Noortje Venhuizen is an Assistant Professor in the Department of Cognitive Science and Artificial Intelligence at the Tilburg School of Humanities and Digital Sciences, Tilburg University. She serves as Academic Director for the BSc Cognitive Science and Artificial Intelligence program since 2024, and has held academic positions at Saarland University (2015-2022) including roles as Scientific Staff member and Principal Investigator in SFB 1102 projects. PhD in Computational Semantics (University of Groningen, 2015) MSc in Logic (ILLC, University of Amsterdam, 2011) BSc in Artificial Intelligence (Utrecht University, 2009) Her research focuses on expectation-based language comprehension, neurocomputational modeling of semantic processing, distributional formal semantics, and pragmatic reasoning in discourse. Key contributions include PDRT-SANDBOX (Haskell NLP library) and DFS Tools (Prolog implementation of distributional formal semantics). Recent publications (2023-2025) explore multimodal word meaning, informativity in reference production, and neurocognitive models of surprisal processing. Her work combines formal semantics with cognitive neuroscience and computational modeling. LOT Grotevragenprijs essay contest - Second Prize She has presented research at major conferences including CogSci, AMLaP, and Sinn und Bedeutung. Current teaching includes courses on artificial intelligence, statistics, and semantic theory.
Jason Poulos is a data scientist and researcher currently working at the intersection of machine learning, causal inference, and health policy. He holds a Ph.D. in Political Science from the University of California, Berkeley, with a designated emphasis in Computational Science and Engineering. His academic journey includes postdoctoral positions at Harvard Medical School's Department of Health Care Policy and Brigham and Women's Hospital. Dr. Poulos's research focuses on developing and applying machine learning methods to address challenges in complex, high-dimensional longitudinal health data. His work spans several interconnected areas including extending causal inference methods for multi-valued treatments, developing frameworks for adversarial machine learning, applying neural networks for health outcome prediction, and evaluating deep learning approaches for missing data imputation in survey data. His recent publications demonstrate expertise in targeted learning methodologies, particularly in observational studies with multi-level treatments, with applications in pharmacoepidemiology. His research output shows a consistent pattern of interdisciplinary work bridging political science, statistics, and health policy. The majority of his recent publications (2021-2024) appear in high-impact journals across biostatistics, political methodology, and machine learning domains, reflecting his ability to contribute to multiple academic communities. His work often employs advanced statistical techniques to address real-world policy questions, particularly in healthcare settings. As evidenced by his publications and research trajectory, Dr. Poulos has established himself as a methodological innovator in causal inference with multi-valued treatments, with particular expertise in applying these methods to evaluate the safety and effectiveness of antipsychotic medications. His work combines deep methodological rigor with practical policy applications.
Irena Vodenska is Professor of Finance and Director of Finance Programs at Boston University’s Metropolitan College, Department of Administrative Sciences. She holds a PhD in statistical finance and an MA in economics from Boston University, an MBA from Vanderbilt University, and a BS in computer information systems from the University of Belgrade. She is also a Chartered Financial Analyst (CFA) charter holder. Her research is at the intersection of finance, complexity science, and artificial intelligence, focusing on systemic risk modeling, ESG investments, and financial network dynamics. She has led major interdisciplinary research projects funded by the National Science Foundation, the European Commission, and the U.S. Army Research Office. PhD, Statistical Finance – Boston University MA, Economics – Boston University MBA – Owen Graduate School of Management, Vanderbilt University BS, Computer Information Systems – University of Belgrade Dr. Vodenska’s research interests include network theory in finance, systemic risk propagation, AI-powered ESG analysis, cryptocurrency price forecasting, and financial regulation. She employs big data, machine learning, and natural language processing to analyze financial news, market dynamics, and corporate sustainability. Her work investigates how climate disinformation spreads via social networks and influences public policy and governance. The recent articles highlight a consistent focus on modeling financial and economic systems using network science and AI. Trends include systemic stress testing, sentiment analysis in financial markets, cascading failures, and the interplay between macroeconomic indicators and financial networks. Her work spans econophysics, behavioral finance, public health economics, and ethical AI in fintech. National Science Foundation (NSF) research grant (2023) NSF EAGER Award (2014–2015) European Commission FET Open Grant (2012–2014) U.S. Army Research Office (ARO) Grant (2020–2021) MEXT Post-K Computer Grant, Japan (2016–2019) Alexander Hamilton Fulbright Fellowship (1994) Owen Graduate School Fellowship (1995–1996) Dr. Vodenska teaches core finance courses such as Investment Analysis and Portfolio Management, Derivatives Securities, and Financial Regulation and Ethics. She co-developed the MET AD 678 course with Professor Tamar Frankel from BU Law, emphasizing real-world case studies and ethical decision-making. Her research grants have supported innovative work in systemic risk modeling, AI for ESG, and financial network stability. She is actively involved in mentoring, conference organization, and editorial roles in leading journals. She is a key organizer of the International School and Conference on Network Science (NetSci) and the Big Data in Economics, Science, and Technology (BEST) Conference. Her lab and research team focus on complexity in financial systems, bringing together economists, physicists, computer scientists, and data analysts to study global financial stability and sustainability.
