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Yun-Nung ChenView profile

Professor

• Natural Language Processing (NLP)
• Dialogue Systems
• Machine Learning
• +3 more

Professor Yun-Nung Chen works at the Department of Computer Science and Information Engineering , National Taiwan University , focusing on Natural Language Processing and Dialogue Systems . With a Ph.D. from Carnegie Mellon University , their research bridges Machine Learning and Language Understanding in conversational AI. Education Ph.D. in Language Technologies, Carnegie Mellon University (2015) M.S. in Computer Science, National Taiwan University (2011) B.S. in Computer Science, National Taiwan University (2009) Research Trends Recent work emphasizes Retrieval-Augmented Generation , Knowledge Editing in LLMs , and Temporal Modeling for dialogue systems. Key themes include cross-modal understanding , semantics-driven dialogue , and robust language modeling across domains. Scientific Recognition Best Student Paper, IEEE ASRU 2013 Best Student Paper, IEEE SLT 2010 Distinguished Master Thesis, ACLCLP 2011 Best Paper Finalist, ISCA INTERSPEECH 2012 Current projects involve StreamBench for continuous agent improvement and Taiwan LLM for culturally aligned language models.

Mikail RubinovView profile

Assistant Professor

• Network Neuroscience
• Computational Neuroscience
• Brain Connectivity
• +4 more

Mikail Rubinov serves as Assistant Professor of Biomedical Engineering (primary appointment), Computer Science, Psychiatry, and Psychology at Vanderbilt University's School of Engineering. His interdisciplinary work bridges computational neuroscience, network science, and clinical applications. His research focuses on integrative statistical models of large-scale neural data , exploring brain network organization across species and scales. Key interests include evolutionary principles of brain networks, transcriptomic basis of neural individuality, information transfer in neural systems, and neuropsychiatric connectivity phenotypes. The Rubinov Lab develops computational frameworks for analyzing complex neural systems and integrates neuroscientific knowledge with multi-omics data. Recent publications reveal strong trends in network neuroscience methodology development (circular analysis frameworks, unbiased sampling techniques) and translational applications (epilepsy networks, autism spectrum connectomics, gut-brain axis interrogation). His work increasingly incorporates transcriptomic data with neuroimaging at biobank scale. NIH Grant Writing Workshop (June 2022) NIH Workshop Short Talks (April 2023) Rubinov actively mentors graduate and undergraduate students across Biomedical Engineering and Computer Science. His lab maintains collaborations with UCSF, HHMI Janelia Research Campus, Weizmann Institute, and international neuroscience consortia. Current projects include integrative models of large-scale neural data and transcriptomic basis of neural individuality. The Rubinov Lab operates within Vanderbilt's Department of Biomedical Engineering with extensive cross-school collaborations. Technical resources include GitHub repositories for constraint network models (cnm-code), volumetric segmentation (voluseg), and brain connectivity toolboxes.

• Software Engineering
• Reverse Engineering
• Program Understanding
• +7 more

Ken Wong is an Associate Professor in the Department of Computing Science at the University of Alberta's Faculty of Science. He also serves as Associate Chair within the same department. Holding a PhD in Computer Science from the University of Victoria (1999), his research focuses on software engineering challenges such as reverse engineering, program understanding, and software visualization. He emphasizes improving software evolution through tools like architecture recovery and root cause analysis, with applications in web/mobile platforms and diverse system understanding. Teaching highlights include developing Massive Open Online Courses (MOOCs) via Coursera, including the 'Software Product Management Specialization' and courses on Agile practices, client needs analysis, and software metrics. His recent publications (2023–2025) span AI-driven healthcare innovations (e.g., medical imaging, photoacoustic tomography) and advanced computer vision techniques (e.g., diffusion models, video inpainting). Notable collaborations include EVAREST studies on heart failure management and lung transplantation outcomes. His work bridges software engineering theory and practical applications in healthcare technology, with contributions to federated learning frameworks (e.g., FedLPPA) and AI-augmented clinical decision support systems. Research also extends to autonomous driving (DriveGPT4-V2) and 3D human avatar generation (DreamAvatar), showcasing interdisciplinary impact.

