Matthew Garver is a Professor and Chair of the Department of Nutrition, Kinesiology, and Health at the University of Central Missouri (UCM). He joined UCM in Fall 2016 after completing his Ph.D. in Exercise Science at The Ohio State University and teaching for five years in Abilene, Texas. Education: Ph.D. in Exercise Science (The Ohio State University) Garver’s research focuses on applied human performance, with emphasis on resistance training, interlimb asymmetry in athletes, and the intersection of exercise science with diversity, equity, and inclusion (DEI). His work spans collegiate sports, track and field, and health interventions for special populations. Recent publications highlight his exploration of artificial intelligence in sport science ethics, DEI initiatives in exercise research, and position-specific training in collegiate female soccer. His studies also address exercise-induced bronchoconstriction, biomechanical asymmetry in American football players, and affective responses to resistance training. Garver has contributed to understanding physical activity’s role in older adults, particularly patients with knee osteoarthritis, through the IMPACT-P trial. His interdisciplinary approach bridges kinesiology, physiology, and social science in promoting health and athletic performance. At UCM, he teaches courses in Kinesiology and leads research initiatives aimed at optimizing human performance and health outcomes through evidence-based strategies.
Roberto Mora Cortez serves as an Associate Professor in the Department of Business and Sustainability at the University of Southern Denmark (SDU), Kolding campus, with a primary focus on Business-to-Business (B2B) marketing research and instruction. His academic contributions span teaching, publication, and industry engagement within the global B2B domain. His research expertise centers on B2B Marketing , Market Segmentation , and Quantitative Methods , with significant extensions into Sales , Trade Shows , Customer Journey mapping, and DEI in B2B contexts. His methodological approach combines systematic reviews, survey research, and empirical analysis, emphasizing practical applications for businesses across diverse economic environments including Chile and Peru. Key investigations address segmentation efficacy, digital transformation of trade shows, and the integration of diversity principles into B2B selling frameworks. Analysis of his 15 most recent publications (2022-2025) reveals dominant trends toward digitalization, sustainability, and inclusivity in B2B practices. His work consistently bridges theoretical frameworks with actionable implementation strategies, particularly in global and emerging markets. Notable thematic clusters include AI-driven business processes, relationship marketing resilience during economic fluctuations, and social media's role in B2B engagement. No scientific awards, prizes, or fellowships are documented in the available records. Mora Cortez has supervised at least one bachelor thesis student (Catherine Scheck) and teaches courses including Business-to-Business Marketing, Advanced Quantitative Analyses, and Scientific Research Processes. His academic service includes peer reviewing for the Journal of Business & Industrial Marketing and participation in conferences such as the AMA Winter Academic Conference. International collaborations with Georgia State University and the University of Chile indicate active research networking. His departmental work within SDU's sustainability-focused business unit involves media engagement on mining industry marketing in Latin America, including contributions to Peruvian business media on value propositions and trade show ROI. Current research trajectories emphasize digital transformation, sustainable innovation, and DEI integration within evolving B2B landscapes.
