جست‌وجوی استاد و پژوهشگر

Julie Yujie Chenمشاهده پروفایل

استادیار

• Artificial Intelligence
• Digital Labour
• Platform Economy
• +۳ مورد دیگر

Julie Yujie Chen is an Assistant Professor at the Institute of Communication, Culture, Information, and Technology (ICCIT) at the University of Toronto Mississauga, with a graduate appointment at the Faculty of Information. She holds a PhD in American Studies from the University of Maryland, College Park, and previously served as a Lecturer at the University of Leicester's School of Media, Communication, and Sociology. Her research focuses on the intersection of culture, digital technologies, and economic structures in shaping work experiences, particularly in China's platform economy. Key areas include ride-hailing and food-delivery sectors, worker resistance in digital capitalism, and the sociocultural impacts of platforms like WeChat. She is the lead author of *Super-sticky WeChat and Chinese Society* (2018), the first academic work analyzing WeChat's societal and political influence. Research interests: Digital labour, platform capitalism, AI-driven work, global labour patterns, and socio-technological change. Current supervision: Hiu-Fung Chung and Mathew Iantorno. Her recent work examines platform labour dynamics, national employment policies in China, and comparative studies of global digital economies. Articles often explore themes like worker agency, algorithmic management, and socio-technical resistance strategies.

• Audio Signal Processing
• Psychoacoustics
• Cochlear Implants
• +۵ مورد دیگر

Prof. Bernhard U. Seeber is an Extraordinary Professor at the Technical University of Munich (TUM), leading the Chair of Audio Signal Processing within the TUM School of Computation, Information and Technology. His work bridges auditory neuroscience and engineering, focusing on improving hearing aids, cochlear implants, and virtual acoustic systems. He holds affiliations with the Bernstein Center for Computational Neuroscience, Munich Institute of Biomedical Engineering, and others. Education: Studied and earned his PhD (2003) in Electrical Engineering and Information Technology at TUM. Postdoctoral research included time at UC Berkeley and the MRC Institute of Hearing Research (UK), where he pioneered studies on binaural hearing and cochlear implant optimization. Research Interests: Combines experimental and theoretical approaches to explore auditory scene analysis, binaural unmasking, and spatial hearing. Key areas include signal coding for cochlear implants, virtual acoustics, and non-destructive acoustic monitoring. His work emphasizes interdisciplinary collaboration with industry and academia. Awards: Lothar Cremer Award (2010), Emmy Noether Fellowship (2007), and recognition from the German Acoustical Society. Teaching: Offers courses on audio communication, computational neuroscience, and technical acoustics. Projects: Leads initiatives like HAPPAA and Auralization, advancing sound field synthesis and hearing aid algorithms. Current Roles: Head of Chair of Audio Signal Processing, Board Member of DEGA, and spokesperson for the ITG Technical Committee on Hearing Acoustics.

Jean Ponceمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Machine Learning
• +۳ مورد دیگر

Jean Ponce is a Professor at Ecole Normale Supérieure - PSL and a Global Distinguished Professor at New York University's Courant Institute and Center for Data Science. He serves as Scientific Director of PRAIRIE Interdisciplinary AI Research Institute and co-founded Enhance Lab, commercializing super-resolution imaging software. His research focuses on computer vision, machine learning, robotics, and image processing. Ponce has held roles at Inria, MIT, Stanford, and the University of Illinois, and is an IEEE and ELLIS Fellow. He has served as chair of major conferences like CVPR, ECCV, and ICCV, and authored the textbook 'Computer Vision: A Modern Approach.' Research interests include statistical models for exoplanet detection, neural networks for 3D reconstruction, and self-supervised learning. His work combines theoretical foundations with practical applications in astrophysics, robotics, and imaging. Notable awards include the IEEE CVPR Longuet-Higgins Prize (2016, 2020) and ICML Test-of-Time Award (2019). Key projects include Enhance Lab's high dynamic range imaging and PRAIRIE's interdisciplinary AI initiatives. Ponce's articles explore cutting-edge topics like neural object priors, geodesic motion planning, and satellite image analysis. His contributions bridge academic research and industrial applications, emphasizing both fundamental theory and real-world impact.

