جست‌وجوی استاد و پژوهشگر

Nuwan Wijewardaneمشاهده پروفایل

استادیار

• Soil Spectroscopy
• Crop Sensing
• Precision Agriculture
• +۴ مورد دیگر

Nuwan Wijewardane is an Assistant Professor in the Department of Agricultural and Biological Engineering at Mississippi State University. His research focuses on soil and crop sensing, precision agriculture, and the application of machine learning and IoT technologies. He leads the Advanced Plant & Soil Sensing Laboratory, developing novel sensing tools for agricultural monitoring. Education: Ph.D., Biological Engineering, University of Nebraska-Lincoln, USA M.S., Agricultural and Biological Systems Engineering, University of Nebraska-Lincoln, USA M.Sc., Agricultural and Biosystems Engineering, University of Peradeniya, Sri Lanka B.Sc., Agricultural Technology and Management, University of Peradeniya, Sri Lanka Research Interests: Soil spectroscopy and crop sensing IoT-driven precision agriculture systems Plant phenotyping via remote sensing Machine learning for spectral data analysis Publications: Dr. Wijewardane has published extensively in soil science, remote sensing, and agricultural engineering journals. His work spans spectral analysis techniques, sensor development, and environmental monitoring applications. Labs/Teams: He directs the Advanced Plant & Soil Sensing Laboratory, emphasizing interdisciplinary collaborations in sensor technology and data science for sustainable agriculture.

Pedro Felipe Felzenszwalbمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Pedro Felipe Felzenszwalb is a Professor of Engineering and Computer Science at Brown University . His work bridges computer vision, artificial intelligence, and optimization, with a focus on structured models and probabilistic inference. His research spans low-level image restoration, mid-level segmentation, and high-level object recognition. Key methodologies include deformable part models, belief propagation, and stochastic grammars. He has mentored numerous PhD students, including Ross Girshick and Anna Grim , and his publications reflect collaborations in machine learning and applied mathematics. Recent work (2020–2025) explores super-resolution, belief propagation dynamics, and convex optimization. Though no formal awards are mentioned, his contributions to computer vision are evident through textbooks, courses like Pattern Recognition and Machine Learning , and tools like deformable part models.

Chris DiMattinaمشاهده پروفایل

استاد

• Vision Science
• Computational Neuroscience
• Cognitive Psychology
• +۴ مورد دیگر

Chris DiMattina is a Professor of Psychology at Florida Gulf Coast University (FGCU), affiliated with the College of Arts & Sciences. He leads the Computational Perception Laboratory focusing on computational models of sensory processing, particularly mid-level vision problems like texture segmentation and shadow detection. His work is supported by an NIH R15 grant (NIH-R15-EY032732-01). He holds a PhD in Neuroscience from Johns Hopkins University and a BA in Mathematics and Psychology from Cornell University. Research interests include visual perception mechanisms, computational neuroscience, and trypophobia studies. He serves as Associate Editor for Frontiers in Psychology: Perception Science and Frontiers in Neuroscience: Perception Science . His lab develops machine learning models to understand human visual behavior, leveraging platforms like Qualtrics® for data collection. Key publications include studies on distinguishing shadows/surface boundaries, trypophobia mechanisms, and adaptive psychophysical methods. His NIH-funded research explores texture segmentation and scene analysis. GitHub repositories showcase code for his modeling work, including 2015 J. Vision and 2022 PLoS Computational Biology papers.

• Machine Learning
• Computer Vision
• Human Activity Recognition
• +۳ مورد دیگر

Fadi Al Machot is an active researcher in artificial intelligence and machine learning, focusing on applications in human activity recognition, emotion detection, and sensor-based systems. His work often explores zero-shot learning, deep learning frameworks, and the integration of symbolic knowledge into neural networks. Collaborations with co-authors such as Kyandoghere Kyamakya highlight interdisciplinary research in complex systems and adaptive technologies. Key research areas include: Human Activity Recognition (HAR), Emotion Recognition, Sensor Networks, Zero-Shot Learning, and Explainable AI. Recent contributions emphasize noise-resilient time series forecasting and transparent AI system development using ontologies and logical reasoning. Publications span prestigious journals like Sensors, IEEE Access, and Symmetry, with notable work in conferences such as WACV, COINS, and AVSS. His research bridges theoretical advancements with practical implementations in healthcare, transportation, and manufacturing domains.

