جست‌وجوی استاد و پژوهشگر

• Food Chemistry
• Engineering Education
• Digital Learning
• +۷ مورد دیگر

Julia Diederen is a Lecturer in the Department of Food Chemistry at Wageningen University & Research, where she contributes to engineering education and digital learning innovation. Her work spans educational design, laboratory skill development, and competency assessment in higher education, particularly within food technology programs. Academic Role: Lecturer Department: Food Chemistry Institution: Wageningen University & Research Research Focus: Engineering Education, Digital Learning, Virtual Labs Her research interests center on enhancing student learning through innovative educational tools and environments. She investigates how digital platforms, such as virtual experiments and interactive protocols, can foster meaningful learning and self-regulated behavior. Her work emphasizes the alignment of learning materials with intended outcomes and the role of student guidance in scientific reasoning. Julia's recent publications reveal a strong trend in applying data mining and educational design to improve laboratory and scientific education. Her work integrates technology with pedagogical theory, focusing on how students engage with digital tools, regulate their learning, and develop higher-order competencies. Topics include sequential pattern mining, virtual experiment environments, and competency-based curricula. She has contributed to advancing engineering education through funded research projects focused on teaching and assessing competencies in food technology. Her collaborative work includes supervising PhD candidates and presenting on emerging topics such as generative AI in student learning. Project: Improving teaching and assessing competencies in Food Technology bachelor programme Project: Fostering students' higher order laboratory research skills using digital educational games Julia Diederen is also active in academic discourse, having delivered oral presentations and contributed expert commentary in the media on educational challenges, including student resits and the integration of AI in education.

• Risk Analysis
• Financial Market Forecasting
• Quantitative Modelling
• +۷ مورد دیگر

Dr Tiejun Ma is an Associate Professor at the University of Southampton , specializing in risk analysis and decision-making. His research integrates quantitative modelling with real-time big data analytics to address financial market forecasting challenges. He has secured over £4 million in research grants across 30 projects funded by EPSRC, ESRC, Innovate UK, and industry partners like Huawei. Member of the Centre for Risk Research Member of CORMSIS (Centre for Operational Research, Management Science and Information Systems) Member of the Centre for Digital Finance Dr Ma's research spans cryptocurrencies, behavioral economics, data anonymization, and financial risk management. His work combines applied data science, mathematical modelling, and behavioral analysis to tackle complex uncertain environments. Key projects include: Huawei-funded Failure Detection Algorithm for SDN/NFV EPSRC-funded Modelling the Wisdom of the Crowd Modelling Complex Uncertain Environments with E.V. Analytics Ltd His publications appear in leading journals like the European Journal of Operational Research , Quantitative Finance , and Journal of Computer Science and Technology . He supervises PhD students in Business Studies & Management.

Jack Janetzkiمشاهده پروفایل

مدرس

• Medication Safety
• Pharmacoepidemiology
• Drug Utilization
• +۳ مورد دیگر

Dr. Jack Janetzki is a Lecturer in Pharmacy and Pharmacology at the University of South Australia, specifically within the UniSA Clinical & Health Sciences school. He completed both his Bachelor of Pharmacy (Honours) and Doctor of Philosophy at UniSA, graduating with his PhD in 2023. As a credentialed pharmacist, he maintains active clinical practice across both rural Victoria and urban/rural South Australia, providing Home Medicine Reviews, Residential Medication Management Reviews, and serving as a vaccinating and prescribing pharmacist. Dr. Janetzki's educational background includes: PhD from University of South Australia (2023) Bachelor of Pharmacy (Honours) from University of South Australia His research focuses on medication safety through an integrated approach examining adverse drug events at both molecular and population levels. Using molecular modeling strategies alongside real-world data analysis, he investigates drug-target interactions and population-level adverse event risks. A significant portion of his work examines medicine utilization patterns, particularly during shortages, and sequential treatment patterns. Dr. Janetzki is an active member of the Quality Use of Medicines and Pharmacy Research Centre (QUMPRC) at UniSA, contributing to numerous studies on medication safety and utilization. His recent publications demonstrate expertise across multiple domains of pharmacy practice, with particular emphasis on medicine shortages, pharmacovigilance, medication safety in mental health, and pharmacy education. His work often employs pharmacoepidemiological methods to analyze Pharmaceutical Benefits Scheme data and other large datasets. Dr. Janetzki has received recognition for his contributions to pharmacy practice, serving on the Local Advisory Committee for Pharmaceutical Defence Limited, a not-for-profit organization supporting pharmacists with risk management advice. He also co-hosts the popular pharmacology educational Instagram page 'jbpharmacology' with colleague Wern Chai, demonstrating his commitment to innovative knowledge dissemination. As an educator, Dr. Janetzki coordinates several key courses including Fundamentals of Pharmacology, Pharmacy Practice Health Services Provision, Managing and Developing Pharmacy Operations, and Pharmacy Healthcare Services. He teaches across all year levels in Pharmacy Practice courses, bringing his real-world clinical experience into the classroom to enhance student learning. His educational research examines effective teaching methods in pharmacology, particularly for health professional students beyond pharmacy.

