جستجوی استاد راهنمای دانشگاهی

Professeure assistante tenure track at EPFL's School of Life Sciences (SV) within the Brain Mind Institute (BMI), Fides Zenk leads the UPZENK research unit. She holds multiple roles, including teaching responsibilities in Neuro-X, Biomedical Engineering, and doctoral program memberships (Programme doctoral Biotechnologie et génie biologique, Programme doctoral Neurosciences). Her lab focuses on epigenetic regulation in developmental systems, using brain organoids and model organisms like Drosophila and axolotl. She advises five PhD students and collaborates across disciplines, including systems biology and regenerative medicine. Her work bridges molecular mechanisms with tissue-level organization in developmental and regenerative contexts. Research emphasizes chromatin dynamics, cell fate decisions, and 3D genome architecture during embryogenesis. She develops multi-modal profiling techniques (e.g., Phospho-seq, CUT&Tag) to study epigenetic and proteomic interactions. Teaching includes courses on neuroscience foundations and single-cell biology. Her lab website: upzenk.epfl.ch .

• Neuroscience
• Developmental Biology
• Epigenetics
• +۳ مورد دیگر

Jiankang Deng is a Lecturer in the Department of Computing at Imperial College London, within the Faculty of Engineering. His research focuses on multi-modality foundation models, generative modeling of physical entities, and computer vision applications. He leads projects such as InsightFace.ai for facial analysis and synthesis. Previously, he earned his PhD (2020) from Imperial College under Prof. Stefanos Zafeiriou, focusing on deep face analysis and modeling. Education: PhD in Computing (2016-2020) from Imperial College London. Research interests include photorealistic facial reconstruction, generative adversarial networks, and multimodal learning. His work bridges computer vision with practical technologies for societal impact. Publications reflect expertise in 3D reconstruction, diffusion models, and cross-modal systems. Notable areas include hand motion analysis, face reflectance estimation, and tactile robotics. His work emphasizes scalability and real-world applicability. No awards listed explicitly, but his research contributions are evident through active publications and industry collaborations. Advising and lab work involve the IBUG research group, focusing on facial recognition and generative AI systems. Labs/Teams: Part of the Imaging and Biomedical Engineering Group (IBUG), collaborating on projects like FaceMAE for privacy-preserving recognition and MogFace for advanced face detection systems.

• Computer Vision
• Generative Modeling
• Multimodal Foundation Models
• +۴ مورد دیگر

Atriya Sen is an Assistant Professor in the Department of Computer Science at Oklahoma State University. Their research focuses on Explainable AI and Biodiversity AI, aiming to enhance understanding of neural network interpretability and applying AI to conservation decision-making. Sen holds a PhD from Rensselaer Polytechnic Institute and completed postdoctoral training at Arizona State University. Their work integrates machine learning with logical reasoning to address challenges in biodiversity science and ecological informatics. Research interests include taxonomic intelligence, data integration for conservation, and ethical frameworks in AI systems. Key projects involve developing tools like the Automated Taxonomic Concept Reasoner (ATCR) and advancing methods to extract virus-host interactions from biological datasets. Sen has secured grants from the National Science Foundation (NSF) and National Institutes of Health (NIH), including a CRII grant for Explainable AI in biodiversity science. Publications span venues like the AAAI Conference, Biodiversity Information Science and Standards, and the Lancet Planetary Health, addressing topics from container angle classification to ethical operating systems for AI. Their interdisciplinary approach bridges computer science, ecology, and philosophy to tackle global challenges in conservation and technological ethics.

