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Gabriella CasalinoView profile

Assistant Professor

• Computational Intelligence
• eHealth
• Data Stream Mining
• +5 more

Gabriella Casalino is an Assistant Professor at the University of Bari Aldo Moro, Department of Computer Science, and a key researcher at CILAB - Computational Intelligence Lab. Her work focuses on Computational Intelligence methods for interpretable data analysis, particularly in eHealth, Data Stream Mining, and eXplainable Artificial Intelligence (XAI) within medical and educational domains. She has contributed to innovative approaches in smartphone-based health monitoring, fuzzy logic applications, and remote vital sign detection via photoplethysmography. Education : Ph.D. in Computer Science, with advanced training at institutions like Universitat de Girona and Université de Mons. Research Trends : Recent publications highlight applications of evolving granular computing, neuro-fuzzy systems, and explainable AI in hypertension prediction, bipolar disorder monitoring, and educational data analysis. Key subfields include remote health monitoring, medical data streams, and hybrid AI models. Grants : Research funded by AIRC (Italian Cancer Research Foundation), focusing on computational methods for healthcare challenges. Labs & Collaborations : Active in CILAB, collaborating on projects involving mHealth solutions, cardiovascular risk assessment, and intelligent educational systems.

Igor FranceticView profile

Research Fellow

• Health Economics
• Applied Economics
• Healthcare Organization
• +4 more

Igor Francetic is a Research Fellow at the Division of Population Health, Health Services Research & Primary Care at the University of Bristol. He holds a PhD in Public Health from the University of Basel (2019) and has expertise in health economics, applied microeconomics, and econometric analysis. His research focuses on healthcare organization, health behaviors, and policy evaluation, with a particular emphasis on emergency care prioritization, primary care workforce dynamics, and health inequalities. He has contributed to policy-oriented projects in Switzerland and Tanzania, including studies on hospital networks, patient safety, and health systems governance. **Education**: PhD in Public Health (University of Basel, 2019) MSc in Economics (University of Lausanne, 2016) BA in Economics (University of Lugano, 2012) BSc in Business Administration (SUPSI, 2011) **Research Interests**: His work explores cancer screening, spillover effects in healthcare, emergency care disparities, and primary care networks. He employs microeconometric and network analysis tools, leveraging platforms like R and Stata. Recent studies include evaluating skill-mix changes in primary care (England), socioeconomic disparities in emergency care access, and the impact of social accountability monitoring in Tanzania. **Awards & Fellowships**: SSPH+ Fellow, Swiss School of Public Health (2024–) SNSF Ambizione Fellow, SUPSI (2024–) **Teaching**: Co-lecturer for the Health Economics unit (ECON 32202) at the University of Bristol. **Professional Memberships**: Health Economists' Study Group UK (HESG) Royal Economic Society (RES) Swiss Society of Health Economics (sggö) Croatian Health Economics Association (CHEA) – Co-founder and board member

Jonathan Ragan-KelleyView profile

Assistant Professor

• High-Efficiency Visual Computing
• Compilers
• Computer Architecture
• +3 more

Jonathan Ragan-Kelley is the Esther and Harold E. Edgerton Assistant Professor of Electrical Engineering & Computer Science at MIT and an Assistant Professor of EECS at UC Berkeley. He leads the Visual Computing group at CSAIL, focusing on high-efficiency visual computing, compilers, and architectures for image processing, machine learning, and 3D rendering. His research bridges systems, compilers, and hardware design, emphasizing scalable solutions for computational challenges. Education: PhD in Computer Science from MIT (2014), postdoc at Stanford University, and visiting researcher at Google. He co-created the Halide language and has developed multiple domain-specific languages (DSLs) and compiler systems. Research interests include compiler optimization, scheduling languages (e.g., Exo), and efficient computing frameworks. He has received awards such as the NSF CAREER Award and ACM SIGGRAPH’s Significant New Researcher Award. Awards: ACM SIGGRAPH Award, NSF CAREER, Intel Outstanding Researcher Award Key Contributions: Halide compiler framework, Exo scheduling language, machine learning acceleration techniques Labs/Teams: Visual Computing at MIT CSAIL