Jerome Busemeyerمشاهده پروفایل

استاد

• Cognitive Science
• Quantum Cognition
• Decision Making
• +۷ مورد دیگر

Jerome Busemeyer is a Distinguished Professor and Provost Professor in the Department of Psychological and Brain Sciences at Indiana University Bloomington, with a joint affiliation in the Cognitive Science Program. He is a Fellow of the American Academy of Arts and Sciences, reflecting his significant contributions to the science of decision-making and cognitive modeling. His educational background includes a Ph.D. in Psychology from the University of South Carolina (1979), an M.A. from the same institution (1976), and a B.A. cum laude from the University of Cincinnati (1973). He completed postdoctoral training in Quantitative Methods at the University of Illinois in 1980. Busemeyer's research focuses on mathematical and computational models of human judgment and decision-making , with pioneering work in quantum cognition , dynamic models , and neural network modeling . He developed Decision Field Theory , a dynamic cognitive model of decision processes, and has led the application of quantum probability to explain cognitive 'errors' such as order effects and violations of rational decision rules. His recent publications reveal a consistent trend toward integrating quantum-theoretic frameworks with empirical decision research, exploring topics such as pricing behavior, confidence judgments, multi-attribute decisions, and medical decision-making. These works appear in top journals including Psychological Review , Trends in Cognitive Sciences , and Proceedings of the National Academy of Sciences , indicating sustained high-impact contributions. Fellow, American Academy of Arts and Sciences Distinguished Professor, Indiana University Provost Professor, Indiana University Busemeyer has advised numerous graduate students and postdoctoral researchers, many of whom have become active collaborators and co-authors. His lab, the Judgment and Decision-Making Laboratory (Decision Research Laboratory), supports interdisciplinary research in cognitive science and receives ongoing funding for theoretical and experimental work. He has authored influential books, including Quantum Models of Cognition and Decision and Cognitive Modeling . The lab, located in Room A300G of the Psychology Building, fosters collaborative research on decision-making, quantum cognition, and computational modeling, with active projects involving fMRI, behavioral experiments, and theoretical development.

• Artificial Intelligence
• Optimization
• Machine Learning
• +۷ مورد دیگر

Sebastian Pokutta is a Professor at Technische Universität Berlin, Vice President at the Zuse Institute Berlin (ZIB), and Chair of the Cluster of Excellence MATH+ and MODAL. His research lies at the intersection of Artificial Intelligence, Optimization, and Machine Learning, with applications in sustainability, quantum computing, and mathematical discovery. Research Interests: Development of novel optimization algorithms, particularly Frank-Wolfe and Conditional Gradient methods. Integration of machine learning with decision-making and combinatorial optimization. AI for Science (AI4Science), including applications in quantum mechanics and ecology. AI and creativity, human-AI co-creativity, and social science modeling using multi-agent LLMs. His recent publications (2025) demonstrate a strong focus on scalable optimization, interpretability, and algorithmic foundations. The work spans theoretical advances in convergence analysis, practical implementations in Julia (FrankWolfe.jl), and real-world deployments in biomass estimation and quantum certification. Scientific Awards: Gödel Prize (2023) STOC Test of Time Award (2022) Science Prize of the Association for Pediatric Orthopedics (2025) Google Research Awards (2021, 2020) NSF CAREER Award (2015) He advises a vibrant research group, with former students and postdocs securing faculty positions at institutions like Inria, Carlos III University, and James Madison University. His group has received funding from Google, DFG, and Math+, and he leads major collaborative efforts such as the Thematic Einstein Semester on Mathematical Optimization for Machine Learning. Labs and Teams: Interactive Optimization and Learning Lab at TU Berlin and ZIB. Leadership in MODAL and MATH+ research clusters, fostering interdisciplinary collaboration in mathematical optimization and AI.

