intelligence
• Game Theory
• Monte Carlo Tree Search
• +۷ مورد دیگر

Martin Müller is a Professor in the Department of Computing Science at the University of Alberta, where he conducts research in artificial intelligence, game theory, and heuristic search. He holds the Canada CIFAR AI Chair at Amii and is an Amii Fellow, underscoring his leadership in AI. His research group focuses on Monte Carlo tree search, reinforcement learning, combinatorial game theory, and automated planning, with applications in games such as Go, Hex, and NoGo. His research interests span Monte Carlo and exact methods in game-tree search , exploration in heuristic search and machine learning , and algorithms in combinatorial game theory . He has developed open-source software like MCGS (Minimax-based Combinatorial Game Solver) and contributes to game-playing systems such as Fuego for Go. His work bridges theoretical foundations with practical implementations in AI-driven game solvers. Recent publications show a strong trend in reinforcement learning , particularly in deep Q-learning, policy gradient methods, and anomaly detection in deep RL. His team also explores combinatorial game solving , sparse reward environments , and imperfect information games . The research integrates machine learning with classical AI techniques, emphasizing empirical validation and algorithmic innovation. Canada CIFAR AI Chair Amii Fellow Best student paper award at IEEE Conference on Games 2024 Best paper award at IEEE COG 2021 Outstanding paper award at AAAI-18 Faculty of Science Dissertation Award (2016) Dissertation Award from the Canadian Artificial Intelligence Association (2013) Müller has supervised numerous PhD and MSc students, including Hongming Zhang, Henry Du, and Timo Bertram, many of whose theses focus on game AI, reinforcement learning, and combinatorial optimization. He is funded by NSERC, Mitacs, and Compute Canada. His group collaborates on projects involving neural networks for game playing, SAT solving, and planning algorithms. He is currently on sabbatical but remains academically active, teaching a graduate course on combinatorial games in 2025 and hosting visiting researchers. His lab is involved in the development of MCGS, a solver for sum games, and contributes to open-source AI software. The team publishes regularly in top venues such as NeurIPS, ICML, AAAI, and IEEE Transactions on Games. Future work includes advancing combinatorial game solvers, improving deep RL robustness, and exploring generalization in game representations.

Claudia Wagnerمشاهده پروفایل

استاد

• Computational Social Science
• Algorithmic Fairness
• Digital Behavioral Data
• +۷ مورد دیگر

Claudia Wagner is a full professor for Applied Computational Social Sciences at RWTH Aachen University and the Scientific Director of the Computational Social Science department at GESIS—Leibniz Institute for the Social Sciences. She is also an External Faculty member at the Complexity Science Hub Vienna. Her work bridges computer science and the social sciences to study algorithmic systems and their societal impacts. Her research focuses on socio-technical phenomena such as inequality, sexism, and perception bias in algorithmically infused societies. She investigates methodological challenges in using digital behavioral data to study human behavior, attitudes, and group dynamics. Her interests span computational social science, algorithmic fairness, network science, and AI ethics. The analysis of her recent publications reveals a strong emphasis on bias, fairness, and methodological rigor in digital data analysis. Her work spans AI psychometrics, gender inequality in online platforms, and validation frameworks for digital traces. She frequently publishes in top-tier venues such as Nature , Science , and AAAI conferences. DOC-fFORTE fellowship from the Austrian Academy of Sciences Four best paper awards at international conferences (ICWSM, CSCW, WWW, AAAI) Associate Editor, EPJ Data Science Steering Committee Member, International AAAI Conference on Web and Social Media Board Member, International Society for Computational Social Science Claudia Wagner has led and co-led substantial research projects funded by national and international agencies. She mentors a diverse group of PhD students working on topics like algorithmic bias, data quality, and dehumanization. She has organized training events such as the CSS Methods Summer School and delivered keynotes globally on inequality and computational social science. She leads the Computational Social Science department at GESIS and collaborates with interdisciplinary teams at RWTH Aachen and the Complexity Science Hub. Her group develops tools for measuring algorithmic impacts and visualizing disparities in socio-technical systems, such as the 'Planets of Disparity' dashboard.

