Ines Zeitlhofer is a PhD student and Research Assistant at the Department of Educational Science, University of Salzburg, affiliated with the School of Education. Her research focuses on metacognition and problem-solving in digital learning environments, with a particular emphasis on pedagogical agents and multimedia learning. Prior to this role, she earned a Master of Educational Sciences from the University of Salzburg and worked as a teacher at Group Scolarie Sophie Barat in Paris and a lecturer for Business German at the Université Paris Cité’s Faculty of Law and Economics. Her work explores cognitive and metacognitive strategies to enhance learning performance, leveraging digital tools and multimedia approaches. Recent publications highlight her contributions to understanding appraisal processes in multimedia learning, the impact of pedagogical agents on motivation, and the application of cognitive prompts in digital platforms. Ines is part of the Digital Learning Research Group (DLRG), collaborating on projects that bridge educational theory and technological innovation. She holds no explicitly stated awards but is actively engaged in advancing evidence-based practices in digital education.
Vladimir Filkov is a Professor in the Department of Computer Science at the University of California, Davis, College of Engineering. He leads two research labs: the DECAL Lab and the AI for Health Lab. He is actively engaged in research, teaching, and service, with a focus on open-source software sustainability, AI in healthcare, and data science. He has held leadership roles such as General Chair of ASE 2024 and inaugural Director of Translational Data Science at UCD DataLab. Professor, Department of Computer Science, UC Davis Director, DECAL Lab Director, AI for Health Lab General Chair, ASE 2024 Director of Translational Data Science, UCD DataLab (2020–2024) His research centers on the sustainability of open-source software, using socio-technical and governance data to forecast project success and evolution. He also investigates AI applications in health, particularly multimodal models for atrial fibrillation and NLP in medicine. His work bridges empirical software engineering, data science, and healthcare informatics, with strong community engagement through forums and podcasts. He has led major NSF, Google, and Sloan Foundation-funded projects on OSS sustainability and UC-wide OSPO initiatives. The recent publications highlight a strong trend in empirical software engineering, particularly around open-source governance, lifecycle analysis, and sustainability forecasting. There is also a growing emphasis on health-related AI, including multimodal models for cardiac conditions and natural language processing in clinical settings. The work combines data-driven modeling with real-world impact in both software ecosystems and healthcare. ACM Distinguished Member ACM SIGSOFT Distinguished Paper Award Vladimir Filkov has successfully secured competitive grants from the NSF (GCR, Phase I and II), Google, and the Sloan Foundation. He advises PhD students including Likang Yin, Raiyan Jahangir, and postdoc Stefan Stanciulescu. His mentoring spans topics in software engineering, AI, and computational biology. He has organized major research forums and collaborative initiatives across the UC system. He leads the DECAL Lab and the AI for Health Lab at UC Davis, fostering interdisciplinary research in software sustainability and healthcare AI. These labs support graduate students, postdocs, and collaborative projects with national and international partners.
Assoc Prof Sau Kim Lum holds a dual role as Associate Vice President (Global Relations) leading the Global Relations Office (GRO) at the National University of Singapore (NUS), alongside his faculty appointment in the Department of Real Estate within the NUS Business School. His academic responsibilities include teaching real estate finance and development, while his administrative role focuses on international partnerships, student mobility programs, and strategic global engagement initiatives. Education & Research: His research expertise spans housing economics, land policy, and asset pricing, with a focus on Singapore's unique housing market dynamics. He has authored seminal works on housing affordability, public policy impacts, and the interplay between land supply and market behavior. His studies often collaborate with governmental bodies to inform policy design. Teaching Philosophy: Prof Lum emphasizes student engagement through real-world applications, structured curricula, and critical thinking development. His teaching modules (e.g., RE4101 Real Estate Development II, GEK2013 Real Estate Finance) integrate interdisciplinary knowledge and practical projects, fostering skills in problem-solving and teamwork. Key Contributions: Notable publications include analyses of Singapore's public housing affordability policies, land-use regulations, and asset pricing frameworks. His work bridges academic rigor with policy relevance, addressing challenges such as housing market segmentation and urban redevelopment.
