Christopher C. Yang is a Professor in Information Science at Drexel University, where he directs the Healthcare Informatics Research Lab and serves as Program Director for the MS in Health Informatics. He has held leadership roles in academic conferences including steering committee chair and general co-chair positions at IEEE International Conference on Healthcare Informatics (ICHI) and IEEE International Conference on Intelligence and Security Informatics. Education: PhD, MSc, and BSc in Computer Engineering from University of Arizona His research spans healthcare informatics , artificial intelligence , and data science , focusing on applications in pharmacovigilance, drug repositioning, sepsis modeling, and health equity. His recent work examines fairness in healthcare AI and large language model applications in medical diagnostics. Grants/Awards: 2022 DoD grant for prostate cancer treatment personalization 2017 NSF BigData grant ($999,993) 2013 PCORI Challenge Award 2025 Artificial Intelligence in Medicine (AIME) Most-cited Paper Award
Benedikt Berger is Professor of Digital Transformation and Society at the University of Münster's School of Business and Economics, Department of Information Systems. He leads research on generative AI implementation impacts and digital business transformation, with ongoing projects funded by industry partners including BASF Coatings GmbH. His research examines AI integration in organizational contexts, voice commerce systems, and human-AI collaboration frameworks. Key projects investigate generative AI's effects on workplace structures and employee experience in industrial settings. Honors include the 2024 Best Paper Award from The Data Base for Advances in Information Systems and 2022 Best Associate Editor recognition from ECIS. He supervises graduate research on topics including robotic process automation implementation.
Dr. Michael Breen is an Associate Professor in the School of Law and Government at Dublin City University, part of the Faculty of Humanities & Social Sciences. He specializes in international political economy, focusing on power dynamics within international financial institutions and the socio-economic impacts of corruption. His research spans over 30 peer-reviewed articles and two books, addressing topics such as IMF governance, regulatory evasion, and corruption in public infrastructure. He co-directs DCU’s Anti-Corruption Research Centre (ARC) and advises the European Commission’s DG Home Affairs on anti-corruption strategies. Notable roles include presenting at the UN and European Commission, as well as supervising numerous PhD students and postdoctoral researchers. Dr. Breen has secured significant research funding from bodies like the Irish Research Council and Enterprise Ireland, leading projects on governance reform and financial stability. His work bridges theoretical frameworks with applied policy analysis, emphasizing the interplay between institutional structures and real-world outcomes. Key themes include regulatory compliance in multinational corporations, crisis management in disaster contexts, and transparency in global financial systems.
Dr. Amin Ibrahim is an Associate Teaching Professor in the Faculty of Business and Information Technology at Ontario Tech University, specializing in evolutionary computation, optimization, and machine learning. He holds a PhD and MASc in Computer Engineering from Ontario Tech University and a BASc in Computer Engineering from the University of Toronto. His research focuses on multi- and many-objective evolutionary algorithms, high-dimensional visualization, and applying optimization techniques to big data challenges. He has won two Teaching Excellence Awards at Ontario Tech University (2008 and 2018) and contributed to peer-reviewed journals, book chapters, and conferences. Education: PhD in Computer Engineering, Ontario Tech University MASc in Computer Engineering, Ontario Tech University BASc in Computer Engineering, University of Toronto Research interests span evolutionary algorithms, machine learning applications, and optimization-driven solutions for complex systems. Recent work includes electric vehicle infrastructure planning and resilient supply chain design. His articles address multi-objective optimization, risk management, and sustainable transportation. Awards and Honors: Teaching Excellence Award (2008, 2018) Graduate Continuing Scholarship (2008) Graduate Entrance Scholarship (2007) Professional Involvement: Session chair at IEEE World Congress on Computational Intelligence (2016), workshops on cybersecurity and supply chain management, and contributions to IBM CASCON (2008). Active in the Business Analytics & AI Research Group, focusing on transforming data into actionable insights for business decision-making. Teaches courses ranging from cryptography to discrete mathematics and business analytics.
