Sanjit A. Seshia is the Cadence Founders Chair Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley . He is affiliated with the Group in Logic and the Methodology of Science and participates in centers like the Industrial Cyber-Physical Systems Center , Berkeley AI Research , and the Simons Institute for the Theory of Computing . Research interests include formal methods for automated verification and synthesis of dependable systems, with applications to cyber-physical systems , AI-based autonomy , and computer security . His work spans SMT solving, model counting, syntax-guided synthesis, and algorithmic improvisation, with tools like UCLID5 , VerifAI , and Scenic for verifying autonomous systems and educational platforms like CPSGrader . Students and collaborators include notable researchers such as Dorsa Sadigh (Stanford), Daniel Fremont (UC Santa Cruz), and Hazem Torfah (Chalmers). He has co-founded startups like Decyphir and 20ⁿ Labs based on his research.
Robert Wellington serves as Associate Professor and Director of the Centre for Art History & Art Theory at the School of Art & Design, Australian National University (ANU), within the College of Arts & Social Sciences. His academic leadership focuses on advancing art historical research and public engagement through institutional initiatives. His research spans Art History, Early Modern European Art (particularly Louis XIV's France), Museum Studies, Public Humanities, and Cultural Heritage. Wellington investigates transcultural object exchange, royal iconography, and identity construction through material culture, with emphasis on Versailles, medals, and marginalia. His work bridges historical scholarship with contemporary public humanities applications in museums and digital archives. Analysis of his 2020-2024 publications reveals concentrated exploration of Louis XIV's cultural legacy, Versailles' global impact, and the evolving role of humanities institutions. He pioneers digital critical editions while examining how objects shape identity across cultural boundaries, consistently connecting historical practices to modern museum and archival challenges. No scientific awards were documented in the provided materials. As Centre Director, Wellington oversees research programs and institutional partnerships, though specific student advisees and grant details remain unlisted in available sources. His leadership facilitates interdisciplinary projects including the Jean-Benjamin de Laborde digital edition initiative. The Centre for Art History & Art Theory functions as a research hub under Wellington's direction, driving public humanities innovation through the ANU's Art & Design Seminar Series, Visiting Artist Program, and collaborative projects with national collections. Current initiatives focus on digital heritage and community-engaged scholarship within the School of Art & Design framework.
Casey Reas is a Professor of Design Media Arts at the University of California, Los Angeles (UCLA), within the UCLA School of the Arts and Architecture. Previously, he served as an Associate Professor at Interaction Design Institute Ivrea (2001–2003). His academic background includes a BSc in Design from the University of Cincinnati (1996) and an MSc in Media Arts and Sciences from MIT (2001). Reas co-created the open-source programming language Processing with Ben Fry, revolutionizing visual arts and education globally. He is renowned for blending art and technology, creating generative works exhibited in institutions like the Centre Georges Pompidou and the Whitney Museum of American Art. His research focuses on computational aesthetics, algorithmic art, and digital media's intersection with music and design. Education: Bachelor of Science in Design, University of Cincinnati, 1996 Master of Science in Media Arts and Sciences, MIT, 2001 Research Interests: Reas explores the synergy between code and creativity, emphasizing generative systems, real-time visualization, and interactive installations. His work often addresses themes of randomness, pattern formation, and human-computer interaction. Notable projects include Processing language development, music video collaborations with The National, and immersive installations like Sketch Machine and Warm Up . Recent Collaborations: Music videos for The National’s Sleep Well Beast (2017), blending algorithmic visuals with Grammy-winning music. Interactive installations such as Impure Functions (2017) and Warm Up (2017), co-created with UCLA’s Conditional Studio. Public commissions like Sketch Machine (2018), an open-source drawing tool for GIPHY. Awards: Contributor to The National’s Grammy Award for Best Alternative Music Album ( Sleep Well Beast , 2018). Labs/Teams: Reas leads the Reas Studio and collaborates with UCLA’s Arts Conditional Studio, focusing on experimental software, generative art, and interdisciplinary projects.
