Takeshi Ikenaga is a Professor at Waseda University’s School of Fundamental Science and Engineering and Graduate School of Information, Production and Systems . He earned his Ph.D. in Information & Computer Science from Waseda University in 2001, following B.E. and M.E. degrees in Electrical Engineering (1988–1990). His career spans roles at NTT LSI Laboratories (1990–2002), Kitakyushu Foundation for Advancement of Industry, Science and Technology (FAIS) (1999–2002), and visiting researcher at the University of Massachusetts (1999–2000). Research Interests : Application-specific SoCs for video/image processing, including compression (H.264/AVC, H.265/HEVC), filters (super-resolution, noise reduction), recognition systems (feature detection, object tracking), and communication (UWB, LDPC). He also works on many-core processor design, ultra-low-delay vision systems, and sports analytics (volleyball, figure skating) with real-time 3D pose estimation and ball tracking. Awards : Recipient of the Furukawa Sansui Award (Waseda University, 1988) IEICE Research Encouragement Award (1992) Multiple Best Paper/Presentation Awards (2006–2022) at conferences including DAC/ISSCC, LSI IP Design, ISOCC, ISPACS, and CVIT APSIPA Distinguished Lecturer Certificate (2015) Waseda University Presidential Teaching Award (2020)
Prof. dr. Nico Van de Weghe is a full Professor of GIScience at the University of Ghent (UGent), affiliated with the CartoGIS research unit. His work bridges computer science, social science, and natural science through geospatial information studies, focusing on enabling machines to reason spatially (GeoAI). Since 2004, he has specialized in knowledge-based AI, particularly spatiotemporal reasoning and moving object analysis, with applications in animal behavior, criminology, healthcare, mobility, and sports. Van de Weghe's research emphasizes hybrid GeoAI systems combining knowledge-driven and data-driven approaches. Keywords include GeoAI, GIScience, Spatiotemporal Analysis, Moving Objects, and Data Mining. Recent publications highlight urban road network analysis, hybrid trajectory modeling, BIM semantic enrichment, and cycling safety studies using virtual reality.
Dr. Simon Beaumont is an Associate Professor in the Department of Chemistry at Durham University , with additional responsibilities as Associate Dean (PGR) in the Faculty of Science. His research program integrates heterogeneous catalysis , nanomaterials , and in situ spectroscopic techniques to develop sustainable chemical processes. BA & MSci Natural Sciences, University of Cambridge (2003-2007) PhD in Heterogeneous Catalysis, University of Cambridge (2010) Postdoctoral Fellowship at UC Berkeley (2010-2012) Research foci include mechanistic studies of catalytic processes, nanoparticle synthesis , and in situ characterization via X-ray absorption (NEXAFS), DRIFTS, and Raman. His work addresses challenges in CO2 hydrogenation , biomass conversion , and environmental remediation , supported by national/EU/industrial funding. Recent publications highlight trends in selective hydrogenation (furfural), multi-functional catalysts (acid-base systems), and nanoparticle stability under reactive conditions. All studies emphasize molecular-level understanding for practical catalyst design. Scientific awards include Leverhulme Trust and Addison Wheeler fellowships. Teaching portfolio spans first-year laboratories , organic chemistry tutorials , and advanced catalysis lectures . Supervision of five research postgraduates and leadership of industry-funded projects further demonstrate his academic impact.
