جست‌وجوی استاد و پژوهشگر

Oleksiy Petrovych Chornyiمشاهده پروفایل

استاد

• energy conversion quality control
• monitoring and diagnostics in electrical systems
• interdisciplinary research into computer technologies and electrical engineering education
• +۳ مورد دیگر

Oleksiy Petrovych Chornyi serves as Professor at Zaporizhzhia Polytechnic National University within the Faculty of Electrical Engineering, Department of Electric Drive and Automation of Industrial Installations. Holding a Doctor of Technical Sciences degree, he has been actively contributing to the university since 2022. His academic journey began at Kryvyi Rih Mining Institute (1986) where he earned his qualification as an Electrical Engineer specializing in 'Electrification and automation of mining operations.' He completed his Candidate of Technical Sciences dissertation in 1991 and his Doctor of Technical Sciences dissertation in 2005 at National Mining University in Dnipro. Professor Chornyi's research focuses on energy conversion quality control, monitoring and diagnostics in electrical systems, and the interdisciplinary intersection of computer technologies with electrical engineering education. His work demonstrates significant theoretical depth coupled with practical applications in motor systems and power electronics. His publication portfolio reveals a consistent research trajectory with emphasis on improving energy efficiency and developing advanced diagnostic methodologies. His recent work particularly addresses challenges in induction motor systems, energy conversion processes, and control system optimization. Member of Expert Council on Energy and Electronics at State Accreditation Commission of Ukraine (2013-2016) Member of Scientific and Methodological Commission on Engineering (Subcommittee 141) Expert on educational program accreditation for National Agency for Quality Assurance in Education Member of Ministry of Education expert group for assessing higher education institution effectiveness since 2019 Professor Chornyi teaches 'Modeling and analysis of electromagnetic processes' and has supervised numerous scientific works. His laboratory activities focus on electromagnetic processes modeling and analysis, with applications spanning industrial power systems and electrical machinery.

Hyun Oh Songمشاهده پروفایل

دانشیار

• Machine Learning
• Combinatorial Optimization
• Algorithms
• +۷ مورد دیگر

Hyun Oh Song is an Associate Professor in the Department of Computer Science and Engineering at Seoul National University , focusing on machine learning, combinatorial optimization, and algorithms. Previously, he was a Research Scientist at Google Research and a Postdoctoral Fellow at Stanford University . Education : Ph.D. in Computer Science (2014) from UC Berkeley , B.S. from Hanyang University Research Interests : Solving combinatorial problems in AI, with applications in neural network compression, adversarial robustness, and reinforcement learning. Teaching : Courses include Deep Learning, Machine Learning, and Probability & Computing at SNU. His startup DeepMetrics raised $2.2M in combined VC and government funding. Scientific Awards : Samsung Lee Kun Hee Scholarship Foundation (5-year Ph.D. fellowship) Publications : 15 recent works span efficient CNN compression (ICML2022), adversarial attack mitigation (ICML2022), neural relation graphs for label noise detection (NeurIPS2023), and Co-Mixup data augmentation (ICLR2021).

Stefano BELLUCCIمشاهده پروفایل

پژوهشگر

• Geophysics
• Nanomaterials
• Environmental Remediation
• +۳ مورد دیگر

Prof. Dr. Stefano BELLUCCI serves as a Scientific Researcher I at the National Institute of Materials Physics (INFIM) in Romania, where he leads research activities in the Laboratory of Optical Processes in Nanostructured Materials. His interdisciplinary work bridges geophysics, nanomaterials science, and environmental engineering, with significant contributions to both theoretical modeling and experimental applications. Dr. BELLUCCI's research interests focus on advanced materials for environmental applications, geological modeling, and nanotechnology. His work encompasses the synthesis and characterization of novel nanomaterials for water purification, development of biodegradable polymers with enhanced properties, and advanced geophysical techniques for subsurface characterization. He employs both experimental approaches and theoretical modeling, including Density Functional Theory simulations, to understand material properties and geological structures at fundamental levels. His recent publications demonstrate a strong trend toward interdisciplinary research that combines materials science with environmental and geological applications. The work spans from fundamental nanomaterial synthesis to practical applications in water management and hydrocarbon exploration, reflecting a consistent focus on solving real-world environmental challenges through advanced materials and analytical techniques. Dr. BELLUCCI currently leads or participates in research projects including the PNRR-funded initiative on Composite materials for applications in water management field (2024-2026). His laboratory work focuses on optical processes in nanostructured materials, with applications ranging from photocatalytic degradation of pollutants to advanced reservoir characterization techniques.

