Rozenn Dahyot is a Professor of Computer Science at Maynooth University within the Faculty of Science & Engineering. She previously held roles as Assistant and Associate Professor in Statistics at Trinity College Dublin (2008-2021) and Lecturer in Computer Science (2005-2008). Her research interests bridge Digital Signal Processing, Computer Vision, Machine Learning, and Statistical Analysis. She organized the European Signal Processing Conference (EUSIPCO2021) in Dublin and served as President of the Irish Pattern Recognition and Classification Society (IPRCS) from 2014-2020. Her work spans topics like semantic scene understanding, CNN compression, and medical image segmentation. Key contributions include advancements in graph-based image analysis, reinforcement learning optimization, and AI-driven systems for disaster management. Dahyot is a member of IEEE, ACM, and EURASIP, contributing to both academic and industrial collaborations.
Mithuna S. Thottethodi is a Professor and Interim Associate Head of Teaching and Learning at the Elmore Family School of Electrical and Computer Engineering at Purdue University. He holds a B.Tech. from the Indian Institute of Technology, Kharagpur (1996), and a Ph.D. in Computer Science from Duke University (2002). His research focuses on computer architecture, interconnection networks, distributed systems, and security, with notable work on sparse tensor accelerators and processing-near-memory architectures. His work has been funded by the NSF, AT&T, and SK Hynix. Research interests include security, interconnection networks in multicores, storage performance optimization, and memory hierarchies. Notable contributions span hardware accelerators for machine learning, secure speculative execution, and datacenter network congestion control. He has advised over 20 graduate students, many of whom have joined top tech firms like Google, Microsoft, and Intel. Awards: NSF CAREER Award (2007) Wilfred Hesselberth Teaching Excellence Award (2021) Multiple Eta Kappa Nu Outstanding Professor Awards Teaching includes undergraduate courses like EE 437 (Computer Design) and graduate courses such as ECE 666 (Advanced Computer Architecture). He also advises VIP and EPICS student teams.
Alis Oancea is Professor of Philosophy of Education and Research Policy at the University of Oxford's Department of Education, part of the Social Sciences Division. She holds additional roles including ESRC Centre for Global Higher Education Deputy Director and Social Sciences Division Advocate for Responsible Engagement. With dual doctoral degrees (DPhil from Oxford and PhD from Bucharest), she also received a Doctor Honoris Causa from University of the West Timisoara. Her research focuses on meta-research, research policy, and higher education governance. Key areas include research assessment frameworks, impact measurement, ethics, open knowledge practices, and teacher education. She co-edited the 2024 Handbook of Meta-Research and leads international studies on research cultures and policies. Education: DPhil (Oxford), PhD (Buc), DipLATHE (Oxford), Dhc (UWT) Roles: Director of Research (2016-20), REF2021 Coordinator, ESRC CGHE Deputy Director Leadership: Over 30 funded projects including the RKEEI initiative and BERA Observatory Her 150+ publications span research policy, impact evaluation, and education systems. Awards include FAcSS Fellowship and numerous international advisory roles across Europe and Norway. Advises on global education reforms and chairs panels for EU, UK, and Nordic research councils. Current doctoral supervision focuses on research policy, higher education systems, and teacher education innovations. Active in editorial roles for Oxford Review of Education and Review of Education .
Kyle C. Hale is an Associate Professor at Oregon State University's School of Electrical Engineering and Computer Science (College of Engineering). He holds a Ph.D. and M.S. from Northwestern University (2016, 2013) and a B.S. in Computer Science from UT Austin (2010). Prior to joining Oregon State in 2024, he served as an Associate Professor at Illinois Tech in Chicago. His research spans operating systems, high-performance computing (HPC), virtualization, computer architecture, and system security. Current work focuses on specialized system software stacks for emerging computing paradigms like memory disaggregation and parallelism optimization. He leads the HExSA Lab and collaborates with the HiPCastor group. Scientific Awards: NSF CAREER Award (2023-2028) Illinois Tech College of Computing Excellence in Research (2023) Illinois Tech College of Computing Excellence in Teaching (2021) Illinois Tech Department of Computer Science Teacher of the Year (2020) EuroSys '22 Best Artifact Award Recent Research Trends: His publications emphasize compiler techniques for memory-disaggregated systems, optimizing parallel runtimes through hardware-software integration, virtualization at fine granularities, and accelerating machine learning workloads via system-level innovations. Keywords include HPC, virtualization, parallelism, and secure execution contexts. Teaching: Courses taught include Computer Architecture (CS/ECE 472), System Security (CSP 544), Operating Systems (CS 450), and advanced topics in serverless/edge computing. He actively recruits PhD students to the HExSA Lab.
