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Andrea MaranzoniView profile

Associate Professor

• Hydraulics
• Numerical Modelling of Free Surface Flows
• Flood Hazard Assessment
• +7 more

Andrea Maranzoni is an Associate Professor of Hydraulics at the Department of Engineering and Architecture, University of Parma, Italy, where he has been serving since February 2019. He previously held assistant professor positions at the University of Parma and the University of Brescia, and has been actively involved in teaching and research in hydraulic and environmental engineering. Education: Ph.D. in Civil Engineering (Hydraulic Engineering), University of Parma, 2004 Degree in Civil Engineering (cum laude), University of Parma, 2000 His research focuses on the numerical and physical modeling of unsteady free surface flows, flood hazard assessment due to dam-breaks and levee breaches, probabilistic flood mapping, shallow water equations, and finite volume methods. He also investigates hydrodynamic actions on structures, mixed flow modeling, and imaging techniques for water surface measurement. His work bridges theoretical hydraulics with practical flood risk mitigation strategies. The recent publications (2023–2024) reflect a strong trend in dam-break modeling, flood hazard quantification, and theoretical advances in fluid mechanics. Key themes include probabilistic risk assessment, extension of classical equations like Bernoulli’s under non-inertial frames, and experimental analysis of hydrophobic sphere impacts. These works demonstrate a consistent focus on improving flood prediction accuracy and safety through advanced numerical and experimental methods. Scientific Awards: None listed in the provided text. Maranzoni has advised one Ph.D. student, Susanna Dazzi, and has supervised approximately 30 Bachelor’s and Master’s theses. He has received research funding from MIUR’s FFABR program (€3,000) and is a participant in the RELAID project (€600,000 total, 2020–2022). He also serves as Vice Coordinator of the Ph.D. Programme in Civil Engineering and Architecture at the University of Parma. He is involved in the research community through editorial roles, notably as Guest Editor for a special issue in Water (MDPI) on dam-break modeling, and as a reviewer for leading journals such as Journal of Hydraulic Engineering (ASCE) and Advances in Water Resources . He is a member of the Italian Hydraulic Group (GII), the Faculty Board of the Ph.D. program, and the CIDEA Interdepartmental Center for Energy and the Environment.

R. Iris BaharView profile

Professor

• Computer Architecture
• Low Power Design
• Machine Learning
• +5 more

R. Iris Bahar is a Professor of Computer Science and Engineering at Colorado School of Mines, where she serves as Department Head of Computer Science since 2022. Previously, she held dual appointments as Professor of Engineering and Computer Science at Brown University for 26 years. Her research focuses on energy-efficient and reliable computing across system-level to device-level domains, with applications in near-data processing, robust machine learning for robotics, and noise-immune nanoscale circuit design. Research Interests include computer architecture, low-power design, and robotic system optimization. She pioneers hybrid generative-discriminative inference techniques for robot scene perception, achieving 40% accuracy improvements in occluded environments through FPGA-based Monte-Carlo sampling implementations. Her work on subthreshold CMOS noise modeling provides 1000x simulation speedups over SPICE-based methods. Near-Data Processing Architectures Approximate Computing Hardware/Software Co-design Nanoscale Noise Modeling Robust AI for Robotics Scientific Awards include the 2024 IEEE Undergraduate Teaching Award, 2019 Marie R. Pistilli Women in Engineering Achievement Award, and NSF CAREER award. She is an IEEE Fellow and ACM Distinguished Scientist. Funded Research spans NSF grants on thermal noise effects ($360k), durable data structures ($500k), and Brown SEED grants for autonomous robotics. Her lab trains 12+ students in hardware acceleration and probabilistic computing.