• Artificial Intelligence
• Bioinformatics and Computational Biology
• Explainable AI
• +۳ مورد دیگر

Maryam Abo-Tabik serves as a Lecturer in Computer Science within the School of Engineering and Computing, where she applies interdisciplinary expertise to develop machine learning solutions for health behaviour prediction. Her work bridges computer science and public health through innovative mobile health applications, particularly targeting smoking cessation interventions using real-world smartphone data. Her research interests center on deep learning architectures and behavioural modelling techniques for health monitoring, with emphasis on predicting smoking lapses through movement dynamics and physiological signals. This includes pioneering work on 1D-CNN models for time-series analysis of hand movements and smartphone sensor data, advancing the field of just-in-time adaptive interventions in digital therapeutics. Analysis of her publication trajectory reveals consistent focus on translating machine learning innovations into practical cessation tools, evolving from foundational biometric research to specialized health applications. Her methodology integrates mobile sensing with predictive analytics to create personalized, real-time behavioural support systems. Professional recognition includes: HEA Fellow Dr Abo-Tabik actively supervises advanced student projects in health technology development, guiding interdisciplinary research that combines technical implementation with behavioural science principles for next-generation mobile health solutions.

• Deep Learning
• Behavioural Modelling
• Mobile Health Applications
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Alexandre Dolgui is Distinguished Professor and Head of the Department of Automation, Production and Computer Sciences at IMT Atlantique in Nantes, France. He serves as Editor-in-Chief of the International Journal of Production Research and has established himself as a leading researcher in production and supply chain optimization. His academic career spans multiple institutions including Jean Monnet University and University of Lorraine Mines Nancy, with a visiting professorship at the Chinese Academy of Sciences. PhD in Industrial Engineering, University of Technology of Compiègne (2000) Doctorate in Computer Science and Control, National Academy of Sciences of Belarus (1990) Bachelor in Automated Data Processing and Management Systems, Belarusian State University of Informatics and Radioelectronics (1986) Dolgui's research primarily focuses on manufacturing line design, production planning and scheduling, and supply chain optimization. His work bridges theoretical operations research with practical industrial applications, particularly in the context of Industry 4.0 and sustainable manufacturing. He has pioneered approaches to address challenges in assembly line balancing, reconfigurable manufacturing systems, and resilient supply chain design under uncertainty. His research integrates mathematical programming, optimization techniques, and increasingly, artificial intelligence methodologies to solve complex production and logistics problems. An analysis of his recent publications reveals a strong emphasis on supply chain resilience, particularly regarding the ripple effect of disruptions, digital supply chain twins, and sustainable manufacturing practices. His work increasingly incorporates AI and machine learning techniques while maintaining strong theoretical foundations in operations research. The interdisciplinary nature of his research connects industrial engineering with computer science, economics, and environmental sustainability. Highly Cited Researcher 2022, 2023, and 2024 by Clarivate Best Paper Award for 2023 by Omega Fellow of IISE (Institute of Industrial and Systems Engineers) As Editor-in-Chief of the International Journal of Production Research, Dolgui has significantly influenced the field's research direction and quality standards. His leadership extends to organizing major conferences including the IFAC Symposium on Information Control Problems in Manufacturing (INCOM). His research group maintains strong international collaborations, particularly with researchers from China, Russia, and other European institutions, securing funding for projects addressing contemporary manufacturing and supply chain challenges. Dolgui leads the Alexandre Dolgui Lab at IMT Atlantique, which focuses on production systems engineering and supply chain management. The lab maintains strong industry partnerships and participates in European research networks addressing advanced manufacturing challenges. Current research directions include Industry 5.0 applications, human-robot collaborative systems, and sustainable supply chain design, reflecting the lab's commitment to addressing both theoretical and practical challenges in modern production environments.

