Dominik Endres is a Professor at the Department of Psychology , Philipps-Universität Marburg , leading the Theoretical Cognitive Science working group. His research spans Cognitive Neuroscience , Sensorimotor Integration , and Machine Learning , with a focus on modeling how the brain represents knowledge and controls movement. His team investigates Movement Primitives in virtual reality, Bayesian Inference in attention control, and Formal Concept Analysis in decoding neural data. Recent work includes reaction time decomposition , VR immersion studies , and assistive technology development for neurological disorders. Publications highlight sensorimotor hierarchies , relational coding , and dynamic human motion modeling . Collaborations include the International Research Training Group 1901 , SFB/TRR 289 , and GRK 2271 . His team includes researchers like Neda Meibodi (PhD student) and Benjamin Knopp (Scientific Staff), with projects involving VR experiments and cognitive modeling .
Aldo Gangemi is a Full Professor at the University of Bologna and Director of the Institute for Cognitive Sciences and Technologies (ISTC) at the Italian National Research Council (CNR). He co-founded the Semantic Technology Lab (STLab) in 2008 and currently coordinates the Horizon 2020 SPICE project. His academic career spans interdisciplinary research at the intersection of semantic technologies, cognitive science, and data science. Affiliations: University of Bologna, CNR-ISTC, IMT School for Advanced Studies Lucca (Board of Directors) Research Interests: Semantic Technologies integrating Knowledge Engineering, Web Science, Cognitive Science, and NLP. Applications span Cultural Heritage, Robotics, Medicine, Law, eGovernment, Agriculture, and Business. His theoretical focus includes hybrid symbolic/sub-symbolic methods for knowledge pattern representation and discovery across data, ontologies, language, and cognition. Scientific Leadership: He has served as Editor-in-Chief or editorial board member for journals like Semantic Web and Web Semantics , chaired major conferences (EKAW2008, WWW2015, ESWC2018/9), and coordinated 8 EU projects.
Dr. Guanhua Chen is an Assistant Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech). His research focuses on Natural Language Processing (NLP), Large Language Models (LLMs), and Multimodal AI, with applications in drug discovery and urban planning. He previously earned his Ph.D. from the University of Hong Kong and B.S./M.S. from Tsinghua University, with internships at Microsoft Research Asia and Huawei Noah’s Ark Lab. Education: Ph.D. in Computer Science (University of Hong Kong, 2022), B.S. and M.S. in Computer Science (Tsinghua University, 2012 & 2014) Dr. Chen's research explores LLMs, data synthesis, multimodal systems, and model compression. He leads a lab equipped with 16 RTX 4090 GPUs, 16 L40 GPUs, and 4 A100 GPUs, providing resources for students to experiment with open-source and proprietary LLM APIs. His recent publications highlight innovations in LLM jailbreaking, parameter-efficient tuning (MiLoRA), cross-lingual transfer (mCLIP), and evaluation frameworks for robustness testing. Notably, he was awarded the Microsoft Research Asia StarTrack Scholar in 2025. Key Publications: mCLIP (ACL 2023), ImPart (ACL 2025), MiLoRA (NAACL 2025) Dr. Chen actively seeks motivated PostDoc, PhD, and Master's students. He serves as an Area Chair for ACL and EMNLP and contributes to advancing AI through open-source projects like mCLIP on GitHub.
Dr. Moritz Herrmann is a postdoc researcher and Reproducibility & Open Science Transfer Coordinator at the Munich Center for Machine Learning (MCML). He is affiliated with the Biometry in Molecular Medicine working group led by Prof. Anne-Laure Boulesteix at Ludwig-Maximilians-Universität München, and contributes to initiatives like the LMU Open Science Center , Open Science Initiative in Statistics (OSIS) , and Open Science Initiative in Medicine (OSIM) . Ph.D. in Statistics from LMU (2022), M.Sc. in Statistics (2018), and B.Sc. in Mathematics/Sports Science (2014) His research focuses on Empirical Machine Learning , Manifold Learning , and Metascience , with emphasis on epistemological foundations and reliability in ML research. He advocates for open science practices and data literacy, as outlined in his ICML 2024 position paper on rethinking empirical ML research. As a member of the Empirical Machine Learning research focus group and the Statistical Learning and Data Science Chair , Herrmann bridges statistical methodology with biomedical applications. His work spans outlier detection, cluster analysis, and reproducibility frameworks, reflected in his recent publications in journals like Biometrical Journal and Data Mining and Knowledge Discovery .