Sonakshi Garg is a Research Fellow (Postdoctoral fellow) at the Department of Computing Science, Umeå University, affiliated with the NAUSICA research group (PrivAcy-AWare traNSparent deCIsions). Her office is located at MIT House, MIT.A.420 in Umeå. Dr. Garg's research focuses on the intersection of artificial intelligence and data privacy, with specific expertise in: Privacy-preserving techniques for high-dimensional data and foundation models Synthetic data generation and distribution learning Differential privacy implementations in machine learning Federated learning security and adversarial robustness Geometric and manifold-based privacy methods Her publication portfolio demonstrates significant contributions to privacy-enhancing technologies, particularly through innovative combinations of k-anonymity with differential privacy, federated learning security frameworks, and manifold-based data protection methods. Recent works explore applications in autonomous driving security, COVID-19 forecasting, and Large Language Model privacy preservation. She collaborates with international researchers across Europe and contributes to major conferences including ESORICS, SEC, and SECRYPT. Dr. Garg is an active member of the NAUSICA research group, advancing privacy-aware decision systems through theoretical and applied research.
Ove Edvard Hatlevik is a Professor at the Faculty of Education and International Studies , Department of Primary and Secondary Teacher Education at Oslo Metropolitan University. His research focuses on Teacher Professional Digital Competence , ICT integration in education , Psychometrics , and Critical Health Literacy . He leads the Teacher Education for a Future in Flux (TEFF Academy) and Teacher Education Panel Study (TEPS) projects, while previously managing initiatives such as Developing ICT in Teacher Education (DiCTE) and Literacies for Health and Life Skills . Email : ove-edvard.hatlevik@oslomet.no Office : Pilestredet 42, Oslo (Q6007) His recent publications explore: AI applications in thesis categorization (2025) Gender differences in early childhood education outcomes (2025) Job satisfaction dynamics in teaching (2024) Digital distraction management in teacher training (2024) Professional digital competence in technology-rich classrooms (2024) Systematic reviews on digital competence dimensions (2023) Key thematic areas include: Algorithmic thinking in mathematics education Long-term impacts of ECEC quality Digital divide in adult education Assessment of critical health literacy ICT access barriers in education centers Psychometric properties of educational tools
Siegfried Nijssen is an Assistant Professor of Data Mining and Artificial Intelligence at the Catholic University of Louvain (UCLouvain) in Belgium, working within the ICTEAM research institute's Artificial Intelligence and Algorithms group. He has been at UCLouvain since 2016, previously serving as an Assistant Professor at University Leiden (2012-2016) and completing postdoctoral work at KU Leuven (2006-2015). He earned his PhD in Computer Science from University Leiden in 2006. His research focuses on making data analysis simpler through intersections between pattern mining, exploratory data analysis, and programming paradigms in Artificial Intelligence, particularly constraint programming and probabilistic programming. He has developed techniques for analyzing diverse data types including graphs, networks, and multi-relational data. His work bridges theoretical foundations with practical applications in decision tree learning, probabilistic networks, and source code analysis. Nijssen's recent publications demonstrate a strong focus on optimal decision trees, constraint-based pattern mining, and applications in bioinformatics and education. His research shows consistent evolution from foundational graph mining work (including the Gaston algorithm developed in 2004) to current work integrating machine learning with constraint programming for interpretable AI solutions. As an educator, he teaches courses including Mining Patterns in Data, Databases, and Artificial Intelligence and Machine Learning seminars at UCLouvain. He has advised numerous PhD students and postdocs, primarily in collaboration with Pierre Schaus, with former students like Tias Guns now holding professorships.
