جست‌وجوی استاد و پژوهشگر

Jasna Martinovicمشاهده پروفایل

مدرس ارشد

• Visual Perception
• Color Vision
• Cognitive Neuroscience
• +۷ مورد دیگر

Jasna Martinovic is a Senior Lecturer in Psychology at the University of Edinburgh's School of Philosophy, Psychology and Language Sciences. Her research focuses on visual perception mechanisms, particularly color vision, luminance processing, and neural representations using EEG and psychophysical methods. She has held academic positions at the University of Aberdeen (2009–2020) and the University of Leipzig (2004–2007), with extensive postdoctoral experience in Liverpool and Aberdeen. She has supervised multiple PhD students and holds a Postgraduate Certificate in Teaching & Learning in Higher Education. Education includes a Diplom(Psych.) from the University of Belgrade, MSc (Distinction) in Cognitive Neuroscience from the University of Liverpool, and a Dr.rer.nat. (Summa Cum Laude) from the University of Leipzig. Her research explores how basic visual signals (e.g., luminance, chromaticity) are transformed into environmental representations, with a focus on attention, categorical effects, and neural plasticity. Key projects include Wellcome Trust grants on lithium’s role in bipolar disorder and Royal Society collaborations on color cognition. She serves on editorial boards for Visual Neuroscience and Royal Society Open Science . Her work bridges basic and applied research, contributing to display technology advancements through EPSRC-funded projects. Major awards include Fellow of the Higher Education Academy and DAAD PhD scholarships. Her teaching spans perception, EEG methodologies, and research design at both undergraduate and postgraduate levels.

Farran Briggsمشاهده پروفایل

دانشیار مدعو

• Neuroscience
• Visual System
• Neuronal Circuits
• +۳ مورد دیگر

Farran Briggs, Ph.D., is an Adjunct Associate Professor in the Department of Neuroscience at the University of Rochester’s School of Medicine and Dentistry (SMD). She previously held an Assistant Professor position at Dartmouth’s Geisel School of Medicine (2011–2017) before moving her lab to Rochester in 2017. Her research focuses on understanding visual processing and attention mechanisms in neuronal circuits, particularly in the early visual system and thalamocortical pathways. She has been recognized with awards such as the 2022 Scholar-in-Residence and the 2016 Mentoring Award. Dr. Briggs earned a B.A. in Biology from Dartmouth College (1997) and a Ph.D. in Biology from UC San Diego (2003). Her postdoctoral training at UC Davis explored visual systems neurophysiology with Drs. Usrey, Chapman, and Mangun. Her current projects investigate corticogeniculate feedback circuits, attention-driven modulation of visual signals, and visual processing in disease models. Her lab employs cutting-edge techniques like optogenetics and functional ultrasound imaging to study how neuronal circuits encode visual information. Collaborators include researchers at the University of Rochester’s Center for Visual Science and Del Monte Institute for Neuroscience. Recent work highlights the role of corticogeniculate feedback in refining visual signal timing and precision. Dr. Briggs mentors graduate students and postdocs, emphasizing interdisciplinary approaches to neuroscience. Her lab has trained researchers like Vanessa Mock and Mike Hembrook-Short, now recognized as new PhDs. She is also engaged in vision restoration studies post-V1 damage and explores naturalistic visual processing in freely moving subjects.

