Find Academic Supervisors

• Medical Imaging Physics
• Neuroimaging
• MRI Technology
• +5 more

Univ.-Prof. Dr. Dr. hc NJ Shah is a prominent academic and researcher in medical imaging physics. He serves as the Institute Director of the Institute of Neuroscience and Medicine – Medical Imaging Physics (INM-4) at Forschungszentrum Jülich and holds a Professorship in the Department of Neurology at RWTH Aachen University. He also co-directs the Jülich-Aachen Research Alliance (JARA-Brain). His research focuses on advanced MRI techniques, multimodal neuroimaging, and applications in neuro-oncology and mental health. Shah has held roles such as Distinguished Professor at Monash Institute of Medical Engineering (2015–2017) and has been recognized with awards including the Veski Award and Honorary Doctorate from the Georgian Technical University. Education: PhD (1987, University of Manchester), Diploma in Advanced Studies in Science (1984, Manchester), BSc (1983, University of Sheffield). Research interests include MRI physics, ultra-high-field imaging (7T/9.4T), brain tumor imaging, and neuroimaging data science. His work bridges clinical and experimental MRI, with contributions to quantitative water content mapping and multimodal integration of MRI-PET-EEG. His publications highlight advancements in neuroimaging methodologies and their applications in understanding neurological and psychiatric conditions. Awards and honors include Fellowships from the Royal Society of Chemistry, Royal Society of Medicine, and Institute of Physics. Shah leads the MR Physics team at INM-4 and collaborates internationally, including roles at Maastricht University and the University of New Brunswick. His work emphasizes translating imaging innovations into clinical practice for precision medicine.

• VLSI Design
• Built-In Self-Test (BIST)
• Test Data Compression
• +7 more

Chr. Kavousianos is an Assistant Professor in the Department of Informatics at the University of Ioannina, Greece. He is actively engaged in research and teaching in the fields of VLSI design, testability, and low-power testing. He has been involved in major national and international research programs such as Heracleitus II, Pythagoras, and NSF-SRC (USA). Research Interests: His research focuses on advanced techniques in built-in self-test (BIST), test data compression, scan-based testing, fault tolerance, and embedded control architectures. He explores methods to reduce test data volume, power consumption during testing, and improve defect coverage in integrated circuits. Publication Trends: His recent publications (2004–2011) show a strong trend toward defect-aware testing, power-efficient test compression, and multicore SoC testing. He frequently collaborates with leading researchers, including Prof. Krishnendu Chakrabarty (Duke University). His work on multilevel Huffman coding and reseeding techniques has been highly influential, with one paper among the most accessed in 2004. Special Distinction for Excellent Academic Performance from TEE, 1996 Doctoral Scholarship 'In Memory of Professor Maritsa', 1999 Postdoctoral Scholarship from IKY, 2002 Advising and Grants: He has supervised multiple postdoctoral researchers, PhD candidates (e.g., Vasilis Tenentes, Emmanouil Kalligeros), and master’s students. He has led research projects such as 'Embedded Control Architectures' (Heracleitus II) and 'Design Techniques for Embedded Self-Control Circuits' (Pythagoras II). His international collaboration with Duke University included a postdoctoral research role in 2009. Labs and Teams: He leads a research group at the University of Ioannina with postdocs, PhD students, and visiting professors, including Prof. Krishnendu Chakrabarty. His team works on cutting-edge VLSI testing and design methodologies.

• Artificial Intelligence
• Neuroscience
• Biomechanics
• +3 more

Dr. Yonatan Hutabarat is a Professor at Universität Bonn, holding the Hertz-Chair for Artificial Intelligence and Neuroscience. He is affiliated with the Center for Artificial Intelligence and Neuroscience (CAIAN) and focuses on interdisciplinary research at the intersection of AI, neuroscience, and biomechanics. His work emphasizes wearable sensor technology and machine learning applications in gait analysis, posture coordination, and human movement modeling. Research interests include neural network-based gait phase estimation, biomechanical modeling under cognitive loads, and sensor integration for healthcare applications. His research has explored temporal convolutional networks, LSTM architectures, and reinforcement learning for prosthetic control. He has published extensively on quantitative gait assessment using minimal IMU sensors, virtual reality experiments, and multimodal data fusion techniques. Affiliations include CAIAN's interdisciplinary team and active participation in collaborative projects involving neuroscience and clinical applications. His laboratory work involves developing AI-driven solutions for movement disorders and wearable sensor systems. No formal awards or grants are explicitly listed, though his research indicates substantial collaborative activity. Current address: Raum 4.06, Am Propsthof 49, 53121 Bonn. Contact: y.hutabarat@uni-bonn.de

