Apurva Gandhi is a Researcher at Carnegie Mellon University , affiliated with the School of Computer Science and the Computer Science Department. Their research focuses on Artificial Intelligence with applications in program synthesis , agentic tasks , and handwritten content analysis . Research Interests : Artificial Intelligence Machine Learning Natural Language Processing Program Synthesis Publications highlight expertise in web agents , AI-driven database systems , deepfake detection , and sequence modeling for cybersecurity . Contact : apurvag@andrew.cmu.edu
Franceska Xhakaj is an Assistant Teaching Professor in the Computer Science Department at Carnegie Mellon University, affiliated with the School of Computer Science. She specializes in educational technology, intelligent tutoring systems, and learning analytics. Her work focuses on leveraging technology to enhance teaching practices, particularly through classroom sensing, data-driven instruction, and dashboard design for educators. She teaches courses such as 15110 ('Fundamentals of Programming and Computer Science'), 15121 ('Great Ideas in Theoretical Computer Science'), and 15890 ('Special Topics in CS'). Her research integrates artificial intelligence and human-computer interaction to improve educational outcomes, including studies on teacher dashboards, gaze-tracking classroom digital twins, and heuristic evaluations via multimodal LLMs. She has contributed to systems like EduSense and Luna, aiming to scale practical classroom analytics and support teacher professional development. Her teaching responsibilities include multiple semesters of introductory programming courses (2023-2025), reflecting her dual focus on both research innovation and foundational computer science education. Key projects emphasize bridging gaps between technological tools and pedagogical needs, with recent work exploring instrumentation-free classroom monitoring and adaptive learning systems.
Claudio Silva is an Institute Professor of Computer Science and Engineering at NYU Tandon School of Engineering and Professor of Data Science at NYU Center for Data Science. He co-founded the Center for Urban Science and Progress (CUSP) and the Visualization, Imaging and Data Analysis (VIDA) Center. His academic journey includes a BS in mathematics from Universidade Federal do Ceará (1990) and PhD in computer science from SUNY Stony Brook (1996). Co-Director, VIDA Center Former Professor, University of Utah (2003-2011) Technology Consultant, MLB Advanced Media (2012-2017) Research Interests : At the intersection of visualization, geometric computing, and urban/sports analytics, his work includes: AR task guidance systems (ARGUS, HuBar) Urban data analysis (3D visualization, sidewalk mapping) LLM multimodal reasoning (POEM, BDIViz) Biomedical data tools (TopoMap++, T-Explainer) 2023-2025 Research Trends : Recent publications focus on neuroadaptive AR systems (AdaptiveCoPilot), LLM prompt optimization (POEM), explainable AI frameworks (T-Explainer), and urban data visualization (Urban Rhapsody). Collaborations span NYU, Northrop Grumman, and NSF/DARPA grants for projects like OSCUR and PTG. Scientific Honors : ACM Fellow (2024) IEEE Visualization Academy (2019) IEEE Technical Achievement Award (2014) Technology Emmy for Statcast (2018) His lab has developed open-source tools like ARGUS on GitHub and pioneered methods in fNIRS-based cognitive workload analysis for pilot training systems. Current projects include NASA-funded OpenSpace and NSF-backed OSCUR.
Isis Hjorth is a Research Associate at the Oxford Internet Institute, University of Oxford, specializing in the socioeconomic implications of digital technologies. Holding a DPhil from Oxford and an MSc from the Department of Education, she brings interdisciplinary expertise spanning cultural sociology, digital ethnography, and technology studies. Her research examines: Networked cultural production and virtual labor markets Gig economy impacts on global economic development Crowdsourcing models and platform-based work ICT for development in Sub-Saharan Africa and Southeast Asia Digital inequalities and symbolic capital in creative industries Current projects investigate microwork and virtual production networks across developing economies, examining how digital labor platforms reshape economic opportunities. Her methodological approach combines digital ethnography with critical sociological theory, emphasizing the dynamics of capital distribution in technology-mediated environments. Prior academic appointments include Researcher (2014-2018) and Research Associate (2018-2025) positions at Oxford. She has contributed to interdisciplinary research on MOOC learning patterns and participatory digital tools for artistic production, with publications spanning computational social science, labor studies, and human-computer interaction.