Jessica RobertsView profile

Assistant Professor

• Learning Sciences & Technologies
• Geospatial Analysis
• Data Visualization
• +6 more

Jessica Roberts is an Assistant Professor in the School of Interactive Computing at Georgia Tech. She holds a PhD in the Learning Sciences and directs the Technology-Integrated Learning Environments (TILEs) Lab. Her work focuses on technology-mediated learning in informal environments like museums and citizen science projects, emphasizing geospatial analysis, data visualization, and accessibility. Prior to academia, she was a middle school teacher. Education: PhD in Learning Sciences (specific institution not listed) Postdoctoral research at Carnegie Mellon University's Human-Computer Interaction Institute Postdoctoral work at Northwestern University's Tidal Lab Research Interests: Dr. Roberts explores how interactive technologies can foster inclusive, socially meaningful learning experiences. Her projects include: Data sonification systems for visually impaired audiences AI literacy frameworks for K-12 classrooms Citizen science platforms that democratize environmental data exploration Embodied interaction designs to enhance public engagement with scientific data Article Trends: Recent work highlights innovations in AI education, climate data visualization, and accessibility technologies. Over half of her 2023-2025 publications address data sonification systems for marginalized audiences or AI literacy challenges in K-12 contexts. Awards: Brook Byers Institute for Sustainable Systems Faculty Fellow Advising & Grants: Leads the TILEs Lab, which collaborates with institutions like the Field Museum of Natural History. Projects have been exhibited at major science museums, indicating significant external partnerships. Labs/Teams: Director of the TILEs Lab, affiliated with GVU Center at Georgia Tech. Collaborates with interdisciplinary teams across education, computer science, and environmental science domains.

• Instrumentation Electronics
• Memristor Modelling
• Signal Integrity
• +6 more

Dr. Christos Papavassiliou is an Associate Professor in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. His research focuses on instrumentation electronics, memristor modeling, signal integrity, and novel device technologies such as SiGe devices, RF MEMS, and ReRAM. He leads the Space Lab and collaborates with the National Centre for Scientific Research in Athens. He holds senior membership in IEEE and is a member of the IET. Education: Ph.D. in Applied Physics, Yale University (1983–1989) MPhil in Applied Physics, Yale University (1983–1988) MS in Applied Physics, Yale University (1983–1985) B.S. in Physics, MIT (1979–1983) Research Interests: Memristor-based neuromorphic computing and stochastic systems High-performance instrumentation hardware and data acquisition Multi-state memristive memory and selectorless arrays Integration of memristors with CMOS for hybrid circuits Applications in biomedical wearables and edge AI deployment Key Contributions: Developed novel memristor models for circuit simulation Pioneered work on memristor-based true random number generators Advanced understanding of resistive drift and energy-constrained storage Designed FPGA-based systems for analog circuit emulation Labs & Teams: Active in the Space Lab at Imperial College, focusing on interdisciplinary research in electronics and space applications.

Kaidi YangView profile

Assistant Professor

• Intelligent Transportation Systems
• Traffic Flow Theory and Control
• Multimodal Transportation Systems
• +5 more

Kaidi Yang is an Assistant Professor at the National University of Singapore (NUS) in the Department of Civil and Environmental Engineering, specializing in Intelligent Transportation Systems and related fields. He holds a PhD from ETH Zurich (2019), an M.Sc. in Control Science and Engineering from Tsinghua University (2014), and dual bachelor’s degrees in Automation and Mathematics from Tsinghua University (2011). His research focuses on advancing traffic control, connected/automated vehicles, shared mobility systems, and data privacy in transportation. He has contributed to developing algorithms for efficient traffic signal control, platooning coordination, and privacy-preserving data sharing in transportation networks. Education: Ph.D., Civil and Environmental Engineering (Transportation), ETH Zurich, 2019 M.Sc., Control Science and Engineering, Tsinghua University, 2014 B.Sc./B.Eng., Dual Degrees in Pure/Applied Mathematics and Automation, Tsinghua University, 2011 Yang has received prestigious awards including the Swiss National Science Foundation’s Postdoc Mobility Fellowship (2021–2022) and the IEEE ITS Conference Best Student Paper Award (2020). He serves as an Associate Editor for the IEEE Conference on Intelligent Transportation Systems (2024). His work bridges theoretical advancements in operations research, robotics, and machine learning with practical applications in urban mobility systems. Recent efforts emphasize integrating privacy-preserving techniques into traffic management and optimizing mixed-autonomy platoon control.