Ana Serranoمشاهده پروفایل

دانشیار

• Visual Computing
• Computational Imaging
• Material Appearance Perception
• +۷ مورد دیگر

Ana Serrano is an Associate Professor at Universidad de Zaragoza, Spain, where she is affiliated with the Graphics & Imaging Lab in the EINA (Edificio Ada Byron) school. She earned her PhD at the same institution under the supervision of Prof. Diego Gutierrez and Prof. Belen Masia, and completed a postdoctoral fellowship at the Max-Planck-Institute for Informatics under Prof. Karol Myszkowski. Her research focuses on visual computing , particularly in computational imaging , material appearance perception and editing , and virtual reality . She is especially interested in developing perceptually-driven methods that leverage knowledge of the human perceptual system to enhance user experiences and assist content creation in immersive environments. Her recent publications (2023–2025) span top-tier venues such as SIGGRAPH, CVPR, IEEE TVCG, and Eurographics. These works explore topics like saliency prediction in 3D and 360° video, crossmodal perception in VR, gloss modeling, radiance fields, and perceptual evaluation of immersive content. The research demonstrates a strong integration of machine learning, human perception, and computer graphics to solve real-world problems in visual computing. She has received several prestigious awards, including: Eurographics 2023 Young Researcher Award VGTC VR 2024 Significant New Researcher Award Eurographics 2020 PhD Award Adobe Research Fellowship (honorable mention, 2017) NVIDIA Graduate Fellowship (2018) Ana Serrano actively supervises PhD and Master’s students and has taught courses such as Virtual Reality, Computational Imaging, and Deep Learning applications. She serves as an Associate Editor for Computer Graphics Forum , ACM Transactions on Applied Perception , and Computers and Graphics , and has held leadership roles in major conferences including Eurographics (Tutorials co-chair, 2023), ACM SAP (Program co-chair, 2022), and CEIG (Program co-chair, 2022). Her professional service includes extensive program committee and reviewer roles for SIGGRAPH, IEEE VR, ISMAR, and others. She leads a vibrant research group focused on human perception in virtual environments, with current projects on computational models of attention and perception, integrated with physiological signals. Her lab, the Graphics & Imaging Lab, fosters interdisciplinary collaboration and innovation in visual computing.

Steven Farberمشاهده پروفایل

دانشیار

• Transport Geography
• Spatial Analysis
• Accessibility
• +۷ مورد دیگر

Steven Farber is an Associate Professor in the Department of Human Geography at the University of Toronto . His research focuses on transport geography , spatial analysis , accessibility , and public transportation equity , with a strong emphasis on Geographic Information Science (GIS) and social sustainability . Current research: Distributional aspects of transit accessibility, personal mobility, activity participation Teaching: Undergraduate courses on multivariate analysis and transportation geography, graduate seminars on transportation and urban form Key research trends include: Transport equity and social inclusion Time-geography in urban mobility Spatiotemporal accessibility modeling Behavioral impacts of transport infrastructure GIS-based spatial econometric methods Grants include multiple SSHRC Insight awards, Ontario Ministry of Research funding, and municipal partnerships for transport equity studies.

