جست‌وجوی استاد و پژوهشگر

David Fouheyمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• AI for Science
• +۶ مورد دیگر

David Fouhey is an Assistant Professor at New York University, jointly appointed between the Courant Institute of Mathematical Sciences (Computer Science) and the Tandon School of Engineering (Electrical and Computer Engineering). He previously held positions at the University of Michigan and was a postdoctoral researcher at UC Berkeley. His research focuses on learning-based computer vision, particularly in 3D reconstruction, AI for science, and human-object interaction. Education: PhD in Robotics from Carnegie Mellon University (2013-2018) Bachelor of Arts in Computer Science from Middlebury College (2007-2011) Research Interests: His work spans 3D reconstruction from images , AI-driven scientific measurement (e.g., solar physics, evolutionary ecology), and human interaction modeling . Notable projects include Stereo4D for 3D motion analysis and SyntheticIA for solar magnetogram fusion. Recent Articles: Recent work emphasizes interdisciplinary applications of vision (e.g., bird morphology analysis) and robust 3D techniques like Perspective Fields for camera calibration. His 2025 Nature Scientific Data paper on bird skeletal traits highlights his AI-for-science focus. Grants & Collaborations: Secured a NASA grant for heliophysics tools and collaborates with institutions like NASA’s SDO mission and the Astrophysical Journal. Labs & Teams: Leads a NYU research group focused on vision and robotics, with active collaborations in astrophysics and ecology.

Farhad Malekiمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Dr. Farhad Maleki is an Assistant Professor in the Department of Computer Science at the University of Calgary, Faculty of Science. He holds a PhD in Computer Science from the University of Saskatchewan (2019). His postdoctoral research at McGill University’s Augmented Intelligence & Precision Health Laboratory focused on machine learning for medical image analysis. He has held leadership roles, including President of the Association of Postdoctoral Fellows at McGill and President of the Computer Science Graduate Council at the University of Saskatchewan. Currently, he serves on the Machine Learning Education Sub-Committee of the Society for Imaging Informatics in Medicine and as a guest editor for journals in medical data analysis. Dr. Maleki’s research spans Artificial Intelligence , Machine Learning , Biomedical Data Analysis , and Computer Vision . His work emphasizes medical applications, including tumor segmentation, clinical outcome prediction, and AI-driven diagnostics in oncology and cardiology. He also explores agricultural challenges, such as wheat head segmentation using generative models and domain adaptation. Key contributions include developing robust medical imaging tools (e.g., Rel-UNet for tumor segmentation) and frameworks for evaluating AI model reliability ( RIDGE ). His work bridges clinical needs with computational innovation, addressing issues like reproducibility, generalizability, and low-annotation learning across healthcare and agriculture domains. Dr. Maleki’s articles focus on advancing AI methods for precision health and agriculture. His recent work highlights interdisciplinary applications, such as integrating clinical and pathology data for cancer survival prediction, optimizing radiation therapy using Bayesian methods, and leveraging synthetic data for crop phenotyping. These studies emphasize practical deployment and ethical considerations in AI adoption.

• Marketing
• Sustainability
• Technology in Business
• +۳ مورد دیگر

Carolina Luis Bassa is a Research Professor at the Universitat Pompeu Fabra Barcelona School of Management (UPF-BSM), serving as Academic Director and leading the Department of Business and Strategic Management. She directs multiple master’s programs and heads the Mercadona UPF-BSM Chair of Circular Economy. Her expertise spans Marketing, Sustainability, and Technology applied to business strategy, with a focus on digital transformation and circular economy practices in industries like agri-food distribution. Her academic journey includes roles as Vice-Dean of Teaching Staff and Academic Director, reflecting her commitment to educational innovation. Research interests emphasize sustainable consumer behavior, data-driven business models, and the integration of technology in CRM and customer experience management. Over 35 years, she has advised students and professionals on strategic applications of marketing and technology. Research Trends: Recent publications highlight circular economy adoption in agriculture, transparency in sharing economies, and data-driven strategies in customer-centric models. She bridges academic insights with industry challenges, particularly in leveraging technology for sustainable business practices. Awards & Grants: No specific awards are listed, but her leadership roles and chair position indicate significant institutional recognition. Grants and partnerships likely stem from her involvement with initiatives like the Mercadona Chair. Labs & Teams: Leads the Mercadona UPF-BSM Chair, focusing on circular economy research and collaboration with industry partners.

