Joshua J. Coon is a Professor at the University of Wisconsin-Madison with appointments in the Department of Biomolecular Chemistry and the Department of Chemistry. He leads the Coon Group, focusing on advancing mass spectrometry technologies for proteomics, metabolomics, and lipidomics. His research addresses fundamental questions in cell biology, including stem cell differentiation, epigenetic regulation, and cancer biomarker discovery. Affiliations : Director of the NIGMS National Center for Quantitative Biology of Complex Systems. Research Emphasis : Instrumentation development, data analysis software, ion chemistry, and biological applications of proteomics. Laboratory : Located in the Genome Center of Wisconsin with a dozen hybrid mass spectrometers, including Orbitrap systems. Collaborations : Long-term partnership with Thermo Fisher Scientific and the Wisconsin Alumni Research Foundation (WARF) for technology commercialization. Training : Mentored 27 Ph.D. students since 2009, emphasizing interdisciplinary research and professional development.
Cécile Mailler is a Reader in Probability at the University of Bath, where she is a member of the probability group Prob-L@B. She has held significant research positions including an EPSRC postdoctoral fellowship (2018-2021) titled "Random trees: analysis and applications" and previously worked as a postdoc at Prob-L@B (2013-2016) as part of Peter Mörters' EPSRC project "Emergence of Condensation in Stochastic Networks". She earned her PhD under the supervision of Brigitte Chauvin and Danièle Gardy at the Laboratoire de Mathématiques de Versailles. Her research focuses on probability theory with emphasis on branching processes, random trees, reinforcement mechanisms, Pólya urns, stochastic approximation, random networks, and statistical physics. She has made significant contributions to understanding preferential attachment models, zero-range processes, and random Boolean trees. Her work bridges theoretical probability with applications in statistical physics and combinatorics. Analysis of her recent publications shows a strong focus on random tree structures, branching processes, and reinforcement learning algorithms, with applications spanning from network theory to statistical mechanics. Her research demonstrates sophisticated mathematical techniques applied to complex stochastic systems, particularly those with reinforcement mechanisms and memory effects. Associate Editor of the Applied Probability Trust (since October 2020) Associate Editor of Stochastic Processes and Their Applications (since March 2022) Author of a general introduction to Pólya urns for the LMS Newsletter (November 2020) Co-organizer of the "Random Walks: Applications and Interactions" conference at CIRM (January 2026) She actively supervises PhD students working on topics including the multi-city ants process, Pólya urns with growing initial composition, large deviations for the Monkey walk, and competing growth processes. She has secured research funding through EPSRC fellowships and has been involved in multiple collaborative projects with prominent researchers in probability theory. Mailler regularly teaches mini-courses on advanced probability topics at international summer schools and workshops, demonstrating her commitment to knowledge dissemination in the field.
Parisa Hosseinzadeh is an Assistant Professor in the Department of Bioengineering at the University of Oregon. Her research focuses on computational protein design and structure-guided rational protein/peptide engineering, with applications in enzyme design, biosensors, and biomedical solutions. She holds a B.Sc. from the University of Tehran, a Ph.D. from the University of Illinois (advisor: Yi Lu), and a postdoc at the University of Washington in David Baker's lab. Her lab emphasizes interdisciplinary approaches at the intersection of computer science, chemistry, and biology, prioritizing diversity and inclusion in STEM. Key projects include designing cyclic peptides as enzyme inhibitors, developing methods for tuning redox potentials in metalloproteins, and creating tools to combat biomedical challenges. Lab members include postdocs, graduate students (e.g., Noora Azadvari, Andrew Powers), and undergraduates. Notable achievements include NSF grants, the Baxter Foundation Award, and the Hans Horse Meyer Award. The lab also emphasizes mentorship, collaborative culture, and outreach initiatives.
Ken Forbus is the Walter P. Murphy Professor of Computer Science and Professor of Education at Northwestern University. He earned his Ph.D. in Artificial Intelligence from MIT in 1984, along with S.M. and S.B. degrees in Computer Science from the same institution. Current research focuses on qualitative reasoning , analogical reasoning , spatial reasoning , sketch understanding , and the Companion cognitive architecture . He has made foundational contributions to qualitative physics , compositional modeling , and cognitive simulation through systems like CyclePad and Companions . His work spans AI, cognitive science, and education technology with applications in intelligent tutoring systems , educational software , and interactive entertainment . Awards and Fellowships: Humboldt Research Award AAAI Fellow Cognitive Science Society Fellow ACM Fellow AAAS Fellow Herbert A. Simon Prize recipient Research trends in recent publications include analogical reasoning frameworks, normative modeling, pretense simulation, qualitative spatial representations, and applications in education and cognitive systems. Articles frequently address intersections between AI, cognitive science, and human-computer interaction. Teaching activities include core courses like Cognitive Science 207 , Design of Problem Solvers , and Conversational AI . He co-developed the open-source Freeciv game framework for AI research in strategy games.
