جست‌وجوی استاد و پژوهشگر

Rasha Abdel Rahmanمشاهده پروفایل

استاد

• Language production
• Neurocognitive psychology
• Visual perception
• +۵ مورد دیگر

Prof. Dr. rer. nat. Rasha Abdel Rahman is a leading figure in Neurocognitive Psychology at the Institute of Psychology, Humboldt-Universität zu Berlin . Her research bridges the domains of language production , visual perception , and semantic memory organization , with particular emphasis on electrophysiological mechanisms (EEG) and emotional influences on cognitive processing. Habilitation in Psychology (2008), Ph.D. in Psychology (summa cum laude, 2001), and M.S. in Psychology (1997) from Humboldt-Universität Since 2010: Heisenberg Professor of Neurocognitive Psychology 2009: Heisenberg Fellow Her research interests focus on: Language production mechanisms Interface between vision, semantics, and language Functional organization of semantic memory Attentional and emotional modulation of perception Face and object perception dynamics Mental imagery processes The laboratory employs behavioral and electrophysiological (EEG) methods to investigate how knowledge shapes perception and language processing. Recent studies examine: Emotional content's impact on social judgment AI-generated face perception Art perception influenced by artist morality Trustworthiness effects in visual consciousness Selected scientific awards include: Heisenberg-Fellowship (DFG, 2008) Heinz Heckhausen Junior Scientist Award (DGPs, 2002)

Katy Ilonka Geroمشاهده پروفایل

مدرس

• Human-Computer Interaction
• Creativity
• Writing Assistants
• +۶ مورد دیگر

Katy Ilonka Gero is a Lecturer at the University of Sydney's School of Computer Science, with a PhD in Computer Science from Columbia University (2022). She holds a BSc in Mechanical Engineering from MIT, where she received the Carl G. Sontheimer Prize for Excellence in Innovation and Creativity. Education : BSc (MIT), PhD (Columbia) Her research focuses on Human-Computer Interaction , Creative Writing , and AI Ethics , particularly examining how language models impact writing processes, ownership, and agency. She advocates for community-driven language models trained on consensual data and explores technical innovations for personalized AI tools. Recent publications span language model ethics (Nature Machine Intelligence 2023), generative AI (CHI 2025 Best Paper), and creative collaboration (CHI 2023). Key trends include user-centered AI design , creative ownership , and data ethics . Scientific Awards : NSF Graduate Research Fellowship, Brown Institute for Media Innovation, Amazon Research Award, CHI Best Paper (2025), CHI Honorable Mention (2024) As co-founder of Ensemble Park and former taper editor, she bridges computational poetry and traditional literary practices. Her work at startups Rest Devices and Soofa demonstrates technical innovation in consumer and urban tech.

• Memory
• Cognitive Control
• Neuroscience
• +۵ مورد دیگر

Mikael Johansson is a Professor in the Department of Psychology at Lund University, where he leads research on the cognitive and neural bases of memory and cognitive control. His academic appointments include membership in eSSENCE: The e-Science Collaboration, LAMiNATE (Language Acquisition, Multilingualism, and Teaching), LU Profile Area: Proactive Ageing, and LU Profile Area: Natural and Artificial Cognition. With 159 research outputs and leadership in 18 projects (8 active), he maintains a prominent position in cognitive neuroscience research. His research focuses on the neural mechanisms of memory using behavioral, electrophysiological (EEG/ERP), and functional magnetic resonance imaging (fMRI) methods. Key interests include interactions between memory systems, formation and retrieval of episodic memories, emotion regulation and emotional memory, mechanisms underlying incidental and intentional forgetting, and the relationship between eye-movements, mental imagery and memory. His work has significant implications for understanding memory functions in psychiatric conditions such as depression and post-traumatic stress disorder. Analyzing his extensive publication record spanning from 2012-2025 reveals a consistent focus on memory mechanisms with increasing integration of eye-tracking methodologies and clinical applications. His research demonstrates an evolving trajectory from basic memory processes toward understanding memory in real-world contexts and clinical populations, with recent work emphasizing the role of eye movements in memory construction and the neural dynamics of memory integration. Mikael Johansson serves as a member of The Swedish National Committee for Psychological Sciences at the Royal Swedish Academy of Sciences since 2011 and has received significant research funding including from The Bank of Sweden Tercentenary Foundation, Swedish Research Council, and Stiftelsen Marcus och Amalia Wallenbergs Minnesfond. His current major projects include TEAM: Transdisciplinary Approaches to Learning, Acquisition, Multilingualism (2024-2029), Tracking cognitive change as a function of normal ageing and different types of degenerative disease, and How the brain constructs the present and reconstructs the past via sequences of eye movements. He leads the Lund Memory Lab where his team investigates how the brain constructs and maintains coherent episodic memories through eye movements. His research group actively collaborates with international partners across multiple disciplines, bridging cognitive psychology, neuroscience, and clinical applications. The lab's work has gained significant attention, with several publications being highlighted in news outlets and academic discussions.

