James Tompkin is an Associate Professor in the Department of Computer Science at Brown University, specializing in visual computing. His research focuses on computer graphics, computer vision, and human-computer interaction, with an emphasis on techniques for image/video creation, editing, analysis, and interaction. His lab develops methods for scene reconstruction (especially from multi-camera systems), dynamic scene modeling, and applications in 2D, multi-view, and VR/AR displays. Research interests include neural radiance fields (NeRFs), Gaussian splatting, time-of-flight sensing, and generative adversarial networks (GANs). He has collaborated extensively with industry partners (Adobe, Amazon, Meta) and received funding from NSF, DARPA, NASA, and UK/EPSRC. His work is disseminated via top-tier venues like CVPR, SIGGRAPH, and ECCV. Teaching includes visual computing topics, and his lab maintains active projects on GitHub and project webpages. Office hours are held weekly, with scheduling via Google Calendar integration.
Mark Hasegawa-Johnson is a Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, where he has been faculty since 1999. He holds affiliations with the College of Engineering and leads the Statistical Speech Technology Group. His academic roles include serving as Editor-in-Chief of the IEEE Transactions on Audio, Speech and Language, and membership in the ISCA Diversity Committee. Education: PhD in Electrical Engineering and Computer Science from MIT (1996), postdoctoral research at UCLA (1996-1999). Research interests span automatic speech recognition, machine learning applied to phonetics and prosody, and accessibility technologies for under-resourced languages and speech disorders. Key projects include the Speech Accessibility Project, which improves speech recognition for individuals with dysarthria, and international competition successes in audio event detection and multilingual broadcast retrieval. Scientific achievements include Fellowships from the IEEE (2020), Acoustical Society of America (2011), and ISCA. Awards also include NIH’s National Research Service Award (1998-1999) and the Frederic Vinton Hunt Post-Doctoral Fellowship (1996-1997). Teaching focuses on courses like Artificial Intelligence, Multimedia Signal Processing, and Speech Processing. Research supervision emphasizes undergraduate projects in signal processing and speech recognition, with notable student contributions to prosody-dependent speech recognition and audio source separation. Labs/Teams: Leads the Speech Accessibility Project and collaborates with interdisciplinary teams on projects like Mandarin language education tools and audio-visual speech models. Current work explores unsupervised learning, cross-lingual speech recognition, and AI-driven accessibility solutions.
Peng Song is an Assistant Professor of Computer Science at Singapore University of Technology and Design (SUTD), affiliated with the Pillar of Information Systems Technology and Design (ISTD). His research focuses on computer graphics, geometric modeling, computational design, and fabrication, emphasizing the development of algorithms for designing functional real-world objects. He holds a PhD from Nanyang Technological University (2013) and prior research roles at EPFL, University of Science and Technology of China, and Nanyang Technological University. Education: PhD in Computer Science, Nanyang Technological University (2013) Master’s Degree, Harbin Institute of Technology (2010) Bachelor’s Degree, Harbin Institute of Technology (2007) His research interests span computational design of assemblies, 3D printing, robotics, and urban modeling. Notable projects include generative urban design, wireframe mesh modeling, and mechanisms for custom-fit medical devices. He has received awards such as the SMI 2024 Best Paper Award and SIGGRAPH 2022 Honorable Mention. Grants & Professional Roles: Leading Singapore MOE Academic Research Fund Tier 2 grants (2023–2024) Associate Editor for Computers & Graphics and Graphical Models (2024–present) Program Committee Member for SIGGRAPH Asia, Pacific Graphics, and SPM He teaches courses such as 'Graphics and Visualization' and 'Extended Reality' at SUTD. His group’s work is showcased on YouTube and his lab’s website.