Anna Choromanska is an Associate Professor in the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering, with affiliations to NYU Center for Data Science (CDS), NYU Center for Urban Science and Progress (CUSP), NYU Center for Advanced Technology in Communications (CATT), and the C2SMART Center. Her research focuses on deep learning optimization, generalization, and applications in autonomous driving and large-scale data analysis. She holds an Alfred P. Sloan Fellowship and NSF CAREER Award, and her work impacts industries like NVIDIA and Facebook. She directs the Learning Systems Laboratory (LSL), emphasizing interdisciplinary experimental/theoretical work. Research Interests: Machine Learning fundamentals, DL optimization, continual learning, autonomous vehicle systems, large data analysis. Her lab explores DNN learning dynamics, training architecture design, and scalable algorithms. Professional Impact: Over 50 invited talks, workshop organization for top ML conferences, and contributions to open-source projects like Vowpal Wabbit. Her algorithms are deployed in production systems at Facebook and Baidu. Awards: NSF CAREER Award Alfred P. Sloan Fellowship IBM Global University Program Academic Award (2x) Columbia University Presidential Fellowship Advising & Labs: Leads LSL, supervising interdisciplinary projects in optimization and autonomy. Actively involved in NYU's Modern AI seminar series and industry partnerships through CATT. Personal Interests: Accomplished pianist, salsa dancer, and fashion design enthusiast with notable performances and certifications in dance and music.
Professor Thomas Blumensath is a Professor of Signal and Image Processing at the University of Southampton and a Fellow at the Alan Turing Institute. He is the Academic Lead in Image Processing and Reconstruction at the University's μ-VIS X-ray Imaging Centre and Director of Research at the Institute of Sound and Vibration Research (ISVR). His research focuses on advanced algorithms for solving inverse problems in tomographic imaging, combining machine learning, optimization, and statistical methods. Key areas include X-ray tomography strategies, GPU-accelerated reconstruction, and multimodal imaging applications. Education: B.Sc. (Hons) Music Technology and Audio System Design, University of Derby (2002) PhD in Electronic Engineering (Bayesian Signal Processing), University of London (2006) Research Interests: Professor Blumensath's work spans theoretical and applied signal/image processing, with emphasis on tomographic imaging techniques. His current projects address efficient reconstruction methods, spectral X-ray CT, and applications in manufacturing and plant science. He collaborates with advanced imaging facilities like Diamond Light Source and ISIS neutron imaging beamline. Key Contributions: His research bridges computational methods (e.g., compressed sensing) with practical imaging challenges, including limited-angle tomography and stereo imaging strategies. He leads the National Research Facility for Lab X-ray CT and has developed the TIGRE reconstruction toolbox. Grants & Projects: Active funding includes EPSRC projects on tomographic sensitivity monitoring and CT-based manufacturing inspections. Completed projects cover constrained reconstruction, AM process verification, and industrial CT metrology. Awards: Alan Turing Institute Fellowship Teaching & Leadership: He teaches modules on machine learning, biomedical image processing, and robotics. Leads the BEng Control Engineering program at the Joint Education Institute with Harbin Engineering University. Labs/Teams: Active in the Signal Processing, Audio and Hearing research group (SPAH) and the Institute for Life Sciences. Oversees the μ-VIS X-ray Imaging Centre's research initiatives.