Judith Driscoll is Professor of Materials Science at the University of Cambridge in the Department of Materials Science & Metallurgy. She holds the prestigious Royal Academy of Engineering Chair in Emerging Technologies and serves as a Visiting Staff Member at Los Alamos National Laboratory. As the founding Editor-in-Chief of APL Materials, she has significantly contributed to the materials science community. Dr. Driscoll's research focuses on Energy Efficient Oxide Materials for Information and Communications Technologies and energy devices. Her work spans the development of non-volatile memory, resistive switching devices, and ferroelectric materials for neuromorphic computing applications. She investigates oxide thin films for applications ranging from data storage to energy generation and conversion, with particular emphasis on creating more energy-efficient device technologies to handle the exponential growth of data-centric applications. Her recent publications demonstrate strong trends in developing novel oxide-based memory devices with improved energy efficiency, particularly for AI applications. The work shows significant progress in hafnium-zirconium oxide ferroelectrics, resistive switching mechanisms, and vertically aligned nanocomposite structures for enhanced device performance. These innovations address critical challenges in reducing the unsustainable energy demands of modern computing, particularly for artificial intelligence systems. Fellow of the Royal Academy of Engineering Fellow of the Materials Research Society Fellow of the American Physical Society Fellow of IOM3, IOP, and Women Engineers Society Fellow of the American Academy of Arts and Sciences Recipient of ERC Advanced Grant Editor-in-Chief of APL Materials Dr. Driscoll leads a vibrant research group that has secured significant funding including her Royal Academy of Engineering Research Chair, an ERC Advanced Grant, and an ECCS-EPSRC grant in collaboration with researchers from the USA. She has founded the Cambridge Centre for Neuromorphic Computing (Neucam) in 2023. Her group operates world-leading growth equipment including pulsed laser deposition with RHEED control, high temperature oxide sputtering, and spatial ALD systems. She collaborates extensively across the University of Cambridge and with international partners to solve complex materials challenges, with her group's role often being to identify optimal materials for functional goals, predict fabrication methods, and then create and characterize these materials.
Manuel R. Amieva is a Professor at Stanford University School of Medicine , holding joint appointments in Pediatrics - Infectious Diseases and Microbiology & Immunology . He is also a member of the Maternal & Child Health Research Institute (MCHRI) . His clinical practice at Stanford Medicine Children's Health focuses on pediatric infectious diseases. Education: Medical Education: Stanford University School of Medicine (1997) Fellowship: Stanford University Pediatric Infectious Disease Fellowship (2004) Internship & Residency: Stanford Health Care at Lucile Packard Children's Hospital (1998-1999) Dr. Amieva's research investigates host-pathogen interactions at epithelial barriers, with specific expertise in Helicobacter pylori , Listeria monocytogenes , Salmonella enterica , and Staphylococcus aureus . His lab develops innovative organoid culture systems with controlled polarity to study microbial colonization and oncogenic mechanisms. Key discoveries include: H. pylori's manipulation of epithelial junctions via the CagA protein Listeria's exploitation of cell extrusion sites for invasion Staphylococcus toxin interactions with adherens junctions Gastric stem cell activation by pathogens Recent publication trends show continued leadership in infectious disease mechanisms (2020-2025), with a focus on: Pathogen-specific epithelial breach strategies Organoid modeling of viral/bacterial interactions Redox-dependent host factor regulation Single-cell spatial transcriptomic analyses Multi-institutional educational frameworks His scientific collaborations span disciplines including: Gastric cancer genomics initiatives COVID-19 lung infection models Stem cell-microbe interactions Medical education reform projects Dr. Amieva maintains active clinical research while mentoring students in both the Microbiology & Immunology and Pediatrics programs. His lab at Stanford employs advanced 3D confocal microscopy and organ-on-a-chip technologies to visualize epithelial colonization dynamics.
Dr. Jillianne Code is an Associate Professor at the University of British Columbia's Faculty of Education, Department of Curriculum & Pedagogy, directing the ALIVE Research Lab. Her work focuses on learner agency, educational technologies, and social media's impact on student success. Specializes in immersive virtual environments for assessment Researches self-regulated learning and formative feedback Investigates health education through digital interventions Her recent publications examine pandemic-era educational transitions, digital literacy in teacher training, and cognitive tools in virtual reality. She has received Outstanding Paper Awards for her work on immersive assessment. Dr. Code leads research projects on learning analytics, student engagement in digital spaces, and technology education sustainability. Her work bridges educational theory with practical interventions through digital badges, mobile health applications, and 3D learning environments.