Sander Stuijkمشاهده پروفایل

دانشیار

• Embedded Systems
• Signal Processing
• Multi-Core Architectures
• +۳ مورد دیگر

Sander Stuijk is an associate professor at the Department of Electrical Engineering of Eindhoven University of Technology, chairing the Electronic Systems (ES) group. His research focuses on design methodologies for embedded signal processing applications in high-tech systems like industrial manufacturing, automotive, and healthcare. He develops high-level compilation strategies for heterogeneous multi-core platforms, with interests in efficient code generation and resource allocation. Prof. Stuijk holds leadership roles including chairing the ES group, serving on the board of 4TU.NIRICT (a Dutch ICT research consortium), and coordinating the Embedded Systems master program. He contributes to national ICT initiatives through ICT Next Generation, a network for mid-career academics. He also chaired the Department Council (2016-2018) and participates in TPC activities. His research spans streaming applications, real-time systems, and multi-processor architectures. Notable projects include the PROMES initiative (programming embedded multi-media systems) and the MNEMEE project (automated MPSoC design). His work emphasizes predictable and energy-efficient computing, with applications in healthcare monitoring (e.g., remote PPG, thermal imaging) and reconfigurable systems. Prof. Stuijk has advised numerous PhD/Master students and led projects like FORSEE, VSM, and SenSafety. His publications include over 70 journal/conference papers, covering topics like neuromorphic computing, GPU optimization, and medical signal processing. He actively engages in academic service, including organizing SCOPES workshops and serving on editorial boards.

Cathleen Mooreمشاهده پروفایل

استاد

• Visual Perception
• Attention
• Object Perception
• +۳ مورد دیگر

Cathleen Moore is a Professor in the Department of Psychological and Brain Sciences at the University of Iowa, holding the Starch Faculty Fellow title. She leads the Iowa Attention and Perception Lab, focusing on mid-level visual processes and their interactions with attention and memory. Education: Ph.D., University of California-San Diego (1994) Her research examines how mid-level visual processes interpret retinal images into 3D object representations, addressing questions about figure-ground segmentation, depth perception, and material properties. Using psychophysical methods, eye tracking, and virtual reality, she investigates real-world applications like lifeguard surveillance and medical-image reading. Recent publications (2019-2024) demonstrate her focus on visual attention mechanisms, object correspondence, and perceptual organization. These works appear in journals such as Journal of Vision and Attention, Perception & Psychophysics . Scientific Award: Starch Faculty Fellow She collaborates closely with Andrew Hollingworth’s lab, sharing research projects and laboratory space in the Psychological and Brain Sciences Building.

• Marketing Research
• Marketing Analytics
• Customer Defection
• +۵ مورد دیگر

Dr. John Dillard is an Associate Professor in the Department of General Business, Marketing and Supply Chain at the University of Houston-Downtown (UHD), where he has taught since 2010. His academic appointments include prior positions at the University of Maryland System, University of Tulsa, Virginia Tech, and University of Arkansas. He holds a Ph.D. in Marketing from the University of Arkansas with supporting concentrations in Management and MIS/Statistics, complemented by industry experience as a founding partner of AVALA Marketing Group and Center for Strategic Management and Marketing. Research Focus: Dr. Dillard specializes in consumer behavior analytics with emphasis on Customer Defection, Net Promoter Score optimization, Loyalty Mechanics, and Leisure Motivation. His work bridges theoretical marketing frameworks with practical applications in automotive, retail, and services industries. Key research streams include digital sales viability, customer retention economics, and motivational drivers in recreational activities. Publications: His scholarly output demonstrates consistent engagement with marketing strategy evolution, spanning 1975-2023. Early career research centered on logistics and transportation economics, transitioning to consumer behavior in the 2000s, with current focus on digital marketing transformation and leadership dynamics. Recurring themes include satisfaction metrics application, decision-making psychology, and strategic adaptation to market disruptions. Awards & Service: Sigma Iota Epsilon Honor Society recognition Program Coordinator for UHD institutional research initiatives including brand strategy development and donor evaluation Committee leadership: Grievance Panel, Faculty Development, Rank & Tenure, and Curriculum committees across university/college/department levels Instructional Portfolio: Teaches core marketing courses including Marketing Research (MKT 4306), Principles of Marketing (MKT 3301), and Marketing Management (MBA 6208) at undergraduate and graduate levels, with consistent teaching load across 2020-2025 academic cycles.

