جستجوی استاد راهنمای دانشگاهی

Rick de Haartمشاهده پروفایل

پژوهشگر

• Posttraumatic Stress Disorder
• Virtual Reality Exposure Treatment
• Neuropsychological Functioning
• +۳ مورد دیگر

Rick de Haart is a PhD researcher at the University of Groningen's Faculty of Behavioural and Social Sciences, Department of Clinical Psychology and Experimental Psychopathology, while simultaneously working as a healthcare psychologist at GGZ Drenthe Mental Health Institute's Trauma Center specializing in PTSD treatment for uniformed personnel. His educational background includes: BSc in Psychology from University of Groningen (2012-2015) MSc in Clinical Psychology from University of Groningen (2015-2016) Postmaster Education as Healthcare Psychologist from PPO Educational Institute (2018-2020) De Haart's research focuses on trauma-related conditions with particular emphasis on virtual reality exposure therapies, neuropsychological aspects of PTSD, and substance use disorders in forensic settings. His work bridges clinical practice and academic research through multicenter collaborations with institutions like GGZ Drenthe. His publications reveal a consistent trajectory in trauma research, examining avoidance learning in firefighters, neuropsychological factors in PTSD treatment, and the role of impulsivity in substance use disorders among forensic patients. His methodology often combines clinical observation with experimental approaches. His recognition includes: Grant: Focus on psychotrauma: improving psychotrauma care at Espria (February 2021) De Haart actively contributes to multiple PTSD research initiatives while maintaining clinical practice. His work demonstrates strong integration between his therapeutic experience and research objectives, particularly in developing motion-assisted virtual reality treatments. He collaborates with leading researchers including Prof. Eric Vermetten and Dr. Mirjam J.J. Lommen. His research network centers on the Trauma Center at GGZ Drenthe and the University of Groningen's clinical psychology department, with emerging focus on 3MDR (Multi-Modal Motion-Assisted Memory Desensitization and Reprocessing) techniques combining physical movement with virtual reality exposure therapy.

Yada Zhuمشاهده پروفایل

پژوهشگر

• Machine Learning
• Time Series Analysis
• Graph Learning
• +۵ مورد دیگر

Dr. Yada Zhu is a Researcher at IBM T. J. Watson Research Center , affiliated with the Future of Computing – Finance Research team. She leads initiatives in foundational AI/ML capabilities for financial decision-making and risk management. Research Focus: High-dimensional time series analysis, heterogeneous data modeling, graph learning, and statistical applications in finance, e-commerce, and smart energy. Grants: Principal Investigator for MIT-IBM AI Lab projects funded by Refinitiv and a major financial institution. Leadership: Technical lead in analytics projects contributing to commercialized IBM products. Scientific Achievements: IBM Research Division Outstanding Technology Achievement Award High Valuable Innovation Award Her publications span AI/ML, statistical modeling, and financial applications, with recent work on graph transformers, time-series unification, and zero-shot tool optimization for LLM agents. She serves on editorial boards for statistics journals and as Senior Program Committee member for AAAI.

Mo Lotfollahiمشاهده پروفایل

دانشیار مدعو

• Machine Learning
• Single-Cell Genomics
• Computational Biology
• +۳ مورد دیگر

Dr. Mo Lotfollahi is a Group Leader in the Cellular Genomics Programme at the Wellcome Sanger Institute and an Adjunct Associate Professor at the Cambridge Centre for AI in Medicine , University of Cambridge. His research focuses on leveraging Artificial Intelligence and Single-Cell Genomics to engineer cells and model their responses to diseases and perturbations. PhD in Machine Learning & Computational Biology from Technical University of Munich Research Interests include: Biologically informed deep learning Single-cell multi-omic data integration Perturbation response modeling AI-assisted drug discovery Spatial transcriptomics Scientific Awards : Bayer Foundation Early Excellence in Science Award for developing ML algorithms in single-cell omics MDSI Best Paper of the Year Award for transfer learning in single-cell data mapping Lab & Collaborations : The Artificial Intelligence for Cell Engineering (AICE) Lab at Cambridge includes 10+ PhD students and postdocs working on interdisciplinary projects. He collaborates extensively with Relation Therapeutics , Meta AI , and Helmholtz Munich .

