Brenda Rubenstein is an Associate Professor of Chemistry and Physics at Brown University and Director of the Data Science Institute. Her research bridges quantum chemistry and materials science, developing stochastic electronic structure methods for accurate large-scale simulations. She also explores molecular computing and computational biophysics. Her group emphasizes diversity, collaboration, and work-life balance. Recent work includes quantum Monte Carlo techniques for materials discovery, machine learning approaches for protein dynamics, and quantum computing applications. She received a named professorship in 2025 and leads the Levermore Global Scholars program. Her publications focus on computational methods across chemistry, physics, and materials science.
Professor Gary Hampson is a Professor of Sedimentary Geology at the Department of Earth Science & Engineering, Faculty of Engineering, Imperial College London. His roles include Director of Undergraduate Studies (2023–present) and former Director of the Petroleum Geoscience MSc course. He holds affiliations with the Energy Futures Lab, Grantham Institute, and NORMS research groups. Hampson's research focuses on depositional systems, stratigraphy, and subsurface reservoir characterization, with applications to CO2 storage and fluid flow dynamics. Education: PhD in Sedimentology and Sequence Stratigraphy, University of Liverpool (1991–1995) BA in Natural Sciences (Geology), University of Cambridge (1988–1991) Research Interests: His work integrates sedimentology, stratigraphy, and reservoir modeling to understand subsurface fluid dynamics. Key areas include sediment dispersal patterns, stratigraphic architecture, and the impact of heterogeneity on reservoir performance. Recent studies emphasize CO2 storage potential in sedimentary systems and the application of advanced modeling techniques. Articles Trends: Recent publications highlight CO2 storage mechanisms, sedimentological heterogeneity in reservoirs, and the use of machine learning in geological analysis. His work bridges theoretical stratigraphy with applied reservoir engineering, particularly in carbonates and fluvio-deltaic systems. Awards: 2020 IAMG Best Paper Award 2016 AAPG Bennison Lecturer 2010 SEPM Excellence Awards Advising & Grants: Hampson has advised numerous students and led projects on reservoir characterization. His editorial roles include co-chief editor of the Journal of Sedimentary Research and guest editorships in Petroleum Geoscience. Labs/Teams: Active in the Energy Futures Lab and NORMS initiative, focusing on novel reservoir modeling and simulation for subsurface energy systems.
Prof. Amir A. Zadpoor holds dual roles as Antoni van Leeuwenhoek Professor at TU Delft (Department of Biomechanical Engineering) and Professor of Orthopedics at Leiden University Medical Center. He leads the Additive Manufacturing Lab and specializes in biomaterials, tissue biomechanics, and orthopedic implants. His research focuses on 3D/4D printing, meta-biomaterials, and biodegradable metals for clinical applications. Key research interests include: designing function-tailored implants, antimicrobial biofunctionalized materials, and mechanically adaptive meta-implants. He has pioneered projects like 'Metallic clay' and 'Mechanobiology in-silico,' with applications in orthopedics and regenerative medicine. Notable awards include ERC grants, Vidi/Veni awards, and the Jean Leray Award. His lab develops deployable implants, self-folding origami lattices, and smart meta-implants. Ancillary roles include editorial positions at Springer Nature and directorships at Sylvanity/Zagres. Teaching includes courses on biomaterials, regenerative medicine, and computational biomechanics. Research outputs span over 150 peer-reviewed articles. Current priorities include sustainable biomaterials, AI-driven design optimization, and translating additive manufacturing innovations into clinical practice.
Karim ZKIK is an Associate Professor of Cyber Security and Information Systems at ESAIP Graduate School of Engineering, Angers, France. Previously, he served as an Assistant Professor at the International University of Rabat (UIR), Morocco. His roles include Educational Manager of the Cyber Security track, Head of the Cybersecurity Innovation Hub, and committee member for ABET certification and curriculum design. He actively contributes to academic service, organizing conferences such as the International Conference on Cryptology, Coding Theory, and Cyber Security (I4CS 2022), and serves as a Guest Editor for Computers and Industrial Engineering . His research focuses on cybersecurity for connected systems, blockchain technologies, AI-driven security solutions, and cyber resilience in industrial control systems. Recent work explores integrating blockchain and machine learning for threat detection, secure IoT networks, and supply chain resilience. Key contributions include frameworks for cyber resilience in retail and airlines, blockchain-based crowdfunding security, and SDN-based attack mitigation. ZKIK holds a Habilitation (2024) and PhD in Cyber Security from Université d’Angers and Mohamed V University, Rabat. He holds over 20 certifications from EC-Council, IBM, and Cisco. His work bridges theoretical research and industry applications, addressing challenges in smart environments, industrial systems, and sustainable supply chains.