Professor Danilo P. Mandic, affiliated with Imperial College London, UK, is a leading researcher in signal processing, machine learning, and biomedical signal analysis. His work spans quaternion algebra, tensor networks, and neural networks for real-world applications. 2025: Published 11+ works on EEG/PPG analysis, quantum learning, and tensor-based LLM compression 2024: Active in interpretable transformers, graph learning for financial data, and hearable devices Research focuses on hypercomplex signal processing, graph neural networks, and medical AI applications. Recent work explores quaternion calculus for signal processing, tensor network structures for LLMs, and hearable device optimization. Key publication trends include: 2025 emphasis on quantum-aware learning, 2024 graph-based time series clustering, and 2023 foundational work on graph CNNs and matched filtering approaches. Collaborates extensively with Dongpo Xu, Sayed Pouria Talebi, Clive Cheong Took, and Tobias Reichenbach on projects involving ear-EEG, ECG enhancement, and financial sentiment analysis.
Yann LeCun is the Chief AI Scientist at Meta and holds the Jacob T. Schwartz Professorship at New York University in Computer Science, Data Science, Neural Science, and Electrical and Computer Engineering. He is a member of the National Academy of Engineering, National Academy of Sciences, and Académie des Sciences, and a fellow of ACM, AAAI, AAAS, and SIF. Research Interests: AI, Machine Learning, Computer Vision, Robotics, Computational Neuroscience, Physics of Computation. Labs: CILVR Lab (Computational Intelligence, Learning, Vision, Robotics), Computational and Biological Learning Lab at Courant Institute, Meta FAIR (Fundamental AI Research). Scientific Awards include the ACM Turing Award (2018), Queen Elizabeth Prize for Engineering (2025), VinFuture Grand Prize (2024), and multiple honoris causa doctorates. He has mentored numerous PhD students and postdocs, including Ying Wang, Yilun Kuang, and Quentin Le Lidec. Recent Publications focus on self-supervised learning, world models, video analysis, and representation learning, with key contributions to autonomous machine intelligence architectures.
Michael Hilton is an Associate Teaching Professor in the Software and Societal Systems Department of the School of Computer Science at Carnegie Mellon University. He also serves as the Associate Department Head for Education and directs both the Software Engineering Minor and Software Engineering Concentration programs. His work bridges academic research with practical software engineering education. Ph.D. in Computer Science, Oregon State University (2017) M.S. in Computer Science, Cal Poly San Luis Obispo (2013) B.S. in Computer Science, San Diego State University (2002) Professor Hilton's research primarily focuses on understanding and improving the developer experience, with particular emphasis on flaky tests, continuous integration practices, and software engineering education. His work combines empirical studies of real-world development practices with educational innovations to enhance how software engineers are trained. He has conducted extensive research on test flakiness, identifying patterns, causes, and potential solutions to this pervasive problem in modern software development. His scholarly contributions reveal a consistent focus on practical software engineering challenges, particularly those affecting developer productivity and software quality. The research trajectory shows increasing attention to educational aspects of software engineering, including team-based learning, structured feedback mechanisms, and the impact of emerging technologies like AI on programming education. Professor Hilton has over 20 years of professional experience in software development, including 9 years at SPAWAR Pacific where he worked on projects for the US Navy, Coast Guard, and White House. This industry background informs his teaching approach, which emphasizes preparing students for real-world challenges they'll face after graduation. He teaches software engineering-focused courses and has developed educational approaches that integrate practical development experience with theoretical foundations. His teaching philosophy centers on providing students with both immediate practical skills and enduring principles that will serve them throughout their careers, with special attention to software engineering in startup environments.
Dan Gutfreund is a Principal Research Scientist and Senior Manager at the MIT-IBM Watson AI Lab, focusing on machine learning with applications to natural language processing and computer vision. He previously held managerial and technical roles at IBM's Haifa Research Lab and was involved in IBM Project Debater. Gutfreund earned his PhD in computer science from the Hebrew University in Jerusalem in 2005. His research spans Neuro-Symbolic AI , Computational Complexity , and Foundations of Cryptography , with notable contributions to datasets like Moments in Time and ObjectNet . His recent work includes multimodal models for the metaverse, generative AI for engineering design, and simulator-assisted training for interpretable systems. Gutfreund's publications reflect expertise in AI applications for supply chain prediction, avatar personalization, and reconciling virtual disputes. He has also explored evolutionary algorithms for software engineering and constraint-based generative models in design tasks.