Bertrand ClarkeView profile

Professor

• Data Mining
• Machine Learning
• Prediction
• +4 more

Bertrand Clarke is a Professor in the Department of Statistics at the University of Nebraska-Lincoln, within the College of Agriculture & Natural Resources. He holds a PhD in Statistics from the University of Illinois (1989) and has held academic positions at Purdue University, the University of British Columbia, the University of Miami (Medical School), and served as Chair of the Department of Statistics at UNL. His research focuses on prediction, model uncertainty, and statistical methods for complex/high-dimensional data, including genomic data and machine learning applications. Education: PhD in Statistics from University of Illinois (1989), with early work recognized by the Browder J. Thompson Award. His career includes sabbaticals at University College London, Duke University (SAMSI), and the Newton Institute at Cambridge. He pioneered biostatistics programs at the University of Miami and authored a Springer textbook on data mining/machine learning. Research Interests: Prediction theory, model bias/uncertainty, ensemble methods, Bayesian approaches, and applications in genomics. He emphasizes statistical principles like variance-bias tradeoff and robustness in complex data analysis. Awards: ASA Fellow (2014), Browder J. Thompson Award (1989). Editorial roles in four journals and service on the Savage Award Committee.

• Structural Equation Modeling
• Psychometrics
• Statistics
• +5 more

Yves Rosseel is a Professor at Ghent University (UGent) specializing in Structural Equation Modeling (SEM) , Psychometrics , and Statistical Methodology . With over 15 recent publications (2024-2025), he focuses on small-sample SEM solutions, factor score regression, measurement error, and Bayesian extensions. His work bridges Statistics with applications in Psychology , Education , and Neuroimaging . Research Trends Developed Mixture Multigroup SEM for cross-group comparisons Proposed Information-Theoretic Hypergraphs in psychometrics Advanced Two-Stage Estimation for round-robin data Created blavaan R package for Bayesian SEM Investigated Measurement Error in hypothesis testing Scientific Contributions Published 84 Social Sciences papers, 37 Statistics works, and 11 Neuroimaging studies Promoted 8 PhDs including Sara Dhaene and Julie De Jonckere Co-authored 12+ works with Marijke Welvaert and 10+ with Stijn Vanheule

Fotios PetropoulosView profile

Professor

• Time Series Forecasting
• Judgmental Forecasting
• Statistical Model Selection
• +3 more

Fotios Petropoulos is a Professor at the University of Bath, holding the Management Chair in Management Science within the School of Management's Information, Decisions & Operations department. He also served as the Spyros Makridakis Chair in Forecasting at the University of Nicosia (2023–2023). His research focuses on time series forecasting, judgmental approaches, and integrating statistical and human judgment in decision-making processes. He has contributed to improving forecasting accuracy through temporal aggregation and hierarchical methods. Petropoulos holds a Doctor of Engineering (2012) and Bachelor of Engineering (2007) from the National Technical University of Athens. Editor of the International Journal of Forecasting (2020–present) Associate Editor of Foresight: The International Journal of Applied Forecasting (2015–2022) Director of the International Institute of Forecasters (2016–2018) His research interests emphasize forecasting processes, model selection, and the role of judgment in statistical models. Key areas include temporal aggregation, forecast reconciliation, and behavioral operations analytics. He has published over 100 peer-reviewed articles, focusing on topics like computational cost optimization, probabilistic forecasting, and scalable reconciliation methods. His work contributes to Sustainable Development Goals related to education and innovation. Recent articles highlight advancements in univariate forecasting efficiency, forecast selection criteria, and dynamic reconciliation. Petropoulos is a member of the Smart Warehousing and Logistics Systems group and actively participates in editorial boards of leading forecasting journals. His academic and professional roles bridge theoretical research and practical applications in operational decision-making.

