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Joonpyo KimView profile

Assistant Professor

• Spatio-Temporal Data Analysis
• Statistics in Asymmetric Norms
• Change Point Detection
• +3 more

Joonpyo Kim serves as Assistant Professor in the Department of Mathematics and Statistics at Sejong University since 2022, specializing in advanced statistical methodologies with applications across medical imaging and industrial engineering. His academic foundation includes doctoral training at Seoul National University where he developed expertise in non-standard statistical frameworks. Education B.S. in Mathematics, Seoul National University (2016) Ph.D. in Statistics, Seoul National University (2022) Dr. Kim's research centers on spatio-temporal data analysis using asymmetric norm frameworks and change point detection methodologies. His work bridges theoretical statistics with practical applications in ophthalmology and semiconductor manufacturing, particularly through functional data analysis and quantile-based techniques. The fingerprint of his research reveals strong connections to quantile theory (100%), functional data analysis (61%), and changepoint detection (61%), demonstrating interdisciplinary impact across medical diagnostics and industrial process control. Recent publications (2024-2025) exhibit a clear trajectory toward solving high-dimensional statistical challenges in both biomedical and engineering contexts. His work on vitelliform macular dystrophy combines multimodal imaging with chromatic perimetry, while semiconductor manufacturing research develops novel model averaging for two-way functional data. The consistent application of quantile curves and extreme value theory across diverse domains highlights his methodological innovation. Scientific Recognition Korean Statistical Society Best Ph.D Dissertation Award (2022) Sejong Research Fellowship (2022-2027) from Ministry of Science and ICT BK21 FOUR Groups Students Award from Seoul National University Dr. Kim leads the Sejong Research Fellowship project (2022-2027), focusing on statistical methodology development with applications in medical diagnostics and industrial quality control. His mentorship contributed to the BK21 FOUR Groups Students Award, reflecting effective guidance in collaborative research environments. Current projects integrate multiscale extreme value analysis with functional data clustering techniques.

• Binocular Vision
• Amblyopia
• Stereopsis
• +4 more

Alexandre Reynaud serves as a Junior Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC) at the Montreal General Hospital site and holds an Assistant Professor position in the Department of Ophthalmology and Visual Sciences within McGill University's Faculty of Medicine and Health Sciences. He is actively affiliated with the Brain Repair and Integrative Neuroscience (BRaIN) Program and Centre for Translational Biology, directing the Binocular Integration in Space and Time research group under the McGill Vision Research Unit. Dr. Reynaud's research centers on elucidating binocular vision mechanisms through psychophysical experiments and brain imaging techniques applied to both normal and clinical populations. His work spans amblyopia pathophysiology, stereopsis processing, visual plasticity, and spatial vision analysis including texture and color perception. He develops computational models to explain neural correlates of visual processing deficits and investigates therapeutic applications like dichoptic training protocols. Analysis of his recent publications reveals a dominant focus on amblyopia treatment innovation, particularly dichoptic training methodologies for adults beyond critical periods. His work consistently examines binocular imbalance quantification, interocular suppression dynamics, and stereopsis restoration mechanisms. Current trends show increasing emphasis on translating laboratory findings into clinical tools like the Dichoptic Contrast Ordering Test and home-based therapeutic applications. Dr. Reynaud leads the Binocular Integration in Space and Time research initiative within the McGill Vision Research Unit, collaborating across the Brain Repair and Integrative Neurosciences Program to develop vision-strengthening systems and investigate neural mechanisms underlying visual perception deficits.

• Surgical Data Science
• 3D Computer Vision
• Medical Imaging
• +3 more

Lennart Bastian is a PhD Student at the Chair of Computer Aided Medical Procedures (Prof. Navab) at Technische Universität München . His research focuses on Surgical Data Science , 3D Computer Vision , and Medical Imaging , with specific interests in 3D Reconstruction , Pointcloud Segmentation , and Scene Understanding . His recent work includes advancements in Operating Room semantics , holistic face anonymization , and scalable statistical shape modeling . Across 2025, his publications demonstrate strong engagement with topological neural networks , SO(3) forecasting , and 3D shape matching algorithms for medical applications. Lennart has supervised numerous student projects, including Learning Robust Deformation Energies Neural Interpolation for Dynamic 6D Pose Weakly Supervised Segmentation in collaboration with the Chair of Computer Science Applications in Medicine.

