Find Academic Supervisors

Andreas BullingView profile

Professor

• Human-Computer Interaction
• Computer Vision
• Ubiquitous Computing
• +4 more

Prof. Dr. Andreas Bulling is Full Professor of Computer Science at the University of Stuttgart , leading the Collaborative Artificial Intelligence research group at the Institute for Visualization and Interactive Systems. He is also a founding director of the Stuttgart ELLIS Unit and serves on multiple prestigious boards including IEEE Transactions on Visualization and Computer Graphics . Education: MSc in Computer Science (KIT), PhD in Information Technology (ETH Zurich) Research Interests: Human-Computer Interaction, Eye Tracking, Wearable Computing, Computer Vision, and Privacy-Preserving AI Scientific Leadership: UbiComp Steering Committee member, ACM ETRA General Chair (2020), and extensive editorial/guest editor roles Key Awards: ERC Starting Grant (2018), Henriette Herz Scout (2024), and multiple best paper awards at CHI, ETRA, and UIST Technical Contributions: Developed datasets (LPW, VisRecall++), created novel methods for gaze estimation, mental face reconstruction, and saliency prediction in visualizations His work bridges AI and Human-Computer Interaction with applications in immersive systems and healthcare technologies.

Abdul-Manan KhanView profile

Lecturer

• Robotics
• Explainable AI
• Trustworthy Autonomous Systems
• +7 more

Dr. Abdul-Manan Khan is a Lecturer in Robotics at the School of Computing and Engineering, University of West London. He holds a PhD in Mechanical Design Engineering from Hanyang University and has prior research experience as a postdoctoral researcher at Imperial College London and as a senior research associate at the University of Bristol. His research focuses on developing transparent and reliable intelligent systems, particularly in the domains of robotics and autonomous navigation. Key interests include explainable AI, trustworthy autonomous systems, and safe navigation in dynamic environments. Dr. Khan contributes to several postgraduate and undergraduate programs, including the BEng (Hons) Electrical and Electronic Engineering, MSc Electronic and Robotic Engineering, and MSc Industrial Internet of Things. His work aims to bridge the gap between complex AI decision-making and human interpretability, ensuring safety and reliability in real-world applications. Although no specific awards or publications are listed in the provided text, his academic trajectory reflects strong engagement with cutting-edge robotics and AI research. He is actively involved in teaching and research, contributing to curriculum development and innovation in robotic engineering education at UWL.

• Machine Learning
• Computer Vision
• Natural Language Processing
• +2 more

Chun-Liang Li is a Research Scientist at Apple MLR and an Affiliate Assistant Professor at the Allen School of Computer Science, University of Washington. He holds a Ph.D. in Machine Learning from Carnegie Mellon University (2014-2019) and a B.S./M.S. in Computer Science and Information Engineering from National Taiwan University (2008-2013). His research spans machine learning, computer vision, and natural language processing, with emphasis on representation learning, efficient model training, and multimodal understanding. Key focus areas include large language model optimization, contrastive learning techniques, document understanding systems, and domain adaptation methods. Recent publications demonstrate strong focus on LLM efficiency (dataset decomposition, distillation techniques), document AI (table reasoning, form extraction), and multimodal alignment (image-text retrieval, prefix conditioning). His work consistently appears in premier venues including NeurIPS, CVPR, ACL, and ICLR. Awards and Honors: IBM Ph.D. Fellowship (2018) Best student paper runner-up, IJCAI (2017) First Place in both tracks of KDD Cup (2011, 2013) He actively mentors students and collaborators, inviting contact for internship opportunities. Current industry-academic position bridges cutting-edge research with practical applications in machine learning systems.