Sylvain Lefebvreمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Texture Synthesis
• Additive Manufacturing
• +۴ مورد دیگر

Sylvain Lefebvre is a permanent researcher at INRIA (Institut National de Recherche en Informatique et en Automatique) in France, where he leads the MFX research team since 2018. Previously, he was part of the ALICE group at INRIA Nancy (2009-2018) and the REVES team in Sophia Antipolis (2006-2009). His career includes a postdoctoral position at Microsoft Research Seattle (2005) following his PhD at INRIA Rhones-Alpes under Fabrice Neyret. His educational background includes a PhD in Computer Graphics from Université Joseph Fourier (Grenoble) in 2005, preceded by a Master in Computer Graphics from INP Grenoble in 2001. His habilitation thesis focused on Runtime Texture Synthesis. Lefebvre's research centers on simplifying content creation for highly detailed patterns, structures, and shapes with applications spanning Computer Graphics to additive manufacturing. He develops fast, controllable by-example synthesis approaches that generate content while enforcing user-specified constraints. His work addresses computational challenges through novel data structures and algorithms optimized for GPUs and FPGAs, including his Silice programming language. The ERC-funded ShapeForge project (2012-2017) advanced shape generation for 3D printing, leading to the IceSL software for digital modeling and fabrication. Analysis of his 15 most recent publications reveals a strong focus on additive manufacturing optimization, with recurring themes in structural integrity, material efficiency, and geometric algorithms. His work bridges computer graphics theory with practical fabrication constraints, particularly in microstructure design, slicing techniques, and mechanical metamaterials. The interdisciplinary nature spans computer science, materials engineering, and robotics. EUROGRAPHICS Young Researcher Award (2010) ERC Starting Grant for ShapeForge project (2012) Lefebvre has advised over 25 PhD students and interns including Marco Freire, Thibault Tricard, and Jimmy Etienne. His ShapeForge project received significant ERC funding, supporting research in computational fabrication. He serves on numerous program committees including SIGGRAPH, Eurographics, and SIGGRAPH Asia, reflecting his leadership in the computer graphics community. As leader of the MFX team since 2018, Lefebvre directs research in computational fabrication, focusing on IceSL software development for 3D printing workflows. The team integrates computer graphics techniques with manufacturing constraints, developing tools that simplify complex object design and fabrication while addressing real-world challenges in material usage and structural integrity.

• Contemporary German-Language Literature
• GDR Literature
• Cultural Studies Didactics
• +۷ مورد دیگر

Dr. Frank Thomas Grub is Professor of German and Head of the Department of Modern Languages at Uppsala University. Educated at Universität des Saarlandes (Dr. phil. 2003) and promoted to Associate Professor at Gothenburg University (2012) before joining Uppsala as Professor (2018), he has been recognized as Excellent Teacher (2021). As founder of the Landeskunde Nord network since 2012 and editor of the Peter Lang book series since 2013, his career bridges academic research, educational innovation, and international collaboration. His research focuses on contemporary German-language literature , GDR cultural studies , and transcultural didactics . Key projects explore autobiographical writing, post-Wall literary representations, and the intersection of travel literature with cultural memory. Recent publications (2024-2025) examine media narratives of reunification and cultural stereotypes in language pedagogy. Prof. Grub contributes to German as a foreign language curriculum development Intercultural competence frameworks Editorial leadership in Northern European Germanistics His 2023-2025 works analyze graphic novels in education, migration discourse in literature, and media transitions during the Cold War.

