Exploratory Teaching Methods Professors and Supervisors | ApplySharif

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Brett WelchView profile

Assistant Professor

University of HoustonUnited States

Brett Welch is an Assistant Professor and Biostatistician at the University of Houston with dual appointments in the Department of Communication Sciences and Disorders and the Department of Psychological, Health, and Learning Sciences within the College of Liberal Arts and Social Sciences. Dr. Welch's educational background includes: Ph.D. in Communication Science and Disorders from the University of Pittsburgh M.S. in Communication Sciences and Disorders from the University of Texas Rio Grande Valley B.S. in Linguistics from the University of Texas at Austin B.A. in Communication Sciences and Disorders – Speech-Language Pathology from the University of Texas at Austin Dr. Welch's research sits at the intersection of psychology and communication. He seeks to understand how psychological processes like personality and psychopathology influence communication behaviors, and conversely, how communication behaviors impact psycho-social well-being. His work leverages quantitative methods, cross-sectional studies, and intensive longitudinal designs to interrogate the dynamic interplay between psychological processes and voice and communication behaviors. This research aims to advance understanding of functional voice disorders, improve assessment of psychopathology, and promote well-being for people with and without communication disorders. Dr. Welch has received several notable awards: Honors Council of Academic Programs in Communication Sciences and Disorders PhD Scholarship (2024) New Century Scholars Doctoral Scholarship from the American Speech-Language-Hearing Foundation (2023) New Investigator Forum from The Voice Foundation (2021) As an academic, Dr. Welch serves as an ad hoc peer reviewer for various scientific journals in speech-language pathology and psychology. He is also a mentor in the Students to Empowered Professional Mentoring Program and a member of the American Speech-Language-Hearing Association. His teaching includes Special Problems: Introduction to Quantitative Modeling (COMD 8398). Dr. Welch maintains an active research program focused on the intersection of psychology and communication, with particular emphasis on voice disorders and their psychological correlates. His work combines biostatistical approaches with clinical insights to advance understanding in this specialized field.

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Steve MannView profile

Professor

University of WarwickUnited Kingdom

Steve Mann is a Professor in Applied Linguistics at the University of Warwick, where he has been affiliated since 2007. His work focuses on English Language Teacher Education (ELTE), teacher development, and qualitative research methodologies. He holds a PGCE from the University of Warwick (1984) and has extensive experience in ELT across Hong Kong, Japan, and Europe. Mann’s research group investigates reflective practice, teacher beliefs, mentoring, and technology integration in education. Notable contributions include co-editing the Routledge Handbook of English Language Teacher Education (2019) and pioneering studies on video-based teacher reflection. He has supervised numerous PhD students exploring aspects of teacher development, though he is currently not accepting new PhD candidates. Education: PGCE, University of Warwick (1984) Pre-service teaching in English and Drama in England (1980s) British Council Teaching Scheme in Hong Kong (1980s) Research Interests: Teacher education, reflective practice, qualitative interview methodologies, and the role of technology in professional development. His work emphasizes bridging theory and practice in ELT, particularly through collaborative dialogue and action research. Grants & Projects: Supporting Sustainable English Teacher Professional Development in Yunnan Province (British Council, 2022–2023) China Course Research: Postgraduate Curriculum Design (2019–2022) Video in Language Teacher Education (British Council, 2016–2018) Labs/Teams: Leads a research group focused on teacher development, mentoring, and blended learning strategies. Collaborates with institutions like the British Council on global teacher education initiatives.