Talia Konkleمشاهده پروفایل

استاد

• High-level vision
• Visual representation
• Object recognition
• +۵ مورد دیگر

Talia Konkle is a Professor of Psychology in the Faculty of Arts and Sciences at Harvard University. She directs the Konkle Lab, which is part of the joint Harvard Vision Sciences Lab co-led with Professor George Alvarez. Her research focuses on understanding how the human visual system is organized and how it represents the world around us, with particular emphasis on high-level vision including object recognition, scene perception, and visual memory. William James Hall 780, 33 Kirkland St, Cambridge, MA Phone: (617) 495-3886 Email: talia_konkle@harvard.edu Professor Konkle's research explores how representational spaces of the mind are mapped onto the surface of the brain. Her work examines how we recognize and organize our knowledge about objects, scenes, and actions and events. She uses human functional neuroimaging as her primary methodology, combined with computational modeling and behavioral approaches. Her recent publications reveal a strong focus on the intersection of neuroscience and artificial intelligence, with particular emphasis on understanding how brain organization relates to machine learning models. The research trends show increasing integration of deep learning approaches to model visual representation, with significant work on cortical topography, visual memory capacity, and the relationship between real-world object properties and neural organization. Lila R. Gleitman Award from Cognitive Science Society Professor Konkle has mentored numerous graduate students, postdocs, and research fellows, including current members like Fenil Doshi, Jacob Prince, and Haley Keglovits. Her lab has received funding through various grants including those from the Kempner Institute for Natural and Artificial Intelligence. She collaborates extensively with Professor George Alvarez and other researchers in the Vision Sciences Lab. The Konkle Lab maintains several important resources for vision research, including the Reachspace Database with over 10,000 images of reachable environments, various stimulus sets for studying object recognition and visual memory, and computational tools for analyzing visual representations. Professor Konkle also values work-life balance, noting that she is a mother of two daughters (ages 7 and 9) and a cancer survivor.

Gemma Roigمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Neuroscience
• +۳ مورد دیگر

Gemma Roig is a Professor (W1 with tenure-track) at the Computer Science and Maths Department of Goethe University Frankfurt since 2020. She holds an honorary research affiliate position at MIT. Previously, she served as an Assistant Professor at Singapore University of Technology and Design, conducted postdoctoral research at MIT's Center for Brains Minds and Machines, and was affiliated with the Laboratory for Computational and Statistical Learning (MIT-Istituto Italiano di Tecnologia). Her academic journey includes roles at ETH Zurich (PhD in Computer Vision), EPFL, Ramon Llull University, and Carnegie Mellon University. Roig's research focuses on computational models of human vision, aiming to uncover underlying principles and develop ethical AI applications. She investigates neural mechanisms in vision, brain-AI alignment, and ethical implications of AI deployment. Notable contributions include the Net2Brain toolbox for comparing artificial vision models with human brain responses and the Algonauts Project (2019–present), bridging biological and artificial intelligence research. Her scientific accolades include the Z-inspection® Teaching Certificate. She has advised projects on multimodal learning, domain adaptation, and brain-inspired predictive coding. Her work spans collaborations with institutions like MIT, IIT, and the Algonauts consortium. Research outputs include over 50 peer-reviewed articles, emphasizing neuro-inspired AI, ethical AI frameworks, and computational vision modeling.