Joseph D. Romanoمشاهده پروفایل

استادیار

• translational bioinformatics
• environmental toxicology
• biomedical artificial intelligence

Joseph D. Romano serves as Assistant Professor of Informatics and Pharmacology within the Department of Biostatistics, Epidemiology and Informatics, leading a research program focused on artificial intelligence applications for predicting chemical toxicity and advancing drug discovery. His core research interests include: Translational Bioinformatics for biomedical data integration Environmental Toxicology mechanisms and prediction Biomedical Artificial Intelligence with emphasis on interpretability Romano's laboratory develops computational frameworks leveraging knowledge graphs, graph neural networks, and automated machine learning to model chemical-biological interactions. Current projects address toxicology prediction, drug-target affinity modeling, and venom-derived therapeutic discovery through multi-omics integration and semantic knowledge representation. Analysis of his 2022-2025 publications reveals consistent innovation in applying graph-based machine learning to toxicology and pharmacology challenges, with particular strengths in knowledge-guided deep learning for interpretable toxicity prediction and federated approaches for clinical data analysis. His research group maintains active development of open-source tools including ComptoxAI for automated predictive toxicology and VenomKB for computational toxinology, supporting collaborative advances in environmental health and drug safety assessment.

Hersh Sagreiyaمشاهده پروفایل

استادیار

• Artificial Intelligence in Radiology
• Medical Informatics
• Diagnostic Radiology
• +۳ مورد دیگر

Hersh Sagreiya is an Assistant Professor of Radiology at the University of Pennsylvania, board-certified in both Diagnostic Radiology and Clinical Informatics. His academic position focuses on advancing AI integration within radiological practice to enhance diagnostic accuracy and clinical workflow efficiency through cutting-edge machine learning applications. Dr. Sagreiya's research centers on the development and implementation of artificial intelligence systems for medical imaging analysis. His primary interests include deep learning for abdominal MRI and CT characterization, vision-language models for 3D medical representations, and natural language processing for radiology report generation. He also investigates critical legal and ethical dimensions of AI adoption in radiology, particularly regarding malpractice risk mitigation and regulatory compliance frameworks. His work consistently addresses real-world clinical challenges in imaging order optimization, diagnostic support, and treatment monitoring through novel algorithmic approaches. Analysis of his 15 most recent publications (2024-2025) reveals three dominant research trajectories: (1) AI-driven clinical decision support systems aligned with radiological guidelines, (2) automated integration of AI results into reporting workflows using standardized data elements, and (3) development of foundational models for medical image analysis. His publications demonstrate exceptional methodological diversity spanning supervised, unsupervised, and generative AI techniques applied across abdominal imaging, pulmonary analysis, and oncology contexts. The work consistently emphasizes practical clinical implementation, safety considerations, and regulatory adherence in AI deployment. No scientific awards, grant funding information, or student mentorship activities were documented in the source material. Similarly, there is no available information regarding laboratory affiliations, research teams, or collaborative networks led by Dr. Sagreiya. His publication record indicates sole or primary authorship on multiple high-impact technical implementations suggesting strong independent research leadership within the radiology informatics domain.