Dr. Min Xu is a Courtesy Professor in the Computational Biology Department within the School of Computer Science at Carnegie Mellon University. His research focuses on advancing computer vision and machine learning for biomedical image analysis, particularly cellular cryo-electron tomography (Cryo-ET) and automated science video analysis. He leads a lab developing cutting-edge computational tools for structural biology and medical imaging. Key research directions include: High-resolution 3D Cryo-ET image analysis AI-driven medical image segmentation Few-shot learning for cryo-EM analysis Video analysis frameworks for laboratory automation Notable contributions include the AITom toolkit for Cryo-ET analysis and pioneering work in adapting foundation models for medical imaging tasks. His work has been published in top venues like CVPR, MICCAI, and Nature-associated journals. No academic awards or grants are explicitly listed in the provided text. He maintains an active lab focused on translating computational methods into impactful biomedical research tools.
Mahsan Nourani is a Research Assistant Professor at Northeastern University, specializing in intersections of human-computer interaction, artificial intelligence, and healthcare informatics. Their research focuses on explainable AI systems, user behavior in AI partnerships, and design of interactive machine learning tools. Current work explores cognitive biases in human-AI collaboration, predictive analytics in healthcare, and activity recognition in videos. Key research areas include user profiling in AI applications, trust dynamics in intelligent systems, and visual debugging tools for machine learning models. Notable projects include the HEART initiative for real-time healthcare analytics and development of the DETOXER explanation framework for temporal classification systems. Recent publications investigate nudge techniques for behavioral change (Romadoro project), anchoring bias effects in AI system trust, and evaluation of view rotation techniques in virtual reality navigation. Their work consistently bridges technical AI advancements with human-centered design principles. No academic awards are listed in the provided materials. Current affiliations include Northeastern University's research groups in AI ethics, human-computer interaction, and healthcare technology innovation.
Dr. John Hastings is a Professor in the Department of Computer Science at Dakota State University, part of the Beacom College of Computer & Cyber Sciences. With nearly 35 years of experience, he specializes in teaching and curriculum development in computer science, combining academic rigor with industry insights from his roles as an AI/ML engineer, team leader, and business owner. Education: Ph.D., Computer Science, University of Wyoming M.S., Computer Science, University of Wyoming B.S., Computer Science, University of Wyoming Research interests include machine learning, AI applications in natural language processing (LLMs), generative AI, computer vision, ecological/environmental AI, AI in games, and gamification in education. Recent publications focus on cybersecurity challenges, AI ethics, and insider threat detection. His work has been recognized with awards such as the AAAI’s Innovative Applications of Artificial Intelligence (IAAI) Award and an International IPM Award for Excellence related to the CARMA AI tool. Teaching emphasizes active learning and practical skills, including courses on programming, data structures, AI, and cybersecurity. He advocates for gamification in education to enhance student engagement and success.
Sandra Paterlini is a Full Professor in the Department of Economics and Management at the University of Trento, Italy. She holds academic roles including Co-Chair of the ERCIM Working Group on Optimization Heuristics and Vice-Chair of the IEEE Task Force on Portfolio Optimization. Her career includes visiting positions at institutions such as the University of Minnesota and Ludwig-Maximilians-Universität München. She earned a PhD in Computational Methods for Financial and Economic Decisions from the University of Bergamo, an MSc in Financial Mathematics from the University of Warwick, and a Laurea in Economics from the University of Modena and Reggio E. Her research focuses on quantitative finance, risk management, portfolio optimization, and network analysis, with applications to ESG, systemic risk, and financial stability. Key research contributions include methodologies for sparse graphical modeling, systemic risk analysis, and ESG scoring frameworks. She has received multiple awards for research excellence and serves on editorial boards of journals like Computational Statistics & Data Analysis and Frontiers in Applied Mathematics and Statistics . Her work bridges academia and policy, with contributions to the European Central Bank’s Financial Stability Directorate and involvement in global conferences on computational finance and econometrics.