Carmen Galaz Garcíaمشاهده پروفایل

عضو هیئت علمی

• Environmental Data Science
• Remote Sensing
• Machine Learning in Ecology
• +۳ مورد دیگر

Carmen Galaz García is an Assistant Teaching Professor at the Bren School of Environmental Science & Management at UC Santa Barbara. She teaches data science courses including EDS 220 and capstone projects, emphasizing accessible technical education and DEIJ initiatives. Previously, she worked at the National Center for Ecological Analysis and Synthesis (NCEAS), analyzing remote sensing data and developing educational resources. Carmen holds a Ph.D. in Mathematics (UCSB) and a B.Sc. in Mathematics from the University of Guanajuato/CIMAT, Mexico. Her research focuses on environmental data science applications such as invasive species mapping using machine learning and geospatial analysis. She actively contributes to reproducible workflows in Python and collaborates on topological data analysis in environmental contexts. Professional affiliations include NCEAS and Bren School's environmental data programs. Education: Ph.D. in Mathematics, UC Santa Barbara (2021) B.Sc. in Mathematics, Universidad de Guanajuato/CIMAT (2015) Research emphasizes interdisciplinary approaches combining mathematics, ecology, and data science to address environmental challenges. She leads capstone projects integrating real-world environmental datasets and mentors students through collaborative data science workflows.

Chris Simpsonمشاهده پروفایل

استاد

• Health Policy
• Cardiology
• Medical Imaging AI
• +۷ مورد دیگر

Dr. Chris Simpson is a Professor at Queen’s University and currently serves as Executive Vice President & Chief Medical Executive at Ontario Health. He previously held roles as Head of Cardiology (2006-2016) and Vice-Dean (Clinical) of the Faculty of Health Sciences (2016-2021). His expertise spans cardiology, health policy, and medical imaging AI. Simpson has authored over 350 peer-reviewed publications and holds fellowships in multiple medical societies. His research focuses on health policy (access to care, wait times), cardiac electrophysiology, and AI-driven medical imaging. Education: BSc (University of New Brunswick), MD (Dalhousie University), Cardiac Electrophysiology Fellowship (University of Western Ontario). Notable roles include CMA President (2014-2015), CAHS President (2020-2021), and leadership in organizations like CorHealth and SEAMO. Awards include Fellowships in the Canadian Academy of Health Sciences and American College of Cardiology. Research interests include medical fitness to drive, atrial fibrillation, sudden cardiac death, and AI applications in oncology imaging. He leads initiatives such as the Terry Fox Research Institute’s Marathon of Hope Network. Simpson also contributes to policy bodies including Ontario Health’s Quality Standards Committee and the World Medical Association.

Alanna (Leni) Greenمشاهده پروفایل

پژوهشگر ارشد

• Bone-Immune-Cancer Interactions
• Cancer Dormancy
• Retinoic Acid Receptor Signaling
• +۳ مورد دیگر