• Machine Learning
• Graph Neural Networks
• Time Series Analysis
• +۷ مورد دیگر

Filippo Maria Bianchi is an Associate Professor in the Department of Mathematics and Statistics at UiT The Arctic University of Norway, where he conducts research at the intersection of machine learning, dynamical systems, and complex networks. He is also a Senior Researcher at NORCE Norwegian Research Centre and actively contributes to the IEEE Task Force on Learning for Structured Data and the ELLIS Society. Department: Department of Mathematics and Statistics School: Faculty of Science and Technology University: UiT The Arctic University of Norway Adjunct Position: Senior Researcher, NORCE Education: Bachelor’s in Computer Engineering, Sapienza University of Rome Master’s in Artificial Intelligence & Robotics, Sapienza University of Rome (cum laude, 2012) PhD in Machine Learning, Sapienza University of Rome His research focuses on graph machine learning, time series analysis, reservoir computing, and probabilistic forecasting , with applications in energy analytics and remote sensing. He has led and contributed to numerous projects involving Arctic power grids, satellite-based environmental monitoring, and deep learning for sustainability. The recent publications reflect a strong trend in graph neural networks —particularly pooling mechanisms, spatiotemporal modeling, and explainability—alongside applications in energy forecasting, avalanche detection, and remote sensing . His work combines theoretical innovation with real-world impact, especially in Arctic and remote environments. Scientific Affiliations and Leadership: Vice-Chair, IEEE Task Force on Learning for Structured Data Member, ELLIS Society Co-founder, Northernmost Graph Machine Learning group Member, IEEE Task Force on Reservoir Computing Visiting Professor, Politecnico di Milano (2024–2025) He actively mentors students and collaborates on interdisciplinary research. He has led projects in power grid reliability, solar fault detection, and unsupervised change detection in satellite imagery . His work is supported by open-source implementations and reproducible research practices. Laboratories and Research Groups: Northernmost Graph Machine Learning group (co-founder) ARC Research Group, UiT Graph Machine Learning Group, Lugano

Walid G. Arefمشاهده پروفایل

استاد

• Databases
• Data Mining
• Spatial Databases
• +۶ مورد دیگر

Walid G. Aref is a Professor of Computer Science at Purdue University since Fall 1999. His research focuses on database systems, spatial and spatio-temporal data management, query processing, and indexing. He has led projects funded by NSF, NIH, and industry partners. Aref is a Fellow of the IEEE and has received awards including the NSF CAREER Award (2001) and VLDB’s Ten-Year Best Paper Award (2016). He serves as Editor-in-Chief of ACM Transactions on Spatial Algorithms and Systems and has authored numerous influential papers. Education: BSc/MSc from Alexandria University (Egypt), PhD from University of Maryland (1993). Research Interests: Extending database functionality for emerging applications like spatial, graph, and sensor databases. Notable contributions include the Chameleon project, AQWA system for big spatial data, and privacy-preserving Casper framework. Selected Projects: NSF-funded work on multi-predicate spatial queries, in-memory graph relational systems, and adaptive spatio-textual processing. Systems like Tornado (spatio-textual streams) and LocationSpark (distributed spatial data) exemplify his contributions. Awards: Multiple best paper awards, IEEE Fellow, and leadership roles in ACM SIGSPATIAL.

Charles C. Davisمشاهده پروفایل

استاد

• Plant Systematics
• Phylogenomics
• Biodiversity Conservation
• +۸ مورد دیگر

Charles C. Davis is a Professor of Organismic and Evolutionary Biology at Harvard University and Curator of Vascular Plants in the Harvard University Herbaria. He leads the Davis Lab, focusing on plant diversity through integrative research in systematics, paleobiology, ecology, and molecular biology. His work emphasizes phylogenetic theory, biogeography, and the application of herbarium collections to address global change challenges. Davis is particularly noted for leveraging herbarium specimens to study plant responses to climate change, phenology, and biodiversity patterns. Research interests include plant-insect interactions, genome architecture evolution in parasitic plants, and the ethical use of herbarium specimens. His lab has pioneered high-throughput phylogenomic pipelines (e.g., PhyloHerb) and explored the future of herbaria in the digital age. Collaborations span global institutions, emphasizing digitization, spectral imaging, and AI-driven analysis of biodiversity data. Recent work highlights the impact of anthropogenic change on plant communities, the role of phenology in species survival, and strategies to mitigate collecting biases in herbaria. Davis teaches courses on plant systematics and evolution, and his lab actively participates in public engagement through exhibits like the HMNH’s *In Search of Thoreau’s Flowers*. His research has been featured in *Trends in Ecology & Evolution*, *Molecular Phylogenetics and Evolution*, and *Current Biology*, with a focus on advancing methodologies for biodiversity science while addressing ethical challenges in specimen sampling.