Dr. Zhi Huang serves as an Instructor and incoming Assistant Professor in the Department of Pathology and Laboratory Medicine, with a secondary appointment in the Informatics Division of the Department of Biostatistics, Epidemiology, and Informatics. His academic work bridges biomedical research with artificial intelligence to advance healthcare solutions. His research expertise spans critical areas in medical AI: Biomedical AI : Developing AI models for clinical decision support Human-AI Collaboration : Designing intuitive interfaces for clinician-AI teamwork Medical Image Platforms : Creating scalable infrastructure for medical imaging analysis Digital Pathology : Implementing AI-driven tissue analysis systems Precision Medicine : Tailoring treatments using genomic and clinical data integration Analysis of his publication record reveals a strong interdisciplinary trajectory connecting computer vision, multi-agent systems, and clinical applications. His work demonstrates consistent innovation in translating autonomous systems research—particularly in scene graph generation, motion planning, and visual question answering—into medical contexts including digital pathology platforms and precision diagnostics. Recent contributions emphasize open-source frameworks for accessible medical AI development.
Professor Sarit Kraus is a Professor of Computer Science at Bar-Ilan University in Ramat Gan, Israel, renowned for foundational contributions to artificial intelligence including multi-agent systems, human-agent interaction, and non-monotonic reasoning. Her research spans theoretical frameworks, experimental validation, and real-world applications in security, healthcare, and automated negotiation. She received her Bachelor's, Master's, and PhD degrees from The Hebrew University of Jerusalem. Her research focuses on developing intelligent agents capable of proficient interaction in cooperative and adversarial scenarios through integration of game theory, machine learning, and optimization. Key areas include: Collaborative planning frameworks and coalition formation Culture-sensitive agents and automated negotiation protocols Non-monotonic reasoning for knowledge representation Security applications (e.g., ARMOR for airport security) Healthcare implementations (e.g., Sheba Project for speech therapy) Professor Kraus has received numerous prestigious honors: ACM Athena Lecturer Award (2020) ACM Fellow (2014) and Fellow of AAAI/EurAI Israel EMET Prize (2010) IJCAI Computers and Thought Award (1995) ACM SIGART Autonomous Agents Research Award (2007) IFAAMAS Influential Paper Award As an educator, she has supervised 62 Master's and 34 PhD students. Her research has produced six books, 122 journal articles, 176 conference papers, and nine patents with applications including law enforcement training systems and intelligent recommendation engines. She actively serves on editorial boards for leading AI journals and holds leadership roles in major conferences like IJCAI and AAMAS. Professor Kraus leads a research group at Bar-Ilan University advancing multi-agent systems with ongoing projects in security robotics, human training systems, and cross-cultural negotiation mediation.