Melanie Tory is a Professor at Northeastern University's Khoury College of Computer Sciences, serving as Professor of the Practice and Director of Data Visualization. Her research focuses on data visualization and human-centered computing, with interdisciplinary applications in healthcare, energy systems, and natural language processing. Her recent work explores the intersection of machine learning and visualization in domains like cardiothoracic care and wind farm optimization. She also investigates conversational interfaces for data visualization, focusing on intent recognition and pragmatic language use in analytical workflows. Key projects include the HEART initiative for real-time medical analytics and schema design for dynamic visualizations. Melanie advises PhD students Carey Barry, Shani Spivak, and Timothy Yim, and contributes to visualization education through research faculty roles. She actively publishes in venues like IEEE PacificVis, addressing challenges in visual utility evaluation, vague command modifiers, and collaborative analysis.
Dr. Xiongcai Cai is an Adjunct Associate Professor at the School of Computer Science and Engineering, University of New South Wales (UNSW). With expertise in Artificial Intelligence , Machine Learning , and Computer Vision , he contributes to advancing Recommender Systems , Natural Language Processing , and Health Informatics . His work bridges theoretical and applied research in technology for human-centric applications. Current roles: Adjunct Associate Professor, UNSW School of Computer Science and Engineering Key research areas: Machine Learning, Recommender Systems, Computer Vision, Generative AI Dr. Cai's research portfolio demonstrates a consistent focus on recommender systems and machine learning over the past decade. His technical contributions span graph convolutional networks , temporal bilinear models , and embedding techniques for collaborative filtering. Recent work in 2025 addresses knowledge distillation for GCNs-based recommenders, while earlier studies tackled cold-start transitions and matrix factorisation boosting. His publication history (2 book chapters, 7 journal articles, and 36 conference papers) reveals a strong emphasis on real-time applications in domains like gait recognition (2020), health data analytics (2016), and social network recommendation (2010-2015). The research applies mathematical rigor to practical challenges in online dating platforms , medical decision support , and object tracking systems . Contact details: Email: x.cai@unsw.edu.au Phone: +61 2 9385 8858
Steven Zhou is an Assistant Professor of Psychological Science at Claremont McKenna College, where he leads the STATS Lab (www.statslabatcmc.com). His academic work bridges quantitative methods with psychological research, focusing on leadership, personality, and vocational psychology. With methodological expertise in psychometrics, machine learning, and data visualization, he actively contributes to both academic and practitioner communities through research, teaching, and consulting. Dr. Zhou's educational background includes: PhD in Organizational Psychology + Certificate in Computational Social Sciences from George Mason University BA in Organizational Psychology + Certificate in Conflict Resolution from Pepperdine University His research program centers on developing and refining psychological assessments while investigating bias in measurement systems. Dr. Zhou specializes in forced-choice personality assessments, leadership evaluation methodologies, and career calling research. His work consistently addresses the gap between academic research and practical application in organizational settings, with recent projects incorporating large language models and agent-based simulations to study complex behavioral phenomena. Dr. Zhou's publication record shows a clear trajectory toward increasingly sophisticated methodological approaches to organizational psychology questions. His recent work demonstrates growing integration of computational techniques (machine learning, NLP) with traditional psychological research, while maintaining strong connections to practical organizational applications. The research consistently addresses measurement challenges in leadership assessment and career development. His notable scientific achievements include: 2025 Jablin Dissertation Award from the International Leadership Association and Jepson School of Leadership Studies 2025 Chapman Dissertation Award from the Network of Leadership Scholars and Academy of Management 2023 Dissertation Research Award from the American Psychological Association 2022 Free Inquiry Grant from the Foundation for Individual Rights and Expression 2022 Graen Grant for Student Research on Leaders and Teams from SIOP 2021 Kenneth E. Clark Student Research Award from ILA and Center for Creative Leadership 2021 Outstanding Graduate Student Instructor from GMU Department of Psychology Dr. Zhou actively mentors both graduate and undergraduate students, with numerous student co-authors across his publication record. He has secured nearly $40,000 in external research funding to support his work on leadership assessment and psychometric methodology. His professional service includes editorial roles for multiple journals and leadership positions within the Society for Industrial and Organizational Psychology. As director of the STATS Lab, Dr. Zhou leads a research team focused on applying quantitative methods to understand leadership, career development, and personality. The lab's five research pillars include Psychometrics & Measurement Science, Bias in Measurement & Analytics, Leadership Assessment & Team Dynamics, Integrating Novel Methods into Research, and Career Development & Vocational Psychology, all emphasizing both methodological rigor and practical application.