Dr. Emily Postan serves as Senior Lecturer and Chancellor's Fellow in Bioethics at the University of Edinburgh's Edinburgh Law School, where she also holds the position of Deputy Director of the Mason Institute for Medicine, Life Sciences and the Law with lead responsibility for policy engagement. Her academic leadership extends to Director of Postgraduate Academic Guidance within the Law School. Her educational background includes a PhD from the University of Edinburgh (2017), LLM (Distinction), MA(Hons), and MLitt by research in Philosophy from the University of Edinburgh and University of Stirling. Prior to her academic career, she spent a decade in policy management at the Scottish Government. Postan's research centers on the intersection of biomedical technologies, personal information, and identity formation, with particular focus on how health informatics shapes our self-conception. Her work critically examines ethical governance frameworks for bioinformation, emphasizing the narrative dimensions of identity. She explores how AI-driven health data classification impacts belonging and social categorization, particularly through her current project 'Identity by Algorithm: Ethical Impacts of Categorisation by Health AI'. Her scholarly output reveals consistent engagement with neuroethics, genomic regulation, and AI ethics, with publications spanning from foundational identity theory to practical policy recommendations. The trajectory shows increasing focus on algorithmic governance in healthcare, culminating in the 2025 international consensus paper on AI trust frameworks. 2018 Rising Star lecture at International Neuroethics Society Annual Meeting Project leadership for Nuffield Council on Bioethics’ 2013 Novel Neurotechnologies report Key contributions to Scottish policy through Mason Institute submissions on organ donation, human tissue legislation, and Adults with Incapacity Act reforms As a doctoral supervisor, she mentors researchers in bioethics and medical law, currently guiding Gokce Kolukisa's work on 'Genome Editing in International Law'. Her Wellcome Trust-funded project 'Confronting the Liminal Spaces of Health Research Regulation' (2017-2021) established her as a leading voice in health research governance. Postan actively engages with media and policymakers on bioethics issues, bridging academic research and real-world application through the Mason Institute's policy portfolio.
Thomas Lasko is an Associate Professor holding dual appointments in the Department of Biomedical Informatics and the Department of Computer Science at Vanderbilt University. His work focuses on advancing machine learning and computational methods to address critical challenges in healthcare, particularly leveraging electronic health records (EHR) for disease prediction, risk stratification, and clinical decision-making. Lasko’s research spans areas such as predictive modeling for chronic diseases (e.g., COPD, systemic lupus erythematosus), multimodal data integration (e.g., imaging and EHR), and improving the generalizability of clinical models across healthcare institutions. His academic contributions emphasize scalable solutions for large-scale EHR analysis, including lightweight natural language processing (NLP) models for document classification and contrastive learning approaches for patient-level representation. Lasko has also pioneered methods for uncovering latent disease signatures using probabilistic independence and has developed tools like pyPheWAS for phenome-disease association studies. His work frequently intersects with clinical practice, aiming to translate algorithmic advancements into actionable clinical decision support systems. Lasko’s recent research trends highlight a focus on longitudinal data analysis, model calibration drift mitigation, and explainable AI (XAI) for enhancing trust in medical AI systems. While his articles emphasize technical innovation, they consistently ground methodologies in real-world healthcare challenges, such as optimizing lung cancer screening protocols and identifying comorbidity patterns in autoimmune diseases. Despite his prolific output, no formal scientific awards or student advising records are explicitly mentioned in the provided texts. He collaborates across disciplines, integrating expertise from computer science, biomedical informatics, and clinical medicine to tackle problems such as automated patient acuity determination and novel disease subtyping. Ongoing work includes refining multimodal fusion techniques for pulmonary nodule classification and developing synthetic data frameworks to improve model robustness in resource-limited settings.
Greg Ridgeway serves as the Rebecca W. Bushnell Professor of Criminology at the University of Pennsylvania's School of Arts and Sciences, with a dual appointment in the Department of Statistics and Data Science. He holds multiple leadership roles including Co-director of the Data Driven Discovery Initiative and Co-editor-in-chief of the Journal of Quantitative Criminology. His affiliations span the Quattrone Center for the Fair Administration of Justice, Penn Injury Science Center, Center for Causal Inference, and Population Studies Center. His educational background includes a Ph.D. in Statistics from the University of Washington (1999), where his dissertation focused on Bayesian inference for massive datasets under advisors David Madigan and Thomas Richardson. Additional degrees include an M.S. in Statistics (1997) and B.S. in Statistics (1995) from the University of Washington and California Polytechnic State University respectively. Ridgeway's research centers on statistical methods for crime analysis and justice system improvement, with major contributions in police use-of-force analysis, racial profiling detection, and justice system benchmarking. His work bridges criminology and data science through innovative applications of propensity scoring, causal inference, and predictive modeling to real-world criminal justice challenges. He has developed methods implemented by police departments in Cincinnati, Los Angeles, and New York City, as well as Federal Public Defender Organizations. His 15 most recent publications demonstrate consistent focus on police behavior analysis, sentencing disparities, and environmental crime prevention. Key trends include the development of conditional likelihood models for officer shooting analysis, benchmarking systems for judicial accountability, and rigorous evaluation of place-based interventions like vacant lot remediation. His methodological contributions span criminology, statistics, and public health with strong emphasis on practical policy applications. Fellow of the American Society of Criminology (2025) Fellow of the Academy of Experimental Criminology (2024) Fellow of the American Statistical Association (2013) ASA Outstanding Statistical Application Award (2007) RAND Gold Medal Award (2007) 8 granted US patents in medical treatment hypothesis testing and resource pre-fetching Ridgeway has secured over $20 million in research funding from entities including Arnold Ventures, National Institute of Justice, and Neubauer Family Foundation. His advisory work includes directing the Master of Science in Criminology program and mentoring students through Penn's Graduate Groups. As former Acting Director of the National Institute of Justice (2013-2014), he led an 80-person agency with a $250M budget, implementing reforms like a $75M school safety research program. Current service includes chairing the American Statistical Association's Committee on Law and Justice Statistics. His leadership extends to directing RAND's Safety and Justice Program and Center on Quality Policing, where he managed 50-person teams and $10M in annual research. Current institutional roles include Co-director of the Data Driven Discovery Initiative, which he launched to develop data science for social good programming, seed grants, and a data science minor.