Dominique Chen is a Professor at Waseda University's School of Culture, Media and Society since 2022, previously serving as Associate Professor from 2017-2022. A French national born in 1981, he holds a Ph.D. in Interdisciplinary Informatics from the University of Tokyo (2013). His work bridges technology, art, and human experience with a focus on digital well-being and more-than-human relationships. Chen's research interests include human-microbe interaction, neo-cybernetics, and the design of systems that foster mutual care between humans and non-human entities. His work with the Nukabot project exemplifies this interdisciplinary approach, exploring how fermentation processes can serve as metaphors for communication and relationship building. He leads the Ferment Media Research group, investigating how fermentation principles apply to digital cultures and communication systems. His recent publications reveal a consistent focus on designing for well-being in digital societies, with particular attention to translation processes, human-microbe relationships, and the creation of systems that support co-adaptive interaction. The Nukabot research series demonstrates how traditional fermentation practices can inform novel interaction paradigms that acknowledge and incorporate more-than-human perspectives. Best Paper Honorable Mention (2024) - ACM Synlogue with Aizuchi-bot ACM SIGGRAPH Special Prize (2023) - Nukabot Best of AppStore 2015/2016 - Picsee/Syncle applications Good Design Award (2008) - Creative Commons Japan Super Creator certification (2009) - IPA Exploratory IT Program Chen has advised numerous projects through his leadership of Ferment Media Research and has served on various committees including the Good Design Award jury (2016-), Yomiuri Shimbun Reading Committee, and advisory boards for art and design institutions. His work extends beyond academia through his founding of Divideal Inc. (acquired by Smart News in 2018) and Creative Commons Japan (now Commonsphere). His laboratory, Ferment Media Research, explores interdisciplinary connections between fermentation processes, digital systems, and human relationships, creating installations like Nukabot that facilitate human-microbe interaction and Last Words/TypeTrace that examines writing processes and communication.
Gizem Kayişoğlu is an Assistant Professor in the Department of Maritime Transportation and Management Engineering at Istanbul Technical University. Her research focuses on cybersecurity challenges in maritime systems, human error probability, and risk assessment methodologies. She actively explores vulnerabilities in shipboard systems such as ECDIS, VDR, and radar while emphasizing regulatory compliance and maritime safety standards. Her work integrates frameworks like CORAS, Fuzzy FUCOM, and SLIM to address cybersecurity dynamics and human factors in maritime operations. Key collaborations involve analyzing ransomware threats, AIS system vulnerabilities, and port infrastructure protection through interdisciplinary approaches. Her articles highlight trends in maritime cybersecurity, including ransomware mitigation, cyber hygiene practices, and critical infrastructure protection. She has contributed to developing risk assessment tools and checklists tailored for maritime environments, emphasizing both technological and human aspects of safety. No scientific awards have been explicitly mentioned. She has not listed formal advisees, and her professional activities focus on advancing maritime cybersecurity through academic and applied research.
N. Rich Nguyen is an Assistant Professor in the Department of Computer Science at the University of Virginia (UVA), where he joined in August 2018. He's part of the School of Engineering and Applied Science and is on a teaching track , focusing on making machine learning accessible and engaging for all students. Research and Innovation: Rich Nguyen's research interests include biomedical image analysis , machine learning , and computer science education . He aims to reinvent instructional activities to make them adaptive and engaging by incorporating art and music elements to help everyone learn coding. Notable research contributions include: Floodwatch : A system for flood monitoring using crowdsourced images TuneScope : A digital music creation tool combining SoundScope and Snaps! technology CAD Library : Open-source design tools for educators AI for early sepsis detection : Highlighted in UVA Today Teaching Accomplishments: Before UVA, Rich taught computer science courses at UNC Charlotte for four years to a total of 1,458 students. At UVA, he teaches several courses including: CS 4774: Machine Learning (multiple semesters) CS 2501: Machine Learning for All (launched in Fall 2021) SYS 6016 / SDS 6050: Deep Learning CS 2150: Data and Program Representation (multiple semesters) CS 6316: Machine Learning (Graduate Level) CS 2910: CS Education Practicum (for Teaching Assistants) He previously taught at UNC Charlotte: ITCS 1600: Computing Professionals ITCS 2600: Computing Professionals for Transfer Students ITCS 4156: Introduction to Machine Learning ITCS 2215: Design and Analysis of Algorithms Academic Achievements: Rich Nguyen has received several notable awards and grants: Google Faculty Award for Machine Learning Education with TensorFlow (2019) Best Paper Award at IEEE BigDataSE (2022) Best Poster Award at SITE Conference (2022) CCI Faculty Innovation Award (2018) NSF grants for Smart and Connected Communities (2022) and Computational Thinking (2021) 3 Cavaliers Grant on Coding and Music (2021) Student Mentorship: Rich has mentored numerous students and teaching assistants who have achieved recognition. Notable students include: Joy Qiu - Published in Clinical Infectious Diseases Louisa Edwards and Zach Boner - Invited to Ken Ono Podcast Mike Ferguson - Winner of CS Louis T. Rader Undergraduate Teaching Award He has also served as faculty advisor for HooHacks (UVA's hackathon) and co-founded CharlotteHack at UNC Charlotte. Labs and Collaborations: Rich Nguyen leads the ML4VA (Machine Learning for Virginia) initiative, engaging students in project-based learning to apply machine learning to real-world problems affecting Virginia communities. He collaborates with institutions for symposiums on smart cities, particularly with ASEAN universities, and has partnered with Premier Healthcare for hackathons and with Glen Bull on educational technology projects.
