Dr. Dicle Yagmur Ozdemir is an Assistant Professor of Business Information Management at the Rotterdam School of Management (RSM), Erasmus University. She joined RSM in September 2023 after earning a PhD in Management Science (Information Systems concentration) from the University of Texas at Dallas, a Master's in Industrial Engineering from Sabanci University, and a Bachelor's in Industrial Engineering from Istanbul Technical University. Her research focuses on user-generated content dynamics in online platforms and the application of generative AI in healthcare. She employs econometric modeling and natural language processing to study how novel information in reviews influences stakeholders' decisions, and designs algorithms to mitigate information overload. In healthcare AI, she experiments with generative AI's impact on patient-provider interactions. Her work has been presented at top conferences including CIST, WITS, ICIS, WCBA, and INFORMS. Key themes in her research include algorithmic fairness in content selection, the psychological effects of AI-driven health advice, and the mediation effects of review novelty on consumer behavior. She has collaborated internationally, with research outputs including 6 works since 2023. Notable contributions address the moderating role of review dissimilarity in credibility assessments and the paradoxical effects of positive/negative review valence in decision contexts.
Tanja Käser is a Tenure Track Assistant Professor at EPFL's School of Computer and Communication Sciences (IC), leading the Machine Learning for Education Laboratory (ML4ED). Her interdisciplinary research bridges machine learning, data mining, and educational technology, focusing on personalized learning systems and human behavior modeling. PhD in Computer Science (ETH Zurich, 2015) - honored with Fritz Kutter Award Former Senior Data Scientist at Swiss Data Science Center (ETH Zurich) Postdoctoral Researcher at Stanford University's Graduate School of Education Research Focus Explainable AI for education Adaptive learning environments Behavioral pattern recognition Generative AI applications in pedagogy User modeling and personalization Learning analytics in unstructured settings Recent Publication Trends Her 2024-2023 work demonstrates: Interpretable clustering of learners Transformer-based language learning prediction GAN applications for creative education Teacher-AI collaboration frameworks Explainability validation methods Modular network architectures Scientific Recognition Fritz Kutter Award for best Swiss computer science thesis (2015) Advising & Collaborations Currently supervises multiple PhD students including: Cock Jade Maï L Glandorf Dominik Güres Fatma-Betül Neshaei Seyed Parsa Radmehr Bahar Shibu Abhinand Shved Ekaterina Research Infrastructure Operates from EPFL's ML4ED laboratory with hybrid on-site and digital educational systems research capabilities.
Flavio P. Calmon is the Thomas D. Cabot Associate Professor of Electrical Engineering at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He holds a Ph.D. in Electrical Engineering and Computer Science from MIT, an M.Sc. from the Universidade Estadual de Campinas (Brazil), and a B.Sc. from the Universidade de Brasília (Brazil). His research focuses on the intersection of information theory, machine learning, and statistics, with applications to privacy, fairness, and trustworthy AI systems. He has received prestigious awards, including the NSF CAREER Award (2018) and the James L. Massey Award (2024). Research interests include developing theoretical foundations for fair and private machine learning, understanding algorithmic bias, and designing systems with provable guarantees. His work spans information-theoretic tools for responsible AI, distributed privacy mechanisms, and understanding the limits of fairness interventions. He has advised numerous students, including Hao Wang, Hsiang Hsu, and Lucas Monteiro Paes, many of whom now hold prominent roles in academia and industry. Calmon's research is supported by grants from NSF, Amazon, Google, and IBM. He has organized workshops on AI in Brazil and leads initiatives to broaden participation in STEM from underrepresented groups. His lab collaborates with institutions globally and emphasizes both foundational theory and practical applications of machine learning.
Tatsunori Hashimoto is an Assistant Professor of Computer Science at Stanford University, specializing in artificial intelligence, machine learning, and natural language processing. His research focuses on developing robust and ethical language models, addressing challenges in bias mitigation, fairness, and transparency. He leads projects like the Stanford Alpaca, exploring instruction-following models and their societal impacts. Key research interests include generative models, AI ethics, and privacy-preserving techniques. His recent work examines language model behaviors, security risks, and the societal implications of AI systems. Notable contributions include frameworks for auditing language models, improving factual accuracy, and reducing disparities in speech recognition. His publications highlight advancements in long-context processing, few-shot learning, and automated benchmarking. He emphasizes practical applications of AI while addressing dual-use concerns and ensuring alignment with human values.