Dr. Alanna (Leni) Green is a Senior Research Fellow at the School of Medicine and Population Health, University of Sheffield. She leads the Cancer and Bone (CAB) Lab, focusing on eradicating chemotherapy-resistant dormant cancer cells in bone. Her academic journey includes a BBiomedSci at Monash University, a PhD at the University of Melbourne, and postdoctoral roles in the Sheffield Myeloma Research Team and Helleday’s lab. Education: BBiomedSci (Hons), Monash University PhD, University of Melbourne Her research explores bone-immune-cancer interactions, particularly how bone microenvironment cells protect tumors from therapy. Key areas include retinoic acid receptor signaling, vitamin A regulation of hematopoiesis, and novel drug development for myeloma and bone-metastatic cancers. Recent publications highlight her work in cancer metabolism (MTHFD2 inhibition), bone repair mechanisms, and microenvironmental regulation of lymphopoiesis. She has received over 20 awards, including Young Investigator Prizes from ECTS and ASBMR. Scientific Awards: Wellcome Trust Career Development Award (2024) Yorkshire Cancer Research Pioneer Advanced Fellowship (2024) Blood Cancer UK Project Grant (2023) Faculty MDH Early Career Researcher Prize (2020) Dr. Green supervises the Cancer and Bone Lab, which includes postdoctoral researchers and PhD students. She lectures on translational oncology and molecular medicine courses and actively contributes to professional societies like the Bone Research Society and ECTS.

Maumita Bhattacharyaمشاهده پروفایل

مدرس ارشد

• Computational Intelligence
• Cyber Security
• Big Data Optimization
• +۵ مورد دیگر

Maumita Bhattacharya is a Senior Lecturer (equivalent to Associate Professor in North America) at the School of Computing, Mathematics & Engineering, Charles Sturt University, Australia. She specializes in Computational Intelligence and Cyber Security, focusing on AI-driven security solutions, electronic fraud detection, and human-centric cybersecurity challenges. Her work addresses critical areas like BYOD security, IoT vulnerabilities, and social engineering through social media. Research Interests: Computational Intelligence techniques for optimization (Big Data, Engineering Design), Cyber Security applications (fraud detection, IoT security), and human dimensions of cybersecurity. She also investigates socio-computing systems and privacy challenges in social networks. Key Achievements: Recipient of the 2024 Excellence Award (Research) and 2017 SOCM Research Excellence Award Published over 65 peer-reviewed papers in top-tier journals/conferences Editorial roles at Human-centric Computing and Information Sciences (Springer Q1 journal) Guest editor of special issues and conference committee member Labs/Groups: Member of Cyber Security Research Group (CSRG), Machine Vision and Digital Health (MaViDH), and Data Mining Research Group (DaMRG). Active in interdisciplinary research collaborations across Australia and globally.

Florian Stahlمشاهده پروفایل

استاد

• Empirical Quantitative Marketing
• Marketing Analytics
• Digital Marketing
• +۱۴ مورد دیگر

Florian Stahl is Professor of Marketing at the University of Mannheim, affiliated with the Department of Business Administration in the Business School. His research focuses on empirical quantitative marketing, digital transformation, and marketing analytics, with applications in digital communications, pricing, and social media. His research interests include: Empirical Quantitative Marketing Marketing Analytics Digital and Social Media Marketing Economics of Data and Digital Products Brand and Advertising Strategy Behavioral and Experimental Marketing Methodologically, he employs applied econometrics, machine learning, and experimental designs. His recent publications center on influencer marketing, online reviews, network effects, and digital content strategies, reflecting a strong trend toward understanding consumer behavior in digital ecosystems using data-driven approaches. His scientific awards include: H. Paul Root Award (2012) MSI Best Paper Award (2012) Donald R. Lehmann Award (2021) IJRM Best Paper Award (2014) Finalist, Paul E. Green Award (2021) Finalist, Harold H. Maynard Award (2012) Florian Stahl has advised numerous research projects and published in top-tier journals, contributing significantly to marketing science. He has held research grants and collaborative projects, though specific grant details are not listed. He leads the Chair of Quantitative Marketing and Consumer Analytics and maintains an active research team focused on digital marketing innovations. His lab, the Quantitative Marketing and Consumer Analytics team, conducts cutting-edge research on user-generated content, influencer dynamics, and digital strategy, leveraging real-world data and experiments.