Professor Peter F. Driessen is a faculty member in the Department of Electrical and Computer Engineering at the University of Victoria, with a cross-appointment in the School of Music. He holds a BSc and PhD from the University of Victoria and is a Professional Engineer (PEng). His research focuses on communication systems, signal processing, control, and interdisciplinary projects in computer music and wireless technologies. Key areas include audio/video signal processing, radio propagation, sound recording, and multimedia systems. He leads the University of Victoria Propagation Laboratory, which explores radio wave propagation and Amateur radio integration with engineering education. His work spans theoretical research and applied projects like ECOSat satellite systems, software-defined radio (SDR), and innovative musical instruments such as the Radio Drum. He supervises undergraduate and graduate projects in these domains through ELEC 499 courses. Notable contributions include the APEGBC Editorial Board Award for Best Paper (2002) and patents in wireless networking and signal processing. His teaching includes courses in signal analysis and electromagnetics, and he collaborates on interdisciplinary programs like the Music/Computer Science degree. Education: BSc in Electrical Engineering, University of Victoria PhD in Electrical Engineering, University of Victoria Research Interests: Audio and video signal processing for music and media Software-defined radio and Amateur radio technologies Satellite communication and ground station development Gesture-based interfaces and musical instrument design Error mitigation in streaming audio/video Optical and microwave-photonic systems Labs & Collaborations: Propagation Laboratory (radio wave research) UVic Experimental Radio Group (Amateur radio club) UVic Satellite Design Team (ECOSat projects) UVic Centre for Aerospace Research Grants & Awards: APEGBC Editorial Board Award (2002) Multiple US patents in wireless systems and signal processing
David Bawden is a Professor in the Department of Library and Information Science (CityLIS) at City, University of London. With a publication record spanning over two decades, he has established himself as a leading scholar in information science, particularly known for his work on the philosophical foundations of information, knowledge organization, and information behavior. His research frequently intersects with Luciano Floridi's Philosophy of Information, which he has applied to library and information science theory and practice. Bawden's research interests focus on the theoretical underpinnings of information science, including information behavior, information literacy, knowledge organization, and the philosophical dimensions of information. His work often explores the conceptual frameworks that shape how we understand information in both physical and digital environments. He has made significant contributions to understanding information privacy through a philosophical lens and has examined temporal aspects of information in contemporary society. His recent publications demonstrate a continued focus on theoretical developments in information science, with particular attention to the implications of Floridi's work for library and information practice. Bawden's scholarship shows a consistent trajectory from examining historical aspects of information to contemporary challenges in the digital age, maintaining a strong theoretical orientation throughout. As a frequent collaborator with Lyn Robinson (also at City, University of London), Bawden has co-authored numerous papers exploring transitions between concepts of information across different domains, information overload, and the philosophical foundations of information science. His work has appeared in leading journals including the Journal of Documentation, Journal of the Association for Information Science and Technology, and Information Research. Bawden has been actively involved in the CityLIS program, contributing to innovative approaches to information science education including the development of curricula addressing data librarianship and the 'onlife' nature of contemporary information experience. His scholarship reflects a deep commitment to advancing the theoretical foundations of the field while maintaining relevance to practical information challenges.
Michael S. Horn is a Professor of Learning Sciences and Computer Science at Northwestern University, serving as Program Coordinator for Learning Sciences. He holds a PhD in Computer Science from Tufts University and a ScB in Computer Science from Brown University. His research focuses on the intersection of human-computer interaction and learning, emphasizing the use of emerging technologies in museums, classrooms, and informal settings. Notable projects include multi-touch tabletops in natural history museums, tangible programming languages for early education, and the TIDAL Lab’s innovative exhibits like Build-a-Tree and DeepTree. Education background includes: PhD in Computer Science (Tufts University, 2009) MS in Computer Science (Tufts University, 2006) ScB in Computer Science (Brown University, 1997) Research interests span tangible interaction design, computational literacy, and museum-based learning. He has led projects such as the ‘Energy Monsters’ board game and the TunePad music-coding platform. His work integrates technology with pedagogical practices to foster collaborative and exploratory learning environments. Active collaborations include the Computer History Museum and the California Academy of Sciences. Advising contributions include mentoring doctoral students like Dr. Mmachi Obiorah and Pei-Yi. His lab, TIDAL, explores interactive technologies for education, with grants from NSF and industry partnerships. Recent articles highlight innovations in computational thinking integration, music-coding hybrid practices, and AI-driven qualitative analysis.
