جست‌وجوی استاد و پژوهشگر

• Brain-Computer Interfaces
• Machine Learning
• Neural Signal Processing
• +۵ مورد دیگر

Professor Guan Cuntai serves as Deputy Dean of the College of Computing & Data Science and holds the President's Chair in Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. He also maintains a courtesy appointment as Professor at the Lee Kong Chian School of Medicine. His leadership extends to multiple research institutes including serving as Director of the Artificial Intelligence Research Institute (AI.R) and Co-Director of both S-Lab for Advanced Intelligence (2020-2025) and the Rehabilitation Research Institute of Singapore (2015-2018 & 2024-2025). Professor Guan's research focuses on Brain-Computer Interfaces (BCI), Machine Learning, Neural Signal & Image Processing, and Artificial Intelligence. His work at the Centre for Brain-Computing Research (CBCR) investigates deep learning algorithms for motor activity decoding, cognitive mechanism understanding, and explainable AI for brain decoding modeling. His research spans applications in motor decoding, attention and emotion detection, silent speech decoding, and olfactory response analysis from brain signals. His significant contributions to the field have been recognized through numerous awards: Annual BCI Research Award (First Prize) King Salman Award for Disability Research Nanyang Research Award IES Prestigious Engineering Achievement Award Achiever of the Year (Research) Award Finalist of the President Technology Award Professor Guan has delivered over 100 keynote speeches and invited talks globally, including at the opening ceremony of the 7th International BCI Meeting and IEEE International Conference on Rehabilitation Robotics. His extensive publication record includes 420 refereed journal and conference papers, and he has secured significant research funding resulting in 26 granted patents and patent applications, with 17 technologies licensed to six companies across Singapore and USA. He leads the Centre for Brain-Computing Research (CBCR) which focuses on fundamental and applied BCI research, and previously established the Brain-Computer Interface research program and founded the Department of Neural & Biomedical Technology at the Institute for Infocomm Research, A*STAR before joining NTU in 2016.

Xilin Liuمشاهده پروفایل

استادیار

• Integrated Circuits
• Biomedical Engineering
• Brain-Machine Interfaces
• +۵ مورد دیگر

Xilin Liu is an Assistant Professor at the Edward S. Rogers Sr. Department of Electrical & Computer Engineering (University of Toronto) and the Center for Advancing Neurotechnological Innovation to Application (CRANIA) . He obtained his PhD from the University of Pennsylvania and previously worked at Qualcomm Inc. in California. Expertise in integrated circuits and systems for brain-machine interfaces , neuromodulation , and edge AI Published in top venues including Nature Electronics , IEEE JSSC , and ISSCC Recipient of multiple best paper awards and IEEE Senior Member His research spans three main themes: High-speed data converters for wireless/wireline communication IC design for neural interfacing Accelerating machine learning via hardware Recent publications focus on closed-loop neuromodulation , ultra-wideband transceivers , and flexible biomedical sensors . These works integrate analog IC design , edge AI , and real-time neural interfacing across medical rehabilitation , parkinson's monitoring , and memory research . Awards include: IEEE Solid-State Circuits Society Predoctoral Achievement Award (2016) Best Paper Award at BioCAS (2015) ECE Department Teaching Award (2022) Multiple conference best paper finalists His lab collaborates with UHN , EMBS , and global institutions while maintaining strong commitments to equity, diversity, and inclusion (EDI) in research practices.

Ju Sunمشاهده پروفایل

استادیار

• Machine Learning
• Numerical Optimization
• Computer Vision
• +۵ مورد دیگر

Ju Sun is an Assistant Professor at the University of Minnesota, Twin Cities, in the Computer Science & Engineering department. He leads the Group of Learning, Optimization, Vision, Healthcare, and X (GLOVEX) and plays key roles in the UMN Data Science Initiative (DSI), Program for Clinical AI, and AI-CLIMATE institute. Research Focus : Theoretical foundations of machine learning, computer vision, and numerical optimization with applications in healthcare, inverse problems, and medical imaging. Grants : $4.5M+ in funding including NSF ACED Program and NIH R01 grants for constrained deep learning and imbalanced classification. Teaching & Leadership : Featured in UMN seminars and AI institutes, with affiliations across Electrical and Computer Engineering, Health Informatics, and Medical School. Recent Publications address inverse problems, federated learning, imbalanced classification, and phase retrieval using deep generative priors and diffusion models. His group website details these innovations. Scientific Awards : McKnight Land-Grant Professorship (2025–2027) 2021 AAAI New Faculty Highlights Advising : Mentored three PhD graduates now at Meta, Amazon, and UCLA. Collaborations span medicine, materials science, and biomedical engineering, integrating physics-informed constraints into AI.