• Operations Management
• Logistics
• Supply Chain Optimization
• +۴ مورد دیگر

Xuan Wang is an Assistant Professor in the Department of Computer Science at Virginia Tech (VT), also affiliated with the Sanghani Center for Artificial Intelligence and Data Analytics. Specializing in Natural Language Processing (NLP), Data Mining, and AI applications in healthcare and sciences, their research focuses on weakly-supervised learning, multi-agent systems, and foundation models. They hold a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC), with additional M.S. degrees in Statistics and Biochemistry from UIUC, and a B.S. in Biological Science from Tsinghua University, China. Education: Ph.D., Computer Science, UIUC (2022) M.S., Statistics, UIUC (2017) M.S., Biochemistry, UIUC (2015) B.S., Biological Science, Tsinghua University (2013) Research Interests: Focus on NLP with limited supervision, LLM agents for reasoning, multi-modal science foundation models, and healthcare AI. Current projects include small language model agents, EEG-to-text translation, and gene regulatory network inference. Grants & Awards: Recipient of NSF NAIRR Pilot Award (2024-2025), Cisco Research Award (2025), and NAACL Best Demo Award (2021). Active in securing funding for projects in healthcare AI, multi-agent systems, and scientific computing. Lab & Students: Mentors PhD/Master students in NLP, data mining, bioinformatics, and AI. Current advisees include Daniel Hajialigol, Zhenyu Bi, Meng Lu, and others. Alumni Yueyan Gu has contributed to large energy models research. Service & Leadership: Organized workshops at ACL 2025, VL/HCC 2025, and SDM 2025. Served as co-chair for ICDM 2025 Undergraduate Symposium. Active in tutorial development on AI for science and trustworthy LLMs.

• Natural Language Processing
• Data Mining
• AI for Sciences
• +۷ مورد دیگر

Pengcheng Shi serves as the Associate Dean for Research and Scholarship and PhD Program Director at the Golisano College of Computing and Information Sciences at Rochester Institute of Technology (RIT). He holds a prominent position within the Department of Computing and Information Sciences, where he oversees research initiatives and doctoral programs while maintaining an active research profile across multiple disciplines. Dr. Shi completed his educational journey with a BS from Shanghai Jiao Tong University (China), followed by MS, M.Phil., and Ph.D. degrees from Yale University. His academic foundation spans both Chinese and American institutions, providing him with a diverse educational background that informs his interdisciplinary research approach. Dr. Shi's research spans an impressive breadth of computational disciplines, with particular focus on artificial intelligence applications in biomedical contexts. His work integrates bioinformatics, data science, and health informatics to develop computational approaches for medical imaging analysis, cardiac electrophysiology modeling, and diagnostic reasoning processes. Recent publications reveal an expanding research portfolio that now includes significant contributions to battery technology, materials science, and advanced 3D computer vision techniques for robotics and autonomous systems. His research demonstrates a unique ability to bridge theoretical computer science with practical applications in healthcare and energy storage. Dr. Shi's scholarly output shows a clear evolution from biomedical imaging and computational physiology toward broader applications in materials science and autonomous systems. While his early work focused primarily on cardiac modeling, medical image analysis, and diagnostic reasoning processes, his recent publications indicate a strategic expansion into energy storage technologies, particularly battery chemistry and interfacial engineering, alongside continued work in 3D computer vision and point cloud processing for robotics applications. As Associate Dean for Research and Scholarship, Dr. Shi plays a critical leadership role in shaping the research direction of the college while actively mentoring doctoral students through his PhD program director responsibilities. His teaching portfolio includes advanced courses such as CISC-810 Research Foundations, CISC-890 Dissertation and Research, and CISC-896 Colloquium in Computing and Information Sciences, indicating his commitment to developing the next generation of computing researchers. Dr. Shi's laboratory work appears to focus on computational biomedical imaging, with recent expansions into battery technology research and 3D vision systems. His interdisciplinary approach connects computer science with biomedical engineering, materials science, and robotics, creating a research environment that bridges traditionally separate domains. This cross-pollination of ideas across disciplines has positioned his work at the intersection of multiple rapidly advancing technological fields.