Naftali Cohenمشاهده پروفایل

استاد مدعو

• Quantitative Finance
• Data Science
• Machine Learning
• +۳ مورد دیگر

Naftali Cohen is an Adjunct Professor at NYU Tandon School of Engineering and Columbia University’s Industrial Engineering and Operations Research Department. He holds a PhD in Applied Mathematics from NYU’s Courant Institute (2013), an MSc and BSc in Atmospheric Science from The Hebrew University of Jerusalem (2008 and 2006). As a Senior Quant in the hedge fund industry, he specializes in systematic, mid-frequency market-neutral equity trading using data-driven approaches. His research interests bridge quantitative finance and data science, focusing on algorithmic trading strategies, machine learning applications in financial markets, time series forecasting, and risk management. He has held roles at JP Morgan Chase, Yale University, and Columbia University prior to his current positions. Notable research trends include AI-enhanced factor analysis for equity prediction, momentum strategies, and volatility modeling. His work on adversarial attacks on ML systems for high-frequency trading highlights cybersecurity concerns in algorithmic finance. He has contributed to climate science research earlier in his career, studying atmospheric dynamics and monsoon patterns. His teaching focuses on graduate-level courses at the intersection of data science and financial engineering.

Drew Hiltonمشاهده پروفایل

استاد آموزشی

• Computer Architecture
• Microarchitecture
• Memory Systems
• +۴ مورد دیگر

Drew Hilton is Professor of the Practice in the Department of Electrical and Computer Engineering at Duke University . His academic appointment focuses on teaching and practical applications in computer engineering, with particular emphasis on bridging theoretical concepts with real-world implementation. His research spans several critical areas in computer systems and architecture: Computer Architecture & Microarchitecture - Advanced processor designs including hybrid pipeline systems Memory Systems & Security - Secure memory architectures and metadata access patterns Hardware Description Languages - Development of functional programming languages like Gemini for hardware design Parallel Computing Systems - CPU/GPU task-parallel runtimes and synchronization mechanisms Disaster Response Computing - Optimization of 3D reconstruction systems for emergency scenarios His publication record demonstrates a consistent focus on improving processor performance, security, and efficiency. The research trajectory shows evolution from foundational work on branch prediction and memory systems (2007-2012) to more recent applications in disaster response and secure computing architectures (2016-2022). Teaching Contributions: Professor Hilton teaches the MIDS Bootcamp course, indicating his involvement in data science education and practical training programs at Duke University. Contact Information: He can be reached at adh39@duke.edu .

• Signal Processing
• Machine Learning
• Acoustic Sensor Arrays
• +۳ مورد دیگر

Jan Lundgren serves as a Professor in the Department of Computer and Electrical Engineering (DET) at Mid Sweden University, specializing in signal processing and AI-driven sensor systems. His research spans acoustic sensor arrays, medical diagnostics, and industrial IoT applications. His primary research interests include: Development of acoustic sensor arrays for source localization and medical auscultation Machine learning applications in medical diagnostics (skin lesion classification, cardiac sound analysis) Industrial IoT systems for timber measurement, snow load monitoring, and surface defect detection X-ray imaging systems and noise coupling in mixed-signal electronics Analysis of his 15 most recent publications reveals a strong focus on interdisciplinary applications of signal processing and deep learning, particularly in medical diagnostics (35% of works) and industrial sensor systems (40%). Key trends include uncertainty-aware AI models (2023-2025) and multi-channel acoustic processing (2024-2025). Lundgren actively collaborates with researchers across Europe on projects including DSS-oral (digital oral healthcare) and IRS TransTech (transformative technology). His work demonstrates consistent funding through Swedish research councils and EU programs focused on industrial digitalization. He leads research within the STC Research Centre, focusing on sensor technology and instrumentation. Current projects emphasize real-world deployment of AI-enhanced sensor systems in healthcare and industrial settings, with particular attention to measurement uncertainty and system robustness.