Raquel Fernandezمشاهده پروفایل

استاد

• Natural Language Processing (NLP)
• Dialogue Systems
• Computational Linguistics
• +۷ مورد دیگر

Raquel Fernández is a Full Professor of Computational Linguistics and Dialogue Systems at the Institute for Logic, Language & Computation (ILLC), University of Amsterdam. She serves as Vice-Director for Research at ILLC and is a board member of the ELLIS Amsterdam Unit. Her research focuses on interdisciplinary approaches at the intersection of computational linguistics, cognitive science, and artificial intelligence, with emphasis on dialogue modeling, multimodal processing, and language grounding in visual/social contexts. Her work is supported by prestigious grants including the European Research Council (ERC Consolidator Grant 819455) and multiple Dutch Research Council (NWO) awards (VENI, VIDI, Aspasia). She has received scientific recognition such as the Outstanding Paper Award at EMNLP and Best Data Award at GenBench Workshop. Her recent publications analyze multimodal dialogue systems, visual storytelling evaluation consistency, and co-speech gesture modeling, reflecting trends in Linguistic-Cognitive Integration , Multimodal AI , and Contextual NLP . She leads the Dialogue Modelling Group and has been actively involved in academic leadership as co-president of SemDial, VP-Elect for SIGDAT, and ethics chair for major conferences like COLM. Scientific Awards ERC Consolidator Grant 819455 NWO VENI/VIDI/Aspasia grants Outstanding Paper Award at EMNLP 2023 Best Data Award at GenBench Workshop Elected ELLIS Fellow 2023

Lianne Lefsrudمشاهده پروفایل

دانشیار

• Risk Management
• Sustainability
• Organizational Theory
• +۵ مورد دیگر

Lianne Lefsrud serves as Associate Professor and Risk, Innovation, and Sustainability Chair (RISC) in the Department of Chemical and Materials Engineering at the University of Alberta's Faculty of Engineering. Her interdisciplinary research bridges engineering, social sciences, and policy to transform risk management practices across energy, mining, construction, and railroading industries, directly influencing regulations, building codes, and industry operations for sustainable development. Her academic credentials include: BSc in Civil Engineering (Cooperative Program), University of Alberta (1994) MSc in Interdisciplinary Civil & Environmental Engineering and Sociology, University of Alberta (1996) PhD in Strategic Management and Organization, Alberta School of Business (2014) Dr. Lefsrud's research centers on risk management frameworks for sustainability challenges. She examines hazard identification, social license to operate, and technology adoption drivers in high-hazard industries, with emphasis on prospective risk assessment (e.g., hydrogen infrastructure design) and retrospective analysis (e.g., microplastic pollution impacts). Her work integrates circular economy principles into energy systems while addressing unintended consequences across UN Sustainable Development Goals. Recent publications (2024-2025) demonstrate heavy focus on machine learning applications for rail and construction safety, hydrogen infrastructure risk analysis, and science denial mitigation. Key patterns show cross-industry adaptation of AI for incident prediction, regulatory gap analysis for emerging energy systems, and socio-technical approaches to reconcile sustainability goals with operational realities. Scientific recognition includes: Erb Post-Doctoral Fellowship (University of Michigan) Dow Sustainability Research Fellowship (Ross School of Business) Dr. Lefsrud mentors graduate students through industry-integrated projects like her Sustainable Design course where teams generated patents and city solutions. Her research secures Alberta Innovates funding with 1:4 industrial-to-federal matching, collaborating with Suncor, Transport Canada, and Canadian Standards Association. Grants target practical implementations including railcar inspection systems and hydrogen safety protocols. She co-founded Insight Risk Systems and leads the Lefsrud Lab, prioritizing inclusive teams with under-represented groups (women, Indigenous, LGBTQ2S+, neurodiverse) to tackle 'wicked problems' in sustainability. The lab leverages interdisciplinary partnerships across engineering, computer science, psychology, and environmental sociology for real-world risk management solutions.