• Artificial Intelligence
• Machine Learning
• Recommender Systems
• +4 more

Özlem Özgöbek is an Associate Professor at the Department of Computer Technology and Informatics, Norwegian University of Science and Technology (NTNU). Her research spans artificial intelligence, machine learning, and recommender systems with a focus on privacy, fake news detection, and educational technology. NTNU - Department of Computer Technology and Informatics Her work explores multimodal fake news detection, privacy implications in recommender systems, and technology-enhanced classroom interaction. Recent publications analyze digital education trends and classroom tools. Özgöbek collaborates with international researchers and contributes to news recommendation workshops. Her projects address ethical AI, environmental sustainability, and real-time information processing.

• machine learning
• robotics
• machine vision
• +9 more

Dr. Hamidreza Mohades Kasaei is an Associate Professor in the Department of Artificial Intelligence at the University of Groningen, Netherlands. He holds positions in both the Faculty of Science and Engineering and the Faculty of Medical Sciences/UMCG, focusing on Robotics and image-guided minimally-invasive surgery. His work bridges theoretical advances in machine learning with practical robotic applications. Dr. Kasaei's research focuses on developing algorithms for adaptive perception systems through interactive environment exploration and open-ended learning. His specific interests include 3D object perception, grasp affordance detection, object manipulation, and active perception. He has evaluated his research on various robotic platforms including PR2, UR5e, Kinova, Franka robotic arms, and humanoid robots. His work enables robots to learn from past experiences and intelligently interact with non-expert human users using data-efficient techniques. Analysis of his recent publications reveals strong trends toward increasingly sophisticated manipulation capabilities, particularly in dual-arm coordination and handling dense clutter. There's a clear progression toward integrating language models with robotic control systems, as seen in works like 'Lifelong Robot Library Learning' and 'Towards Open-World Grasping with Large Vision-Language Models.' His research consistently addresses real-world challenges in agricultural robotics, assistive technologies, and service robotics applications. Gratama Science Award (2022) Google Research Scholar Award in Machine Learning (2023) Outstanding Associate Editor for IEEE Robotics and Automation Letters (2023) Dr. Kasaei has successfully supervised multiple PhD students including Zhenxing Zhang (thesis on 'Generative Adversarial Networks for Diverse and Explainable Text-to-Image Generation') and Hamed Ayoobi (thesis on 'Explain What You See: Argumentation-Based Learning and Robotic Vision'). His research is supported by significant grants including the Google Research Scholar Award for 'Continual Robot Learning in Human-centered Environments' and various conference organization roles including workshops at RSS 2023 and NeurIPS 2022. He leads the Lifelong Interactive Robot Learning Lab (IRL-Lab), which focuses on six key research directions: Perception and Perceptual Learning, Object Grasping and Manipulation, Lifelong Interactive Robot Learning, Dual-Arm Manipulation, Dynamic Robot Motion Planning, and Exploiting Multimodality. The lab develops cutting-edge approaches for robots to learn in open-ended fashion through interaction with non-expert human users, with applications in assistive robotics for people with disabilities.

• Information visualization
• Visual analytics
• Eye tracking
• +10 more

Tanja Blascheck is a PostDoc Researcher and Margarete von Wrangell Fellow at the Institute for Visualization and Interactive Systems (VIS) at the University of Stuttgart. Her work focuses on visual analytics , eye tracking , and microvisualizations for smartwatches and other wearable devices.

• Computer Vision
• Machine Learning
• Human-Computer Interaction
• +3 more

Olga Russakovsky is an Associate Professor in the Computer Science Department at Princeton University. She serves as Associate Director of the Princeton AI Lab and Chair of the Board of Directors at AI4ALL, a nonprofit dedicated to diversity in AI leadership. Her research focuses on computer vision, machine learning, human-computer interaction, and fairness in AI. She specializes in developing AI systems that reason about the visual world, emphasizing fairness, accountability, and transparency. Her work integrates computer vision with ethical AI frameworks, and she is affiliated with Princeton’s Center for Statistics and Machine Learning and Center for Information Technology Policy. Her publications address biases in datasets, explainable AI, and generative models. Her recent research trends include: Bias detection in datasets (e.g., CelebA, ImageNet) Interactive and explainable AI systems Generative models like diffusion and vision-language integration Deepfake detection and AI forensics Conceptual learning and few-shot training Scientific awards: NSF CAREER Award for fairer computer vision systems Co-founder of AI4ALL and Stanford AI4ALL outreach programs She advises students through AI4ALL initiatives and leads the Visual AI Lab, which focuses on robust, inclusive AI development. Her work bridges technical innovation with societal impact, particularly in diversity-focused education.

