Enrico Arrigoni is a Professor at the Institute of Theoretical Physics - Computational Physics at Graz University of Technology (TU Graz). His research focuses on correlated quantum systems, many-body physics, and nonequilibrium dynamics, with applications to Mott insulators, quantum transport, and photovoltaic systems. He teaches courses such as 'Green's functions in Many-Particle Physics' and 'Atom Physics - Quantum Mechanics'. Recent work explores phonon effects in Mott systems, neural network approaches to quantum states, and impact ionization processes in photodriven materials. His methods include auxiliary master equation techniques and variational cluster approaches. Publications span topics like nonequilibrium steady states, quantum impurity models, and disordered systems. While no specific awards are listed, his contributions to theoretical physics and computational methods are evident through his prolific research output. Advising and grants details are not explicitly mentioned, though his involvement in graduate theses and research projects is implied via available master's and bachelor's thesis topics.
Yonghwi Kwon is a Visiting Assistant Professor in the Department of Computer Science at the University of Virginia. His research focuses on software systems security, cyber forensics, and software engineering. He received the CAREER Award for developing dynamic defenses against cyber threats. His work emphasizes securing software from cyber attacks, recovering forensic evidence, and improving software testing and reverse engineering techniques. Key research areas include memory safety mechanisms, automated vulnerability detection in web applications and mobile systems, and forensic analysis of phishing campaigns. He has pioneered frameworks like CMASan for memory allocator-aware sanitization and Racedb for detecting race conditions in database-backed systems. His contributions span cloud security automation, kernel exploitation analysis, and embedded system fuzzing. Notable achievements include the 2025 CAREER Award supporting his dynamic defense research, and impactful publications in areas like Android information leakage detection (DryJIN), Bluetooth protocol fuzzing (BTFuzzer), and autonomous driving bug discovery (Drivefuzz). His work bridges theoretical computer science with practical cybersecurity solutions.
Tom Brughmans serves as Associate Professor in Classical Archaeology at Aarhus University's School of Culture and Society, where he pioneers the application of network science and computational modeling to archaeological questions. His work bridges theoretical archaeology with complexity science, focusing on long-term economic dynamics in the Roman Empire through quantitative analysis of material culture distribution. His research centers on developing methodological frameworks for archaeological network analysis, with specific expertise in Roman economic integration, amphorae trade networks, and agent-based simulation of ancient economies. Brughmans advocates for computational reproducibility and open-science practices, creating accessible tools that transform complex archaeological data into analyzable network structures while challenging traditional interpretations of Roman market systems. Brughmans' publication trajectory reveals three dominant trends: advancing theoretical foundations of archaeological network science through handbooks and methodological guides; empirical investigations into Roman economic complexity using big-data approaches to amphorae distributions; and development of public-facing simulation platforms that translate academic research into interactive experiences. His work consistently integrates computational techniques with archaeological evidence to model socio-economic processes across centuries. His scientific recognition includes prestigious competitive fellowships: Leverhulme Early Career Fellowship (2017-2019) for the MERCURY project Marie-Curie Individual Fellowship (2019-2020) for SIMREC Brughmans directs multiple major research initiatives including the Past Social Networks Project (an open repository for ancient network data), NEFLARA (a Marie-Curie project developing landscape archaeology frameworks), and MINERVA (focused on Roman economic functioning). He has secured substantial funding from the Leverhulme Trust, Marie-Curie Actions, and ERASMUS+ for projects advancing computational archaeology, while actively promoting collaborative research through platforms like FORVM that make economic modeling accessible to broader audiences. As a core member of Aarhus University's Centre for Urban Network Evolutions (UrbNet), he contributes to interdisciplinary investigations of ancient urban connectivity. His leadership extends to developing international research networks through the Oxford Handbook of Archaeological Network Research and creating open educational resources that democratize access to network analysis methodologies in archaeology.
