جست‌وجوی استاد و پژوهشگر

• Urban and Regional Planning
• Sustainable Mobility
• Post-growth Planning
• +۵ مورد دیگر

Dr. Kim Carlotta Von Schönfeld is a Research Fellow at the Department of Civil Engineering and Environmental Sciences, Western Norway University of Applied Sciences. Currently holding a Marie Skłodowska-Curie Post-Doctoral Fellowship , she leads the MobileWorlds project exploring mobility as a socio-cultural phenomenon through transdisciplinary lenses. Collaborating with Wendy Tan Fieldwork in Bergen (Norway) and Porto (Portugal) Teaching courses: MOA256 (Scientific Theory and Methods) and MOA270 (Urban Design for Sustainable Mobility) Her research bridges urban planning with: art-based methods, post-growth economics, children's mobility, and water governance. Key areas include transportation , proximity planning , and collaborative urbanism . Recent publications address: Artistic engagement with youth in coastal planning Critiques of car-centric mobility paradigms Water heritage as socio-cultural practice Post-growth urban principles Scientific awards include the prestigious Marie Skłodowska-Curie Fellowship . She contributes to the New European Bauhaus project Minante and advances participatory water governance frameworks.

• Medical Physics
• Biomedical Imaging
• Neurosciences
• +۴ مورد دیگر

Dr. Oun Al-Iedani is a Research Fellow at the University of Newcastle within the School of Biomedical Sciences and Pharmacy . His work focuses on neuroimaging techniques for studying Multiple Sclerosis (MS) and other neurological conditions. PhD in Magnetic Resonance in Medicine (University of Newcastle, 2020) MSc in Science (University of Basrah, Iraq) His research interests span Medical Physics , Biomedical Imaging , and Neurosciences . He specializes in MRI/MRS techniques like FAST MRSI , Diffusion Imaging , and Quantitative Susceptibility Mapping to analyze neurometabolic profiles and disease progression in MS patients. Recent publications (2023-2024) demonstrate his expertise in multimodal neuroimaging for predicting cognitive decline, analyzing white matter networks, and characterizing lesion biochemistry in MS. His work combines advanced MRI methodologies with machine learning for clinical applications. He has secured significant grant funding totaling $699,325 from organizations like Multiple Sclerosis Australia and Hunter Medical Research Institute , including a $225,000 Postdoctoral Fellowship (2025-2027) for personalized MS progression prediction using multi-modal MRI. As a supervisor, he guides PhD and Honours students in projects related to brain tumor imaging , neurochemical analysis , and diffusion tensor imaging applications.

Corentin Dumeryمشاهده پروفایل

پژوهشگر

• Computer Vision
• Computer Graphics
• 3D Reconstruction
• +۴ مورد دیگر

Corentin Dumery is a Doctoral Assistant at École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the Computer Vision Laboratory (CVLAB) and Machine Learning and Optimization Laboratory (MLO) within the School of Computer and Communication Sciences . His research bridges Computer Vision and Computer Graphics , focusing on 3D scene reconstruction, garment modeling, and neural rendering techniques. Previously, he interned at Meta Redmond , worked at CEA Paris-Saclay on polycube mapping, and was a visiting researcher at ETH Zurich under Prof. Olga Sorkine-Hornung . Corentin holds dual MSc degrees in Computer Science from National University of Singapore (NUS) and Télécom Paris . His work emphasizes 3D content creation for AR/VR Diffusion models for garment reconstruction Neural radiance field optimization Polycube mapping for hexahedral meshing His recent publications (2022–2025) span top venues like SIGGRAPH , ICCV , and CVPR , addressing challenges in 3D Gaussian splatting, view-consistent NeRF training, and single-view garment recovery. He also contributes to academic service as an Outstanding Reviewer at CVPR25 and co-organizes workshops like OpenSUN3D . At EPFL, he serves as Head Teaching Assistant for courses CS433 Machine Learning (2023–2024) and CS442 Computer Vision (2023–2024). Additionally, he is the VP/Treasurer of EPIC , EPFL's computer science PhD association.