Shweta YadavView profile

Assistant Professor

• Natural Language Processing
• Healthcare Informatics
• Biomedical Text Mining
• +3 more

Shweta Yadav is an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago (UIC). Prior to this, she was a Bridge to the Faculty (B2F) fellow at UIC and a postdoctoral research fellow at the U.S. National Library of Medicine, NIH. She holds a Ph.D. in Computer Science from the Indian Institute of Technology Patna, India. Education: Ph.D. in Computer Science, Indian Institute of Technology Patna, India Research Interests Her research focuses on the intersection of Natural Language Processing (NLP), Healthcare Informatics, Biomedical Text Mining, and Computational Social Science. She develops machine learning algorithms to advance AI applications in healthcare, particularly in medical document summarization , disease progression modeling , and health outcome prediction using electronic health records and social media data. Her work emphasizes interdisciplinary collaboration to address real-world healthcare challenges. Recent Publications Her recent publications highlight advancements in Multimodal Mental Health Analysis , Perspective-aware Healthcare Summarization , and Biomedical Relation Extraction . She employs techniques like Transformer models , Contrastive Learning , and Attention Frameworks to tackle low-resource settings and extract insights from complex data sources.

Summer HanView profile

Associate Professor

• Statistical genetics
• Molecular epidemiology
• Cancer screening
• +5 more

Dr. Summer Han serves as Associate Professor of Medicine, Neurosurgery, and Epidemiology at Stanford University School of Medicine. She leads research through the Quantitative Sciences Unit (QSU) in the Biomedical Informatics Research Division of the Department of Medicine and maintains joint appointments in the Department of Neurosurgery. Her work bridges statistical methodology development with clinical applications in cancer screening and neuroscience. Her research program focuses on statistical genetics, molecular epidemiology, and risk prediction modeling for complex diseases. Key areas include developing novel methods for analyzing high-dimensional genomic data, creating dynamic risk prediction models under competing risks, and establishing evidence-based cancer screening strategies. Her team integrates genetic, environmental, and clinical factors to improve early detection of lung cancer and second primary malignancies, with particular attention to reducing racial disparities in screening outcomes. Dr. Han's scientific contributions have been recognized through prestigious awards including the NCI R37 MERIT Award for Early-Stage Investigators and the Department of Medicine Teaching Award in Biomedical Informatics Research. Her team has developed impactful tools such as the SPLC-RAT for second primary lung cancer risk assessment and RAMBO for brain metastasis prediction in lung cancer patients. She actively mentors PhD students and postdoctoral fellows, with several former trainees securing faculty positions at institutions including Cornell University and IIT Roorkee. Current research initiatives include the Oncoshare-Lung database integrating EHRs from Stanford Health Care and 23+ Sutter Health sites across Northern California, and the Cancer Data Science Shared Resources Core which she co-directs at the Stanford Cancer Institute. NCI R37 MERIT Award (Early-Stage Investigator) 2022 Department of Medicine Teaching Award in Biomedical Informatics 2024 SCI Equity Impact Research Grant 2024 Neurosurgery Research Seed Grant Award Multiple NCI R01 grants (CA226081, CA282793) Her laboratory collaborates extensively across Stanford Medicine, working with thoracic oncologists, neurosurgeons, and epidemiologists to translate statistical innovations into clinical practice. Current projects address socioeconomic factors in cancer risk stratification, real-time physical activity monitoring in spine surgery recovery, and machine learning approaches for genomic data analysis.

• Biomedical Image Analysis
• Computational Neuroscience
• Biomedical Informatics
• +4 more