Golan Levin is Professor of Electronic Art at Carnegie Mellon University's School of Art, with courtesy appointments in the School of Design, School of Architecture, School of Computer Science, and Entertainment Technology Center. From 2009 to 2022, he served as Director and Co-Director of CMU's Frank-Ratchye STUDIO for Creative Inquiry, a laboratory dedicated to supporting atypical, anti-disciplinary research at the intersection of arts, science, technology, and culture. Levin's research spans interactive art, digital fabrication, information visualization, and generative design. He explores the critical potential of visualization, generative computation for expressive form, and how abstraction connects to realities beyond language. His practice engages interactive gestural robotics, nonverbal interaction aesthetics, and tactical applications of personal digital fabrication through responsive artifacts and media provocations that highlight human-machine relationships. His publications reveal consistent innovation in real-time audiovisual systems, interactive fabrication interfaces, and pedagogic tools for artists. Key trends include slit-scan video techniques, tangible augmented-reality performance, and noise-based visualizations, demonstrating his commitment to expanding human action vocabulary through digital media while bridging artistic expression with computational systems. Levin's work has received significant recognition: Permanent collection inclusion at Museum of Modern Art (MoMA) Whitney Biennial exhibition "50 Designers Shaping the Future" by Fast Company (2012) Competitive grants from National Endowment for the Arts, National Endowment for the Humanities, Creative Capital, and Rockefeller MAP Fund As an educator, Levin teaches "studio courses in computer science" on interactive art, experimental capture, and generative design, emphasizing computation as a medium for critical inquiry. His research funding from major cultural institutions supports interdisciplinary exploration at the arts-technology nexus. The Frank-Ratchye STUDIO for Creative Inquiry, which he directed for 13 years, remains a vital hub for anti-disciplinary collaboration, continuing his legacy of fostering creative agency through technology-infused artistic practice.
Adam Finkelstein is a Professor in the Department of Computer Science at Princeton University, where he has been a faculty member since 1997. He holds a PhD and Master's in Computer Science from the University of Washington and a dual degree in Physics and Computer Science from Swarthmore College. Finkelstein is renowned for his interdisciplinary work at the intersection of computer graphics, audio processing, and machine learning, and he co-organized the Art of Science exhibition at Princeton. Education: PhD, Computer Science, University of Washington MS, Computer Science, University of Washington BA, Physics and Computer Science, Swarthmore College His research spans audio processing (e.g., speech enhancement, voice conversion, audio metrics), computer graphics (e.g., line drawing algorithms, stylized rendering, image manipulation), and machine learning (e.g., self-supervised learning, differentiable programming). His work often bridges technical and creative domains, exemplified by collaborations at Pixar and Adobe Creative Technologies Lab. Recent publications highlight advancements in audio super-resolution and voice conversion using deep learning frameworks, as well as stylized line rendering for animated 3D models. His contributions to perceptual audio metrics and shader optimization further underscore his impact on human-centric computational systems. Scientific Awards: NSF CAREER Award Alfred P. Sloan Fellowship Fellow of the Association for Computing Machinery (ACM) Finkelstein has secured foundational grants for his research and actively mentors students, though no specific advisees are listed. He also explores collaborative tools for internet music performance, reflecting his broader interest in distributed systems and user interfaces.