Gina-Maria Pomann is an Associate Professor of Biostatistics & Bioinformatics at Duke University's School of Medicine, where she serves as Director of the Biostatistics, Epidemiology, and Research Design (BERD) Methods Core. She leads a diverse team of quantitative experts including biostatisticians, data scientists, and bioinformaticians who contribute to groundbreaking research across clinical and translational domains. Dr. Pomann earned her Ph.D. from North Carolina State University (2015) and has developed expertise in novel statistical methodology for functional data and brain imaging. She has directed 30 collaboration teams across medical fields including Pediatrics, Global Health Institute, and Neurosurgery. Her primary research focuses on the science of team science, developing administrative structures and workforce development programs to support data-intensive biomedical research. Her research program demonstrates a consistent focus on improving how quantitative scientists collaborate with biomedical researchers. Recent publications examine integrating large language models in biostatistical workflows, methods for building quantitative collaboration units, workforce development for biostatisticians, and the organizational aspects of team science. Her 15 most recent articles (2023-2025) span topics from AI in healthcare to statistical education and collaborative research structures, reflecting her dual expertise in methodological statistics and research organization. Dr. Pomann has developed significant workforce development initiatives: BERD Core Training and Internship Program (BCTIP) for Masters of Biostatistics students Duke AI Health Fellowship Program (two-year postgraduate training) R25 grant: "Quantitative Methods for HIV/AIDS Research" as MPI She holds a joint appointment at Duke National University of Singapore and has secured multiple NIH grants including CTSA UM1 (2025-2032), Quantitative Team Science Program (2024-2029), and Quantitative Methods for HIV/AIDS Research (2018-2028). Her leadership has enabled the BERD Core to assist over 1,100 investigators and produce more than 550 collaborative manuscripts, while training over 100 student interns and 40 staff members in data-intensive biomedical research.
Professor Tomoji Kishi is a distinguished faculty member at Waseda University's School of Creative Science and Engineering, where he has been serving since 2009. Previously, he held academic positions at Japan Advanced Institute of Science and Technology (2003-2009) following a 21-year career at NEC Corporation (1982-2003). He earned his Ph.D. in Information Science from Japan Advanced Institute of Science and Technology in 2002, building upon his earlier engineering graduate studies at Kyoto University. Professor Kishi's research focuses on software engineering, particularly in software product line development, model checking, formal verification, and aspect-oriented modeling. His work bridges theoretical formal methods with practical applications in embedded systems, automotive software, and IoT technologies. He has made significant contributions to scalability challenges in model checking for configurable systems and has pioneered approaches to variability management and approximate modeling techniques. His publication record demonstrates remarkable consistency and evolution, with 42 papers and 153 citations according to Scopus data (h-index: 7), spanning from foundational work in software architecture in the 1990s to cutting-edge research on AI-enhanced verification methods in 2025. His recent work shows increasing application of machine learning techniques to traditional formal methods problems, particularly in the context of highly configurable systems and IoT applications. ITS Standardization Activity Merit Prize (2022) from Society of Automotive Engineers of Japan IPSJ/ITSCJ Standardization Contribution Award (2017) IPSJ/ITSCJ Project Editor Award (2016 and 2013) Information Processing Society of Japan Society Activity Contribution Award (2010) IPA/SEC Journal Best Paper Award (2007) Information Processing Society of Japan Yamashita Memorial Research Award (1998) Professor Kishi has led multiple JSPS-funded research projects, including recent work on 'variability management methods prioritizing usability through variability mining' (2020-2023) and 'utility-first modeling method' (2017-2020). His industry collaborations, particularly with automotive systems developers, demonstrate the practical impact of his research. He maintains active membership in major professional societies including IEEE Computer Society, ACM, and the Information Processing Society of Japan.