Angel Xuan Chang is an Associate Professor at Simon Fraser University's School of Computing Science, where she leads research at the intersection of natural language processing, computer vision, and 3D scene understanding. She holds the prestigious Canada CIFAR AI Chair position and is affiliated with multiple research groups including 3DLG, GrUVi, SFU NatLang, SFU AI/ML, and VINCI. PhD in Computer Science, Stanford University MSc in Computer Science, Stanford University M.Eng in Electrical Engineering and Computer Science, MIT BSc in Computer Science and Engineering, MIT Professor Chang's research primarily focuses on connecting language to 3D representations of shapes and scenes, with particular emphasis on grounding language for embodied agents in indoor environments. Her work spans natural language processing and understanding, linking natural language with visual and 3D representations, multimodal grounding of language, embodied AI, and machine learning applications for biodiversity monitoring through the BIOSCAN project. She has developed methods for synthesizing 3D scenes and shapes from natural language and created various datasets for 3D scene understanding. Her recent publications reveal a strong trend toward integrating language understanding with 3D scene generation and manipulation, with increasing focus on practical applications in embodied AI and biodiversity monitoring. The research shows progression from foundational work on text-to-3D scene generation to more sophisticated approaches for evaluating semantic coherence in generated scenes and developing efficient methods for zero-shot scene modeling. Canada CIFAR AI Chair TUM-IAS Hans Fischer Fellow (2018-2022) Best paper award at 3DV 2025 for 'An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion' Professor Chang actively advises numerous graduate students who appear as first authors on her publications, indicating a strong mentoring program. Her research is supported through multiple channels including the CIFAR AI Chair position and likely various research grants supporting her BIOSCAN-related work and 3D scene understanding projects. She has been involved in organizing multiple workshops at major conferences including ICML, CVPR, and ICLR. Her research is conducted through several interconnected groups: 3DLG (3D Language and Graphics), GrUVi (Graphics, Vision, and Interaction), SFU NatLang (Natural Language Processing), SFU AI/ML, and VINCI. These groups work collaboratively on problems spanning language grounding, 3D scene understanding, embodied AI, and biodiversity applications, creating a rich interdisciplinary research environment.
Dr. Wahab Hamou-Lhadj is a Professor and Chair at the Department of Electrical and Computer Engineering , Concordia University, and an Affiliate Researcher at NASA JPL, Caltech . He leads research in Artificial Intelligence for IT Operations (AIOps) , Software Observability , and Model-Driven Engineering , focusing on improving the reliability of digital systems in AI-driven environments.
Anastasia Ailamaki is a Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL), renowned for her work in database systems and data management . Her research focuses on optimizing query processing for modern hardware, particularly GPUs and heterogeneous systems, and advancing cloud data analytics with serverless architectures like PixelDB . She has co-authored influential frameworks for adaptive query optimization , hardware-conscious database engines , and model-relational data management . Key research areas: GPU acceleration , HTAP , query approximation , spatial data processing , and cloud-native databases . Recent work emphasizes cross-task optimizations in distributed environments, efficient sampling , and context-aware joins integrating vector embeddings. In 2023, she contributed to adaptive recursive query optimization and speculative K-means clustering, while 2024 publications addressed proportional caching (HPCache) and model-relational systems . Her collaborations span institutions such as MIT, Microsoft, and ETH Zurich, with publications in top venues like SIGMOD , VLDB , and ICDE .
Maciej A Mazurowski is an Associate Professor at Duke University School of Medicine, with dual appointments in the Department of Biostatistics & Bioinformatics and Radiology. He is also affiliated with the Department of Electrical and Computer Engineering and is a member of the Duke Cancer Institute. His research focuses on applying machine learning to medical imaging for improved diagnosis and treatment. Ph.D. in Computer Science from the University of Louisville (2008) Dr. Mazurowski's research emphasizes medical imaging , machine learning , and computer vision applications in radiology. His work includes automated segmentation , domain adaptation , prognostic modeling , and foundation models for MRI/CT analysis. His recent publications highlight trends in universal segmentation models (SegmentAnyBone, SegmentAnyMuscle), foundation models for MRI (MRI-CORE), and AI-driven diagnostic tools for breast cancer, glioblastoma, and thyroid nodules. Key challenges addressed include domain generalization , image harmonization , and ethical considerations in clinical AI. Incubation Award for innovative research commercialization Dr. Mazurowski has secured significant research funding from agencies including the National Institutes of Health , National Institute of Biomedical Imaging and Bioengineering , and American Roentgen Ray Society . His work spans CT segmentation , MRI analysis , and AI-based quality assessment across multiple imaging modalities.