N. Rich Nguyen is an Assistant Professor in the Department of Computer Science at the University of Virginia (UVA), where he joined in August 2018. He's part of the School of Engineering and Applied Science and is on a teaching track , focusing on making machine learning accessible and engaging for all students. Research and Innovation: Rich Nguyen's research interests include biomedical image analysis , machine learning , and computer science education . He aims to reinvent instructional activities to make them adaptive and engaging by incorporating art and music elements to help everyone learn coding. Notable research contributions include: Floodwatch : A system for flood monitoring using crowdsourced images TuneScope : A digital music creation tool combining SoundScope and Snaps! technology CAD Library : Open-source design tools for educators AI for early sepsis detection : Highlighted in UVA Today Teaching Accomplishments: Before UVA, Rich taught computer science courses at UNC Charlotte for four years to a total of 1,458 students. At UVA, he teaches several courses including: CS 4774: Machine Learning (multiple semesters) CS 2501: Machine Learning for All (launched in Fall 2021) SYS 6016 / SDS 6050: Deep Learning CS 2150: Data and Program Representation (multiple semesters) CS 6316: Machine Learning (Graduate Level) CS 2910: CS Education Practicum (for Teaching Assistants) He previously taught at UNC Charlotte: ITCS 1600: Computing Professionals ITCS 2600: Computing Professionals for Transfer Students ITCS 4156: Introduction to Machine Learning ITCS 2215: Design and Analysis of Algorithms Academic Achievements: Rich Nguyen has received several notable awards and grants: Google Faculty Award for Machine Learning Education with TensorFlow (2019) Best Paper Award at IEEE BigDataSE (2022) Best Poster Award at SITE Conference (2022) CCI Faculty Innovation Award (2018) NSF grants for Smart and Connected Communities (2022) and Computational Thinking (2021) 3 Cavaliers Grant on Coding and Music (2021) Student Mentorship: Rich has mentored numerous students and teaching assistants who have achieved recognition. Notable students include: Joy Qiu - Published in Clinical Infectious Diseases Louisa Edwards and Zach Boner - Invited to Ken Ono Podcast Mike Ferguson - Winner of CS Louis T. Rader Undergraduate Teaching Award He has also served as faculty advisor for HooHacks (UVA's hackathon) and co-founded CharlotteHack at UNC Charlotte. Labs and Collaborations: Rich Nguyen leads the ML4VA (Machine Learning for Virginia) initiative, engaging students in project-based learning to apply machine learning to real-world problems affecting Virginia communities. He collaborates with institutions for symposiums on smart cities, particularly with ASEAN universities, and has partnered with Premier Healthcare for hackathons and with Glen Bull on educational technology projects.
Nichole Denise Pinkard is the Alice Hamilton Professor of Learning Sciences and Faculty Director of the Office of Community Education Partnerships at Northwestern University's School of Education and Social Policy. She founded the Digital Youth Network (DYN) and developed L3, a social learning platform connecting youth's learning across multiple environments. Education: PhD, Learning Sciences, Northwestern University, 1998 MS, Computer Science, Northwestern University BS, Computer Science, Stanford University Research Focus: Designs socio-technical systems to support ecological models of learning, emphasizing community-level learning ecosystems and pedagogical-based social networks. Her work bridges technology and urban education contexts to reimagine learning documentation and opportunities. Awards: 2010 Common Sense Media Award 2004 Jan Hawkins Award 2000 NSF Early Career Fellowship Projects & Initiatives: Leads Chicago City of Learning and Digital Youth Divas programs, advancing digital equity and youth empowerment through technology. Collaborates with city agencies to create existence proofs for innovative urban learning models.
Michael S. Horn is a Professor at Northwestern University with a joint appointment in Computer Science and the Learning Sciences. He directs the Tangible Interaction Design and Learning (TIDAL) Lab and coordinates the Learning Sciences PhD Program. His work focuses on leveraging interactive technology to design innovative learning experiences, such as tangible programming languages (e.g., Tern) and music-coding platforms (e.g., TunePad). He holds a PhD from Tufts University and has collaborated with institutions like the California Academy of Sciences and the Museum of Science, Boston. Education: PhD in Computer Science, Tufts University MS in Computer Science, Tufts University BS in Computer Science, Brown University Research Interests: Dr. Horn explores how emerging technologies can create engaging learning environments. His projects include museum exhibits (e.g., DeepTree, Build-a-Tree), educational tools for computational literacy (e.g., TunePad, Strawbies), and collaborative design with educators. Recent work emphasizes integrating music and coding to foster computational thinking. Publications: His recent work addresses computational thinking in STEM education, AI-driven qualitative analysis, and hybrid music-coding practices. Themes include equitable participation, teacher professional development, and immersive AR/VR tools for learning. Awards: Recipient of the 2018 Edith Ackermann Award for innovative work in child-computer interaction. His research has led to commercial products like Osmo Coding and Kibo Robotics. Grants & Labs: Principal investigator on NSF grants (e.g., #1612619, #1451762) and collaborator with labs like the Center for Connected Learning (CCL) and the Collaborative Technology Laboratory (CollabLab).