Professor Jean Burgess is a leading scholar in digital media studies, holding positions as Distinguished Professor of Digital Media at Queensland University of Technology (QUT) and Associate Director of the ARC Centre of Excellence for Automated Decision-Making and Society (ADM+S). She is affiliated with the Digital Media Research Centre (DMRC) and School of Communication at QUT. Her work focuses on the social implications of digital platforms, algorithmic culture, and innovative digital methods. Education: PhD (Queensland University of Technology) M.Phil (University of Queensland) B. Arts (Hons) and B. Mus (Hons) (University of Queensland) Research Interests: Burgess explores digital media technologies' societal impacts, platform governance, and algorithmic systems. Her recent work includes studies on GenAI, Instagram's visual culture, and automated decision-making. She co-authored Everyday Data Cultures (2022) and contributes to platforms like The Conversation. Awards: Fellow, Australian Academy of the Humanities Member, ARC College of Experts Recipient of the Vice-Chancellor’s Award for Excellence Grants & Projects: Key projects include the ARC-funded ADM+S Centre, research on Instagram’s machine vision, and studies on algorithmic transparency in advertising. She collaborates with industries like Australia Post and the Australian Centre for the Moving Image. Labs/Teams: Leads the DMRC and ADM+S QUT node, fostering interdisciplinary research on digital media's role in society.
Dr. Brady D. Lund is an Assistant Professor at the University of North Texas, focusing on interdisciplinary research at the intersection of information science, artificial intelligence, and ethics. His work addresses AI adoption in libraries, data privacy, academic integrity, and international development. He holds a Ph.D., M.S., and B.S. from Emporia State University and Wichita State University. Education: Ph.D., Emporia State University M.S., Emporia State University B.S., Wichita State University Research Interests: Dr. Lund explores how AI impacts information seeking behaviors, data privacy literacy, and library services. His work emphasizes ethical AI deployment in academic and clinical settings, with a focus on marginalized communities. Key areas include AI-driven library systems, blockchain applications for academic integrity, and the societal implications of generative AI. Research Trends: Recent publications analyze AI's role in health information, cybersecurity threat intelligence, and library leadership in minority-serving institutions. He critiques AI authorship policies, evaluates large language models, and advocates for equitable AI access in developing countries. Labs & Teams: Leads the Computational Humanities and Information Literacy Lab and the CyberCrews initiative, focusing on AI ethics, digital literacy, and interdisciplinary collaboration.
Prof. Dr. Peter Gomber is Chair of e-Finance at the Faculty of Economics and Business, Goethe University of Frankfurt, Germany. He serves as Co-Chairman and member of the Board of the 'efl – the Data Science Institute', an industry-academic partnership between Frankfurt and Darmstadt Universities and leading industry partners. Additionally, he is a member of the Exchange Council of the Frankfurt Stock Exchange, Supervisory Board of Clearstream Banking AG, and Research Fellow at the Leibniz Institute for Financial Research SAFE in Frankfurt. Prof. Gomber received his Ph.D. at the Institute of Information Systems at the University of Giessen in 1999 after graduating in Business Administration. Before joining Goethe University in 2004, he worked for five years as Director, Head of Market Development Cash Markets and Xetra Research at Deutsche Börse AG, where he developed new market models and products for cash market trading on Xetra. His research focuses on market microstructure theory, digital finance and fintech, regulatory impact on financial markets, and electronic trading systems. With over 150 publications in leading international journals, his work has significantly influenced the field, particularly his highly cited papers on the Fintech Revolution. His recent research examines market fragmentation, circuit breakers, research unbundling under MiFID II, and the application of AI in financial markets. Prof. Gomber's extensive publication record shows a clear evolution from traditional market microstructure and electronic trading systems toward digital finance, fintech innovations, and regulatory impact analysis. His work bridges technical aspects of financial markets with regulatory considerations, demonstrating how technological innovations interact with market structure and regulation. His scientific recognition includes: IBM Shared University Research Grant (2007) Reuters Innovation Award (2000) Best Paper Award of the Journal of the Association for Information Systems (2020) Best Information Systems Publications Award (2020) Top 1 and Top 3 most cited articles in Fintech research (2025 bibliometric analysis) Prof. Gomber has successfully supervised numerous PhD students, including Tino Cestonaro who won the Best PhD Paper Award 2025. He has acquired significant research funds from both public institutions and the private sector. Notably, a market model invention by Prof. Gomber was granted a patent by the United States Patent and Trademark Office, with two additional market model inventions filed for patent in Europe and the US. He leads an active research team at the Chair of e-Finance, including researchers like Benjamin Clapham, Micha Bender, and Tino Cestonaro. The team collaborates closely with the efl – the Data Science Institute and the Leibniz Institute for Financial Research SAFE, bridging academic research with practical applications in financial markets.