Marco Cuturiمشاهده پروفایل

استاد

• Machine Learning
• Optimal Transport
• Statistics
• +۳ مورد دیگر

Marco Cuturi is a Professor of Statistics at ENSAE (École Nationale de la Statistique et de l'Administration Économique), part of Institut Polytechnique de Paris, where he has been affiliated since 2016, working part-time since 2018. He is also a CREST Permanent Member and a Research Scientist at Apple ML Research in Paris, having previously worked at Google Brain from 2018 to 2022. His academic journey includes positions as an Associate Professor at Kyoto University's Graduate School of Informatics (2010-2016), a lecturer at Princeton University's ORFE department (2009-2010), and postdoctoral research at the Institute of Statistical Mathematics in Tokyo (2005-2007). Dr. Cuturi earned his Ph.D. from École des Mines de Paris in 2005 and holds a master's degree from ENS Cachan, having graduated from ENSAE in 2001. His research centers on the application of optimal transport theory to machine learning and statistics, with significant contributions to computational optimal transport, kernel methods, and time series analysis. His influential work includes the development of the Sinkhorn distance for fast computation of optimal transport, soft-DTW for time series alignment, and various applications of optimal transport in machine learning. His research program explores how geometric structures can be leveraged in statistical learning, with recent work focusing on entropic optimal transport solvers, Gromov-Wasserstein distances, and applications to single-cell biology and multimodal learning. Dr. Cuturi's publications reveal a consistent trajectory of developing theoretically grounded yet computationally efficient methods that bridge optimal transport theory with practical machine learning applications across diverse domains including computer vision, natural language processing, and computational biology. Among his professional recognitions is a Notable Paper Award at AISTATS 2020 for his work on regularization in optimal transport. He serves as an Associate Editor for JMLR and has held senior area chair positions at NeurIPS and ICML. Dr. Cuturi has organized major machine learning events including the Machine Learning Summer School in Kyoto (2012, 2015) and co-organized workshops on optimal transport at major conferences. Dr. Cuturi has supervised numerous doctoral students including Nina Vesseron, Théo Uscidda, Charlotte Bunne, and Othmane Sebbouh, with research spanning optimal transport applications in generative modeling, representation learning, and biological data analysis. His current research group at ENSAE and Apple continues to push the boundaries of optimal transport methodology and its applications to cutting-edge machine learning problems.

Slobodan Ilicمشاهده پروفایل

استاد مدعو

• 3D Computer Vision
• Computer Vision
• Medical Imaging
• +۳ مورد دیگر

PD Dr. Slobodan Ilic is a Senior Key Expert Research Scientist at Siemens AG (since 2014) and an Adjunct Professor at the Chair of Computer Science Applications in Medicine , Technical University of Munich (TUM). His work bridges 3D computer vision and medical imaging , with a focus on real-time object detection, deformable surface modeling, and depth data analysis. Current Roles: Adjunct Professor at TUM, Senior Key Expert at Siemens Research Themes: 6D pose estimation, non-rigid 3D reconstruction, RGB-D data processing Labs: CAMP Chair (TUM), Siemens AG Research Division His recent work explores LLM-driven control systems , semantic-aware 3D generation , and cross-modal medical imaging . Articles highlight advancements in point cloud registration , rotation-invariant descriptors , and hyperspectral calibration . While no specific awards are documented in the provided text, his team at Siemens/TUM advises PhD candidates in 3D vision for robotics and medical applications .

• Robotic Ultrasound
• Implicit Neural Representations
• Medical Image Segmentation
• +۲ مورد دیگر

Dr. Mohammad Farid Azampour is a Researcher at the Chair of Computer Aided Medical Procedures within the Department of Informatics at the Technical University of Munich (TUM). His work bridges robotics, medical imaging, and deep learning, with affiliations extending to the German Heart Center Munich (DHM) and collaborations across TUM's NARVIS and IFL labs. Research Focus: Azampour specializes in physics-inspired AI for healthcare, including robotic ultrasound systems, neural representations for medical image reconstruction, and anatomy-aware deep learning. His projects emphasize real-time surgical guidance, weakly supervised learning for clinical data, and multi-modal registration (e.g., CT-US/MRI fusion). Teaching & Supervision: He coordinates courses such as Medical Augmented Reality , Advanced 3D Computer Vision , and Innovation Generation in Healthcare . Azampour actively mentors students through IDPs, theses, and practical projects in computer-aided medical procedures. Publications: His recent work (2020-2025) demonstrates consistent focus on enhancing medical imaging through neural networks (e.g., Ultra-NeRF for ultrasound, diffusion models for simulation) and improving surgical robotics via real-time navigation/registration. Key themes include reducing annotation dependency, leveraging physics-based models, and enabling intraoperative decision-making.

Yang Liuمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Pattern Recognition
• +۸ مورد دیگر

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.