LEE Wee Sun is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he previously served as Head of Department, Vice Dean of Undergraduate Studies, and Vice Dean of Research. His academic journey began with a B.Eng. in Computer Systems Engineering from the University of Queensland (1992) and a Ph.D. from the Australian National University (1996), followed by research roles at the Australian Defence Force Academy and MIT. Education: Ph.D., Australian National University, Canberra, Australia (1996) B.Eng. in Computer Systems Engineering, University of Queensland, Brisbane, Australia (1992) Research Focus: Professor Lee pioneers work in Machine Learning , Planning Under Uncertainty , and Approximate Inference , with emphasis on integrating AI subfields for holistic reasoning. His current projects include "Learning to Decompose for Reasoning and Planning" (enhancing LLMs via self-supervised problem decomposition) and "Learning to Reason with Visual-Linguistic Inputs" (unifying vision, language, and reasoning in single architectures). Publication Trends: Recent work (2023-2025) centers on bridging LLMs with classical AI techniques, featuring breakthroughs in uncertainty quantification, multi-task optimization, and graph-based reasoning. Key themes include sparsity-aware vehicle routing, epistemic uncertainty for reliable LLMs, and differentiable neural solvers for combinatorial problems. Awards: IJCAI-JAIR Best Paper Prize (2022) RSS Test of Time Award (2021) RoboCup Best Paper Award (IROS 2015) HRATC 1st Place (2015) IPPC POMDP Track 1st Place (2011, 2014) UAI Google Best Student Paper (2014) Semeval-1 1st/2nd Place (2007) J.G. Crawford Prize (ANU 1996) Leadership & Service: As steering committee chair for ACML and area chair for NeurIPS/ICML/AAAI/IJCAI, Professor Lee shapes global AI discourse. His administrative roles at NUS and collaborations with MIT/Singapore-MIT Alliance demonstrate commitment to advancing AI education and research infrastructure. While student advisees aren't listed, his leadership positions imply extensive mentoring. Research Ecosystem: His work drives NUS's AI initiatives including Knowledge@Computing projects on reasoning frontiers. Current efforts focus on making AI systems robust through uncertainty-aware planning and multi-modal integration, with applications in robotics, verification systems, and combinatorial optimization.
Matthew W. Kunz is an Associate Professor of Astrophysical Sciences at Princeton University, serving as Associate Chair of the Department of Astrophysical Sciences and Director of Graduate Studies for the Program in Plasma Physics. He holds a B.S. in Astronomy-Physics and B.A. in Music from the University of Virginia (2003), and a Ph.D. in Physics from the University of Illinois at Urbana-Champaign (2009). His research focuses on astrophysical plasma dynamics, including instability, turbulence, and transport in weakly collisional and poorly ionized plasmas, with applications to galaxy clusters, accretion disks, and the solar wind. Dr. Kunz's work employs analytical and numerical methods to study multi-scale plasma dynamics, aiming to understand angular momentum transport in accretion disks, kinetic turbulence cascades, and magnetic field evolution. His research has been recognized with several awards, including an NSF CAREER Award (2020-25), Alfred P. Sloan Research Fellowship (2017-20), and NASA Einstein Postdoctoral Fellowship (2011-14). He teaches courses on plasma astrophysics (AST 521), irreversible processes in plasmas (AST 554), and astrophysical research methods (AST 303). His publications demonstrate a consistent focus on plasma turbulence, magnetic reconnection, and cosmic ray propagation, with recent work emphasizing collisionless plasma dynamics and high-energy astrophysical phenomena.
