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Jeffrey L. KrichmarView profile

Professor

• Neurorobotics
• Embodied Cognition
• Biologically Plausible Neural Models
• +6 more

Jeffrey L. Krichmar is a Professor in the Department of Cognitive Sciences and Department of Computer Science at the University of California, Irvine. His academic journey includes a B.S. in Computer Science from the University of Massachusetts Amherst (1983), an M.S. in Computer Science from The George Washington University (1991), and a Ph.D. in Computational Sciences and Informatics from George Mason University (1997). Prior to UCI, he served as Assistant Professor at George Mason University (1997-1999) and Senior Fellow at The Neurosciences Institute (1999-2007). University of California, Irvine (2007-present) George Mason University (1997-1999) The Neurosciences Institute (1999-2007) His research focuses on neurorobotics , exploring how embodied cognition and biologically plausible neural models can enhance robotic systems. Key areas include spiking neural networks , neuromodulation , path planning , and interactive tactile robots for therapeutic applications. His work bridges neuroscience , robotics , and cognitive science , with applications in autonomous vehicles , neuroprosthetics , and AI explainability . Recent publications emphasize spiking neural networks for navigation , neuromodulated attention , and neuromorphic hardware integration. The development of CARLsim, a GPU-accelerated spiking neural network simulator now in version 6.0, represents a major technical contribution. His team's work on socially assistive robots like CARL-SJR targets therapeutic applications for autism and ADHD. Scientific Awards IJCNN 2020 Best Paper Award Finalist for Best Student Paper at IJCNN 2018 Best Paper Award at IEEE IJCNN 2009 Grants include National Science Foundation funding for neural models of decision-making (2009). His lab (Cognitive Anteater Robotics Laboratory) develops systems that use large-scale brain simulations for autonomous behavior , with applications in adaptive robotics , sensorimotor learning , and neuroethology . Current projects explore neuromodulatory influences on attention systems and cognitive flexibility .

Jørgen BakkeView profile

Associate Professor

• Art History
• Classical Archaeology
• Byzantine Studies
• +4 more

Jørgen Bakke is an Associate Professor in History of Art at the Department of Linguistic, Literary and Aesthetic Studies, University of Bergen. His academic work spans classical archaeology, Byzantine studies, and visual culture with a particular focus on ancient Greek art, landscape archaeology, and the cultural history of the Mediterranean region. Bakke has been actively involved in archaeological research in Greece, particularly in the Tegea region of Arcadia. His research interests encompass a broad range of topics in art history and classical studies. Bakke specializes in visual culture of ancient Greece, Byzantine garden traditions, landscape archaeology, and the intersection of art with memory and knowledge representation. His work often bridges disciplines, connecting classical literature with visual analysis and exploring how ancient visual culture functioned within historical and religious contexts. Bakke's research demonstrates particular expertise in the interpretation of ancient texts related to art (ekphrasis), the visual poetics of ancient Greek culture, and the reception of classical themes in later periods. Bakke's scholarly output reveals a consistent focus on Mediterranean cultural landscapes, particularly examining the Tegea region through multiple historical periods from classical antiquity through the Byzantine era and into the Ottoman period. His work shows a progression from early studies on ancient Greek visual culture toward more specialized research on Byzantine visual traditions and garden culture. A notable thread throughout his publications is the examination of how visual culture functions within religious and devotional contexts across different historical periods. His recent work increasingly engages with digital humanities approaches to art historical analysis. Bakke is actively involved in multiple research groups at the University of Bergen, including the Images of Knowledge Research Group, the Antiquity and the Classical Tradition Research Group, and the Literature & Religion Research Group. These affiliations reflect the interdisciplinary nature of his work, which bridges art history, classical studies, religious studies, and literary analysis. His research demonstrates how visual culture serves as a critical lens for understanding historical, religious, and philosophical developments across different eras.

