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Kostas DaniilidisView profile

Professor

• Computer Vision
• Robotics
• Machine Learning
• +5 more

Kostas Daniilidis is the Ruth Yalom Stone Professor at the University of Pennsylvania in the School of Engineering and Applied Science , specifically the Department of Computer and Information Science . He is also affiliated with the GRASP Laboratory and Archimedes, Athena Research Center, Greece . Education : PhD in Computer Science (1992) from the University of Karlsruhe with Hans-Hellmut Nagel Diploma in Electrical Engineering (1986) from the National Technical University of Athens Research Interests : Kostas Daniilidis is a leading researcher in Computer Vision and Robotics , with significant contributions to event-based vision , equivariant learning , 3D human pose estimation , and hand-eye calibration . His work spans neural rendering , dynamic scene modeling , and low-latency sensing systems . Article Trends : Daniilidis’s recent publications focus on event cameras for low-light and high-speed applications, Gaussian splatting for real-time 3D reconstruction, and equivariant neural architectures for robust motion estimation. His work bridges deep learning with geometric vision , emphasizing human mesh recovery and multi-agent coordination . Scientific Awards : Best Conference Paper Award at ICRA 2017 IEEE Fellow (2012) Teaching : He has taught courses such as CIS580: Machine Perception and CIS121: Data Structures , alongside advanced topics in robotics and computer vision. Lab & Collaborations : As director of the GRASP Laboratory (2008–2013), he fostered interdisciplinary research in robotics, and currently collaborates with institutions like the Athena Research Center in Greece.

Martin HollerView profile

Professor

• Mathematics of data science
• Machine learning
• Generative models
• +7 more

Martin Holler is a Professor at the Institute of Mathematics and Scientific Computing at the University of Graz, Austria, where he leads the research group Applied Mathematics and Machine Learning . His work bridges theoretical mathematics with practical applications in imaging and machine learning. Research Focus: His primary research areas include the mathematics of data science, variational methods in imaging, dynamic and multi-modality inverse problems, and biomedical imaging. He has made significant contributions to model-based regularization techniques, particularly with Total Generalized Variation (TGV) approaches for image and video reconstruction. Publication Trends: Over the past decade, Holler's research has evolved from traditional variational methods for image reconstruction toward increasingly sophisticated machine learning approaches. His recent work (2021-2023) focuses on integrating deep learning with variational methods, particularly for motion separation in medical imaging and learning-informed parameter identification in partial differential equations. His publications demonstrate a consistent thread of applying rigorous mathematical frameworks to solve practical problems in medical imaging and computer vision. Mathematics of data science and machine learning Generative models in machine learning Variational methods in imaging Dynamic and multi-modality inverse problems Model-based regularization Biomedical imaging Image and video decompression Technical Leadership: Holler has developed several open-source software packages implementing advanced reconstruction algorithms, particularly for multi-modal imaging problems. His GitHub repositories show active maintenance and development of these tools, which have been cited in the medical imaging community.

Weitao WangView profile

Assistant Professor

• Head and Neck Oncology
• Facial Plastic and Reconstructive Surgery
• Skull Base Surgery
• +4 more

Dr. Weitao Wang serves as an Assistant Professor in the Department of Otolaryngology at the University of Rochester School of Medicine and Dentistry. Board-certified in both Otolaryngology-Head and Neck Surgery and Facial Plastic and Reconstructive Surgery, he practices clinically at UR Medicine and the Wilmot Cancer Institute in Rochester, NY, specializing in complex head and neck reconstruction and facial plastic procedures. Medical Degree: University of Virginia School of Medicine Residency: Otolaryngology-Head and Neck Surgery, University of Rochester Medical Center (2015-2019) Fellowship: Facial Plastic and Reconstructive Surgery, The Institute for Rehabilitation and Research (2019-2020) His research focuses on advancing head and neck reconstruction through tissue engineering (particularly bone/cartilage grafting) and optimizing surgical education via point-of-view video technology. Clinical investigations prioritize patient-reported outcomes in facial plastic surgery to enhance functional recovery and satisfaction metrics. Current work bridges engineering solutions with complex defect reconstruction. Recent publications (2020-2025) demonstrate concentrated expertise in microvascular free tissue transfer, virtual surgical planning for craniofacial defects, and innovative skull base/orbital reconstruction techniques. His work consistently addresses oncologic safety while improving functional outcomes in head and neck cancer patients, with increasing emphasis on cost-effective surgical training methodologies. Robert Joynt Kindness Award 2021 Leslie Bernstein Resident Research Grant: Optimizing bone allograft in craniofacial defect reconstruction Wallace K. Dyer Clinical Investigation Grant: Cost-effective surgeon POV video in otolaryngology training Dr. Wang secures competitive funding from the American Academy of Facial Plastic and Reconstructive Surgery to advance surgical education and reconstruction techniques. His clinical mentorship emphasizes multidisciplinary collaboration, particularly in head and neck oncology cases where tumor resection requires immediate reconstruction. Current projects integrate virtual planning with intraoperative navigation to optimize complex defect repair. He operates within the Facial Plastic and Reconstructive Surgery division at the University of Rochester, collaborating with Wilmot Cancer Institute's head and neck oncology team. His practice incorporates virtual surgical planning technology and microvascular reconstruction expertise, with active participation in multidisciplinary tumor boards for complex craniofacial cases.