Gerard de MeloView profile

Professor

• Artificial Intelligence
• Large Language Models
• Natural Language Processing
• +9 more

Gerard de Melo is a Professor and Chair of Artificial Intelligence and Intelligent Systems at the Hasso Plattner Institute (University of Potsdam, Germany). He also serves as a member of the Cognitive Sciences Research Focus and ELLIS Unit Potsdam . Research Interests: AI and Machine Learning Natural Language Processing Cross-modal AI (vision-language models, knowledge graphs) Societal and philosophical aspects of AI Medical Informatics Recommendation Systems Graph Neural Networks Recent Article Trends: His work spans generative AI (Vector Grimoire), medical LLM evaluation (CliMedBench), distributed training (Efficient Parallelization), and social media analysis (WallStreetBets) with a focus on multimodal systems and ethical AI . Scientific Recognition: Rutgers CSGSS Best Professor Award (Teaching, Advising) Best Paper Awards: NeurIPS Workshop, CIKM, EACL LANTERN IEEE TCSE Distinguished Paper Award First Runner-Up ACM WebSci Advising & Grants: Mentored students like Rohan Sawahn (Better World Award) and Maximilian Schall (IEEE TCSE Award). Secured major funding including German BMBF Grant for AI Service Center Berlin-Brandenburg and DARPA SocialSim Program support.

• LLM-assisted agent systems
• Explainable AI
• Multimodal artificial intelligence
• +12 more

Dr. Yifei Dong is a Research Fellow at the Data Science Institute , University of Technology Sydney, with expertise in LLM-assisted agent systems , explainable AI , and multimodal artificial intelligence . Holding a PhD in Computer Science from UNSW Sydney and over a decade of fintech industry experience including CTO roles, he has secured $2.4 million in competitive funding for AI solutions bridging academia and real-world applications in healthcare, education, and finance. Education : PhD in Computer Science from UNSW Sydney Current Role : Research Fellow at UTS Data Science Institute (2023–present) Past Academic Appointments : Lecturer at Southern Cross University and Western Sydney University Dr. Dong’s research focuses on making AI systems transparent and socially beneficial , with contributions to adversarial AI, trustworthy digital societies, and wireless sensor networks. His recent work includes: Developing AICAttack (2025) for adversarial image captioning attacks Creating the QMAD fairness metric (2025) for dynamic environments Advancing explainable ECG diagnosis systems (2025) via multimodal LLMs As a scientific awardee (2025 RegTech Social Impact of the Year), he has pioneered AI solutions for vulnerable populations, such as NDSI participants through the "My Complaint Assistant" tool. His supervision of PhD and Honours students emphasizes technical rigor and ethical responsibility.

Laurent CharlinView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Recommender Systems
• +6 more

Laurent Charlin is an Associate Professor at HEC Montréal and holds an adjunct appointment in Computer Science at Université de Montréal. His research focuses on machine learning for decision-making with applications in recommender systems, reinforcement learning, and optimization.

Noboru MatsudaView profile

Associate Professor

• Artificial Intelligence
• Learning Sciences
• Educational Technology
• +5 more

Noboru Matsuda is an Associate Professor in the Department of Computer Science at North Carolina State University, affiliated with the Chancellor’s Faculty Excellence Program cluster in Digital Transformation of Education . He leads the Innovative Educational Computing Laboratory and is an affiliate of the Center for Educational Informatics . His research bridges artificial intelligence , cognitive science , and education to develop adaptive learning technologies. Master of Science in Math Education, Tokyo Gakugei University Ph.D. in Intelligent Systems, University of Pittsburgh Matsuda’s work focuses on intelligent tutoring systems , generative AI for educational content, and adaptive online courses . He has pioneered methods like PASTEL (evidence-based learning engineering) and explored SimStudent , a teachable agent for tutor learning. His studies span question generation , metacognitive scaffolding , and AI-driven skill modeling . Recent publications highlight trends in large language models for education , automated content validation , and student-agent interaction . Though no scientific awards are explicitly listed, his leadership in educational computing and cross-disciplinary research underscores his impact. Matsuda’s lab continues to innovate infrastructure for adaptive learning systems with embedded ITS technologies.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +7 more