Marco Pedersoliمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Marco Pedersoli serves as an Assistant Professor at École de technologie supérieure (ETS) in Montreal since February 2017, where he leads research in computer vision and machine learning. His work focuses on reducing computational costs and annotation requirements for deploying vision algorithms on embedded devices, positioning ETS at the forefront of Montreal's AI ecosystem. His academic journey includes: Ph.D. from Autonomous University of Barcelona (UAB) under Jordi Gonzàlez and Juan José Villanueva Post-doctoral research at INRIA Grenoble with Cordelia Schmid and Jakob Verbeek (2015-2016) Research at KU Leuven with Tinne Tuytelaars (2012-2015) Dr. Pedersoli's research tackles deep learning bottlenecks through weakly-supervised methodologies and computational efficiency innovations . His three core projects address: Reduced Supervision : Developing weakly/semi-supervised learning for images, video, audio and text Exploration Learning : Optimizing data selection in unstructured environments Efficient Computation : Accelerating deep learning training and inference These efforts enable vision algorithms to run on resource-constrained portable devices. Publication trends (2014-2022) reveal consistent focus on weak supervision (60% of works) and computational efficiency (30%), with recent expansion into medical imaging and multimodal emotion recognition. Key venues include CVPR, ICCV, NeurIPS and ECCV. His accolades include: Best Paper Award at ICIAR 2019 NVIDIA Titan X Pascal hardware donation Dr. Pedersoli actively mentors 18 graduate students across PhD and MSc programs, with notable placements at Huawei and Radio Canada. His lab secures competitive tax-free funding for projects with international collaborations, including Element AI and European institutions. Current openings emphasize Python/C++ proficiency and deep learning expertise. He leads a dynamic research group at ETS developing open-source tools for Roi-Pooling, weakly-supervised detection, and 3D object recognition, maintaining active GitHub repositories with community contributions. Recent WACV 2023 acceptances demonstrate ongoing productivity following medical leave.

Duncan Astleمشاهده پروفایل

استاد

• Developmental cognitive neuroscience
• Brain connectivity and network organization
• Neurodevelopmental disorders
• +۵ مورد دیگر

Duncan Astle is the Gnodde Goldman Sachs Professor of Neuroinformatics at the Department of Psychiatry, University of Cambridge. He serves as a Programme Leader at the Medical Research Council's Cognition and Brain Sciences Unit (MRC CBU) and is a Fellow of Robinson College. Astle heads the 4D Lab (Development, Dynamics, Disorders, Data Science), which provides a research home for approximately 15 Early Career Researchers working at the intersection of developmental cognitive neuroscience and advanced data science methodologies. Astle's research focuses on understanding childhood development through innovative analytical approaches. His work employs transdiagnostic methods to study children with attention, learning, and memory difficulties, moving beyond traditional diagnostic categories. He investigates how neural systems develop in childhood, how they relate to developmental disorders, and how they respond to intervention. His research integrates network science, machine learning, and generative modeling to capture the complexity of neurodevelopmental diversity, examining how cognitive skills, literacy, numeracy, and mental health interrelate over developmental time. His publication record reveals a strong focus on brain connectivity and organization across development. Recent work explores structural and functional neurodevelopmental trajectories, brain wiring economics, and the impact of environmental factors on neural development. Astle's research frequently employs advanced data science techniques to identify sub-populations of children with different cognitive or brain profiles, regardless of diagnosis, and to map non-linear relationships between brain organization and cognitive difficulties. His work has increasingly focused on transdiagnostic approaches to understanding developmental disorders and the application of computational models to developmental neuroscience. Astle actively supervises PhD students and has built a substantial research group that contributes to major projects including the Centre for Attention Learning and Memory (CALM) and Resilience in Education and Development (RED). His work has been supported by prestigious funding bodies including the Royal Society, the British Academy, the Medical Research Council, and the Economic and Social Research Council, as well as multiple charitable foundations. The 4D Lab, under Astle's leadership, utilizes state-of-the-art facilities at the University of Cambridge, including on-site magnetic resonance imaging and magnetoencephalography scanners. The lab contributes to building specialist cohorts such as CALM (800 children with cognitive difficulties plus 200 comparison children) and RED, which study children's development, resilience, and educational outcomes. Astle's team explores how growing up in adverse environments affects children's brains, behavior, and mental health, with the aim of identifying early markers of risk and resilience.