Dr. Yang Liu is an Assistant Professor in the Department of Computer Science at Hong Kong Baptist University's Faculty of Science. He also serves as the Associate Director of the Health Informatics Center. His academic career spans prestigious institutions including Yale University and Carnegie Mellon University, demonstrating his expertise in both theoretical and applied aspects of computer science. Dr. Liu received his B.Eng. and M.Eng. degrees in Automation from National University of Defense Technology in 2004 and 2007, respectively. He earned his Ph.D. in Computing from The Hong Kong Polytechnic University in 2011. His academic journey included a Visiting Scholar position at Carnegie Mellon University's Robotics Institute (Feb.-Aug. 2010) and a Postdoctoral Research Associate position in the Department of Statistics at Yale University (2011-2012). Dr. Liu's research spans the intersection of artificial intelligence, machine learning, and practical applications in health and complex systems. His work focuses on artificial intelligence , machine learning , pattern recognition , dimensionality reduction , and subspace learning , with particular emphasis on multi-way/multi-view/multi-label/multi-task learning approaches. His research extends to modeling complex dynamical systems with applications in computational epidemiology and infectious disease modeling , addressing critical public health challenges through data-driven approaches. Analysis of Dr. Liu's recent publications reveals a strong focus on applying machine learning techniques to epidemiological challenges, particularly in modeling infectious disease transmission patterns. His work bridges theoretical advances in graph neural networks, subspace learning, and multi-view analysis with practical applications in public health. A significant portion of his research addresses the challenges of high-dimensional and heterogeneous data analytics, with applications ranging from malaria transmission modeling in Cambodia to uncovering COVID-19 transmission patterns in Hong Kong. Dr. Liu is an IEEE Senior Member and ACM Member, reflecting recognition of his contributions to the field. His paper "What are the underlying transmission patterns of COVID-19 outbreak? – An age-specific social contact characterization" was recognized as one of the most cited articles in EClinicalMedicine, Lancet Discovery Science, during 2020-2021, highlighting the impact of his work on pandemic response research. Dr. Liu actively mentors research students and regularly has research student and RA positions available. His professional service includes serving on editorial boards for SPJ Health Data Science and as a journal guest editor for special issues on cross-media analysis. He has extensive experience as a journal reviewer for top publications including IEEE TNNLS/TNN, IEEE T-Cyber/TSMC-B, IEEE TAC, IEEE TKDE, IEEE TMM, IEEE TCSVT, ACM TIST, ACM TOMM, and others, and serves on program committees for major conferences including WWW, IJCAI, and AAAI. Dr. Liu is affiliated with the Centre for Health Informatics and the Artificial Intelligence and Machine Learning Laboratory (AIML) at Hong Kong Baptist University. These research centers provide the infrastructure and collaborative environment necessary for his work in health informatics and machine learning applications. His role as Associate Director of the Health Informatics Center positions him at the forefront of interdisciplinary research connecting computing with public health challenges.
Justin Johnson is an Assistant Professor at the University of Michigan and a Research Scientist at Facebook AI Research (FAIR) . His work spans computer vision and machine learning , with a focus on visual reasoning, vision-and-language integration, image generation, and 3D reasoning through deep neural networks. Education : PhD from Stanford University under Fei-Fei Li His research combines visual reasoning and 3D reconstruction with applications in image generation and neural rendering . He has contributed to advancing style transfer , super-resolution , and 3D deep learning through frameworks like PyTorch3D . Recent publications emphasize scalable 3D modeling , neural fields , and vision-language grounding . He advises PhD students including Karan Desai , Mohamed El Banani , and Chris Rockwell (co-advised with David Fouhey). He has taught courses like EECS 498/598: Deep Learning for Computer Vision at Michigan and CS 231N: Convolutional Neural Networks for Visual Recognition at Stanford.
Manfred Eppe serves as Chief Engineer at the Institute for Data Science Foundations, Hamburg University of Technology, where he leads the cognitive robotics laboratory and contributes to teaching the machine learning lecture. His research centers on computational cognitive models of representation learning and reinforcement learning for simulated and physical robotic agents, with applications spanning neuro-semantic systems and cognitive robotics. His educational background includes a Ph.D. in Computer Science from the University of Bremen (2014), followed by postdoctoral research at the University of Hamburg (2016-2021), UC Berkeley (2015), and IIIA-CSIC in Barcelona (2015) within the COINVENT project. These positions focused on data-driven neuro-semantic systems, human-robot interaction using neurocognitive methods, and AI-based concept representation. Eppe's research integrates reinforcement learning with cognitive modeling to develop agents capable of representation learning, concept blending, and commonsense reasoning. His work bridges symbolic AI and neural approaches, emphasizing neurocognitively inspired methods for natural language understanding and robotic applications in physical environments. He has secured over €1.5 million in research funding as principal investigator for DFG projects MoReSpace and LeCAREbot, alongside prior grants from Volkswagen Stiftung and DAAD. His teaching portfolio includes Data Science at West Coast University of Applied Sciences and the "From Data to Knowledge" course at Universität Hamburg, with thesis supervision limited to reinforcement learning topics using the Scilab-RL virtual robotics platform. Eppe directs the cognitive robotics laboratory, which develops the Scilab-RL framework for simulating robotic agents and testing learning algorithms. The lab emphasizes open collaboration and practical applications of computational cognitive models in real-world robotic systems.