Professor Janet McColl-Kennedy is a leading figure in marketing and service science at The University of Queensland's Business School. She serves as Program Lead for Innovation Pathways (FaBA) and co-founded the Service Innovation Alliance (SIA) research hub, focusing on customer experience, AI, digital transformation, and sustainability. With Fellow status in the Academy of the Social Sciences in Australia and the Australian and New Zealand Marketing Academy, she holds international recognition including Research.com's World's Best Business Scientists ranking and the Christopher Lovelock Career Contributions to the Services Discipline Award (2025). University of Queensland Business School Honorary Visiting Professor, Cambridge Service Alliance Research Collaborator, University of Cambridge Her research spans customer experience management, service ecosystems, digital transformation, and healthcare services. She has pioneered frameworks for customer value co-creation and service recovery strategies, integrating AI and behavioral science. Her 2019 paper on customer experience insights was implemented by a major B2B organization, while her work on value co-creation improved outcomes at Lutheran Community Care. Recent publications focus on AI impacts in food and beverage services, sustainable service ecosystems, and digital health interventions. She has secured over $89 million in competitive grants, including multiple ARC Linkage and Discovery Projects, and leads cross-disciplinary teams with institutions like Cambridge University and Arizona State University. 2024 - ARC College of Experts appointment 2023 - Bo Edvardsson Industry Impact in Services Award 2022 - Elected Fellow of Academy of the Social Sciences in Australia 2021 - Clarivate Highly Cited Researcher 2018 - Ranked most influential marketing academic in Australia With over 220 publications and a Google Scholar H-index of 62, she has mentored 15 PhD students and examined theses at multiple universities. Her teaching spans 30+ years across undergraduate, postgraduate, and executive programs in Australia, USA, Italy, China, and Korea, with awards for blended learning and corporate education.
Sebastian Pfotenhauer is a Carl von Linde Professor for Innovation Research at Technical University of Munich (TUM) and Deputy Head of the STS Department. He specializes in the societal dimensions of innovation, including governance of emerging technologies, regional innovation cultures, and responsible innovation practices. His work bridges academic research and policy, with leadership roles in major initiatives like the €50M Munich Cluster for the Future of Mobility (MCube) and the EU-funded SCALINGS project on co-creation in innovation. He holds a PhD in Physics from the University of Jena and postdoctoral training at MIT and Harvard. Research interests include innovation policy, science governance, and the interplay between technology and societal change. Notable projects include comparative studies of innovation cultures, governance frameworks for AI and neurotechnology, and global partnerships in science and innovation. He advises governments and international bodies like the OECD and the German Engineers’ Association (VDI). Publications span journals like Research Policy , Social Studies of Science , and Nature Biotechnology , addressing topics from regulatory sandboxes to neurotechnology ethics. Awards include the Leading Technology Policy Fellowship (MIT) and NSF grants for studying complex international partnerships.