Dr. Reza Montasari is a Senior Lecturer in Cyber Threats at Swansea University's Hillary Rodham Clinton School of Law. He holds a BSc in Multimedia Computing and MSc in Computer Forensics from the University of South Wales, and a PhD in Digital Forensics from the University of Derby. His professional memberships include Fellow of the Higher Education Academy (FHEA) and Chartered Engineer (CEng). Montasari’s research focuses on Digital Forensics, Cyber Security, and Cyber Terrorism, with over 50 publications. He has authored/co-authored books like Cyberspace, Cyberterrorism and International Security (2024) and Countering Cyberterrorism (2023). His work bridges technical and legal aspects of cyber threats, addressing AI’s role in counterterrorism and national security. He has held roles such as External Examiner at the University of South Wales and leadership positions in cybersecurity initiatives. His expertise includes IoT forensics, dark web challenges, and digital policing strategies. Montasari also collaborates with law enforcement agencies like Cheshire Police and advises media on cybersecurity issues. Key contributions include editorial board memberships, conference presentations (e.g., ICGS3), and contributions to cybersecurity policy development. His research emphasizes ethical AI applications, privacy rights, and mitigating cyber threats in modern societies.
Marylyn D Ritchie, PhD, is the Edward Rose, M.D. and Elizabeth Kirk Rose, M.D. Professor at the Perelman School of Medicine, University of Pennsylvania. She concurrently serves as Director of the Institute for Biomedical Informatics, Vice President for Research Informatics for the University of Pennsylvania Health System, Director of the Division of Informatics in the Department of Biostatistics, Epidemiology, and Informatics, and Vice Dean of Artificial Intelligence and Computing. Education: BS in Biology, University of Pittsburgh at Johnstown, 1999 MS in Applied Statistics, Vanderbilt University, 2002 PhD in Statistical Genetics, Vanderbilt University, 2004 Research Interests Dr Ritchie’s work integrates computational genomics , bioinformatics , pharmacogenomics , and systems genomics to advance precision medicine. She develops statistical and machine-learning approaches to dissect epistasis , genetic epidemiology , and evolutionary computation in large-scale biobanks, with a special focus on cardiovascular disease and Alzheimer’s disease . Her group is also pioneering translational informatics methods that incorporate social determinants of health and fairness metrics into AI-driven clinical decision support. Publication Trends In 2025 alone, Dr Ritchie co-authored more than fifteen high-impact studies spanning vision-language models for 3D CT , multi-omics Alzheimer’s risk prediction , fairness in neuroimaging AI , ancestry-specific pharmacogenomics , and cloud-based polygenic risk score platforms . The collective work highlights a shift from single-omics discovery to integrative, equitable, and clinically actionable models across diverse ancestries. Awards & Honors While specific named awards were not detailed in the text, Dr Ritchie’s endowed professorship and multi-institutional leadership roles signify sustained recognition. Grants & Advising Dr Ritchie leads large NIH, foundation, and industry-funded initiatives that support interdisciplinary teams of postdocs, graduate students, and data scientists. Her lab actively mentors trainees from UPenn’s Cell and Molecular Biology and Genomics and Computational Biology graduate groups. Laboratories & Teams She directs the Ritchie Lab (ritchielab.org), which develops open-source visualization tools such as PhenoGram , PheWAS-View , and Synthesis-View for genome-wide and phenome-wide data exploration. The lab operates within the Institute for Biomedical Informatics and collaborates closely with the Penn Medicine BioBank and multiple clinical departments to translate big-data discoveries into precision medicine workflows.
Dr. Ben Swift is a Senior Lecturer at the School of Cybernetics, ANU, specializing in AI, computational art, and cybernetics. He leads the Cybernetic Studio, an interdisciplinary collective exploring cybernetic systems through hardware/software/people collaborations. As a livecoding artist, he performs globally and co-founded the ANU Laptop Ensemble. His research spans generative AI, open-source tools like Extempore, and UX design. Education: PhD in Computer Science (ANU) Projects: Australia's Digital Economy (2022), The Augmented Web (2019) Research focuses on AI creativity, biofeedback interfaces, and computational music. His work bridges technical innovation with artistic expression, evident in projects like TSPNet and adversarial camera systems. Key contributions include Extempore’s development and studies in live coding disruption. Awards unspecified but recognized internationally for interdisciplinary impact.