• Intelligent Robotics
• Multi-Robot Systems
• Human-Robot Interaction
• +3 more

Sérgio Paulo Carvalho Monteiro is an Assistant Professor in the Department of Industrial Electronics at the School of Engineering, University of Minho, and a Senior Researcher at Centro Algoritmi. He serves as Director of the Doctorate in Electronics and Computer Engineering since 2021 and previously led the Integrated Masters program (2011-2013), with core expertise in robotics and signal processing education. His academic credentials include a Doctor of Engineering (DEng), Master of Science (MSc), and PhD. Monteiro's research centers on intelligent robotics with dual focus on single-robot autonomy and multi-robot coordination . His work pioneers dynamic neural field approaches for human-vehicle interaction, particularly in learning driver routines through machine learning. Key application domains span industrial logistics , autonomous transportation , and collaborative workstations , emphasizing safety in human-robot shared environments. Analysis of his 42 publications reveals a clear trajectory from foundational formation control (2002-2010) toward contemporary AI-driven solutions for intelligent cockpits (2020-2024), with increasing emphasis on human-centered design and real-world industrial implementation. He has secured funding through major European initiatives including Horizon 2020 (iFACTORY), FP6 (CoopDyn), and national FCT projects, focusing on autonomous logistics and multi-robot collaboration. His academic leadership extends to directing doctoral programs and shaping curriculum for over 500 engineering students. As core member of the Mobile and Anthropomorphic Robotics Lab within Centro Algoritmi's CAR R&D Group, he develops bimanual manipulators (RAMBO) and safety-critical systems for human-robot collaboration, with recent expansion to CCG/ZGDV Institute affiliation (2023).

Youness DiouaneView profile

Research Fellow

• Artificial Intelligence
• Computational Biology
• Network Theory
• +4 more

Youness Diouane is a Postdoctoral Research Fellow at Boston College in the Department of Computer Science, collaborating with Prof. George Mohler and Prof. Jose Bento. His research focuses on network-based models for Artificial Intelligence (AI) and computational biology, emphasizing mathematical properties of network alignment algorithms. Education Ph.D. in Statistical Field Theory, Scuola Internazionale Superiore di Studi Avanzati (SISSA), Italy (2022) M.S. in Condensed Matter and Statistical Physics, International Center for Theoretical Physics (ICTP), Italy (2018) M.S. in Quantum Information Theory, Faculty of Sciences, Rabat, Morocco (2017) His interdisciplinary work spans network alignment, spatial-temporal point process modeling, fairness-aware machine learning in criminal justice, and synthetic control methods. Publications reflect expertise in Hawkes processes, critical phenomena, and statistical field theory. He is active in applying mathematical models to public health and societal challenges.

Inger SkredeView profile

Professor

• Speciation
• Population genomics
• Comparative fungal genomics
• +7 more

Inger Skrede is a Professor in the Section for Genetics and Evolutionary Biology at the University of Oslo's Faculty of Mathematics and Natural Sciences. Her research spans fungal evolutionary biology, population genomics, and fungal ecology, with particular focus on speciation mechanisms and wood decay processes. Skrede's research interests center on fungal evolution and ecology, with emphasis on speciation mechanisms in Trichaptum abietinum , evolutionary processes leading to functional transitions like efficient wood decay and ectomycorrhizal symbiosis, and brown rot decay in Serpula lacrymans . She also investigates fungal systematics in the ascomycete family Helvellaceae , fungal diversity in built environments, responses to modified wood, and conservation biology of rare fungal wood decay species. Her work integrates population genomics, comparative genomics, and phylogenetic approaches to understand evolutionary processes in fungi. Analysis of her recent publications reveals a strong focus on fungal genomics and ecology, with particular attention to how fungi adapt to different environments including built structures and forest ecosystems. Her research demonstrates how genomic tools can illuminate speciation mechanisms, adaptive processes, and the ecological roles of fungi in diverse habitats ranging from natural forests to human-built environments. Skrede leads multiple research projects including Characterization of yeast biodiversity from Oslo and Kveik beer, Effects of polyploidization during adaptive evolution in yeasts (PloidYeast), Fungal Conservation Genetics, and The genomic and physiological basis of invasiveness in a harmful house-invader (InHouse). She is actively involved with the Centre for Integrative Microbial Evolution (CIME) and the Oslo Mycology Group (OMG), contributing to collaborative research efforts in fungal biology and evolution.