Marzyeh GhassemiView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Healthcare
• +6 more

Marzyeh Ghassemi is an Associate Professor at MIT in the Department of Electrical Engineering and Computer Science (EECS) and the Institute for Medical Engineering & Science (IMES). She also holds a Canadian CIFAR AI Chair and is a faculty member at the Vector Institute. Her affiliations include the Jameel Clinic and CSAIL. Dr. Ghassemi's work focuses on ethical AI, algorithmic fairness in healthcare, and mitigating bias in medical datasets. She has pioneered projects analyzing racial disparities in end-of-life care, vocal disorder diagnosis using accelerometers, and improving subgroup robustness in machine learning. Education: PhD in Computer Science, MIT MSc in Biomedical Engineering, University of Oxford (Marshall Scholar) B.S. in Computer Science and Electrical Engineering, New Mexico State University (Goldwater Scholar) Research Interests: Her work bridges AI and healthcare, emphasizing fairness, reproducibility, and ethical deployment. Key areas include: - Algorithmic Fairness: Addressing biases in medical imaging and clinical decision systems. - Bias Mitigation: Developing methods to decorrelate sensitive information from predictions. - Health Equity: Studying disparities in end-of-life care and organ procurement. - Generative AI: Applications in mental health and medical imaging. Publications: Her recent work highlights challenges in vision-language models (e.g., handling negation), regulatory gaps in health AI, and bias reduction techniques. These efforts aim to ensure AI systems are both accurate and equitable in clinical settings. Awards: Herman L. F. von Helmholtz Career Development Professorship CIFAR Azrieli Global Scholar MIT Tech Review’s 35 Innovators Under 35 Canada Research Chair Leadership & Impact: She co-chaired the NeurIPS Workshop and founded the Association for Health Learning and Inference. Her projects like Quantifying Racial Disparities in End-of-Life Care reveal systemic biases in ICU treatments. She advocates for transparent AI deployment through frameworks like FUTURE-AI and TRIPOD+. Labs & Teams: Leads MIT’s HealthyML group, collaborating on projects such as voice misuse detection, federated learning for medical imaging, and AI-driven mental health prediction.

Ioana JivetView profile

Research Professor

• Learning Analytics
• Educational Technology
• Artificial Intelligence in Education
• +5 more

Ioana Jivet is a Research Professor and Head of the Learning Analytics Research Professorship at FernUniversität in Hagen, Germany. She leads interdisciplinary research focused on leveraging learning analytics to enhance educational practices through data-driven insights. Her role in the CATALPA Graduate School involves collaboration across disciplines to translate research into actionable educational strategies. Educational Background: PhD in Learning Analytics from Open Universiteit Nederland (2021) Postdoc at TU Delft (2019–2021) Research roles at DIPF (2021–2024) and studiumdigitale (2021–2024) Research Interests: Self-regulated learning mechanisms and feedback systems Cultural and ethical dimensions of learning analytics adoption Design of human-centered learning dashboards Privacy concerns in educational technology AI-driven educational decision support systems Her work emphasizes practical applications, such as developing adaptive feedback tools and investigating cross-cultural usage patterns of learning analytics platforms. Professional Contributions: Secretary of the Society for Learning Analytics Research (SoLAR) General Chair for the 2024 European Conference on Technology Enhanced Learning (EC-TEL) Co-editor of Springer volumes on technology-enhanced learning Lab & Teams: Active in the Learning Analytics research group at FernUniversität, focusing on translational research between academic theory and classroom practice.