Khoa Do (Bin) is an Assistant Professor (UK Lecturer) of Marketing at Royal Holloway-University of London. His research focuses on services marketing (AI/service robots, organizational frontlines) and transformative consumer research (prosocial/sustainable behaviors). He holds a Ph.D. in Marketing from National Tsing Hua University, Taiwan, and has been a visiting scholar at Florida State University, University of Houston, and SolBridge International School of Business. Education: Ph.D. in Marketing, National Tsing Hua University (2017-2023) MBA in Marketing, SolBridge International School of Business (2015-2017) Bachelor's in Economics, Foreign Trade University, Vietnam (2010-2014) Research interests blend experimental methods (field experiments, A/B tests) with text mining and secondary data analysis. His work addresses AI ethics in service robots, consumer sustainability, and frontline service dynamics. Contributions include the 2023 Psychology of Technology Dissertation Award and 2024 SERVSIG Best Dissertation Award. Key awards: 2023 Psychology of Technology Dissertation Awards Honorable Mention 2024 SERVSIG Best Dissertation Award Research trends emphasize human-robot collaboration, empathetic creativity in service roles, and GM food consumption ethics. His articles appear in Journal of Service Research , Psychology & Marketing , and International Journal of Contemporary Hospitality Management .
Anh T. Ninh is an Associate Professor in the Department of Mathematics at William & Mary, where he also contributes to the M.S. program in Computational Operations Research within the Department of Computer Science. His academic work bridges mathematics, computer science, and healthcare applications, with a strong focus on optimization and machine learning. Research Interests: His primary research areas include optimization under uncertainty, machine learning, and their applications in healthcare systems, particularly in clinical trial design and pharmaceutical supply chain management. He develops advanced mathematical models to improve decision-making under uncertainty in complex operational environments. Publication Trends: His recent publications reflect a consistent focus on integrating stochastic and robust optimization with real-world healthcare logistics, clinical operations, and pharmaceutical planning. The works demonstrate a strong interdisciplinary approach, combining operations research, data science, and domain-specific knowledge to solve critical problems in health systems. Scientific Awards & Recognition: While no specific awards are listed in the provided text, his research is supported by the Bill & Melinda Gates Foundation, indicating significant recognition and impact in the field. He has also collaborated with major pharmaceutical companies such as Lifecell (now Abbvie), Sandoz, and IntegriChain, highlighting the practical relevance of his work. Advising and Grants: Dr. Ninh advises students through the Computational Operations Research program and leads externally funded research, including an active project on site selection funded by the Bill & Melinda Gates Foundation. His work bridges academia and industry, contributing to both theoretical advances and practical implementations in healthcare operations. Labs and Research Teams: While no formal lab name is mentioned, Dr. Ninh leads a research group focused on computational optimization and machine learning applications in healthcare. His team likely includes graduate students and collaborators from both mathematics and computer science, working on projects related to supply chain resilience, clinical trial efficiency, and data-driven healthcare decision-making.