Ranjay Krishna is an Assistant Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he co-directs the RAIVN lab and leads the computer vision team at the Allen Institute for AI (Ai2). His research intersects computer vision , natural language processing , robotics , and human-computer interaction . PhD in Computer Science from Stanford University (2021) Bachelor's and Master's degrees from Stanford and Cornell His work has received best paper , outstanding paper , and orals at top conferences like CVPR, ACL, CSCW, NeurIPS, UIST, and ECCV. Media outlets including Science , Forbes , and PBS NOVA have covered his research. He has been supported by grants from Google , Apple , NFS , and others. Ranjay advises a diverse group of 15 PhD and postdoctoral researchers , including Jieyu Zhang, Benlin Liu, and Cheng-Yu Hsieh. His teams have developed benchmarks like MemoryBench and The Colosseum , and his PathFinder framework achieved 74% accuracy in skin melanoma diagnosis—surpassing human experts by 9%. Notable contributions include: Perception Tokens for visual reasoning in MLMs SAM2Act for robotic manipulation with memory Synthetic Visual Genome dataset with 5.6M relationships
Mark Lee is an Adjunct Professor in the People Analytics department at NYU’s Tandon School of Engineering, specializing in Technology Management and Innovation. He holds a Ph.D. in Engineering Psychology from Georgia Institute of Technology (1996). Currently, he serves as Head of Research, Analytics, and Business Development at UL ComplianceWire, focusing on pharmaceutical and medical device manufacturing training. His research leverages large datasets to improve healthcare safety through regulatory compliance and best practices. Courses taught include Human Factors Engineering, Workplace Design, and Predictive Analytics. Education: Ph.D. in Engineering Psychology, Georgia Tech (1996) Key Roles: Adjunct Professor, Head of Research at UL ComplianceWire Research Focus: Human Factors, Training Systems Design, Healthcare Compliance His work spans auditory display systems for aviation (e.g., 3D audio cockpit interfaces) and ergonomic design for industrial products. Recent projects emphasize data-driven solutions for regulatory challenges in life sciences. Publications highlight studies on visual search strategies, age-related cognitive performance, and application of signal detection theory in decision-making. He actively collaborates with industry and government entities, exemplified by the FDA-UL Cooperative Research Agreement.
Changhuei Yang is the Thomas G. Myers Professor of Electrical Engineering, Bioengineering, and Medical Engineering at California Institute of Technology, serving as Executive Officer for Electrical Engineering and Investigator at Heritage Medical Research Institute. He holds a Ph.D. and three master's degrees from MIT, with appointments at Caltech since 2003. Research focuses on: Advanced microscopy techniques including Fourier Ptychography Wavefront shaping for biological tissue imaging Optical phase conjugation for deep-tissue applications Compact medical devices for cerebral monitoring Publications demonstrate leadership in computational imaging, with recent advances in stain-free embryo analysis, portable cerebral blood flow monitors, and high-resolution volumetric imaging techniques using neural representations. Honored as National Academy of Inventors member. Research applications span deep-tissue biochemical imaging, incisionless surgery, and optogenetic activation systems.
Parastoo Abtahi is an Assistant Professor in the Computer Science Department at Princeton University. She leads the Situated Interactions Lab (Ψ Lab) as part of the Princeton HCI Group, focusing on AR/VR and spatial computing. Prior to Princeton, she worked at Meta Reality Labs Research. PhD in Computer Science from Stanford University BSc in Electrical and Computer Engineering from University of Toronto (Engineering Science program) Her research explores wearable interactive technologies that augment human intelligence in physical contexts through: Input on-the-go: Low-effort, privacy-preserving multimodal interfaces Intelligent output: Timely, minimal multisensory systems Situated AI: Personalizable, predictable, safe physical-digital interaction Recent publications analyze: Drone-based haptic illusions for AR/VR Microgestures for mobile AR input Interactivity in explainable AI systems Temporal dynamics in VR haptics Awards include: 2025 Google Research Scholar Award CHI 2019 & 2018 Honorable Mentions She teaches courses like: COS 436: Human-Computer Interaction COS IW: AR Meets AI
