Hadar Averbuch-Elor is an Assistant Professor at the Cornell Ann S. Bowers College of Computing and Information Science and Cornell Tech , with prior roles at Tel Aviv University and postdoctoral work at Cornell Tech. Her research bridges computer graphics and computer vision , focusing on integrating 3D geometry and natural language into multimodal perception systems. Education: B.S. in Electrical Engineering, Technion Israel Institute of Technology Ph.D. in Computer Science, Tel Aviv University Her recent publications highlight advancements in diffusion models , 3D reconstruction , and image generation , with applications in text-guided shape editing , floorplan localization , and cuneiform digitization . Papers span top conferences like ICCV , SIGGRAPH , CVPR , and Eurographics . Scientific Awards: Zuckerman Postdoctoral Scholar Fellowship Schmidt Postdoctoral Award for Women in Mathematical and Computing Sciences Rising Star in Electrical Engineering and Computer Sciences, UC Berkeley She advises PhD students including Morris Alper , Etai Sella , and Daniel Garibi , and has mentored former MS/BS students like Gal Fiebelman and Rachel Mikulinsky .
Franck Davoine is a CNRS Senior Researcher (equivalent to Full Professor) at the LIRIS Laboratory (UMR 5205) affiliated with INSA Lyon, where he coordinates the Imagine research team and contributes to environmental transition initiatives. His career spans 25+ years across France, Sweden, and China, including roles at Heudiasyc Lab (UTC), LIAMA Beijing, and Peking University. His research integrates computer vision , autonomous systems , and deep learning , with emphasis on: Sensor fusion for LiDAR and event cameras Real-time perception in autonomous vehicles Uncertainty modeling using Dempster-Shafer theory Self-supervised depth estimation He has supervised 14 PhD students and 21+ interns, with current projects funded by ANR (e.g., ANNAPOLIS for autonomous navigation). Awards include: Most Influential Paper over the Decade (2023) Grenoble INP PhD Thesis Prize (1995) Recent publications focus on neural networks for calibration, depth estimation, and event-based vision, reflecting his lab's work on efficient embedded systems for robotics.
Fabrice Guillaume is a Professor at the Laboratory of Cognitive Psychology (LPC) affiliated with Aix Marseille University and the Centre National de la Recherche Scientifique (CNRS). His research focuses on fundamental memory processes and their clinical applications, particularly in schizophrenia and cognitive disorders. He leads research within the MeMoPsy Technology platform, investigating memory through multiple approaches including electrophysiology and psychophysics. Dr. Guillaume's research interests span multiple domains within cognitive psychology and clinical neuroscience. His work primarily investigates memory systems, with specific emphasis on face recognition, false memories, mental imagery, and the neural correlates of memory processes. He employs electrophysiological methods (ERP) to examine the temporal dynamics of memory encoding and retrieval, particularly how contextual information influences recognition. His schizophrenia research explores the dissociation between different memory processes and how they are affected in psychotic disorders. Analysis of his recent publications reveals a consistent focus on the intersection of memory, perception, and clinical disorders. His work demonstrates how memory processes interact with perceptual information during object and face recognition. A significant portion of his research examines schizophrenia, particularly how memory deficits manifest in this condition and what they reveal about normal memory functioning. His methodological approach combines behavioral measures with electrophysiological recordings to provide a comprehensive understanding of memory processes. Dr. Guillaume has established strong collaborative networks across multiple institutions, working with researchers from Université de Lyon, École normale supérieure de Lyon, and Université de Montréal. His work bridges basic cognitive research with clinical applications, particularly in developing diagnostic and remediation tools for memory disorders.
Sakeena Muntaha serves as a Junior Researcher at the University of Applied Sciences St. Pölten, affiliated with the Institute of Creative\Media/Technologies and the Department of Media and Digital Technologies since 2017. Currently on leave, she contributes to the institution's research mission through interdisciplinary projects spanning computer vision and applied machine learning. Her academic foundation includes a Master's degree in Computer Engineering from the National University of Sciences and Technology (NUST), Pakistan (2016) and a Bachelor's degree in Computer System Engineering from the NFC Institute of Engineering and Technology (NFCIET), Pakistan (2012). These qualifications underpin her technical expertise in visual computing systems. Dr. Muntaha's research program centers on machine learning and computer vision with dual application tracks: medical diagnostics (skin lesion segmentation, dermoscopy analysis) and environmental/urban systems (building footprint extraction, flood monitoring, real estate analysis). Her methodological approach integrates deep learning architectures with classical image processing techniques like level sets and Gabor filters, demonstrating versatility across domains from cultural heritage preservation to cybersecurity. Recent work shows increasing focus on robustness evaluation and real-world deployment challenges in vision systems. Analysis of her 15 most recent publications reveals strong thematic continuity in computer vision applications, with growing sophistication in handling real-world data constraints. Early work focused on medical imaging and malware detection, while recent publications emphasize urban infrastructure analysis and environmental monitoring, reflecting strategic alignment with societal challenges. The consistent use of deep learning frameworks across diverse domains highlights her technical agility. As an active member of the Media Computing Research Group, she contributes to projects including IMREA (Intelligent Multimodal Real Estate Assessment), Scribe ID AI (cultural heritage analysis), and ImmBild (location assessment via computer vision). Her collaborative research involves partnerships with institutions across Austria and Pakistan, though specific grant details and advising activities are not documented in available sources.