Rong Chen is a Distinguished Professor and Chair of the Department of Statistics at Rutgers University, within the School of Arts and Sciences. With a Ph.D. from Carnegie Mellon University, Professor Chen has established himself as a leading researcher in statistical time series analysis, Monte Carlo methods, and their applications across various fields. Professor Chen's research focuses on: Nonlinear and Multivariate Time Series Analysis Monte Carlo Methods, Statistical Computing and Bayesian Analysis Statistical Applications in Science, Engineering and Business His research trajectory has evolved significantly over the years, beginning with foundational work on nonlinear time series and moving toward more complex high-dimensional tensor time series analysis. Recent publications show a strong emphasis on matrix and tensor factor models for high-dimensional time series, reflecting the growing importance of analyzing complex structured data in modern applications. His work bridges theoretical statistical innovation with practical problem-solving across finance, engineering, and computational biology. Professor Chen has been recognized for his contributions to the field with prestigious fellowships: ASA Fellow (American Statistical Association) IMS Fellow (Institute of Mathematical Statistics) As Chair of the Department of Statistics, Professor Chen oversees academic programs including the Master in Financial Statistics and Risk Management (FSRM) and the Master in Data Science (Statistics Track) programs. His leadership extends to guiding research directions in the department and fostering collaborations across disciplines. Professor Chen has secured numerous research grants supporting work in time series analysis, statistical computing, and applications in finance, engineering, and bioinformatics. Professor Chen's research group maintains active collaborations with researchers in finance, engineering, and computational biology, applying statistical innovations to real-world problems including financial time series analysis, protein folding studies, HIV infection dynamics modeling, wind power forecasting, and nuclear material detection systems.
Francis de Véricourt is Professor of Management Science and the founding Academic Director of the Institute for Deep Tech Innovation (DEEP) at ESMT Berlin, where he also holds the Joachim Faber Chair in Business and Technology. He has held faculty positions at Duke University and INSEAD and was a post-doctoral researcher at MIT, reflecting a global academic footprint across France, the USA, Germany, and Singapore. His educational background includes a PhD from Université Paris VI and an engineering degree from ENSIMAG (Grenoble Institute of Technology), establishing a strong foundation in applied mathematics and computer science. Francis's research focuses on decision science, analytics, and operations, with impactful applications in healthcare, sustainability, and human-AI interaction. He investigates how mental models—'framing'—enable individuals and organizations to transcend data and generate better alternatives for decision-making. His work emphasizes cognitive agility, translational innovation, and the role of human intuition in the age of artificial intelligence. The analysis of his recent publications reveals a consistent trajectory in understanding cognitive frameworks in decision-making, the integration of AI in human contexts, and the ethical and strategic dimensions of deep-tech innovation. His writings bridge academic rigor with practical insight, targeting both scholarly and industry audiences. ENRE Best Publication Award, INFORMS MSOM Best Publication Award, INFORMS He has been a Department Editor for Operations Research and MSOM , and his academic leadership includes establishing the Center for Decisions, Models, and Data at ESMT. He has received multiple teaching awards for his work with MBA and Executive MBA students and is deeply engaged in executive education and corporate learning solutions. His book Framers , published by Penguin Random House and listed among the Financial Times' Best Books, has amplified his influence in both academic and public spheres. Francis leads DEEP—the Institute for Deep Tech Innovation—which fosters research, education, and entrepreneurial action in areas like AI, quantum computing, and biotechnology. The DMD Center, now integrated into DEEP, explores how modeling and representation enhance decision-making beyond data. These initiatives reflect his commitment to cultivating cognitive and entrepreneurial capabilities within scientific and business communities.
Joane Nagel is a University Distinguished Professor in the Department of Sociology at the University of Kansas, where she has established a prolific career spanning over four decades. Her work bridges sociology, gender studies, and environmental research with a focus on structural inequalities and identity politics. Education: Ph.D. in Sociology, Stanford University, 1977 Professor Nagel's research interrogates the persistence of racial and ethnic boundaries through the lenses of gender, sexuality, and power. She examines American Indian activism, the militarization of science, and climate change, employing comparative-historical methods to analyze how political and cultural forces shape identity formation. Her foundational works include American Indian Ethnic Renewal (1996) and Race, Ethnicity & Sexuality (2003), while her current scholarship focuses on gendered dimensions of climate policy as evidenced by Gender and Climate Change: Impacts, Science, Policy (2025). Analysis of her recent publications reveals a critical shift toward climate justice, consistently exposing gendered power dynamics in environmental discourse. Her 2020-2025 work demonstrates how hegemonic masculinity permeates climate science and policy, while earlier scholarship established frameworks for understanding ethnic boundary maintenance in multicultural societies. This trajectory reflects her commitment to intersectional analysis across temporal and geopolitical contexts. No scientific awards were explicitly documented in the source materials. Professor Nagel's academic leadership is demonstrated through workshop organization including the SGER-funded Sociological Perspectives on Global Climate Change (2007), though specific grant amounts and student mentorship details remain unreported. Her collaborative publications with scholars like Trevor Scott Lies and Sam Kendrick indicate sustained interdisciplinary partnerships. While no formal lab affiliations are specified, her research methodology emphasizes collaborative knowledge production through workshops and interdisciplinary teams focused on climate justice and indigenous rights.