• Artificial Intelligence
• Bioinformatics
• Data Science
• +۴ مورد دیگر

Prof. Dr. Jasmina Bogojeska is a Professor for Artificial Intelligence and Machine Learning at ZHAW School of Engineering, where she leads the Explainable Artificial Intelligence Group since March 2024. She previously held roles as Senior Principal Data Scientist at Roche (2022-2024) and Research Staff Member at IBM Research Zurich (2013-2022), with postdoctoral work at Max Planck Institute for Informatics (2011-2013). Her affiliations include ZHAW Datalab and Digital Health Lab. PhD in Computer Science (2011), Saarland University/Max Planck Institute MSc in Computer Science (2007), Saarland University BSc in Computer Science (2004), University Ss. Cyril and Methodius Her research bridges Explainable AI with applications in Healthcare , IT Infrastructure Management , and Critical Care Time-Series Analysis . Key focus areas include domain-specific foundation models , conversational data systems , and low-resource NLP solutions . Recent work explores multi-modal medical AI for chest X-ray interpretation and large-scale clinical time-series datasets . Notable contributions include GIT-CXR for automated radiology reports and domain-specific protein language models in immunology. Her publications span IEEE, BMC, and Nature journals, emphasizing practical AI deployment in healthcare and IT operations. Scientific Recognition: Edelman Prize Finalist (2020) IBM Research Accomplishment Award (2019) IBM Corporate Award (2017) Best Paper Award at CNSM (2013) She actively develops reliable AI systems for server incident reduction (PASIR) and child injury monitoring, while advancing automated medical analytics through projects like Antibiotika-Resistenz Tracker. Her teaching covers Machine Learning , Data Mining , and Safer AI at bachelor and master levels.

• Machine Learning
• Deep Learning
• Explainable Artificial Intelligence
• +۳ مورد دیگر

Gloria Menegaz is a Full Professor of Bioengineering at the Department of Engineering for Innovation Medicine, University of Verona, where she directs the BraiNAVLab. She has been serving in this position since 2021, following her appointment as Full Professor in Computer Science in 2017. Previously, she held Associate Professor and Assistant Professor positions at the University of Verona, University of Siena, and University of Fribourg (Switzerland). Her research spans multiple interdisciplinary fields including bioengineering, artificial intelligence, machine learning, and neuroimaging. She specializes in explainable AI methods applied to biomedical data, with particular focus on brain connectivity analysis using multi-modal neuroimaging data (fMRI, EEG) integrated with genetic information. Her work has resulted in over 160 publications in prestigious journals including IEEE Transactions on Image Processing, IEEE Transactions on Medical Imaging, and Frontiers in Neuroscience. Her recent research trends emphasize the development of explainable AI frameworks for clinical applications, particularly in neurological disorder diagnosis and treatment planning. She has pioneered approaches for integrating structural and functional brain connectivity data with genetic markers to identify numerical biomarkers for various neurological conditions. Senior Member of IEEE (2017) Chair of Steering Committee of IEEE Transactions on Medical Imaging Member of IEEE-SPS Challenges and Data Collection Technical Committee Vice-Chair of IEEE Bioimaging and Signal Processing Committee Recipient of Rita Levi Montalcini grant Throughout her career, Professor Menegaz has secured over 1 million euros in research funding from international institutions including the European Commission (H2020, FP7), European Science Foundation, MIUR (PRIN, SIR, FIRB), and ANR (France). She maintains extensive international collaborations with Georgia Tech University, INRIA (France), EPFL (Switzerland), Queen Mary University, and University College London. Her laboratory, BraiNAVLab, focuses on developing advanced computational methods for brain imaging analysis and navigation.