• Digital Humanities
• Art History
• Urban History
• +۱۱ مورد دیگر

Isabella Di Lenardo is a Lecturer and Scientist at the Digital Humanities Institute (DHI) at École Polytechnique Fédérale de Lausanne (EPFL), where she also serves as the coordinator of the EPFL Time Machine Unit and the European Local Time Machines. She holds affiliations across multiple departments, including DHI-GE, SAR-ENS, SHS-ENS, and EDDH-ENS, reflecting her interdisciplinary role in teaching and research. Her educational background includes a PhD in Theories and Art History, with postdoctoral and faculty experience at institutions such as INHA (Paris), EPFL, and IUAV (Venice). Her research spans Digital Humanities, Art History, Urban History, and GIS , with a focus on digital urban reconstruction, historical cadastres, and AI applications in cultural heritage. She employs advanced computational methods including machine learning, 4D modeling, and semantic segmentation to analyze historical maps, cadastral records, and art archives. Her work bridges humanities scholarship with computer science, particularly in reconstructing urban evolution and analyzing visual patterns. The recent publications reveal a consistent trend in AI-powered historical data analysis , especially in processing non-standardized historical documents, reconstructing urban spaces, and developing open-source tools for digital heritage. Her work frequently involves large-scale datasets from Venice, Lausanne, Paris, and Jerusalem, demonstrating a transnational and interdisciplinary approach. She has contributed to significant collaborative projects such as the Venice Time Machine , Parcels of Venice , and Time Machine Organization , often acting as a principal investigator or project leader. Her role involves coordinating diverse teams of researchers, engineers, and cultural institutions. Scientific contributions include: Development of the Morphograph tool for visual pattern recognition in art archives Automatic vectorization and analysis of Napoleonic cadastres Creation of 4D models for historical cities AI-driven text and pattern extraction from historical maps Building discovery engines for digital art history She actively teaches ex cathedra courses in Digital Urban History and Art History at EPFL and internationally. Her work in grants and projects emphasizes open data, reproducibility, and interdisciplinary collaboration. She has led research funded by organizations supporting digital heritage innovation. She is a key member of the Digital Humanities Laboratory at EPFL and the Time Machine Organization , where she fosters collaboration between computer scientists, historians, and cultural institutions. Her work in the Replica Project and ARCHiVe center highlights her leadership in digitizing and making accessible large art historical archives.

• Explainable AI
• Human-AI Interaction
• Machine Learning
• +۷ مورد دیگر

Andreas Holzinger is a Professor at Graz University of Technology, with additional affiliations at Medical University Graz and University of Natural Resources and Life Sciences Vienna in Austria. He is recognized as an IFIP Fellow (2021) for his significant contributions to information processing and computer science. His work spans multiple institutions across Europe, with notable collaborations extending to the University of Alberta in Canada. Professor Holzinger's research focuses on Human-Centered AI, Explainable AI (XAI), and their practical applications across diverse domains. His work bridges theoretical AI advancements with real-world implementations in healthcare, forestry, and human-robot interaction. He has pioneered approaches in counterfactual explanations, graph neural networks, and human-in-the-loop systems that emphasize transparency and trustworthiness in AI decision-making processes. His recent publications demonstrate a strong trend toward integrating large language models with traditional AI systems while maintaining explainability. Holzinger's work consistently emphasizes the human element in AI systems, ensuring that technological advancements serve human needs rather than obscuring decision processes. His research in medical AI, smart forestry, and agricultural applications shows a commitment to solving practical problems with human-centered technological solutions. Scientific Awards: IFIP Fellow (2021) Professor Holzinger has been instrumental in establishing design guidelines for explainable AI systems, particularly through his work on post-hoc versus ante-hoc explanations. His research on Kandinsky Patterns has provided valuable experimental frameworks for pattern analysis and machine intelligence. He has secured significant research funding for projects bridging AI with practical applications in healthcare and environmental monitoring. His leadership extends to the organization of major conferences and workshops, including the CD-MAKE conference series, where he has fostered interdisciplinary collaboration between AI researchers and domain experts. His work on the CLARUS platform demonstrates practical implementations of interactive explainable AI for medical applications.