• Machine Learning & Artificial Intelligence
• Probabilistic Modelling
• Neuro-Symbolic Modelling
• +۷ مورد دیگر

Dr. Robert Lieck is an Assistant Professor in the Department of Computer Science at Durham University. His research focuses on interdisciplinary applications of machine learning (ML) and artificial intelligence (AI), emphasizing interpretability, robustness, and ethical considerations. He explores computational models in music cognition, communication dynamics, and medical image analysis, aiming to bridge theory and practical tools for domain experts. Before Durham, he was a postdoctoral researcher at EPFL's Digital and Cognitive Musicology Lab (2018–2021) and earned his PhD from the Learning and Intelligent Systems Lab in Stuttgart/Berlin (2012–2017). His work combines probabilistic modelling, neuro-symbolic systems, and reinforcement learning to address challenges in music analysis, autonomous decision-making, and medical robotics. Key research themes include: Music structure and perception modelling Symbol emergence in multi-agent communication Ethical AI and autonomous systems governance Medical imaging applications (CT/MRI analysis) Recent projects involve developing patient-agnostic diabetes management systems using deep reinforcement learning and surgical workflow anticipation through graph learning algorithms. He actively contributes to conferences such as NeurIPS, ISMIR, and AAAI, with publications spanning music informatics, robotics, and biomedical engineering. Current supervision includes four postgraduate students focusing on AI applications in healthcare, music technology, and autonomous systems. His work bridges technical innovation with societal impact, addressing challenges in policy, legislation, and interdisciplinary collaboration.

• Machine Learning
• Computational Biology
• Bioinformatics
• +۴ مورد دیگر

Catherine Mooney is a Professor in the School of Computer Science at University College Dublin (UCD), leading the Life Science Data Analytics Group (LiSDA). Her research focuses on applying machine learning to healthcare challenges, including biomarker development, clinical decision support systems, and addressing ethical and technical barriers in healthcare AI. Education: BSc in [field unspecified] from Trinity College Dublin PhD in Computer Science from UCD Prof Dip University Teaching & Learning from UCD Research Interests: Machine Learning applications in healthcare Biomarker discovery for neurological and metabolic conditions Explainable AI for clinical decision support Promoting diversity and inclusion in STEM education Her work bridges computational methods with medical challenges, emphasizing ethical AI and translational research. Notable Awards: Best Paper Award in Applied Biosciences (2023) UCD Long-Service Award (2022) Best Poster Award at ITiCSE ’20 (2020) Her research has led to impactful tools like LiSDA’s clinical decision support systems for epilepsy and pregnancy care. She also advocates for gender diversity in computing, serving in leadership roles like Vice Principal for EDI in UCD’s College of Science (2022–2024).

• Human-Centered Artificial Intelligence
• Natural Language Processing
• Computer Vision
• +۵ مورد دیگر