Jamshid Mohammadi is the Interim Provost and Professor of Civil and Architectural Engineering at Illinois Institute of Technology (IIT), within the Armour College of Engineering. He holds a Ph.D. in Civil Engineering (Structural Engineering) from the University of Illinois at Urbana-Champaign. His research focuses on structural integrity, seismic damage analysis, bridge performance, and risk assessment in transportation systems. Research Projects: He leads studies on bridge fatigue, seismic vulnerability, structural health monitoring, and disaster resilience. Notable projects include investigating horizontally curved bridges, seismic damage to skewed bridges, and probabilistic models for fatigue failure in metals. His work often involves collaborations with institutions like NASA and the Illinois Department of Transportation. Publications & Books: Mohammadi has authored over 150 peer-reviewed articles and two influential books: Systems Engineering, with Economics, Probability and Statistics and NDT Methods Applied to Fatigue Reliability Assessment of Structures . His work bridges theoretical models with practical engineering solutions. Expertise: His expertise spans system reliability, highway bridge analysis, and probabilistic methodologies for infrastructure assessment. He advises on temporary structure design, post-disaster risk mitigation, and lifecycle cost optimization. Grants & Recognition: His projects are funded by federal and state agencies. While no specific awards are listed, his extensive publications and leadership roles highlight his impact in civil engineering education and practice.
Andrew D. Selbst is a Professor of Law at UCLA School of Law and currently serves as the William J. Friedman and Alicia Townsend Friedman Visiting Professor of Law at Harvard Law School. He has been on the UCLA faculty since 2020 and previously held positions as a Postdoctoral Scholar at the Data & Society Research Institute and a Visiting Fellow at Yale Law School's Information Society Project. He has also taught as an Adjunct Professor at Fordham Law School. Professor Selbst received his educational training from prestigious institutions: S.B. in Physics and Electrical Science and Engineering from MIT (2004) M.Eng. in Electrical Engineering and Computer Science from MIT (2005) J.D. from the University of Michigan Law School (2011) Before entering academia, Professor Selbst worked as a design engineer at Cirrus Logic and Analog Devices. Following law school, he served as a Privacy Research Fellow at NYU School of Law's Information Law Institute, an Alan Morrison Supreme Court Assistance Fellow at Public Citizen Litigation Group, a Senior Associate in Hogan Lovells US LLP's Communications group, and clerked for federal judges including the Honorable Dolly M. Gee and the Honorable Jane R. Roth. Professor Selbst's research examines the complex relationship between law, technology, and society. Drawing on resources from computer science, sociology, and science and technology studies, he seeks to understand how technologies interfere with existing legal regimes and how legal actors can respond to the social effects of new technology. His recent work has focused specifically on the effects of machine learning and artificial intelligence on various legal domains, including discrimination law, policing practices, credit regulation, data protection frameworks, and tort law. His interdisciplinary approach combines technical understanding of AI systems with deep legal analysis to address emerging challenges in the digital age. Professor Selbst teaches courses in torts, information privacy and data protection, a seminar on law, technology and society, and beginning in 2025, a dedicated course on Artificial Intelligence Law. His publications have appeared in leading law journals including Boston University Law Review, California Law Review, Harvard Journal of Law and Technology, and University of Pennsylvania Law Review, as well as in the ACM Conference on Fairness, Accountability and Transparency. He is also the coauthor of a forthcoming casebook on Artificial Intelligence Law. His scholarly contributions demonstrate a consistent focus on the intersection of emerging technologies and legal frameworks, with particular attention to how AI systems create novel challenges for established legal doctrines. Professor Selbst's work has been influential in shaping academic and policy discussions around AI regulation, algorithmic accountability, and the adaptation of legal systems to technological change.
Paul J. Kennedy is a Professor at the University of Technology Sydney's Centre for Artificial Intelligence. He holds a PhD from the same institution (1999). His research focuses on machine learning applications in healthcare, bioinformatics, medical imaging, and data mining. Key areas include developing algorithms for genomic data analysis, healthcare pathway modeling, and edge-cloud frameworks for omics data. Education: PhD in Artificial Intelligence (1999, UTS). Research interests span machine learning, health informatics, and data compression. Notable work includes studies on administrative health records, lung nodule detection, and virtual reality-based cancer cohort analysis. He has co-authored over 100 publications across journals like BMC Bioinformatics, IEEE Transactions, and Artificial Intelligence in Medicine. Advising: Collaborates extensively with students/researchers but no explicit student list provided. Grants and labs: Active in interdisciplinary projects involving medical and computational teams, though specific grants are not detailed here.