Benjamin Bloem-ReddyView profile

Assistant Professor

• Statistics
• Machine Learning
• Probabilistic Modeling
• +4 more

Benjamin Bloem-Reddy is an Assistant Professor of Statistics at the University of British Columbia (UBC), affiliated with the Department of Statistics within the Faculty of Science. His research focuses on probabilistic approaches in statistics and machine learning, emphasizing symmetry, causality, and model-driven scientific knowledge acquisition. Prior to UBC, he completed his PhD under Peter Orbanz at Columbia University and a postdoc with Yee Whye Teh at the University of Oxford. He holds a physics background from Stanford and Northwestern Universities. Education: PhD in Statistics, Columbia University Postdoctoral Research, CSML Group, University of Oxford Physics degrees from Stanford and Northwestern Universities Research Interests: Bloem-Reddy explores symmetry in modeling and inference, causal discovery, and integrating scientific models with statistical frameworks. His work includes developing hypothesis tests for symmetry, causal inference via cocycles, and leveraging invariance properties in neural networks. He collaborates with scientists to apply statistical methods to domain-specific problems. Awards: Best Student Poster Award at NeurIPS 2014 Workshop on Networks Teaching & Advising: He teaches courses like STAT 460/560 (Statistical Inference) and advises a vibrant research group. His students include Boyan Beronov, Kenny Chiu, and Johnny Xi. He emphasizes recruiting curious, mathematically skilled students aligned with his research themes. Grants & Funding: Supported by NSERC, CANSSI, and UBC, with computational resources from ARC at UBC.

FENG MenglingView profile

Associate Professor

• Biostatistics
• Healthcare Data Analytics
• Medical AI
• +3 more

**FENG Mengling** is an Associate Professor at the National University of Singapore (NUS) and holds primary affiliation with the Saw Swee Hock School of Public Health. She serves as the Domain Leader for the Biostatistics, Modelling, AI and Data Analytics (B.MAD) Domain and Director of the AI for Public Health (AI4PH) Program. Her academic credentials include a Senior Post-doc from Harvard-MIT Health Science Technology Division, a PhD from Nanyang Technological University (2009), and a Bachelor's degree (2003) from NTU. Research & Teaching: Her research focuses on causal inference for evidence-based medicine, generative models for medical time-series analysis, and healthcare data analytics. She teaches courses on big data technologies for healthcare problems and healthcare data analytics. Professional Roles & Awards: She has led the Biomedical and Healthcare Analytics Lab at the Institute for Infocomm Research (2014–2015) and currently serves as an Affiliate Scientist at Harvard-MIT. Notable accolades include the MIT Teaching & Learning Laboratory Kaufman Teaching Certificate and recognition as a finalist in MIT’s 2013 Innovation Showcase. Her work has been featured in prominent media outlets like The Straits Times and Channel NewsAsia, highlighting breakthroughs such as AI nurses and Singlish-speaking healthcare assistants. Publications & Impact: Over 50 peer-reviewed publications span AI-driven clinical decision support, medical imaging analysis, and predictive modeling in critical care. Key contributions include frameworks like MedDreamer (reinforcement learning for EHR analysis) and DivScore (LLM-generated text detection). Her research bridges causal inference, generative AI, and scalable healthcare solutions. Labs & Initiatives: As a leader in NUS’s Public Health AI Innovation Center (launching early 2025), she drives initiatives like FxMammo (AI for breast cancer screening) and the Biomedical and Healthcare Analytics Lab. Her work emphasizes ethical AI deployment and cross-disciplinary collaboration in healthcare.

Gustav HenterView profile

Assistant Professor

• Deep Generative Models
• Speech Synthesis
• 3D Character Animation
• +3 more

Gustav Henter is an Assistant Professor in Intelligent Systems at KTH Royal Institute of Technology, specializing in Machine Learning. He is affiliated with the Division of Speech, Music and Hearing (TMH) within the School of Electrical Engineering and Computer Science. His research focuses on deep generative models for applications like speech synthesis, 3D character animation, and human-computer interaction. He holds a Docent degree from KTH and has held post-doctoral positions at the University of Edinburgh and the National Institute of Informatics in Tokyo. Education: PhD in Electrical Engineering (KTH, 2013), MSc in Engineering Physics (KTH, 2007). He supervises doctoral students in areas like gesture synthesis and multimodal interaction. His work is supported by grants from the Wallenberg AI, Autonomous Systems, and Software Program (WASP) and South Korea's MOTIE. He co-founded Motorica AB to commercialize motion synthesis research. Awards include Best Paper Awards at ICMI 2020 and IVA 2020, and recognition for student theses. His research spans generative AI, perceptual evaluation, and robust statistical models. He organizes the GENEA Challenge and Workshop series for gesture generation benchmarking.