Yoel Inbar is an Associate Professor at the University of Toronto, affiliated with the Department of Psychology and the Morality, Affect, and Politics (MAP) Lab. His research explores the intersection of moral intuitions, emotions, and political/social beliefs, with a focus on disgust sensitivity and its implications. He also investigates public acceptance of emerging technologies like genetic engineering. Contact: yoel.inbar@utoronto.ca . PhD, Cornell University BA, University of California at Berkeley His research spans moral psychology, political ideology, and behavioral responses to technological innovation. Recent studies employ natural language processing to analyze morality in real-world contexts, such as political discourse and environmental attitudes. The MAP Lab emphasizes interdisciplinary approaches, integrating psychology, behavioral economics, and computational methods to study moral decision-making and its societal consequences. Alumni from the lab include researchers now at institutions like UC Berkeley, University of the Fraser Valley, and Cornell University, reflecting his mentorship of advanced psychological and behavioral science scholars.
Xuezhe Ma is an Assistant Professor in the Department of Computer Science at the University of Southern California's Viterbi School of Engineering. Previously, he was a Ph.D. student at Carnegie Mellon University's Language Technologies Institute, where he worked under the supervision of Professor Eduard Hovy. His academic journey includes a Master's degree from Shanghai Jiao Tong University's Center for Brain-like Computing and Machine Intelligence and a Bachelor's degree in Computer Science from the same institution. Ph.D. in Computer Science, Carnegie Mellon University (completed ~2020) M.S. in Brain-like Computing, Shanghai Jiao Tong University B.S. in Computer Science, Shanghai Jiao Tong University Dr. Ma's research spans multiple areas at the intersection of Natural Language Processing and Machine Learning, with particular focus on structured prediction, syntactic and semantic parsing, machine translation, language generation, and deep generative models. His recent work has expanded into vision-language models, large language model architectures, and applications across computer vision tasks. His research combines theoretical foundations with practical implementations, as evidenced by his development of tools like NeuroNLP2 and MaxParser. His publication record shows a clear trajectory from foundational NLP work during his PhD (including papers on dependency parsing and sequence labeling) to more recent contributions in generative models and large language systems. The 15 most recent publications reveal a strong focus on addressing fundamental challenges in generative modeling, context handling, and multimodal integration, with applications spanning literary translation, medical imaging, and news diffusion analysis. AI2 Outstanding Intern Award (2018) Dr. Ma has secured research funding supporting his work in generative models and language technologies, with projects focusing on improving the efficiency and capabilities of large language models. His research group at USC is actively working on next-generation language understanding and generation systems, with particular emphasis on context-aware modeling and multimodal integration. He has established collaborations with industry partners including the Allen Institute for AI and has contributed to open-source projects like Texar. At USC, Dr. Ma leads research in the Information Sciences Institute, directing projects on efficient large language model architectures and multimodal reasoning systems. His lab focuses on developing novel approaches to context handling, model efficiency, and multimodal integration, with applications across diverse domains including healthcare, literary analysis, and news media.
Hanbyul Joo is an Assistant Professor in the Department of Computer Science and Engineering at Seoul National University (SNU). Prior to joining SNU, he was a Research Scientist at Facebook AI Research (FAIR) in Menlo Park. He completed his Ph.D. in the Robotics Institute at Carnegie Mellon University, working with Yaser Sheikh, and received his M.S. in Electrical Engineering and B.S. in Computer Science from KAIST, Korea. Dr. Joo's educational journey began at KAIST, where he earned both his Bachelor's and Master's degrees. He then pursued his Ph.D. at Carnegie Mellon University's Robotics Institute, completing his dissertation titled "Sensing, Measuring, and Modeling Social Signals in Nonverbal Communication." His doctoral work focused on developing the Panoptic Studio, a unique sensing system with over 500 synchronized cameras for capturing social interactions. Dr. Joo's research primarily focuses on endowing machines and robots with the ability to perceive and understand human behaviors in 3D . His goal is to build "social Artificial Intelligence" that can interact with humans using social signals (body languages). He pursues this direction using data-driven methods where data is collected by measuring the wide spectrum of social signals transmitted during interpersonal social interaction. His research spans computer vision, machine learning, computer graphics, and robotics , with particular emphasis on 3D human pose estimation, human-object interaction, and social signal processing. His recent publications demonstrate a clear trend toward leveraging diffusion models for 3D reconstruction and generation tasks, with a focus on human-centric applications. His work bridges the gap between 2D image understanding and 3D scene reconstruction, often utilizing pre-trained models to overcome data limitations. The research consistently addresses fundamental challenges in understanding human behavior, interaction with objects, and social dynamics in 3D space. Dr. Joo is a recipient of several prestigious awards including the Samsung Scholarship and the CVPR Best Student Paper Award in 2018 . His paper "Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies" received this honor at CVPR 2018. His research has been widely recognized in top computer vision and AI conferences, with multiple oral presentations at venues like CVPR, ICCV, and ECCV. Dr. Joo actively mentors a large group of students, with approximately 15 current students working toward MS/PhD degrees under his supervision. His lab, the SNU VCLab, focuses on cutting-edge research in computer vision and graphics. He has secured significant research funding through his work, though specific grant details aren't provided on his website. Dr. Joo frequently serves as an area chair for major conferences including CVPR, ICCV, and NeurIPS, demonstrating his standing in the academic community. Dr. Joo leads the SNU VCLab, which has developed several notable datasets and tools including SNU ParaHome, FrankMocap, and the CMU Panoptic Studio Dataset. His lab maintains strong industry connections, with students interning at leading companies like Meta. The lab's research focuses on building the infrastructure and algorithms needed for social AI, with an emphasis on practical applications that can be deployed in real-world settings.