• Mobile operating systems
• Mobile computer architecture
• Low-power mobile systems
• +۵ مورد دیگر

Robert Likamwa is an Associate Professor at Arizona State University, affiliated with the School of Arts, Media and Engineering and the School of Electrical, Computer and Energy Engineering. His research focuses on the intersection of mobile computing, augmented reality, and sensor design through Meteor Studio. Ph.D., Rice University M.S., Rice University B.S., Rice University His work spans three core research arcs: (i) advanced visual capture systems, (ii) hybrid virtual-physical immersion through sensory augmentation, and (iii) data-driven frameworks for AR/VR storytelling. He explores mobile operating systems, low-power architectures, computational imaging, and holographic computing. Recent publications analyze multi-resolution visual sensing, geospatial cross-virtuality collaboration, planetary data visualization, spatial audio optimization, and multi-sensory virtual environments. His 2013 paper on energy-proportional image sensors received the Best Paper Award at ACM MobiSys. Best Paper Award, ACM MobiSys 2013 LiKamWa advises graduate students through research and thesis courses and leads grants related to mobile vision systems, haptic interfaces, and energy-efficient sensor design. He directs Meteor Studio, a research group focused on immersive technology innovation.

Sally Gibsonمشاهده پروفایل

پژوهشگر

• Geochemistry
• Volatiles cycling in Earth's mantle
• Mantle and volcanic processes
• +۳ مورد دیگر

Sally Gibson is a researcher at the Department of Earth Sciences, University of Cambridge, specializing in mantle geodynamics and volatile cycling processes. Her work integrates field observations, geochemical analysis, and numerical modeling to investigate how deep Earth processes influence surface environments over 3.5 billion years of planetary evolution. Research focuses on volatile cycling (CO₂, H₂O, F, Cl, S) in mantle systems Key projects include mantle plume-ridge interactions with collaborators in the US and Ecuador Operates a LA-ICP-MS laboratory for high-resolution geochemical analyses Supervises PhD students in petrology, geochemistry, and numerical modeling Her research addresses fundamental questions about Earth's habitability through studies of mantle-derived volatiles critical for climate regulation and energy transition metal deposits. Fieldwork in remote regions like Antarctica, Lesotho, and the Galápagos Islands provides empirical data for her interdisciplinary approach. Recent publications highlight her expertise in mantle xenolith analysis, plume dynamics, and volatile quantification in large igneous provinces. Her group's work combines 3He/4He isotopic analysis with seismic tomography to constrain lithospheric evolution and mineral deposit formation. Students under her supervision develop expertise in petrology and geochemical modeling while engaging with environmental and societal impacts of geological research. She actively promotes scientific outreach and community engagement, fostering connections between academia and broader society.

Markus Bachmayrمشاهده پروفایل

استاد

• Numerical Analysis
• Partial Differential Equations
• High-dimensional Problems
• +۴ مورد دیگر

Prof. Dr. Markus Bachmayr is a full professor at the Institute for Geometry and Practical Mathematics, RWTH Aachen University, holding the chair for Applied Mathematics. His research focuses on nonlinear approximation, high-dimensional partial differential equations (PDEs), uncertainty quantification, and numerical methods in quantum chemistry. He leads the ERC Consolidator Grant project Computational Complexity of Highly Nonlinear Approximations (COCOA) and contributes to CRC 1481 Sparsity and Singular Structures, and RTG 2326 Energy, Entropy, and Dissipative Dynamics. His recent work emphasizes adaptive low-rank and sparse approximation techniques for parametric and stochastic PDEs, including applications in radiative transfer and poroviscoelastic flow modeling. He serves as Editor-in-Chief of Foundations of Computational Mathematics and Associate Editor for multiple journals. Scientific Awards: John Todd Award 2013 Borchers Plakette 2014 Erwin Wenzl Preis 2007 He has taught courses such as Numerische Analysis I/II, Numerische Mathematik für Elektrotechniker, and seminars on numerical methods and approximation theory.