David Sewellمشاهده پروفایل

مدرس ارشد

• Attention
• Learning
• Memory
• +۷ مورد دیگر

Dr. David Sewell is a Senior Lecturer and Deputy Head of School (Teaching & Learning) at the School of Psychology, The University of Queensland. His research focuses on attention, learning, memory, and decision-making, with a strong emphasis on formal mathematical models of human cognition. He is affiliated with the Centre for Perception and Cognitive Neuroscience within the Faculty of Health, Medicine and Behavioural Sciences. Education: Bachelor (Honours) of Arts and Doctor of Philosophy, both from the University of Western Australia. David's research explores the intersection of cognitive psychology and computational modeling. Key areas include perceptual decision-making, attentional mechanisms, and the application of diffusion models to understand cognitive processes. His work also extends to sustainability and collective self-regulation through cognitive frameworks. The 15 most recent articles highlight his contributions to modeling decision thresholds in memory prioritization, analyzing gaze cueing effects, and investigating neural correlates of confidence in multisensory decisions. Collaborative projects frequently involve interdisciplinary approaches, combining neuroscience, psychology, and computational methods. He has supervised multiple PhD candidates, serving as Principal or Associate Advisor, with research topics ranging from visual categorization to metacognition in children. Current and past funding includes ARC Discovery Projects on collective self-regulation and category learning constraints.

• Computational social science
• Social computing
• Information systems
• +۹ مورد دیگر

Björn Ross is a Lecturer in Computational Social Science at the University of Edinburgh's School of Informatics, where he is affiliated with the Institute for Language, Cognition and Computation. He serves as Director of the SMASH research group and is part of the management team for the Centre for Doctoral Training in Natural Language Processing (CDT in NLP). His educational background includes: PhD (2019) from the University of Duisburg-Essen MSc in Computer Science (2016) from the University of Münster Exchange year (2014-2015) at the University of Strasbourg BSc in Information Systems (2013) from the University of Münster Ross's research focuses on computational social science, particularly using natural language processing, social network analysis, and agent-based modeling to study social media phenomena. His work examines misinformation, hate speech, bot activity, and the ethical implications of computational methods. He also investigates how social media can be leveraged for social good, especially in crisis communication contexts. A significant portion of his recent research addresses bias and fairness issues in AI systems, particularly regarding marginalized communities and LGBTQ+ identities. His work spans both technical development of computational methods and critical examination of their societal impacts. Analysis of his recent publications reveals a strong trend toward examining bias in AI systems, particularly regarding marginalized communities. His work spans multiple methodologies including computational analysis of social media data, development of NLP techniques, and critical examination of AI ethics. He frequently collaborates across disciplines, working with researchers in computer science, social sciences, and healthcare domains. His professional recognition includes: Best Paper Award at the Hawaii International Conference on System Sciences (HICSS) in 2018 Associate Editor for Business & Information Systems Engineering Active membership in professional organizations including AIS, ACL, and ACM Ross currently supervises multiple PhD students including Agostina Calabrese, Eddie Ungless, Sandrine Chausson, Wendy Zheng, Seraphina Goldfarb-Tarrant, and Mahmoud Ibrahim. At the University of Edinburgh, he teaches courses such as 'Text Technologies for Data Science' and 'Evidence, Argument and Persuasion in a Digital Age,' reflecting his expertise at the intersection of computational methods and social implications.