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Keigo KobayashiView profile

Associate Professor

Waseda UniversityJapan

Keigo Kobayashi is an Associate Professor at Waseda University's Faculty of Science and Engineering, School of Creative Science and Engineering, Department of Architecture. He has served in this position since 2016, previously holding the rank of Assistant Professor from 2012 to 2016. His professional journey includes significant experience at the Office for Metropolitan Architecture (OMA) in Rotterdam, where he worked as a Senior Architect and Project Architect from 2007 to 2012. His educational background includes a Master of Architecture II from Harvard University's Graduate School of Design (2005), a Master of Architecture from Waseda University's Graduate School of Science and Engineering (2003), and a Bachelor's degree in Architecture from Waseda University's School of Science and Engineering (2002). Kobayashi's research focuses on architectural planning and city planning, with particular interests in food-related architecture, spatial behavior, and sustainable design. His work explores the intersection of architecture with everyday life, examining how spatial configurations influence human behavior and community formation. Recent research has investigated food systems in urban contexts, playful approaches to spatial design, and the relationship between architectural form and environmental performance. His publications demonstrate a strong focus on practical applications of architectural theory, with projects spanning from food truck design in Shodoshima to zero-energy housing solutions and innovative approaches to architectural education. His work shows a consistent interest in how architecture can respond to contemporary social challenges while maintaining theoretical rigor. Excellence in Concrete Construction Award 2019, American Concrete Institute (ACI), Qatar National Library / OMA Kobayashi actively contributes to architectural education through various committee memberships, including serving as Chief of the Subcommittee on Strategies for International Compatibility in Architectural Education at the Architectural Institute of Japan and leading the Caberra Accord Correspondence Subcommittee at the Japan Accreditation Board for Engineering Education. His teaching portfolio at Waseda University is extensive, covering architectural design studios from undergraduate to doctoral levels. He leads several research initiatives, including the Wakusei Project in Tosa, Kochi, NPO PLAT (Platform for Architectural Thinking), and the European Thinkbelt/McDonald's Radio University project which represents an innovative approach to architectural education and community engagement, particularly in contexts of migration and cultural exchange.

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Brad KnoxView profile

Associate Professor

University of Texas at AustinUnited States

Brad Knox is a Research Associate Professor in the Department of Computer Science at the University of Texas at Austin . His work bridges machine learning, human-computer interaction, and computational cognitive science, with a focus on developing systems that learn from human feedback. Key research areas: Reinforcement Learning, Human-AI Interaction, Reward Design, Autonomous Systems Notable contributions: TAMER framework for human-guided learning, empirical studies on reward misdesign, and human preference modeling for autonomous agents Research Trends : His recent work (2023-2025) emphasizes reward alignment, safety in autonomous systems, and preference-based learning frameworks. Earlier studies (2012-2020) established foundational methods for integrating human feedback into reinforcement learning architectures and exploring behavioral signatures in decision-making. Scientific Honors : Bert Kay Dissertation Award (2013) Victor Lesser Distinguished Dissertation Award (IFAAMAS, Runner-up, 2013) NSF SBIR Grant (PI, 2016) NSF Graduate Research Fellowship (2008-2011) IEEE Intelligent Systems AI 10 to Watch (2013) Teaching & Leadership : Knox served as Principal Lecturer for MIT's Interactive Machine Learning course (2013) and held organizational roles at major conferences including Reinforcement Learning Conference (Scheduling Chair, 2025) and RLDM workshop (Co-chair, 2022).

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Li CaiView profile

Professor

University of California, Los AngelesUnited States

Li Cai is a Professor and Director at the National Center for Research on Evaluation, Standards, and Student Testing (CRESST) within the Graduate School of Education and Information Studies at the University of California, Los Angeles (UCLA). His work focuses on quantitative methods in education, particularly psychometrics and statistical modeling. Ph.D. in Quantitative Psychology from the University of North Carolina – Chapel Hill Research and teaching interests center on psychometrics, latent variable models, item response theory, nonlinear mixed models, and statistical computation. His methodological work addresses advanced techniques for educational assessment and model evaluation. His representative publications include studies on covariance structure models, item response theory, bifactor analysis, and goodness-of-fit testing. These works often emphasize computational algorithms and practical applications in educational measurement. Li Cai is affiliated with CRESST at UCLA, a leading center dedicated to rigorous research, assessment design, and evaluation methodology across diverse educational contexts.

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Daniel FrischemeierView profile