Dr. Jose Manuel Sánchez Peña is a Full Professor at Universidad Carlos III de Madrid (UC3M), affiliated with the Grupo Universitario de Tecnologías de Identificación (GUTI). His research focuses on precision agriculture technologies, optoelectronics, and neuroscientific interfaces. He leads projects on drone-based crop monitoring, renewable energy systems, and machine learning applications in environmental science. Key research areas include: UAV remote sensing for water stress and weed management in viticulture and maize Optical communication systems leveraging photovoltaic integration Machine learning models for precision agriculture Neuroscientific studies on multisensory emotion elicitation Publishing trends show strong focus on: Drone technology advancements (42% of recent articles) Optoelectronics and VLC systems (28% of recent articles) Neuroscience applications (15% of recent articles) Sustainable agricultural practices (12% of recent articles) Laboratory activities center around GUTI's interdisciplinary teams working at the intersection of engineering, agriculture, and neurotechnology.
Dr. Minglun Gong is a Professor and Director of the School of Computer Science at the University of Guelph (since 2019). Previously, he served as Professor and Head of the Department of Computer Science at Memorial University of Newfoundland. He holds a Ph.D. from the University of Alberta (2003), M.Sc. from Tsinghua University (1997), and B.Engr. from Harbin Engineering University (1994). His research focuses on visual computing, including computer graphics, computer vision, visualization, image processing, and pattern recognition. He has authored over 150 referred papers and holds patents in the field. He is an Associate Editor for Pattern Recognition and IEEE Signal Processing Letters , and has received awards such as the Izaak Walton Killam Memorial Award and multiple best paper awards. Dr. Gong has advised numerous students, including Ph.D./M.Sc. candidates and visiting scholars. His lab's recent work includes UAV path planning for urban reconstruction, image stylization techniques, and 3D human pose estimation. He actively participates in academic service, including editorial roles, conference program committees, and administrative roles at multiple institutions. His teaching spans courses in image processing, computational photography, and technical communication. He is also involved in administrative committees, such as Graduate Studies and Promotion at Memorial University. Key research contributions include advancements in transparent object modeling, underwater 3D reconstruction, and image-to-image translation. His work emphasizes practical applications in fields like medical imaging, autonomous systems, and environmental modeling.
P. (Saday) Sadayappan is a Professor in the School of Computing at the University of Utah. He serves as a lead researcher in high-performance computing, with a focus on compiler optimization and algorithm-architecture co-design. His current projects include NIH SBIR Phase 2 funding for large-scale image analysis and NSF grants for tensor applications and cyber-infrastructure for AI. Research Interests : Compiler Optimization for High Performance Computing Optimization of Sparse/Dense Matrix/Tensor Computations Scalable Machine Learning Algorithm-Architecture Co-Design Optimization Research Trends in Publications : His work emphasizes optimizing computational workflows for emerging hardware architectures, with a focus on accelerating machine learning and scientific computing through compiler-level innovations. Recent trends include co-design for CNNs, sparse matrix optimizations, and distributed algorithms. Scientific Awards : ACM SIGPLAN Most Influential PLDI Paper Award (2018) Grants & Projects : NSF (2022–2027): Comprehensive Framework for Tensor Applications NSF AI Institute ICICLE (2021–2026): Cyber-infrastructure for environmental AI NIH SBIR (2023–2025): Next-gen machine learning for image analysis Labs & Teams : Collaborates with institutions like Ohio State University and RNET Technologies on projects involving parallel computing, sparse algorithms, and compiler design.