Meike Akveld is a Professor in the Department of Mathematics at ETH Zurich, where she has held roles since 1997, including her current position as Titularprofessorin since 2024. Her work focuses on mathematics education, outreach, and diversity initiatives. 2024–Present: Titularprofessorin, ETH Zurich 2016–2024: Senior Scientist, ETH Zurich Research Interests : Meike Akveld specializes in mathematics education, with emphasis on innovative teaching methods, mathematical competitions, and the popularization of knot theory. Her work includes designing automated assessment tools (e.g., STACK) and promoting inclusive education practices. Publications Trends : Recent articles address educational technology, flipped classrooms, and the role of competitions in STEM learning. Themes include calculus instruction, linear algebra pedagogy, and diversity in mathematics education. Scientific Awards : Finalist, KITE Award (2024) – Digital Math Assessment Credit Suisse Award for Best Teaching (2021) Finalist, KITE Award (2020) – Brückenkurs Project Goldene Eule des VSETH (2014, 2009) – Teaching Excellence
Muzaffar Rao serves as an Associate Professor in the Department of Electronic and Computer Engineering within the Faculty of Science and Engineering at the University of Limerick, Ireland. His academic base is located at C0-065 on campus, with primary contact via muzaffar.rao@ul.ie. His research spans critical cybersecurity domains with emphasis on IoT security infrastructure , FPGA-based hardware security solutions , and operational technology protection . Specialized expertise includes zero-trust architectures for smart manufacturing, intrusion detection for autonomous systems, and aviation cybersecurity. His work bridges theoretical cryptography with industrial applications through LabVIEW-FPGA implementations. Analysis of his 15 most recent publications reveals a consistent trajectory toward real-time security processing for emerging technologies. Key thematic clusters include: (1) Hardware-accelerated cryptography for resource-constrained IoT devices, (2) Zero-trust frameworks for Industry 4.0 environments, and (3) Aviation/maritime cybersecurity solutions addressing physical-layer threats. His methodology prominently features FPGA-based prototyping and machine learning integration. Rao's technical leadership manifests in developing practical security implementations rather than theoretical frameworks. His publications consistently address implementation challenges in constrained environments, with recent work focusing on latency-sensitive applications like autonomous vehicles and aviation systems. The progression from cryptographic core development (2012-2018) to system-level security architectures (2019-2025) demonstrates evolving research maturity. His educational contributions include pioneering cyber range development for OT/IT security training, directly addressing workforce skill gaps in critical infrastructure protection. The absence of formal award mentions suggests industry-focused rather than academically decorated output, with impact measured through applied solutions rather than traditional academic recognition. Rao maintains active research in laboratory environments focused on hardware security prototyping, particularly utilizing FPGA platforms for real-world security validation. His work with LabVIEW middleware indicates strong industry collaboration potential, especially in manufacturing and transportation sectors where safety-critical systems demand robust security integration.
Jing-Rebecca Li is a Professor and Research Scientist at ENSTA Paris, affiliated with the Applied Mathematics Unit (UMA) and INRIA Saclay as part of the IDEFIX research team. Her work bridges advanced mathematical techniques with medical imaging applications, particularly in diffusion MRI. She maintains a dual affiliation between ENSTA Paris, a leading engineering school in France, and INRIA, the French national research institute for digital science and technology. HDR (Habilitation à Diriger des Recherches) in Mathematics, Université Paris-Sud, 2013 Ph.D. in Mathematics, Massachusetts Institute of Technology, 2000 B.Sc. in Mathematics, University of Michigan, 1995 Dr. Li's research focuses on developing sophisticated numerical methods to solve partial differential equations with applications in diffusion magnetic resonance imaging. Her work spans brain and cardiac imaging, numerical linear algebra, machine learning algorithms for inverse problems in PDEs, and natural language processing tools. She has pioneered approaches to simulate diffusion MRI signals in complex biological tissues, enabling more accurate interpretation of imaging data for neuroscience and cardiology applications. Her research has significant implications for understanding brain microstructure and cardiac tissue organization through non-invasive imaging techniques. Her recent publications demonstrate a clear trend toward increasingly sophisticated modeling of biological tissues, with growing emphasis on cardiac applications alongside her foundational work in brain imaging. She has developed robust computational frameworks that incorporate permeable interfaces, geometrical deformations, and realistic neuronal geometries to better simulate diffusion MRI signals. Her work increasingly integrates machine learning with traditional numerical methods, creating hybrid approaches that leverage the strengths of both paradigms for microstructure estimation. Householder Prize for the best dissertation in Numerical Algebra (2002) Dr. Li has supervised numerous doctoral students across multiple institutions, with a focus on computational methods for diffusion MRI. Her current research is supported by significant grants including the Engineering for Health (E4H) interdisciplinary center project investigating biomarkers for Multiple Sclerosis through diffusion MRI (2023-2025). Previously, she led the ANR-funded SIMUDMRI project (2010-2014) and participated in the US-French Collaboration project on Computational Imaging of the Aging Cerebral Microvasculature (2013-2016). Her work demonstrates strong interdisciplinary collaboration between mathematics, computer science, and medical imaging communities. As leader of the IDEFIX research team at INRIA Saclay, Dr. Li directs a group focused on inversion methods for differential equations applied to imaging and physics problems. Her team has developed the SpinDoctor software package, a widely used MATLAB toolbox for diffusion MRI simulation that has become a standard tool in the field. The team maintains strong collaborations with Neurospin (CEA) and international research groups working on advancing diffusion MRI methodology and applications.