Talin Louderمشاهده پروفایل

استادیار

• Biomechanics
• Aquatic Exercise Therapy
• Neuromuscular Control
• +۳ مورد دیگر

Talin Louder serves as an Assistant Professor in the Department of Kinesiology and Health Science at Utah State University, where he directs the Dennis G. Dolny Movement Research Clinic within the Sorenson Center for Clinical Excellence. His academic journey spans biomechanics research, teaching, and clinical application with focus on human movement analysis. His educational credentials include: PhD in Disability Disciplines, Utah State University (2017) MS in Health and Human Movement, Utah State University (2013) BS in Exercise Science, Utah Valley University (2010) AS from Southern Utah University Dr. Louder's research centers on wearable technologies for movement analysis , sensorimotor and neuromuscular determinants of agile movement , and therapeutic applications of aquatic environments . His work integrates biomechanical principles with rehabilitation science to optimize movement training and recovery protocols, frequently comparing land-based and aquatic interventions for diverse populations. Analysis of his 15 most recent publications reveals consistent emphasis on biomechanical measurement techniques (particularly vertical stiffness and reactive strength indices), muscle activation patterns during water immersion, and innovative training methodologies like eccentric overload and stroboscopic vision. The research demonstrates strong interdisciplinary connections between sports science, geriatrics, and clinical rehabilitation. His scientific recognition includes: Graduate Faculty Mentor of the Year Award (2022) from the Department of Kinesiology and Health Science Dr. Louder has successfully mentored nine Master's students to completion while teaching core courses including Anatomical Kinesiology (KIN 3250) and Topics in Biomechanics (KIN 6610). His teaching philosophy emphasizes practical application of movement science principles to real-world rehabilitation scenarios. He leads the Dennis G. Dolny Movement Research Clinic, which provides specialized facilities for biomechanical analysis in both aquatic and land-based environments, supporting his research on movement rehabilitation across age groups and ability levels.

• Visual Perception
• Cognitive Psychology
• Computational Aesthetics
• +۳ مورد دیگر

Johan Wagemans is a full professor at the Faculty of Psychology and Educational Sciences, KU Leuven, leading the Laboratory for Experimental Psychology. He actively contributes to DigiSoc, LBI, and Leuven.AI institutes. Research Focus: Interdisciplinary work at the intersection of visual perception, computational aesthetics, and AI-mediated art. Key themes include perceptual organization, mid-level vision, and the application of machine learning to aesthetic assessment. Current projects cover symmetry detection, cross-cultural aesthetics, AI-generated art, and neural modeling of perceptual processes Recent publications emphasize computational tools, dataset development, and Gestalt principles in modern perception research Methodological Innovations: Pioneering techniques in deep learning, psychophysics, and naturalistic shape analysis to bridge cognitive science and technological applications.

Laila Riedmillerمشاهده پروفایل

پژوهشگر

• History and Contemporary Right-Wing Thinking
• Conservatism, Extreme Right and Right-Wing Terrorism
• Anti-Semitism and Conspiracy Theories
• +۶ مورد دیگر

Laila Riedmiller is a Research Assistant at the Chair of Political Theory and History of Ideas at Friedrich-Alexander University Erlangen-Nuremberg (FAU), where she also serves as Erasmus coordinator and mid-level representative in the Faculty Council. Based in Room 0.034 in Erlangen, she maintains regular consultation hours (Mondays 16:00-18:00 and Tuesdays 14:00-16:00) both in person and via Zoom, demonstrating her commitment to student engagement and accessibility. Her academic background includes: Master's degree in Political Science from the University of Bonn (2019-2022), awarded the Gender Studies Prize Two concurrent Bachelor's degrees from the University of Bonn: Comparative Religious Studies, Philosophy, and Arabic (2016-2022), and Political Science and English Studies (2013-2019) Recipient of the Deutschlandstipendium scholarship (2019-2021) Dr. Riedmiller's research focuses on the history and contemporary manifestations of right-wing thinking, including conservatism, the extreme right, and right-wing terrorism. She examines antisemitism and conspiracy theories through gendered lenses, analyzes concepts of temporality in political movements, critiques scientific epistemology, and explores democratic theories alongside feminist perspectives. Her work on religion and religious fundamentalism maintains a regional focus on the MENA region, connecting theoretical frameworks with empirical analysis of contemporary political phenomena. This interdisciplinary approach reveals how gender concepts function within extreme right conspiracy thinking and how right-wing movements conceptualize historical time. Her scholarly output demonstrates consistent theoretical rigor with increasing institutional relevance. While her early work focused on specific manifestations of right-wing ideology (like Turkish ultranationalism in Germany), her recent publications examine structural aspects of academia itself - particularly the intersection of diversity management and internationalization strategies in higher education. This evolution shows her ability to connect micro-level analysis of political ideologies with macro-level institutional challenges, making significant contributions to both political theory and practical university policy development. Her notable achievements include: Receiving the Gender Studies Prize from the University of Bonn Authoring a significant monograph on gender concepts in extreme right conspiracy thinking (Springer VS, 2023) Contributing to high-impact journals like the European Journal of Higher Education Presenting at major academic conferences including the DVPW Congress and European Association for the Study of Religions As an educator, Dr. Riedmiller supervises doctoral projects on topics like the intellectual history of accelerationism and teaches a diverse range of courses at FAU Erlangen-Nürnberg. Her teaching portfolio spans from undergraduate seminars on feminist political thought to advanced courses on memory culture and political thinking beyond the nation-state. Through regular workshops on antisemitism (including collaborations with the Jewish Museum Fürth) and panel discussions at venues like the Leipzig Book Fair, she actively bridges academic research with public education and social intervention. Dr. Riedmiller maintains a dynamic scholarly presence through multiple channels, including traditional academic publications, conference presentations, media interviews, and social media engagement. Her TikTok presence (@notwithoutmybooks) with over 400 followers demonstrates her commitment to making political theory accessible beyond academic circles. This multi-platform approach reflects her belief in the public relevance of political theory and the importance of engaging diverse audiences in discussions about contemporary political challenges.