James Elderمشاهده پروفایل

استاد

• Computer Vision
• Human Vision
• Artificial Intelligence
• +۴ مورد دیگر

James Elder is a Professor at York University, holding a joint Lassonde School of Engineering/Faculty of Health Tier I Chair. He is affiliated with the Centre for Vision Research, the VISTA program, and the Centre for AI & Society. Electrical Engineering & Computer Science Psychology His research integrates computer vision and biological vision studies, focusing on developing algorithms through understanding human visual processing. Key areas include computer vision , 3D shape perception , and AI applications in urban mobility . He leads projects like AttentiveVision and Agile AI-Powered Robotics . Recent publications reveal critical differences between human and AI visual processing, including Monocular Depth Estimation and Shape Frequency Analysis studies. His work has been recognized with the Premier’s Research Excellence Award and Young Investigator Award from the Canadian Image Processing and Pattern Recognition Society. York Research Chair CFI Exceptional Opportunities Grant recipient ORF Research Excellence Grant recipient Professor Elder supervises graduate students including Maria Koshkina, Kumar Vaibhav Jha, and Nizwa Javed. His lab collaborates with institutions like Justus Liebig University Giessen and the Vector Institute for AI, with applications in hockey analytics and autonomous systems .

Zhixiang Chenمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Human Analysis
• +۲ مورد دیگر

Dr. Zhixiang Chen is a Lecturer in Machine Learning at the Department of Computer Science, University of Sheffield, and an active member of both the Machine Learning and Computer Vision research groups. Previously, he held postdoctoral positions at Imperial College London and Tsinghua University. Education: PhD in Computer Science, Tsinghua University, China B.Eng. in Computer Engineering, Xi'an Jiaotong University, China His research focuses on advancing Computer Vision and Machine Learning through human analysis, object detection, and image retrieval systems. Recent work integrates deep learning for industrial applications like PCB inspection and autonomous systems, while earlier contributions pioneered video hashing techniques for efficient multimedia search. His methodology emphasizes robust feature representation and scalable algorithm design. Publication trends reveal a strategic shift from foundational hashing research (2016-2019) toward applied 3D vision and industrial automation (2020-2024), with increasing emphasis on uncertainty-aware models and sensor fusion for real-world deployment. Scientific Awards: China Society of Image and Graphics Doctoral Dissertation Award (2019) IEEE International Conference on Multimedia and Expo Best Paper Award (2018) China National Postdoctoral Program for Innovative Talents (2017) Dr. Chen currently leads the EPSRC-funded project 'VIM: towards a Vision based System for Inventory Management with Deep Learning' (2024-2026, £46,354) as Principal Investigator. He serves as a Grant Reviewer for UKRI-FLF and NSFC, Senior Program Committee Member for AAAI 2023, and regularly reviews for top-tier conferences (CVPR, ICCV, NeurIPS) and journals (IEEE T-PAMI, IEEE T-MM). As a core member of Sheffield's Machine Learning and Computer Vision research groups, he contributes to collaborative projects involving industrial partners and interdisciplinary teams focused on AI-driven solutions for healthcare, manufacturing, and autonomous systems.

Aaron Fathمشاهده پروفایل

استاد آموزشی

• Motion Perception
• Sensation and Perception
• Perception and Action
• +۳ مورد دیگر

Aaron Fath is an Associate Teaching Professor in the Department of Psychology at the University of Southern Mississippi. He earned his PhD in Mathematics from Indiana University-Bloomington (2016) and holds Master's and Bachelor's degrees in Cognitive Science and Mathematics from Rensselaer Polytechnic Institute (2010), teaching core psychology courses including Sensation and Perception, Statistics, and Research Methods. His educational qualifications include: PhD in Mathematics, Indiana University-Bloomington (2016) MS in Cognitive Science, Rensselaer Polytechnic Institute (2010) BS in Mathematics, Renssela Polytechnic Institute (2010) AS, Hudson Valley Community College (2006) Dr. Fath's research centers on human vision with a primary focus on motion perception and its critical role in higher visual processing and action control. His work integrates Experimental, Cognitive, and Ecological Psychology frameworks to investigate Sensation & Perception, Perception & Action, and Motion Perception. He examines how visual motion information guides motor behavior across the lifespan, studying children, adults, and older adults in contexts ranging from time-to-contact estimation to affordance-based navigation and manual control tasks. His publication record (2010-2018) reveals a cohesive research program at the perception-action interface, emphasizing motion perception's application to motor control. Key themes include monocular/binocular time-to-contact perception, age-related differences in motor learning, geometric/dynamic affordances for reaching, and perceptuo-motor adaptation across the lifespan. His experimental work with diverse age groups demonstrates how visual information structures action capabilities and learning trajectories in real-world tasks. Scientific awards: No awards mentioned As an instructor of PSY 475 (Senior Seminar) and PSY 718 (Graduate Seminar in Modern Psychology), Dr. Fath mentors undergraduate and graduate students in advanced research. While specific advisees aren't listed, his teaching portfolio indicates active supervision of capstone projects and thesis work. No research grants are documented in the provided materials. No dedicated research laboratories or specialized teams are described, though his experimental publications suggest collaboration with human subjects in motion perception and motor control studies.