Qi Yuمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Science
• +۶ مورد دیگر

Qi Yu is a Professor in the School of Information at the Golisano College of Computing and Information Sciences at Rochester Institute of Technology (RIT). He serves as the Graduate Program Director and directs the Machine Learning and Data Intensive Computing Lab. His research focuses on machine learning, deep learning, and data-driven knowledge discovery, particularly in knowledge-rich domains like medicine and bioinformatics. He holds a B.E. from Zhejiang University, an M.E. from the National University of Singapore, and a Ph.D. from Virginia Tech. His work emphasizes interpretable models, multimodal data fusion, and human-in-the-loop learning. He has secured significant grants, including a $500K NSF award and a $1.6M ONR grant, supporting projects on Bayesian learning frameworks and decision-making under uncertainty. His lab actively explores active learning, few-shot learning, and uncertainty quantification. He advises a vibrant group of Ph.D. and MS students and teaches courses such as Data-Driven Knowledge Discovery and Thesis/Project Capstones. Education: B.E., Electrical Engineering, Zhejiang University (2001) M.E., Computer Engineering, National University of Singapore (2003) Ph.D., Computer Science, Virginia Tech (2008) Research Interests: Machine Learning, Deep Learning, Vision-Language Models, Uncertainty Quantification, Active Learning, Multimodal Data Fusion, Bayesian Methods, and Applications in Healthcare and Cybersecurity. Recent Work Trends: His articles emphasize label-efficient learning, interactive systems, and applying ML to complex domains like medical imaging and anomaly detection. Notable projects include Bayesian learning for dynamic decision-making and evidential optimization for robust models. Awards/Grants: NSF IIS Award ($500K, 2018–2023); DoD/ONR Award ($1.6M, 2018–2023); multiple conference recognitions (NeurIPS, ICML, CVPR). Advising spans over 20 students, many securing roles at Amazon, Samsung, and academia. Labs/Teams: Leads the Mining Lab, collaborating on interdisciplinary projects with domain experts in medicine, cybersecurity, and material science.

Kenneth M. Andersonمشاهده پروفایل

استاد

• Software Engineering
• Crisis Informatics
• Human-Computer Interaction
• +۳ مورد دیگر

Professor Kenneth M. Anderson is Chair of the Department of Computer Science and holds the Palmer Endowed Chair at the University of Colorado Boulder's College of Engineering & Applied Science. He co-directs Project EPIC, a $4M NSF-funded initiative on social media use during mass emergencies, and serves as Co-Director of the Center for Software and Society. Ph.D. in Computer Science, University of California, Irvine (1997) Joined CU Boulder in 1998; tenured in 2005 Former Associate Dean for Education (2016-2019) Former Associate Chair (2010-2013) His research bridges software engineering, crisis informatics, and human-computer interaction. Current projects focus on large-scale social media analytics, crisis response systems, and software architecture for data-intensive environments. Recent publications explore multilingual social media analysis (ML-EPIC), bug fix patterns in software development (FIXR), and collaborative big data platforms. Themes include disaster risk communication, data modeling challenges, and asynchronous analysis tools. ATLAS Fellow (2010) As department chair, he spearheaded initiatives for inclusivity, including creating a Bachelor of Arts in Computer Science and establishing the NSF Broadening Participation in Computing plan. He oversees major academic reforms and departmental operations.

Guoquan Huangمشاهده پروفایل

استادیار

• Robotics
• Computer Vision
• Robot Learning
• +۵ مورد دیگر

Guoquan Huang is an Assistant Professor in the Department of Mechanical Engineering at the University of Delaware. He holds a B.Eng. in Automation from the University of Science and Technology, Beijing (2002), and M.Sc. and Ph.D. degrees in Robotics from the University of Minnesota (2009 and 2012). Prior to his current role, he was a Postdoctoral Associate at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on robotics, computer vision, and autonomous systems, emphasizing probabilistic perception, estimation, and control for ground, aerial, and underwater vehicles. He leads the development of the OpenVINS platform for visual-inertial estimation and has contributed to advancements in SLAM (Simultaneous Localization and Mapping), sensor fusion, and multi-robot coordination. Education: B.Eng in Automation (Electrical Engineering), University of Science and Technology, Beijing, 2002 M.Sc. in Robotics, University of Minnesota, Twin Cities, 2009 Ph.D. in Robotics, University of Minnesota, Twin Cities, 2012 His research interests span robotics, computer vision, and autonomous systems , with a focus on: Visual-inertial navigation and SLAM Sensor fusion (LiDAR, IMU, camera) Autonomous vehicle control and safety Multi-robot cooperative localization His recent publications (2023–2025) emphasize robust algorithms for navigation in GPS-denied environments, real-time sensor calibration, and dataset development for aerial visual localization. He has pioneered techniques like decoupled error-state estimation and consistent parallel frameworks for SLAM. Labs/Teams: Leads the development of the OpenVINS research platform, focusing on visual-inertial state estimation. Collaborates on projects involving human-swarm interactions and resilient ground vehicle navigation.

