جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Artificial Intelligence
• Probabilistic Modelling
• +۱۰ مورد دیگر

Dr. Robert Lieck is an Assistant Professor in the Department of Computer Science at Durham University. He holds a PhD from the Machine Learning and Robotics Lab (now Learning and Intelligent Systems Lab) in Stuttgart/Berlin, Germany, and completed a postdoctoral position at the Digital and Cognitive Musicology Lab at EPFL, Switzerland. His research focuses on interdisciplinary applications of machine learning and artificial intelligence, with particular emphasis on cognitive modelling, music cognition, and ethical AI. Education: PhD in Machine Learning and Robotics, Stuttgart/Berlin, Germany (2012–2017) MSc Physics and Philosophy, Freie Universität Berlin Research Interests: Probabilistic Modelling (Bayesian inference, graphical models) Neuro-Symbolic Modelling (differentiable parsing algorithms) Structure Learning (feature discovery, hierarchical systems) Applications in music analysis, medical imaging, and autonomous systems Ethical implications of AI in policy and legislation Publications: Recent work includes advancements in deep reinforcement learning for diabetes management, recursive Bayesian networks, and computational models of musical expectancy. His research bridges theoretical AI with practical applications in musicology and healthcare. Students: Supervising four postgraduate students: Ishaq Ibrahim, Megan Finch, Ningxiang Xie, Xiaotang Zhang Labs: Active contributor to the Digital and Cognitive Musicology Lab (EPFL) and Durham's Computer Science research groups.

• Data Visualization
• Machine Learning
• Neural Networks
• +۵ مورد دیگر

Professor Masa Takatsuka is a faculty member in the School of Computer Science at The University of Sydney, holding the rank of Professor. He serves as Associate Head of School (Postgraduate). His research focuses on data visualization, machine learning, and human-computer interaction, with notable contributions in spectral clustering, neural networks, and collaborative systems. He teaches courses such as COMP5048 Visual Analytics, COMP5116 Design of Networks & Distributed Systems, and others. Research interests include: Data visualization techniques (e.g., SOM-based methods) Graph neural networks and spectral clustering algorithms Interactive systems for large displays and collaborative environments Geospatial analysis and visualization tools like GeoVISTA Studio Recent publications span topics like random projection forests in graph convolutional networks (2023), topology-aware clustering (2018), and crowd-powered evaluation visualization (2017). His work emphasizes practical applications in domains like medical data analysis and social media sentiment exploration. He has secured grants including ARC Linkage Projects (2011, 2010) and industry partnerships. Current research students include Michael ASHSHIDDIQ PODBURY working on topological clustering methods. Labs/teams: Collaborations involve UnderworldGUI (geodynamic modeling tools) and projects leveraging self-organizing maps (SOM) for multidimensional data analysis.

Gao Minمشاهده پروفایل

استاد

• Personalized Recommendation Systems
• Anomaly Detection
• Social Media Mining
• +۷ مورد دیگر