Prof. Dr.-Ing. Gerrit Meixner is a Research Professor for Human-Computer Interaction at Heilbronn University's Faculty of Computer Science. He serves as Managing Director of the UniTyLab and Program Dean for the SEM Faculty. His research focuses on Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and innovative interaction technologies, with applications in health, industrial training, and software development. He leads projects addressing usability engineering, rehabilitation systems, and AR/VR integration in industry 4.0 environments. Prof. Meixner's work emphasizes practical applications, including VR-based therapy for cerebral palsy, gamified alcohol disorder treatments, and ergonomic training solutions using HoloLens 2. He actively contributes to advancing AR/VR tools for CAD design, automotive interfaces, and smart factory systems. His research also explores human factors in virtual environments, including motion sickness mitigation in automated driving simulations and user experience optimization for industrial workflows. As a program dean, he oversees academic programs while advancing interdisciplinary research through the UniTyLab. Notable collaborations include projects with Audi AG for smartglass applications in manufacturing and AR systems for medical training. His contributions bridge academic research with real-world implementations, focusing on usability, accessibility, and technological innovation.
James Wolfer is a Professor in the Computer Science and Information Systems department at Indiana University South Bend (IUSB) since 2001. His research focuses on biologically-inspired computing, medical imaging applications, and computer science pedagogy. He holds a Ph.D. in Computer Science from Illinois Institute of Technology and has contributed to fields such as genetic programming, deep learning, and haptic interfaces. Education: 1993: Ph.D., Computer Science, Illinois Institute of Technology 1981: M.S., Computer Information Science, Andrews University 1975: B.A., Religion, Andrews University His research interests include applying natural algorithms to medical imaging, remote sensing, and art, alongside innovating computer science education through robotics and haptic technologies. He has served on committees for IEEE EDUCON and the Eurographics Education Committee, and is an executive member of the International Society for Engineering Pedagogy (IGIP). Key Contributions: Developed haptic interfaces for teaching computer graphics and biomedical applications. Introduced robotics into the computer organization curriculum via an AT&T/SBC Fellowship. Authored over 10 publications on pedagogical models, medical imaging algorithms, and parallel computing. Awards: AT&T/SBC Fellow (for integrating robotics into CS education) He has advised multiple student projects, including SMART Summer Fellowships focused on self-organizing maps, mammography analysis, and haptic palpation. His work bridges theoretical computing with practical biomedical and educational applications.
Kyle Bellucci Johanson is a contemporary artist and educator working at the intersection of performance architecture, critical theory, and social practice. He currently serves as faculty in the Critical Studies department of the MFA program at the New York Academy of Art, where he contributes to shaping emerging artists through critical pedagogy and conceptual frameworks. His educational background includes a BA in Art and Reconciliation Studies from Bethel University, studies in peace and conflict at the University of Ulster in Derry/Londonderry, Northern Ireland, and an MFA from California Institute of the Arts. He was also a founding fellow of At Land's Edge, an artist-led autonomous free school focused on intergenerational mentorship in community-run spaces across Los Angeles, and a 2022-23 participant in the Whitney Independent Study Program. Johanson's research interests center on visualizing and critiquing power structures through performance-architecture. His work explores how objects, language, and media can instigate imaginary futures while examining the intersections of labor, identity, and emancipation. Recent projects investigate radical hospitality, worker solidarity, and speculative community building, often through immersive installations that challenge conventional spatial and social arrangements. His practice blends conceptual rigor with participatory elements, creating spaces for critical discourse and collective imagination. His most recent exhibition series, KLUB WRKR (2024), represents a significant evolution in his exploration of worker solidarity, transforming Alexander Rodchenko's early 20th-century Workers' Club concept into a nomadic, cross-country art project that engages with contemporary labor conditions. This work exemplifies his broader thematic concerns with community assembly, spatial politics, and alternative economic models. Whitney Independent Study Program Participant (2022-23) Humanity in Action Landecker Fellow Johanson has curated and directed significant projects including table (2018-2022), a project space dedicated to situating artists' practices through exhibition, discursive meals, and publication. His collaborative approach extends to numerous group exhibitions and symposia, including the Quantum Unlearning Symposium with Charles Gaines and Kathryn Shaffer at the School of the Art Institute of Chicago (2018). His work often involves creating temporary communities and gathering spaces that challenge conventional institutional boundaries while fostering critical dialogue around social and political issues.