Prof. Dr. Michael Guckert is a Professor of Business Informatics - Artificial Intelligence at the Department of MND (Management, Natural Sciences, Design) at Technische Hochschule Mittelhessen (THM). He serves as Spokesperson of the KITE Competence Center, Board member at hessian.AI, and Director at StudiumPlus. His research spans multiple domains with a strong focus on AI applications in medical contexts and multi-agent systems. Dr. Guckert's research interests center on applying artificial intelligence to solve complex problems across multiple domains. His primary focus is on medical applications, particularly using AI for electrocardiogram (ECG) analysis, cardiac pathology detection, and clinical decision support systems. He also maintains significant research in multi-agent systems for traffic simulation, model-driven development approaches, and trustworthy/explainable AI systems. His work bridges theoretical AI development with practical applications in healthcare, manufacturing, and transportation. Analysis of Dr. Guckert's recent publications reveals a clear trend toward medical AI applications, particularly in cardiology. His work on the RisKa project demonstrates how AI can interpret ECG data to detect heart conditions, with significant emphasis on explainability and trustworthiness. Parallel to this medical focus, he maintains active research in multi-agent traffic simulation systems (AGADE), acoustic emission analysis, and model-driven development approaches. The publications show increasing collaboration with medical researchers, indicating a strategic focus on interdisciplinary AI applications in healthcare. Dr. Guckert leads the RisKa project (2022-2024), a €736,682 initiative funded by the Ministry for Digital Strategy and Development, which develops an AI-based clinical decision support system for ECG analysis. His research group at THM KITE Competence Center collaborates with Justus Liebig University in Giessen on biomarker research. He has secured multiple research grants for projects involving AI in medical diagnostics, traffic simulation, and manufacturing quality control. His work demonstrates strong industry-academia collaboration, particularly in applying AI to solve real-world problems in healthcare and transportation. Dr. Guckert directs the KITE Competence Center at THM, which focuses on information technology research and applications. His team includes researchers specializing in medical AI, multi-agent systems, and model-driven development. The RisKa project team specifically brings together computer scientists and cardiologists to develop clinically useful AI tools. His work with hessian.AI positions him at the forefront of AI development in the Hesse region of Germany, facilitating collaboration between academic and industry partners.

• Explainable Artificial Intelligence (XAI)
• Time Series Classification
• Cardiology
• +6 more

Prof. Dr. Jennifer Hannig is a leading researcher in Explainable Artificial Intelligence (XAI) and computational biology at the Technical University of Mittelhessen's KITE Competence Center. Her work bridges AI transparency with applications in cardiology, digital pathology, and smart home technologies, focusing on interpretable algorithms for safety-critical domains. Junior Research Group Leader, TimeXAI project (2024-2027, €1.09M) Collaborations with Kerckhoff Clinic, CRS medical GmbH, and Veli GmbH Member of good scientific practice committee Mentor at Mentoring Hessen and AI Grid initiative Research Focus : Developing human-understandable XAI methods for time series classification, particularly in cardiovascular diagnostics and smart home monitoring. Her approach combines mathematical modeling (Petri nets) with deep learning to ensure reliable AI decision-making. Scientific Contributions : Key publications in PLOS Computational Biology , Frontiers in Digital Health , and Bioinformatics , with h-index of 8. Developed AI models for myocardial scar detection, Hodgkin lymphoma analysis, and manufacturing predictions. Poster Award – 1st Place (German Conference on Bioinformatics, 2013) ECCB 2016 Travel Award Contact: jennifer.hannig@kite.thm.de

• Supply Chain Management
• Logistics
• Artificial Intelligence
• +3 more

Batin Latif AYLAK serves as Associate Professor in the Department of Industrial Engineering at the Faculty of Engineering, Turkish-German University, Turkey. Previously, he held an Assistant Professor position at Hitit University starting in 2015. His academic career spans over a decade of research at the intersection of industrial engineering, logistics, and artificial intelligence. His educational foundation includes: Doctorate in Mechanical Engineering from University of Duisburg-Essen (2010-2015) Master's degree in Mechanical Engineering from University of Duisburg-Essen (2008-2010) Bachelor's degree in Industrial Engineering from Istanbul Kültür University (2001-2006) with full scholarship Dr. AYLAK's research pioneers the integration of artificial intelligence in supply chain optimization, with particular emphasis on sustainable logistics systems. His work bridges theoretical advancements with practical applications in blockchain implementation for supply chain transparency, machine learning-driven energy forecasting, and intelligent warehouse management. He has developed novel frameworks like SustAI-SCM for agentic AI in sustainable supply chains and contributed significantly to understanding digital transformation in Turkey's logistics sector. His publication trajectory (2021-2025) reveals a strategic evolution from foundational logistics research toward cutting-edge AI applications. Early work focused on optimization algorithms and blockchain integration, while recent publications demonstrate leadership in agentic AI systems for sustainability, advanced predictive modeling for renewable energy, and pandemic-responsive supply chain solutions. This progression highlights his adaptation to emerging technological paradigms while maintaining core focus on supply chain resilience. No scientific awards or fellowships were documented in the provided materials. Dr. AYLAK has secured competitive research funding including project BAP TAÜ - 2018BM0028 for digital trend analysis in Turkey's logistics sector. While his publications indicate collaborative research with multiple co-authors across institutions, no formal graduate student advisement relationships were specified in the available documentation.