Hyun Soo Parkمشاهده پروفایل

دانشیار

• Computational Social Intelligence
• Computer Vision
• Robotics
• +۴ مورد دیگر

Hyun Soo Park is an Associate Professor at the University of Minnesota, Twin Cities in the Department of Computer Science & Engineering . As a McKnight Presidential Fellow , his research focuses on computational social intelligence and egocentric perception , developing algorithms to model human interaction through gaze, facial expressions, and body gestures. He leads the Gemini-Huntley Robotics Research Lab and has secured major grants including NSF CAREER , Toyota Research Institute , and multiple NSF awards. Education : Ph.D. in Computer Science Teaching : Courses on 3D Computer Vision, Computer Vision, and Computational Linear Algebra (2017-2021) Research : Pioneering egocentric video analysis , 3D social signal modeling , and behavioral imaging with applications to robotics and human-computer interaction Scientific Awards include: NSF CAREER Award McKnight Presidential Fellow Guillermo E. Borja Award CVPR Best Paper Honorable Mention Grants secured: TRI (2021-2024) NSF NRI (2020-2023) NSF NCS (2020-2023) NIH R34 (2020-2022) NSF CRII (2018-2021) Minnesota Futures (2018-2021) Team includes: Meng-Yu Jennifer Kuo (Postdoc) 5 Ph.D. students (Yasamin Jafarian, Zhixuan Yu, etc.) Co-advised students with Prof. Roumeliotis and Prof. Guy Past members now at 3M and Adobe Research

Alessandro Leonardiمشاهده پروفایل

مدرس ارشد

• Geomechanics
• Numerical Modelling
• Geohazards
• +۷ مورد دیگر

Dr. Alessandro Leonardi is a Senior Lecturer in Geomechanics at the University of Sheffield's Department of Civil and Structural Engineering, within the School of Mechanical, Aerospace and Civil Engineering. He holds a PhD from ETH Zurich, completed with support from a Marie Curie Fellowship, and previously served as a postdoctoral fellow at Politecnico di Torino where he led software development for mountain hazard assessment. His research focuses on: Numerical modelling of multiphase particle-fluid interactions using DEM-LBM coupling Geohazard prediction including landslides, debris flows, and submarine mass movements Climate-resilient infrastructure through physical modeling and smart mitigation structures Recent publications demonstrate strong emphasis on multiscale simulation frameworks, geotechnical centrifuges, and barrier interaction dynamics, with consistent applications to climate adaptation challenges. His HYBIRD simulation tool development represents significant innovation in multiphase flow analysis. Awards: Marie Curie Fellowship for doctoral research at ETH Zurich Research Leadership: Leads the HYBIRD simulation tool development initiative and supervises PhD candidates including Hanpeng Chen and Hannah Nichols. Collaborates extensively with the Geotechnical Engineering Group and Geotechnical Centrifuge facility.