Sohum Sohoni is a Professor in the School of Computing and Augmented Intelligence within the Ira A. Fulton Schools of Engineering at Arizona State University. His research spans computer science education, programming pedagogy, computer architecture, and image quality assessment with GPU acceleration. He has developed innovative educational approaches for computer architecture courses using the PLP instruction set architecture and has contributed significantly to understanding programming education through analysis of error messages and embedded questions. His research interests focus on Computer Science Education , particularly programming education techniques, computer architecture pedagogy, and engineering education methods. He has made substantial contributions to understanding how students learn programming concepts, develop effective debugging skills, and comprehend computer architecture principles. His work bridges theoretical computer science concepts with practical educational applications, emphasizing hands-on learning experiences and innovative assessment methods. His publication trends reveal a strong focus on computer science education research with particular emphasis on programming education (2015-2018), computer architecture education using PLP (2014-2017), and image quality assessment with GPU acceleration (2012-2018). His work consistently combines theoretical computer science concepts with practical educational applications, demonstrating his commitment to improving how computing concepts are taught and learned. His advising work includes mentoring students like Christopher Mar, Harsha B. M. Kadekar, Shaowen Lu, Thien D. Phan, and Vignesh Kannan, who have co-authored publications with him across various computing education topics. His research has been supported through various educational technology projects focused on online learning environments, software engineering education, and computer architecture instruction. He has been actively involved in developing virtualized learning environments for IoT education, creating innovative approaches for software engineering education, and designing effective methods for teaching computer architecture concepts. His work demonstrates a consistent commitment to improving computing education through evidence-based pedagogical approaches and technological innovation.
Paul Schrater is a Professor at the University of Minnesota, holding a joint appointment in Psychology and Computer Science and Engineering. He earned his Ph.D. in Neuroscience from the University of Pennsylvania (1999) and completed a postdoctoral fellowship in Computational Vision at the University of Minnesota. His research focuses on cognitive science, computer vision, statistical pattern recognition, and motor control. He teaches courses in Cognitive Science, Pattern Recognition, and Virtual Reality. Education & Specialties: Ph.D.: Neuroscience, University of Pennsylvania, 1999 Specialties: Cognitive Science, Computer Vision, Human and Computer Vision, Motor Control, Neuroscience, Pattern Recognition, Virtual Reality Research Interests: Dr. Schrater explores the intersection of human and machine intelligence, focusing on sensory integration, decision-making, and neural coding. His work bridges cognitive science, neuroscience, and artificial intelligence, with applications in virtual reality and neural decoding. Recent studies include curiosity-driven learning, strategic foraging, and the computational underpinnings of human behavior. Publications Trends: His recent articles emphasize metacognitive frameworks (e.g., KIX), neural decoding of depression severity, and the explore-exploit trade-off in knowledge networks. Themes include information theory, decision-making under uncertainty, and computational models of human cognition. Labs & Collaborations: His research draws from the Computational Vision Lab (postdoctoral affiliation) and current interdisciplinary collaborations. He contributes to initiatives like Neuromatch Academy, promoting global computational neuroscience education.
Dr. Christoforos Christoforou is an Associate Professor in the Division of Computer Science, Mathematics, and Sciences at St. John’s University’s Collins College of Professional Studies. He serves as Program Director for the Master’s in Computer Science program and leads the Neuro-Intelligence and Innovation Lab. He holds a PhD in Computer Science from the CUNY Graduate Center, complemented by advanced degrees from The City College of New York. Education: PhD, Computer Science, City University of New York - The Graduate Center MPhil, Computer Science, City University of New York - The Graduate Center MS, Computer Science, The City College of the City University of New York Research Interests: Dr. Christoforou’s work bridges computer science and neuroscience, focusing on machine learning and AI algorithms to decode neurophysiological signals (EEG) for neurotechnology applications. Key areas include brain-computer interfaces, neuro-cinematics, and understanding neurocognitive processes in reading disorders and spatial cognition. His research has pioneered computational frameworks for studying neural underpinnings of dyslexia and emotional responses to media. Publications Overview: His most recent work (2023–2021) explores neural congruency in phonological processing, EEG-based emotion recognition, and computational models for reading remediation. Earlier contributions include foundational studies on cortically coupled computer vision and predictive modeling of movie audience engagement. Lab & Innovation: The Neuro-Intelligence and Innovation Lab under his direction develops neurotechnology solutions through interdisciplinary collaboration, emphasizing real-world applications like assistive robotics and adaptive educational tools.
Dr. Zhaoxing Li is a Research Fellow at the University of Southampton, specializing in Citizen-Centric Artificial Intelligence Systems (CCAIS). His work focuses on integrating Large Language Models (LLMs), Deep Reinforcement Learning, and Multi-Agent Systems to bridge technological innovation with societal impact. He emphasizes human-AI interaction, explainable AI, and ethical AI development. Zhaoxing collaborates on projects like FATES of Africa and contributes to educational technology through AI-driven learning systems. His research interests include Large Language Models Deep Reinforcement Learning Multi-Agent Systems Human-AI Interaction Explainable AI . Recent work highlights include advancing consensus-building algorithms, personalized learning recommendations via LLMs, and gesture recognition for multimodal interfaces. His publications span conferences and journals like Universal Access in the Information Society and Neurocomputing. No scientific awards are listed. Current projects emphasize citizen-centric AI architectures and ethical AI deployment.
