Kyle Bellucci Johanson serves as a Visiting Critic in Cornell University's College of Architecture, Art, and Planning, Department of Art. Holding an MFA from California Institute of the Arts and a BA in Art and Reconciliation Studies from Bethel University, he merges artistic practice with critical theory through performance architecture and collaborative media. Whitney Independent Study Program (2022–23) Founding fellow at land's edge free school (Los Angeles) Teaching experience at SAIC, UIC, CUNY, and Cooper Union His research investigates: Power structures through spatial interventions Critical theory applications in art practice Post-capitalist imaginaries Hauntology and historical materialism Transdisciplinary approaches to social critique Project trends reveal: Reinterpretation of Soviet design principles Investigation of worker identity in post-industrial contexts Temporal and spatial dislocation in artistic practice Monument critique through performative methods Quantum theory-inspired architectural concepts Reimagining of common spaces as political sites Scientific awards include: Creative Research Grant (2025) Whitney ISP participation (2022–23) Automata Studio Residency (2022) Artists Run Chicago Grant (2021) BOLT Residency (2020–21) Through projects like KLUB WRKR —a nomadic workers' club reimagined for the gig economy—and table (2018–2022), a Chicago-based experimental space for artistic practice, he interrogates institutional frameworks while fostering community through discursive meals and collaborative exhibitions.
Ron Fedkiw is the Canon Professor of Computer Science at Stanford University's School of Engineering. He holds a PhD in Applied Mathematics from UCLA. His research focuses on computational algorithms for applications in computational fluid dynamics, computer graphics, biomechanics, and machine learning. Fedkiw has pioneered techniques for simulating natural phenomena in film and video games, earning two Academy Awards for his contributions to visual effects. He leads the PhysBAM lab and collaborates with industry through consulting roles at Epic Games and former work with Industrial Light & Magic. Education: PhD in Applied Mathematics, UCLA (1996). Notable awards include the National Academy of Science Award, Packard Fellowship, and multiple teaching honors. His lab has graduated 40 PhD students, many of whom have made significant impacts in academia and industry. Research interests span fluid dynamics, cloth simulation, facial animation, and integrating machine learning with physical models. Key contributions include algorithms for two-way fluid-solid coupling, muscle-based facial modeling, and neural network approaches for cloth and deformable bodies. Current projects explore physics-informed machine learning and real-time interactive simulations. Scientific Awards include two Oscars, PECASE, and Okawa Foundation grants. His work bridges computational physics and visual effects, with over 140 research papers and a textbook on level set methods. Advising and grants: Supervised 40 PhD students, securing funding through NSF, ONR, and industrial partnerships. Lab collaborations include SAIL (Stanford AI Lab) and Epic Games. Future work focuses on AI-driven physical simulations and biomedical applications.
Howie Choset is a Professor of Robotics at the Robotics Institute, Carnegie Mellon University. He directs the Undergraduate Robotics Minor and leads the Biorobotics Laboratory, where his research focuses on snake robots, motion planning, and medical robotics. He is also affiliated with the Manufacturing Futures Institute. Ph.D., Mechanical Engineering, California Institute of Technology (1996) M.S., Mechanical Engineering, California Institute of Technology (1995) B.S.E., Computer Science and Engineering, University of Pennsylvania (1990) B.S., Economics, The Wharton School of Business (1990) Choset's research centers on robotics for confined and complex environments, particularly through the development of snake robots. His work integrates mechanism design, path and motion planning, and estimation to enable applications in surgery, manufacturing, infrastructure inspection, and search and rescue. He is a pioneer in medical robotics and has co-founded Medrobotics to commercialize minimally invasive surgical robots. His recent publications highlight a strong focus on ergodic exploration, multi-agent systems, motion planning under uncertainty, and medical robotics. Themes include optimizing robot trajectories for information gathering, solving complex path planning problems with dynamic obstacles, and advancing autonomous systems for disaster response and space exploration (e.g., the EELS robot for Enceladus). MIT Technology Review Top 100 Innovators under 35 (2002) Best Paper Award, RIA (1999) Best Paper Award, ICRA (2003) Best Paper, IEEE Bio Rob (2006) Best Video, ICRA (2011) Nominations for best papers at ICRA, IROS, and CLAWAR Choset has advised numerous students, many of whom have won top awards. His lab has received significant funding for robotics research, including projects in surgical robotics, additive manufacturing, and autonomous exploration. He is the lead author of the textbook Principles of Robot Motion and is actively involved in educational innovation through custom robotics labs. He leads the Biorobotics Laboratory at CMU, which develops advanced robotic systems like snake robots and the EELS (Exobiology Extant Life Surveyor) robot for NASA missions. The lab collaborates with industry and government agencies on applications ranging from surgery to space exploration.