Christoph Heinzlمشاهده پروفایل

استاد

• Scientific Visualization
• Visual Analytics
• Immersive Analytics
• +۳ مورد دیگر

Christoph Heinzl is a Professor of Cognitive Sensor Systems at the University of Passau since September 2022. He leads the Knowledge-based Image Processing research group at the Fraunhofer Development Center X-ray Technology (EZRT) . His academic background includes a PhD in Informatics and a Habilitation in 2022 , both from TU Wien . Research Focus: Scientific visualization, visual analytics, immersive analytics, virtual/augmented reality, machine learning, and X-ray computed tomography (XCT). Key Trends: Development of novel visualization techniques for complex volumetric data (e.g., dynamic volume lines, visual coherence frameworks), parameter space analysis, and cross-virtuality collaboration tools. Applications: Aerospace component inspection, defect analysis in composites (CFRP, GFRP), porosity quantification, and 4DCT time-series exploration.

Ben Liangمشاهده پروفایل

استاد

• Networked Systems
• Mobile Communications
• Distributed Machine Learning
• +۴ مورد دیگر

Professor Ben Liang is a faculty member at the Department of Electrical and Computer Engineering , University of Toronto, holding the L. Lau Chair . He has served on editorial boards of IEEE Transactions on Mobile Computing , IEEE Transactions on Wireless Communications , and Wiley Security and Communication Networks . His research focuses on networked systems , mobile communications , and distributed machine learning , with applications in wireless network virtualization, edge computing, and resource optimization. He explores stochastic scheduling , computation-communication co-design , and multi-resource fair allocation . Key publication trends: Wireless federated learning (2024-2025) Network virtualization and MIMO systems (2022-2025) Online distributed optimization (2023-2025) Stochastic resource management (2020-2024) Scientific Awards: Fellow of IEEE Best Paper Award, ACM MSWiM 2013 INFOCOM 2010 Finalist Ontario ERA Award 2007 IFIP Networking 2005 Best Paper Intel Foundation Graduate Fellowship 2000 Polytechnic University valedictorian 1997 Affiliated with IEEE , ACM , and Tau Beta Pi , he teaches courses like ECE368: Probabilistic Reasoning and ECE421: Machine Learning , emphasizing stochastic networks and random processes .

Zhenghao Chenمشاهده پروفایل

مدرس

• Computer Vision
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

Dr. Zhenghao Chen is a Lecturer in Data Science at the University of Newcastle, affiliated with the School of Information and Physical Sciences. He earned his Ph.D. from the University of Sydney in 2022, following a B.Eng. H1 degree from the same institution in 2017. Prior to his current position, Dr. Chen served as a Postdoctoral Research Fellow at the University of Sydney (2022-2024), a Research Engineer at TikTok (2024), and as a Visiting Research Scientist at Microsoft Research and Disney Research (2022-2023). Dr. Chen's educational background includes: Doctor of Philosophy, University of Sydney (2022) Bachelor of Information Technology (B.Eng. H1), University of Sydney (2017) Dr. Chen's research spans multiple domains within artificial intelligence, with particular expertise in Computer Vision, Natural Language Processing, and Machine Learning. His work in Generative AI has garnered significant recognition, with applications in both academic and industrial settings. His research interests are reflected in his Fields of Research percentages: Deep Learning (30%), Computer Vision (30%), Natural Language Processing (20%), and Multimodal Analysis and Synthesis (20%). His publications in top-tier venues like CVPR, ICCV, ECCV, and journals like IEEE TPAMI demonstrate the breadth and impact of his work. Analysis of Dr. Chen's recent publications (2022-2025) reveals a consistent focus on neural compression techniques, 3D perception, and multimodal AI systems. His work spans medical imaging (CXR bone suppression), video compression, point cloud processing, and neural surface reconstruction. A notable trend is his exploration of efficient AI systems that work well under resource constraints, as evidenced by his involvement in the EMCLR workshop. His research often bridges theoretical advances with practical applications across multiple domains. Dr. Chen has received several prestigious awards: Microsoft Research Asia StarTrack Fellowship (2025) ACM SIGMM Award for Outstanding PhD Thesis in Multimedia Computing (2024) Australia Government Research Training Program (RTP) Fellowship (2019) Google Australia Prize for Excellence in Computer Science (2017) Dr. Chen is actively involved in the academic community, serving on the Program Committee for major AI conferences including CVPR, ICCV, ECCV, SIGGRAPH, AAAI, and others. He also organizes workshops in Multimedia and ICCV conferences, and serves as a reviewer for prestigious journals. His teaching responsibilities include courses on Intelligent Visual Signal Understanding, Video Intelligence and Compression, Database and Information Management, and Computing Fundamentals at both the University of Sydney and University of Newcastle.