Guillermo GallegoView profile

Professor

• Robotics
• Computer Vision
• Event-based Vision
• +5 more

Guillermo Gallego is a Professor of Robotic Interactive Perception at the Faculty of Electrical Engineering and Computer Science , Technische Universität Berlin , holding the Einstein Center Digital Future (ECDF) Professorship since 2019. His research bridges robotics , computer vision , and applied mathematics , focusing on optimization methods for interdisciplinary imaging and control problems. Education : PhD in Electrical and Computer Engineering (Georgia Tech, 2011), MS in Mathematics (Georgia Tech, 2009), MS in Electrical Engineering (Georgia Tech, 2007), MS in Mathematical Engineering (Universidad Complutense de Madrid, 2005). Gallego's work explores event-based vision to enhance robot perception through low-latency sensing and real-time 3D reconstruction . He previously held postdoctoral positions at the Institute of Neuroinformatics (University of Zurich/ETH Zurich) and Technical University of Madrid (Marie Curie Experienced Researcher). His interdisciplinary projects span applications in ocean remote sensing , autonomous driving , and space exploration . Key scientific awards include the Fulbright Fellowship (2005-2010) and Marie Curie Experienced Researcher (2011-2014). His recent publications focus on event camera algorithms for optical flow , SLAM , and noise estimation , reflecting his leadership in event-based vision research. Collaborations include institutions like University of Zurich , Georgia Tech , and University of Pennsylvania . Research Grants : Funded through ECDF and Marie Curie programs. Labs : Affiliated with the Einstein Center Digital Future and Institute of Neuroinformatics (Zurich/ETH Zurich).

Xiaolin WuView profile

Professor

• Image Processing
• Multimedia Coding and Communication
• Computer Vision
• +7 more

Dr. Xiaolin Wu is a Professor in the Department of Electrical & Computer Engineering at McMaster University's Faculty of Engineering. With expertise in image processing, multimedia coding, computer vision, and artificial intelligence, he has made groundbreaking contributions to visual/multimedia computing and communication. His research has resulted in over 350 publications and four patents, including the renowned CALIC algorithm for lossless image coding and L3 codec for digital cinema. B.Sc. from Wuhan University (1982) Ph.D. from University of Calgary (1988) Dr. Wu's research spans critical areas such as image restoration (demosaicing, denoising, superresolution), multimedia coding (JPEG, wavelet transforms), and information display (Temporal Psychovisual Modulation). His work on TPVM, featured in MIT Technology Review, redefines display technology by leveraging human vision properties for multi-user VR/AR experiences. Recent publications focus on deep learning applications for image/video restoration, including multi-modality approaches and l∞-constrained compression. His algorithms, such as fast color quantizers and compressive sensing recovery methods, have been widely adopted in industries like medical imaging and digital cinema. Dr. Wu also holds an NSERC Senior Industrial Research Chair and has collaborated with global tech leaders including Microsoft, Nokia, and Huawei. IS&T/SPIE Best Paper Award UWO Distinguished Research Professor Award McMaster Distinguished Engineering Professor Award IEEE Fellow As an educator, Dr. Wu teaches courses in image processing, discrete methods, numerical analysis, and parallel computing, emphasizing algorithm design, data structures, and real-time systems. His work bridges academic research with industrial applications, ensuring practical impact across biomedical imaging, digital security, and smart display technologies.