Manuel Kaufmann is a Lecturer in the Department of Computer Science at ETH Zürich. His work focuses on advanced 3D human motion capture, sensor-based systems, and computer vision applications. He is affiliated with the Institute of Informatics (inf.ethz.ch) and contributes to research in real-time motion tracking, dataset development, and machine learning integration for human-robot interaction. Research interests include holistic human-scene reconstruction from monocular videos, gaze estimation using EEG signals, and expressive avatar creation. His projects emphasize practical applications in robotics, sports analytics, and biomedical engineering, often leveraging electromagnetic and inertial sensors for high-precision data acquisition. His publications reflect a trend toward multi-modal data fusion, real-world dataset creation (e.g., WorldPose, ARCTIC), and addressing challenges in loose garment modeling (Reloo). These efforts aim to improve markerless motion capture, crowd analysis, and human-robot collaboration. No scientific awards or grants are explicitly listed. He has no documented advisees, though his research may involve collaborations with students or teams. His office is located at OAT X 23, Andreasstrasse 5, Zürich, Switzerland, and contact details include a phone number and professional email.
Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.
Dr. Zara Ersozlu is a Senior Lecturer in Mathematics Education within the School of Education at the University of Newcastle, Australia. With a distinguished international career spanning multiple continents, she has held academic positions at prestigious institutions including North Carolina State University (USA), Gazi and Gaziosmanpasa Universities (Turkey), National Taiwan Normal University (Taiwan), The University of Western Australia, Murdoch University, and Deakin University. Her academic journey includes tenured positions as an Associate Professor and leadership roles as Department Head and Chair in teacher education disciplines. Currently, she teaches undergraduate and postgraduate courses in mathematics education, including Effective Pedagogies in Primary Mathematics, K-6 Mathematics, K-6 Numeracy, and Digitally Supported Learning. Dr. Ersozlu earned her Doctor of Philosophy from Firat University in Turkey and her Master of Art from Sakarya University. Her extensive academic preparation is complemented by five years of practical teaching experience in public schools prior to entering academia. This blend of theoretical knowledge and practical classroom experience informs her approach to teacher education and educational research. At the broadest level, Dr. Ersozlu's research investigates solutions to real-life problems impacting people's well-being, success, and capacity to achieve. Her scholarly work spans primary and secondary mathematics education, the psychology of mathematics (including metacognition, self-regulation, and anxiety), cross-cultural educational studies, teacher education, virtual simulated learning environments, and educational assessment. She has increasingly focused on the transformative potential of AI and machine learning in education, exploring how these technologies alter teaching, learning, and research processes. Her methodological expertise encompasses both quantitative and qualitative approaches, allowing her to effectively analyze both small and large educational datasets. Analysis of Dr. Ersozlu's recent publications reveals a strong emphasis on mathematics anxiety, teacher education, and the integration of technology in learning environments. Her work demonstrates a consistent focus on practical applications of educational research to address real-world challenges in mathematics education. The interdisciplinary nature of her research connects educational psychology, technology integration, and cross-cultural perspectives, with particular attention to how these elements intersect in teacher preparation and student learning outcomes. 