Rynson W.H. LauView profile

Professor

• Computer Graphics
• Computer Vision
• Deep Learning
• +3 more

Rynson W.H. Lau is a Professor of Computer Science at City University of Hong Kong (CityU), leading research in Computer Graphics, Computer Vision, and Deep Learning. He holds an Honorary Professorship at Swansea University. Previously, he served on faculties at Durham University and The Hong Kong Polytechnic University. His work focuses on advancing graphics and vision techniques, including deep learning applications for graphics/vision problems, with publications in top venues like SIGGRAPH, CVPR, and NeurIPS. He has received the Adobe Research Gift (2023) and the Springer Nature Editorial Contribution Award (2025) for his editorial contributions to the International Journal of Computer Vision . Education: B.Sc. (First-class Honors) in Computer Systems Engineering from University of Kent Ph.D. in Computer Science from University of Cambridge Research Interests: Computer Graphics: Focused on 3D reconstruction, rendering, and real-time performance capture. Computer Vision: Specializing in saliency detection, object recognition, and low-light scene enhancement. Deep Learning: Developing generative models and diffusion-based frameworks for graphics and vision tasks. Editorial Roles: Editorial Board Member, International Journal of Computer Vision and IET Computer Vision . Guest Editor for special issues in journals like ACM Transactions on Internet Technology and IEEE Transactions on Multimedia. Teaching: 2024/25 Academic Year: CS4185: Multimedia Technologies and Applications CS4188/CS5188: Virtual Reality Technologies and Applications Research Team: Advises over 20+ students and collaborates internationally. Recent projects include AI-driven VR systems for healthcare and advanced 3D content generation using diffusion models.

Michael MarklView profile

Professor

• Cardiac Imaging
• Biomedical Engineering
• Cardiovascular Hemodynamics
• +4 more

Michael Markl is the Lester B. and Frances T. Knight Professor of Cardiac Imaging and Professor of Biomedical Engineering at Northwestern University's McCormick School of Engineering and Feinberg School of Medicine. His research focuses on developing multi-parametric imaging techniques, particularly 4D Flow MRI, to understand cardiovascular hemodynamics in diseases like heart failure, stroke, and aortic valve disorders. He leads the Markl Lab, advancing applications in clinical diagnostics and therapeutic assessment. Key areas include AI integration for automated analysis, environmental sustainability in MRI, and translational imaging for pediatric and adult cardiovascular conditions. Education: PhD from University of Freiburg (2000). Research emphasizes hemodynamic biomarkers for disease progression, surgical outcomes, and therapy efficacy. Notable contributions include establishing 4D Flow MRI as a standard for aortic valve and pulmonary hypertension evaluation. Scientific Awards: None explicitly listed. Grants and funding details are inferred through lab activities and collaborative initiatives like the Center for Translational Imaging. Advising and Grants: Oversees a multidisciplinary team in the Markl Lab, collaborating on NIH-funded projects and industry partnerships. Focus areas include AI-driven diagnostics, MRI efficiency, and cardiovascular disease modeling. Labs/Teams: Director of the Cardiovascular MRI Group and Co-Director of the Center for Translational Imaging. Active in professional societies like the Society for Cardiovascular Magnetic Resonance (SCMR).

Santiago OntañónView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Game AI
• +10 more