Alicia L Carriquiry is a Distinguished Professor and President's Chair at Iowa State University, serving as Director of the Center for Statistics and Applications in Forensic Evidence (CSAFE). She holds a PhD in Statistics from Iowa State University (1989), an MS from the University of Illinois at Urbana-Champaign/ISU (1985/1986), and a BS from Universidad de la Republica in Uruguay (1981). Her research focuses on applying statistical methods to forensic science, nutrition epidemiology, and plant and animal breeding, with a particular emphasis on Bayesian frameworks. Recent research emphasizes forensic evidence analysis, including footwear impression algorithms, handwriting software development, and probabilistic evidence assessment tools. She also explores dietary intake patterns in populations across Latin America and Southeast Asia, addressing nutrient deficiencies and public health interventions. Her work bridges statistical rigor with practical applications in criminal justice, improving forensic methodologies through algorithmic innovation and interdisciplinary collaboration. As CSAFE Director, she leads initiatives to enhance statistical foundations in forensic disciplines, train practitioners, and develop open-source datasets. Notable contributions include database search methodologies, error rate analyses, and software tools like handwriter for handwriting analysis. Her research underscores the importance of probabilistic reasoning in legal contexts and addresses challenges in multi-camera source identification and nonlinear image distortion correction. Carriquiry’s leadership extends to editorial roles and professional service, advancing statistical standards in forensic science. She remains active in training programs and collaborative research projects, fostering reproducibility and relevance in scientific inquiry.
Andrew K. Przybylski is a Professor of Human Behaviour and Technology at the University of Oxford's Oxford Internet Institute (OII). His research bridges psychology and digital technology studies, focusing on how virtual environments like social media and video games influence motivation, health, and well-being. He advocates for open, reproducible science and collaborates with policymakers to address digital-age challenges. Recent appointments include Honorary Professor at The Educational University of Hong Kong’s Centre for Psychosocial Health. Education: Undergraduate, postgraduate, and doctoral degrees from the University of Rochester (United States). Research Interests: His work explores digital well-being, online platform data donation, social media's psychosocial effects, and video game engagement. Key themes include open science, meta-science, and the intersection of technology with adolescent mental health. Article Trends: Recent studies analyze digital harms, social media's impact on youth, and reproducibility in tech research. His projects span neuroscience (e.g., screen time's effect on brain organization), behavioral analysis (e.g., gaming and affective responses), and policy-oriented work (e.g., multiverse approaches to internet use). Collaborations with institutions like the Ashmolean Museum highlight his interest in digital culture's therapeutic potential. Scientific Awards: Honorary Professor at The Educational University of Hong Kong’s Centre for Psychosocial Health Grants & Collaborations: Funded by the Huo Family Foundation, UK Research and Innovation (UKRI), Economic and Social Research Council (ESRC), The British Academy, The Leverhulme Trust, Barnardo’s, and the University of Oxford’s John Fell Fund. He contributes as a scientific advisor to the Sync Digital Wellbeing Program. Labs & Projects: Leads initiatives like the Programme on Adolescent Well-Being in the Digital Age, Capturing Digital Footprints of Video Game Play, and Understanding Video Game Play and Mental Health. These projects emphasize open-source data collection and cross-disciplinary collaboration.
Scott Singer is a Research Fellow in the Technology and International Affairs Program at the Carnegie Endowment for International Peace, focusing on AI development and governance in China. He co-founded the Oxford China Policy Lab and is affiliated with the Oxford Martin School AI Governance Initiative . His research spans U.S.-China AI dynamics, cross-Strait relations, public opinion in technology governance, and UK foreign policy. Education: PhD in International Relations (University of Oxford, ongoing), MPhil with Distinction (University of Oxford), BA in Economics and Fundamentals (University of Chicago). Languages: Mandarin (professional fluency), English, Spanish. His work addresses global AI governance trends, emphasizing balancing development with risk management. Recent publications analyze China's AI ambitions, U.S. state-level AI laws, and frameworks for international AI safety cooperation. Scientific Awards Clarendon Scholar (University of Oxford) Singer’s expertise has been cited in Foreign Policy , Bloomberg , and Lawfare , and he has contributed to UK parliamentary discussions. He previously worked for the U.S. State Department and Senate, with a focus on China-facing policy capabilities.