Judit Csanádi is an Associate Professor in the Department of Scenic Design at the Hungarian University of Fine Arts, where she has taught Set Design and Spatial Language Studies since 2002. Her academic career includes previous teaching positions at ELTE (1999-2002), the National Theatre School of Canada (1993-2011), and Concordia University (1991-1993). Her educational background features: Architectural Engineer from Budapest University of Technology, Faculty of Architecture (1976) Stage Designer qualification from Banff School of Fine Arts, Canada (1980) Csanádi's research centers on the linguistic characters of architectural space and theatrical design language. She investigates historical theater architecture while pioneering contemporary experimental spaces, with particular focus on urbanism's relationship to stage design. Her work bridges architectural engineering principles with theatrical innovation, exploring how spatial semantics shape audience perception and narrative expression in performance environments. Her publications reveal consistent exploration of Hungarian scenographic identity across decades, with increasing emphasis on spatial theory and pedagogical innovation in theater design education. The trajectory shows evolution from practical set design documentation toward theoretical frameworks for understanding space as dramatic language. Her scientific recognition includes: Főnix Award (2010, 2007) World Stage Design silver medal (2009) Hevesi Sándor Award (2008) Jászai Mari Award (2001) Hungarian Academy of Sciences Research Scholarship (1982-84) METESZ Diploma Award (1976) While her professional work includes extensive stage design for major Hungarian theaters, the available documentation does not specify student advising activities or research grant funding. Her teaching philosophy emphasizes organic integration of craft fundamentals with artistic creation within theater production teams.
Filip Biljecki is an Assistant Professor at the National University of Singapore, jointly appointed in the Department of Architecture (College of Design and Engineering) and the Department of Real Estate (NUS Business School). He founded and leads the NUS Urban Analytics Lab, which serves as a research hub for urban data science and geospatial AI applications. His work bridges architecture, geomatics, and data science to create smarter, more sustainable urban environments. Dr. Biljecki earned his PhD in 3D GIS from Delft University of Technology with highest honors (top 5%) and completed his MSc in Geomatics at the same institution. His educational background in geospatial science forms the foundation for his innovative research in urban analytics. His research focuses on leveraging emerging urban data sources, particularly street view imagery and other visual data, to advance 3D city modeling, urban digital twins, and GeoAI applications. He investigates spatial data quality, crowdsourcing through platforms like OpenStreetMap, and develops methods to assess urban form and human perception of built environments. His work integrates computer vision, machine learning, and geospatial analysis to address pressing urban challenges related to sustainability, comfort, and equity. Analysis of his recent publications reveals a strong trend toward integrating AI with urban analytics, with particular emphasis on using street view imagery to understand urban environments. His work spans from technical aspects of 3D modeling and digital twins to human-centered applications assessing walkability, thermal comfort, and visual perception. A significant portion of his research addresses sustainability challenges through carbon analysis, urban heat island mitigation, and sustainable urban design. Presidential Young Professorship (NUS), 2020 Top 2% scientists worldwide (Stanford University), 2021 Multiple teaching excellence awards (2021-2025) Best paper awards at 3D GeoInfo (2017, 2023) EuroSDR award for best PhD thesis related to GIS in Europe, 2017 Dr. Biljecki actively supervises PhD students and research fellows through his Urban Analytics Lab, with research supported by various grants and collaborations. He serves as Associate Editor for Computers, Environment and Urban Systems and holds editorial positions with several other leading journals in geography and urban studies. His work bridges academia and practice through collaborations with industry and government agencies focused on urban development. As founder of the NUS Urban Analytics Lab, he leads a vibrant research team exploring the intersection of cities and AI. He also chairs the 3D Information Management Domain Working Group at the Open Geospatial Consortium and serves as Chair of WG IV/1 at the International Society for Photogrammetry and Remote Sensing. His leadership extends to the Future Cities Lab Global at the Singapore-ETH Centre where he serves as Principal Investigator.