Mark Coeckelbergh is a Professor of Philosophy at the University of Vienna, specializing in the philosophy of technology, AI ethics, and robot ethics. He is affiliated with the Department of Philosophy and the Research Network Data Science at the University of Vienna. In addition to his academic position, Coeckelbergh has held significant roles including Former President of the Society for Philosophy and Technology (SPT) and Member of the European Commission's High-Level Expert Group on Artificial Intelligence (AI HLEG). His research focuses on the ethical, political, and philosophical implications of emerging technologies, particularly artificial intelligence and robotics. Coeckelbergh has published extensively in these areas, authoring influential books such as Robot Ethics (2022), The Political Philosophy of AI (2022), and Why AI Undermines Democracy and What To Do About It . His work explores how AI systems affect democratic processes, human agency, and social relationships. Recent publications demonstrate a strong focus on the relationship between AI, democracy, and ethical governance, examining how AI systems can both threaten and potentially enhance democratic processes through algorithmic transparency and participatory design approaches. Finalist of the World Technology Award 2017 Coeckelbergh teaches various courses including 'Introduction to Philosophy of LLMs,' 'LLMs and the Future of Writing,' 'Ethics and Robotics,' and 'Global Governance of AI.' He has supervised numerous students working on topics related to technology ethics and philosophy. He has been involved in several major research projects including H2020 PERSEO, WWTF Democracy Responsible Entrepreneurship, and FP7 DREAM (Development of Robot-Enhanced therapy for children with Autism spectrum disorders). Coeckelbergh has also announced upcoming guest professorships at the Institute of Philosophy of the Czech Academy of Sciences and Uppsala University, where he will work on environmental and technology ethics projects.
Catherine Tucker is the Sloan Distinguished Professor of Management and Professor of Marketing at the MIT Sloan School of Management. She serves as the faculty director of the EMBA program and has chaired the MIT Sloan PhD Program. Her research focuses on the intersection of technology, marketing, and public policy, with expertise in digital privacy, online advertising, and blockchain applications. She co-founded the MIT Cryptoeconomics Lab and has testified before Congress on digital privacy issues. Tucker holds a PhD in Economics from Stanford University and a BA from the University of Oxford. Education: PhD in Economics, Stanford University Bachelor of Arts, University of Oxford Research Interests: Algorithmic bias and fairness Privacy regulation and consumer data Blockchain technology applications Healthcare technology and digital health Marketing strategy in digital ecosystems Awards: NSF CAREER Award (2010s) William F. O’Dell Award (Long-term Impact in Marketing) Garfield Economic Impact Award (2020) Editorial Roles: Senior editor at Marketing Science , former co-editor at Quantitative Marketing and Economics , and associate editor roles at Management Science and Journal of Marketing Research . Labs & Initiatives: Co-director of the NBER Digital Economics and AI program, and co-founder of the MIT Cryptoeconomics Lab studying blockchain applications.
Prof. Diane DE SAINT AFFRIQUE is a full-time Professor at SKEMA Business School (France) since 2019, specializing in Business Law, Ethics, and Governance. She holds a Doctorat d'Etat en Droit (2002) from Université Paris 2 Panthéon-Assas, with additional qualifications from ESSEC Business School and other institutions. Her academic leadership roles include Head of the Contract Law Master program (2016–present), Head of the Law Department (2003–2014), and responsible for the Master in Business Law (2006–2016). Research Focus: Her work centers on corporate governance, sustainable finance, AI ethics in healthcare and business, and legal frameworks for responsible business practices (RSE). She explores tensions between regulatory compliance and corporate autonomy, with recent emphasis on duty of vigilance legislation and AI accountability. Key Contributions: Over 30+ publications include analyses of: Legal implications of AI in medicine and business Corporate social responsibility and judicialization trends Religious freedom in French workplaces Data sovereignty and AI governance Affiliations & Impact: She chairs the French Academy of Legal Studies and Business, serves on the Board of VITAMINE T, and advises institutions like IFSI Ambroise-Paré. Her work frequently bridges legal theory with practical business challenges, emphasizing interdisciplinary solutions for global governance issues.