Professor

University of MünsterGermany

Daniel Frischemeier is a Professor of Mathematics Didactics with a focus on Primary Education at the University of Münster's Faculty of Mathematics and Computer Science. He has established himself as a leading researcher in statistics and data science education for primary school students, with extensive contributions to educational methodology and teacher training. University of Münster (2021-present) TU Dortmund (2020-2021) University of Paderborn (2009-2020) Ludwig-Maximilians-Universität München (2017-2018) Dr. Frischemeier completed his doctoral studies at the University of Paderborn with a dissertation on statistical thinking and research using TinkerPlots software. His educational background includes graduate studies in Mathematics and undergraduate studies in Mathematics and Physics for teaching at various school levels. His research focuses on the design and testing of teaching-learning environments for primary mathematics education, particularly in the areas of data analysis, probability, and statistics. He conducts qualitative analysis of learners' cognitive processes related to the guiding principle of 'data and chance' in primary education. His work also includes the design and evaluation of teaching materials in data science and civil statistics, the use of learning videos to promote process-related skills, and the implementation of Fermi tasks and computer science education within primary mathematics lessons. Analysis of Dr. Frischemeier's recent publications reveals a strong emphasis on data literacy development in primary education, with increasing focus on the integration of digital tools and the conceptual understanding of data as models. His work bridges mathematics education with emerging fields of data science, addressing both theoretical frameworks and practical classroom applications. The research demonstrates a progression from basic statistical concepts toward more complex data modeling approaches suitable for young learners. Elected member of the International Statistical Institute (ISI) Chair of the Local Organizing Committees for IASE Satellite 2025 Conference Council-Member of the International Statistical Institute Special Edition Editor of the Statistics Education Research Journal Member of International Program Committees for major statistics education conferences Co-Leader of CERME Thematic Working Group 5 on Probability and Statistics Education Dr. Frischemeier serves in numerous editorial capacities and review roles for prominent journals in mathematics and statistics education. He leads significant research projects including 'Promoting Data Science Education for Teacher Education at the University level (DataSETUP)' and 'Data Science Education in STEAM for Civic Engagement and Social Justice from the Early Years (DataScEd4CiEn)'. His work has substantial impact on teacher education programs and curriculum development in statistics and data science for primary schools. He is actively involved in the development and leadership of the Math Center Münster (MaZ), which promotes mathematical potential for all students. His team includes numerous research assistants and doctoral candidates working on various aspects of mathematics education research, particularly focusing on data literacy and statistical reasoning in primary education contexts.

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George MagoulasView profile

Professor

Birkbeck , University of LondonUnited Kingdom

Prof. George Magoulas is a Professor of Computer Science at the University of London's School of Computing and Mathematical Sciences and Director of the Birkbeck Knowledge Lab. He specializes in machine intelligence, machine learning algorithms, and AI system architectures, with applications in healthcare (e.g., neurodegenerative disease diagnosis) and educational technologies. His research has received awards from IEEE, ACM, and others. He holds a PhD in Nonlinear Optimization for Neural Networks and a PGCE in Higher Education. Education: BEng/MEng (Integrated Master's in Systems & Control Engineering), University of Patras, Greece PhD in Nonlinear Optimization for Neural Networks Learning, University of Patras, Greece PGCE in Teaching and Learning (Higher Education) Research & Leadership: He leads the Birkbeck Knowledge Lab, focusing on AI's impact on learning and communication. His work includes designing learning algorithms for psychophysiological data modeling and developing the cloudUPDRS app for Parkinson's disease assessment. He has supervised over 12 PhD students and contributed to 200+ publications. Awards & Recognition: Stanford’s “World’s top 2% of Scientists” (2024) Best Paper Awards at IEEE, ACM, and EUNITE Keynote speaker at major AI and e-learning conferences Honorary membership in the Hellenic Artificial Intelligence Society Administrative Roles: Director of Teaching & Learning Quality (2016–2023) Chair of Postgraduate Programmes Exam Board (2010–2022) Editor-in-Chief, International Journal on Artificial Intelligence Tools Teaching: He teaches courses on Artificial Intelligence, Neural Networks, and Project Management at both undergraduate and postgraduate levels. Labs & Collaborations: He directs the Birkbeck Knowledge Lab and is a member of the Data Science and AI Research Group. His projects include analyzing violent cycles using AI and collaborating on EU-funded initiatives.

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Carol FrostView profile

Assistant Professor

University of AlbertaCanada

Carol Frost, PhD, is an Assistant Professor in the Department of Renewable Resources at the University of Alberta's Faculty of Agricultural, Life and Environmental Sciences. Her research focuses on arthropod community ecology, particularly biodiversity conservation and the impacts of human activities on ecological functions. She holds a PhD in Ecology from the University of Canterbury (2014), an MSc in Entomology from McGill University (2009), and a BSc in Animal Biology from the University of Alberta (2006). Her work spans three key areas: (1) identifying low-cost industrial/urban modifications for biodiversity conservation, (2) advancing ecological network approaches to predict community dynamics, and (3) documenting Alberta's arthropod biodiversity. Recent studies include analyzing pollinator networks, habitat edge effects, and agricultural landscape impacts on hoverflies and canola crops. Frost teaches courses such as Principles of Managing Natural Diversity (REN R 364/765), Exploratory Data Analysis (REN R 581), and Statistical Methods for Environmental Sciences (REN R 582). Her research outputs emphasize ecosystem resilience, keystone species roles, and multitrophic interactions, with a focus on applied conservation solutions.