Alex Chohlas-Wood is an Assistant Professor of Computational Social Science at New York University’s Steinhardt School of Culture, Education, and Human Development. His work bridges computational methods with public policy, focusing on algorithmic fairness, criminal justice reform, and equitable decision-making frameworks. He holds a Ph.D. in Computational Social Science from Stanford University, an M.S. in Applied Urban Science from NYU CUSP, and a B.A. in Studio Art from Carleton College. His research emphasizes practical applications of AI to reduce bias in legal systems, including prototyping algorithms to mitigate racial disparities in charging decisions and testing behavioral nudges to reduce incarceration rates. Supported by organizations like the MacArthur Foundation and Arnold Ventures, his work has directly influenced state and local policymaking. As faculty co-director of the Computational Policy Lab since 2018 and former director of analytics for the New York City Police Department, he leads interdisciplinary teams collaborating with government agencies to design evidence-based solutions. His publications span topics from equitable algorithm design to policing practices analysis.
Professor David Leslie is a Professor of Ethics, Technology, and Society at Queen Mary University of London and Director of Ethics and Responsible Innovation Research at The Alan Turing Institute. His research focuses on digital ethics, algorithmic accountability, and the societal impacts of AI, synthetic biology, and geoengineering. He has contributed to UK government AI ethics guidelines and international frameworks like the UNESCO Ethics of AI Recommendation. Education background: Previously taught at Princeton, Yale, and Harvard with notable teaching awards. Leadership roles: Co-authored key documents such as the ICO’s 'Explaining Decisions Made with AI' and advised the Council of Europe on AI governance. Research emphasizes cross-cultural and intercultural perspectives on data justice, with projects involving global collaborations. His work bridges academia, policy, and industry, addressing ethical challenges in AI deployment.
Jake Goldenfein is a Senior Lecturer at Melbourne Law School and a Chief Investigator in the ARC Centre of Excellence for Automated Decision-Making and Society. His research focuses on platform regulation, data governance, digital surveillance, and automated decision-making systems. He authored the monograph Monitoring Laws (2019) and has contributed to critical analyses of facial recognition technology, AI governance frameworks, and the ethical dimensions of automated systems. Before joining Melbourne Law School, Goldenfein was a Postdoctoral Research Fellow at Cornell Tech’s Digital Life Initiative. His work bridges legal scholarship with technological critique, emphasizing how law constructs data economies and shapes digital rights. Recent projects include submissions to parliamentary inquiries on AI, social media regulation, and privacy in automated systems. His research outputs span policy submissions, academic articles, and interdisciplinary studies, addressing themes such as algorithmic transparency, human-in-the-loop systems, and the intersections of labor rights with automated decision-making. Goldenfein’s writing often critiques corporate influence in digital rights advocacy and explores the societal implications of emerging technologies. Key contributions include analyses of facial recognition’s privacy risks, critiques of AI ethics commodification, and proposals for regulatory frameworks balancing innovation with accountability. His work is informed by both academic rigor and practical policy engagement, aiming to shape legal responses to technological change.
Loup Cellard is a Postdoctoral Research Fellow at the ARC Centre of Excellence for Automated Decision-Making and Society (ADM+S) at the University of Melbourne, affiliated with Melbourne Law School. His research focuses on the ecological impacts of data centres and automated decision-making (ADM), drawing on science and technology studies (STS) and materialist media studies. He holds a PhD from the University of Warwick (UK), where his thesis explored algorithmic transparency through ethnographic fieldwork at France's Etalab open data initiative. Before joining ADM+S, he worked as a data visualisation designer at Sciences-Po Paris and EPFL Lausanne, and as an editor for the design criticism journal Strabic . His current project examines environmental challenges of data centre cooling infrastructures in Marseille, France. He has authored policy-relevant white papers on algorithmic transparency cited in Algorithm Watch's reports and contributed to interdisciplinary collaborations across law, computer science, and social sciences. Active in cross-national research networks, Cellard's work bridges technical practices with societal implications of ADM systems, emphasizing ethical governance and policy innovation.