2023 ATEA Research Recognition Award from the Australian Teacher Education Association 2021 Fellow of the Higher Education Academy (Advance HE, UK) 2010 Fellowship Program for Postdoctoral Researchers from the Council of Higher Education of Turkey Dr. Ersozlu is deeply committed to mentoring the next generation of scholars, currently supervising four PhD students and having successfully guided ten students to completion. Her grant portfolio includes significant funding for projects such as Best Practice Guidelines for RPL in Initial Teacher Education Programs ($60,000), Exploring the Reciprocal Relationship Between Mathematics Anxiety and Mathematical Resilience ($2,599), and multiple conference travel awards. She serves as an Associate Editor for several prominent journals including the International Electronic Journal of Mathematics Education and as Editor for Interdisciplinary STEM Education. Her editorial work reflects her standing as a respected voice in mathematics education research. Dr. Ersozlu's academic leadership extends to her role in developing innovative teaching approaches that integrate virtual simulation technology and learning analytics. Her work with TeachLivE™, a mixed-reality classroom simulation platform, demonstrates her commitment to creating authentic learning experiences for teacher education students. Through these initiatives, she bridges the gap between educational theory and classroom practice, preparing future educators to effectively implement evidence-based teaching strategies in diverse learning environments.
Masood Masoodian is an Associate Professor in the Department of Art and Media at Aalto University, Finland. He leads the Visual Communication Design research group, focusing on interactive visualization for health, energy, and sustainability contexts. Previously, he held roles at the University of Waikato (2000-2016), University of Southern Denmark, and Massey University. Education: Doctoral degree in Other disciplines from the University of Waikato (1999) Research interests include design thinking, visualization of complex data, and creative aging interventions. Notable projects include the EU-funded INT-ACT initiative (2024-2026) addressing intangible cultural heritage. He has received an award for collaborative work on video game ludonarrative analysis (2019). Recent activities include organizing workshops on map-based interfaces, co-creating cultural heritage methods, and delivering public talks on digital design. Supervised two theses and contributed to 131 peer-reviewed outputs, emphasizing human-centered design and sustainability. Grants: Principal investigator for multiple EU projects totaling over 3 years of active funding. Awards: Prize for 'Comedy in the Ludonarrative of Video Games' (2019). Labs/Teams: Visual Communication Design group, collaborating internationally on projects like INT-ACT's cultural heritage mapping.
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.
Wenping Wang is a Professor in the Department of Computer Science & Engineering at Texas A&M University, part of the College of Engineering. His research focuses on computer graphics, computer vision, geometric modeling, and visualization. He holds Fellowships from ACM and IEEE, and has received notable awards including the 2021 AsiaGraphics Outstanding Technical Contributions Award and the 2017 John Gregory Memorial Award. Wang's educational background includes a Ph.D. from the University of Alberta and M.Eng. and B.Sc. degrees from Shandong University. His work spans advancements in neural implicit surfaces, 3D reconstruction, and medical imaging applications such as orthodontic treatment prediction. He has authored numerous influential papers in top-tier conferences like SIGGRAPH and journals like ACM Transactions on Graphics. His research interests emphasize bridging geometric modeling with machine learning, particularly in neural rendering, surface parameterization, and medical visualization. Recent projects include developing frameworks for automatic tooth alignment and high-fidelity 3D geometry generation. Wang's contributions have significantly impacted both theoretical foundations and practical applications in computer graphics.