Christopher S. Tang is a UCLA Distinguished Professor and Edward W. Carter Chair in Business Administration at the Anderson School of Management , where he researches global supply chain management with a focus on social innovation in developing countries . He also serves as Senior Associate Dean for Global Initiatives and Faculty Director of the Center for Global Management . Education: Ph.D. in Management Science (1985, Yale University) M.Phil. in Administrative Science (1983, Yale University) M.A. in Statistics (1983, Yale University) B.Sc. in Mathematics (First Class Honors, 1981, King’s College, University of London) His research explores the intersection of corporate responsibility and supply chain innovation , addressing topics like microfinancing , mobile platforms for developing economies , direct agricultural procurement , and disaster response logistics . He emphasizes visibility, integrity, and agility in uncertain environments. Recent work highlights AI adoption benefits for supply chains , strategies to reduce forced labor risks , and policy impacts on ride-sharing platforms . His research bridges operations management and social justice , advocating for environmental stewardship alongside business growth. Scientific Awards: Salzberg Medallion (2017) Lifetime Fellow, INFORMS (2011) Responsible Research in Management Award (2017) Teaching Excellence Award (multiple years, UCLA-NUS) Dean’s Excellent Service Award (2014) As an influential adviser and consultant , Tang has worked with Amazon, HP, IBM, Nestlé, GKN , and Accenture . He has taught at Stanford University, UC Berkeley, Hong Kong University of Science and Technology , and served as visiting professor at Cambridge University and the Institute of Advanced Study at HKUST .
Univ.-Prof. Torsten Möller, PhD is a Professor at the University of Vienna and serves as Head of the Research Group Visualization and Data Analysis and Head of the Research Network Data Science. His work spans data visualization, visual analytics, and human-computer interaction, with a focus on biomedical, environmental, and societal data applications. Academic rank: Professor Research group: Visualization and Data Analysis Network: Data Science Email: torsten.moeller@univie.ac.at Research interests include: Visual data analysis for complex systems Interdisciplinary applications in climate science and medicine Human-computer interaction in data exploration Image processing and computer graphics Recent publication trends show expertise in: Visualizing climate change and pandemic data Multi-volumetric and network analysis Algorithmic transparency and user-centered design Interdisciplinary collaborations (e.g., astrophysics, medical imaging) Statistical and uncertainty visualization Design frameworks for visualization recommendation Teaching includes courses in: Computer graphics and visualization Image processing and analysis Human-computer interaction Data analysis projects Doctoral research seminars
Ke Wang is a Professor in the School of Computing Science at Simon Fraser University . His research focuses on Data Mining , Database Systems , Data Privacy , and Graph and Network Data . He holds a Ph.D. and M.Sc. from the Georgia Institute of Technology (1986 and 1984, respectively). Teaching includes courses like Database Systems II , Introduction to Data Mining , and Special Topics in Databases . He has advised numerous students and alumni, many of whom now work in tech, academia, and industry. Notable awards include the 2013 Faculty of Applied Sciences Research Excellence Award and the ECIR 2019 Best System Paper . His work emphasizes privacy-preserving techniques and has led to contributions like the Introduction to Privacy-Preserving Data Publishing textbook. He has served as a conference chair for major data mining events like SDM 2015/2016 and holds editorial roles in journals like ACM TKDD. His lab, the Database and Data Mining Laboratory , focuses on actionable solutions for real-world data challenges.