Tim Pearce is a Reader in Bioengineering at the School of Engineering, University of Leicester. He holds a PhD from Warwick University and has served as a Research Assistant Professor at Tufts University Medical School. His work focuses on machine olfaction, neuromorphic engineering, and interdisciplinary applications of chemical sensing technologies. Key contributions include the development of electronic noses, biomimetic infochemical communication systems, and neuromorphic implementations inspired by insect olfactory pathways. Research Interests: Machine olfaction and sensor arrays Neuromorphic engineering and bio-inspired computation Integration of neuroscience principles into engineering systems Applications in environmental monitoring, healthcare, and security Publications highlight advancements in odor classification, spatio-temporal signal processing, and AI-driven world modeling. Notable collaborations include EU projects like Neuro-IT and AMOTH, which translated biological olfactory principles into real-world technologies. He has authored over 100 articles, including the seminal Handbook of Machine Olfaction . Awards include Fellowships from the Institute of Physics and the Higher Education Academy. Editorial roles span Frontiers in Neuromorphic Engineering and Connection Science . He actively contributes to global initiatives in neuroengineering and computational neuroscience.
Professor Kerstin Bach is affiliated with the Department of Computer Technology and Informatics at NTNU's Faculty of Information Technology and Electrical Engineering. Her research focuses on artificial intelligence, machine learning, and their applications in health informatics and robotics. Notable projects include the selfBACK app for musculoskeletal pain management and reinforcement learning for robotic systems. She has supervised numerous doctoral students in areas like activity recognition and explainable AI. Education: Formal academic degrees not explicitly listed in text. Research interests span: - Case-Based Reasoning for clinical decision support - EHealth/mHealth applications (e.g., wearable sensor analytics) - Explainable AI methodologies - Human activity recognition using accelerometer data Recent work emphasizes: - Machine learning models for health monitoring (sleep/wake detection, gait analysis) - Reinforcement learning for robotics and autonomous systems - AI-driven clinical tools for pain management Key Projects: selfBACK app: Digital self-management tool for musculoskeletal pain (RCT validated) UtiliGEM: Energy management framework for IoT devices Labs/Teams: Active in NTNU's AI research groups focusing on healthcare informatics and robotics. Collaborates with medical institutions on clinical AI applications.
Mohammad Naser Sabet Jahromi is an Assistant Professor at the Department of Architecture, Design and Media Technology, Aalborg University, Denmark. He is affiliated with the Visual Analysis and Perception Centre for AI Ethics, Law and Policy. His research focuses on explainable AI (XAI), biometrics, machine learning, and ethical AI applications in legal and medical domains. He actively participates in interdisciplinary projects like REPAI: Responsible AI for Value Creation (2023-2027), which explores AI ethics, computational discourse analysis, and value-driven AI systems. His educational background is not explicitly detailed in the provided text, but his research trajectory indicates strong expertise in computer science and AI systems. Key research interests include interpretable machine learning models, privacy-preserving biometric systems, and AI applications in asylum adjudication and educational assessment. Recent work emphasizes developing XAI frameworks like SIDU-TXT for NLP, verifying machine unlearning mechanisms, and automating large-classroom assessments. His projects bridge technical AI advancements with societal implications through collaborations with legal and ethical scholars. Notable contributions include datasets evaluating XAI methods in medicine and methodologies for transparent AI decision-making. He has participated in conferences such as ICPR 2024 and JURISIN 2023, showcasing interdisciplinary research impact.
Justin Oakley is Professor and Deputy Director of the Monash Bioethics Centre at Monash University. His research focuses on virtue ethics, moral psychology, and applied bioethics, particularly in healthcare contexts. He leads projects on ICU resource allocation during COVID-19, religion in healthcare, and dignity of risk in aged care. Oakley's research explores: Virtue ethics applications in professional roles (e.g., physicians, researchers) End-of-life decision-making , including terminal sedation and Islamic bioethics Healthcare policy design through ethical frameworks for AI, surgeon report cards, and antimicrobial prescribing Moral psychology of hope, blame, and professional boundaries His 15 most recent publications (2020-2025) cluster around: Clinical ethics of hope and conviction Regulatory challenges in AI/health technology Religious pluralism in medical practice Virtue-based approaches to professional accountability Awards: ACU Eureka Prize for Research in Ethics (2004) Academic Leadership: Oakley teaches in Monash's Master of Bioethics program and undergraduate ethics courses. He edits the Monash Bioethics Review and leads interdisciplinary projects involving philosophers, clinicians, and policymakers. Current work examines virtue ethics in professional regulation and assisted reproduction ethics.