Maciej A Mazurowski is an Associate Professor at Duke University School of Medicine, with dual appointments in the Department of Biostatistics & Bioinformatics and Radiology. He is also affiliated with the Department of Electrical and Computer Engineering and is a member of the Duke Cancer Institute. His research focuses on applying machine learning to medical imaging for improved diagnosis and treatment. Ph.D. in Computer Science from the University of Louisville (2008) Dr. Mazurowski's research emphasizes medical imaging , machine learning , and computer vision applications in radiology. His work includes automated segmentation , domain adaptation , prognostic modeling , and foundation models for MRI/CT analysis. His recent publications highlight trends in universal segmentation models (SegmentAnyBone, SegmentAnyMuscle), foundation models for MRI (MRI-CORE), and AI-driven diagnostic tools for breast cancer, glioblastoma, and thyroid nodules. Key challenges addressed include domain generalization , image harmonization , and ethical considerations in clinical AI. Incubation Award for innovative research commercialization Dr. Mazurowski has secured significant research funding from agencies including the National Institutes of Health , National Institute of Biomedical Imaging and Bioengineering , and American Roentgen Ray Society . His work spans CT segmentation , MRI analysis , and AI-based quality assessment across multiple imaging modalities.
Michael Riegler is a Researcher at the AI Department, Simula Research Laboratory , focusing on interdisciplinary applications of Artificial Intelligence in healthcare, sports analytics, and multimedia systems. His work bridges Machine Learning , AI Alignment , and Applied AI across clinical and real-world domains. Key Affiliations: Simula Research Laboratory (AI Department Head) Research Themes: Explainable AI in medicine, multimodal data analysis, and AI-driven health monitoring Research Interests include: Developing AI/ML algorithms for medical imaging (e.g., polyp detection, embryo analysis) Addressing missing data challenges in healthcare through novel imputation techniques Creating multimodal virtual avatars for investigative interview training Designing edge AI systems for sports analytics and sustainable fishing Recent Publications highlight collaborations with institutions in Norway and globally, with a focus on: Medical Applications: Polyp segmentation, ECG analysis, and explainable models for disease detection Sports Analytics: Athlete performance prediction and soccer video processing Data Infrastructure: Lifelogging datasets (ScopeSense), semantic representation frameworks Labs & Teams include leadership in Simula’s AI Department and participation in projects like Medico Multimedia Task , ImageCLEF , and MediaEval workshops. His work emphasizes responsible AI innovation in public sectors and privacy-preserving systems for edge environments.
Golnoosh Farnadi is an Assistant Professor at McGill University's School of Computer Science and an Adjunct Professor at the University of Montréal. She serves as a Visiting Faculty Researcher at Google, a Core Academic Member at MILA (Quebec Institute for Learning Algorithms), and holds a prestigious Canada CIFAR AI Chair. Farnadi co-directs McGill's Collaborative for AI & Society (McCAIS) and founded the EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms), which focuses on advancing algorithmic fairness and responsible AI. Her educational background includes a Ph.D. in Computer Science from KU Leuven and Ghent University (2017), with postdoctoral research at the University of Montreal/MILA (2018-2020) and the University of California, Santa Cruz (2017-2018). During her doctoral studies, she was a visiting scholar at UCLA, University of Washington, Tsinghua University, and Microsoft Research. Dr. Farnadi's research centers on developing mathematical tools and algorithms for fairness-aware machine learning systems. Her work addresses bias and discrimination in AI decision-making across critical domains including healthcare, criminal justice, financial services, and social media. She has pioneered approaches to ensure fairness in deep learning models, particularly in sequential decision-making under uncertainty. Her research bridges theoretical foundations with practical applications, examining how AI systems can be designed to promote equity while maintaining performance. Analysis of her recent publications reveals a strong focus on practical implementations of fairness mechanisms across diverse