Shizhe Chenمشاهده پروفایل

پژوهشگر

• Vision-and-Language
• Embodied AI
• 3D Computer Vision
• +۲ مورد دیگر

Shizhe Chen is a researcher at the WILLOW team in INRIA Paris , focusing on vision-and-language and embodied AI. Previously, he completed postdoctoral research at WILLOW with Ivan Laptev and Cordelia Schmid. PhD in Computer Science from Renmin University of China (2020) BSc in Computer Science from Renmin University of China (2015) His research bridges vision-and-language with embodied AI , emphasizing 3D scene understanding , robotic manipulation , and vision-language navigation . Recent projects include the GRAIL workshop (CVPR'25) on generalizable robotic manipulation and NextBestPath (ICLR'25) for efficient mapping. Key publication trends show expertise in 3D reconstruction (HORT, Online 3D Scene Reconstruction), robotic navigation (ViViDex, Object Goal Navigation), and vision-language systems (DUET, Think Global Act Local). Collaborations span INRIA, Microsoft Research Asia, and Carnegie Mellon University. 2022 REVERIE Challenge (2nd place) 2021 REVERIE & SOON Challenges (winner) 2019-2024 multiple TRECVID, CVPR, and ACM Multimedia awards 2020 Jingdong Scholarship & Beijing Outstanding Graduate Award 2016-2017 National Scholarships As co-supervisor, he guides PhD students in robotics and computer vision projects at INRIA Paris, collaborating with teams at Microsoft, Carnegie Mellon, and Louis Vuitton. His DUAL-SCALE GRAPH TRANSFORMER (CVPR'22 Oral) framework significantly advanced vision-language navigation benchmarks.

• Maritime Economics
• Shipping Industry Analysis
• International Trade
• +۵ مورد دیگر

Sigbjørn Sødal serves as Principal at NLA University College in Kristiansand, Norway, providing academic leadership and strategic direction for the institution. His role as Principal represents the highest academic and administrative position at the university college. His educational background demonstrates remarkable interdisciplinary breadth: 1998: Dr. oecon. (Doctor of Economics) from NHH (Norwegian School of Economics) 1994: Cand. polit. (Master of Political Science) from University of Oslo with major in economics, intermediate studies in music, and basic studies in religious education 1986: Siv. ing. (Master of Science in Engineering) from NTNU in industrial mathematics 1986: Pedagogical exam from NTNU Sødal's research spans maritime economics, real options theory, and regional economic structures. His work examines shipping market dynamics, international trade flows, and investment decision-making under uncertainty. He has made significant contributions to understanding how maritime connectivity influences economic development patterns. His methodological approach combines economic theory with quantitative analysis, particularly in regional economic structures and shipping market behavior. His publication record shows a clear evolution from theoretical work on real options in the early 2000s toward more applied maritime economics research in recent years. This trajectory reflects both his theoretical expertise and practical engagement with shipping industry challenges. His research has been published in leading journals across economics, finance, and maritime studies, demonstrating consistent scholarly productivity over two decades. Sødal's professional interests extend well beyond traditional economics, encompassing over 50 fields including public administration, education, theology, music, philosophy, and social sciences. This exceptionally broad disciplinary scope informs his interdisciplinary approach to complex economic and maritime industry problems, connecting technical economic analysis with broader societal and cultural contexts.