Gian Antonio Susto is an Associate Professor at the Department of Information Engineering , University of Padova . With a Ph.D. in Information Technology and post-doctoral experience at National University of Ireland, Maynooth, he leads research in Machine Learning , Semiconductor Manufacturing , and Industrial IoT . His work bridges Anomaly Detection , Continual Learning , and Algorithmic Fairness with applications in Hydroelectric Power Plants , Particle Accelerators , and Smart Mobility . B.Sc. and M.Sc. in Controls Engineering, University of Padova (cum laude) Ph.D. in Information Technology, University of Padova (2013) Post-Doc at National University of Ireland, Maynooth (2012-2013) Assistant Professor at University of Padova (2013-2021) His research focuses on Explainable AI , Virtual Metrology , and Deep Learning for manufacturing and infrastructure monitoring. Recent projects include the AIMS5.0 (AI for Manufacturing Sustainability) and MICS (Circular Economy in Italy) initiatives. His publications span Engineering Applications of Artificial Intelligence , IEEE Transactions , and Information Processing & Management , with 15+ recent papers on topics like Fault Diagnosis , Continual Learning , and Fair Ranking . Key scientific awards include: IEEE CCTA Best Student Paper Award (2021) IP&M 2020 Ph.D Paper Award Best Industry Paper Award, European Workshop on Advanced Control and Diagnosis (ACD 2019) He has supervised Ph.D. students on projects involving Particle Accelerators , Plant Behavior Modeling , and Explainable AI , with alumni now at institutions like Max Planck Institute , IBM , and Scripps Research . Current teaching includes Reinforcement Learning and Explainable Machine Learning at graduate and Ph.D. levels.
Risto Miikkulainen is a Professor of Computer Science and Neuroscience at the University of Texas at Austin and VP of AI Research at Cognizant AI Lab. He directs the UTCS Neural Networks Research Group and is currently on leave from UT, working on Evolutionary Computation and Deep Learning at Sentient Technologies, Inc. Education: Ph.D. in Computer Science, UCLA, 1990 M.S. in Applied Mathematics, Helsinki University of Technology (now Aalto University), 1986 Risto Miikkulainen's research focuses on biologically-inspired computation such as neural networks and evolutionary computation. His work spans three main areas: (1) Neuroevolution, evolving complex deep learning architectures and recurrent neural networks for sequential decision tasks in robotics, games, and artificial life; (2) Cognitive Science, developing models of natural language processing, memory, and learning that shed light on disorders such as schizophrenia and aphasia; and (3) Computational Neuroscience, studying the development, structure, and function of the visual cortex, episodic memory, and language processing. His research combines theoretical understanding of biological information processing with practical applications for developing intelligent artificial systems. His recent publications (2025) show a strong focus on evolutionary approaches to AI development, particularly in neural architecture search, loss function optimization, and explainable AI. Many papers explore the intersection of evolutionary computation with deep learning, creating more efficient and transparent AI systems. His work spans theoretical foundations and practical applications in areas ranging from environmental control systems to cognitive modeling. Scientific Awards: College of Fellows, International Neural Network Society, 2024 Best Pathway to Impact Award, NeurIPS Climate Change workshop, 2024 AAAI Fellow, 2023 IEEE CIS Evolutionary Computation Pioneer Award, 2020 Gabor Award, International Neural Network Society, 2017 Outstanding Paper of the Decade Award, International Society for Artificial Life, 2017 IEEE Fellow, 2016 Multiple Best Paper Awards at GECCO, CIG, and CEC conferences Deployed Application Award, AAAI/IAAI-2013, AAAI/IAAI-2018 Miikkulainen has extensive experience mentoring students through undergraduate research courses like CS378 Computational Intelligence in Game Design I and II, where students develop independent research projects on the OpenNERO research platform. He has received multiple awards for deployed applications, demonstrating the practical impact of his research. His work has led to the development of the NERO game platform, which serves as both an educational tool and research platform for AI. He directs the UTCS Neural Networks Research Group, which focuses on neuroevolution, cognitive science models, and computational neuroscience. The group has developed the NERO (Neuro-Evolving Robotic Operatives) platform, a machine learning game that allows users to train intelligent agents through evolutionary computation. The group's work spans theoretical research and practical applications in AI, with connections to both academic and industry partners.