Robert J Brunnerمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Statistical Analysis
• +۳ مورد دیگر

Robert J. Brunner is a Professor at the University of Illinois with primary appointments in the Gies College of Business (Department of Accountancy) and the School of Information Sciences. He holds affiliate roles across multiple departments including Astronomy, Computer Science, and Statistics, as well as research centers like the Beckman Institute and NCSA. His research focuses on applying statistical/machine learning to solve complex problems in astronomy, finance, and large-scale data science. Education: Ph.D. in Astrophysics from Johns Hopkins University (advisor: Alex Szalay). Postdoctoral work at Caltech on the Digital Sky project. Research Interests: Machine learning applications, computational techniques, data management/visualization, and observational cosmology. His work bridges astrophysical data analysis with modern data science methodologies. Recent work includes developing spatio-temporal neural networks for forecasting, evaluating AI-driven financial analysis tools, and planning for the Vera C. Rubin Observatory. He collaborates internationally on large-scale surveys like the Dark Energy Survey and SDSS. Labs/Teams: Leads data science initiatives at the University of Illinois Research Park. Active in interdisciplinary teams at NCSA and Beckman Institute focusing on algorithm optimization and data-intensive research.

• Computer Architecture
• Accelerated Machine Learning
• Computer Science Education
• +۳ مورد دیگر

Thomas Yeh is an Assistant Professor of Teaching in the Department of Computer Science at the University of California, Irvine. His academic background includes a Ph.D. in Computer Science from UCLA and a BS in Electrical Engineering and Computer Science from UC Berkeley. Prior to academia, he gained industry experience across research, architecture, design, verification, marketing, and management roles. His educational credentials: Ph.D. in Computer Science, UCLA BS in Electrical Engineering and Computer Science, UC Berkeley Dr. Yeh's research spans computer architecture, accelerated machine learning, and computer science education. In architecture, he pioneers error-tolerant physics simulation and heterogeneous computing. His ML work focuses on adaptive precision techniques for energy-efficient acceleration. In education, he develops interactive tools for novice programmers and experiential learning frameworks for computer architecture. His cross-disciplinary approach bridges hardware-software co-design with pedagogical innovation. Publication trends reveal consistent focus on computational efficiency across physics simulation, ML acceleration, and educational technology. His work connects real-time systems optimization with emerging AI applications, particularly in interactive environments and physics-based animation. No scientific awards are documented in the provided materials. While advising details and grant funding specifics are absent from available information, his industry-academia transition informs practical research directions. Teaching responsibilities include core courses like Introduction to CS, Data Structures, and Efficient ML Computing. Research infrastructure details remain unspecified, though his publications suggest collaborations in physics simulation and heterogeneous computing environments.