Dr. Crystal Fausett is an Assistant Professor in the School of Information at San José State University. Her academic work bridges human factors, cybersecurity, and human-computer interaction, with a strong focus on how individuals and teams interact with technology in high-stakes environments such as healthcare, emergency services, and cyber operations. She teaches courses including ISDA 121 – Human Centered Cybersecurity and ISDA 130 – User Centered Interface Architecture and Prototyping, reflecting her interdisciplinary expertise. Ph.D. in Human Factors, Embry-Riddle Aeronautical University (2024) M.S. in Human Factors, Embry-Riddle Aeronautical University (2022) B.A. in Psychology, San José State University (2020) A.A. in Social and Behavioral Sciences, Santa Barbara City College (2018) Her research interests include human-centered cybersecurity, human-computer interaction, usability and UX design, teamwork and transactive memory systems, training methodologies (particularly simulation-based), and information behavior across diverse user groups such as healthcare providers, cyber defenders, and naval personnel. She applies both qualitative and quantitative methods, including expert interviews, board game simulations, and meta-analytic techniques. The recent publications highlight a strong trend in applying human factors principles to cybersecurity team performance, adaptive training, and healthcare safety. Her work increasingly explores innovative methods such as gamified simulations to study team dynamics and cognitive load in complex operational environments. There is a consistent emphasis on improving system efficiency, safety, and collaboration through human-centered design. Dr. Fausett has no listed scientific awards in the provided text. She has served as an Instructor of Record in Human Factors and Behavioral Neurobiology at Embry-Riddle Aeronautical University (2023–2024) prior to her current role. There is no mention of current grant funding or student advising in the provided materials. Her research often involves collaboration with experts in human factors and healthcare, particularly Dr. Joseph R. Keebler and Dr. E. Salas, suggesting active participation in research teams focused on team performance and safety-critical systems. She is involved in research labs or teams centered on human factors in cybersecurity and healthcare, particularly through collaborative projects involving simulation-based training and team cognition studies. Her use of board games like [d0x3d!] as experimental testbeds indicates innovative lab-based methodologies for studying real-world team interactions in controlled environments.
Tuğba KANMAZ is an Assistant Professor at Kırşehir Ahi Evran University, Faculty of Education, Department of Basic Education since 2024. Previously, she held academic positions at Kütahya Dumlupınar University from 2018-2024, including roles as Department Head and Vice Dean . PhD in Preschool Education (Gazi University, 2023) Master's in Preschool Education (Gazi University, 2017) Bachelor's in Primary Education (Muğla Sıtkı Koçman University, 2014) Her research focuses on Preschool Education and Early Childhood Education , with emphasis on Computational Thinking , Creative Drama , and Technology Integration . Her work explores: Robotics and coding education for preschoolers Peer relationships and bullying prevention Movement programs for children with disabilities Parental involvement in digital education 21st-century skills in early childhood Recent publications analyze trends in drama-based pedagogy, coding tools like ScratchJr, and pandemic-induced learning losses. Awards include the 2023 Academic Incentive Award and 2022 Best Oral Presentation at international conferences. Collaborations span institutions including Gazi University and Kafkas University.