Sherrie Wangمشاهده پروفایل

استاد

• Earth Observation
• Machine Learning
• Sustainable Development
• +۵ مورد دیگر

Sherrie Wang is a Professor and Principal Investigator at Massachusetts Institute of Technology , leading the Earth Intelligence Lab . Her research focuses on developing machine learning methods to monitor planetary systems and advance sustainable development, particularly in low- and middle-income regions where ground truth data is scarce. She obtained her PhD in Computational and Mathematical Engineering from Stanford University in 2021 under David Lobell , following a Ciriacy-Wantrup Postdoctoral Fellowship at UC Berkeley with Solomon Hsiang and the Global Policy Lab . Her academic work bridges Earth observation , machine learning , and global sustainability , with a focus on agricultural management and climate change mitigation . Recent publications highlight her leadership in benchmarking vision-language models for Earth data ( CVPRW 2024 ), mapping Clean Water Act-regulated water bodies ( Science 2024 ), and advancing weakly supervised cropland segmentation techniques ( Remote Sensing 2020 ). Her work frequently utilizes satellite imagery , LiDAR , and crowdsourced data to address environmental challenges. Scientific recognition includes the Best Paper Award at ICLR ML4RS Workshop (2024) and the Sloan Research Fellowship . She collaborates with institutions like USDA , NSF , and Stanford Human-Centered AI , with funding from USAID and the Global Innovation Fund . Advising and collaboration : She mentors PhD students like Chenhui Zhang and Jordi Laguarta , with notable partnerships at the Stanford Sustainability and AI Lab . Her team's work mapping Thai agricultural systems ( MIT News 2024 ) exemplifies her commitment to real-world impact.

Maximilian Rettingerمشاهده پروفایل

پژوهشگر

• Extended Reality
• Human-Computer Interaction
• Computer-based Training
• +۲ مورد دیگر

Maximilian Rettinger is a researcher at the Chair of Human-Machine Communication within the Technical University of Munich (TUM), holding an M.Sc. degree. He actively contributes to research and teaching in Extended Reality (AR/VR/MR), Human-Computer Interaction, and computer-based training systems, with publications spanning 2021-2025. His research centers on VR/AR applications for high-stakes training domains, particularly healthcare and robotics. Key investigations include cognitive load dynamics in complex VR environments, multi-user collaborative systems for medical device operation, tangible interaction techniques, and visual complexity impacts on learning. His work bridges human factors with practical training solutions for critical scenarios like hemodialysis, explosive ordnance disposal, and pandemic response. Analysis of his 12 recent publications reveals consistent focus on medical VR training efficacy, with emerging trends in multi-user expert guidance systems and cognitive load optimization. His 2024-2025 work shows increasing specialization in robotic programming interfaces and visual perception metrics within XR. Rettinger participates in two major funded projects: the DAAD-supported Sino-German Graduate College (CDHK) involving five universities (2022-2028), and a BMWi/ZIM-funded VR training system for explosive ordnance disposal with industry partners EMC Kampfmittelbeseitigungs GmbH and Golden West Humanitarian Foundation (2019-2021). As part of Prof. Hemmert's research chair, he contributes to TUM's human-machine interaction initiatives through conference presentations at IEEE ICRA, VR, and ISMAR, while teaching courses including Ergonomic User Interface Design and Human-Machine Communication since 2019.