Brian Y. ChenView profile

Associate Professor

• Bioinformatics
• Structural Biology
• Computational Biology
• +5 more

Dr. Brian Y. Chen is an Associate Professor and Doctoral Program Director in the Department of Computer Science & Engineering at Lehigh University. His research focuses on bioinformatics, structural biology, and machine learning applications in computational biology. He holds a Ph.D. in Computer Science from Rice University and B.A. degrees in Mathematics and Computer Science from Rutgers University. Dr. Chen's work emphasizes developing algorithms to analyze protein structures, protein-protein interactions, and ligand binding mechanisms. He has contributed to tools like DeepVASP-S and MechPPI, which explain molecular interactions and predict binding specificity. His recent projects include Alzheimer’s disease diagnosis using multimodal data and containerization frameworks for bioinformatics software. He previously served as a postdoctoral researcher in Barry Honig's Lab at Columbia University, where he contributed to the Center for Computational Biology and Bioinformatics. His research spans structural bioinformatics, computational methods for protein function prediction, and interdisciplinary applications in medicine and materials science. Key achievements include a nomination for Outstanding Mentorship (2017) and collaborative projects funded by the Army Research Lab and Lehigh University. His lab explores cutting-edge AI techniques for biomedical problems, including interpretable machine learning models and scalable bioinformatics pipelines.

Ryo SuzukiView profile

Assistant Professor

• Human-Computer Interaction
• Augmented Reality (AR)
• Virtual Reality (VR)
• +5 more

Ryo Suzuki is an Assistant Professor at the ATLAS Institute within the University of Colorado Boulder's Computer Science department. His research focuses on innovative intersections of Human-Computer Interaction (HCI), Augmented Reality (AR), and robotics. He explores systems that blend AI, haptics, and shape-changing interfaces to create enriched user experiences. Key areas of investigation include embedding interactivity into static educational materials (e.g., textbooks), developing AI-driven AR tools for procedural instruction, and creating shape-changing robotics for tactile feedback. His work often emphasizes practical applications in education, remote collaboration, and creative industries. Recent projects include MapStory (LLM-driven map animation), RealityEffects (3D volumetric video augmentation), and HoloDevice (holographic cross-device collaboration).

Hedvig KjellströmView profile

Professor

• Computer Vision
• Human Behavior Analysis
• Animal Behavior Understanding
• +2 more

Hedvig Kjellströmeröm is Professor at KTH Royal Institute of Technology and affiliated with the Max Planck Institute for Intelligent Systems. Her research develops methods for interpreting human and animal behavior through computer vision, with applications in computational aesthetics, communicative behavior analysis, and embodied AI. She serves as Editor-in-Chief for CVIU and was Program Chair for CVPR 2025.

Zhen LiuView profile

Assistant Professor

• Generative Models
• 3D Representation Learning
• Computer Vision
• +2 more

Zhen Liu is an Assistant Professor at the School of Data Science, CUHK-Shenzhen. His research focuses on generative models, 3D representations, and the synergy of spatial and semantic understanding in AI systems. With a PhD from Mila and Université de Montréal, he develops foundational methods for physics simulation, 3D assembly, and semantic reasoning in neural networks. His work bridges machine learning with applications in computer vision and graphics, emphasizing: Generative architectures for 3D content creation Diffusion model alignment techniques Efficient parameter finetuning strategies Dr. Liu mentors students in AI research and contributes to advancing 3D generative modeling paradigms.