Dhruv Shah is an Incoming Assistant Professor of Electrical and Computer Engineering at Princeton University starting January 2026 and currently serves as a Senior Research Scientist at Google DeepMind. He is also an Associated Faculty member in Princeton's Center for Statistics and Machine Learning, focusing on the convergence of machine learning and robotics for real-world deployment. His academic credentials include a Ph.D. and M.S. in Electrical Engineering and Computer Sciences from the University of California, Berkeley (2024) and a B.Tech. (Honors) in Computer Science and Engineering from the Indian Institute of Technology, Bombay (2019). Shah's research pioneers foundation models for robotics, emphasizing large-scale robot learning, out-of-distribution generalization, and long-horizon reasoning. His group adopts a full-stack methodology spanning algorithmic innovation to system design, drawing from cognitive science to develop physical AI systems at the perception-learning-control interface. Key focus areas include reinforcement learning, human-robot interaction, and continual learning for challenging environments. Analysis of his 15 most recent publications reveals dominant trends in foundation models for visual navigation, language-conditioned policies, and multi-agent systems. His work increasingly integrates multimodal inputs (vision, language) while addressing generalization gaps in real-world settings, with strong emphasis on efficient data curation and scalable robot learning frameworks. His accolades feature the Microsoft Future Leaders in Robotics & AI Fellowship (2024), two IEEE ICRA Best Conference Paper Awards (2024), multiple ICRA finalist awards across cognitive robotics and manipulation categories, and the Berkeley Fellowship (2019-2024). Shah will recruit PhD students for Princeton's upcoming admissions cycle, establishing a research group dedicated to full-stack robotics development. While specific grant details aren't provided, his trajectory indicates significant funding for AI-robotics convergence projects, particularly in foundation model development and real-world deployment challenges. His laboratory at Princeton will integrate algorithmic innovation with system design, focusing on physical AI systems that bridge perception, learning, and control while maintaining strong ties to cognitive science principles for human-aligned robotic intelligence.
Reed Stevens is a Professor in the Learning Sciences department at Northwestern University's School of Education and Social Policy (SESP). He holds a PhD in Cognitive Studies in Education from UC Berkeley (1999), an MA from UC Berkeley (1994), and a BA in Mathematics from Pomona College (1987). His research focuses on understanding learning across diverse settings including classrooms, workplaces, and museums, emphasizing how mathematical and technological practices shape cognition. Stevens develops tools like VideoTraces for media annotation and designs innovative educational models such as FUSE Studios to create scalable, interest-driven learning environments. His work bridges cognitive science and sociocultural studies, employing methods like audio-video ethnography and conversation analysis. Key themes include spatial reasoning in making activities, teacher adaptation to facilitator roles, and the diffusion of educational innovations. Stevens collaborates with industry partners to integrate real-world practices into school curricula, addressing challenges in scaling STEAM initiatives and fostering equitable participation. His research has implications for teacher training, curriculum design, and the development of technology-mediated learning tools. Stevens' recent work explores civic data science applications, professional learning in trades like coffee roasting, and the redefinition of scientific practices in education. He actively contributes to debates on expanding what counts as valid scientific inquiry and has published extensively on learning pathways, institutional adoption of innovations, and the role of agency in student-driven projects.
Ryo Suzuki is an Assistant Professor in the Department of Computer Science at the University of Calgary's Faculty of Science. His research focuses on Human-Computer Interaction (HCI) and robotics, particularly in tangible user interfaces, swarm robotics, and shape-changing interfaces. He holds a PhD in Computer Science from the University of Colorado Boulder (2020) and has conducted research internships at Stanford University, UC Berkeley, the University of Tokyo, and Adobe Research. His educational background includes advanced coursework in robotics and HCI, with a strong emphasis on interdisciplinary innovation. He teaches courses such as Human-Computer Interaction II, Human-Robot Interaction, and Special Topics in Mixed Reality Application Design. Research interests include designing novel interfaces for AR/VR/MR systems, haptic feedback mechanisms, and collaborative robotics. His work often bridges physical and digital spaces, such as through projects like LiftTiles (shape-changing building blocks) and RoomShift (room-scale haptic environments). Key awards include the UIST 2020 Honorable Mention Paper Award, DIS 2019 Best Paper Award, and the Ministry of Internal Affairs and Communications in Japan Innovation Award. His research has been featured in IEEE Computer Graphics and Applications, TechXplore, and Wired. He actively explores future work in symbiotic AI systems, AI-in-the-loop AR applications, and scalable shape-changing robotics. His lab emphasizes hands-on prototyping and user-centered design principles.