Tianming Liu serves as a Distinguished Research Professor in the School of Computing at the University of Georgia, with courtesy faculty appointments in the Department of Epidemiology and Biostatistics at the College of Public Health and the Institute of Bioinformatics. His academic career at UGA spans from Assistant Professor (2008-2013) to Associate Professor (2013-2015) to full Professor (2015-present), culminating in his recognition as a Distinguished Research Professor in 2017. He also serves as Graduate Program Faculty in the School of Computing. Education: Ph.D. in Computer Engineering, Shanghai Jiaotong University, China (2002) Master of Science in Computer Science, Northwestern Polytechnical University, China (1999) Bachelor of Arts in Computer Science, Northwestern Polytechnical University, China (1998) Dr. Liu's research focuses on the intersection of computer science and neuroscience, with particular expertise in biomedical image analysis, computational neuroscience, and biomedical informatics. His work centers on cortical architecture imaging and discovery, developing advanced computational methods for analyzing brain structure and function. His research spans multiple disciplines including neurosciences, cognitive sciences, biomedical engineering, and clinical sciences, with applications in understanding Alzheimer's disease progression, brain connectomics, and neural architecture. Analysis of Dr. Liu's recent publications reveals a strong trajectory in applying deep learning techniques to neuroimaging data. His work increasingly focuses on developing sophisticated neural network architectures specifically designed for brain connectome analysis, with particular attention to spatiotemporal dynamics and hierarchical organization of brain networks. Recent publications demonstrate his leadership in applying neural architecture search methods to optimize brain network analysis pipelines, with applications spanning from Alzheimer's disease research to fundamental neuroscience questions about cortical folding patterns. Scientific Recognition: Distinguished Research Professor at the University of Georgia (2017) Dr. Liu has secured substantial research funding through multiple competitive grants from NIH and NSF, demonstrating the significance and impact of his work. His most notable projects include the NIH R01 grant "Developing an Individualized Deep Connectome Framework for ADRD Analysis," the NIH R01 grant "Mapping Trajectories of Alzheimer's Progression via Personalized Brain Anchor-nodes," and the NSF CRCNS grant "Exploring the Mechanism of 3-Hinge Gyral Formation and its Role in Brain Networks." These projects highlight his leadership in applying computational methods to address critical challenges in neuroscience and medicine, particularly in the domain of Alzheimer's Disease and Related Dementias (ADRD). Dr. Liu collaborates extensively across disciplines, working with researchers at institutions including University of Virginia, Emory University, UNC Chapel Hill, and UT Arlington. His work has contributed to the development of BiomedGPT, an open-source visual-language foundation model for biomedical applications, demonstrating his commitment to creating accessible tools for the broader research community.

Tyson Govan PhillipsView profile

Senior Lecturer

• Autonomous Systems
• Robotics
• Mining Automation
• +4 more

Dr. Tyson Phillips serves as Senior Lecturer and Director of Teaching and Learning at The University of Queensland's School of Mechanical and Mining Engineering within the Faculty of Engineering, Architecture and Information Technology. He is an active Affiliate of the Future Autonomous Systems and Technologies research group, focusing on translating robotics innovations into practical mining applications. His academic leadership includes curriculum development for engineering programs and direct industry engagement with major mining equipment manufacturers. He earned his Doctor of Philosophy (PhD) from The University of Queensland in 2016, with thesis research centered on LiDAR-based perception systems for autonomous excavators. His doctoral work established foundational methods for object pose verification in mining contexts. Phillips' research specializes in robotics perception for extreme mining environments, developing LiDAR-centric solutions for autonomous equipment operation amid dust, fog, and unstructured terrain. Key contributions include evidential reasoning frameworks for uncertainty management, real-time pose estimation algorithms, and sensor fusion techniques for excavators and bulldozers. His work bridges theoretical computer vision with industrial deployment, targeting operational safety and efficiency in mineral extraction. Publication analysis reveals consistent focus on mining robotics since 2012, with recent works (2021-2024) emphasizing minimal-sensor configurations, probabilistic terrain mapping, and vibration-assisted gripper technology. His 14 scholarly outputs demonstrate evolution from sensor evaluation (2012-2015) toward integrated autonomy systems (2018-2024), predominantly in Journal of Field Robotics and Sensors . He actively supervises graduate researchers as Principal Advisor for a PhD on multimodal perception mapping and Associate Advisor for two PhD projects involving spreader systems and physics-informed neural networks. Completed supervision includes a 2024 PhD on bulldozer terrain mapping and a 2021 Master's on shovel/hopper interaction strategies. Research funding spans 14 projects from 2012-2026, including current Australian Coal Association Research Program support (2025-2026) and major Caterpillar Inc. collaborations for ERS self-protection and articulated truck automation. Phillips operates within The University of Queensland's Future Autonomous Systems and Technologies group, which develops field-deployable autonomy solutions for mining partners. This team conducts real-world testing of perception systems using Caterpillar and FMG operational sites as validation environments.

Almut Sophia KoepkeView profile

Researcher

• Multi-Modal Learning
• Computer Vision
• Audio-Visual Processing
• +3 more

Dr. Almut Sophia Koepke is a junior research group leader and TUM Junior Fellow at the Technical University of Munich (TUM) and University of Tübingen. She leads the multi-modal learning research group focusing on video understanding through sound, vision, and text integration. University: Technical University of Munich School: TUM School of Computation, Information and Technology Department: Informatics 9 Academic Rank: Researcher Her research spans multi-modal learning, audio-visual foundation models, and cross-modal attention mechanisms. Key themes include: Advancing zero-shot learning through language-guided audio-visual models Developing explainable AI systems via attention pattern translation in VQA Exploring temporal understanding in video-adverb retrieval Building robust multi-modal representations for self-driving applications Recent publications analyze foundation model capabilities in audio-visual tasks (ICCV 2025), temporal reasoning (ACMMM 2024), and cross-modal attention frameworks (ECCV 2022). She co-organizes CVPR workshops on foundation model evaluations and serves as area chair/reviewer for major conferences.