Ganesh Mani is an Adjunct Assistant Professor at the Software and Societal Systems Department, School of Computer Science, Carnegie Mellon University. He specializes in applying artificial intelligence to scale human expertise, particularly in financial services and group decision-making. His research spans machine learning, swarm intelligence, and cross-disciplinary AI applications. Mani holds a PhD in Artificial Intelligence and Software Engineering from the University of Wisconsin-Madison, an MBA in Finance, and an undergraduate degree in Computer Science from IIT Bombay. His work explores hybrid collaborative decision-making systems, financial forecasting, and educational technology innovations. His research focuses on artificial intelligence applications in financial services, swarm intelligence for group deliberation, and educational technology. Key trends include leveraging large language models, analyzing alternative data sources, and developing AI-driven tools for collaborative problem-solving across domains.
Xiusi Chen is a Postdoctoral Research Fellow in the Blender Lab at the University of Illinois Urbana-Champaign (UIUC), working under Prof. Heng Ji. His research focuses on improving reasoning, alignment, and decision-making capabilities of Large Language Models (LLMs). Previously, he completed his Ph.D. in Computer Science at UCLA under Prof. Wei Wang, and earned M.S. and B.S. degrees in Computer Science from Peking University under Prof. Jun Gao. His educational background includes: Ph.D. in Computer Science, University of California, Los Angeles (UCLA), advised by Prof. Wei Wang M.S. in Computer Science, Peking University, advised by Prof. Jun Gao B.S. in Computer Science, Peking University, advised by Prof. Jun Gao Dr. Chen's research program targets three interconnected areas: advancing Large Language Models (particularly in low-resource reasoning and alignment), developing NLP applications for AI in Science and recommendation systems, and modeling complex decision-making processes in sports domains. His work bridges theoretical foundations with practical implementations, resulting in numerous publications in top-tier conferences including ACL, ICML, ICLR, and KDD. Analysis of his recent publications reveals a strong focus on making LLMs more efficient, reliable, and capable of complex reasoning tasks across diverse domains. His significant academic contributions include: 2023 Best Poster Award (Honorable Mention) at SDM 2023 2023 SIAM Student Travel Award 2021 and 2022 SIGIR Student Travel Grants from ACM SIGIR Multiple academic scholarships from Peking University Co-creation of the widely adopted Amazon Reviews'23 dataset (500k+ HuggingFace downloads) Dr. Chen actively serves the research community as Workshop Organizer for KDD 2025, Program Committee member for major conferences (KDD, WSDM, ICML, NeurIPS, ICLR, AAAI), and journal reviewer. He maintains a strong commitment to mentoring, offering dedicated time for students (especially from underrepresented groups) to discuss research and career development. Starting Fall 2025, he will be seeking academic positions to continue his research on language agents and decision-making systems. Currently based in the Siebel Center for Computer Science, Dr. Chen collaborates with the Blender Lab team on advancing NLP and LLM capabilities. His work has practical impact through widely adopted resources like the Amazon Reviews'23 dataset and theoretical contributions through his publications on reasoning frameworks and alignment techniques.
Marcus Birkenkrahe serves as an active Professor at the Berlin School of Economics and Law within the Faculty of Business Administration and Department of Business Informatics. His academic work bridges information technology with business management, focusing on practical applications in corporate and educational environments across Germany and Europe. His research spans E-Learning methodologies, Knowledge Management frameworks, Digital Community dynamics, and Leadership strategies. He pioneered virtual teaching systems and organizational learning tools, notably developing systemic constellations for change management and lean IT solutions for enterprise optimization. His work consistently explores technology-driven transformations in both academic and multinational business contexts. Publication analysis from 2000-2010 reveals dominant themes in knowledge management evolution within the new economy, organizational learning systems, and digital community governance. His research trajectory shows progression from theoretical foundations (2000-2001) toward practical applications in multinational corporations (2002-2004) and innovative educational models (2006-2010), with recurring emphasis on systemic approaches to complex organizational challenges. His distinguished awards include: Best article in the UA Business Review (2002) Fellow of the Royal Society of Arts (RSA) London (1998) Best campus-wide information site on the WWW (1994) Best online course offering on the WWW (1994) Professor Birkenkrahe has significantly advanced digital education infrastructure and knowledge-sharing platforms, though specific current advising roles and grant details aren't documented in available sources. His early 1990s web innovations established foundational models for online course delivery and campus information systems that received international recognition.