Christos Dimitrakakisمشاهده پروفایل

استاد

• Reinforcement Learning
• Reverse Reinforcement Learning
• Preference Elicitation
• +۱۱ مورد دیگر

Christos Dimitrakakis is a Full Professor of Data Science at the University of Neuchâtel, Switzerland, where he is affiliated with the Institute of Computer Science within the Faculty of Science. His research focuses on artificial intelligence, particularly reinforcement learning, fairness, and algorithmic privacy. He has held academic positions at several prestigious institutions including the University of Oslo, Chalmers University of Technology, and the University of Lille. His educational background includes: 1997: BEng from the University of Manchester 1998: MSc from the University of Essex 2006: PhD from IDIAP/EPFL under the supervision of Samy Bengio Professor Dimitrakakis specializes in reinforcement learning and decision making under uncertainty, with particular expertise in reverse reinforcement learning, preference elicitation, and human-AI collaboration. His research bridges theoretical foundations with practical applications, especially in autonomous systems and privacy-preserving AI. He has made significant contributions to understanding representations of uncertainty, differential privacy, and group fairness in algorithmic decision-making. His work often combines Bayesian inference with reinforcement learning frameworks to develop robust and fair AI systems that can operate effectively in uncertain environments. His recent publications demonstrate a strong focus on theoretical foundations of reinforcement learning while addressing practical challenges in fairness, privacy, and decision making. The research shows an evolving trajectory from core reinforcement learning techniques toward applications in fair and transparent AI systems, with increasing emphasis on real-world constraints and ethical considerations. His work spans multiple domains including game theory, matching markets, and bandit algorithms, reflecting an interdisciplinary approach to AI research. Notable scientific achievements include: Best Paper Award at Cooperative-AI@NeurIPS 2021 for "Interactive Inverse Reinforcement Learning for Cooperative Games" Student paper award at EAAMO'22 for "On Meritocracy in Optimal Set Selection" Professor Dimitrakakis actively mentors PhD students and has supervised numerous researchers who have gone on to academic and industry positions. His current research group at the University of Neuchâtel includes five PhD students working on reinforcement learning, fairness, and uncertainty in AI systems. He has secured funding for projects including a national initiative to develop intelligent bird deterrent systems for crop protection, demonstrating the practical impact of his research. His group collaborates with industry partners such as Agroscope, and he maintains academic connections with institutions worldwide. His research group at the University of Neuchâtel is actively working on interdisciplinary projects, most notably the development of an "intelligent scarecrow" system for bird pest control in agriculture. This project combines computer vision, reinforcement learning, and behavioral modeling to create adaptive AI systems that can minimize crop damage while accounting for bird behavior patterns. The group maintains expertise in both theoretical foundations and practical implementations of AI systems.