Gao Min is a Professor and Doctoral Supervisor at the School of Big Data and Software, Chongqing University. He is a member of IEEE, CCF, and CAAI, and has held visiting scholar positions at Arizona State University and Reading University. His research focuses on personalized recommendation systems, anomaly detection, and social media mining, with strong emphasis on security aspects such as shilling attacks and fake news detection. His research interests include: Personalized Recommendation Systems Anomaly and Attack Detection in Recommender Systems Social Media Mining and Fake News Detection Graph-based and Contrastive Learning for Recommendations Domain Adaptation and Meta-Learning Time Series and Behavioral Forecasting The recent articles highlight a consistent trend in adversarial and robust learning for recommender systems and misinformation detection. His team leverages contrastive learning, graph neural networks, and meta-learning to enhance model robustness against poisoning and shilling attacks. There is a growing focus on simulating user behaviors, modeling fine-grained discrepancies, and applying domain adaptation techniques, particularly in detecting fake news and securing recommendation platforms. His scientific awards are primarily reflected through the recognition of his students, including multiple recipients of the National Graduate Scholarship, Huawei Scholarship, and Chongqing Outstanding Master’s Thesis Award. National Graduate Scholarship (awarded to students: Tian Renli, Yu Junliang, Song Yuqi, Zhao Zehua, Zhang Junwei, Wang Jia, Peng Lin, Ma Hao) Huawei Scholarship (awarded to students: Tan Kan, Wang Jia, Huang Yinqiu) Chongqing Outstanding Master's Thesis Award (awarded to students: Yu Junliang, Zhao Zehua, Zhang Junwei) Aerospace Scholarship (awarded to student: Zhang Junwei) Gao Min has secured significant research funding as principal investigator, including two National Natural Science Foundation projects, a sub-project of the National Key R&D Project, two Chongqing Natural Science Foundation projects, and one China Postdoctoral Fund project. He has also contributed as a main researcher in major national programs such as the 973 Program, National Key R&D Program, and National Science and Technology Support Program. He advises a vibrant research group that values autonomy, academic freedom, and practical research, with students regularly publishing in top venues and securing top-tier industry and academic positions. His team has developed key research platforms including QRec (Recommendation Algorithm Experiment Platform), Yue (Music Recommendation), ARLib (Data Pollution Attack Platform), and SDLib (Shill Attack Detection Platform). He serves as a reviewer for major journals and is a PC member of top conferences including CIKM, IJCAI, and AAAI.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Jing Zhao is a Professor at Beijing Institute of Technology's School of Automation, Department of Computer Science, with an extensive publication record spanning artificial intelligence, computer vision, medical imaging, and robotics. Their interdisciplinary research bridges theoretical computer science with practical applications in healthcare, agriculture, and engineering. Research interests focus on cutting-edge AI methodologies including deep learning architectures, neural rendering techniques, and multimodal systems. Notable work includes advancements in 3D reconstruction (Gaussian Splatting), medical image analysis for cancer diagnosis, and novel approaches to EEG-based emotion recognition. The research demonstrates strong interdisciplinary connections between computer science and domain-specific applications. Recent publications reveal a consistent trend toward practical AI implementations across diverse fields. Jing Zhao's work shows expertise in adapting theoretical machine learning concepts to solve real-world problems in medical diagnostics, agricultural monitoring, and robotics. The research portfolio demonstrates particular strength in developing novel neural network architectures for specialized applications. As a corresponding author on numerous high-impact publications, Jing Zhao appears to lead a productive research group with collaborations spanning multiple institutions and disciplines. The publication record indicates significant contributions to both theoretical computer science and applied AI solutions across various domains.

Georg Grohمشاهده پروفایل

استاد مدعو

• Applied Computer Science
• Social Signal Processing
• Social Network Analysis
• +۴ مورد دیگر

Georg Groh is an Adjunct Professor at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology . His research focuses on modeling social context, social interaction mediated by IT systems, and ML-based natural language processing. He holds a doctorate (2005) and habilitation (2012) from TUM, with prior studies in physics and computer science. Key research areas include social signal processing, network analysis, and bias detection in AI systems. Notable awards include the 2019 Supervisory Award and 2016 Honorary Teaching Certificate. His work bridges computational methods with societal impacts, particularly in health informatics and ethical AI. Recent projects explore LLM hallucination detection, bias profiling, and cross-lingual text classification. Education: PhD in Computer Science (2005), TUM Habilitation in Computer Science (2012), TUM Studies in Physics (University of Kaiserslautern) and Computer Science (Universities of Hamburg, Kaiserslautern, TUM) Research Interests: Groh’s work spans social computing, NLP, and ethical AI . Current projects address bias in language models, hate speech detection, and data-driven health interventions. His methodologies emphasize contextual analysis of social interactions, leveraging ML and graph-based techniques. Awards: 2nd place Supervisory Award (2019) Best Paper Awards (2016, 2008) Advising & Grants: Advised on projects like Nutrilize (nutrition recommender system) and contributed to EU-funded initiatives on mHealth systems. Active in designing AI systems for dietary logging and stress management.

Andreas Fenderمشاهده پروفایل

پژوهشگر

• Human-Computer Interaction
• Augmented Reality
• Virtual Reality
• +۶ مورد دیگر

Andreas Fender is a researcher at the Visualization Institute of the University of Stuttgart (VISUS), affiliated with the Schmalstieg Working Group. He has held postdoctoral positions at ETH Zurich (Switzerland) and the University of Sussex (England), following his PhD at Aarhus University (Denmark) with an internship at Microsoft Research (USA). His research focuses on Human-Computer Interaction, particularly in Augmented and Virtual Reality (AR/VR) and camera networks. He develops novel input methods for productivity and artistic expression in Mixed Reality environments, creating hybrid physical-digital systems like OptiBasePen, PressurePick, InfinitePaint, and DeltaPen. Recent work includes contributions to Mixed Reality input methods (UIST 2024, CHI 2022) and collaborative systems (Asynchronous Reality). Projects like GuitarPie (UIST 2025) and OptiBasePen (UIST 2024) demonstrate his focus on mobile interaction and ergonomic design. Scientific accolades include the Best paper award at CHI 2022 Best application paper award at ISS 2019 . At VISUS, he collaborates with Dieter Schmalstieg and leads hiring for PhD students and PostDocs exploring physical-digital workflows, artistic tools, and critical AI engagement. His work integrates machine learning, hardware prototyping, and spatial user interfaces to redefine future workplaces.