• Human-Computer Interaction
• Image Processing
• Digital Signal Processing
• +4 more

Yoichi Kato serves as a Professor (without tenure) at Waseda University's School of Creative Science and Engineering and Global Center for Science and Engineering. He maintains additional affiliations with the Graduate School of Creative Science and Engineering and the Global Education Center. His academic profile spans decades of research and teaching in information and communication technologies, with particular expertise in video coding algorithms, image processing, and human-computer interaction systems. Professor Kato's research interests focus on Human interface and interaction, Image Processing, Digital Signal Processing, and Design Thinking. His scholarly trajectory demonstrates a progression from foundational work in signal processing during the 1980s-1990s toward more applied, interdisciplinary research in recent years. Early publications established his expertise in motion picture coding algorithms and image restoration techniques, while his current work explores IoT applications, machine learning prototyping methods, and user-centered design approaches. Analysis of his publication history reveals significant contributions to video compression standards and image processing algorithms, with recent research expanding into collaborative systems, disaster prevention technologies, and educational applications. His work bridges theoretical foundations with practical implementations, particularly evident in his recent projects involving drone-based terrain analysis and automated plant monitoring systems. Professor Kato actively supervises graduate students through Master's Thesis projects in the Department of Modern Mechanical Engineering and Research on Neuro Robotics. His teaching portfolio includes diverse courses such as SHIP Field Practice, Modern Information and Communication Systems, Exercise for Co-Creation workshop, and Introduction to Python Programming, reflecting his interdisciplinary approach to education. His laboratory activities center on practical applications of information technologies, with current projects examining drone applications for disaster prevention, remote plant monitoring systems using IoT technology, and prototyping methodologies for machine learning applications. These initiatives demonstrate his commitment to translating theoretical knowledge into real-world solutions that address contemporary challenges.

• Information Technology
• Communication Engineering
• Image Processing
• +3 more

Hiroshi Watanabe is a Professor at Waseda University 's Department of Communications and Computer Engineering , School of Fundamental Science and Engineering. With a Doctor of Engineering from Hokkaido University (1985), his career spans NTT Human Interface Laboratories (1985-2000) and academic leadership at Waseda since 2000, including chairing ISO/IEC JTC 1/SC 29 (1999-2006). A Fellow of IEEE , IEICE , and other institutions, he focuses on image/video coding , machine vision , and multimedia distribution . Education: BE, ME, Ph.D. in Electronic Engineering (Hokkaido University) Professional Affiliations: IEEE, IEICE, IPSJ, ITE, IIEEJ His research bridges image/video processing with deep learning , emphasizing machine-centric coding , real-time detection , and multimodal analysis . Key recent work includes: Novel image coding frameworks combining edge learning and diffusion models Super-resolution techniques for QR codes and medical imaging Advanced object detection using non-local modules and attention networks 3D pose estimation and point cloud analysis for robotics and healthcare He has received prestigious awards including the 2005 Information Processing Society of Japan Standardization Contribution Award and multiple earlier honors. His recent publications (2024-2025) demonstrate leadership in machine vision coding , real-time medical detection , and multimodal signal processing , often integrating stable diffusion , feature fusion , and parameter-efficient models .

Evan McHughes PalmerView profile

Associate Professor

• Cognitive Science
• Human Factors
• Vision
• +6 more

Evan McHughes Palmer is an Associate Professor and Chair in the Department of Psychology at San Jose State University's College of Social Sciences. His interdisciplinary research integrates cognitive science with human factors, focusing on the interaction between modern technologies and human cognition, particularly in visual perception, attention, and training methodologies. Academic Focus: Cognitive Science, Human Factors, Vision Science Key Research Areas: Gamification, Medical Image Perception, Road Hazard Detection His recent publications highlight trends in visual attention dynamics, perceptual learning applications, and technology-mediated cognitive training across domains like healthcare and transportation. Research emphasizes quantifying cognitive processes through eye movement analysis, workload measurement, and attentional capture mechanisms. Palmer's lab investigates how gamification elements (points, badges, leaderboards) enhance motivation and performance in repetitive tasks. This aligns with broader interests in human-technology interaction, including web advertising blindness, medical diagnostics, and aviation interface design. His work bridges theoretical cognitive science with applied human factors solutions.