• Decision making
• Cognitive processes
• Mathematical psychology
• +۷ مورد دیگر

Dr. Guy Hawkins is an Associate Professor in the School of Psychological Sciences at the University of Newcastle, Australia. His research focuses on developing and testing computational and mathematical models of cognitive processes, with a primary interest in decision-making. His work spans from low-level speeded perceptual decisions through to high-level cognition, including statistical reasoning and consumer preferences. Dr. Hawkins earned his PhD in 2013 and his Bachelor of Psychology (Honours 1) in 2008, both at the University of Newcastle. Prior to his current position, he held postdoctoral research positions at the University of Amsterdam's Brain and Cognition Center (2014-2016) and UNSW Sydney (2013-2014). In 2017, he was awarded an Australian Research Council Discovery Early Career Researcher Award (DECRA). His research examines the decision mechanisms and strategies that people use to select consumer products and service options. A key finding is that people make fewer reasoning errors when information is presented as counts ('8 out of 10') rather than probabilities ('80%'). This has practical applications in healthcare, where physicians could describe prognoses using counts to help patients make better-informed treatment choices. His work also investigates how people update their decision caution relative to time constraints and option quality. Dr. Hawkins' recent publications reveal a strong focus on evidence accumulation models, decision-making under time pressure, and the relationship between cognitive processes and neural activity. His research often employs computational modeling approaches to understand the psychological processes underlying decision behavior. 2024 John Keats Early Career Award, Society for Mathematical Psychology 2020 William K. Estes Early Career Award, Society for Mathematical Psychology 2018 Fellow of the Psychonomic Society 2017 Australian Research Council Discovery Early Career Researcher Award 2017 Vice-Chancellor's Award for Early Career Research and Innovation Excellence, University of Newcastle 2013 Clifford T. Morgan Best Article Award, Psychonomic Society Dr. Hawkins collaborates extensively with researchers across the globe, including at universities in Australia, USA, Canada, UK, The Netherlands, and Norway. He is part of the Newcastle Cognition Laboratory and the Functional Neuroimaging Laboratory. His current research projects include investigating cognitive neuroscience frameworks for attentional control, evaluating human-machine interfaces in vehicles, and studying perceptual inference anomalies in schizophrenia.

• Learning Classifier Systems
• Evolutionary Computation
• Machine Learning
• +۷ مورد دیگر

Will N. Browne is a Professor specializing in Artificial Intelligence with extensive contributions to Learning Classifier Systems, Evolutionary Computation, and Machine Learning. His research spans multiple disciplines including Robotics, Computer Vision, and Explainable AI, with publications in top-tier conferences and journals across these fields. Dr. Browne's research interests primarily center around Learning Classifier Systems, which are rule-based machine learning systems combining reinforcement learning, supervised learning, and evolutionary algorithms. His work has significantly advanced the field by developing methods to scale these systems for complex problems, addressing perceptual aliasing through lateralized learning approaches, and extending them to handle continuous features. He has pioneered the integration of attention mechanisms with rule-based learning, creating more robust systems for applications like emotion recognition from partially covered faces. His recent research strongly emphasizes interpretable and explainable AI, developing evolutionary methods that maintain model transparency while achieving high performance. His scientific contributions show a clear progression from theoretical foundations to practical applications. Early work focused on core Learning Classifier System algorithms and their application to Boolean problems, while recent publications demonstrate successful applications in multi-robot systems, emotion recognition, and human-robot interaction. His publications in IEEE Robotics and Automation Letters, Evolutionary Computation, and Neurocomputing reflect the interdisciplinary nature of his work. Dr. Browne has mentored numerous researchers, with frequent collaborations indicating his role in guiding students and postdocs. His work often bridges theoretical advances with practical implementations, as evidenced by applications ranging from robot navigation and collision avoidance to smart home technology adoption frameworks. He has been instrumental in developing Learning Classifier Systems for real-world problems, particularly focusing on making these systems applicable to continuous domains and enhancing their interpretability. His laboratory appears to focus on creating AI systems that can be understood by humans, which addresses a critical need in the deployment of AI technologies across various domains.

Kevin Darbyمشاهده پروفایل

استادیار

• Cognitive Psychology
• Developmental Psychology
• Cognitive Aging
• +۷ مورد دیگر

Kevin Darby is an Assistant Professor in the Department of Psychology at Florida Atlantic University's Charles E. Schmidt College of Science. His research focuses on cognitive development, memory binding, interference effects, and attention allocation across the lifespan. He leads the Lifespan Cognition Lab, which emphasizes computational modeling and transparency in cognitive aging research. Ph.D. from The Ohio State University (2017) M.S. from The Ohio State University (2014) B.S. from University of Houston (2010) His research explores how memory binding mechanisms evolve from childhood to old age, using eye-tracking and computational models. Recent work examines temporal delay effects, confidence judgment dynamics, and cross-species cognitive flexibility comparisons. He advocates for replicable methodologies in cognitive aging studies. Key article trends from 2013–2025 reveal expertise in memory interference, developmental attention allocation, computational modeling, and lifespan cognitive changes. Subfields span object-scene integration, forgetting prevention, and neural binding mechanisms.