Carlos Toxtli is an Assistant Professor at Clemson University where he leads the Human-AI Empowerment Lab. His research applies Human-Centered AI to create fair workplace tools, focusing on NLP, computer vision, and crowdsourcing ethics. He holds a PhD in Computer Science from Northeastern University and previously worked at Google, Microsoft Research, and Snap Inc. Education: Ph.D. Computer Science, Northeastern University M.S. Innovation & Technological Entrepreneurship, Monterrey Institute of Technology MBA, IEDE Business School B.S. Computer Science, University of the Valley of Mexico B.S. Computer Engineering, National Autonomous University of Mexico Research Focus: Dr. Toxtli develops AI systems promoting workplace fairness through ethical frameworks. His work spans: 1) Human-AI collaboration for task management, 2) Bias mitigation in gig economies, 3) Multimodal interfaces for accessibility, and 4) Culturally adaptive systems. His lab explores how AI can augment human capabilities while ensuring transparency. Publication Trends: Recent works demonstrate strong focus on LLM reliability (7 papers), human-AI teaming (4 studies), and ethical AI frameworks (3 publications). Over 60% of 2024-2025 output addresses real-world deployment challenges. Awards: UNESCO IRCAI Global Top 100 project Google Apps Developer Challenge 1st Place Facebook Developers World Hack 1st Prize Intel Innovation Latin App 1st Prize NSF STIR Labs Research Grant Leadership: Founded the Human-AI Empowerment Lab securing $1.2M in NSF/industry grants. Supervises 8 PhD students in human-centered computing projects. Previously co-founded 5 tech startups including ComproPago (acquired by Coca-Cola FEMSA).

• Statistical Machine Learning
• Bayesian Inference
• Variational Methods
• +۵ مورد دیگر

Michael C. Hughes ("Mike") is an Assistant Professor in the Department of Computer Science at Tufts University's School of Engineering, where he develops statistical machine learning methods for healthcare applications. His work focuses on building predictive models that extract actionable insights from complex clinical data, including electronic health records and medical imaging. PhD, Computer Science, Brown University (2016) MS, Computer Science, Brown University (2012) BS, Computer Science, Franklin W. Olin College of Engineering (2010) Research interests center on: Bayesian hierarchical models for documents, sequences, and medical images Optimization algorithms for approximate inference Model fairness and interpretability in clinical contexts Semi-supervised learning for medical diagnostics Recent publications demonstrate these capabilities through applications in cardiovascular disease diagnosis, opioid overdose forecasting, and ICU risk prediction. His lab emphasizes reproducibility through open datasets like TMED-2 and open-source tools like BNPy. Grants include NIH R01 funding for heart valve disease detection, NSF CAREER support for model interpretability, and NSF GCR funding for educational uncertainty research. Scientific awards include: NIH R01 Award (PI) for heart valve disease detection (2025) NSF CAREER Award (2024) NSF GCR Grant (2024) Best Poster Award at Time Series Workshop (ICML 2021) Top 10% Reviewer Awards at AISTATS (2023, 2022) Teaching activities include courses on Bayesian Deep Learning, Introduction to Machine Learning, and Statistical Pattern Recognition. He previously served as postdoctoral fellow at Harvard SEAS.

Forest Agostinelliمشاهده پروفایل

استادیار

• Deep Learning
• Reinforcement Learning
• Search Algorithms
• +۷ مورد دیگر

Forest Agostinelli is an Assistant Professor in the Department of Computer Science and Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina, where he is also affiliated with the AI Institute. His research focuses on designing AI algorithms for pathfinding problems, integrating deep learning, reinforcement learning, heuristic search, and formal logic. He holds a Ph.D. in Computer Science from the University of California, Irvine, an M.S. from the University of Michigan, and a B.S. in Electrical and Computer Engineering from The Ohio State University. Research Overview : Agostinelli’s work emphasizes solving pathfinding problems in domains like robotics, theorem proving, and molecular optimization. His group develops explainable AI methods to enable collaboration between humans and machines. Key projects include DeepCubeA (solving the Rubik’s Cube via deep reinforcement learning) and neural activation function research. Funding & Awards : He has secured grants from NSF, NASA EPSCoR, and South Carolina’s ASPIRE and MADE programs. Notable awards include the NSF Graduate Research Fellowship and the Graduate Education for Minority Students Fellowship. Teaching : He teaches courses in Artificial Intelligence (CSCE 580) and Deep Reinforcement Learning and Search (CSCE 790), mentoring over 15 students at undergraduate and graduate levels. Labs & Collaborations : Active in AI-driven education and interdisciplinary projects, his lab contributes to tools like ALLURE for children’s learning and Bioinformatics platforms like CircadiOmics.