Byron BootsView profile

Professor

• Machine Learning
• Artificial Intelligence
• Robotics
• +5 more

Byron Boots is the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he directs the UW Robot Learning Laboratory. He also serves as a Principal Research Scientist in the Seattle Robotics Lab at NVIDIA Research and co-chairs the IEEE Robotics and Automation Society Technical Committee on Robot Learning. Dr. Boots received his Ph.D. from the Machine Learning Department in the School of Computer Science at Carnegie Mellon University, where he was a member of the Sense, Learn, Act (SELECT) Lab co-directed by Carlos Guestrin and his advisor Geoff Gordon. Prior to joining the University of Washington faculty, he was an Assistant Professor in the School of Interactive Computing within the College of Computing at Georgia Tech, and before that, he completed a post-doc in the Robotics and State Estimation Lab directed by Dieter Fox at the University of Washington. Professor Boots' research focuses on the intersection of machine learning, artificial intelligence, and robotics, with particular emphasis on developing theory and systems that tightly integrate perception, learning, and control. His work spans computer vision, state estimation, localization and mapping, high-speed navigation, motion planning, and robotic manipulation. His group develops algorithms drawing from deep learning and neural networks, nonparametric statistics, graphical models, nonconvex optimization, quantum physics, online learning, reinforcement learning, and optimal control. The research demonstrates a strong theoretical foundation while maintaining practical relevance to real-world robotic systems. His recent publications reveal a clear trend toward integrating advanced machine learning techniques with robotics, particularly in model predictive control, motion planning, and learning-based approaches to robot control. His work shows increasing focus on developing theoretically grounded methods that can handle the complex, nonlinear dynamics of real-world robotic systems while maintaining computational efficiency. The publications span top venues including ICRA, CoRL, IROS, and NeurIPS, demonstrating broad impact across multiple subfields of robotics and AI. Finalist for Best Systems Paper at Conference on Robot Learning (CoRL-2021) Multiple papers selected for oral presentations at top robotics conferences Work recognized for theoretical contributions and practical applications in robot learning As director of the UW Robot Learning Laboratory, Boots leads a vibrant research group focused on fundamental and applied research in robot learning. The lab maintains strong collaborations with NVIDIA Research and has produced numerous high-impact publications that bridge theory and practice. Professor Boots teaches courses in autonomous robotics, machine learning, and reinforcement learning, contributing to both undergraduate and graduate education at the University of Washington.

Garreth TigwellView profile

Assistant Professor

• Human-Computer Interaction (HCI)
• Accessibility Design
• Digital Accessibility
• +5 more

Garreth Tigwell is an Assistant Professor in the School of Information at RIT, co-directing the CAIR Lab with Dr. Kristen Shinohara. His research focuses on accessibility in digital design, particularly for disabled users, addressing challenges faced by novice and expert creators. His work spans topics like accessible prototyping tools, cultural considerations in design, and inclusive mixed reality interfaces. Education: BSc in Psychology, University of Dundee, 2012 MSc (Distinction) in User Experience Engineering, University of Dundee, 2014 PhD in Human-Computer Interaction (HCI), University of Dundee, 2019 Research Interests: Designing accessible digital systems for blind, deaf, and low-vision users Adaptable user interfaces for mixed reality and textured surfaces Cultural dimensions in accessibility pedagogy Authentication methods for visually impaired users Articles Trends: Recent work emphasizes AR/VR accessibility, cultural design frameworks, and haptic authentication. His studies often involve collaborations with global researchers and industry partners. Awards: Best Paper (MobileHCI 2022) CHI Honorable Mention (2023, 2024) NSF-funded research projects Advising & Grants: Active in mentoring graduate students and securing grants. His lab focuses on born-accessible AI tools and inclusive design education. Teaches courses like HCI Research Methods and Future Interactions. Labs/Teams: Co-leads the CAIR Lab, which develops technologies to bridge accessibility gaps in digital design and prototyping.

Paul CheungView profile

Professor

• Demography
• Public Policy
• Official Statistics
• +3 more

Professor Paul Cheung is a Professor (Practice) at the Lee Kuan Yew School of Public Policy and serves as Interim Director of the Asia Competitiveness Institute (ACI) at the National University of Singapore (NUS). Previously, he was Director of the United Nations Statistics Division (2004-2012), where he spearheaded global statistical system development and initiated the UN-GGIM platform. He also served as Singapore’s Chief Statistician (1991-2004), leading national statistical reforms. His expertise spans demography, geospatial information, and policy analysis. He holds a PhD from the University of Michigan (1983) and an Honorary Doctorate from Mongolia’s National University (2008). Notable awards include Singapore’s Public Administration Medal (Gold), Cambodia’s Royal Order of Sahametrei, and the Von Neumann-Spallart Medal. Research focuses on official statistics, data policy, and aging populations. Courses taught include 'Big Data and Official Statistics in Public Policy.' He advises governments worldwide on statistical systems, including Oman’s Vision 2040 and China’s National Bureau of Statistics. His work bridges statistical innovation with global governance through initiatives like UN-GGIM.