• Dental biomechanics
• Instrumentation design
• Systems engineering
• +۶ مورد دیگر

Dr. Jie Chen is a Professor and Chair of the Department of Mechanical Engineering at Purdue University (Indianapolis campus, with a courtesy appointment in West Lafayette). He holds dual roles in Mechanical Engineering and Orthodontics. His research focuses on dental biomechanics, energy systems, and instrumentation design, with significant contributions to orthodontic load systems and biomedical engineering. Education: B.S. in Mechanical Engineering, Tianjin University (1982) M.S. in Biomedical Engineering, Shanghai Second Medical University (1985) Ph.D. in Mechanical Engineering and Mechanics, Drexel University (1989) Research Interests: Energy efficiency in HVAC systems and electrical demand forecasting Quantification of orthodontic force systems using finite element analysis Biomechanical validation of orthodontic appliances CT-based quantification of dental displacement and root resorption Optical and haptic interfaces for medical and automotive systems His publications span energy optimization, orthodontic biomechanics, and human-machine interaction. Notable works include real-time energy management platforms and finite element modeling for orthodontic simulations. Recent research explores AI-driven visual analytics and autonomous vehicle systems. Grants & Labs: Active in interdisciplinary collaborations at Purdue, with lab work focused on biomechanical testing and smart energy systems. Supervises projects in orthodontic engineering and advanced manufacturing.

Kimberly M. Davidsonمشاهده پروفایل

استادیار

• Corrections
• Programming and Rehabilitation
• Community Reentry
• +۳ مورد دیگر

Kimberly M. Davidson is an Assistant Professor at the College of Criminology & Criminal Justice at Florida State University. Her work focuses on corrections, rehabilitation, community reentry, and substance use. She holds a Ph.D. in Criminology (2021) from Penn State University, along with a B.M. in Music Therapy and Psychology from the University of Kansas. Education : Ph.D. in Criminology, The Pennsylvania State University (2021) M.A. in Criminology, The Pennsylvania State University (2017) B.M. in Music Therapy (Major) and Psychology (Minor), University of Kansas (2008) Research Interests : Dr. Davidson investigates correctional programming efficacy, reentry challenges for offenders with substance use disorders, and the role of therapeutic communities in prisons. Her work emphasizes mixed-methods approaches to understanding desistance processes and systemic inequities in criminal justice systems. Recent Work Trends : Her publications explore prison-based therapeutic communities, parole supervision dynamics, and the intersection of fatherhood/recovery with criminal justice outcomes. She has conducted extensive research on substance use among probation populations and gendered experiences in incarceration. Awards : Academy of Criminal Justice Sciences Top Honors for Book (2021) National Institute of Justice Graduate Research Fellowship (2020-2021) Grants & Collaborations : Principal Investigator: $91k NIJ Fellowship on opioid crisis impacts on prison reentry (2020-2021) Lead Researcher: $109k PA DOC pilot on recovery specialists in prisons (2019-2020) Key contributor to $685k NIJ grant on women’s prison networks (2017-2020) Teaching : She instructs courses including CCJ2020 and CJC4410 , emphasizing practical applications of criminological theory.

• Artificial Intelligence
• Machine Learning
• Social Network Analysis
• +۴ مورد دیگر

Andreas Kaltenbrunner is a Part-Time Lecturer at the Universitat Pompeu Fabra (UPF) in Barcelona, specializing in Data Driven Social Analytics and Information Retrieval. He serves as Director of Research on AI and Data Science at IN3 - UOC R&I and is an Affiliate Researcher at ISI Turin, Italy. His research focuses on computational social science, user behavior analysis, and applications of AI/ML in social media and network dynamics. Key interests include large language models (LLMs), personality assessment simulations, and digital epidemiology. His work bridges theoretical frameworks with practical applications, such as predicting urban mobility patterns and evaluating AI systems’ performance in political contexts. He collaborates with institutions like UNICEF and the ISI Foundation, contributing to projects on data-driven decision-making and social impact. His interdisciplinary approach integrates network science, computational methods, and socio-cultural analysis. Recent publications highlight innovations in LLM validation, cross-lingual Wikipedia reliability modeling, and climate-related forecasting. While no explicit awards are listed, his prolific output reflects sustained contributions to computational social science and AI ethics.