Seunghoon Hong is an Associate Professor at the School of Computing, Korea Advanced Institute of Science and Technology (KAIST), where he leads the KAIST Vision and Learning Lab (VLLab@KAIST). His research focuses on advancing machine learning and computer vision with emphasis on reducing human supervision requirements and enhancing model interpretability. Prior to joining KAIST, he was a visiting faculty researcher at Google Brain and completed a postdoctoral fellowship at the University of Michigan working with Professor Honglak Lee. Education: PhD in Computer Science and Engineering, 2017, POSTECH, Korea (Supervised by Professor Bohyung Han) BS in Computer Science and Engineering, 2011, POSTECH, Korea Professor Hong's research primarily explores scaling up machine learning algorithms for visual perception while minimizing human supervision requirements. He investigates techniques to make machine learning algorithms more interpretable, enabling users to better understand and participate in the decision-making process. His work spans few-shot learning, generative models, vision transformers, and equivariant neural networks, with applications across various computer vision tasks. His recent publications reveal a strong focus on advancing foundation models for computer vision with emphasis on data efficiency, interpretability, and generalization. Key themes include developing novel approaches for few-shot learning, improving model efficiency through token merging and compositionality, and creating robust vision systems capable of handling real-world challenges like weather degradation. His work frequently appears in top-tier conferences including NeurIPS, ICLR, CVPR, and ECCV. Scientific Awards: Outstanding Paper Award at ICLR 2023 Professor Hong actively mentors a substantial group of students, including multiple Ph.D. candidates, master's students, and undergraduates. He serves in leadership roles for major conferences, including as Senior Area Chair for NeurIPS 2025 and Workshop Chair for ICCV 2025. His research has significant practical implications for developing more efficient, interpretable, and adaptable computer vision systems. He leads the KAIST Vision and Learning Lab, which focuses on cutting-edge research at the intersection of machine learning and computer vision, with particular emphasis on developing algorithms that require minimal human supervision while maintaining high performance and interpretability.
Dr. Chiara Benassi is a Lecturer in Human Resource Management at King's Business School, King's College London. She holds a PhD from the London School of Economics (2014), a Master's from Free University/Humboldt University Berlin, and a BA from the University of Bologna. Previously, she was a Postdoctoral Fellow at the Max-Planck Institute for the Study of Societies and a Lecturer at Royal Holloway, University of London. Her research focuses on comparative human resource management, exploring how institutions and economic conditions shape HR strategies in areas like flexibility, training, and knowledge management. She has received grants from ESRC and the Hans Boeckler Foundation, leading projects such as the 'Managing human capital in different institutional contexts' study. Her work appears in journals like Organization Studies and British Journal of Industrial Relations . Education: PhD in Employment Relations and Organisational Behaviour, London School of Economics (2014) Masters in International Relations, Free University/Humboldt University Berlin BA in Development and Cooperation, University of Bologna Research Interests: Comparative HRM, political economy, workplace flexibility, vocational training, collective bargaining, knowledge management, and labor market segmentation. Her recent work examines lean management systems across nations and robotic process automation's impact on jobs. Grants & Awards: Principal Investigator: ESRC-funded project (2016-2018) on automotive industry HR strategies ESRC Future Research Leader Fellowship (2016-2018) Fellow of the Higher Education Academy Projects: Ongoing studies include the political economy of growth models, automation in knowledge-intensive services, and AI's role in occupations. She also investigates unions' responses to precarious work and externalization challenges. Collaborations: Active in international research networks, with projects comparing Germany, Italy, and the UK. Her work contributes to UN SDGs related to decent work (SDG 8) and reduced inequalities (SDG 10).