• Medical imaging physics
• Positron emission tomography (PET)
• Machine learning for image reconstruction
• +۴ مورد دیگر

Jinsong Ouyang, PhD is an Associate Professor on term in the Department of Radiology & Biomedical Imaging at Yale School of Medicine, with additional affiliations to the Bioimaging Sciences division and the Center for Molecular Imaging Technology and Translation (CMITT). He earned his doctorate in Physics from the University of Colorado in 1992 and has since focused on computational methods that advance quantitative PET imaging. Education: PhD, Physics – University of Colorado, 1992 Research interests: Ouyang’s work integrates medical physics and machine learning to solve core PET imaging challenges—denoising low-count data, correcting for patient motion, and estimating kinetic parameters from dynamic scans. His recent projects leverage diffusion probabilistic models, transformer architectures, and adversarial domain generalization to enhance image quality and quantitative accuracy across varied scanner types and patient cohorts. A parallel stream of research addresses simultaneous PET/MR motion correction using subspace-based real-time MR navigators and synthesizes missing MR contrasts via disentangled multimodal translation networks. Publication trends 2022-2025: The lab’s output is heavily concentrated in IEEE and Society of Nuclear Medicine venues, with 15 first- or co-authored papers since 2023. Recurring themes are deep-learning denoisers tailored to PET count statistics, Bayesian posterior estimation for kinetic modeling, and motion-robust reconstruction algorithms. Collaborative studies with clinicians demonstrate translation to tau-PET neurology and oncology receptor occupancy mapping. Scientific awards: None listed in the supplied pages. Advising & grants: While specific trainee names are not excerpted, Ouyang is embedded within Yale’s residency and fellowship training programs and is frequently senior author on conference abstracts alongside early-career researchers, indicating active mentorship. Grant support is implied by continuous publication, core facility utilization, and participation in multi-investigator programs such as CMITT, but direct funding statements are absent from the text. Labs & teams: He is part of the PET Core within the Yale Biomedical Imaging Institute and contributes to the Mental Health PET Radioligand Development (MHPRD) program, indicating access to cyclotron, radiochemistry, and simultaneous PET/MR scanners for translational studies.

• Computer Vision
• Machine Learning
• Visual Reasoning
• +۵ مورد دیگر

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.

• Computer Vision
• Pattern Recognition
• Human Behavior Understanding
• +۵ مورد دیگر

Anis KACEM is a Researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the University of Luxembourg, part of the Signal Processing and Satellite Communications (SIGCOM) research group. His work focuses on Computer Vision and Pattern Recognition, particularly in Human Behavior Understanding from visual data. He received his PhD in Computer Science from the University of Lille (France) in 2018. Research interests include advanced topics such as Earth Observation via multi-modal autoencoders, domain adaptation for image classification, vulnerability-aware deepfake detection, and CAD system reverse engineering. His contributions span neural network pruning, 3D shape analysis, and generative models for medical imaging. Publications highlight innovations in spatio-temporal learning for deepfake detection, hybrid attention mechanisms for pedestrian detection, and tool-augmented CAD task solvers. His work bridges theoretical advances with practical applications in autonomous systems and space technology. Notably, he has contributed to challenges like the SHARP 2023 Challenge on CAD history recovery and developed frameworks like Picasso for CAD sketch inference using self-supervised learning.