Min Liuمشاهده پروفایل

استاد

• Lean Construction
• Digital Twin Design
• Data Mining
• +۵ مورد دیگر

Dr. Min Liu is a Professor and the Abdallah H. Yabroudi Endowed Professor in Sustainable Civil Infrastructure at Syracuse University, where she directs the Syracuse University Infrastructure Institute. She holds a Ph.D. in Engineering Project Management from UC Berkeley, and prior degrees from National University of Singapore and Xi’an University of Architecture and Technology. Her research focuses on integrating human and engineering aspects in construction planning, with emphasis on Lean Construction, Digital Twin design, and machine learning applications. She has published over 50 articles in top-tier journals and won prestigious awards such as the 2021 ASCE Thomas Fitch Rowland Award. Dr. Liu advises numerous graduate students and postdocs, offering positions in Construction Engineering and Management. Her lab develops innovative approaches for infrastructure project delivery, worker mental health, and bridge preservation strategies. Education: Ph.D. in Engineering Project Management, UC Berkeley (2007) MSCE, Xi’an University of Architecture and Technology (1997) MSc in Building Science, National University of Singapore (2001) BSc in Civil Engineering, Qingdao University of Technology (1994) Research Highlights: Large language models for construction planning reliability Ontology-based knowledge systems for construction methods Lean techniques for worker mental health improvement Socioeconomic analysis of bridge preservation strategies Awards: 2021 ASCE Thomas Fitch Rowland Award Multiple Best Paper Awards (2017-2018) "Thank a Teacher" awards (2011-2018) Her advising record includes notable students like Chuanni He (2023 Chinese Government Award) and Gongfan Chen (2022 Three-Minute Thesis Award). Current opportunities include Ph.D. financial support and postdoc positions. Dr. Liu’s work appears in journals like ASCE Journal of Management in Engineering and Engineering, Construction and Architectural Management.

Shuangquan Wangمشاهده پروفایل

استادیار

• Mobile & Wearable Computing
• Activity Recognition
• Smart Health
• +۳ مورد دیگر

Shuangquan (Peter) Wang is an Assistant Professor of Computer Science at Salisbury University. He holds a PhD in Computer Science from the College of William & Mary (2020) and a PhD in Pattern Recognition and Intelligent Systems from Shanghai Jiao Tong University (2008), along with earlier degrees from Wuhan University of Technology and Wuhan Institute of Technology. His research focuses on mobile/wearable computing, activity recognition, smart health, and machine learning. He has over 10 years of experience in academia and industry, including roles at Philips Research East Asia and Nokia Research Center (Beijing). His work emphasizes wearable sensor-based health monitoring, such as fall detection, mastication analysis, and Parkinson’s disease monitoring. He leads the WISH Research Lab and serves as an Associate Editor for Elsevier's Smart Health Journal. Recent contributions include papers on salinity anomaly detection (2024), LLM-based user requirement analysis (2024), and socially acceptable food recognition (2022). His research trends emphasize interdisciplinary applications of machine learning in healthcare and sensor-driven human activity analysis. Professional service roles include coordinating Salisbury University’s Center for Applied Mathematics and Science (2021–2024) and chairing ACM/IEEE CHASE conferences. He has delivered invited talks on artificial intelligence and its societal impacts to diverse audiences, including the Institute of Retired Persons at Salisbury University. His lab, WISH Research Lab, explores innovative solutions in smart health and mobile computing, integrating wearable technologies with machine learning for real-world health applications.

Zezhou Chengمشاهده پروفایل

استادیار

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

Zezhou Cheng is an Assistant Professor of Computer Science at the University of Virginia, leading the Computer Vision Lab. He holds a Ph.D. from UMass Amherst (2023), a postdoctoral position at Caltech, and a Bachelor's degree from Sichuan University (2015). His research focuses on computer vision, machine learning, and their applications in ecology, materials science, and autonomous systems. Key areas include 3D understanding, self-supervised learning, and AI-driven ecological monitoring. He has received awards such as the Best Synthesis Award (2020) and Outstanding Reviewer (CVPR 2021). His work spans publications in top venues like CVPR, ICCV, and ECCV, addressing challenges in 3D reconstruction, generative models, and ecological data analysis. Education: Ph.D. in Computer Science, UMass Amherst (2023) Bachelor's Degree, Sichuan University (2015) Postdoctoral Researcher, Caltech (advised by Georgia Gkioxari) Research Highlights: Developed LU-NeRF for unposed scene reconstruction and camera pose estimation Contributed to AI for ecology via bird roost detection using weather radar data Advanced 3D representation learning through procedural programs and self-supervised techniques Awards & Recognition: Outstanding Reviewer, CVPR 2021 Best Poster Award, New England Computer Vision Workshop 2019 National Scholarship (China, 2014 and 2016) His lab explores cutting-edge topics in computer vision, with a focus on interdisciplinary applications. Teaching roles include leading Caltech's AI Bootcamp and serving as a Teaching Assistant at UMass Amherst. Industry collaborations include internships at Google Research, Snap, and Amazon.