• Data Compression
• Wireless Communications
• Image Processing
• +2 more

Dr. Nariman Farvardin serves as President and Professor of Electrical and Computer Engineering at Stevens Institute of Technology. Previously Senior Vice Provost at University of Maryland, he holds a PhD from Rensselaer Polytechnic Institute. His research focuses on data compression, wireless communications, and image processing. He has led major projects including a $5.5M Army Research Laboratory initiative on telecommunications and received honors including the Presidential Young Investigator Award and Academic Leadership Award from Carnegie Corporation. His patented inventions include image compression algorithms and wireless protocols. Funded by agencies including NSF, NASA and Mitsubishi, his work spans theoretical foundations to practical implementations like VLSI-based compression and CDMA network optimization. He has supervised over 30 graduate students and published 100+ works on communication systems.

Lee A. TanView profile

Associate Professor

• Spinal biomechanics
• Motion-preservation surgical techniques
• Spinal deformity correction
• +3 more

Lee A. Tan, MD, is Associate Professor of Neurological Surgery at the University of California, San Francisco (UCSF) School of Medicine, specializing in complex spinal disorders, deformities, and minimally invasive and robotic spine surgery. He holds dual neurosurgical and orthopedic spine training, enabling an interdisciplinary approach to patient care. Education & Training MD, Indiana University School of Medicine, 2010 Residency, Neurological Surgery, Rush University Medical Center, 2016 Fellowship, Adult & Pediatric Comprehensive Spine Surgery, Columbia University Medical Center, 2017 B.S. Electrical Engineering, Purdue University Research Focus Dr. Tan’s research encompasses spinal biomechanics, motion-preservation techniques, robotic-assisted surgery, and clinical outcomes in spinal deformity. His work aims to refine surgical techniques, improve patient safety, and enhance postoperative quality of life through evidence-based innovation. Publications Trend Recent publications emphasize predictive modeling for cervical spine fusion outcomes, novel osteotomy techniques, and systematic reviews on complications such as C5 palsy and proximal junctional kyphosis. Operative videos illustrate cutting-edge minimally invasive approaches, while biomechanical studies validate instrumentation strategies. Professional Memberships American Association of Neurological Surgeons Congress of Neurological Surgeons North American Spine Society Clinical Practice He treats scoliosis, kyphosis, spondylolisthesis, cervical and lumbar disc herniations, stenosis, and OPLL, employing both minimally invasive and open techniques. His practice locations include UCSF Parnassus and affiliated hospitals. Laboratory & Teams Dr. Tan collaborates within the UCSF Spine Center, integrating multidisciplinary teams of neurosurgeons, orthopedic surgeons, and researchers to advance translational spine care.

Henryk RichterView profile

Researcher

• Data Compression
• Image and Video Coding
• Real-Time Video Codecs
• +3 more

Dr.-Ing. Henryk Richter is a postdoctoral scientific assistant at the University of Rostock, affiliated with the Communications Engineering research group. His work spans multimedia engineering, focusing on video coding and real-time systems. Education : PhD in Electrical Engineering from University of Rostock (2006), thesis on cross-standard video decoding concepts. Research Interests : Data compression, image/video coding, multistandard video decompression, and real-time signal processing. Publications : 15 most recent articles (2005–2024) highlight advancements in video codecs, biomedical signal processing, and hardware optimization. Contact : henryk.richter@uni-rostock.de

João Manuel Cardoso MartinsView profile

Researcher

• Veterinary Medicine
• Feline Surgery
• Anesthesia and Analgesia
• +3 more

João Manuel Cardoso Martins is a Researcher at the Faculty of Veterinary Medicine (FMV) of Universidade Lusófona , specializing in Veterinary Medicine , with a focus on feline surgery, canine hip dysplasia, and anesthesia. He is affiliated with the Veterinary Medicine Research (CECAV) group. Research Interests : Veterinary anesthesia, surgical margin analysis, hip laxity in dogs, parasympathetic tone monitoring, and neurorehabilitation. Article Trends : His recent works (2024-2025) explore analgesic techniques in cats, canine hip imaging, and spinal cord rehabilitation. Keywords span veterinary surgery, anesthesiology, and regenerative medicine. Collaborations : Frequently co-authors with colleagues like Dos-Santos, J. D. , Cabral, P. , and Campos, S. , with international partnerships implied by media coverage.