Dr. Lijing Zhu serves as an Assistant Professor of Data Science within the College of Science and Engineering at the University of Houston-Clear Lake, where she teaches foundational data science courses and conducts research in artificial intelligence. Academic Background Ph.D. in Data Science, Bowling Green State University (August 2025) Research Focus Dr. Zhu's research spans machine learning, graph-based deep learning, continual graph learning, and computer vision. Her work addresses critical challenges in knowledge representation through continual knowledge graph learning, human-object interaction detection, and graph representation learning. She develops innovative algorithms that enhance the robustness and efficiency of deep learning models for complex structured data, with particular emphasis on overcoming catastrophic forgetting in dynamic knowledge graphs. Publication Trends Her active 2024-2025 publication record in venues like ECML PKDD, CIKM, and IEEE Big Data demonstrates a cohesive research trajectory across three interconnected domains: (1) advancing graph neural network robustness against adversarial attacks, (2) developing continual learning frameworks for evolving knowledge graphs, and (3) applying multimodal deep learning to drug discovery and computer vision problems. This cross-cutting work positions her at the intersection of theoretical machine learning and practical applications. Teaching and Mentorship Dr. Zhu teaches DASC 5133 (Introduction to Data Science), DASC 5333 (Database Systems for Data Science), and DASC 5431 (Data Analytics and Machine Learning). As an early-career faculty member building her research program, she offers graduate students opportunities to contribute to high-impact publications while developing expertise in graph-based AI systems and multimodal learning.
Thanh Nguyen Canh is a Lecturer at University of Engineering and Technology, Vietnam National University (UET VNU) and Teaching/Research Assistant at Japan Advanced Institute of Science and Technology (JAIST), School of Information Science. Primary affiliation is with the Robotics Lab at both institutions where he conducts research in SLAM systems and computer vision. PhD in Information Science at JAIST (2024.10-present) MS in Information Science at JAIST (2022.9-2024.9) BS in Robotics Engineering at UET VNU (2018.8-2022.8) Research focuses on advancing SLAM technologies through semantic understanding and active exploration. Key specialties include Visual-inertial odometry , probabilistic semantic mapping , and multi-sensor fusion for UAV navigation. Current projects integrate deep learning with traditional SLAM pipelines to create robust environmental representations. Publication activity centers on practical robotics applications, with the 2023 ICCAIS paper demonstrating object-oriented semantic mapping for UAV navigation. Research output emphasizes implementable solutions with active GitHub repositories showing continuous development in SLAM systems and reinforcement learning. Supervision involves teaching robotics curriculum at UET while mentoring research assistants at JAIST. Current projects provide hands-on experience with ROS, point cloud processing, and deep learning frameworks. Laboratory work occurs within the Robotics Lab environment at JAIST/UET, utilizing GitHub-hosted tools like probabilistic_semantic_mapping and S3M_SLAM for collaborative development. The lab maintains strong focus on real-world robotics applications with particular emphasis on aerial vehicle navigation systems.
Fabrice Lamarche serves as an Associate Professor at Université de Rennes 1 within the ESIR School of Engineering, while maintaining dual affiliation with the MimeTIC research team at IRISA / INRIA Rennes. His academic career spans uninterrupted service since 2004, evolving from Assistant Professor roles at IFSIC (2003-2009) to current positions at ESIR. As co-founder of Golaem (2009), he bridges academic research with commercial application in crowd simulation technology. His institutional journey includes sequential membership in SIAMES (2004-2006), Bunraku (2007-2011), and MimeTIC (2011-present) research teams at INRIA. Lamarche earned his PhD in Computer Sciences from Université de Rennes 1 in 2003 with thesis work on virtual human autonomy. His educational foundation includes a Master of Computer Sciences specializing in Computer Graphics and AI (1999-2000), a Master of Engineering from INSA de Rennes (1997-2000), and a Technical degree from IUT de Limoges (1995-1997). His research program centers on virtual human behavior modeling with emphasis on decision-making systems, path planning under environmental constraints, and crowd simulation architectures. Key innovations include TopoPlan for human-scale navigation and frameworks integrating high-level task scheduling with low-level motion planning. This work addresses fundamental challenges in creating autonomous virtual characters capable of navigating complex 3D environments while exhibiting realistic behaviors, with applications spanning virtual reality, gaming, and simulation-based training systems. Publication analysis reveals consistent output from 2001-2014, evolving from foundational behavioral animation (2001-2004) to sophisticated crowd simulation systems (2013-2014). A notable trajectory shows increasing integration of cognitive modeling with motion planning, alongside exploration of Brain-Computer Interfaces for virtual navigation. Recent work demonstrates particular strength in semantic decomposition of urban environments and time-space constrained task scheduling. His scientific contributions have earned significant recognition: Rennes city medal (2009) for research excellence Second prize at Deutsch Telekom Awards (FMX 2008) for TopoPlan/MKM integration As an active researcher and educator, Lamarche advises students through Université de Rennes 1 while leveraging INRIA resources and Golaem industry partnerships. His publication record indicates sustained grant funding, particularly through INRIA channels, with collaborative projects extending to neuroscience applications via Brain-Computer Interface research. The MimeTIC team affiliation provides infrastructure for multimodal interaction research in complex virtual environments. Lamarche's laboratory work through MimeTIC focuses on developing practical implementations of virtual human autonomy systems. His research group maintains strong industry connections via Golaem, which commercializes crowd simulation technology. Current efforts emphasize semantic understanding of virtual urban spaces and robust path planning under dynamic constraints, building on foundational work in topological navigation and behavioral decision systems.