Daniel WichsView profile

Associate Professor

• Cryptography
• Computer Security
• Algorithms
• +5 more

Daniel Wichs is an Associate Professor at the Khoury College of Computer Science , Northeastern University , and Senior Scientist at NTT Research . His work focuses on Cryptography , particularly in computing on encrypted data, lattice-based cryptography, and information-theoretic security. Education: PhD in Computer Science (NYU, 2011), BS/MS in Mathematics and Computer Science (Stanford, 2005). His research spans Cryptography , with emphasis on theoretical foundations , program obfuscation , and secure computation . Recent work explores computationally bounded error correction , succinct arguments , and RAM-based cryptographic systems . He has received the Alfred P. Sloan Foundation Fellowship (2018), NSF CAREER Award (2018), and IBM Josef Raviv Fellowship (2011-2013). His students include Manu Kondapaneni , LaKyah Tyner (co-advised), and Ethan Mook , many of whom now hold academic positions. He leads the Cryptography and Privacy Group at Northeastern and has organized major conferences like the Simons Summer Program in Cryptography (2025). His teaching includes advanced courses on Foundations of Cryptography and Theory of Computation .

• Automata Theory
• Formal Verification
• Logic in Computer Science
• +3 more

Christof Löding , currently an Adjunct Professor at the Lehrstuhl für Logik und Theorie diskreter Systeme (Informatik 7) department of RWTH Aachen University , is a leading researcher in Automata Theory , Formal Verification , and Logic in Computer Science . His work bridges theoretical foundations with practical applications in software verification, automata minimization, and game theory. Research Interests include automata theory, formal verification, logic, tree automata, game theory, and computational models. Publications span topics like Finite-valued Streaming String Transducers , Deterministic Parity Automata , and Stochastic Game Strategies . Collaborations with researchers like Emmanuel Filiot , Sarah Winter , and León Bohn highlight his contributions to automata and verification. Email : loeding@informatik.rwth-aachen.de He has actively published in venues such as ICALP , LICS , and STACS , focusing on deterministic automata, transducers, and logic-based computational systems. His work on Hyperlogic for Strategies in Stochastic Games (2025) and Minimal History-Deterministic Automata (2025) showcases his ongoing influence in formal methods and automata theory.

Cheryl J. HapkeView profile

Research Professor

• Coastal Geology
• Sea-Level Rise Adaptation
• Coastal Hazards
• +6 more

Cheryl J. Hapke is a Research Professor at the University of South Florida's College of Marine Science, specializing in coastal geology. She holds a Ph.D. in Coastal Geology and serves as Coordinator for the Florida Coastal Mapping Program, an initiative to deliver high-resolution bathymetric data across Florida's coastal waters within a decade. Her research addresses coastal hazards like inundation, storms, and erosion through geomorphic evolution studies of barrier islands, rocky coasts, and estuarine systems. Academic Rank: Research Professor University: University of South Florida School: College of Marine Science Role: Coordinator, Florida Coastal Mapping Program Dr. Hapke's work emphasizes probabilistic frameworks for forecasting coastal resilience, leveraging multibeam bathymetry and LiDAR technologies. Her research spans global coastal systems, with a focus on Fire Island (NY), Tampa Bay (FL), and the California coast, addressing sediment budgets, shoreline change, and storm impacts. The Florida Coastal Mapping Program supports applications in navigation, restoration, fisheries management, and community resilience planning. Dr. Hapke's recent publications highlight advancements in bathymetric data collection, morphodynamic modeling, and coastal hazard adaptation tools. Her interdisciplinary approach integrates remote sensing, historical data, and numerical models to understand coastal vulnerabilities and inform policy decisions.

Michael WilliamsView profile

Research Fellow

• Gravitational Wave Physics
• Neutron Stars
• Black Holes
• +3 more

Dr. Michael Williams is a Research Fellow at the Institute of Cosmology & Gravitation under the Faculty of Technology , University of Portsmouth. His work focuses on gravitational wave physics, neutron star dynamics, and black hole binaries through collaborations with LIGO, Virgo, and KAGRA detector networks. Active in gravitational wave detection and multi-messenger astronomy Developing AI algorithms for telescope targeting and signal processing Recipient of the IOP Trusted Reviewer status (2025) His recent research includes premerger characterization of black hole binaries , continuous wave searches from pulsars , and gravitational wave-FRB counterpart detection . He contributes to open-source tools like nessai and bayesbeat , with 25 datasets released. Williams participates in conferences like the European AI for Fundamental Physics Conference and workshops on milli-hertz gravitational wave astrophysics. Key collaborations: LIGO, Virgo, KAGRA, GEO600 Technical expertise: matched filtering , Bayesian inference , normalizing flows