• Bioengineering
• Artificial Intelligence
• Explainable AI
• +۷ مورد دیگر

Qiang Sun is an Associate Professor of Statistics and Computer Science at the University of Toronto (UofT), holding cross-appointments in the Department of Computer Science and the Department of Computer and Mathematical Sciences. He leads the StatsLE (Statistics, Learning, and Engineering) Lab, focusing on generative AI, trustworthy AI, and next-generation statistics. His research bridges statistical theory with applied challenges in industry, emphasizing ensemble learning, transfer learning, and reinforcement learning, with applications in tech, finance, and science. Education: PhD from the University of North Carolina at Chapel Hill (UNC-CH) BS in SCGY from the University of Science and Technology of China (USTC) Research Interests: Sun’s work spans foundational AI and statistical methods, including GenAI, robust algorithms, and interdisciplinary applications. His lab explores topics like resistant clustering, physical commonsense violation detection in gameplay, and microbiome analysis. Recent trends in his publications emphasize robustness (e.g., heavy-tailed errors), multimodal learning, and lifelong learning frameworks. Scientific Awards: Connaught Award James E. Grizzle Distinguished Alumni Noah’s Ark Distinguished Lecture Advising & Grants: While student names are not listed, his grants and collaborations reflect industrial and academic partnerships. He serves as an associate editor for the Electronic Journal of Statistics and as an area chair for conferences like AISTATS and COLT. Labs & Teams: The StatsLE Lab at UofT drives his research, integrating statistical theory with cutting-edge AI applications.

• Generative AI
• Trustworthy AI
• Next-generation Statistics
• +۸ مورد دیگر

Dr. Xi Wang is an Assistant Professor in the Department of Materials Science and Engineering at the University of Delaware, where he has been since September 2018. Prior to this, he served as a postdoctoral researcher in the Department of Materials Science and Engineering at the University of California, Berkeley from 2014. He holds a PhD in Electrical Engineering from the State University of New York at Buffalo (2014) and a B.S. in Microelectronics from Tsinghua University, China (2007). Research Interests: Dr. Wang's work focuses on Nanophotonics , Dynamic tunable photonic devices , Micro/Nano-Electro-Opto-Mechanical Systems (MEOMS/NEOMS) , Semiconductor plasmonics , Metamaterials , Metasurfaces , Phase transition materials , and Two-dimensional materials . His research integrates theoretical and experimental approaches to advance optoelectronic and photonic technologies. Awards: University of Delaware Research Foundation (UDRF) Award (2020) Advising & Grants: Dr. Wang currently advises no listed students but has contributed to funded research through grants such as the UDRF Award. His work often involves collaborations with industry and academic partners to explore applications of novel materials and devices. Labs/Teams: While specific lab affiliations are not explicitly mentioned, his research likely aligns with interdisciplinary groups at the University of Delaware focused on advanced materials and photonics.

• Nanophotonics
• Dynamically tunable photonic devices
• Micro/Nano-Electro-Opto-Mechanical Systems (MEOMS/NEOMS)
• +۷ مورد دیگر

Dr. Fabio Papacchini is a Senior Lecturer in Computer Science at Lancaster University. His research focuses on modal logic, automated planning, formal verification, agent systems, and ontology-mediated querying. He contributes to theoretical foundations of logic and their applications in artificial intelligence and software engineering. Papacchini is actively involved in the Security Lancaster research groups, specifically in Software Security and Systems Security domains. His recent work includes advancements in modal logic reduction techniques, efficient model construction, and formal verification of autonomous systems. He has published extensively in top-tier conferences and journals, with notable contributions to automated planning, BDI agents, and runtime verification frameworks. Current projects include fostering professional development through collaborative academic initiatives. Awards and recognitions are not explicitly listed in the provided texts. Papacchini’s research has been supported by grants and collaborations, though specific funding details are not detailed here. He is affiliated with the Department of Computer Science at Lancaster University and maintains an active presence in international academic communities.