Ellen Zhongمشاهده پروفایل

استادیار

• Machine Learning
• Computational Biology
• Structural Biology
• +۷ مورد دیگر

Ellen Zhong is an Assistant Professor of Computer Science at Princeton University, leading the E.Z. Lab for Molecular Machine Learning. Her research bridges AI and structural biology, focusing on cryo-EM/cryo-ET data analysis for protein structure-dynamics characterization. Ph.D. in Computer Science from MIT (2022) Former DeepMind AlphaFold team member Ex-D. E. Shaw Research in drug discovery Key research domains include 3D Reconstruction , Protein Dynamics , Cryo-EM Heterogeneity , and Interpretability in Biomedical AI . She develops open-source tools like cryoDRGN-AI and cryoDRGN for high-throughput structural biology applications. Recent publications demonstrate AI-cryo-EM synergy (2025) and heterogeneous dataset benchmarking (NeurIPS 2024). Her work has received Princeton's Junior Faculty Award (2025) for excellence in research and teaching. Scientific Recognition : Junior Faculty Award, Princeton (2025) Notable Collaborations : AlphaFold3 development (Nature 2024) CryoDRGN-AI (Nature Methods 2025) Lab members include computational biologists and machine learning researchers working on generative modeling and biomedical imaging . Current projects span in situ proteomics , diffusion-based structural sampling , and multimodal biomedical data fusion .

• Machine Learning
• Autonomous Driving
• Natural Language Processing
• +۴ مورد دیگر

Tsvetomila Mihaylova is a Postdoctoral Researcher in the Department of Computer Science at Aalto University , specializing in machine learning and human-robot interaction. Her work bridges theoretical advancements in neural networks with practical applications in autonomous systems. Fields of Interest : Latent structure learning, autonomous driving, visual-language models, and natural language processing Email : tsvetomila.mihaylova@aalto.fi Research Focus : • Latent structure modeling using discrete and undirected neural networks • Autonomous driving safety through conflict simulation and trajectory prediction • Cross-modal integration in robotic vision-language systems Publication Trends : Recent work explores 2025 in structured neural architectures for autonomous vehicles and 2024 in human-robot interaction quality evaluation, with foundational contributions to natural language processing fact-checking systems since 2019 .

• Urban Planning
• Sustainable Development
• Flood Risk Management
• +۳ مورد دیگر

Susa Eräranta serves as Professor of Practice at Aalto University's Department of Built Environment, conducting interdisciplinary research at the intersection of urban planning, resilience, and sustainability with a Nordic focus. Her work critically examines governance structures and educational frameworks through Finnish case studies. Research interests center on: Urban Resilience and Sustainable Development Flood Risk Governance Networks More-than-human Planning Education Nordic Planning Theory Evolution Digital Participation in Urban Processes Recent publications (2022-2025) reveal a methodological emphasis on social network analysis applied to flood risk management, alongside growing exploration of ecological multicrisis responses and post-pandemic educational transformation. Key trends include the integration of non-human perspectives in planning theory, comparative analysis of Nordic governance models, and adaptive strategies for mixed-modality planning education in crisis contexts.

Amr Gomaa Mohamed Elhady Gomaaمشاهده پروفایل

پژوهشگر

• Human Computer Interaction
• Multi-modal Interaction
• Applied Machine Learning
• +۴ مورد دیگر

Amr Gomaa Mohamed Elhady Gomaa is a Doctoral Researcher at the German Research Center for Artificial Intelligence (DFKI) within the Cognitive Assistance Systems department. His work focuses on human-computer interaction, adaptive interfaces, and machine learning applications in autonomous systems. Human-Computer Interaction Multi-modal Interaction Applied Machine Learning Computer Vision Reinforcement Learning Adaptive Interfaces Amr's research combines machine learning with multimodal interaction to develop adaptive systems for autonomous vehicles and robotics. His recent publications address gesture recognition, object referencing, and workload adaptation in driving scenarios. He has contributed to 16 publications since 2020, with a focus on hybrid neuro-symbolic learning, synthetic gesture generation, and human-in-the-loop machine teaching. His work has been presented at IEEE IV, UIST, AutomotiveUI, and ICMI conferences. Amr serves on program committees (AutomotiveUI WIP 2023, HITLAML Workshop 2023) and reviews for journals/conferences including CHI, IEEE VR, and IMWUT. He is part of the Ubiquitous Media Technology Lab (UMTL) at Saarland Informatics Campus.