Abhraneel SarmaView profile

Researcher

• Uncertainty Visualization
• Data Analysis
• Multiverse Analysis
• +3 more

Abhraneel Sarma is a PhD candidate in Computer Science at Northwestern University, advised by Professors Matthew Kay and Jessica Hullman. His research focuses on addressing uncertainty in data analysis through tools like multiverse (an R package for sensitivity analysis) and Milliways (a visualization system for multiverse analysis validation). Uncertainty visualization Multiverse analysis Bayesian inference Reproducibility in research His work combines empirical studies and system development to improve data-driven decision-making. Publications include best paper awards at CHI 2019 and honorable mentions at CHI 2024, CHI 2023, and VIS 2022. 2025 : CHI 2024 : CHI (Best Paper Honorable Mention) 2023 : CHI (Best Paper Honorable Mention) 2022 : VIS (Best Paper Honorable Mention) 2019 : CHI (Best Paper Award) Email: abhraneel@u.northwestern.edu

Måns MagnussonView profile

Associate Professor

• Bayesian Statistics
• Probabilistic Machine Learning
• Statistical Inference from Textual Data
• +4 more

Måns Magnusson is an Associate Professor at the Department of Statistics, Uppsala University, with affiliations at the Institute for Analytical Sociology (Linköping University) and the Institute for Future Studies. His work bridges Bayesian statistics, probabilistic machine learning, and textual analysis, focusing on model evaluation, diagnostics, and inference algorithms. He contributes to computational social science and digital humanities through text-as-data methods. Research Themes: Bayesian inference, probabilistic machine learning, statistical inference from textual data Key Applications: Sociology, political science, law, education statistics, public health Current Projects: Improving probabilistic programming generalizability (Swedish Research Council grant), SWERIK research infrastructure (Riksbankens jubileumsfond) His recent publications emphasize text mining, model comparison, and legal data challenges. He has developed tools for national ID number validation, hate crime estimation, and parliamentary corpus construction. Notable awards include the Cramér Prize (2018), Statistician of the Year (2023), and membership in the Swedish Young Academy (2023) and ELLIS (2024). Scientific Contributions: Botten Ada Bayesian election model, 'loo' package for cross-validation Collaborative Work: AI4Research sabbatical (2024), Riksbankens jubileumsfond funding Industry Background: Statistician roles at Swedish Agency for Education, Crime Prevention, and Public Health

• Computational Physiology
• Biomechanics
• Cardiac Electromechanics
• +7 more

Joakim Sundnes is a Chief Research Scientist and Research Professor at the Department of Scientific Computing, Simula Research Laboratory. He specializes in computational physiology, cardiac biomechanics, and mathematical modeling of cardiovascular systems. Key Research Areas: Cardiac electromechanics, computational fluid dynamics in cardiology, uncertainty quantification in cardiac models, and mechano-electric feedback mechanisms Recent Trends: Focus on patient-specific modeling, left atrial flow dynamics, right ventricular mechanics in pulmonary hypertension, and personalized treatment simulations Scientific Contributions: Active participant in international conferences and editorial work. Co-author of multiple benchmark studies and educational texts on physiological modeling.