Arpan Man SainjuView profile

Assistant Professor

• Spatial Big Data Analytics
• Spatial and Spatiotemporal Data Mining
• Geographic Information Systems (GIS)
• +3 more

Dr. Arpan Man Sainju is an Assistant Professor and Internship Coordinator in the Department of Computer Science at Middle Tennessee State University (MTSU). He holds a PhD (2021) and MS (2020) from the University of Alabama, and a B.E. (2011) from Tribhuvan University. His research focuses on spatial big data analytics, spatiotemporal data mining, and GIS applications in environmental modeling, disaster management, and geospatial science. He develops innovative algorithms for Earth imagery segmentation, flood inundation mapping, and physics-aware machine learning models. Education: PhD in Computer Science, University of Alabama (2021) MS in Computer Science, University of Alabama (2020) B.E. in Computer Science, Tribhuvan University (2011) Key research interests include deep learning for geospatial tasks, semi-supervised learning with limited labels, and parallel computing for big spatial data. His work bridges computer science and environmental science, addressing challenges in hydrology, urban safety, and disaster response. He has published extensively in top journals like ACM TIST, IEEE TKDE, and Environmental Modelling & Software, focusing on applications like flood modeling, road safety analysis, and 3D shape analysis. Dr. Sainju collaborates on interdisciplinary projects involving physics-guided models, hidden Markov structures, and GPU-accelerated algorithms. His research has been applied to real-world scenarios such as hurricane flood analysis and malware detection through Windows log analysis.

Diogo Carbonera LuvizonView profile

Researcher

• Computer Vision
• Computer Graphics
• Deep Learning
• +4 more

Diogo Carbonera Luvizon is a Researcher at the Max-Planck-Institut für Informatik (MPI-INF) in Saarbrücken, Germany, and a member of the Visual Computing and Artificial Intelligence (VIA) Research Center. He holds a PhD in Computer Vision and Machine Learning from CY Cergy Paris University (2019), and Bachelor's and Master's degrees in Engineering and Applied Computing from UTFPR, Brazil. His research focuses on solving complex problems in Computer Vision, Computer Graphics, and Deep Learning, particularly in human modeling and real-time systems. Education: PhD (2019) - CY Cergy Paris University; M.Sc. (2015) - UTFPR; B.Sc. (2011) - UTFPR. Research interests include 3D human pose estimation, action recognition, multitask learning, and novel view synthesis. He has contributed to patents on multiplane image generation (Samsung) and vehicle speed measurement systems. His work has been recognized with awards like the Best Paper Honorable Mention at GCPR-VMV 2022 and Best Presentation Award at ETIS Lab (2018). He has developed open-source tools, including the deephar repository for human action recognition and pose estimation. His current affiliations include MPI-INF and the VIA Research Center, a partnership between MPI-INF and Google.

Maneesh AgrawalaView profile

Professor

• Computer Graphics
• Human-Computer Interaction
• Information Visualization
• +2 more

Maneesh Agrawala is the Forest Baskett Professor of Computer Science at Stanford University and Director of the Brown Institute for Media Innovation. His research spans computer graphics, human-computer interaction, and information visualization. Agrawala's lab develops computational tools for visual communication, investigating how design principles improve media effectiveness. Current projects include diffusion models for image and video generation, sketch-based interfaces, and visualization tools for scriptwriting. His team creates systems that enable new forms of content creation and analysis. His publications demonstrate consistent innovation in visual computing, with recent advances in controllable generative models, video understanding, and visualization design. Agrawala has received numerous honors including the MacArthur Fellowship and ACM Fellowship for his contributions to visual computing.

Junier OlivaView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Nonparametric Statistics
• +8 more

Junier Oliva is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill and Lead Faculty of the Master of Applied Data Science program. His research focuses on machine learning, artificial intelligence, and nonparametric statistics, particularly in high-dimensional density estimation, sequential modeling, and learning from complex/structured data. He holds a B.S., M.S., and Ph.D. in Computer Science from Carnegie Mellon University, with prior industry experience at Yahoo! and Uber ATG. Research Interests: Machine learning, artificial intelligence, nonparametric statistics, deep learning, statistical data mining, signal processing, kernel methods, and scalability. His work bridges machine and human learning via collective approaches, emphasizing simple yet flexible models for massive datasets. Awards/Grants: $592K AIM-AHEAD/NIH Grant for Human+AI Collaboration $594K NSF Grant for Scientific Discovery $500K NSF Grant for 'Machine Detectives' Project ACM BCB Best Paper Award (2022) for transparent single-cell classification work Labs/Teams: Director of the LUPA Lab, which develops machine learning techniques for holistic data understanding across domains like healthcare, earth science, and computer vision.