Rich ShivenerView profile

Associate Professor

• Writing
• Media
• Emotion Studies
• +4 more

Rich Shivener is an Associate Professor in the Writing Department at York University's Faculty of Liberal Arts & Professional Studies (LA&PS). He holds a PhD from the University of Cincinnati and an MA from Northern Kentucky University. His research focuses on digital media composing practices, emotions in rhetoric, and the intersections of technology and communication. He has served as a section editor for Kairos: A Journal of Rhetoric, Technology, and Pedagogy from 2016 to 2024. Before academia, he worked in journalism and technical writing, contributing to local newspapers, national magazines, and software documentation. Shivener's recent work explores virtual reality (VR) writing, remote collaboration in game development, and environmental rhetoric in digital infrastructure. His articles analyze topics such as affective labor in online spaces, infrastructural storytelling for environmental justice, and pandemic-era remote work adaptations. He teaches in the digital authoring stream, emphasizing multimodal and hybrid approaches to writing. His research trends highlight a focus on emerging technologies’ impact on communication practices, with particular attention to equity, emotion, and sustainability. He has contributed to discussions about digital authorship, technical communication ethics, and the rhetorical dimensions of webcomics and gaming communities. Shivener’s career reflects a commitment to bridging academic and applied contexts, leveraging his industry experience to inform teaching and research. He remains active in methodological innovations, such as discourse-based interviews and infrastructural narrative analysis, to explore tacit knowledge and systemic inequities in technical fields.

Ahmad HaririView profile

Professor

• Psychology
• Neuroscience
• Molecular Genetics
• +3 more

Professor Ahmad Hariri is a faculty member in the Department of Psychology & Neuroscience at Duke University, part of the Trinity College of Arts & Sciences. He holds affiliations with the Duke-UNC Brain Imaging and Analysis Center, the Duke Institute for Brain Sciences, and the Duke Initiative for Science & Society. His research integrates neuroimaging, pharmacology, and molecular genetics to study biological pathways underlying individual differences in behavior and psychopathology risk. Education: Ph.D. in Psychology from UCLA (2000), M.S. and B.S. in Psychology from the University of Maryland, College Park (1997, 1994). Research interests include understanding how genetic and environmental factors influence brain function and behavior, with a focus on psychopathology, stress, and resilience. His work explores neural correlates of traits like psychopathy, childhood adversity effects on brain structure, and biomarkers of aging. Recent studies highlight links between lead exposure and neurodegeneration, neighborhood disadvantage and dementia risk, and the role of brain connectivity in self-regulation. Key achievements include over 200 publications, including high-impact papers in Nature Aging , Neuron , and Biological Psychiatry . Honors include the APA Distinguished Scientific Award for Early Career Contribution (2009) and being named a Highly Cited Researcher (2014). Grants include leadership roles in the Duke-NCCU Postdoctoral Training Program in Child Psychiatric Conditions and the Duke Psychiatry Physician-Scientist Residency Program. He has contributed to editorial boards of journals like Cortex and Biology of Mood and Anxiety Disorders . Professional activities include mentoring in the Summer Neuroscience Program and serving as a Bass Connections Faculty Team Leader.

Edward JohnsView profile

Associate Professor

• Robot Learning
• Robot Manipulation
• Artificial Intelligence
• +4 more

Dr. Edward Johns is an Associate Professor in the Department of Computing at Imperial College London and Director of the Robot Learning Lab. He specializes in robot learning, focusing on enabling robots to learn tasks through imitation and language-based reasoning. His expertise spans robotics, machine learning, and computer vision, with a particular emphasis on manipulation tasks requiring physical interaction with objects. He holds a BA and MEng from the University of Cambridge and a PhD from Imperial College London. Prior to his current role, he was a postdoc at UCL, a founding member of the Dyson Robotics Lab, and led the robot manipulation team there. He also served as Head of Robot Learning at Dyson (part-time, 2021–2022). His research has produced state-of-the-art capabilities such as one-shot imitation learning and language-driven task execution. Key areas of interest include sim-to-real transfer, self-supervised learning, and adaptive robotic systems. His work bridges foundational AI research with practical robotics applications, emphasizing real-world deployment and human-robot collaboration. Dr. Johns has published over 60 peer-reviewed papers, with over 4,000 citations, and has received prestigious awards including the UK-RAS Early Career Award (2023) and the Best Conference Paper Award at ICRA (2024). He is also actively involved in industry through advisory roles for robotics and AI startups. His teaching includes graduate courses on reinforcement learning and robot learning, and he collaborates extensively with labs such as the Robotics Forum and the Artificial Intelligence Network at Imperial College.