• Control Systems
• Autonomous Marine Operations and Systems
• Aerospace Engineering
• +۳ مورد دیگر

Tor Arne Johansen is a Professor at the Department of Technical Cybernetics, Norwegian University of Science and Technology (NTNU), and a key researcher at the Center for Autonomous Marine Operations and Systems (AMOS). His work bridges control theory, autonomous systems, and remote sensing technologies. Education : Civil Engineering Degree Doctorate in Technical Cybernetics His research focuses on autonomous marine and aerospace systems , with emphasis on control algorithms , collision avoidance, and hyperspectral imaging . Recent work explores UAV navigation in extreme conditions, MPC for maritime operations, and adaptive Kalman filters for spectroscopic correction. The 15 most recent articles highlight trends in autonomous navigation , UAV control systems , hyperspectral satellite data processing , and risk-aware industrial drone applications . Subfields include RRT algorithms, wave-motion compensation, intention-aware trajectory prediction, and onboard classification for earth observation. Advising : Supervised numerous Master’s and PhD students on topics like collision avoidance, UAV recovery, and hyperspectral analytics. Collaborates with institutions like SINTEF and international researchers in maritime and aerospace domains.

Thomas Langerakمشاهده پروفایل

مدرس

• Human-Computer Interaction
• Reinforcement Learning
• Haptic Feedback Systems
• +۳ مورد دیگر

Thomas Langerak is a PhD student and Lecturer at ETH Zurich's Advanced Interactive Technologies lab under Prof. Otmar Hilliges. His academic journey includes a double-degree in Human-Computer Interaction and Design from Aalto University (Finland) and University of Twente (Netherlands), and a B.Sc. in Industrial Design from Eindhoven University of Technology. His research spans Human-Computer Interaction , Reinforcement Learning , and Haptic Systems , with recent work focusing on multi-agent RL for adaptive interfaces and novel electromagnetic actuation for VR/AR. Earlier contributions centered on custom haptic hardware like the Hedgehog pin array and Omni volumetric system. Publication trends reveal an evolution from hardware prototyping (2018-2020) toward intelligent interface optimization (2021-2024), with increasing emphasis on user modeling and adaptive automation. Key venues include ACM UIST, CHI, and IEEE Transactions. Honorable Mention - Best Paper Award at World Haptics Conference 2021 As an academic mentor, he supervised Mengfan Wu's thesis on electromagnetic tracking and Aline Abler's Hedgehog interface development. His service includes UIST organizing roles (Data Co-Chair 2021-2022) and session chairing. Teaching spans HCI, Computational Haptics, and ML fairness courses since 2019. Laboratory work occurs within ETH's Advanced Interactive Technologies group, specializing in computational interaction and electromagnetic systems for immersive environments.

Tian Lanمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Object Detection
• +۴ مورد دیگر

Tian Lan is a Senior Research Scientist at Amazon, focusing on computer vision and machine learning. He previously held a post-doctoral position at Stanford University and completed his academic training at Simon Fraser University (Ph.D. and M.Sc. in Computer Science) and Huazhong University of Science and Technology (B.Eng.). Ph.D. in Computer Science, Simon Fraser University (2013) M.Sc. in Computer Science, Simon Fraser University (2010) B.Eng. in Electrical Engineering, Huazhong University of Science and Technology (2008) His research spans object detection , activity recognition , image retrieval , and structured visual models , with a focus on hierarchical representations for semantic scene understanding. Current work at Amazon involves applying these techniques to real-world problems like Amazon Go ’s “just walk out” technology. Recent publications highlight trends in action localization , weakly supervised learning , and spatio-temporal modeling , often integrating latent variable frameworks and max-margin learning . He plays a key role in workshops like Understanding Human Activities: Context and Interactions (ICCV 2013). CIPPRS Doctoral Dissertation Award (2013) Lan’s collaborations with leading researchers like Greg Mori and Silvio Savarese underscore his impact in the field. His work bridges academic research and industrial applications, particularly in human activity recognition and computer vision systems .