Jason J. Jungمشاهده پروفایل

استاد

• Computer Science
• Knowledge Engineering
• Social Media Analysis
• +۳ مورد دیگر

Jason J. Jung is a Professor in the Department of Computer Engineering at Chung-Ang University, Seoul, Korea. His academic work focuses on knowledge engineering , social media analytics , and data mining within the Knowledge Engineering Laboratory. Research Interests : Computer Science, Social Knowledge, Data Modeling, Sentiment Analysis Projects : IoT-based cultural systems, real-time social event detection, transmedia storytelling models The 15 most recent publications (2014-2018) demonstrate expertise in social network analysis , multimodal data processing , and context-aware systems applied to urban services, cultural tourism, and digital storytelling. Key trends include real-time analytics , trust modeling , and collaborative frameworks for O2O services. Professional activities include editorial contributions, invited talks, and patent developments. Students at all levels (PhD/MSc/BSc) conduct research under his supervision at the Knowledge Engineering Laboratory. Personal interests include travel, film, painting, literature, and music.

Amir-Homayoun Javadiمشاهده پروفایل

مدرس ارشد

• Cognitive Neuroscience
• Memory Enhancement
• Neuroimaging
• +۷ مورد دیگر

Dr. Amir-Homayoun Javadi is a Senior Lecturer in Cognitive Neuroscience at the School of Psychology, University of Kent . He holds additional roles as an Honorary Research Associate at the Institute of Behavioural Neuroscience, University College London and a Visiting Professor at the School of Rehabilitation, Tehran University of Medical Sciences . His research focuses on non-pharmacological interventions to enhance memory, learning, and mental health through methods like physical exercise, brain stimulation, music, and sleep studies. Key Research Themes: Memory consolidation and enhancement Neurostimulation (tDCS, tACS) Sleep and circadian rhythm effects Emotion processing Computational modeling The 15 most recent publications highlight his work in neurostimulation during memory tasks, music-induced emotional states, exercise neuroscience, and Alzheimer’s disease diagnostics. Scientific awards are not explicitly mentioned. He supervises PhD students in areas including memory enhancement, declarative memory, and procedural skill learning, while maintaining memberships in the Federation of European Neuroscience Societies (FENS) and British Neuroscience Association (BNA). The Javadi Lab investigates cognitive mapping, neural oscillations, and brain-body interactions using EEG, fMRI, and AI.

• Theoretical computer science
• probabilistic programming
• probability theory
• +۲ مورد دیگر

Dr Fredrik Dahlqvist serves as a Lecturer in Computer Science at Queen Mary University of London's School of Electronic Engineering and Computer Science, where he contributes to the Centre for Fundamental Computing and AI. His academic profile reflects a strong commitment to theoretical foundations in computing with direct applications to artificial intelligence research. His research spans theoretical computer science with focused expertise in probabilistic programming, probability theory, category theory, and mathematical logic. Dahlqvist investigates the mathematical frameworks governing probabilistic computation, developing rigorous methodologies for uncertainty quantification in computational systems. His work establishes formal connections between abstract category theory and practical probabilistic programming implementations, creating bridges between pure mathematics and applied AI. Recent publications (2023-2024) demonstrate cohesive thematic development across probabilistic programming semantics, neural network optimization, and formal verification. Key trends include error analysis in probabilistic floating-point systems, neural network pruning techniques based on geometric similarity, and categorical foundations for graded computation. His research consistently merges theoretical depth with practical AI challenges, particularly in verification of probabilistic models and optimization of learning architectures. Dr Dahlqvist has not received any documented scientific awards or fellowships according to available sources. He actively supervises two PhD students: Gregor Meehan researching "Representation Learning For Musical Audio Using Graph Neural Network-Based Recommender Engines" and Niki Omidvari conducting theoretical work in computer science foundations. Dahlqvist currently holds a £15,000 grant from the Academy of Medical Sciences for "Learning from Each Other: Neural Networks and Finite Automata" (March 2025-March 2026), investigating formal connections between neural architectures and automata theory. As an integral member of Queen Mary's Centre for Fundamental Computing and AI, Dahlqvist collaborates within a multidisciplinary research environment focused on advancing theoretical underpinnings of artificial intelligence. His work contributes to the Centre's mission of developing mathematically rigorous frameworks for next-generation AI systems through category-theoretic approaches and probabilistic reasoning.