AI applications. Her work spans technical domains from generative models and large language models to recommender systems and healthcare optimization. A unifying theme is the development of mathematically rigorous frameworks that balance performance with fairness considerations, with increasing attention to cultural diversity in multilingual AI systems and privacy-preserving fairness approaches. Google Scholar Award (2021) Facebook Research Award (2021) Rising Stars in AI Ethics (2021) Google Award for Inclusion Research (2023) WAI Responsible AI Leader of the Year Finalist (2023) 100 Brilliant Women in AI Ethics (2023) Canada CIFAR AI Chair Dr. Farnadi advises numerous doctoral and master's students across McGill University, University of Montréal, and MILA, with research focusing on fairness, privacy, and responsible AI. Her EQUAL Lab brings together researchers from computer science, social sciences, and policy domains to address systemic challenges in AI ethics. She has secured significant research funding from Google and other major organizations to support her work on fairness-aware AI systems, with applications spanning healthcare, social media safety, and public policy. The EQUAL Lab serves as a hub for interdisciplinary research on algorithmic fairness, bringing together computer scientists, social scientists, and policy experts. The lab's work spans theoretical foundations of fairness metrics, practical implementations in real-world systems, and policy recommendations for responsible AI deployment. Current projects include developing frameworks for fair kidney exchange programs, mitigating cultural stereotypes in multilingual language models, and creating privacy-preserving approaches for detecting online harms while protecting user data.
Abigail Jacobs is an Assistant Professor at the University of Michigan , jointly appointed in the School of Information and the College of Literature, Science, and the Arts . She is also affiliated with the Center for Ethics, Society, and Computing (ESC) and the Michigan Institute for Data Science (MIDAS) . Education: PhD in Computer Science, University of Colorado Boulder (2015-2019) BA in Mathematical Methods in the Social Sciences and Mathematics, Northwestern University (2011-2015) Research Interests: Dr. Jacobs examines measurement and validity in machine learning , focusing on hidden assumptions in AI systems, governance structures in sociotechnical systems, and inequality in algorithmic design. Her work bridges AI, data science, and social science methodologies, emphasizing interdisciplinary collaboration. Recent Publications (2024-2025) explore generative AI evaluation, algorithmic transparency in government (e.g., US Census Bureau), motion capture data ethics, and sociotechnical frameworks for AI governance. Earlier works (2023) address fairness in ranking systems and racial categorization in algorithmic bias studies. Scientific Awards: Microsoft Research AI & Society Fellowship (2024) NSF Graduate Research Fellowship (during PhD) Advising & Grants: She co-advises Ph.D. students Amina Abdu and Meera Desai . Her research includes a Notre Dame-IBM Tech Ethics Lab grant on AI audits and collaborations with institutions like UC Berkeley, Microsoft Research, and the National Academies .
Professor David Clifton is the Royal Academy of Engineering Chair of Clinical Machine Learning at the University of Oxford’s Institute of Biomedical Engineering. He leads the Computational Health Informatics (CHI) Lab, focusing on AI-driven healthcare solutions with a strong emphasis on translational research in low- and middle-income countries (LMICs). His work spans digital health technologies, medical imaging analysis, and AI ethics. Clifton holds multiple fellowships, including from the Alan Turing Institute and Fudan University. Key affiliations include co-directorship of the Oxford-CityU Centre for Cardiovascular Engineering and involvement in the Wellcome Trust’s Flagship Centre in Vietnam. His research has been commercialized through spinouts like OBS Medical and Oxehealth. Notable projects include AI tools for non-invasive vital sign monitoring and pandemic response strategies using audio-based health data. Clifton’s awards include the IEEE Early Career Award (2022) and the Vice-Chancellor’s Innovation Prize. His lab’s Suzhou branch focuses on open-source digital health research using public datasets. Current research themes include multimodal data integration, generative AI in healthcare, and equitable AI deployment across global health systems.