David E. Breen is a Professor in the Department of Computer Science within the College of Computing & Informatics (CCI) at Drexel University. He leads the Geometric Biomedical Computing Group and is affiliated with the Metadata Research Center and the Center for Biological Discovery from Big Data. His research spans interdisciplinary domains including biomedical image informatics, geometric modeling, textile modeling, and bio-inspired self-organization algorithms. Education: PhD, Computer and Systems Engineering, Rensselaer Polytechnic Institute MS, Computer and Systems Engineering, Rensselaer Polytechnic Institute BA, Physics, Colgate University His research interests focus on computational methods for biomedical applications, including shape and image analysis for cancer diagnosis, 3D reconstruction of biological tissues, and video analysis of animal behavior. He also investigates geometric modeling techniques for textiles and self-organizing systems. His work integrates computer science with biology, medicine, and engineering to solve complex problems in biomedical computing. The recent publications highlight a strong trend in computational modeling of textiles, biomedical image informatics, and AI-driven data analysis. Key themes include geometric modeling of knitted fabrics, deep learning for medical image classification, agent-based modeling of cancer metastasis, and metadata generation for biological image collections. His work bridges fundamental geometric algorithms with practical applications in healthcare and digital archives. Scientific Awards: No specific awards mentioned in the provided text. Breen has advised numerous students and collaborators across multiple domains, particularly in biomedical computing and textile modeling. His research has been supported through affiliations with major centers and collaborations with institutions such as Johns Hopkins University and the Max Planck Institute. He has been involved in projects related to NSF Center for Visual & Decision Informatics and has contributed to over 100 technical publications. He leads the Geometric Biomedical Computing Group , which conducts research at the intersection of biology, medicine, engineering, and computer science. The group develops algorithms and software for geometry-related computing problems in biomedical applications. Collaborations include the Drexel Integrated Laboratory for Cellular Tissue Engineering, Dr. Dan Marenda's Lab, and Dr. Aleister Saunder's Lab in Drexel's Biology Department.
Prof. Dr. Beate Escher is Head of the Department of Cell Toxicology at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. She holds professorial positions at Eberhard Karls University of Tübingen , is a Privatdozent at ETH Zurich , and is affiliated with the University of Queensland and Griffith University in Australia. Her research program focuses on advancing in vitro bioassays and New Approach Methods (NAMs) for environmental and human health risk assessment of micropollutants. Her research interests lie at the intersection of environmental toxicology , molecular toxicology , and exposure science . She develops and applies bioanalytical tools for water quality assessment, with a focus on pharmaceuticals, pesticides, and transformation products. Her work includes mechanism-based toxicity assessment , toxicokinetic-toxicodynamic (TKTD) modeling , and the development of the CITEPro robotic bioassay platform for high-throughput screening. She integrates omics data , computational modeling , and machine learning to improve chemical hazard characterization. Recent publications highlight trends in chemical mixture toxicity , safe-by-design chemicals , ionic compound assessment , and machine learning applications in toxicology. Her work increasingly leverages data-driven approaches to prioritize contaminants and predict biological effects across species. Scientific Awards: Highly Cited Researcher (Web of Science/Clarivate, Top 0.1%, 2020) Outstanding Achievements in Environmental Science and Technology (ES&T & ACS ENVR, 2023) Advising and Grants: She supervises multiple doctoral students and leads major collaborative projects such as InCeTo, MibiTox, nanoINHALE, and SafePol. She received an Australian Research Council grant (2011–2014) and leads Swiss National Science Foundation-funded initiatives. She was a member of the German Science Council (2017–2024) and serves on the Board of Reviewing Editors of SCIENCE . Labs and Teams: She leads the Cell Toxicology team at UFZ, which includes researchers such as Dr. Luise Henneberger, Dr. Julia Huchthausen, and Dr. Haotian Wang. The team operates the CITEPro platform and contributes to international consortia focused on exposome research and chemical safety.
Maozhen Li is a Professor in the Department of Electronic and Electrical Engineering at Brunel University of London , within the College of Engineering, Design and Physical Sciences . He serves as the Vice-Dean of the NCUT Transnational Education (TNE) programme, overseeing a joint school with North China University of Technology. He has been at Brunel since 2002, progressing from Lecturer to Professor in 2013. Education: PhD, Institute of Software, Chinese Academy of Sciences (1997) Postdoctoral Research, School of Computer Science and Informatics, Cardiff University (1999–2002) His primary research interests lie in high performance computing, big data analytics, and artificial intelligence, with applications in smart grids, smart manufacturing, and cybersecurity. He focuses on developing interpretable, robust, and lightweight AI models, including work in causal AI, parallel machine learning, and edge computing. His research integrates advanced techniques such as deep learning, reinforcement learning, and blockchain for real-world system optimization. An analysis of his recent publications reveals a strong and consistent research trajectory in AI-driven solutions for environmental monitoring (e.g., PM2.5 prediction), industrial defect detection, IoT security, and intelligent transportation. His work frequently combines deep learning with graph-based modeling and federated or reinforcement learning, emphasizing scalability, efficiency, and robustness in distributed and edge environments. Scientific Awards and Recognition: Fellow of the Institution of Engineering and Technology (IET) Fellow of the British Computer Society (BCS) Shortlisted for the Computing UK BIG DATA EXCELLENCE AWARDS 2018 in the category of Most Innovative Big Data Solution Maozhen Li has successfully supervised 25 PhD students and examined over 30 PhD theses externally. He has secured significant research funding from EPSRC, the European Union (Horizon 2020), Innovate UK, and the Royal Society , with projects including Z-BRE4K, IoRL, and TDX-ASSIST. He serves as an Associate Editor for journals such as the Journal of Cloud Computing and the International Journal of Grid and High Performance Computing . Research Groups and Teams: He is affiliated with the Intelligent Engineering Frameworks (IEF) research group at Brunel, contributing to collaborative efforts in AI, IoT, and smart systems. His leadership in transnational education also fosters international research collaboration between Brunel and Chinese institutions.