Seyedeh Neelufar PayrovnaziriView profile

Research Fellow

• AI in Medicine
• Explainable Artificial Intelligence (XAI)
• Medical Informatics

Seyedeh Neelufar Payrovnaziri is a Postdoctoral Fellow affiliated with the Aghaeepour lab. Her research focuses on predictive modeling of electronic health records data for maternal and child health using artificial intelligence methods. PhD in Information from Florida State University Master’s and undergraduate degrees in Computer Engineering, specializing in Artificial Intelligence and Hardware Prior work experience at Mobile Telecommunications Network (MTN) in Iran Her research interests include AI in Medicine, Explainable Artificial Intelligence (XAI), and Medical Informatics. She is currently engaged in applying XAI techniques to healthcare data for improved clinical decision-making. Dr. Payrovnaziri collaborates with the Aghaeepour lab, focusing on applications of AI to maternal and child health outcomes. Her work emphasizes transparency and interpretability in predictive models.

• Healthcare Data Analysis
• Machine Learning
• Deep Learning
• +5 more

Awais Ashfaq is a Postdoctoral Researcher at Halmstad University's School of Information Technology, focusing on healthcare data analytics and AI applications in clinical decision-making. His work bridges the gap between advanced machine learning techniques and practical healthcare solutions, with particular emphasis on emergency department diagnostics and cardiovascular disease analysis. Dr. Ashfaq's research interests center around healthcare data analysis, applying statistics, traditional machine learning, deep learning, and language models to extract insights for informed clinical and management decisions. His work demonstrates strong expertise in interpretable AI models for medical diagnostics, particularly in emergency settings for conditions like dyspnea. He has made significant contributions to the development of evidence-based diagnostic tools that integrate seamlessly with clinical workflows. His publication record shows a consistent focus on leveraging complete healthcare system data to address real clinical challenges. The research trends indicate increasing sophistication in model interpretability while maintaining high diagnostic accuracy. His work spans multiple healthcare domains including emergency medicine, cardiology, and pediatric care, demonstrating the versatility of his methodological approaches. Dr. Ashfaq collaborates extensively with physicians across all care levels to ensure his research translates effectively to clinical practice and improves patient outcomes. His work on regional healthcare information platforms demonstrates commitment to building infrastructure that supports ongoing medical research and quality improvement initiatives.

Mohammad Mahdi KhaliliView profile

Assistant Professor

• Machine Learning
• Theoretical Machine Learning
• Applied Machine Learning
• +6 more

Mohammad Mahdi Khalili is an Assistant Professor in the Department of Computer Science and Engineering at The Ohio State University's College of Engineering. He also serves as a part-time Research Scientist at Yahoo! Research, focusing on theoretical and applied machine learning with emphasis on robustness, interpretability, and model compression for large-scale systems including LLMs. Dr. Khalili earned his Ph.D. in Electrical Engineering and Computer Science and MSc in Applied Mathematics from the University of Michigan, Ann Arbor. Prior to OSU, he was a research scientist at Yahoo Research and a postdoctoral researcher at UC Berkeley. His research spans mechanistic interpretability of foundation models , privacy-aware model compression , and fairness in sequential decision-making . Current projects include counterfactual reasoning for fair ML, physiological signal analysis for worker health monitoring, and corruption analysis in model mechanisms. His work bridges theoretical guarantees with practical healthcare and security applications. Recent publications reveal strong focus on model compression techniques (block-wise sparsity, quantization) and interpretability methods for LLMs, with growing emphasis on physiological signal processing applications. The 2024-2025 papers show increasing integration of healthcare domains like ECG analysis and construction worker monitoring. Dr. Khalili currently advises four PhD students: Zhiqun Zuo: Counterfactual Reasoning Zhongteng Cai: Privacy-Aware Model Compression (UAI travel grant recipient) Ding Zhu: Trustworthy Model Compression & Time Series Analysis Vishnu Chhabra: Mechanistic Interpretability His research is supported by two NSF grants (health monitoring systems and dynamic environment robustness), a Translational Data Analytics Institute grant for medical AI, and a College of Engineering grant for large-scale systems. The lab recently acquired a dedicated GPU server for intensive computations. He actively contributes to the ML community through invited talks (Midwest Machine Learning Symposium) and publications in top venues including NeurIPS, ICML, and UAI.