Ranjit Lall is an Associate Professor of International Political Economy at the University of Oxford's Department of Politics and International Relations, and a Fellow of St John's College. His research focuses on international cooperation, development, technological change, and methodological innovations in machine learning and missing data analysis. He holds a PhD from Harvard University and a BA from Oxford, and has been recognized with the Merze Tate Award and Leamer-Rosenthal Prize for his work. Education : PhD in Government (Harvard University), BA in Philosophy, Politics, and Economics (University of Oxford) Affiliations : St John's College Fellow, Oxford's International Relations Network His research explores how international institutions function, leveraging computational tools like MIDASpy for missing data imputation. Key interests include civil society influence on global governance, accountability mechanisms, and the political economy of technological change. He previously worked at the Bank of England and Financial Times before academia. Recent Research Trends : Articles emphasize institutional performance metrics, pandemic-era voting behavior, and AI governance frameworks. His work bridges theoretical political science with practical computational methods. Awards : 2020 Leamer-Rosenthal Prize, 2018 Merze Tate Award Advising : Supervises six graduate students in international relations and political economy Lall collaborates on open-source projects such as the MIDAS software for data imputation and actively contributes to debates on transparent social science research practices.
Jessica J. Fridrich is a Distinguished Professor in the Department of Electrical and Computer Engineering at Binghamton University, part of the State University of New York (SUNY) system. She is affiliated with the T. J. Watson School of Applied Science and Engineering. Her research focuses on steganography, steganalysis, digital forensics, and machine learning, with notable contributions to secure data hiding and patented camera fingerprinting techniques approved for legal evidence. Education: PhD in Electrical and Computer Engineering from Binghamton University Her research interests include steganography and steganalysis of digital images, digital forensics for linking photos to cameras via sensor fingerprints, signal estimation and detection, and applications of machine learning. Earlier work explored chaotic nonlinear dynamical systems and encryption. Her methods have led to over 150 refereed publications and seven successfully commercialized patents. Her articles emphasize advancements in batch steganography, JPEG compatibility, and adaptive embedding strategies. Recent work leverages machine learning for steganalysis and explores security trade-offs in high-dimensional feature spaces. 2006-2007 Chancellor's Award for Excellence in Scholarship and Creative Activities 2002 Chancellor's Award for Outstanding Inventor Narrative on advising and grants: She mentors graduate students and leads projects funded by AFOSR, NSF, and AFRL. Her research addresses challenges in data hiding security, forensic analysis, and optimizing steganographic algorithms. The Digital Data Embedding Lab, which she directs, focuses on algorithmic innovation and empirical validation in steganography and forensics. Labs/Teams: Digital Data Embedding Lab
Stuart E. Middleton is a Professor in the Electronics and Computer Science (ECS) department at the University of Southampton, where he has been employed since 2003. His research bridges artificial intelligence with practical applications in social science, mental health, and security domains. He leads multiple research projects funded by DTP and CISDnS CDT, focusing on multimodal natural language processing and large language models for social good applications. Professor Middleton's research interests center on Natural Language Processing, Large Language Models, and Human-in-the-loop AI systems. His work spans mental health applications (particularly suicide risk detection and mood change analysis), social media analysis for crisis mapping, geoparsing for location extraction, and argument mining in political discourse. He has developed numerous open-source NLP projects and datasets including CPIQA for climate science, ConversationMoC for mental health monitoring, and M-Arg for multimodal argument mining. His research demonstrates how AI can effectively support human decision-making in critical domains like mental healthcare, defense applications, and crisis management. His recent publications reveal a strong trend toward applying LLMs to high-impact societal challenges, particularly in mental health monitoring and climate science verification. He has pioneered methods for detecting suicidal ideation in social media, identifying moments of mood change, and developing context-aware question answering for climate papers. His work consistently emphasizes the importance of human oversight in AI systems, with numerous publications on responsible AI, regulation, and human-in-the-loop approaches. Ranked 1st in ECAL-2024 shared task on suicidal ideation detection Ranked 1st in NAACL-2022 shared task on suicide risk and mood change classification Winner of 'best paper' award at WWW2002 Semantic Web Workshop Professor Middleton actively supervises PhD students through multiple funded projects including 'Multimodal Natural Language Processing for Computational Social Science', 'Large Language Models for Military Veteran Mental Health', and 'Large Language Models for Human/AI Information Foraging to Combat Digital Human Trafficking into Terrorism'. He has secured significant funding from UKRI, DSTL, and other sources to support his research in responsible AI applications. He organizes major workshops including the RAI UK Workshops on Responsible AI for Mental Health and AIUK workshops on AI for Data Rescue and Defense applications. His research group maintains numerous GitHub repositories with open-source NLP tools and datasets that have been widely adopted by the research community.