Alhusain A. NagmView profile

Lecturer

• Neurosurgery
• Skull Base Surgery
• Endoscopic Neurosurgery
• +3 more

Alhusain A. Nagm, PhD, serves as an Instructor in the Department of Neurosurgery, focusing on advanced surgical techniques for complex intracranial pathologies. His work bridges cadaveric anatomical studies with clinical applications, emphasizing innovation in skull base and endoscopic procedures. Dr. Nagm's research spans skull base surgery , endoscopic neurosurgery , and cerebrovascular procedures , with particular expertise in transorbital approaches, CMK-UHC modifications, and pediatric applications. He investigates surgical nuances for meningioma resection, aneurysm management, and ventriculostomy, often utilizing augmented reality and robotic assistance to enhance precision. His anatomical studies prioritize surgical corridor optimization and complication prevention. Analysis of his 2015-2025 publications reveals a dominant focus on skull base accessibility (75% of recent work), with key contributions to transorbital/transpetrosal techniques and internal carotid artery exposure. Emerging themes include augmented reality integration for neuralgia treatment and pediatric adaptations of endoscopic procedures, reflecting his commitment to translating anatomical insights into safer surgical workflows. No scientific awards, fellowships, or medals were documented in the provided materials. While no student advisees or research grants were specified, Dr. Nagm's extensive operative video documentation and cadaveric studies suggest active involvement in surgical training. His publications frequently address technical challenges in complex tumor resection and vascular procedures, indicating hands-on clinical mentorship. Details regarding dedicated laboratories, research teams, or collaborative networks were absent from the source materials, though his work implies affiliation with neurosurgical training programs focused on skull base surgery innovation.

• Epilepsy
• Functional Neurosurgery
• Neurosurgery
• +5 more

Layton Lamsam is a resident in the Department of Neurosurgery at Yale School of Medicine , focusing on epilepsy and functional neurosurgery . He completed his MD and BS in Biology at Stanford University , where he developed expertise in statistical programming and causal inference on observational data. Education: MD, Stanford University School of Medicine (2019) BS, Stanford University, Biology (2014) Research Interests: Layton's work spans epilepsy surgery , neuroimaging , and scRNA-Seq analysis for brain tumors. His recent studies examine structural network remodeling post-surgery, portable MRI applications, and therapeutic interventions like laser interstitial thermal therapy. Key Trends: His publications highlight neurosurgical outcomes , epilepsy , and neuroimaging , with a focus on clinical translational research , minimally invasive techniques , and neuro-oncology . Lab Involvement: He contributes to the Damisah Lab at Yale, which investigates neurosurgical innovations and epilepsy management strategies.

Mathieu DelalandreView profile

Associate Professor

• Image Processing
• Computer Vision
• Document Analysis
• +7 more

Mathieu Delalandre is an Associate Professor at LIFAT Laboratory, University of Tours, France, specializing in image processing and document/video analysis since 2009. His research focuses on local approaches including local detectors, template matching, and transform domain processing, with applications in video copy detection, scene text detection, and document image networking. He received his PhD in Computer Science from Rouen University in 2005, followed by research fellowships at SCSIT (Nottingham, UK), L3i (La Rochelle, France), and CVC (Barcelona, Spain) from 2006-2009. His educational background established his foundation in document analysis and pattern recognition. Delalandre's research spans multiple domains of computer vision with emphasis on practical applications. His work bridges theoretical image processing techniques with real-world problems in document analysis and video content monitoring. Key contributions include robust symbol localization, performance evaluation frameworks for symbol recognition systems, and real-time text detection algorithms. His approach often combines structural and statistical methods for enhanced accuracy. His publication record demonstrates consistent focus on video and document analysis, with recent work emphasizing partial video copy detection (2021-2023), real-time text detection (2019-2021), and JPEG document processing (2015-2017). The trajectory shows evolution from fundamental symbol recognition research to applied multimedia analysis systems addressing contemporary challenges in copyright protection and content monitoring. As head of the StationTV project, Delalandre leads research on real-time processing of multi-channel television content. He has participated in numerous national and international research projects, contributing to advancements in document image analysis and video processing. His work has been supported through various research grants focusing on practical applications of computer vision. Delalandre is an active member of the RFAI research group at LIFAT Laboratory, where he contributes to the development of innovative image processing techniques. His team collaborates across European institutions, maintaining strong connections with previous research sites in the UK, Spain, and other French laboratories. Current work focuses on multimedia fact-checking systems and real-time video analysis solutions.