Yading Yuan, PhD is an Associate Professor of Radiation Oncology (Physics) at Columbia University Irving Medical Center and a member of the Data Science Institute. He holds a PhD in medical physics from the University of Chicago (2010) and completed clinical residency at Harvard Medical Physics Program (2013). His research focuses on AI-driven innovations in radiation oncology, including automated medical image analysis systems, federated learning frameworks for tumor segmentation, and data-driven approaches to personalized cancer treatment. He is certified by the American Board of Radiology and licensed in New York State. Education: PhD in Medical Physics (University of Chicago, 2010); Clinical Residency (Harvard Medical Physics Program, 2013). Research interests include: automated knowledge-based treatment planning, large-scale clinical AI systems, medical image reconstruction algorithms, and panomics integration for precision oncology. His work emphasizes translating data science advancements into clinical practice to improve patient outcomes. Key trends in his publications include federated learning for privacy-preserving medical AI, tumor segmentation in multi-modal imaging (PET/CT, MRI), and AI-driven prediction of treatment outcomes and recurrence risks. Recent work emphasizes decentralized learning architectures and cross-institutional collaboration systems. Scientific Awards: Distinguished Reviewers 2013 (selected by peer review committees) Advising/grants: No specific student names or grant details listed in provided text. His work is supported through institutional and collaborative research initiatives. Labs/teams: Active member of Columbia's Data Science Institute and Radiation Oncology department, contributing to interdisciplinary medical AI research groups.
Jian Tang is an Assistant Professor at HEC Montréal and a core member of the Montreal Institute for Learning Algorithms (MILA). His research focuses on graph representation learning, generative models, and their applications in drug discovery and material science. Prior to this, he was a postdoctoral researcher at the University of Michigan and Carnegie Mellon University, and a researcher at Microsoft Research Asia (2014-2016). He has received several prestigious recognitions, including the Canada CIFAR Artificial Intelligence Chairs (CCAI Chair) and best paper nominations at WWW’16. His work on LINE (WWW’15) was recognized as the most cited paper in its year. Tang’s research spans theoretical foundations and practical systems, such as GraphVite for scalable graph embedding and TorchDrug for drug discovery. Key research interests include geometric deep learning for molecular structures, generative models for protein design, and neural-symbolic reasoning for knowledge graphs. He actively collaborates with leading biological labs and leverages industry partnerships for GPU resources. Recent publications emphasize molecular property prediction, 3D conformation generation, and algorithmic reasoning frameworks. He has secured grants from IBM/MILA, Amazon, and the National Research Council Canada, supporting projects like molecular pretraining and geometric representation learning. Tang teaches courses on graph representation learning and deep learning, and mentors a vibrant team of PhD and master’s students. His lab has developed impactful software tools like LINE, PTE, and LargeVis, widely used in the research community.
Anders Karlström is a Professor at KTH Royal Institute of Technology, specializing in Transport Modelling and Economics. His research focuses on sustainable transportation systems, emissions reduction, and energy efficiency. Key interests include activity-based modelling, dynamic discrete choice frameworks, and policy analysis for urban mobility. He has contributed to studies on travel behavior, infrastructure planning, and environmental impacts of transport systems across multiple international cities. His work integrates advanced methodologies such as recursive logit models, spatial regression, and machine learning for predictive analytics. Notable research areas involve evaluating weather variability effects on travel patterns, optimizing traffic state estimation with sensor data, and developing scenario-based models for future employment growth. Karlström collaborates with industries to enhance the competitiveness of sustainable transport solutions globally.
Emine Ayaz is a Professor at Istanbul Technical University's Department of Electrical Engineering. Her research spans fault detection in electric motors, signal processing, and nuclear power plant monitoring, with recent work integrating deep learning (e.g., dual RNN architectures) and medical applications (e.g., parasitology, plant-based wound healing). Key Collaborations : International partnerships in motor diagnostics and nuclear engineering. Projects : Led grants on high-voltage training and predictive maintenance for TEİAŞ and industrial processes. Research Trends : Recent publications emphasize neural networks for motor fault classification, coherence analysis for insulation diagnostics, and interdisciplinary work in plant biotechnology and parasitology. Labs & Teams : Involved in projects analyzing vibration signals, wavelet transforms, and sensor fusion for industrial and nuclear systems.
Weiwei Lin is an Associate Professor in the Department of Civil Engineering at Aalto University, specializing in structural engineering with a focus on bridge systems, composite materials, and structural health monitoring. His research explores fatigue behavior of steel structures, seismic performance of composite systems, and innovative repair techniques. He holds a PhD from Waseda University (2012), MSc from Southeast University (2009), and BEng from Southwest Jiaotong University (2006). Key research areas include: steel-concrete composites, bridge redundancy evaluation, replaceable energy dissipaters, and AI-driven infrastructure diagnostics. Lin leads projects like CCU Structure (EU Horizon Europe) and RCF Mobility initiatives, focusing on sustainable construction and material recyclability. He has published 120+ peer-reviewed articles and secured 6 major grants. Lin has received prestigious awards including the IABMAS Young Award (2014) and Outstanding Reviewing Award (2017). His lab collaborates globally, hosting researchers from institutions like Israel Institute of Technology and Tsinghua University. Current work emphasizes crowdsourcing-based bridge monitoring and physics-guided AI frameworks for infrastructure diagnostics.