Nadine Bergner is a Professor in the Teaching and Research Area of Learning Technologies (Informatik 9) at the Faculty of Computer Science, Dresden University of Technology (TU Dresden). Her work focuses on innovative approaches to computer science education across various educational levels, from primary schools to university settings. She leads research on learning technologies, VR applications in education, and teacher training in computer science. Prof. Bergner's research spans multiple dimensions of computer science education. She has developed and evaluated numerous educational approaches including VR serious games, physical computing with Arduino, and specialized learning environments like the InfoSphere student lab. Her work emphasizes making computer science accessible and engaging for diverse learners, including young children, senior citizens, and pre-service teachers. She has made significant contributions to understanding how students form mental models of computer science concepts and how these can be effectively developed through targeted educational interventions. Her recent publications reveal a strong focus on emerging technologies in education, particularly virtual reality applications for computer science learning. She has been exploring how different interaction modalities in VR affect learning outcomes, presence, and enjoyment. Another major thread in her recent work involves machine learning education, where she has developed concept inventories and game-based learning approaches to make complex ML concepts accessible to learners. She has also been actively researching how to integrate computer science and AI competencies into teacher education across various disciplines. Prof. Bergner has made substantial contributions to the development of educational materials and frameworks for computer science education. Her work includes creating resources for programming education ("Apps programmieren für Dummies Junior"), designing concept inventories for machine learning assessment, and developing comprehensive approaches to building computational thinking skills from early childhood through higher education. She has been instrumental in shaping computer science education policy through initiatives like the Dagstuhl Declaration on education in the digital networked world. Her research often involves collaborative projects with schools and teachers, focusing on practical implementation and evaluation of computer science education approaches. She has been particularly active in developing teacher training programs and materials to support the integration of digital competencies into school curricula. Her work with the InfoSphere student lab has provided valuable insights into how extracurricular learning environments can shape students' perceptions of computer science.
Dr. Arie Levit is a Senior Lecturer (tenure track) in the Department of Theoretical Mathematics at Tel Aviv University's School of Mathematics, a position he has held since 2021. Previously, he served as a Gibbs Assistant Professor at Yale University from 2017. His academic career centers on pure mathematics with emphasis on structural properties of discrete groups and dynamical systems. His educational background includes: B.A in Mathematics from the Hebrew University of Jerusalem (2004) M.A in Mathematics from the Hebrew University of Jerusalem (2012) Ph.D. in Mathematics from the Weizmann Institute of Science (2017) under Prof. Tsachik Gelander Levit's research spans discrete groups, geometric and analytic group theory, and ergodic theory, with significant contributions to lattice theory, invariant random subgroups, character rigidity, and group stability. His work integrates algebraic, geometric, and probabilistic frameworks to solve fundamental problems in classification and rigidity of group actions, particularly in non-Archimedean and hyperbolic settings. Analysis of his 14 publications (2014-2024) reveals evolving focus from foundational lattice theory toward contemporary stability phenomena and character theory, with 60% of recent work (2022-2024) addressing permutation stability, Hilbert-Schmidt representations, and ergodic properties of group actions. Key methodological threads include the application of ergodic theory to group-theoretic classification and the development of analytical tools for stability problems. His scholarly recognition includes: Klein Prize (2017) ISF-BSF research grant (2020) As principal investigator of the ISF-BSF grant, Levit leads research on group stability and ergodic theory. His extensive collaborations with Gelander, Lubotzky, and Lazarovich demonstrate active mentorship within the global mathematics community. His work is conducted within Tel Aviv University's Theoretical Mathematics department, which maintains strong international partnerships in geometric group theory and dynamics.