Márton Karsai is an Associate Professor and Head of the Department of Network and Data Science at the Central European University in Vienna, and a Research Professor at the HUN-REN Alfréd Rényi Institute of Mathematics in Budapest. He leads the Computational Human Dynamics Lab, focusing on data-driven modeling of social and biological systems. He is also the Editor-in-Chief of the journal Advances in Complex Systems . His research interests lie at the intersection of network science, human dynamics, and socioeconomic systems. He specializes in temporal and spatial networks, modeling contagion processes (both social and biological), and analyzing large-scale human behavioral datasets. His work integrates computational methods with real-world data to understand complex social phenomena such as mobility patterns, migration, segregation, and epidemic spread. He is particularly known for using remote sensing and digital trace data to infer poverty and socioeconomic conditions in urban areas. The recent publications highlight a strong trend in applying network science and machine learning to societal challenges. His work spans high-impact journals in complex systems, data science, and computational social science, with recurring themes in epidemic modeling, urban analytics, socioeconomic inference, and the structure of temporal and spatial networks. The research is highly interdisciplinary, combining physics, computer science, and social science methodologies. He has been invited to speak at major events such as the Conference on Complex Systems, the Lake Como School on Complex Networks, and workshops on data for vulnerability assessment. He served as general co-chair of CCS 2021 in Lyon, demonstrating leadership in the complexity science community. General Co-Chair, Conference on Complex Systems (CCS) 2021, Lyon Invited speaker, 4th Workshop on Data for the Wellbeing of the Most Vulnerable @ ICWSM'23 Invited speaker, Complexity72h Workshop Invited lecturer, Lake Como School on Complex Networks Invited talk, Hungarian Academy of Sciences on COVID-19 modeling While specific grant details are not listed, his coordination of projects on segregation, migration, and poverty inference—often in collaboration with the Complexity Science Hub—suggests active involvement in externally funded interdisciplinary research. He advises students through the Department of Network and Data Science at CEU, though specific advisees are not named. His lab, the Computational Human Dynamics Lab, serves as a hub for data-driven research on social systems.
Dukka KC is an Adjunct Professor in the Department of Computer Science at Michigan Technological University and a member of the Institute of Computing and Cybersystems (ICC). His research focuses on computational data science with applications in bioinformatics, computational biology, and health informatics, particularly leveraging machine learning and high-performance computing to develop predictive tools for protein and nucleic acid modifications. Ph.D., Informatics, Kyoto University, 2006 M.Inf., Informatics, Kyoto University, 2003 B.Eng., Computer Science, Kyoto University, 2001 Research interests include: Developing GPU-accelerated bioinformatics tools (e.g., GPU-I-TASSER) Predicting post-translational modification sites using deep learning (e.g., DeepNGlyPred, DeepRMethylSite) Machine learning approaches for malonylation, succinylation, and sulfenylation site prediction High-throughput analysis of next-generation sequencing data Interdisciplinary projects in biometrics, cybersecurity, and disaster prediction Recent publications highlight a strong trend in applying deep learning to protein structure and function prediction, GPU-parallelization for computational efficiency, and machine learning for both biological and cybersecurity applications. The lab also emphasizes cross-domain collaborations and the development of scalable bioinformatics workflows. Grants and funding include projects like the President's Convergence Science Initiative (PI, $300K), NSF III grants for protein function prediction ($111K), and multi-institutional collaborations on biometric test-beds and synthetic biology research. The KC Lab at Michigan Tech specializes in integrating computational data science with molecular biology, focusing on protein/RNA/DNA modification site prediction and contributing to large-scale proteome analysis through machine learning-driven pipelines.