Jacob Andreas is an Associate Professor of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), affiliated with the MIT-IBM Watson AI Lab and the School of Engineering. His research focuses on advancing artificial intelligence, machine learning, and natural language processing, with particular emphasis on neurosymbolic methods, language model reasoning, and interdisciplinary applications such as AI governance and bioacoustic analysis of sperm whale communication. He explores the intersection of language models and robotics, developing techniques for scalable system-level reasoning, code generation, and efficient computation allocation. His work also addresses societal challenges, including mitigating misinformation through generative AI and informing policy through white papers on AI governance. Andreas’s contributions span foundational ML theory (e.g., scaling laws) and applied systems (e.g., WhaleLM for bioacoustic analysis). He collaborates across disciplines, integrating cognitive science insights into model design and advocating for ethically aligned AI development. His research has been recognized in MIT’s EECS Faculty Awards and contributes to MIT’s broader mission of impactful technological innovation. While no specific awards are listed, his active role in high-impact projects underscores his academic leadership. His advising and grant work drive collaborative research in AI, with a focus on training next-generation researchers through the MIT-IBM Watson AI Lab and interdisciplinary initiatives.
Darko Odic is an Associate Professor in the Department of Psychology at the University of British Columbia, Faculty of Arts. He holds a PhD from Johns Hopkins University (2014) and is affiliated with the Early Development Research Group, focusing on language, learning, and social understanding in infants and children. His research spans cognitive development, psychophysics, and the interface between language and number representation. Odic teaches courses in developmental, social, and clinical psychology, and his work has been recognized through awards like the Jacobs Foundation Fellowship and APS Rising Star honor. Research Interests: His primary areas include numerical cognition (approximate number system), developmental psychophysics, and how language acquisition interacts with semantic-cognitive processes. Notable projects explore how children’s intuitive number sense develops, the role of non-numeric features in perception, and metacognitive confidence judgments. Publications & Awards: Odic has published extensively on topics like number representation, perceptual discrimination, and educational interventions. His work appears in journals like Developmental Psychology and Perception. Awards highlight his contributions to both research and teaching. Academic Engagement: He advises students in cognitive and developmental psychology and collaborates on interdisciplinary projects involving robotics education and visual perception studies.
Siddharth Narayanaswamy is an Associate Professor (Reader) in Explainable AI at the University of Edinburgh's School of Informatics. He holds a part-time Senior Research Fellow role at The Alan Turing Institute and is a Visiting Fellow at the University of Oxford’s Department of Engineering Science. His research bridges machine learning, computer vision, natural language processing, cognitive science, robotics, and neuroscience, focusing on unsupervised learning of structured representations and human-AI interaction. Education: PhD in Electrical and Computer Engineering from Purdue University. Previously, he was a Senior Researcher at the University of Oxford and a Postdoctoral Scholar at Stanford University’s Computation and Cognition Lab. Research Interests: Explainable AI, Human-Like Learning, Unsupervised Representation Learning, Probabilistic Programming, and applications to global health decisions via Simulation-Based Inference. Key projects include DGPose (human body analysis) and neuro-symbolic generative models. Publications highlight contributions to generative models, variational autoencoders, and interpretable AI systems. He collaborates with the Torr Vision Group and ELLIS Society. Supervision: Open to motivated PhD/MSc students exploring structured representations and probabilistic inference in AI. Labs/Teams: Member of the Torr Vision Group and ELLIS Scholar Network. Active in interdisciplinary collaborations across robotics, cognitive science, and healthcare AI.
Prof. Amel BOUZEGHOUB is a Professor at Telecom SudParis, affiliated with the SAMOVAR research center. Her work focuses on AI, IoT, and data-driven systems with applications in smart environments, robotics, and education. She has contributed to over 50 peer-reviewed publications spanning machine learning, reinforcement learning, and semantic data processing. Research Interests: Her research bridges theoretical advances in machine learning with practical applications in smart homes, autonomous systems, and educational technology. She explores topics like human activity recognition, anomaly detection in social networks, and real-time data stream processing. Recent Trends: Her 2023-2024 work emphasizes explainable AI, reinforcement learning for autonomous systems, and multi-agent frameworks for stream reasoning. Earlier contributions include IoT-based supply chain traceability and distributed human activity recognition models. Grants & Projects: Key contributions include the ANR INCOME project on multi-scale context management for IoT systems and ACMES initiatives in educational technology. Labs/Teams: Active within the SAMOVAR lab at Telecom SudParis, collaborating with international teams in AI and robotics research.