Francois ChaumetteView profile

Research Professor

• Robotics
• Computer Vision
• Visual Servoing
• +17 more

François Chaumette is a Senior Research Scientist (Directeur de recherche) at Inria, affiliated with IRISA and the Centre Inria de l'Université de Rennes. He has been a key researcher in robotics and computer vision since 1990 and led the Lagadic research team from 2004 to 2017. His research interests are centered on robot vision, particularly visual servoing and active perception . He has made foundational contributions to image-based and position-based visual servoing, and his work integrates control theory, computer vision, and robotics. His research spans applications in mobile robotics, aerial systems, medical robotics, space robotics, and soft object manipulation. The recent publications highlight a consistent focus on visual servoing under complex constraints—such as motion blur, occlusions, and deformations—applied to drones, cable-driven robots, and space systems. There is a strong emphasis on robustness , stability analysis , and hybrid sensing (e.g., vision + proximity, vision + force). His work with the RemoveDebris mission demonstrates real-world impact in space robotics. AFCET/CNRS Prize for best Ph.D. in Automatic Control Best paper awards at RFIA 1996 & 2004 Best paper in IEEE T-RA (2002) Best paper in IEEE RA-L (2019) Best paper in IEEE RAM (2020) IEEE Fellow (2013) He has advised over 30 Ph.D. students, many of whom have become active researchers in robotics. He has served in editorial roles for top journals including IEEE Transactions on Robotics , IEEE Robotics and Automation Letters , and the International Journal of Robotics Research . He was elected to the IEEE RAS Administrative Committee (2016–2018) and served on ERC grant panels for robotics. Chaumette is the main developer of ViSP (Visual Servoing Platform), a widely used C++ library for visual tracking and servoing. His leadership in both theoretical advances and software tools has significantly shaped the visual servoing community.

Shibashis GuhaView profile

Associate Professor

• Formal Methods
• Logic in Computer Science
• Automata Theory
• +12 more

Shibashis Guha is an Associate Professor (Reader) at the School of Technology and Computer Science, Tata Institute of Fundamental Research (TIFR), where he conducts research and teaches in formal methods, logic, automata theory, and verification. His work focuses on reactive controller synthesis, probabilistic systems, timed automata, and the integration of machine learning with formal verification. Institution: Tata Institute of Fundamental Research School: School of Technology and Computer Science Department: Department of Computer Science Academic Rank: Associate Professor His research interests span formal methods, logic in computer science, automata theory, probabilistic systems, algorithmic game theory, and reinforcement learning. He investigates the synthesis of reactive controllers, behavioral equivalences, and the application of learning in verification. His work often intersects with infinite games and descriptive complexity. The recent publications highlight a strong trend in stochastic games, mean-payoff objectives, window properties, probabilistic model checking, and the synthesis of controllers under logical specifications. There is a clear emphasis on bridging formal methods with learning, especially in continuous-time and probabilistic settings. Scientific Awards: Best paper award at MFCS 2021 Guha advises several students and collaborates widely across institutions. His research is funded by the DST-SERB project on zero-sum and nonzero-sum games for controller synthesis. He has served on program committees for major conferences including CAV, ATVA, CONCUR, LICS, and VMCAI, and has organized workshops such as iVerif. He teaches advanced graduate courses like Automata and Computability, Descriptive Complexity, and Automata, Verification, and Infinite Games. He is actively involved in the research community, delivering invited talks at institutions like IST Austria, ENS Paris-Saclay, and Indian Statistical Institute. He also leads seminar series and participates in panels on AI and verification.

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence
• +7 more

Jing Jiang is a prominent researcher at Singapore Management University, specializing in Natural Language Processing (NLP), Computational Linguistics, and Artificial Intelligence. His work spans diverse areas including Vision-Language Models, Machine Translation, Knowledge Graph Reasoning, Sentiment Analysis, and Social Media Discourse Modeling. Key contributions include frameworks for consistent client simulation in mental health counseling and counterfactual contrastive prefix-tuning for many-class classification. He has pioneered methods in zero-shot VQA with interpretable reasoning graphs , cross-lingual understanding with universal syntax , and modularized zero-shot architectures . His research often combines theoretical insights with practical implementations, as seen in works on stereotypical bias in vision-language models (VLStereoSet), tensorized self-attention for dependency modeling, and collaborative relation-augmented attention for knowledge graph completion. Jing Jiang's collaborations span global experts in NLP and AI, with co-authors from institutions like SMU, Waseda University, and Microsoft Research.