• Knowledge Representation
• Answer Set Programming
• Non-monotonic Reasoning
• +5 more

Pedro Cabalar is Full Professor at the Department of Computer Science of the University of Corunna, Galicia, Spain, and current coordinator of the inter-university Master in Artificial Intelligence (Universities of A Coruña, Santiago de Compostela and Vigo). He also serves as Area Editor (Theory Foundations) for Theory and Practice of Logic Programming and as Standard Editor for the Artificial Intelligence journal. Education: PhD in Computer Science, University of Corunna, 2001 Master in Computer Science, Politéchnic University of Madrid, 1993 Bachelor in Computer Science (3-year degree), University of Santiago de Compostela / University of Corunna, 1989 Research interests revolve around Knowledge Representation & Reasoning , especially Answer Set Programming , non-monotonic reasoning , temporal and modal logics , and causal reasoning . He investigates theoretical foundations (equilibrium logic, temporal extensions, deontic operators) and practical systems (telingo, eclingo, aspBEEF), with applications ranging from planning and diagnosis to explainable AI and healthcare decision support. His recent articles (2023-2025) exhibit a clear trend toward temporal and metric extensions of ASP , explainability , and hybrid reasoning systems , often combining logic programming with deontic or probabilistic features. Scientific awards & recognition: University of Corunna Dissertation Award, 2003 Best Paper Award at LPNMR 2019 Best Student Paper at ICLP 2020 Best Student Paper at JELIA 2019 Grants & projects: He currently leads or co-leads nationally funded Spanish projects (GEISER 2024-2028, ARLEKIN 2021-2024) and has coordinated EU COST actions (DigForASP) as well as earlier MINECO projects on temporal ASP and medical reasoning (TARDIS, MERLOT, FEAST, etc.). PhD supervision: He has successfully supervised three PhD theses (Martín Diéguez, Jorge Fandiño, Brais Muñiz) and continues to advise students within the Information Retrieval Laboratory (IRLab) and the Spanish node of Potassco Solutions.

Francesco LeofanteView profile

Research Fellow

• Explainable AI
• Counterfactual Explanations
• Robustness
• +3 more

Francesco Leofante is a Research Fellow at Imperial College London , affiliated with the Centre for Explainable AI . His work focuses on Explainable AI (XAI) , particularly counterfactual explanations with formal robustness guarantees against perturbations. Imperial College Research Fellowship DAAD AINet Fellowship (Safety and Security in AI) Imperial PFDC Supporting Research Staff and Students Award 2023 Research Interests center on Explainable AI , emphasizing counterfactual explanations , robustness , model multiplicity , and formal verification in critical systems like energy and aviation. His work bridges AI , formal methods , and human-AI collaboration . Publications include studies on robust counterfactual explanations , parametric ReLUs for verification, and multi-agent systems with formal guarantees. These appear in top venues like AAAI , KR , IJCAI , and AAMAS , often addressing AI safety and trustworthy systems . Scientific Awards include the Imperial PFDC Supporting Research Staff and Students Award 2023 , DAAD AINet Fellowship , and Imperial College Research Fellowship . He also contributes to workshops and program committees at conferences like AAAI, KR, and IJCAI. Future Work includes expanding robust XAI into critical infrastructure systems (energy, aviation) and developing tools like OMTPlan for AI planning and verification .