Harsh Mathur is a Professor in the Department of Physics at Case Western Reserve University , where he also serves as Associate Dean for Academic Affairs. His research spans theoretical physics, with primary contributions to condensed matter theory and cosmology, and interdisciplinary projects linking physics to art and history. Education B. Tech., Indian Institute of Technology, Kanpur (1987) Ph.D., Yale University (1994) Research Interests Condensed Matter Theory: Focus on mesoscopic systems, quantum dots, and localization phenomena. Cosmology: Investigates primordial gravitational radiation and cosmic microwave background physics. Interdisciplinary Work: Applies statistical physics to art authentication (Pollock’s fractal analysis) and language evolution. Publications Recent work (2008) explores gravitational radiation from phase transitions and critiques of fractal authentication of Pollock paintings. Earlier publications (1992-2003) address quantum transport, Berry phase effects, and localization models.
Umberto Nanni is a full Professor of Information Management Systems at the University of Rome "La Sapienza" since November 2005. He has held significant leadership roles including Director of the Research Center for Distance Learning and Technologies for Learning at Telematic University Unitelma Sapienza (2015-2022) and President of the Area Council in Information Engineering at the Latina location of University of Rome "La Sapienza" (2014-2020). Professor Nanni's research spans multiple domains with primary focus on Algorithm Engineering (particularly path algorithms and combinatorial problems), Health IT, Information Systems (with applications in Transport and Logistics, Cultural Heritage), and Technology Enhanced Learning. His work integrates Big Data, Graph and Text mining, Incremental Algorithms, Dynamic Data Structures, and Internet of Things technologies to address complex challenges in these areas. His recent publications demonstrate a strong trend toward biomedical applications, particularly in biospecimen management and digital twins for precision medicine, while maintaining his foundational work in algorithm design and educational technology. The research shows increasing interdisciplinary collaboration, especially between computer science and healthcare domains, with notable publications in Cancer Genomics & Proteomics and other high-impact journals. He has advised over 400 undergraduate and master's theses along with 2 PhD students He has led numerous significant research projects including "Artificial Intelligence algorithms to track and detect Covid-19 vaccine-related infodemic on social media" (2023-2025) He coordinated the "ADCATER - Advanced Digital Solutions for Professional Food and Nutrition Catering Service" project (2021-2023) He served as Principal Investigator for the eLF-eLearning Fitness project (2011-2014) with 19 partners and 42 associated partners Professor Nanni co-founded and was instrumental in developing SPRECware, software tools for Standard PREanalytical Code labeling to improve biospecimen management. His work has resulted in over 100 scientific papers with an h-index of 27, demonstrating substantial impact across computer science, healthcare informatics, and educational technology domains.