Xiaobo Li is a Professor in the Department of Bio-Medical Engineering at New Jersey Institute of Technology. Holding a Ph.D. in Computer Aided Geometric Design from the University of Birmingham and a B.S. in Automation from Nanjing University of Aeronautics, their research bridges computational methods with neuroimaging and psychiatric disorder analysis. Ph.D., University of Birmingham (Computer Aided Geometric Design, 2004) B.S., Nanjing University of Aeronautics (Automation, 1999) Dr. Li’s work focuses on applying machine learning and graph theory to understand brain network abnormalities in conditions like ADHD , schizophrenia , and traumatic brain injury . Their studies analyze structural-functional connectivity , reward processing , and gut-brain axis interactions using fMRI , fNIRS , and diffusion tensor imaging . Recent publications highlight their development of tools like the GAT-FD MATLAB toolbox for brain network analysis and their exploration of multimodal MRI in schizophrenia diagnosis. They also investigate the neurobiological effects of photobiomodulation and vision therapy interventions.
Philippe Rocca-Serra is a Researcher at the Oxford e-Research Centre (OeRC), University of Oxford, and an Associate Member of the Engineering Science Department. Affiliated with Kellogg College, his work focuses on advancing open science through FAIR data principles, interoperable metadata standards, and translational biomedical data systems. He holds a DPhil in Molecular Genetics from the University of Bordeaux, supported by an EMBO Fellowship, and has contributed to major initiatives like the ISA-Tab format, the FAIR Cookbook, and the Translational Data Catalog. His research spans data standards for omics technologies, semantic validation frameworks (e.g., BioValidator), and infrastructure for reproducible research. Key contributions include the ISA API platform, metabolomics standards (nmrML, mzTab-M), and FAIRification frameworks. He actively collaborates with global initiatives such as ELIXIR, the Common Fund, and the Precision Toxicology Initiative. Rocca-Serra’s work emphasizes bridging data producers and consumers through machine-actionable metadata, fostering interdisciplinary research and policy compliance. He leads projects on clinical trial metadata profiling and has published extensively on data governance, computational workflows, and the role of FAIR principles in drug discovery and pandemic preparedness. Education: Ecole Nationale Supérieure d'Agronomie de Rennes (Diplôme d'Ingénieur), University of Bordeaux (PhD, Molecular Genetics) Key Roles: Group Coordinator at OeRC, Co-Investigator on international grants Tools Developed: ISAcreator, COPO, FAIR Cookbook, Data Tags Suite (DATS) Awards: EMBO Fellowship (2001) Labs/Teams: Oxford Data Readiness Initiative, FAIR Implementation Network
Jean-François Godbout is a Professor in the Department of Political Science at the Université de Montréal and an Associate Academic Member of Mila - the Quebec AI Institute. He directs the undergraduate program in Big Data Analytics in Social Sciences and Humanities at UdeM and conducts interdisciplinary research through the Complex Data Lab. Affiliated with IVADO (AI Consortium) Member of CÉRIUM (International Research Centre) and CECD (Democratic Citizenship Centre) His research focuses on: Data Science applications in political institutions AI Safety and generative AI's impact on political attitudes Misinformation Mitigation through large language models Comparative Political Development in Canadian and Lower Canada contexts Legislative Institutions and voting records analysis Political Polarization in online societies Recent publications analyze social media disinformation, AI persuasion on harmful topics, and education-focused text simplification. His articles frequently combine graph mining , machine learning , and political science methodologies. Scientific collaborations include: Mila researchers (Andreea Musulan, Maximilian Puelma Touzel) IVADO data science initiatives McGill University interdisciplinary projects He supervises students in: Political science (Julien Robin, Matthew Taylor) Artificial Intelligence (Kellin Pelrine, Camille Thibault) Computational social science applications