• Formal verification
• Algorithmic game theory
• Infinite-state systems
• +9 more

Patrick Totzke is a Professor in the Department of Computer Science at the University of Liverpool’s School of Electrical Engineering, Electronics and Computer Science. As a leading researcher at the intersection of mathematics and computer science, he specializes in the foundations of formal verification, algorithmic game theory, and infinite-state systems. Education: While specific degrees are not listed, his expertise and role as Professor strongly suggest advanced graduate training in computer science and mathematics. Research Interests: Algorithmic game theory, particularly strategy complexity and games on infinite graphs Decidability and complexity of verification problems such as bisimulation, language inclusion, model checking, and synthesis Counter automata, vector addition systems, Petri nets, and process algebras Computational logics with fixed-points, temporal or probabilistic modalities, and associated games Real-time systems including timed automata, timed Petri nets, and timed games His recent focus centers on timed automata and stochastic games on graphs. Research Grants: Below the Branches of Universal Trees – EPSRC (March 2023 – August 2024) Unambiguity in Infinite-state Systems – Royal Society (March 2021 – March 2023) COSTRA: The Cost of Winning Strategies – EPSRC (July 2021 – September 2024) Teaching: Module Co-ordinator for COMP122 Object-Oriented Programming (2024–25). Labs & Teams: While no specific lab names are provided, his grants and publications indicate active leadership of research teams in formal methods and verification.

Markus SteinbachView profile

Professor

• German Grammar
• Semantics
• Pragmatics
• +8 more

Markus Steinbach is a Professor of German Linguistics at the University of Göttingen, specializing in theoretical and experimental linguistics with a focus on sign languages, spoken language interfaces, and discourse structures. He serves as Spokesperson of the Göttingen Centre 'Text Structures' (2021–present) and has held prestigious fellowships, including a 2017 Distinguished Guest Scientists Fellowship at the Hungarian Academy of Sciences. Core research areas: German grammar, sign languages, syntax-semantics interface, grammaticalization. Active projects: TypMod (CNRS), Parts of Speech and Iconicity in DGS (DFG), IDEALISM (DFG-AHRC), and Sign Hub (Horizon 2020). His recent work explores temporal and causal pragmatics, polar response strategies, and neurophysiological aspects of sign language processing. Awards include a 2017 MTA fellowship, and he contributes to editorial leadership via co-editing Linguistische Berichte (since 2014) and the Sign Languages and Deaf Communities series (since 2012). Collaborations span institutions like the DFG and CNRS.

• Geodesy
• GNSS Technology
• Remote Sensing
• +3 more

Jacek Rapiński is a Professor at the University of Warmia and Mazury (UWM) in the Department of Geoengineering. His research focuses on geodesy, GNSS technology, LiDAR data processing, and structural monitoring, particularly in mining and environmental contexts. He has supervised doctoral students at UWM and UPC and contributed to numerous publications. Over 58 publications in WoS/Scopus 307 citations (WoS/Scopus) H-index 12 (WoS/Scopus), 14 (Google Scholar) Supervised 1 doctoral student at UWM and 2 total His recent research explores GNSS multipath mitigation , low-cost sensor integration , and machine learning applications for infrastructure monitoring. Projects include innovative tools for GNSS mission planning and climate-focused land consolidation strategies. While no explicit awards are listed, his work involves advanced methodologies like Msplit estimation and MEMS sensor fusion . He emphasizes promoting doctoral research with available funding and infrastructure at UWM.

Cedric VegaView profile

Researcher

• Remote Sensing
• Forest Ecology
• Lidar Technology
• +5 more

Cedric Vega is a Research Director at the National Institute of Geographic and Forest Information (IGN) in France, where he leads research in the Laboratory of Forest Inventory (LIF) since March 2014. His work focuses on developing advanced methodologies for forest monitoring using remote sensing technologies, particularly lidar, photogrammetry, and satellite imagery. He has established himself as a leading expert in integrating National Forest Inventory data with remote sensing sources to create high-resolution forest attribute maps across France. Dr. Vega completed his education with a focus on Environmental Sciences at the University of Quebec in Montreal (2000-2006) and Ecology at Université Toulouse III - Paul Sabatier (1999-2000). His professional journey includes significant international experience as Head of the Laboratory of Applied Informatics and Geomatics at the Institut Français de Pondichéry in India (2010-2014) and teaching positions in Lidar remote sensing at AgroParisTech. Vega's research interests center on remote sensing of forest ecosystems, with particular emphasis on developing methods for monitoring forest structure and dynamics using lidar, photogrammetry, and very high-resolution optical imagery. His work addresses critical challenges in estimating forest biophysical parameters, mapping forest attributes at high spatial resolution, and improving the accuracy of national forest inventories through multisource approaches. Recent research increasingly incorporates machine learning and deep learning techniques to process complex remote sensing data streams. Analysis of his recent publications reveals a strong trend toward integrating multiple remote sensing data sources (particularly GEDI, Sentinel-1, and Sentinel-2) with traditional forest inventory data to create high-resolution forest attribute maps. His work demonstrates consistent innovation in solving spatial misalignment issues between satellite measurements and ground plots, improving geolocation accuracy, and developing robust small-area estimation techniques for forest management applications. Vega has established significant collaborations across European research institutions, particularly with French National Institute for Agriculture, Food, and Environment (INRAE), and has contributed to advancing forest monitoring methodologies that support carbon stock assessment and climate change adaptation strategies. As a Research Director at IGN, Vega leads the Laboratory of Forest Inventory which serves as a critical hub for developing and implementing advanced forest monitoring techniques in France. His team works at the intersection of remote sensing technology and practical forest management needs, focusing on creating operational tools that can be deployed at national scale while maintaining statistical rigor.