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Özlem İlk DağView profile

Professor

Middle East Technical UniversityTurkey

Prof. Dr. Özlem İlk Dağ is a Professor of Statistics at the Middle East Technical University (METU), Ankara, Turkey. She holds the position of Department Head of Statistics and serves on advisory boards for TUBITAK and the Journal of Biostatistics. Her academic journey includes roles from Research Assistant (1997) to Professor (2018), with affiliations in Actuarial Sciences and Biostatistics. Education: Ph.D. in Statistics (Iowa State University, 2004), M.S. and B.S. from METU. Research focuses on longitudinal data analysis, multilevel modeling, Bayesian methods, and biostatistics. She authored three editions of R Yazilimina Giris and a foundational book on multivariate longitudinal data analysis. Key contributions include developing marginalized transition random effects models (MTREM) and R packages like 'mmm'. Awards include METU's 20-year service recognition and teaching excellence. She has supervised numerous research projects and co-authored over 50 peer-reviewed articles across biostatistics, genomics, and statistical computing. Her work bridges statistical theory and application, with notable contributions to gene expression clustering, maternal antibody studies in veterinary science, and computational methods for longitudinal data. She maintains active collaborations in genomics and biostatistics, and her lab focuses on integrating statistical computing with biomedical research.

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Steven Paul ReiseView profile

Research Professor

University of California, Los AngelesUnited States

Steven Paul Reise is a Research Professor in Quantitative Psychology at the University of California, Los Angeles (UCLA), where he has been a faculty member since 1998. He co-directs the Advanced Quantitative Methods training program, supporting graduate students in measurement, latent variable modeling, and modern experimental design. Dr. Reise earned his Ph.D. in Psychology from the University of Minnesota (1990), mentored by IRT pioneer David Weiss and personality researcher Auke Tellegen. Dr. Reise specializes in applying latent variable measurement models—including Structural Equation Modeling (SEM) and Item Response Theory (IRT)—to personality, psychopathology, and health outcomes research. His methodological work focuses on hierarchical models, MIMIC modeling, person-fit statistics, and bifactor modeling. He has been recognized with prestigious awards, including the Raymond B. Cattell Award (1998), UCLA Psychology Department Distinguished Teaching Award (2008), and University of California Campus-Wide Distinguished Teaching Award. His recent publications explore multidimensionality in SEM, bifactor modeling, person-fit indices, and applications to psychopathology and health outcomes. Dr. Reise collaborates on projects spanning gene-to-phenotype modeling, impulsivity, and advanced psychometrics. Scientific Awards : Raymond B. Cattell Award (1998), UCLA Psychology Teaching Award (2008), UC Campus-Wide Teaching Award, Outstanding Paper of the Year (2012).

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Patrick DallasegaView profile

Associate Professor

Free University of Bozen-BolzanoItaly

Patrick Dallasega is an Associate Professor in the Department of Industrial Plants at the Faculty of Science and Technology of the Free University of Bolzano (Italy). He holds a PhD from the University of Stuttgart and has been a Visiting Scholar at Chiang Mai University (Thailand) and Worcester Polytechnic Institute (USA). His expertise spans supply chain management, Industry 4.0 integration in SMEs, lean construction methodologies, and sustainable production planning in ETO/MTO environments. He teaches Project Management and Industrial Plants courses in Industrial Mechanical Engineering programs. His research focuses on digital transformation in manufacturing, including smart mobile factories, augmented reality applications for training, and synchronization of production and on-site assembly processes. Collaborative projects like the AR-enhanced industrial training initiative with Memc aim to reduce errors and costs in complex industrial setups. His work emphasizes human-centered technology integration, sustainability, and real-time data utilization for adaptive production strategies. Education Bachelor/Master: Free University of Bolzano (Italy) MSc: Polytechnic University of Turin (Italy) PhD: University of Stuttgart (Germany) Research Interests Professor Dallasega’s research explores the intersection of Industry 4.0 technologies with lean manufacturing principles, particularly in complex Engineer-to-Order (ETO) and Make-to-Order (MTO) sectors. He investigates how digital twin frameworks, augmented reality (AR), and real-time data analytics can enhance supply chain resilience, reduce operational losses, and improve workforce training efficiency. His work also addresses sustainability challenges in mobile and distributed manufacturing systems, emphasizing eco-friendly logistics and smart factory design. Key Projects Recent collaborations include: Development of an AR-based training module to boost procedural knowledge retention in machinery setups Comparative studies on Industry 4.0 adoption in SMEs across Europe and Asia Framework for digital twin-driven quality control in precast manufacturing Grants & Advising No specific grants or student advisees are listed in the provided data. His focus remains on collaborative industry projects and institutional teaching responsibilities. Labs & Teams Involved in cross-disciplinary teams at the Free University of Bolzano, particularly in the NOI Techpark innovation hub. Leads initiatives on smart mobile factories and human-centered robotics applications in manufacturing environments.