William T. Freemanمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

William T. Freeman is the Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science (EECS) at MIT and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). Since 2015, he has also served as a research manager at Google Research in Cambridge, MA. Freeman previously held the role of Associate Department Head of EECS from 2011 to 2014. Research Interests: Mid-level vision, computational photography, 3D object structure, video synthesis, and vision-learning integration. Projects: Imaging privacy threats from ambient light sensors, Single-Image 3D Interpreter Network, Visual Dynamics, 3D-GANs, and motion magnification. His work spans foundational computer vision techniques and modern AI applications, emphasizing the importance of combining classical methods with neural networks. Recent projects include rapid video generation and ethical considerations in vision systems. Scientific Awards: 2020 Breakthrough Prize in Physics, 2019 PAMI Distinguished Researcher Award, multiple Outstanding Paper and Test-of-Time Awards. Freeman co-authored the 2024 textbook Foundations of Computer Vision , which bridges classical and deep learning-based approaches. He is actively involved in CSAIL and MIT’s Visual Computing and Vision Groups, impacting fields like cybersecurity, healthcare, and autonomous systems.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Roman Bachmann is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Visual Intelligence and Learning Lab (VILAB) within the School of Computer and Communication Sciences (IC). He holds a doctoral status as an Assistant-doctorant in the Programme doctoral en informatique et communications. His research focuses on advanced computer vision, machine learning, and AI-driven multimodal systems, with contributions to generative models, vision-language integration, and 3D scanning technologies. Key research interests include visual personalization (ViPer), multimodal learning (4M series), and scalable vision pipelines (Omnidata). His work bridges theoretical advancements with practical applications in embodied AI, robotics, and creative technologies. He is actively involved in the VILAB, contributing to projects that address challenges in cross-modal understanding and adaptive systems. Roman’s publications reflect a strong emphasis on innovation in AI, with recent work exploring flexible tokenization (Flextok) and task-agnostic vision models (4M-21). His research has implications for robotics, generative AI, and data-driven decision-making in complex environments.

• Visual Perception
• 3D Shape and Material Perception
• Lightness and Gloss Perception
• +۳ مورد دیگر

Professor Bart Anderson is a faculty member at the University of Sydney's Faculty of Science, specializing in visual perception. His research focuses on how humans perceive 3D shape, surface materials, lightness, gloss, and optical illusions. Key areas include the interplay between material properties and 3D shape perception, the role of color and motion in visual processing, and computational models of perception. Anderson has secured significant grants, including an Australian Research Council Discovery Project in 2020 exploring emergent cues in perception and a 2018 grant investigating perceptual misperceptions of optical structure. His work bridges neuroscience, computer vision, and cognitive psychology, with over 50 peer-reviewed publications. His lab explores topics like surface material inference, depth perception, and the neural mechanisms underlying visual illusions. While no current students are listed, his research has influenced theories of mid-level vision and unsupervised learning in perception.