Andrew Witt is an Associate Professor in Practice of Architecture at the Harvard Graduate School of Design (GSD). He co-directs the Master in Design Engineering Program and leads research at the intersection of geometry, machines, and cultural perception. His work bridges architecture, mathematics, and computational design through projects like Certain Measures, a design-technology studio addressing complex spatial challenges for clients ranging from cultural institutions to infrastructure firms. Witt holds an M.Arch and M.Des (History and Theory) from the GSD, and has been recognized with grants from the Graham Foundation and Harvard Data Science Initiative, as well as awards like the World Frontiers Forum Pioneer distinction. Education: M.Arch (Distinction, AIA Medal, John E. Thayer Scholarship), Harvard GSD M.Des (History and Theory, Distinction), Harvard GSD Affiliations: Co-founder of Certain Measures (architecture/design studio) Laboratory for Design Technologies Research Interests: Witt explores how computational methods and mathematical rigor shape architectural design, with a focus on parametric geometry, material systems, and interdisciplinary exchanges between design and science. His publications include Formulations: Architecture, Mathematics, Culture (MIT Press, 2021) and Light Harmonies (on Heinrich Heidersberger’s light-drawing machines). His work emphasizes technically synthetic approaches to form while critically engaging cultural narratives in design. Key Achievements: Exhibitions at Pompidou Center, Barbican Centre, and Haus der Kulturen der Welt Patents for geometric rationalization and collaborative software systems Finalist for Zumtobel Award (2017) Teaching & Grants: Witt teaches collaborative design engineering courses at GSD and SEAS. His grants include projects on AI-driven design methodologies and machine vision applications in architecture.
Kyle Ormsby is an Associate Professor and Chair of the Department of Mathematics at Reed College, with an affiliate appointment as Associate Professor at the University of Washington. His expertise lies at the intersection of algebraic topology, algebraic geometry, and combinatorics. He holds a Ph.D. from the University of Michigan (2010) and a B.A. from the University of Chicago (2006). Ormsby's research focuses on motivic and equivariant homotopy theory, including studies of transfer systems, operads, and applications to geometric data analysis in neuroimaging. He has been awarded multiple NSF grants, including leadership roles in the Electronic Computational Homotopy Theory (eCHT) Research Community and the National Alzheimer’s Coordinating Center. He has advised numerous undergraduate theses and research projects, mentoring students in topics ranging from knot theory to persistent homology. His teaching spans calculus, linear algebra, topology, and advanced analysis, emphasizing inclusive pedagogy and visualization. Key honors include the NSF Mathematical Sciences Postdoctoral Fellowship (2011–2014), Rackham Fellowship (2010), and Honorable Mention for the NSF Graduate Research Fellowship (2006).
Alexandra Golby, MD is a Professor of Neurosurgery and Radiology at Harvard Medical School, and Haley Distinguished Chair in the Neurosciences at Brigham and Women's Hospital. She directs the Golby Lab, a surgical brain mapping laboratory focused on advanced imaging technologies for neurosurgical applications. Her clinical expertise centers on brain tumor and epilepsy surgery, with a focus on lesions near critical brain structures. Dr. Golby holds multiple leadership roles including Director of Image-guided Neurosurgery and Co-Director of AMIGO at Brigham and Women's Hospital. Her research integrates disciplines such as computer science, applied mathematics, and biomedical engineering to improve surgical planning and intraoperative decision-making. Notable innovations include technologies for real-time tumor resection monitoring and low-cost neuronavigation systems (e.g., NousNav) for low-resource settings. Dr. Golby completed her BA at Yale University and MD at Stanford University School of Medicine, followed by neurosurgery residency at Brigham and Women's Hospital. Dr. Golby's translational work emphasizes global health equity, including Fulbright-supported initiatives to develop locally adapted medical technologies in Rwanda and Morocco. Her research spans image-guided neurosurgery, brain-computer interface applications, and neuro-oncology advancements.
Liangyan Gui is a Research Assistant Professor at the University of Illinois at Urbana-Champaign, affiliated with the Department of Computer Science within the College of Engineering. His research focuses on cutting-edge topics in Artificial Intelligence, particularly in computer vision, 3D modeling, human motion prediction, and robotics. He teaches courses such as CS 446 (Machine Learning) and CS 598 GUI (Efficient & Predictive Vision). Gui's research interests span 3D scene understanding, human-object interaction, and generative models. He explores AI-driven solutions for tasks like motion editing, physics-based simulation, and multimodal reasoning. His work bridges computer vision and robotics, with applications in 3D reconstruction, animation, and AI for dynamic environments. Recent publications highlight advancements in 3D photo editing, human-object interaction generation, and self-supervised learning. His research often integrates textual inputs with visual data to enhance AI systems' capabilities in reasoning and generation. Gui has been involved in initiatives such as the AICE Center grants (as part of a team), though specific grant details are not explicitly attributed to him in the provided text. His contributions extend to foundational AI models and their practical implementations in real-world scenarios.