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Katherine Rose AdamsView profile

Associate Professor

University of North GeorgiaUnited States

Katherine Rose Adams serves as an Associate Professor in the College of Education at the University of North Georgia (UNG), where she joined in August 2018 and assumed coordination of the Higher Education Leadership and Practice (HELP) Ed.D. program in 2019. Starting Fall 2025, she will expand her role as Assistant Director of the UNG Center for Teaching, Learning, and Leadership while maintaining her academic position. Her educational foundation includes: Ph.D. in Adult Education from The University of Georgia (2013) Graduate Certificate in Interdisciplinary Qualitative Studies (2013) M.Ed. in Human Resource/Occupational Development (2007) B.S. in Psychology (2005) Dr. Adams' research centers on boundary spanning within university-community partnerships, higher education leadership, minority-serving institutions, and hidden student populations including those experiencing foster care or homelessness. Her work critically examines higher education affordability, open educational resources, and equity considerations across rural contexts and institutional types, with particular attention to Historically Black Colleges and Universities (HBCUs) and student mental health. Analysis of her 15 most recent publications reveals consistent thematic threads: boundary spanning behaviors in research-practice partnerships (2013-2017), evolving frameworks for community engagement through systems theory (2014-2021), and contemporary equity-focused investigations into rurality, race, and leadership (2023). Her scholarship spans qualitative, historical, and organizational perspectives while maintaining practical applications for graduate education and institutional development. As HELP program coordinator, Dr. Adams developed a grant writing academy, peer mentoring program for adult learners, and dissertation support workshops. She chairs doctoral committees and teaches core courses including Dissertation Development, Qualitative Research Methods, and specialized seminars on organizational theory, ethics, and finance in higher education. Her leadership extends to program design through the HELP Ed.D. curriculum and upcoming collaboration with UNG's Center for Teaching, Learning, and Leadership, reflecting her commitment to developing educational leaders through experiential, community-engaged pedagogy.

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Hari SubramonyamView profile

Assistant Professor

Bangor UniversityUnited Kingdom

Hari Subramonyam is an Assistant Professor (Research) at Stanford University's Graduate School of Education with a courtesy appointment in Computer Science. He serves as the Ram and Vijay Shriram Faculty Fellow at Stanford's Institute for Human-Centered AI (HAI) and is a core faculty member of Stanford HCI. Subramonyam earned his PhD in Information from the University of Michigan under advisor Eytan Adar. His research focuses on the intersection of Human-Computer Interaction (HCI) and Learning Sciences, specifically developing AI systems to augment human learning through cognitively informed design, co-design with educators, and transformative learning experiences. His work prioritizes ethical AI, responsible design practices, and human values in technology creation. Research spans generative AI for education, human-AI interaction paradigms, and accessible learning technologies. Subramonyam's publications demonstrate strong focus on human-centered AI systems for education, visualization, and creative applications. His recent work (2023-2025) concentrates on generative AI interfaces for writing assistance, educational tools, and collaborative systems, while maintaining consistent exploration of visualization techniques and AI transparency frameworks. Awards & Honors: Best Paper Award at CHI (2025, 2020, 2019) Honorable Mention Award at CHI (2025) Best Paper Award at IUI (2021) Ram and Vijay Shriram Faculty Fellow HAI Hoffman Yee Grant (2024) Cover Story in Interactions Magazine (2024) Advising & Grants: Leads 27 students including PhD advisee Neha Rajagopalan (co-advised) and diverse MS/BS researchers. Received HAI Hoffman Yee Grant (2024) for "Integrating Intelligence: Building Shared Conceptual Grounding for Interacting with Generative AI" as co-investigator. Teaches courses on data visualization (CS 448B) and educational technology design (EDUC 432). Labs & Leadership: Core faculty at Stanford HCI group, directing research on human-centered AI systems. Organizes workshops including "Tools for Thought" (CHI 2025) and "Human–AI Coevolution" (ICLR 2025). Maintains collaborations with National University of Singapore and University of Michigan.