• Gaze-based Interaction
• Eye Tracking
• Mobile Eye Tracking
• +۵ مورد دیگر

Dr. Michael Johannes Barz is an Associated Member at the Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) in Saarbrücken, Germany, where he conducts research within the Ubiquitous Media Technology Lab (UMTL). His work bridges human-computer interaction with artificial intelligence, focusing on gaze-based interaction, eye tracking, and interactive machine learning systems. Dr. Barz has established himself as a significant contributor to the field with over 50 publications spanning from 2015 to 2024. His research interests center on gaze-based interaction , mobile eye tracking , user modeling , and interactive machine learning , with recent work expanding into cognitive load measurement using digital pen technology and mixed reality applications for industrial training. Dr. Barz has developed influential tools like IMETA for eye tracking annotation and pEncode for visualizing pen signals, demonstrating his commitment to creating practical research methodologies. Dr. Barz's publication record shows consistent contributions to top-tier conferences including ACM ETRA, IEEE VR, and the International Conference on Intelligent User Interfaces. His 2022-2024 work reveals increasing focus on making AI systems more transparent and interactive, with publications on explaining machine learning model explanations and interactive deep learning frameworks. His research often involves interdisciplinary collaboration across computer science, cognitive science, and educational technology. Special recognition for an outstanding review at ACM ETRA 2020 Active conference reviewer for IJCAI, ACM IUI, KI, ACM ETRA, and IEEE VR Journal reviewer for Journal of Eye Movement Research Dr. Barz has contributed to teaching as an assistant for 'Intelligent User Interfaces' at TU Kaiserslautern and 'Artificial Intelligence' at Saarland University. His current research suggests strong engagement with both theoretical advancements in interactive machine learning and practical applications in educational and industrial contexts, particularly through the MASTER-XR project for mixed reality manufacturing training. The Ubiquitous Media Technology Lab provides the collaborative environment where Dr. Barz develops his innovative approaches to human-AI interaction.

Yongsoo Kimمشاهده پروفایل

استاد

• Neuroscience
• Neurodevelopment
• Aging
• +۵ مورد دیگر

Yongsoo Kim, PhD, is a Professor in the Department of Neuroscience and Experimental Therapeutics at Penn State College of Medicine, where he also directs the Neuroscience Graduate Program. His research focuses on understanding brain architecture through advanced 3D mapping techniques. Dr. Kim holds a PhD from Northwestern University and completed postdoctoral training at Cold Spring Harbor Laboratory. Research Focus The Kim Lab investigates: Cellular organization in neurodevelopmental disorders (autism) and neurodegenerative conditions (Alzheimer's) Oxytocin system functions in social behavior regulation Development of neuroinformatics tools including digital brain atlases (DevCCF) Using murine models, the lab integrates high-resolution mapping with computational approaches to study brain-wide cellular changes across lifespan. Publication Trends Recent works (2024-2025) demonstrate a strong focus on: Brain asymmetry and neuroanatomical mapping techniques Oxytocin transport mechanisms Aging-related cerebrovascular changes Computational integration of multi-scale brain data Honors Biobehavioral Research Award (NICHD, 2018) NARSAD Young Investigator Award (2013) Junior Faculty Research Scholar Award (2017) Research Leadership Dr. Kim currently leads multiple NIH-funded projects including studies on: Oxytocinergic system development (NICHD R01) Cell-type vulnerability in Alzheimer's disease (NIA R01) Cerebrovascular mechanisms in cognitive impairment (NINDS R01) He directs the Neuroscience Graduate Program and supervises interdisciplinary collaborations across Penn State institutes.