• Computer Vision
• Pattern Recognition
• Human Behavior Understanding
• +5 more

Anis KACEM is a Researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the University of Luxembourg, part of the Signal Processing and Satellite Communications (SIGCOM) research group. His work focuses on Computer Vision and Pattern Recognition, particularly in Human Behavior Understanding from visual data. He received his PhD in Computer Science from the University of Lille (France) in 2018. Research interests include advanced topics such as Earth Observation via multi-modal autoencoders, domain adaptation for image classification, vulnerability-aware deepfake detection, and CAD system reverse engineering. His contributions span neural network pruning, 3D shape analysis, and generative models for medical imaging. Publications highlight innovations in spatio-temporal learning for deepfake detection, hybrid attention mechanisms for pedestrian detection, and tool-augmented CAD task solvers. His work bridges theoretical advances with practical applications in autonomous systems and space technology. Notably, he has contributed to challenges like the SHARP 2023 Challenge on CAD history recovery and developed frameworks like Picasso for CAD sketch inference using self-supervised learning.

Koushik SinhaView profile

Associate Professor

• Mobile Computing
• Wireless Ad hoc Networks
• Wireless Sensor Networks
• +6 more

Koushik Sinha is an Associate Professor at the School of Computing, Southern Illinois University Carbondale, where he directs the Complex Networks & Applications Lab. With extensive experience spanning academia and industry—including roles at Qatar Computing Research Institute, Hewlett-Packard Labs, and Honeywell—he brings practical expertise to his research in wireless networking, complex systems, and human-machine collaboration. His work bridges theoretical foundations with real-world applications across multiple domains. Education Ph.D. in Computer Science, Jadavpur University, India (2007) M.S. in Computer Science, Clemson University, USA (2003) B.Tech. in Computer Science, Kalyani Government Engineering College, India (2001) Research Focus Dr. Sinha's research centers on energy-efficient wireless communication protocols, 5G/6G networking, and complex network analysis. His "Communication through Silence" paradigm represents a novel approach to minimizing energy consumption in wireless networks. Current projects address critical challenges in ultra-dense 5G deployments, smart city infrastructure, and the CrowdCloud platform for hybrid human-machine computing. His work increasingly intersects with societal challenges in rural connectivity, agricultural technology, and public health. Publication Trends Analysis of Dr. Sinha's recent publications reveals a strategic progression from core wireless networking research toward interdisciplinary applications. While maintaining expertise in energy-efficient protocols and network optimization, his work now addresses smart city infrastructure, rural broadband expansion, precision agriculture, and pandemic response systems. His publications demonstrate growing integration of machine learning techniques and increasing focus on real-world impact in underserved communities. Scientific Recognition Senior Member, IEEE Grand Prize Winner of 9th OSS World Challenge for AIDR (2015) N.V. Gadadhar Memorial Award from IETE (2011) Young Scientist Award from Indian Science Congress Association (2009) Mentorship and Leadership Dr. Sinha has successfully guided multiple Ph.D. and MS students through research in wireless networking and complex systems. His leadership extends to conference organization as General Co-Chair for IEEE ANTS 2018 and TPC Co-Chair for previous editions. Within his department, he has shaped curriculum as Course Coordinator for key offerings including Distributed Systems, Mobile and Wireless Computing, and Computer Architecture. Research Infrastructure The Complex Networks & Applications Lab under Dr. Sinha's direction supports cutting-edge research in wireless protocols, network optimization, and hybrid computing systems. Current projects include 5G network optimization, ultra-dense base station placement, and the CrowdCloud platform for human-machine collaborative computing, providing students with opportunities to address real-world challenges in smart cities, rural connectivity, and emergency response systems.