Anand Bhattadمشاهده پروفایل

استادیار

• Computer Vision
• Computer Graphics
• Generative Modeling
• +۲ مورد دیگر

Anand Bhattad is an Assistant Professor in the Department of Computer Science at Johns Hopkins University, starting Fall 2025. Previously, he held positions as a Research Assistant Professor at the Toyota Technological Institute at Chicago (TTIC) and a visiting scholar at UC Berkeley. His research focuses on the intersection of computer vision, generative modeling, and physical reasoning, aiming to develop perception-driven and physics-aware visual models. His academic journey includes a PhD in Computer Science from the University of Illinois Urbana-Champaign under David Forsyth, with mentorship from Derek Hoiem, Svetlana Lazebnik, Greg Shakhnarovich, and Shenlong Wang. Prior to his PhD, he earned dual master’s degrees in Computer Science and Civil and Environmental Engineering at UIUC and a bachelor’s in Civil Engineering from NITK Surathkal, India. Research interests center on how generative models encode physical and perceptual knowledge, with key contributions in intrinsic image emergence, projective geometry limitations, and physics-aware relighting techniques. His work bridges classical computer vision concepts with modern deep learning, producing state-of-the-art methods for 3D scene synthesis and image editing. Articles span topics like 3P Vision , diffusion models, and 360° video datasets, reflecting interdisciplinary approaches in computer graphics and computational photography. Scientific awards include Outstanding Reviewer at ICCV 2023, CVPR 2022 Best Paper Finalist, and multiple conference service roles as workshop organizer and area chair. He designed the TTIC course Past Meets Present: A Tale of Two Visions , teaching connections between historical and modern computer vision research.

Jinjin Guمشاهده پروفایل

استادیار

• Computer Science
• Artificial Intelligence
• Computer Vision
• +۷ مورد دیگر

Jinjin Gu is a tenure-track Assistant Professor at Sofia University "St. Kliment Ohridski" 's INSAIT (Institute for Computer Science, Artificial Intelligence, and Technology), leading research on visual cognition and intelligence. Her work spans visual perception, processing, generation, and reasoning. Education: Ph.D. in Electrical and Computer Engineering (2024), University of Sydney B.Sc. in Computer Science and Engineering (2020), Chinese University of Hong Kong, Shenzhen Her research focuses on visual cognition , including agentic systems , diffusion models , GAN architectures , model interpretability , super-resolution , and multimodal vision-language systems . She has developed novel paradigms like HYPIR for diffusion-quality restoration at GAN speeds. Recent publications highlight advancements in image/video restoration , generative modeling , and visual reasoning . Her work addresses critical challenges in model generalization , causal interpretation , and real-world application robustness . Scientific Awards: Stanford University's World's Top 2% Scientists (2024) Yunfan Award at World Artificial Intelligence Conference (WAIC) (2023) She has advised students contributing to TPAMI, CVPR, and ICLR publications, and serves as Area Chair for ICLR 2026, NeurIPS 2025, and ICML 2025.

So, Man Cho, Anthonyمشاهده پروفایل

استاد

• Optimization
• Algorithm Design
• Data Analytics
• +۷ مورد دیگر

Anthony Man-Cho So is a Professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong (CUHK). He currently serves as Dean of the Graduate School and Deputy Master of Morningside College . With a BSE from Princeton University and a PhD in Computer Science from Stanford University, his career at CUHK began in 2007. Academic Leadership: Dean, Graduate School (2023–present); Deputy Master, Morningside College (2019–present) Education: BSE (Princeton), MSc/PhD (Stanford) His research focuses on optimization theory and its interdisciplinary applications in computational geometry, machine learning, signal processing, and statistics. Key projects include non-convex optimization for wireless networks, robust graph learning, and decentralized learning algorithms. His publications span high-impact journals like Mathematical Programming , SIAM Journal on Optimization , and conferences such as NeurIPS and ICML . Recent work emphasizes dynamic regret analysis , low-rank matrix recovery , and stochastic beamforming . He has authored over 50 refereed papers and a monograph on semidefinite programming. Awards include IEEE Fellow (2023), CUHK Research Excellence Award (2016–17), and multiple IEEE/INFORMS best paper and teaching accolades. He has served on editorial boards of journals like Mathematical Programming and SIAM Journal on Optimization , and as Lead Guest Editor for IEEE Signal Processing Magazine . Teaching roles include courses on optimization, discrete mathematics, and machine learning. Scientific Awards IEEE Fellow (2023) CUHK Outstanding Fellow (2019) Multiple IEEE/INFORMS Best Paper Awards (2010–2022) IEEE/UGC Teaching Awards (2008–2022) His methodology integrates theoretical rigor with practical applications, particularly in wireless communication systems, sensor networks, and financial engineering. Collaborations span institutions in Hong Kong, mainland China, and the U.S., reflecting a global academic influence.