• Marine Mammal Behavior
• Conservation Biology
• Diving Physiology
• +3 more

Sascha Kate Hooker is a Professor and Deputy Head of the School of Biology at the University of St Andrews. She holds affiliations with the Scottish Oceans Institute, Institute of Behavioural and Neural Sciences, Sea Mammal Research Unit, and Marine Alliance for Science & Technology Scotland. Her research focuses on marine mammal foraging and diving behavior, conservation biology, and diving physiology. Notable projects include studying predator foraging ecology using video cameras, investigating diving physiology risks in marine mammals, and applying spatial management strategies for marine protected areas. Awards: 2017 Zoological Society of London Marsh Award for Marine and Freshwater Conservation Fellow of the Royal Society of Edinburgh (2023) Grants & Projects: ISSF-funded initiatives on mental health and women in STEM ORBS project on beaked whale acoustic behavior Her work contributes to UN Sustainable Development Goals related to life below water and climate action. She leads interdisciplinary teams and has supervised multiple research projects, including datasets on marine mammal behavior and physiology.

Keith WinsteinView profile

Associate Professor

• Networked Systems
• Computer Networks
• Data Compression
• +6 more

Keith Winstein is an Associate Professor of Computer Science at Stanford University, with a courtesy appointment in Electrical Engineering. His research focuses on creating innovative networked systems, particularly in communication, compression, and computing. Notable projects include Mosh (an interactive remote shell for mobile clients), Puffer (a video-streaming platform), Lepton (a compression tool), Mahimahi (network emulators), and the gg framework for distributed computing. He has received prestigious awards such as the SIGCOMM Rising Star Award, Sloan Research Fellowship, and NSF CAREER Award. Winstein's academic journey includes undergraduate and graduate studies at MIT. Before academia, he worked at The Wall Street Journal as a reporter and at Ksplice (now part of Oracle), where he held roles in product management and business development. His research spans network protocols, video streaming optimization, cloud computing, and machine learning applications in networking. His work emphasizes practical systems that bridge theoretical concepts with real-world implementation. Recent projects explore computation-centric networking, in-network performance enhancements, and low-latency video streaming. He advocates for reproducible experiments through tools like Mahimahi and has contributed to open-source software widely used in academia and industry. Key Projects: Mosh, Puffer, Lepton, Mahimahi, gg Awards: SIGCOMM Rising Star Award, Sloan Fellowship, NSF CAREER Expertise: Networked Systems, Compression Algorithms, Distributed Computing

Ethan WinklerView profile

Assistant Professor

• Cerebrovascular Neurosurgery
• Stroke Mechanisms
• Vascular Malformations
• +3 more

Ethan Winkler, MD, PhD, is an Assistant Professor of Neurological Surgery at the University of California, San Francisco (UCSF) Weill Institute for Neurosciences and the UCSF School of Medicine. As a dual-trained cerebrovascular neurosurgeon, he specializes in both open microsurgery and endovascular techniques for treating vascular disorders of the brain, neck, and spine in adults and children, including brain aneurysms, arteriovenous malformations, and stroke-related conditions. Education & Training BA in Neuroscience, University of Southern California (2006) MD and PhD in Medicine/Neurobiology, University of Rochester School of Medicine & Dentistry (2014) Postdoctoral Fellowship in Genomics, UCSF (2019) Residency in Neurological Surgery, UCSF (2021) Fellowship in Endovascular Neurosurgery, Barrow Neurological Institute (2022) Fellowship in Cerebrovascular & Skull Base Neurosurgery, Barrow Neurological Institute (2023) Research Focus Dr. Winkler directs the UCSF Vascular Anomalies & Stroke Computation (VASC) Laboratory, where his team integrates genomic, stem cell, and bioengineering approaches to study stroke pathogenesis. His research specifically investigates: vascular malformation biology, aneurysm formation mechanisms, and neurovascular dysfunction. The lab develops novel diagnostics like endovascular biopsy and precision therapeutics targeting cerebrovascular diseases. Publication Trends Dr. Winkler's recent publications (2024-2025) demonstrate three key themes: 1) Innovative surgical techniques for trigeminal neuralgia and complex aneurysms, 2) Classification and management of spinal/endovascular complications, and 3) Molecular investigations of vascular malformations. His work consistently bridges clinical neurosurgery with vascular biology. Laboratory Leadership The VASC Laboratory employs single-cell genomics and bioengineering to create cellular atlases of human cerebrovasculature. Current projects focus on identifying genetic drivers of arteriovenous malformations and developing minimally invasive diagnostic platforms. The lab collaborates with computational biologists and stem cell experts to translate findings into clinical applications.