• Semantics
• Pragmatics
• Syntax
• +2 more

Kyle Rawlins is a Professor in the Department of Cognitive Science at Johns Hopkins University (JHU), where he heads the Semantics Lab located in Krieger Hall 105. His research focuses on semantics, pragmatics, syntax, and their intersections, often collaborating with labs like the Van Durme Lab in Computer Science. The Semantics Lab facilitates weekly meetings for discussing research, structured as seminars or traditional lab sessions, and co-organizes projects such as the Decomp Project and MegaAttitude Project . Current lab members include PhD students Karl Mulligan and Natalia Talmina. Notable collaborators: Ilaria Frana (University of Enna), Barbara Landau (JHU), Ben Van Durme (JHU CS), and Aaron Steven White (Rochester). His research spans topics like: Questions Under Discussion (QUD) frameworks Evidentiality and bias in language Multimodal semantic parsing Decompositional semantics Pragmatic particles in Italian Recent publications analyze polar questions, neural network representations, and discourse particles. The lab maintains a mailing list for event announcements, emphasizing collaborative research in semantics and computational linguistics.

David BrownView profile

Professor

• Accessibility
• Virtual Reality
• Multimodal Affect Recognition
• +5 more

David Brown is a Professor in Interactive Systems for Social Inclusion at Nottingham Trent University's School of Science & Technology, Department of Computer Science. He serves as Director of the Computing and Informatics Research Centre (CIRC) and Research Group Leader for the Interactive Systems Research Group (ISRG). Director, Computing and Informatics Research Centre Research Group Leader, Interactive Systems Research Group Governor, Oak Field School for students with severe learning disabilities Conference Chair, International Conference on Disability, Virtual Reality and Associated Technology (ICDVRAT21) Associate Editor, Frontiers: Virtual Reality in Medicine Professor Brown's research focuses on developing inclusive technologies for people with disabilities. His work spans accessibility for students with learning, physical and sensory impairments; virtual reality applications for rehabilitation; multimodal affect recognition systems; social robotics for education; accessible visual programming toolkits; and serious games for developing physical and cognitive skills. His research is characterized by strong interdisciplinary collaboration and practical application in educational and healthcare settings. His recent publications demonstrate a consistent focus on applying emerging technologies like virtual reality, machine learning, and social robotics to address real-world challenges in accessibility and inclusion. The research shows a clear trajectory toward increasingly sophisticated multimodal systems that can detect user states and adapt accordingly, with applications ranging from autism support to mental health interventions. Extensive EU-funded research projects including Horizon 2020, Erasmus+, and EPSRC grants Notable projects: DIVERSIA, MaTHiSiS, Pathway, AI-TOP, EDUROB, No One Left Behind, Real Life, RISE Professor Brown has supervised numerous PhD students and collaborates extensively with international partners across Europe and Asia. His work bridges computer science, psychology, education, and healthcare to create technologies that promote social inclusion and improve quality of life for people with disabilities.

Wai Kiong Oswald ChongView profile

Associate Professor

• Artificial Intelligence
• Engineering Systems
• Optimization
• +10 more

Dr. Wai Kiong Oswald Chong is an Associate Professor at Arizona State University's School of Sustainable Engineering and the Built Environment, with a dual affiliation as Senior Global Futures Scientist at the Global Futures Scientists and Scholars program. He holds a PhD in Civil Engineering from the University of Texas-Austin, MSc and BSc in Building from the National University of Singapore, and focuses on integrating artificial intelligence with sustainable engineering systems. PhD (2005): Civil Engineering, University of Texas-Austin MSc (1999) & BSc (1997): National University of Singapore His research bridges lunar construction with Earth-bound sustainable systems, covering topics like: Space habitat modularization Resource circularity systems AI-enhanced building codes Climate-resilient infrastructure Advanced energy modeling Construction supply chain optimization Publications demonstrate consistent focus on: Semiconductor facility HVAC optimization Building energy consumption anomalies Life cycle assessment frameworks Construction risk management Deconstruction and material reuse AI-driven system modeling Current research projects include: Lunar MVI (Moon Village Initiative) Semiconductor fab design optimization Human-AI knowledge interfaces Thermal insulation systems for extreme environments Smart grid energy modeling