Yannick Hillمشاهده پروفایل

استادیار

• Sports Psychology
• Human Performance
• Resilience
• +۳ مورد دیگر

Yannick Hill is an Assistant Professor at the Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, with affiliations in the Learning and Performance department. He holds concurrent roles as Assistant Professor at IBBA and AMS-Sports. His research focuses on resilience, stress adaptation, and human performance in sports and virtual environments. He is a member of institutions like the Lyda Hill Institute for Human Resilience (Colorado), Instituut voor Sport- en Prestatiepsychologie (Groningen), and Amsterdam Institute of Sport Science (AISS). His work bridges psychology, sports science, and technology, emphasizing dynamic systems and personalized approaches to resilience. Research interests include stressors' impact on motor performance, home advantage analysis in sports, and immersive VR applications. He has published extensively on topics such as pressure training, network analysis in education, and resilience frameworks. Teaching includes courses like 'Sportpsychologie' and 'Gespreksvoering in de Sportpsychologie'. Recent articles highlight themes like stress protocols in gymnastics, gender differences in field hockey performance, and VR's role in medical training. His work emphasizes practical recommendations for technology integration and ethical considerations in resilience research.

• Multimedia Computing
• Computer Vision
• Computer Graphics
• +۷ مورد دیگر

Professor Zhiyong Wang is a faculty member at the School of Computer Science, University of Sydney, serving as Deputy Director of Sydney Informatics Hub and Director of the Multimedia Computing Laboratory. His research focuses on multimedia computing, including information retrieval, computer vision, AI, and applications in agriculture, healthcare, and environmental monitoring. He holds a B.Eng., M.Eng. from South China University of Technology, and a Ph.D. from The Hong Kong Polytechnic University. His work bridges theoretical advancements and real-world applications, with over 200 peer-reviewed publications. He is a member of IEEE and former President of Australia Pattern Recognition Society (APRS). Research Interests: Enabling computers to understand and create multimedia content, with emphasis on human-centric computing, remote sensing, and interdisciplinary applications. Notable areas include action recognition, medical image analysis, and environmental monitoring. Current Roles: Leads the Multimedia Computing Lab, collaborates with Sydney Institute of Agriculture and Brain and Mind Centre. Teaches COMP5216 (Mobile Computing), COMP5405 (Digital Media Computing), and COMP5425 (Multimedia Retrieval). Students: Supervises 9 Ph.D. students focusing on topics like precision agriculture, biomedical engineering, and AI-driven video analytics. Key projects include soil moisture forecasting, biomechanical risk analysis for knee osteoarthritis, and explainable machine learning.

Edwin Lockمشاهده پروفایل

پژوهشگر ارشد

• Algorithmic Game Theory
• Auctions and Markets
• Algorithms & Complexity
• +۳ مورد دیگر

Edwin Lock is a Research Fellow in the Department of Computer Science at the University of Oxford. His research focuses on algorithmic game theory, auctions, and computational economics, with an emphasis on mechanism design, pricing strategies, and market equilibria. He holds a BSc in Mathematics from FernUniversität Hagen, an MSc in Mathematics and Computer Science from the University of Oxford, and a BA in Music from Oxford. His work bridges theoretical foundations and practical implementations, including projects like Test and Contain—a resource-optimal testing strategy for pandemic response—which earned awards at EC’23 and SIGecom. Recent activities include a visiting scholar position at Harvard SEAS in 2025 and presentations at AAAI’25 and JPMorgan AI Research. Education: BSc Mathematics, FernUniversität Hagen MSc Mathematics & Computer Science, University of Oxford BA Music, University of Oxford Research interests include algorithmic solutions for complex markets, decentralized pricing mechanisms, and computational methods for equilibrium analysis. His work on pooled testing strategies and product-mix auctions demonstrates cross-disciplinary impact in economics and public health. Awards include the ACM GCEC’20 grant and best paper recognitions for contributions to algorithmic epidemiology and market mechanisms. Awards: Best Paper Award at EC’23 Best Poster Video Award at ACM SIGecom Workshop Exemplary Track Paper Award at EC’24 Collaborations span academia and industry, with recent focus on AI-driven preference elicitation and decentralized trading systems. His lab’s projects aim to advance both theoretical understanding and practical applications in algorithmic economics.