Anand Bhattad is an Assistant Professor in the Department of Computer Science at Johns Hopkins University, starting Fall 2025. Previously, he held positions as a Research Assistant Professor at the Toyota Technological Institute at Chicago (TTIC) and a visiting scholar at UC Berkeley. His research focuses on the intersection of computer vision, generative modeling, and physical reasoning, aiming to develop perception-driven and physics-aware visual models. His academic journey includes a PhD in Computer Science from the University of Illinois Urbana-Champaign under David Forsyth, with mentorship from Derek Hoiem, Svetlana Lazebnik, Greg Shakhnarovich, and Shenlong Wang. Prior to his PhD, he earned dual master’s degrees in Computer Science and Civil and Environmental Engineering at UIUC and a bachelor’s in Civil Engineering from NITK Surathkal, India. Research interests center on how generative models encode physical and perceptual knowledge, with key contributions in intrinsic image emergence, projective geometry limitations, and physics-aware relighting techniques. His work bridges classical computer vision concepts with modern deep learning, producing state-of-the-art methods for 3D scene synthesis and image editing. Articles span topics like 3P Vision , diffusion models, and 360° video datasets, reflecting interdisciplinary approaches in computer graphics and computational photography. Scientific awards include Outstanding Reviewer at ICCV 2023, CVPR 2022 Best Paper Finalist, and multiple conference service roles as workshop organizer and area chair. He designed the TTIC course Past Meets Present: A Tale of Two Visions , teaching connections between historical and modern computer vision research.
Serkut Ayvasik is a Researcher at the Chair of Communication Networks at Technical University Munich (TUM). He joined TUM in March 2019 as a research and teaching associate, following his M.Sc. in Communications Engineering (2019) and B.Sc. in Electrical and Electronics Engineering (2016) from Middle East Technical University. His research focuses on: Wireless Network Resource Management for heterogeneous latency-critical 5G applications Channel State Information Prediction using depth images Network Slicing and Quality of Service optimization Machine Learning for proactive network configuration Telemedicine Applications in cross-border communication Key article trends include 5G/6G technology , IoT sustainability , digital twins , and haptic feedback systems . He contributes to IEEE and ACM journals, with recent work on Digiot (2025) and OCTOPUS (2024). Collaborations include researchers like Wolfgang Kellerer (Chair), Edwin Babaians , Alba Jano , and Fidan Mehmeti . His work spans projects such as 6G Future Lab Bavaria , DFG GGI QCDE , and ERC FlexNets .
Ellen Zegura is the Stephen Fleming Chair and Professor in the School of Computer Science at Georgia Tech's College of Computing. She holds multiple degrees from Washington University in St. Louis: BS in Computer Science, BS in Electrical Engineering, MS in Computer Science, and DSc in Computer Science. Her research focuses on computer networking, social responsibility in STEM education, and computing for development. She co-founded the Computing for Good initiative, emphasizing project-based learning to address societal challenges. Zegura is an IEEE and ACM Fellow, and serves on the Computing Research Association (CRA) Executive Board. Her education spans interdisciplinary fields at Washington University, combining computer science and electrical engineering. She has held leadership roles at NSF and CRA, advocating for equitable technology policies. Notable contributions include advancing QoE metrics for video conferencing, analyzing mobile broadband infrastructure disparities, and developing ethics education frameworks for computing curricula. Research interests include network measurement, community-empowered data practices, and bridging technical innovation with social impact. Recent work examines tribal mobility during pandemics, sensor co-design with Indigenous communities, and ethical pedagogy for teaching assistants. Her labs and collaborations, such as CERCS, emphasize interdisciplinary problem-solving. Zegura’s awards reflect her dual impact in technical innovation and societal engagement.