• Building Information Modeling (BIM)
• Geographic Information Systems (GIS)
• Digital Twin Technology
• +۳ مورد دیگر

Prof. Bernd Domer is an Associate Professor at the Geneva School of Landscape, Engineering and Architecture (HES-SO) specializing in Building Information Modeling (BIM) , Geographic Information Systems (GIS) , and digital transformation of civil engineering . He leads multiple ongoing research projects including CU_OFROU_PAB (CHF278,844) focused on BIM-GIS workflows for noise barriers, and SousEtoile (CHF50,000) developing subsurface prediction models for urban planning. His work addresses critical challenges in software interoperability and point cloud processing for infrastructure digital twins. BA HES-SO in Architecture (HEPIA) BSc Civil Engineering (EPFL) BSc HES-SO in Civil Engineering (HEPIA) MSc HES-SO in Engineering (HES-SO Master) His research explores digital workflows for infrastructure projects, with over 15 recent publications examining topics like: Semantic segmentation of point clouds (2024) IFC standard optimization (2023) Underground confidence level modeling (2021) Swiss BIM implementation frameworks (2020) Construction waste management platforms (2018) He serves as Head of the MIC Group and co-directs the CAS in BIM Coordination . Active in international committees like EG-ICE and Bauen digital Schweiz , his work bridges academic research with practical implementation through collaborations with HEPIA , HEIG-VD , and institutions like the Swiss Federal Roads Office (OFROU) .

Mariano Cabezasمشاهده پروفایل

پژوهشگر

• computer vision
• medical imaging
• magnetic resonance imaging (MRI)
• +۵ مورد دیگر

Mariano Cabezas is a researcher in medical imaging and computer vision, currently affiliated with Macquarie University and as an affiliate at the University of Sydney . His work focuses on automating brain MRI analysis for pathologies like multiple sclerosis, Alzheimer's disease, and tumors, with additional contributions to UAV image analysis. PhD in Computer Science (2013), University of Girona MSc in Automation, Computation, and Systems (2010), University of Girona BSc in Computer Science (2009), University of Girona Research Interests : Specializes in magnetic resonance imaging , lesion detection , deep learning , and image processing , with applications in multiple sclerosis , hearing loss , and UAV-derived ecological data . His recent work includes federated learning frameworks for cross-site MS lesion segmentation and pseudo-labeling techniques for longitudinal brain volume estimation. Publication Trends : Over the past five years, his research has emphasized federated learning (4 articles), lesion segmentation (9 articles), and UAV image analysis (3 articles), with a strong focus on clinical validation and cross-institutional collaboration. Labs & Collaborations : Contributed to the NIC-VICOROB group at the University of Girona and maintains affiliations with the Research Institute of the Hospital Vall d'Hebron (VHIR) in Barcelona and Macquarie University in Sydney. Actively develops open-source tools hosted on GitHub.