Francesca Grisoni serves as an Assistant Professor in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e), where she currently leads the Molecular Machine Learning team. She additionally holds appointments as an ICMS Core member and Associate Professor at EAISI (Eindhoven Artificial Intelligence Systems Institute), reflecting her cross-disciplinary role at the intersection of computational science and biomedical applications. Academic Background : Grisoni completed her Environmental Sciences degree and earned a Ph.D. in 2016 from the University of Milano-Bicocca, where her dissertation focused on interpretable machine learning for molecular property prediction. During doctoral studies, she conducted research at ETH Zurich's Department of Chemistry and Applied Biosciences and the U.S. EPA's National Center for Computational Toxicology. Ph.D., University of Milano-Bicocca, 2016 (Dissertation: Interpretable machine learning for molecular property prediction) Environmental Sciences, University of Milano-Bicocca Her research integrates artificial intelligence, chemistry, and biology to develop computational methods for drug discovery, emphasizing wet-lab experimental validation alongside algorithmic innovation. Key focus areas include overcoming activity cliffs in molecular machine learning, generative modeling for scaffold hopping, and AI-augmented decision-making in therapeutic development, with the ultimate goal of achieving 'better decisions faster' in drug discovery pipelines. Analysis of her recent 2025 publications reveals a concentrated trend toward chemical language models and generative deep learning frameworks, specifically addressing low-data drug discovery challenges through active learning and neural network architectures. These works bridge computer science with pharmacology, targeting bioactivity prediction, molecular representation, and enzyme design while maintaining strong ties to experimental validation. Scientific Awards : Lush Young Researcher Prize Early Career Award 2022 from the Dutch Royal Netherlands Academy of Arts and Sciences (KNAW) ERC Starting Grant (2022) Grants and Supervision : Dr. Grisoni secured the prestigious ERC Starting Grant in 2022 to advance her molecular machine learning research. Institutional records indicate she has supervised 7 students (as shown in TU/e's 'Supervised Work (7)' repository section), though specific names aren't provided in the source material. Her group maintains active industry collaborations, including past engagement with Bracco Pharmaceuticals. Laboratory and Team : The Molecular Machine Learning team operates under the ICMS and EAISI frameworks, merging computational AI development with experimental wet-lab validation. This collaborative unit focuses on fragment-based molecular design, chirality representation (evidenced by fragSMILES work), and high-throughput nanoparticle identification using machine learning, as highlighted in recent press coverage and datasets.
Brooks Casas, Ph.D., is a Professor at the Fralin Biomedical Research Institute at VTC, with joint appointments in the Department of Psychology (College of Science), Department of Biomedical Engineering and Mechanics (College of Engineering), and the Department of Psychiatry and Behavioral Medicine (School of Medicine) at Virginia Tech. He is also a College of Science Faculty Fellow, recognized for his contributions to decision neuroscience and computational psychiatry. Ph.D. in Psychology, Harvard University Postdoctoral Fellowship, Baylor College of Medicine Former Assistant Professor of Neuroscience and Psychiatry, Baylor College of Medicine Brooks Casas investigates the neural computations underlying social decision-making, focusing on how valuation, learning, and social preferences shape human choices. His research integrates decision neuroscience, behavioral economics, and social psychology to understand both normative and pathological decision processes. Key areas include trust, risk preferences, social influence, and impaired decision-making in psychiatric disorders such as substance abuse and borderline personality disorder. His lab employs fMRI, computational modeling, and longitudinal studies to explore these phenomena. His recent publications span topics such as machine learning applications in diagnosing borderline personality disorder, neural predictors of adolescent risk behaviors, and the role of cognitive control in substance use. His work often involves large-scale longitudinal studies, such as the decade-long investigation into early life adversity and brain development with Jungmeen Kim-Spoon. He has not received any explicitly mentioned scientific awards in the provided text. Casas leads the Casas Lab within the Center for Human Neuroscience Research and collaborates extensively with students and researchers across disciplines. His work is supported by grants from the National Institutes of Health and the Institute for Society, Culture, and Environment. He advises multiple graduate students and early-career researchers, contributing significantly to training in computational psychiatry and decision neuroscience. His lab, the Casas Lab, is part of the Fralin Biomedical Research Institute and focuses on human neuroscience research, particularly using neuroimaging and behavioral experiments to study social and economic decision-making.