• AI
• Robotics
• Sequential Decision Making
• +۵ مورد دیگر

Siddharth Srivastava is an Associate Professor at the School of Computing and Augmented Intelligence , Arizona State University , where he directs the Autonomous Agents and Intelligent Robotics (AAIR) Lab. His research focuses on principled AI systems for generalizable planning , sequential decision-making under uncertainty , and user-driven AI assessment . He previously served as Staff Scientist at United Technologies Research Center and postdoctoral researcher at UC Berkeley . Education Ph.D. in Computer Science, University of Massachusetts-Amherst M.S. in Mathematics, Indian Institute of Technology (IIT) Kanpur His work spans AI planning , robotics , and neuro-symbolic methods , emphasizing safe, reliable AI systems that can learn abstractions , transfer knowledge , and self-assess capabilities . Recent publications focus on user-aligned explanations , integrated task-motion planning , and stochastic reinforcement learning . Scientific recognition includes: NSF CAREER award Top 5% Faculty Award (Fulton Schools of Engineering) Best Paper at ICAPS Outstanding Dissertation (UMass Amherst) Best Final Year Thesis (IIT Kanpur) Chambliss Medal (AAS) He has supervised multiple Ph.D. students including Pulkit Verma , Naman Shah , and Rushang Karia , and developed the JEDAI system for teachable robot planning. His research has been featured in BBC , Motherboard , and IEEE Spectrum .

Daniel Sheehanمشاهده پروفایل

دانشیار

• Consumer Psychology
• Behavioral Economics
• Decision Making
• +۳ مورد دیگر

Daniel Sheehan, PhD, is currently the Department Chair and Kincaid Endowed Associate Professor at the Gatton College of Business and Economics, University of Kentucky. He holds a PhD in Behavioral Sciences from Georgia Institute of Technology (2015) and a BA in Psychology from Virginia Tech (2001). PhD, Georgia Institute of Technology, 2015 BA, Virginia Tech, 2001 His research integrates psychological and economic frameworks to analyze consumer judgments and decisions influenced by prior behaviors. Key research themes include sequential purchase decisions and identity signaling , utilizing laboratory and field experiments alongside secondary data sources. Publications highlight dynamics in consumer spending and discount reactions within retail environments. Scientific awards include the prestigious Kincaid Endowed Associate Professor recognition. His work appears in journals like Journal of Consumer Research and Journal of Retailing and Consumer Services , reflecting expertise in behavioral economics and marketing strategy.

Charilaos Zarakovitisمشاهده پروفایل

پژوهشگر ارشد

• Telecommunications
• Intelligent Transportation Systems
• Wireless Networks
• +۳ مورد دیگر

Dr. Charilaos Zarakovitis is a Senior Research Associate at Lancaster University's Department of Computing and Communications. His research focuses on the intersection of telecommunications, machine learning, and intelligent transportation systems. Key research areas include: 6G network design and control mechanisms AI-driven decision-making in communication systems Semantic communication frameworks Non-terrestrial networks for transportation His recent work explores socially-inspired communication codecs for next-generation transportation networks and LLM applications in 6G control systems.

Lisa Torreyمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۲ مورد دیگر

Lisa Torrey is an Associate Professor in the Department of Mathematics, Computer Science, and Statistics at St. Lawrence University. She teaches courses in programming, algorithms, artificial intelligence, machine learning, and computational theory. She earned her Ph.D. in Computer Science from the University of Wisconsin-Madison in 2009, where she researched relational transfer in reinforcement learning under Professor Jude Shavlik. Her research focuses on: Developing intelligent agents using reinforcement learning Human-inspired techniques for task transfer and multi-agent collaboration Educational applications of AI in computer science classrooms Machine learning approaches to crowd simulation and game AI She has recently shifted toward studying human learning dynamics in CS education. Her publications (2010-2020) predominantly explore reinforcement learning adaptations, educational AI frameworks, and knowledge-transfer mechanisms. A clear evolution is visible from theoretical machine learning toward applied educational research. She actively mentors students in AI/machine learning projects, with recent advisees including: Rodrick Mpofu (Explainable Image Classification) Lily Kendall (Sensitivity Analysis for Image Generation) Cooper Anderson (Audio Source Separation) Ellie Nichols (Avalanche Forecast Analysis) 15+ additional students across 2020-2025 No research labs or major grants are explicitly mentioned in available materials.