• Modal Logic
• Automated Planning
• Formal Verification
• +۵ مورد دیگر

Dr. Dominik Klein is an Assistant Professor in Theoretical Philosophy at the Department of Philosophy and Religious Studies, part of the Faculty of Humanities at Utrecht University. His research focuses on social interaction and its implications, particularly in the domains of collective patterns, epistemic rationality, and decision-making quality. He works at the intersection of logic, social epistemology, and political theory, with additional contributions to philosophy of science and action. Key research themes include agent-based social simulation, human-centered artificial intelligence, and institutions for open societies. He is affiliated with projects such as 'Simulations of Scientific Inquiry' (Bochum) and 'Coalition and Epistemic Logic' (Bayreuth/Prague). Klein teaches courses like 'Mathematics for AI' and actively contributes to interdisciplinary collaborations in social science modeling and formal epistemology. His work spans theoretical explorations of trust dynamics, information cascades, and epistemic decision-making, leveraging formal methods to analyze societal challenges. Klein's publications reflect a blend of philosophical inquiry and computational modeling, addressing topics like non-classical probabilities, dynamic epistemic logic, and the epistemic quality of democratic processes.

• Agent-Based Social Simulation
• Logic
• Epistemology
• +۶ مورد دیگر

Professor Yi-Zhe Song is a Professor of Computer Vision and Machine Learning at the University of Surrey's Centre for Vision, Speech and Signal Processing (CVSSP), one of Europe's largest AI research centers. He leads the SketchX Lab, focusing on understanding human sketching to advance computer vision and cognitive science. His roles include Co-Director of the Surrey Institute for People-Centred AI and Programme Lead of Surrey’s MSc in Artificial Intelligence. He holds a PhD (2008) and MSc (2004, Best Dissertation Award) from the University of Cambridge and Bath. Research interests span sketch-based systems, fine-grained visual classification, domain adaptation, and generative models. His lab has produced over 67 publications in top-tier conferences (CVPR, ICCV, ECCV) and journals, including a Best Paper Award at BMVC 2015. He is an Associate Editor for IEEE TPAMI and Frontiers in Computer Science, and has served as Programme Chair for BMVC 2021. Recent trends in his work emphasize explainable AI (SketchXAI), 3D reconstruction from sketches, and practical applications like virtual try-on and fashion-focused V+L models. His MSc AI programme integrates technical, ethical, and business aspects, preparing students for diverse AI roles. Awards: Best Paper Award (BMVC 2015), Senior Member of IEEE, Fellow of the Higher Education Academy. Lab Leadership: 3 academics, 2 postdocs, 14 PhD students in the SketchX Lab. Grants & Partnerships: Collaborations with industry and international funding bodies (e.g., EPSRC, São Paulo Research Foundation). Future Work: Expanding AI’s societal impact through inclusive education, human-centric AI, and democratizing generative AI tools.

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

Roya Firoozi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada. She holds a Ph.D. in Control Theory from UC Berkeley (2021) and a Postdoctoral Research position at Stanford University's Multi-Robot Systems Lab (2024). Her research focuses on advancing safe robot autonomy in interactive environments through generative AI, distributed optimization, and game theory. Key areas include multi-agent systems, autonomous vehicles, and perception-driven control. Education: Ph.D. in Control Theory, UC Berkeley (2021) Bachelor's in Mechanical Engineering, UC Berkeley (2014) Postdoctoral Research, Stanford University (2024) Research Interests: Robotics-aided generative AI, multi-modal perception, interactive autonomy in multi-agent systems, game-theoretic optimization, and safe navigation in dynamic environments. Her work bridges theoretical foundations with real-world experiments in robotics and autonomous systems. Awards: NSF Postdoctoral Research Fellowship ASEE Fellowship (2021-2023) Rising Stars in Aerospace Engineering (2022) Outstanding Graduate Instructor Award (2021) Advising & Grants: Currently supervising graduate students (PhD/Master's) focused on robotics and control systems. Research is supported by grants from NSF and industry collaborations. She has pioneered algorithms for distributed multi-vehicle coordination and occlusion-aware navigation systems. Labs & Teams: Leads the Autonomous Systems Lab at Waterloo, focusing on robotics, AI, and control. Collaborates with Stanford's Multi-Robot Systems Lab and UC Berkeley's MPC Lab on projects involving autonomous vehicles and fault detection systems.

• Robotics
• Artificial Intelligence
• Control Theory
• +۷ مورد دیگر