Thomas McCurdyView profile

Professor

• Asset Pricing
• Capital Markets
• Financial Institutions
• +3 more

Thomas McCurdy is a Professor of Finance at the University of Toronto's Rotman School of Management and holds the Bonham Chair in International Finance. He has a status-only cross-appointment to the Department of Economics. McCurdy founded the Financial Research and Trading Lab in 1999, later expanded as the BMO Financial Group Finance Research and Trading Lab in 2013. His expertise spans asset pricing, capital markets, and simulation-based learning pedagogy. PhD, University of London (LSE) MA, University of British Columbia Honours BA, University of Guelph McCurdy's research focuses on asset pricing, capital markets, and financial institutions. He has pioneered simulation-based learning tools, including co-developing the RIT Market Simulator package with over fifty decision cases used globally. His recent work includes textual analysis of stock return jumps, probabilistic modeling of regime changes during crises, nonlinear pricing kernels for risk, and real-time structural break detection. McCurdy has served as an Associate Editor for the Journal of Financial Econometrics and an Associate Fellow at CIRANO Research Institute. He teaches courses in MBA, Master of Finance, Master of Financial Risk Management, and Commerce programs, emphasizing quantitative modeling and risk-informed decision-making. BMO Financial Group Finance Research and Trading Lab (Founded 1999, Expanded 2013) RIT Market Simulator package co-developer Best Research Paper Award Best Teaching Award

• Knowledge representation and reasoning
• Planning
• Nonmonotonic reasoning
• +2 more

Yannis Dimopoulos is a Professor in the Department of Computer Science at the University of Cyprus. He has previously held research positions at the Max-Planck Institute for Computer Science in Saarbrücken and the University of Freiburg in Germany. He earned his B.Sc. and Ph.D. in Computer Science from the Athens University of Economics and Business. His primary research interests include: Knowledge representation and reasoning Planning Nonmonotonic reasoning Constraint satisfaction Machine learning His recent research, based on publications from 2017 to 2024, focuses on the theoretical and computational aspects of abstract argumentation, particularly control argumentation frameworks, probabilistic extensions, and the integration of argumentation with Boolean networks and negotiation under incomplete information. He has also contributed significantly to Answer Set Programming (ASP) for planning, developing the plasp 3 framework for effective ASP-based planning solutions. His scholarly work appears in top-tier venues such as AAAI, IJCAI, ECAI, KR, AAMAS, and journals like Artificial Intelligence and Autonomous Agents and Multi-Agent Systems. His most frequent collaborators include Pavlos Moraitis, Jean-Guy Mailly, Antonis C. Kakas, and Wolfgang Dvorák. No scientific awards or honors are mentioned in the provided text. Yannis Dimopoulos has advised or collaborated with several researchers, including Jean-Guy Mailly, Pavlos Moraitis, Nabila Hadidi, and Muhammad Adnan Hashmi, though formal student-advisor relationships are not explicitly detailed. His work often involves theoretical and computational modeling, and while specific grants or funding sources are not listed, his sustained publication record suggests active research support. He is actively involved in research teams and collaborations focused on argumentation, multi-agent systems, and automated reasoning, as evidenced by his extensive co-authorship network.

Angelos AlexopoulosView profile

Assistant Professor

• Theoretical and Applied Econometrics
• Bayesian Analysis
• Computational Statistics
• +4 more

Angelos Alexopoulos is an Assistant Professor at the Department of Economics , Athens University of Economics and Business . He has held Research Associate positions at the University of Cambridge, University College London, and University of Exeter in the UK. PhD: Athens University of Economics and Business Research Focus: Computational Statistics, Econometrics, Bayesian Analysis, Network Modelling Publications span Bayesian inference, epidemic forecasting, machine learning for fraud detection, and econometric methodology. Key journals include Journal of the Royal Statistical Society , Journal of Computational and Graphical Statistics , and Statistics and Computing . 2024: Gaussian invariance in MCMC 2024: Epidemic nowcasting models 2023: VAT fraud detection with ML Awards include certifications in Deep Learning (Coursera), Blockchain (edX), and Object-Oriented R Programming (DataCamp).