Santiago Ontañón is an Associate Professor in the Department of Computer Science at Drexel University's College of Computing and Informatics. He is also a Senior Research Scientist at Google DeepMind, reflecting a strong dual affiliation in both academic and industrial AI research. His work bridges theoretical AI with practical applications in gaming and machine learning. PhD in Computer Science (Artificial Intelligence), cum laude, Autonomous University of Barcelona Postdoctoral Researcher, Georgia Institute of Technology Researcher, Artificial Intelligence Research Institute (IIIA), Barcelona, Spain Dr. Ontañón's research focuses on artificial intelligence, machine learning, and robotics, with a particular emphasis on game AI. His interests span case-based reasoning, reinforcement learning, Monte Carlo tree search, player modeling, and procedural content generation. He has made significant contributions to AI in real-time strategy games and explainable AI systems. His recent publications reflect a consistent trend in AI for games, hierarchical planning, and learning from demonstration. The articles span topics such as reproducible deep reinforcement learning, adaptive player modeling, and integrating domain knowledge into search algorithms, indicating a mature and impactful research trajectory in AI and game technologies. Senior Research Scientist, Google DeepMind Organizer, microRTS AI Competition Advising multiple PhD students in AI and game-related topics He has advised numerous PhD students, many of whom have completed their theses on advanced AI topics in games and reasoning. His research is supported by access to substantial computational resources and collaborative networks in both academia and industry. He actively promotes open science by releasing software, data, and teaching materials. He leads research efforts in AI for games and maintains an active lab focused on game AI, with projects like microRTS, FTL, and Darmok. His team develops systems for reinforcement learning, planning, and natural language understanding in game environments.

• Finance
• Portfolio Management
• Financial Econometrics
• +3 more

Nathan Lassance is a Lecturer at the Louvain School of Management (LSM), Université catholique de Louvain (UCLouvain), and a member of the Louvain Finance (LFIN) research division. His work bridges financial theory, statistical modeling, and data-driven portfolio optimization, with a focus on addressing parameter uncertainty and improving risk-return tradeoffs in asset allocation. Research Interests: Portfolio management, covariance matrix estimation, financial econometrics, risk analysis, quantitative finance, and non-Gaussian return distributions. Publications: His recent work explores shrinkage methods for high-dimensional portfolio selection, sentiment-aligned covariance matrices, and the economic value of statistical metrics like mean squared error. He has also contributed to understanding the limitations of factor-based mispricing models and the statistical properties of mean-variance portfolios. Labs/Teams: Affiliated with the Louvain Institute of Data Analysis and Modeling (LIDAM) and the Louvain Finance (LFIN) group.

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +7 more

Maxime CORDY is a Research Scientist at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) , University of Luxembourg, within the Security, Design and Validation group (SerVal) . He holds a PhD from the University of Namur (Belgium, 2014) and specializes in software engineering, applied artificial intelligence, and cybersecurity. His work focuses on adversarial machine learning, deep learning robustness, and model checking for critical systems. Research interests include adversarial attacks on tabular data , energy system optimization , code understanding models , and software quality assurance . Recent projects address challenges in secure AI deployment, automated test generation, and fault detection in large language models. Publications emphasize empirical studies on adversarial defenses, data augmentation for code models, and energy consumption forecasting. He contributes to tools like Daedalux (variability-aware model checking) and benchmarks like Tabularbench for adversarial robustness evaluation. Current affiliations include leadership within the SerVal group and collaborations on interdisciplinary projects such as MALETSQUE (Machine Learning Techniques for Software Quality Evaluation). His work bridges theoretical computer science with practical applications in energy systems, medical imaging, and space program design.

• Mobile and cloud computing
• Energy-efficient computing
• Image and video processing
• +2 more

Yung-Hsiang Lu is a Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering. His research focuses on mobile/cloud computing, energy-efficient computing, and image/video processing. He holds a BSEE from National Taiwan University (1992), an MSEE (1996), and a PhD (2002) from Stanford University. Dr. Lu's academic background includes significant contributions to VLSI and circuit design, with primary emphasis on computer engineering. His work spans theoretical and applied domains, including optimizing neural networks for edge devices, securing deep learning models, and leveraging large language models for software development. Recent research trends in his articles emphasize energy efficiency in AI systems, interdisciplinary applications of transformers (e.g., music analysis), and challenges in model interoperability and security. His publications also highlight innovations in global camera networks and real-time visual data analysis. While no specific grants or awards are explicitly mentioned, his extensive list of publications reflects sustained academic engagement. His educational contributions include developing C programming resources and teaching large-scale image processing using global camera networks. Dr. Lu's professional address is at Purdue's Materials and Electrical Engineering Building in West Lafayette, Indiana, where he maintains an active research lab focused on embedded systems and low-power computing innovations.