• Applied Probability
• Statistics
• Reliability Theory
• +۲ مورد دیگر

Florian Simatos is a Professor of Probability and Statistics at ISAE-SUPAERO's Department of Complex Systems since February 2015, where he leads the Applied Mathematics research group (2020-2025). His academic journey includes positions as a Researcher at Inria (2014), Lecturer at École Normale Supérieure de Paris (2014), Post-doc at Eindhoven University of Technology (2012-2013), Post-doc at CWI (2010-2011), PhD at Inria under Philippe Robert (2006-2009), MSc in Electrical Engineering from Stanford (2005), and undergraduate studies at École Polytechnique (2001-2004). His research spans three interconnected domains: Applied Probability (branching processes, scaling limits, heavy traffic analysis), Reliability Theory (importance sampling in high dimensions, sensitivity analysis), and Sustainable Aviation (environmental impact beyond climate, planetary boundaries). His work integrates rigorous mathematical frameworks with real-world aeronautical challenges, particularly in decarbonizing air transport. Current projects include analyzing high-dimensional rare events (Jason Beh's PhD) and broadening aviation's environmental impact assessment (Bastien Païs' PhD). His publication trends reveal a strategic pivot from foundational probability (2009-2015) toward reliability engineering (2015-2021) and sustainable aviation (2021-present), reflecting both theoretical depth and applied relevance. This evolution demonstrates his ability to bridge abstract mathematics with pressing industrial challenges. ACM SIGMETRICS Rising Star Researcher Award (2014) ACM SIGMETRICS 2010 Best Paper Award (for Load balancing via randomised local search ) As a supervisor, he mentors seven PhD candidates across probability, reliability, and sustainable aviation, often co-supervising with domain experts like Jérôme Morio (reliability) and Lorie Hamelin (environmental science). His service includes editorial roles for Queuing Systems (2016-2021) and extensive journal reviewing (>50 reviews across probability, queuing theory, and environmental science). He actively collaborates with aerospace stakeholders through ISAE-SUPAERO's Applied Mathematics group, focusing on quantitative sustainability frameworks for aviation.

• Exploration Geophysics
• Magnetic Data Interpretation
• Gravity Surveys
• +۴ مورد دیگر

Pierre Keating is an Adjunct Professor at the School of Earth, Environment & Society , McMaster University , with a focus on geophysical data interpretation for mineral exploration and structural geology. His work spans magnetic, gravity, and electromagnetic survey techniques , emphasizing stochastic inversion , regional-residual separation , and 3D modeling . His research involves exploration geophysics for intrusion-hosted rare metals , seismotectonic analysis in Quebec seismic zones, and fractal-based data gridding . He has contributed to gravity and magnetic data validation in resource exploration and multi-platform geophysical integration in Hudson Bay and Anticosti Basin studies. Selected publication trends include advanced inversion techniques (2016), structural complexity mapping in eastern Canada (2014), and hydrocarbon potential assessment in Tunisia (2012). His work bridges field data analysis with theoretical geophysics .

• Earth Observation
• Satellite Geodesy
• Polar Science
• +۳ مورد دیگر

Mal McMillan is a Professor in Earth Observation at Lancaster University, where he serves as Co-Director of the Lancaster-UKCEH Centre of Excellence in Environmental Data Science (CEEDS) and Co-Director of Science for the UK Centre for Polar Observation and Modelling (CPOM). His work focuses on satellite geodesy and polar ice sheet dynamics to understand sea level rise. Education: PhD in Satellite Geodesy (2012), MSc in Remote Sensing (2006), MA in Mathematics & Philosophy (2003), all from the University of Edinburgh. Research interests include Earth Observation , Polar Science , Remote Sensing , and Climate Change . His recent articles highlight Satellite Altimetry for polar ice sheet monitoring, Subglacial Hydrology , and Environmental Data Science . He supervises PhD students in Polar Science and advises on ESA satellite missions . Current projects span ESA and UKRI funding, including Digital Twin Components for ice sheets and Arctic Extreme Weather studies.

Animesh Mandalمشاهده پروفایل

دانشیار

• Near Surface Geophysics
• Geophysical Data Enhancement
• Subsurface Modeling
• +۷ مورد دیگر