Benoit Combemaleمشاهده پروفایل

استاد

• Software Engineering
• Model-Driven Engineering
• Software Language Engineering
• +۷ مورد دیگر

Benoit Combemale is a Full Professor of Software Engineering at the University of Rennes , currently on leave as Research Director at Inria . He is affiliated with the DiverSE research team (joint between IRISA and Inria) and the SM@RT team at IRIT. He serves as Editor-in-Chief of the Springer-Nature journal Software and Systems Modeling (SoSyM) and holds leadership roles in academic conferences like ACM SIGPLAN Intl. Conference on Software Language Engineering and MODELS . Education : Habilitation (2015) and PhD (2008) in Software Engineering from University of Rennes and University of Toulouse, respectively. Research : Focuses on Model-Driven Engineering , Digital Twins , and ICT for Sustainability , with applications in cyber-physical systems, scientific computing, and industrial systems. Recent Publications highlight trends in digital twin modeling, energy-aware software engineering, polyglot programming, and formal verification for heterogeneous systems. His scientific awards include: Editor-in-Chief of SoSyM Steering Committee member of ACM SIGPLAN Intl. Conference on Software Language Engineering General Chair for ICT4S 2023 and MODELS 2016 PC Chair for MODELS 2024, ECMFA 2019, and SLE 2014 Coordinator of Dagstuhl Seminars and Bellairs workshops Advising includes 30+ students in topics ranging from digital twin engineering to software testing and language design. He leads the GEMOC Initiative and contributes to projects like SciHook and GEMOC Studio .

Bryan Parnoمشاهده پروفایل

استاد

• Secure Systems
• Formal Software Verification
• Verifiable Computation
• +۷ مورد دیگر

Bryan Parno is a Professor at Carnegie Mellon University in the Departments of Electrical & Computer Engineering and Computer Science . He is the recipient of the Kavčić-Moura Chair and leads the Secure Foundations Lab , focusing on end-to-end secure systems through formal verification. Research spans secure systems , applied cryptography , distributed systems , and zero-knowledge proofs Developed Verus (verified Rust systems) and Project Everest (verified HTTPS stack) Key contributions include Ironclad , Flicker , and Pinocchio , with impacts on Intel CPUs and Windows/iOS security models His work emphasizes open-source tools and reproducibility , often published in top venues like POPL, PLDI, and IEEE S&P. Recent projects address WebAssembly security and formal verification of complex distributed systems . Major Awards Jay Lepreau Best Paper Award (OSDI 2025) IEEE Cybersecurity Award for Practice (2024) Sloan Research Fellowship (2018) Test-of-Time Awards (IEEE S&P 2023, IEEE S&P 2020) Best Paper Awards at USENIX Security, OOPSLA, and PLDI