Cecilia O. Alm is a Professor in the Department of Psychology within the College of Liberal Arts at Rochester Institute of Technology (RIT), where she serves as the Artificial Intelligence Program Director. She holds multiple leadership roles including Director of the Center for Human-aware AI and Director of the Computational Linguistics and Speech Processing Lab (CLaSP). Her institutional affiliations span the School of Information, Ph.D. Programs in Cognitive Science and Computing and Information Sciences, Department of Computer Science, and MS in Data Science program. Dr. Alm earned her Ph.D. from the University of Illinois at Urbana-Champaign. Her research focuses on human-centered artificial intelligence with particular emphasis on linguistic and multimodal sensing, affective computing, and natural language processing. She investigates how AI systems can better understand and respond to human communication through multimodal dialogue processing, with applications in accessibility, education, and healthcare. Her recent publications demonstrate a strong trend toward developing inclusive AI systems, particularly through projects addressing Deaf community needs (MULTICOLLAB-ASL), subtle emotion recognition (FUSE corpus), and bias mitigation in NLP. The work consistently integrates multimodal data streams (speech, gaze, gesture) to create more responsive human-AI interaction frameworks. Current research directions emphasize diversity in AI education, visual prosody in sign languages, and human-in-the-loop AI development. Dr. Alm leads several significant NSF-funded initiatives including the AWARE-AI program, IRES AI-PROWIL international research experience, and collaborative projects with Gallaudet University focused on Deaf scientist-centered AI research. She has secured over $2.5 million in external funding for her work on human-aware AI systems. She directs the CLaSP lab which provides research opportunities for PhD, MS, and undergraduate students, with graduates employed at major technology companies including Amazon, Apple, Microsoft, and Facebook. The lab focuses on real-world AI applications in accessibility, human-robot interaction, and multimodal communication systems.
Rhonda Hadi is an Associate Professor of Marketing at the Saïd Business School, University of Oxford. She is also a Fellow of Green Templeton College and an Associate Editor at the Journal of Consumer Psychology , serving on editorial review boards for Journal of Consumer Research , Journal of Marketing Research , and Journal of the Academy of Marketing Science . Her research focuses on emerging technologies reshaping consumer experiences, including augmented reality, AI, and wearable/mobile computing. She explores crossmodal perception, cultural influences on consumer behavior, and sensory-driven decision-making. Rhonda teaches the Core Marketing course for the MBA programme and contributes to Executive MBA, Diploma in AI for Business, and executive education courses. Her engagements span industry collaborations with Fortune 500 firms, startups, and non-profits, emphasizing practical applications of her research. She is a key member of the Oxford Future of Marketing Initiative , fostering academic-industry partnerships for thought leadership. Her research interests prominently feature technology-augmented consumer behavior , particularly in multisensory interactions (e.g., haptics, temperature, scent) and how cultural contexts modulate these responses. She has pioneered studies on anthropomorphic products, chatbot ethics, and the psychological impact of digital tools like AR and the Metaverse. Rhonda received the John Elliot Teaching Award and regularly engages with media outlets like the BBC to disseminate her findings. She has delivered over 50 invited talks globally, addressing North America, Asia, Africa, and Europe. In advising and grants, she mentors students across MBA and executive programmes and secures funding via Oxford’s initiatives. Her work intersects with the Major Programme Management and Private Equity Institute , though direct grant references are not explicitly detailed. She actively participates in interdisciplinary teams at Oxford Saïd, such as the Oxford Future of Finance and Technology Initiative and the Social Thermoregulation Project , integrating insights from psychology, technology, and business strategy.
Dr. Thomas E. Doyle is an Associate Professor at the McMaster School of Biomedical Engineering and the Department of Electrical & Computer Engineering at McMaster University. His research focuses on biomedical signal processing, human-computer interfacing (HCI), and machine learning applications for healthcare augmentation, rehabilitation, and enhancement. He holds a Ph.D. from Western Ontario, Canada, and teaches courses like COMPENG 2DI4 (Logic Design). His work bridges cybernetics and clinical applications, emphasizing AI-driven solutions for medical diagnostics, patient monitoring, and space exploration. Education: B.E.Sc, B.Sc, M.E.Sc, Ph.D. from Western Ontario, Canada Recent Projects: Developed AI systems for remote healthcare diagnostics (2023) Collaborated with NASA on medical emergency simulators for deep space missions (2017–2023) Led ventilator development efforts for local hospitals during the pandemic (2020) His research interests span machine learning for mental health diagnostics, trust quantification in medical AI, and extended reality (XR) for medical training. He emphasizes interdisciplinary approaches, integrating computational methods with healthcare challenges. Recent publications highlight applications in pediatric emergency care, chronic pain management, and reliable medical device design. Dr. Doyle actively engages in educational initiatives, including first-year engineering pedagogy and experiential learning programs. He has received funding for projects such as the Educating the Engineer of 2025 (EtE-25) awards and contributes to initiatives like the Digital & Smart Systems and Health & Bio-innovation research clusters at McMaster.