Guoying Zhaoمشاهده پروفایل

دانشگاهی

• Computer Vision
• Machine Learning
• Affective Computing
• +۵ مورد دیگر

Guoying Zhao is an Academy Professor at the University of Oulu, Finland, and a visiting professor at Aalto University and Stanford University. She has held a tenured professorship since 2017 at the Center for Machine Vision and Signal Analysis (CMVS), progressing from Senior Researcher (2005-2007) to Academy Postdoctoral Researcher (2008-2011), Academy Research Fellow (2011-2017), and Associate Professor (2014-2017) before her full professorship. Current affiliations: Academy Professor (University of Oulu), Visiting Professor (Aalto University, Stanford University) Academic background: PhD in Computer Science (Chinese Academy of Sciences, 2005) Her research spans multiple domains in computer science: Computer Vision : Facial expression/micro-expression recognition, human motion analysis Signal Analysis : Remote physiological signal measurement Machine Learning : Multi-modal learning frameworks With over 340 publications and 31,900 Google Scholar citations (h-index 87), her work has been featured in MIT Technology Review and Finnish national media. She holds editorial leadership in journals like Computer Vision and Image Understanding and chairs major conferences including ACII 2025. Scientific awards include: IAPR Maria Petrou Prize (2024) Multiple fellowships (IEEE, IAPR, ELLIS, AAIA) Membership in Finnish Academy of Sciences and Letters (2022), Academia Europaea (2023) Her students and researchers have secured prestigious positions and grants such as Academy of Finland Research Fellowships, Nokia Scholarships, and Tauno Tönning Research Funding.

Giorgos Bouritsasمشاهده پروفایل

استادیار مدعو

• Graph neural networks
• Geometric deep learning
• Weight space learning
• +۴ مورد دیگر

Giorgos Bouritsas is a machine learning scientist serving as a postdoctoral fellow at the Archimedes AI unit and the University of Athens, while also holding an adjunct lecturer position at NCSR Demokritos. He received his PhD in computer science from Imperial College London and his MEng in electrical and computer engineering from the National Technical University of Athens. His academic journey includes research stints at Google DeepMind, École Polytechnique Fédérale de Lausanne, KU Leuven, NCSR Demokritos, and Universitat Politècnica de Catalunya. Dr. Bouritsas specializes in geometric and graph deep learning, with research focusing on neural network architectures for geometric data, weight space learning, and applications in biology and chemistry. His work bridges theoretical analysis with practical implementations, particularly in developing methodologies for complex networks, physical systems, 3D objects, and neural network weight spaces. He has made significant contributions to graph neural networks, including novel approaches to improving expressivity through subgraph isomorphism counting and developing spiral convolutional networks for 3D shape representation. His recent publications demonstrate a strong trajectory in geometric deep learning, with papers accepted at top conferences including NeurIPS (with an oral presentation in 2024), ICML, CVPR, and ICCV. His 2024 workshop proposal on Neural Network Weights as a New Data Modality was accepted for ICLR 2025. He regularly serves as a reviewer for major machine learning conferences, earning outstanding reviewer distinctions at NeurIPS, ICML, and LoG. Outstanding Reviewer at NeurIPS 2021, 2024 Outstanding Reviewer at ICML 2022, 2024 Outstanding Reviewer at LoG 2022, 2023 Dr. Bouritsas teaches Deep Learning in the MSc in AI program at NCSR Demokritos, demonstrating his commitment to academic service and education. His research has practical applications spanning cryo-EM image analysis, 3D facial recognition for medical diagnostics, and theoretical foundations of contrastive learning frameworks.

• Machine Learning
• Parallel and Distributed Computing
• Graph Theory
• +۶ مورد دیگر

Giuseppe Agapito is an Associate Professor and senior research scientist at Magna Graecia University of Catanzaro, working within the Department of Law, Economics and Sociology. His academic profile spans both teaching responsibilities and active research in computational fields. His research focuses on computational intelligence techniques applied to complex real-world problems, particularly in machine learning, parallel and distributed computing, and graph theory. His work demonstrates strong interdisciplinary applications, especially in biology and omics sciences where he develops methods to leverage the vast amounts of data being produced. Agapito has published over 100 papers in international journals and conference proceedings. His recent publications show a strong focus on graph-based machine learning approaches, sentiment analysis techniques, and bioinformatics applications. His research trajectory indicates a progression from bioinformatics applications toward more general machine learning frameworks with applications across multiple domains. As an active member of the academic community, he serves as a reviewer for scientific journals and participates as chair and program committee member for national and international conferences. His editorial work for PeerJ Computer Science demonstrates his standing in the academic community. His teaching responsibilities include courses on Computer Science and Data Analysis within the Department of Law, Economics and Sociology, suggesting an interdisciplinary approach that applies computational methods to social sciences domains.