Rob Tillyerمشاهده پروفایل

استاد

• Criminal justice system decision making
• Victimization
• Crime prevention
• +۹ مورد دیگر

Dr. Rob Tillyer serves as Professor and Associate Dean of Student Success in the Department of Criminology and Criminal Justice at the University of Texas at San Antonio's College for Health, Community and Policy (HCAP). With a distinguished academic career focused on criminal justice system dynamics, Dr. Tillyer has established himself as a leading researcher in police decision-making, victimization patterns, and crime prevention strategies. Dr. Tillyer earned his Ph.D. in Criminal Justice from the University of Cincinnati, following an M.A. in Criminology from Simon Fraser University and a B.A. with Honors from the same institution. His educational background has provided a strong foundation for his research in criminal justice system operations and outcomes. Dr. Tillyer's research interests span multiple critical areas of criminology and criminal justice, with particular emphasis on criminal justice system decision making, victimization patterns across different demographic groups, crime prevention strategies, and the dynamics of criminal events. His work frequently examines racial and ethnic disparities in policing practices, the effectiveness of various crime reduction strategies, and the complex relationship between victimization and offending behaviors. Notably, Dr. Tillyer has made significant contributions to understanding hot spots policing, officer decision-making during street stops, and the measurement of racial disparities in police-citizen encounters. Analysis of Dr. Tillyer's recent publications reveals a consistent focus on methodological rigor in studying police practices and criminal justice outcomes. His work often employs sophisticated statistical techniques to examine complex relationships between officer characteristics, citizen demographics, and police actions. A significant portion of his research investigates how contextual factors influence police decision-making and crime patterns, with particular attention to geographic concentrations of crime and the sequential nature of police-citizen interactions. Dr. Tillyer's scholarship bridges theoretical criminology with practical implications for law enforcement agencies seeking to improve community relations while maintaining effective crime control. Dr. Tillyer has contributed significantly to the field through his numerous publications in top-tier criminology and criminal justice journals, including Police Quarterly, Justice Quarterly, and the Journal of Research in Crime and Delinquency. His research has been instrumental in advancing methodological approaches to studying racial disparities in policing and has provided valuable insights for evidence-based policing practices. As Associate Dean of Student Success, Dr. Tillyer plays a key leadership role in supporting student achievement within the College for Health, Community and Policy. His administrative responsibilities complement his scholarly work by connecting academic research to practical student outcomes and institutional effectiveness.

Juuso Välimäkiمشاهده پروفایل

استاد

• Microeconomic Theory
• Game Theory
• Mechanism Design
• +۳ مورد دیگر

Professor Juuso Välimäki is a Professor in the Department of Economics at Aalto University. He is a leading microeconomic theorist specializing in dynamic incentive problems and mechanism design. Ph.D. in Economics, University of Pennsylvania (1996) His research focuses on theoretical frameworks for dynamic games, auctions, and contests, with a particular emphasis on participation costs, information aggregation, and allocational efficiency. He has collaborated extensively with Professor Pauli Murto on topics such as auction efficiency and intertemporal incentives. Notable trends in his publications include applications of game theory to spectrum auctions, dynamic pricing strategies, and mechanism design for experience goods. His work bridges theoretical rigor with practical policy implications in markets and resource allocation. Scientific awards include: Fellow of Econometric Society (2008-present) Fellow of the European Economic Association Member of the Finnish Academy of Arts and Sciences He has served on the editorial boards of leading journals, including as co-editor of the Journal of Economic Theory , past main editor of the Journal of European Economic Association (2018), and managing editor at the Review of Economic Studies (2003-2007).