Dr. Farha Sattar is a Lecturer in Education (Mathematics) at the Faculty of Arts and Society, Charles Darwin University. With over thirty years of teaching experience in Mathematics, Science education, GIS, Remote Sensing, and e-Learning technologies at undergraduate and postgraduate levels, Dr. Sattar brings extensive expertise to her academic role. She holds a PhD in Geospatial Science from Charles Darwin University, focusing on Spatial Mathematical Modelling for 3D Gully Mapping and Erosion Quantification. Her educational background includes: PhD in Geospatial Science (Three Dimensional Gully Mapping and Erosion Quantification within a Geoinformatics Framework), Charles Darwin University (2012) Prior academic experience at several Asian and European universities Dr. Sattar's research spans multiple interdisciplinary fields with a strong focus on the intersection of education and technology. Her primary research interests include Mathematics Education, STEM Education, Geoscience Education, Spatial Mathematical Modelling, Drone Technology, Remote Sensing, Geographic Information Systems, and Innovative Pedagogies such as experiential learning and inquiry-based approaches. She has developed expertise in cognitive development, particularly in fostering critical and creative thinking skills through iSTEM approaches. An analysis of Dr. Sattar's recent publications reveals a consistent trajectory toward integrating emerging technologies like drones, augmented reality, and geospatial artificial intelligence into educational contexts. Her work demonstrates a progression from foundational geospatial research on erosion mapping to innovative applications in STEM education. Recent publications increasingly focus on drone technology for educational purposes, spatial data infrastructure for policy development, and computational thinking development through technology-enhanced learning. Dr. Sattar has received numerous accolades for her contributions to education and research: NT Science Week Awards – Inspired NT STEM Hero of the Year award (2021) Long Service Recognition Award (2023) Finalist, CDU Alumni Award (2023) Multiple Best Presentation Awards (2008, 2010, 2017) Australian College of Educators - 2021 College Medal Nominee As a supervisor, Dr. Sattar mentors postgraduate research students including Ayesha Farhan, who is working on "A Framework for Cognitive Development: Fostering Critical and Creative Thinking Skills, Using iSTEM Approach." She actively secures research funding through various projects such as "DBELaSTEM: Drone-Based Experiential Learning and STEM Education," "STEM_D & VR: Youth STEM Learning and Empowerment with Drones and VR," and "CodProg Drone Lab." Her research is supported by grants from NT Government projects and other external funding sources. Dr. Sattar leads the CodProg Drone Lab, an innovative research space exploring drone technology applications in education. She is a certified drone pilot and aeronautical radio operator, which enables her to bridge theoretical knowledge with practical applications. Her work with the "Flying Forward: Drones, STEM Equity, and Indigenous Empowerment" initiative demonstrates her commitment to community engagement and addressing educational disparities through technology.
Jason Ritt is an Associate Professor of Brain Science (Research) and Scientific Director of Quantitative Neuroscience at the Robert J. and Nancy D. Carney Institute for Brain Science, Brown University. He holds affiliations with the Data Science Institute and collaborates across disciplines on quantitative research methods. Education : B.S., M.A., and Ph.D. in Neuroscience from Boston University (1997–2003). Research : Focuses on neural processing during active sensing and neuroengineering for neurostimulation. Combines electrophysiology, optogenetics, and theoretical approaches in rodent models. Develops closed-loop systems for studying sensory neural prosthetics and brain-machine interfaces. Key areas include synaptic diversity, neurocontrol algorithms, and sensory restoration. Teaching : Instructs NEUR 2100 NeuroPracticum, integrating hands-on neuroscience research training.
Ilker Yildirim is an Assistant Professor of Psychology at Yale University, where he leads the Cognitive and Neural Computation Lab (CNCL). He received his Ph.D. from the University of Rochester in 2014. His research focuses on understanding how perception transforms raw sensory signals into meaningful representations of objects and scenes through computational modeling approaches. His research interests include: Computational modeling of visual perception and cognition Intuitive physics and physical reasoning Bayesian inference and probabilistic models in cognition Integration of graphics and physics engines in cognitive modeling Neural mechanisms of object representation Attention and resource allocation in dynamic scenes Yildirim's lab develops computational frameworks that bridge cognitive processes with neural mechanisms, using tools including probabilistic models, simulation engines, and deep learning. His work often involves testing these models through behavioral and neural experiments to create unified accounts of perception and cognition. His recent publications reveal a strong focus on how humans perceive physical properties of objects, particularly soft materials and liquids, and how attention dynamically allocates resources during scene perception. Among his notable achievements is receiving an NSF CAREER award for his project "CAREER: CompCog: Reverse-engineering neural mechanisms of object cognition with multilevel computational modeling," which will run from 2025 to 2030. His Nature Human Behaviour paper by Qi Lin was featured on CBS News, Germany's NZZ, and Wired Italy. Yildirim mentors a diverse group of graduate students and postdocs working on various aspects of perception, cognition, and computational modeling. His students have received prestigious awards including the Jane Olejarczyk Award and the Leonard J. Savage Prize.