Michel Versluisمشاهده پروفایل

استاد

• Physical and Medical Acoustics
• Microbubbles
• Microdroplets
• +۵ مورد دیگر

Michel Versluis is a Full Professor at the University of Twente, Netherlands, specializing in Physical and Medical Acoustics within the Physics of Fluids group. His work focuses on microbubbles and microdroplets for medical imaging and therapy, as well as microfluidic applications in medicine and nanotechnology. University of Twente, Physics of Fluids group His research bridges physics and biomedical engineering, with publications in high-impact journals like PNAS and IEEE Transactions. Recent work emphasizes ultrasound-driven microbubble dynamics, additive manufacturing of flow phantoms, and deep learning for super-resolution imaging. 2025 publications: vascular phantoms, PROTEUS simulator, acoustic microbubble control 2024 innovations: 3D-printed medical devices, immunogenic cell death optimization Contact: m.versluis@utwente.nl

Yogananda Isukapalliمشاهده پروفایل

استاد آموزشی

• Signal Processing
• Computer Engineering
• Digital Design
• +۵ مورد دیگر

Yogananda Isukapalli is a Teaching Professor and Vice Chair in the Computer Engineering Program at the Electrical and Computer Engineering Department , University of California, Santa Barbara . He joined the faculty in Winter 2017 after a career as a staff scientist at Broadcom (2010–2017), where he designed Wi-Fi chips (11n/11ac/11ax) and worked on underwater wireless communication models during a postdoctoral stint at Scripps Institution of Oceanography (2009–2010). His PhD in Communication Theory and Systems from UC San Diego (2009) forms the basis of his expertise in wireless systems and digital design .

Angelika Ramboldمشاهده پروفایل

استادیار

• Immunology
• Cell Biology
• Metabolism
• +۳ مورد دیگر

Dr. Angelika Rambold is a Group Leader at the Max Planck Institute of Immunobiology and Epigenetics in Freiburg, Germany, heading the Laboratory for Metabolic Organelle Networks in Immunology within the Department of Developmental Immunology. Previously affiliated with the University of Münster's Center for Molecular Biology of Inflammation (ZMBE) and Institute of Medical Biochemistry until January 2025, she investigates how intracellular organelle networks regulate immune cell function during inflammation, infection, and metabolic stress. Her research centers on dynamic interactions between mitochondria, lysosomes, lipid droplets, and autophagosomes during cellular adaptation to nutrient deprivation and pathogen challenge. Key interests include organelle communication mechanisms in immune cell activation, metabolic reprogramming in T cells and macrophages, and how defects in organelle networks drive primary immunodeficiencies like Chediak-Higashi syndrome. She employs advanced live-cell microscopy, super-resolution imaging, metabolomics, and single-cell transcriptomics to dissect these processes in primary immune cells and human disease models. Analysis of her publication record reveals consistent focus on mitochondrial dynamics as a central regulator of immune cell metabolism and fate determination. Landmark studies demonstrate TFEB-mediated itaconate synthesis for bacterial control in macrophages and coordinated organelle network responses during starvation, establishing critical links between organelle communication, immunometabolism, and disease pathogenesis across multiple immune cell types. Dr. Rambold serves as a supervisor in the Cells in Motion International Max Planck Research School (CiM-IMPRS) Graduate Programme, mentoring PhD students in interdisciplinary research. Her laboratory maintains active collaboration with the Center for Chronic Immunodeficiency (CCI) at the University of Freiburg to translate basic findings on organelle-mediated immune defects into clinical insights for patient-oriented research.

Markus Heiligمشاهده پروفایل

استاد

• Addiction Neuroscience
• Alcohol Dependence
• Neuropharmacology
• +۴ مورد دیگر

Markus Heilig is a Professor of Psychiatry and Head of the Department of Biomedical and Clinical Sciences (BKV) at Linköping University. He serves as Director of the Center for Social and Affective Neuroscience (CSAN), integrating basic and clinical neuroscience to address addictive and affective disorders. His work bridges neuropharmacology, molecular psychiatry, and clinical trials. Linköping University (Current) Center for Social and Affective Neuroscience (CSAN) (Director) Wallenberg Center for Molecular Medicine (Collaborator) His research focuses on alcohol dependence mechanisms, stress-neurotransmitter interactions (e.g., CRF, substance P, endocannabinoids), and personalized medicine approaches. Using animal models and human neuroimaging (fMRI), his lab explores epigenetic changes in frontal lobe and amygdala neurons that drive compulsive alcohol use. Recent publications highlight his work on endocannabinoid modulation for PTSD treatment, neural correlates of reward choices, and pharmacological interventions targeting GABA and opioid receptors. His studies often combine brain imaging, behavioral analysis, and pharmacology. Scientific awards include the 2019 Wallenberg Clinical Scholar grant and 2017 CAN/Nordic Drugs prizes. Heilig's research is supported by Vetenskapsrådet (SEK 57 million center funding) and international donations like the Andrew J. Bock Memorial Fund.