• Medical Data Science
• Machine Learning
• Clinical Informatics
• +3 more

Dr. Daphné Chopard is a Researcher affiliated with the Professorship for Medical Data Science at ETH Zürich. Her work focuses on advancing medical data science through machine learning, clinical informatics, and multimodal learning applications in healthcare. She specializes in areas such as time-series analysis in critical care, generative models for medical data, and natural language processing for clinical texts. Her research emphasizes improving healthcare outcomes through innovative data-driven approaches, including projects like the SwissPedHealth pediatric data network and foundational work on multimodal variational autoencoders. Dr. Chopard’s contributions span clinical decision support systems, adverse event detection in trials, and acronym disambiguation in medical narratives. Her recent projects include studies on ventilation protocols in pediatric critical care and weakly-supervised learning applied to medical imaging datasets like MIMIC-CXR. She collaborates on initiatives to enhance representation learning in multimodal healthcare contexts, reflecting her commitment to bridging AI advancements with practical clinical applications.

Dongming XuView profile

Associate Professor

• IT innovation and entrepreneurship
• Social media use in business
• Disaster management
• +10 more

Dongming Xu is an Associate Professor in Business Information Systems at the University of Queensland Business School. She holds a PhD from the City University of Hong Kong in Information Systems and has established herself as a prominent researcher in the field of information systems with over 100 publications in top-tier journals and conference proceedings. Her educational background includes a PhD from City University of Hong Kong in Information Systems, though specific details about earlier degrees are not provided in the available text. Dr. Xu's research focuses on the confluence of information technology use and innovation, with particular emphasis on IT entrepreneurship, social media applications in business contexts, and business intelligence systems. Her work explores how information systems influence society and business performance, with applications spanning disaster management, eFinance, eHealth, and knowledge management. She combines theoretical model building with laboratory and field experiments, often developing prototype systems to validate her research. Her publication record demonstrates consistent high-quality output across multiple domains of information systems research, with recent work emphasizing digital disruption, platform ecosystems, social media in disasters, healthcare technology, and micro-learning applications. Her research shows a clear trajectory from foundational work on intelligent agents and decision support systems toward contemporary topics in digital transformation and platform-based innovation. Associate Editor, Information & Management Associate Editor, Journal of Electronic Commerce Research Associate Editor, Australasian Journal of Information Systems Dr. Xu has supervised numerous PhD students to completion, with research topics spanning digital disruption, IT startup development, social media in disasters, conceptual modeling, and environmental management. She has received multiple research grants, including current funding for 'Empowering Australia's Visual Arts via Creative Blockchain Opportunities' (2023-2026) and past projects on 'Smart micro learning with open education resources' (2018-2022). Her research has been supported by various agencies including the Hong Kong Government Research Grant Council, The National Natural Science Foundation of China, The University of Queensland, and City University of Hong Kong. She leads research in several key areas including IT entrepreneurship, business intelligence systems, and social media applications across multiple domains. Her work often involves developing innovative systems such as web-service-agent-based family wealth management systems, decision support systems for securities exception management, and knowledge management systems for disaster management.

J. Christopher LoveView profile

Professor

• Nano-based drugs
• Precision medicine
• Immunology
• +3 more

J. Christopher Love is the Raymond A. (1921) and Helen E. St. Laurent Professor of Chemical Engineering at MIT, with affiliations to the Koch Institute for Integrative Cancer Research, Broad Institute, and Ragon Institute. He earned a BS in Chemistry from the University of Virginia and a PhD in Physical Chemistry from Harvard University under George Whitesides, followed by postdoctoral work under Hidde Ploegh at Harvard Medical School. His research focuses on single-cell analysis, precision medicine, and biomanufacturing. The Love Lab develops technologies for drug discovery, vaccine development, and equitable biologic medicine production. Notable successes include pioneering single-cell analysis platforms, advancing metastatic cancer diagnostics via liquid biopsies, and engineering yeast-based vaccine manufacturing. Recent articles highlight innovations in liquid biopsy sensitivity, AI-driven ECG diagnostics, and CAR T-cell therapies. Awards include the Keck Young Scholar (2009), Dana Scholar (2009), and Camille Dreyfus Teacher-Scholar. He co-founded OneCyte, HoneyComb, and Sunflower Therapeutics, and advises multiple biotech companies. His lab emphasizes translational research, integrating chemical and biological engineering principles to address global healthcare challenges. Current work explores manufacturability-by-design for vaccines, tumor immunology, and mucosal vaccine delivery systems.