Matthew Allen is a Visiting Assistant Professor at Washington University’s Sam Fox School of Design & Visual Arts, specializing in architectural history, theory, and digital culture. His work bridges avant-garde practices, computational techniques, and interdisciplinary methodologies. Allen holds a PhD and Master of Architecture from Harvard University, with research supported by institutions like the Andrew W. Mellon Foundation and the Canadian Centre for Architecture. Education: PhD (2019) and Master of Architecture (2010) from Harvard University’s Graduate School of Design; dual Bachelor’s degrees in Physics and Comparative History of Ideas (2006) from the University of Washington. Research Interests: Focus on the interplay between architecture, technology, and media studies, particularly in 20th-century avant-garde movements, parametric design, and the socio-technical dimensions of digital tools. His book Flowcharting: From Abstractionism to Algorithmics in Art and Architecture (2023) examines the historical roots of algorithmic techniques in modernist practices. Publications: Over 30 peer-reviewed articles and book chapters, including analyses of SOM’s Hajj Terminal, structuralist art movements, and the role of screenshots in representing computational design. Recent work explores corporate architecture’s relationship to global networks and the ethical implications of data-driven design. Key Contributions: Organized exhibitions on computational history (e.g., “When the GSD Designed Software”) and symposia on topics like object-oriented ontology. His writing appears in journals such as Journal of the Society of Architectural Historians , Log , and Avery Review . Professional Experience: Previous roles include Visiting Assistant Professorships at Pratt Institute and the University of Toronto, alongside collaborations with firms like MOS and Preston Scott Cohen. Active in both academic research and public-facing architecture discourse.
Conghui HU is a Lecturer (Educator Track) at the School of Computing, National University of Singapore. Currently teaching courses such as CS1010A Programming Methodology and CS2109S Introduction to AI and Machine Learning. Research Interests Artificial Intelligence and Machine Learning Computer Vision with focus on sketch-based methods Cross-domain Image Retrieval Video Processing and Segmentation Point Cloud Analysis and 3D Vision Publications Trends Recent research outputs emphasize sketch-based video segmentation, cross-modal audio-visual analysis, and domain-generalized image retrieval. Key methodologies include deep learning, unsupervised feature representation, and differentiable particle filters, with applications in video object segmentation, point cloud segmentation, and audio-visual conditioned prediction.
Martin Eisemann is Professor of Computer Science and Director at the Computer Graphics Lab within the Computer Science Department of the College of Engineering at Technical University of Braunschweig. Previously, he served as full professor for Computer Graphics at TH Köln (2015-2020) where he co-founded and led the Advanced Media Institute. His academic journey includes a Diploma (2006) from University of Koblenz-Landau and PhD (2011) from TU Braunschweig, followed by post-graduate work at TU Delft. His research spans visual computing with emphasis on computer vision, image/video processing, computer graphics, ray tracing, Monte-Carlo simulations, information visualization, and visual analytics. Recent work demonstrates strong focus on neural rendering techniques including neural point clouds, Gaussian splatting, and holography applications, reflecting evolving trends toward AI-integrated graphics pipelines and immersive visualization systems. His publications consistently address real-time performance challenges while advancing visual quality metrics. VMV'16 Best Paper Award EGSR'16 Best Paper Award ACM Multimedia 2015 Best Student Paper Award Graphics Interface 2015 Best Student Paper Award SAP 2025 Best Student Paper Honorable Mention As Dean of Studies (2023-2027) and former Audit Committee member, he actively shapes academic policy. His community service includes program committee roles for SAP, WACV, and Computational Visual Media conferences. Current research includes a planned sabbatical (April-October 2025) focusing on visual computing challenges. The Computer Graphics Lab maintains active collaborations with DLR, Ford, and European institutions through projects spanning planetary visualization, video conferencing security, and ADHD cognitive support tools.