• Artificial Intelligence
• Music Technology
• Deep Learning
• +۳ مورد دیگر

Alexander Williams is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. His research focuses on AI-driven music generation, integration of deep learning models into digital audio workstations, and computational creativity tasks such as music inpainting and harmonization. He explores user-centered workflows for musicians and extends symbolic/audio dataset integration to develop mid-level representations. His work also spans reinforcement learning applications in powertrain optimization, robotics, and energy-efficient systems for wireless sensor networks. Research interests include the intersection of AI with music technology, reinforcement learning in engineering systems, and sustainable energy solutions. His recent projects emphasize practical applications in automotive design, renewable energy harvesting, and robotic control mechanisms. No scientific awards or grants are explicitly mentioned. His current projects involve collaborations with musicians to refine AI tools for creative workflows. Alexander holds a visiting role without part-time status and no listed students or email addresses.

• Computer Vision
• Image Processing
• Machine Learning
• +۲ مورد دیگر

Ronan Sicre is a researcher affiliated with the Texmex Project Team at IRISA/INRIA Rennes. His work focuses on mid-level feature learning and image classification, as highlighted in his 2015 seminar presentation. He has been actively involved in numerous academic events, including workshops, conferences, and thesis defenses across computer science disciplines. His research interests emphasize discriminative mid-level features for recognition tasks, combining automatic part discovery and alignment in visual models. He has participated in events such as the 15th IAPR International Workshop on Document Analysis Systems (DAS 2022) and the CLEF-HIPE-2020 competition, demonstrating expertise in document analysis and image representation. Ronan Sicre contributes to academic activities through participation in conferences like CICLing 2019, CORIA-RJCRI, and PFIA 2024, as well as internal seminars and open days at institutions like the University of La Rochelle. His involvement spans both technical and interdisciplinary initiatives, including projects like CircularSeas and AI4Industry.

Sinisa Todorovicمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Sinisa Todorovic is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University (OSU). He holds a Ph.D. (2005) and M.S. (2002) from the University of Florida, and a B.S./M.S. from the University of Belgrade (1994). Before joining OSU, he was a postdoc at the Beckman Institute, University of Illinois, and a software engineer at Siemens (1998–2001). His research focuses on computer vision, machine learning, and AI, particularly semantic/instance segmentation, action segmentation in videos, few-shot learning, weakly-supervised learning, and cross-domain adaptation. He leads projects like fruit orchard segmentation datasets and transformer-based cross-domain semantic segmentation. Key contributions include the Volleyball dataset for group activity analysis, Hough Forest Random Fields for object segmentation, and boundary flow estimation. His work bridges theory and applications, including robotics, medical imaging, and sports video analysis. Todorovic advises over 20 graduate students and collaborates on grants involving AI ethics, explainable systems, and agricultural automation. He is affiliated with OSU's Data Science and Engineering and AI/Robotics research groups.

• Quantum Dots
• Mid-infrared Sensing
• Nanocrystalline Materials
• +۴ مورد دیگر

Dr. Dong Ko is an Associate Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology (NJIT). He specializes in nanomaterials, colloidal quantum dots, and mid-infrared sensing technologies. His research focuses on developing novel optoelectronic devices, photodetectors, and infrared sensing systems through advanced material synthesis and device engineering. Dr. Ko leads or co-leads multiple National Science Foundation (NSF) projects, including initiatives on retinomorphic vision systems, resonant energy transfer in hybrid lasers, and high-performance thin-film transistors for IoT devices. His work integrates nanocrystal engineering, thin-film fabrication, and device optimization to address challenges in infrared detection, energy-efficient optoelectronics, and flexible electronics. His research interests span quantum dot materials, mid-infrared photonics, and energy-efficient sensor technologies. Notable contributions include colloidal Ag 2 Se quantum dots for intraband photodetection, PbSe-based mid-wavelength infrared photoconductors, and low-temperature 2D material growth for flexible electronics. Received 4 NSF grants focusing on infrared sensors, hybrid lasers, and flexible electronics Published 44 peer-reviewed articles with 2,366 citations and an h-index of 23 His lab collaborates internationally on topics like nanocrystal assembly and thermoelectric materials. Current projects aim to reduce power consumption in infrared vision systems and advance scalable quantum dot technologies for widespread applications.