Andriy I Bandosمشاهده پروفایل

دانشیار

• Biostatistics
• Medical Imaging
• Diagnostic Tests Evaluation
• +۳ مورد دیگر

Andriy I Bandos is an Associate Professor and Director of the MS Program in the Department of Biostatistics and Health Data Science at the University of Pittsburgh , with a joint appointment in Radiology . His work bridges biostatistical methodology and medical imaging research , focusing on improving diagnostic accuracy and developing statistical frameworks for evaluating imaging technologies. Education: 2000: MS in Mathematics, V.N. Karazin Kharkiv National University (Ukraine) 2005: PhD in Biostatistics, University of Pittsburgh Research Interests center on receiver operating characteristic (ROC) analysis , free-response ROC (FROC) methodology, and statistical evaluation of diagnostic tools . His contributions include advancing techniques for assessing detection-localization accuracy in medical imaging, particularly for breast cancer screening and radiology applications . Notable Publications focus on digital breast tomosynthesis , contrast-enhanced mammography , and ROC/FROC analysis , with recent work addressing COVID-19 severity quantification and AI automation in medical imaging . He has developed statistical tests for comparing diagnostic systems and analyzing observer performance variability. Professional Service includes serving as Associate Editor for the Biometrical Journal , peer-review activities for journals like Biometrics and Radiology , and involvement in NIH study sections (MCDC), FDA advisory panels , and UK Medical Research Council grant reviews.

• Computer Science
• Biomedical Engineering
• Artificial Intelligence
• +۵ مورد دیگر

Ali Rahmani Nejad is a researcher active in the fields of Computer Science , Biomedical Engineering , and Neuroscience . His work focuses on object recognition , medical imaging , and eye movement analysis , particularly in clinical and prosthetic vision applications. Research Trends: His publications emphasize AI-driven brain tumor segmentation using multi-modal MRI data Enhancing prosthetic vision through synthetic viewing point generation Development of memory-efficient segmentation frameworks for clinical use Innovative applications of Krawtchouk moments in image retrieval Automated gaze event classification in natural viewing conditions Collaborations: He collaborates with institutions like the Donders Institute for Brain, Cognition and Behaviour and contributes to the Data Science Center in Health (DASH) program at UMCG. His work aligns with UN Sustainable Development Goals, particularly in health and technology.

Mingmin Zhaoمشاهده پروفایل

استادیار

• Wireless Technologies
• Machine Learning
• Digital Health
• +۳ مورد دیگر

Mingmin Zhao is an Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania, with a secondary appointment in the Electrical and Systems Engineering Department. His research bridges wireless technologies, machine learning, digital health, and robust robotic perception. Research Interests: Wireless sensing systems with novel propagation modeling Multi-modal machine learning for signal and visual data Contactless health monitoring and biomarker discovery Robotic perception in adverse conditions Scientific Awards: Best Demo Award at MobiCom 2024 First Place in Student Research Competition at MobiCom 2024 ACM SIGMOBILE Doctoral Dissertation Award Runner-up CACM Research Highlights ACM SIGMOBILE Research Highlights Advising: Mentoring PhD students Haowen Lai, Richeek Das, and Yiduo Hao. Professional service includes organizing tutorials at CVPR 2020 and ICCV 2021. Labs & Collaborations: Conducts research in the WAVES Lab at UPenn and collaborated with Emerald Innovations to deploy his PhD work at scale.

Sarra Alqahtaniمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Multi-Agent Reinforcement Learning
• Explainable AI
• +۳ مورد دیگر

Dr. Sarra Alqahtani is an Associate Professor in the Computer Science Department at Wake Forest University. With a Ph.D. from the University of Tulsa (2015) and a two-year postdoc there, she focuses on safety and explainability in reinforcement learning (RL) and multi-agent RL (MARL), particularly for environmental applications. NSF CRII grant for safe MARL research NASA grant for drone navigation in Amazonian rainforests Her work addresses adversarial vulnerabilities in RL systems, develops robust safety policies, and combines local/global explanations to enhance AI interpretability. She applies these techniques to autonomous vehicle platoons, environmental monitoring, and illegal gold mining detection. Recent publications appear in top venues like AAMAS, IJCAI, and Nature. Research emphasizes transitioning MARL algorithms from simulations to real-world deployment while ensuring safety and reliability. Email: sarra-alqahtani@wfu.edu | Personal Site