• Antibodies
• Bioinformatics
• Cheminformatics
• +۷ مورد دیگر

Dr. Ed E. Moret is an Associate Professor of Computational Medicinal Chemistry at Utrecht University, where he serves as Managing Director of the Utrecht Institute for Pharmaceutical Sciences. He is a member of the Departmental Executive Board and Chair of the Board of Examiners of the School of Pharmacy. His academic career spans over three decades with significant contributions to pharmaceutical sciences. Utrecht University, Utrecht Institute for Pharmaceutical Sciences School of Pharmacy, Department of Chemical Biology and Drug Discovery Managing Director since January 2010 Dr. Moret's educational background includes completing Gymnasium-b at Gymnasium Camphusianum in Gorinchem in 1979, followed by pharmacy studies at Utrecht University until 1988. He earned his PhD in 1993 with research on calculations and simulations of DNA-alkylating cytostatics under supervision of Prof. L.H.M. Janssen and Prof. J.P.A.E. Tollenaere. He also conducted postdoctoral research at the Scripps Research Institute with Prof. A.J. Olson. His primary research interests focus on molecular recognition, particularly in auto-immune diseases, with expertise spanning computational medicinal chemistry, computer-aided drug discovery, cheminformatics, and bioinformatics. Dr. Moret's work bridges the gap between theoretical calculations and experimental validation in drug design. His research portfolio demonstrates a consistent trajectory from fundamental molecular interactions to applied drug discovery, with particular emphasis on enzyme inhibitors, carbohydrate-protein interactions, and molecular recognition processes. Analysis of his publication record reveals a strong focus on structure-based drug design, with significant contributions to the development of inhibitors for enzymes like β-glucocerebrosidase, NNMT, and neuraminidase. His work spans multiple therapeutic areas including lysosomal storage disorders, cancer metabolism, and infectious diseases. The interdisciplinary nature of his research is evident in the integration of computational approaches with experimental validation across biochemistry, pharmacology, and medicinal chemistry. Teacher of the Year (awarded three times by Pharmacy students) Member of editorial boards for Medicines and Conceptuur journals Secretary of Board of FIGON (2016) Secretary of Raad voor de Farmaceutische Wetenschappen (2024) Member of Board of Stichting Farmaceutische Erfgoed (2024) Dr. Moret has been actively involved in educational innovation, developing and coordinating the master's programme Drug Innovation, the profile Drug Regulatory Sciences, and the Honours programme Pharmaceutical Sciences. He has taught courses for pharmacy, chemistry, UCU and medical sciences students, as well as PhD courses in bioinformatics and computer-aided drug discovery. His educational contributions include developing an inquiry-based elective course on drug discovery, for which he published educational research. He holds BKO and SKO teaching qualifications and participated in the Centre of Excellence in University Teaching program. As Managing Director of the Utrecht Institute for Pharmaceutical Sciences, Dr. Moret leads research initiatives across chemical biology, drug discovery, and pharmaceutical sciences. His leadership extends to multiple advisory and editorial roles within the pharmaceutical research community, reflecting his significant contributions to both academic and professional spheres of pharmaceutical sciences.

Peter Müllerمشاهده پروفایل

استاد

• Formal Methods
• Program Verification
• Programming Languages
• +۴ مورد دیگر

Prof. Peter Müller is a Full Professor at the Department of Computer Science at ETH Zurich since 2008. Previously, he held positions as Assistant Professor at ETH Zurich (2003-2008), Researcher at Microsoft Research Redmond (2007-2008), and IT project manager at Deutsche Bank. He earned his Diploma in Computer Science from Technical University of Munich (1996) and his Dr. rer. nat. from University of Hagen (2001) with a dissertation on modular verification of object-oriented programs. His research focuses on enabling correct software development through programming languages, verification methods, and tools. Key areas include formal verification for Rust and Go programs (Prusti and Gobra projects), separation logic, security protocols, and distributed systems verification. Müller's work emphasizes practical verification techniques for real-world systems, including secure router implementations and smart contract verification. Recent research trends highlight advancements in hyperproperties, modular reasoning for iterators and closures in Rust, and formal validation of verification tools. His methodologies bridge theoretical foundations with industrial applications, addressing challenges in concurrency, memory safety, and security assurance. Notable contributions include the SCION internet architecture, the Prusti verifier for Rust, and formal verification frameworks for distributed systems. His work often integrates rigorous mathematical foundations with scalable software engineering practices.

• Climate Change Adaptation in Agriculture
• Land Use Modeling
• Biodiversity Conservation
• +۳ مورد دیگر

Thomas Berger is a Professor at the University of Hohenheim , affiliated with the Faculty of Agricultural Sciences and leading the Department of Economics of Land Use . He also contributes to the Computational Science Hub and Hohenheim Tropics initiatives. Focus Areas: Climate change adaptation, land-use modeling, biodiversity-productivity trade-offs, agent-based simulation, and machine learning in agricultural systems. Key Projects: Simulation frameworks for smallholder resilience in Ethiopia, bioeconomic modeling in the Amazon, and hybrid intelligence applications in European agricultural policy. Recent Publications: 2025 study on climate change effects on insecticide reduction in Germany, 2024 work on reconciling biodiversity with productivity via hybrid models, and 2023 methodological contributions to surrogate modeling and seasonal forecast integration. Research Trends: Interdisciplinary integration of climate science, agricultural economics, and computational modeling, with increasing emphasis on AI-assisted decision support systems and sustainability policy validation. Teaching & Outreach: Offers Agricultural Economics seminars and Hohenheim Tropics discussions, requiring advance email registration for office hours.