Animesh Mandal serves as an Associate Professor in the Department of Earth Sciences at Indian Institute of Technology Kanpur (IITK) , where he has been a faculty member since April 2015. His academic journey includes a B.Sc. in Physics from University of Calcutta (2005), M.Sc. in Physics from IIT Delhi (2007), and Ph.D. in Geophysics from IIT Kharagpur (2013). Prior to joining IITK, he worked as a Project Scientist at the National Geophysical Research Institute (NGRI). Research Interests : Dr. Mandal specializes in near-surface geophysical studies , geophysical data enhancement , and subsurface modeling . His work focuses on integrated geophysical approaches to understand shallow crustal configuration and delineate natural resources, utilizing gravimeter, magnetometer, electrical, and EM equipment. He has pioneered research in machine learning-assisted interpretation of geophysical data, particularly in seismic impedance inversion, reservoir characterization, and geothermal system analysis. His recent publications demonstrate expertise in applying deep learning techniques to solve complex geophysical problems. Research Trends : Analysis of his recent publications reveals a strong focus on geothermal energy systems , particularly non-volcanic hot springs in the Eastern Ghats Mobile Belt. His work combines gravity-magnetic studies with advanced computational methods to understand crustal configuration and thermal structure. Another major trend involves machine learning applications in seismic data processing, where he develops novel deep learning architectures for impedance inversion and reservoir characterization. His research bridges traditional geophysical methods with cutting-edge AI techniques, creating innovative approaches to subsurface imaging. Scientific Awards : Supervised students who received Prime Minister's Research Fellowship (PMRF) Advisees awarded FARE Fellowship, ONGC-IGU Best Poster Presentation Award, and EAGE PACE grants Students received FULL sponsorship to attend major international conferences like IMAGE 2024 Mentored students who secured postdoctoral positions at prestigious institutions including KAUST and University College Dublin Academic Leadership : Dr. Mandal has successfully supervised multiple Ph.D. and M.Tech. students to completion, with recent graduates securing positions at institutions like University College Dublin, Queen's University, and industry roles at companies including Verisk Analytics and ONGC. His research group maintains active collaborations with international institutions and regularly presents at major geoscience conferences including EAGE, SEG, and AOGS. The group has secured competitive research funding that supports field studies, laboratory work, and student training in advanced geophysical techniques.

Paul Lintonمشاهده پروفایل

پژوهشگر ارشد

• 3D Vision
• Visual Perception
• Cognitive Neuroscience
• +۱۲ مورد دیگر

Paul Linton is a NOMIS Foundation Fellow at the Italian Academy for Advanced Studies and an Affiliate of the Visual Inference Lab at Columbia University's Zuckerman Mind Brain Behavior Institute. His work bridges visual perception , cognitive neuroscience , and philosophy of mind , focusing on challenging traditional inferential models of vision through novel illusions and theoretical frameworks. Focus Areas : 3D vision, depth perception, perceptual-cognitive distinction, visual illusions. Research Highlights : Linton proposes that 3D visual shape reflects uncorrected retinal disparities rather than environmental geometry, linking this to cognitive inferences about scale. He developed five groundbreaking illusions (e.g., Linton Stereo Illusion, Linton Scale Illusion) to demonstrate that visual experience is simpler than previously thought, with cognitive processes driving apparent depth and size constancy. Publications span topics like stereopsis , monocular depth cues , and the physiology of V1 . His 2025 preprint on five illusions and 2023 Phil. Trans. R. Soc. B article are pivotal. Awards include the NOMIS Fellowship and Presidential Scholar at Columbia. Collaborations include Niko Kriegeskorte’s lab and the MIT Consciousness Club. He organizes events like the Royal Society’s New Approaches to 3D Vision and contributes to debates on computational consciousness and metacognitive science.

Ranjay Krishnaمشاهده پروفایل

استادیار

• Computer Vision
• Human-Centered AI
• Machine Learning
• +۷ مورد دیگر

Ranjay Krishna is an Assistant Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, with a Ph.D. in Computer Science from Stanford University (2021). He co-directs the RAIVN Lab and leads the computer vision team at Ai2 . His research sits at the intersection of computer vision , human-computer interaction , robotics , and natural language processing .

• Nonlinear Dynamics
• Optical Fiber Communication
• Signal Processing
• +۴ مورد دیگر

Prof. Sander Wahls is a Professor at the Institute of Industrial Information Technology (IIIT) at Karlsruhe Institute of Technology (KIT). His research focuses on nonlinear dynamics, optical fiber communication, signal processing, and machine learning applications. He leads the KIT-IIIT team and has contributed significantly to advancing methodologies in nonlinear Fourier transforms, fiber parameter identification, and rogue wave analysis. His work bridges theoretical frameworks with practical implementations in optical systems and marine engineering. Key research interests include nonlinear wave phenomena, soliton-based signal processing, and data-driven approaches for communication systems. His publications highlight innovations in fiber nonlinearity monitoring, machine learning for optical channels, and experimental validations of novel signal recovery techniques. Prof. Wahls' recent work explores the implications of solitons in rogue wave occurrences, leveraging nonlinear Fourier transforms for spectral analysis of buoy measurements. He also investigates fiber parameter estimation using Koopman operators and develops algorithms for blind demultiplexing in optical communication. His research has been applied to real-world scenarios such as oceanographic wave analysis, high-speed optical transmission systems, and environmental monitoring. Collaborations span interdisciplinary areas like ocean engineering and computational mathematics.