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Marcelo WorsleyView profile

Associate Professor

Northwestern UniversityUnited States

Marcelo Worsley is the Karr Family Associate Professor in Computer Science and Learning Sciences at Northwestern University's School of Education and Social Policy (SESP). His research focuses on promoting STEM education for underserved populations through hands-on, project-based learning. He holds a PhD and MS in Learning Sciences and Computer Science from Stanford University, along with dual bachelor's degrees in Chemical Engineering and Portuguese. His work emphasizes inclusive learning technologies, multimodal learning analytics, and fostering agency in students to address real-world challenges. Research Interests: Worsley’s research spans four core areas: (1) designing meaningful hands-on learning experiences; (2) extracting and analyzing multimodal data; (3) developing naturalistic interfaces for education; and (4) bridging engineering education with conceptual change. His Technological Innovations for Inclusive Learning and Teaching (tiilt) Lab aims to address inequities in education by co-designing tools with teachers and learners. Grants & Projects: Worsley has led initiatives such as Multimodal Learning Analytics (funded by NSF EAGER) and PE++ , integrating computer science with physical education. His projects often involve intergenerational making, refugee youth engagement, and culturally responsive computing. Publications: His work appears in journals like Journal of Learning Analytics and conferences such as ICLS and EDM. Recent themes include AI in education, embodied learning in games like Minecraft, and leveraging sports for computational thinking.

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Jilles VreekenView profile

Professor

Helmholtz Institute for Pharmaceutical ResearchGermany

Jilles Vreeken is a Professor of Computer Science at Saarland University and tenured faculty at the CISPA Helmholtz Center for Information Security, where he leads the Exploratory Data Analysis research group. He is also an ELLIS Fellow and Faculty of the Saarbrücken Unit on AI and ML. His work bridges theoretical foundations with practical applications in causal inference, unsupervised learning, and exploratory data analysis. Dr. Vreeken's research focuses on developing theory and algorithms for answering fundamentally exploratory questions about data: "what is going on in my data?", "what causes what and how?", and "what can we learn from this model?" without making unnecessary or unjustified assumptions. He takes a principled approach based on information theory to identify what is worth knowing, then develops efficient algorithms for extracting useful interpretable results. His work spans causal inference under realistic conditions (including hidden confounding, selection bias, and non-i.i.d. data), summarizing complex data and models in understandable terms, and combining these threads to create more robust and useful models across diverse data types. His recent publications demonstrate a strong trend toward causal discovery in increasingly realistic settings, including non-stationary time series, event sequences, and scenarios with hidden confounders. He has made significant contributions to federated learning, interpretable machine learning, and pattern mining. His work consistently applies information-theoretic principles to develop methods that are both theoretically sound and practically useful for extracting insights from complex data. Dr. Vreeken has received numerous prestigious awards including: IEEE ICDM'18 Tao Li Award for Excellence in Research IEEE ICDM'18 Best Paper Award UdS-CS'15 Busy Beaver Teaching Award ACM SIGKDD'11 Best Student Paper Award ACM SIGKDD'10 Doctoral Dissertation Runner-Up Award ECML PKDD'09 Best Student Paper Award As an advisor, Dr. Vreeken has mentored numerous doctoral researchers to completion, including Dr. Osman Ali Mian, Dr. David Kaltenpoth, Dr. Boris Wiegand, Dr. Sebastian Dalleiger, Dr. Janis Kalofolias, Dr. Jonas Fischer, Dr. Alexander Marx, Dr. Panagiotis Mandros, Dr. Kailash Budhathoki, Dr. Roel Bertens, Dr. Koen Smets, and Dr. Michael Mampaey. He has secured significant research funding as PI for multiple projects including "AI for Prediction and Therapy Guidance in Acute Stroke" (HAICU, 2025-2028), "Neuro-Explicit Models of Language, Vision and Action" (RTG, DFG, 2023-2028), and "Crushing Antimicrobial Resistance using Explainable AI" (HAICU, 2021-2024). Dr. Vreeken leads the Exploratory Data Analysis (EDA) research group at CISPA, which focuses on developing theory and algorithms for discovering novel insights from data, learning inherently interpretable models, and drawing reliable causal conclusions. The group has produced numerous influential algorithms and frameworks in causal inference, pattern mining, and exploratory data analysis, with applications spanning healthcare, materials science, and cybersecurity.

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