Vladimir Todorovic is an Associate Professor and Chair of Fine Arts at the School of Design, University of Western Australia (UWA), and a Visiting Professor at the University of Arts Belgrade. His work bridges art, technology, and environmental discourse through immersive storytelling, AI-driven generative art, and experimental film. He has exhibited globally at festivals like Annecy, IFFR, and Ars Electronica, winning over a dozen awards for his innovative projects. Research interests include generative art systems, artificial intelligence aesthetics, virtual reality narratives, and the ethical implications of technology in creative practices. He has organized major events such as ISEA2008 and the Environmental Visions conference (2014), emphasizing interdisciplinary collaboration between artists, scientists, and technologists. Current projects explore AI ethics, Indigenous storytelling through extended reality, and procedural modeling in digital media. Grants: 2023: 'Illustrating Nyangumarta' (AUD 57k) from WA's Department of Local Government 2022: 'Cabinet of Algodreams' (AUD 15k) Awards: 1st Prize Experimental, 19th Athens Animfest (2024) Philip K. Dick Science Fiction Festival Award (2024) Early-Career Research Award, UWA (2022) Labs/Teams: Leads the UWA Fine Arts Lab focusing on experimental digital media and collaborates with Indigenous communities on XR storytelling initiatives.
Baidaa Al-Bander is a Lecturer at Keele University's School of Computing and Mathematics. She holds an MSc in Computer Engineering from the University of Baghdad and a PhD in Electrical Engineering from the University of Liverpool (2018), followed by a Postdoctoral Research position at the University of Dundee. Her research focuses on AI-driven solutions for healthcare and real-world applications, including medical imaging analysis, explainable AI, and deep learning algorithms. She collaborates with experts in medicine, engineering, and computer science to address practical challenges, emphasizing interdisciplinary approaches. Al-Bander's work spans glaucoma diagnosis, melanoma detection, and autonomous medical systems, with publications in top-tier journals like PLoS One and Electronics . She actively reviews for leading conferences and journals, contributing to advancing AI ethics and applications. Her educational background and industry partnerships position her as a key figure in computational healthcare research. Education: MSc Computer Engineering, University of Baghdad (Iraq) PhD Electrical Engineering, University of Liverpool (2018) Research Interests: AI in Healthcare Computer Vision Explainable AI Deep Learning for Medical Imaging Data Science in Structural Engineering Key Contributions: Developed AI models for automated software testing using large language models. Pioneered emotion-aware mental health chatbots integrating BERT and GPT frameworks. Advanced glaucoma diagnosis techniques via deep learning-based retinal image analysis. Benchmarked deep learning algorithms for skin cancer and melanoma detection. Collaborations: Works with interdisciplinary teams in medicine, civil engineering, and cybersecurity to apply AI to real-world problems like seismic risk assessment and network intrusion detection.
Michael Adjeisah is a Research Fellow at Bournemouth University's CfACTs Research Centre, focused on interdisciplinary research in machine learning and artificial intelligence. His work spans computer vision, natural language processing, health informatics, and data science, with applications in cultural heritage technology, low-resource language processing, and medical systems. Notable research areas include Adinkra symbol recognition using deep learning, graph neural networks for classification tasks, and sentiment analysis models leveraging attention mechanisms. He has contributed to advancements in neural machine translation for low-resource languages and blockchain-enabled privacy solutions for electronic health records. Adjeisah's publications reflect a strong emphasis on practical applications of AI, with recent work addressing challenges in spoken digit recognition for Amharic and respiration-based biometric systems. His research often integrates multi-sensor fusion and data augmentation techniques to enhance model performance in real-world scenarios.
Dr. Samer Sarsam is an Assistant Professor in Coventry University's School of Strategy and Leadership. His research specializes in Business Analytics, Data Mining, Machine Learning, and AI-driven decision-making with applications in healthcare, finance, and education. He mentors undergraduate/postgraduate students and consults for tourism/healthcare industries. Research focuses on: Artificial Intelligence and predictive modeling Sentiment and opinion mining techniques Time-series analysis for behavioral prediction Text mining applications in healthcare Recipient of the 2021 Market Research Society Silver Medal for excellence in analytics research. Published extensively on machine learning applications, with recent work exploring generative AI in healthcare management (blood donation supply chains) and education. Consulting work helps organizations leverage data analytics for strategic decision-making across multiple sectors.