Ira Kemelmacher-Shlizerman is a Full Professor of Computer Science at the Paul G. Allen School of Computer Science & Engineering at the University of Washington and Director of the UW Reality Lab. She also serves as a Principal Scientist at Google, where she leads the Shopping Gen AI visuals teams focusing on Virtual Try-On, 3D, and product videos. Her research spans computer vision, computer graphics, and Generative AI, with particular contributions to virtual try-on technology, 3D modeling, and augmented reality applications. Professor Kemelmacher-Shlizerman's research interests focus on Generative AI applications in visual computing. Her work bridges the gap between theoretical computer vision and practical applications, particularly in e-commerce and virtual reality. She has made significant contributions to virtual try-on technology, 3D editing with generative models, and AI applications for shopping experiences. Her research combines deep learning with traditional computer vision techniques to solve challenging problems in image and video synthesis. Her recent publications demonstrate a strong trend toward Generative AI applications for visual shopping experiences, virtual try-on technology, and 3D content creation. The work spans multiple top conferences including CVPR, SIGGRAPH, and ICCV, with a focus on practical applications of computer vision and graphics. Her research has evolved from foundational work in face reconstruction and aging to current applications in virtual shopping and 3D content generation. Google faculty award Madrona prize GeekWire Innovation of the Year Award Covers of CACM and SIGGRAPH Best student paper honorable mention at CVPR'21 Best demo runner up MobiSys'22 Senior member of IEEE Distinguished Member of ACM Professor Kemelmacher-Shlizerman has successfully tech-transferred multiple research projects to industry. She founded Dreambit, a startup acquired by Meta, and previously built and launched the Face Movies feature at Google. She currently leads Google's Shopping Gen AI visuals teams, focusing on 10x improvements to shopping journeys. Her UW Reality Lab serves as a hub for AR/VR research with industry partnerships. She has mentored numerous PhD students who have become researchers in both academia and industry, with several publications featuring student co-authors receiving recognition at top conferences. Professor Kemelmacher-Shlizerman leads the Graphics and Imaging Laboratory (GRAIL) and the UW Reality Lab, which focuses on augmented and virtual reality research with industry partnerships including Google. The labs work on cutting-edge projects in virtual try-on, 3D modeling, and immersive experiences, bridging academic research with real-world applications.
Mani Golparvar Fard is a Professor at the University of Illinois at Urbana-Champaign, holding joint appointments in the Siebel School of Computing and Data Science and the Department of Civil and Environmental Engineering. He also contributes to the Technology Entrepreneur Center. His research focuses on integrating artificial intelligence, computer vision, and data analytics to advance construction management, infrastructure monitoring, and automation. Key areas include BIM integration, reality capture systems, and deep learning-based progress tracking. His work emphasizes automated construction progress monitoring through semantic segmentation, vision-language models, and UAV-based data collection. He has pioneered methods like Scan2BIM-NET for converting point clouds into BIM models and developed frameworks for worker safety analysis using machine learning. Awards: Walter L. Huber Civil Engineering Research Prize (2018) Daniel W. Halpin Award for Scholarship (2016) Advising & Grants: While no specific grant details are provided, his research is supported by collaborations with industry and government initiatives, such as the Japanese national bridge inspection project. He advises a team focused on AI-driven construction solutions and maintains active partnerships with engineering firms. Labs & Teams: Leads research groups in vision-based construction analytics, automated scheduling systems, and BIM integration. His work is disseminated through platforms like the VisualSiteDiary system and the InstaDam open-source platform for structural damage analysis.