Mahmudul Hassanمشاهده پروفایل

مدرس

• Immersive Technologies (VR/AR/XR)
• Serious Game-based Procedural Training
• Machine Learning in Serious Games
• +۶ مورد دیگر

Mahmudul Hassan is a Lecturer in Immersive Technologies at Teesside University's Department of Computing & Games. He holds a PhD in Computer Science (Computer Vision & Machine Learning) from Monash University (Malaysia). His primary research focuses on applying immersive technologies (VR/AR/XR) and machine learning to develop serious games for procedural training and performance assessment. Current projects include virtual bio-reactor simulators and digital twin applications in healthcare and industry. Education: PhD in Computer Science (Monash University), BSc/MSc in related fields (not explicitly stated) Research interests span VR-based training systems, AI-driven serious games, IoT integration in extended reality, and computer vision applications in agriculture and healthcare. He has developed operator training modules for bio-processes through projects like THYME and ATSTN. Key collaborations include the OptiWare project (warehouse optimization via generative AI and computer vision) and cross-disciplinary work with healthcare and engineering teams. His publications address cybersecurity models against social engineering attacks, generative AI in medical education, and bioreactor simulation systems. Advising focuses on immersive tech applications in smart farming, industrial training, and healthcare digital twins. He leads development of exergames for disability health support and IoT-XR integration in industrial safety protocols. Labs/Teams: Core contributor to Teesside's immersive tech research group, collaborating with SHLS and SCEDT departments on applied training solutions.

• Visual Perception
• Cognitive Neuroscience
• Computer Vision
• +۳ مورد دیگر

Richard Murray is a Full Professor in the Department of Biology at the Faculty of Science. His research focuses on visual perception, particularly lightness constancy, and explores how humans and AI models perceive lightness in varied environments such as virtual reality (VR), flat-panel displays, and real-world settings. He investigates the interplay between natural lighting cues, rendering artifacts, and perceptual mechanisms, employing deep learning models and psychophysical experiments. His work bridges computer vision and neuroscience, analyzing mid-level lightness illusions, intrinsic image decomposition, and decision spaces in complex scenes. Murray has developed novel visualization techniques like the 'noise prism' and contributed to VR display calibration methodologies. His research often addresses discrepancies between human perception and computational models, emphasizing the limitations of current AI in replicating human visual constancy. No scientific awards or grants are explicitly listed in the provided information. His advising activities and student collaborations are not detailed here. Laboratory or team affiliations are not specified, though his work suggests involvement with multidisciplinary teams in vision science and computational modeling.

• Computational Systems Oncology
• Systems Biology
• Mathematical Modeling of Cellular Systems
• +۴ مورد دیگر

Professor Lan Nguyen leads the Computational Systems Oncology Program at the South Australian Immunogenomics Cancer Institute (SAiGENCI), University of Adelaide. His research focuses on integrating computational modeling with experimental biology to understand cancer mechanisms and develop predictive tools for personalized treatment. He holds a PhD in Computational Systems Biology and previously led a lab at Monash Biomedicine Discovery Institute. Research Interests: Systems-level analysis of oncogenic signaling networks, drug resistance mechanisms, and therapeutic strategies. Current projects include modeling PI3K-Akt-mTOR pathways, YAP's dual role in cancer, and TCR signaling in immune cells. His work employs ODE modeling, agent-based simulations, and CRISPR experiments. Key Achievements: $5.5M in competitive grants, ARC Centre of Excellence (MACSYS), and high-impact publications in Nature Cell Biology , Cell Systems , and Cancer Research . Awards include a Mid-Career Fellowship and editorship of a computational modeling textbook. Lab Vision: The Nguyen Lab develops interdisciplinary approaches combining computational models with wet-lab experiments to uncover network-level behaviors of cellular systems. Collaborations include Prof. Roger Daly (Monash) and clinicians at Peter McCallum Cancer Centre.