John F. Bradyمشاهده پروفایل

استاد

• fluid mechanics
• transport processes
• complex and multiphase fluids

John F. Brady is the Chevron Professor of Chemical Engineering and Mechanical Engineering at the California Institute of Technology. He earned his B.S. from the University of Pennsylvania (1975), M.S. (1977) and Ph.D. (1981) from Stanford University, and has held academic roles at Caltech since 1985, including Executive Officer for Chemical Engineering (1993-99; 2013-19). His research focuses on fluid mechanics, transport processes, and complex/multiphase fluids. Elected to the National Academy of Sciences (20XX) Elected to the American Academy of Arts and Sciences (20XX) Brady's publications reveal expertise in active matter dynamics, microrheology, and non-equilibrium systems. His work spans fundamental fluid mechanics to applied biomedical device design, with a strong emphasis on computational modeling and experimental validation in colloidal and soft matter physics.

Jean-Pierre Gattusoمشاهده پروفایل

استاد پژوهشی

• Ocean Acidification
• Marine Ecosystems
• Climate Change Mitigation
• +۳ مورد دیگر

Jean-Pierre Gattuso is a CNRS Research Professor at the Laboratoire d'Océanographie de Villefranche (Sorbonne University) and Associate Scientist at the Institute for Sustainable Development and International Relations (IDDRI-SciencesPo, Paris). His research focuses on the impacts of ocean acidification and warming on marine ecosystems, as well as ocean-based solutions to climate change mitigation and adaptation. Current Roles : CNRS Research Professor, Associate Scientist at IDDRI-SciencesPo Past Positions : Research Professor at Shantou University (2006–2009), Visiting Scientist at Rutgers University and NCAR (2004–2005), Group Leader at Laboratoire d'Océanographie de Villefranche (1998–2004), and others. His research interests span ocean acidification , carbon and carbonate cycling , climate change impacts on marine ecosystems , and ocean-based mitigation strategies . He co-edited the first book on ocean acidification and contributed to IPCC AR5 and AR6 reports. Recent publications highlight his work on ocean alkalinity enhancement , CO2 sequestration , and climate policy integration . He has developed tools like FOCE systems for in situ ocean acidification experiments and standardized ocean carbonate chemistry analysis. Scientific Awards : Ruth Patrick Award (2020), Blaise Pascal Medal (2014), Vladimir Vernadsky Medal (2012), and others. Academic Memberships : Foreign member of the Chinese Academy of Sciences, elected member of the European Academy of Sciences, Academia Europaea, and founder of the European Geosciences Union. Gattuso has led the Ocean Acidification International Coordination Centre at the International Atomic Energy Agency and will co-chair the 2025 One Ocean Science Congress.

Stephan Staudacherمشاهده پروفایل

استاد

• Aircraft Propulsion
• Gas Turbine Engineering
• Thermodynamics
• +۳ مورد دیگر

Prof. Dr.-Ing. Stephan Staudacher is the Director of the Institute of Aircraft Propulsion at the University of Stuttgart. His work focuses on aircraft propulsion systems, gas turbine performance, and turbomachinery design. He holds a professorship in the Faculty of Mechanical Engineering and Aerospace, leading research in advanced engine technologies, erosion effects, and fault detection algorithms. Research interests include engine reliability, computational fluid dynamics (CFD), and experimental validation of propulsion systems. His publications emphasize topics like neural network applications for fault detection, ice crystal icing simulations, and particle transport in additive manufacturing processes. Recent studies highlight the optimization of composite-cycle engines and assessment of mission severity caused by erosion. Key contributions include advancements in engine condition monitoring, transient performance analysis, and the development of Stuttgart University’s Altitude Test Facility (ATF). His work bridges theoretical models with industrial applications, addressing challenges in both civil and military aviation propulsion systems.