Francesco CasettiView profile

Professor

• Film and Media Studies
• Semiotics
• Cinema Theory
• +5 more

Francesco Casetti is the Sterling Professor of Humanities and Film and Media Studies at Yale University, affiliated with the Yale School of Architecture. His academic journey spans teaching at institutions like the University of Genova, Harvard, and the University of Iowa. Casetti specializes in semiotics, film and media theories, post-cinema, and the interplay between media and spatial environments. His research explores the evolution of cinematic experience in modernity, audience negotiations, and the relocation of media in technological spaces. Notable works include Eye of the Century (2008) and The Lumière Galaxy (2015), which analyze cinema’s role in cultural and technological shifts. He co-founded the Permanent Seminar on Histories of Film Theories, fostering international scholarship in media studies. Casetti’s contributions extend to editorial roles in academic journals and prestigious fellowships at institutions like Bauhaus University Weimar. His current research investigates historical media anxieties and optical-spatial arrangements. Education: Taught at multiple Italian and international universities without explicit degree details provided. Research focuses on semiotic analysis, ethnographic audience studies, and media ecology. Awards include accolades for his books but specific prizes are not named. Grants and advisory roles are tied to his academic leadership and editorial activities. Labs/teams affiliation includes the Permanent Seminar and collaborations with global media scholars.

• Machine Learning
• Natural Language Processing
• Computational Linguistics
• +3 more

Jordan Boyd-Graber is a Full Professor at the University of Maryland, affiliated with the Department of Computer Science and the University of Maryland Institute for Advanced Computer Studies (UMIACS). His research focuses on machine learning, natural language processing, computational linguistics, topic models, and question answering. He has made significant contributions to the development of interactive topic modeling systems and evaluations of large language models. His work often bridges theoretical advancements with practical applications, such as improving human-AI collaboration and enhancing the interpretability of machine learning models. Boyd-Graber’s research also addresses challenges in adversarial examples, calibration of models, and the ethical implications of AI systems. His recent publications span cutting-edge topics like evaluating topic models through ProxAnn, mitigating hallucinations in vision-language models, and exploring human-AI complementarity in question answering. He collaborates extensively with researchers in computer science, linguistics, and social sciences to advance interdisciplinary applications of NLP and ML.

Habeeb OlufowobiView profile

Assistant Professor

• Cyber-Physical System Security
• Embedded Systems Security
• IoT Security
• +10 more

Habeeb Olufowobi is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), where he leads the Cyber-Physical System Security Lab. He holds a PhD in Computer Science from Howard University (2019) and previously served as a Lecturer at Howard before joining UTA in 2020. His research is centered on the security and trustworthiness of embedded and distributed systems, particularly in the domains of autonomous vehicles, IoT, and healthcare AI. He investigates cybersecurity challenges at the hardware-software interface in real-time systems and develops AI/ML models that are transparent, explainable, and equitable. His interdisciplinary work integrates principles from cybersecurity, real-time systems, and machine learning. The recent publications reflect a strong trend in securing cyber-physical systems using advanced AI techniques, with emphasis on intrusion detection, secure communication (e.g., named data networking), and robustness of autonomous systems. His work frequently appears in top-tier venues such as IEEE Transactions, VehicleSec, and ICMLA. Project Management Professional (PMP), PMI (2012–Present) Member, Institute of Electrical and Electronics Engineers (IEEE) (2020–Present) Habeeb has secured significant research funding, including an NIH grant on ethical AI for Chagas disease prediction and an AIM-AHEAD grant focused on health equity. He mentors several graduate students, including Paul Agbaje and Afia Anjum, who have received awards and internships at prestigious institutions like Los Alamos National Laboratory. He teaches courses in cloud computing, embedded systems, and information security, and serves as a faculty advisor for the National Society of Black Engineers (NSBE) at UTA. His lab, the Cyber-Physical System Security Lab, focuses on developing scalable and reliable security solutions for critical infrastructure, with growing emphasis on healthcare applications and fairness in algorithmic decision-making.