Jim Smith is a Professor in Interactive Artificial Intelligence at the University of the West of England (UWE), Bristol, affiliated with the School of Computing and Creative Technologies and the Department of Computer Science and Creative Technologies. He serves as Director of the Computer Science Research Centre and leads the AI@UWE theme. His research is supported by UKRI, Innovate UK, and partnerships with organizations including Health Data Research UK, Office for National Statistics, NHS Scotland, and DSTL. University: University of the West of England School: School of Computing and Creative Technologies Department: Department of Computer Science and Creative Technologies Role: Professor in Interactive Artificial Intelligence Leadership: Director, Computer Science Research Centre Research Interests : Jim Smith's work focuses on Interactive Artificial Intelligence, particularly at the intersection of AI and privacy preservation when using sensitive data for public good. His research includes statistical disclosure control, privacy leakage from AI models, evolutionary computation, machine learning, and systems that learn through human interaction or self-adaptation. He explores how AI can automate privacy checks in research outputs and assess vulnerabilities in trained models. Recent Publications : His recent work spans AI privacy in trusted research environments (e.g., SACRO, SDC-Reboot), dialogue act classification, human-robot interaction, and visualization of deep learning models. Themes include privacy-preserving AI, automated disclosure control, interactive machine learning, and neuromorphic computing. Machine Learning & Privacy Evolutionary Computation Interactive AI Systems Human-Computer Interaction Statistical Disclosure Control Federated Learning Security Scientific Awards : No specific awards are mentioned in the provided texts. Advising and Grants : He currently supervises PhD students on topics including spatio-temporal air quality modeling, federated learning privacy, and threat detection in mobile networks. He leads Innovate UK and UKRI-funded projects such as SACRO and SDC-Reboot, focusing on AI-driven solutions for data confidentiality in public sector research. Interactive Machine Learning for Claim Settlement (Innovate UK) SDC-Reboot (DARE UK/Health Data Research UK) Threat Identification in Mobile Networks (Ribbon Communications) Labs and Teams : He leads the AI@UWE initiative and the Computer Science Research Centre at UWE. His work involves collaboration through DARE UK and open-source development via the AI-SDC GitHub organization, which hosts tools from SACRO and GRAIMATTER projects.
Jonathan Santo is a Professor of Psychology at the University of Nebraska at Omaha (UNO), where he serves as Director of the Graduate Developmental Psychology Program and as a faculty member in the Office of Latino/Latin-American Studies. His research explores peer relations, cultural differences in self-esteem, and classroom-level influences on child and adolescent development, with a focus on improving school environments and fostering positive social interactions. ORCA Faculty Fellow His research spans broad areas including Developmental Psychology , Social Support Systems , and Cross-Cultural Peer Dynamics , with specific attention to peer victimization , self-continuity , school climate , and developmental trajectories in Brazilian and Colombian youth . Recent publications analyze topics like hikikomori experiences , friendship security , and HPA axis dysregulation in adolescents. Scientific awards include the ORCA Faculty Fellow recognition. His work often involves longitudinal assessments of peer relationships cross-cultural comparisons (Brazil, Colombia, China, Nigeria, Singapore, U.S.) policy-focused blog posts on family separations and school interventions
Dr. Paul Cook is an Associate Professor at the University of New Brunswick Faculty of Computer Science, joining in 2014. He received his PhD in Computer Science from the University of Toronto in 2010. His research focuses on lexical semantics and multiword expressions within Natural Language Processing and Computational Linguistics .
Professor Alessandra Russo leads the Structured and Probabilistic Knowledge Engineering (SPIKE) research group at Imperial College London's Department of Computing. With expertise spanning computational logic, symbolic machine learning, and neuro-symbolic AI, she develops foundational AI techniques applied to security, network management, healthcare, and adaptive systems. Professor Russo holds a PhD in Computing from Imperial College London and an MSc in Computer Science from Ionian University. Her research pioneers logic-based learning systems for intelligent adaptive technologies, with projects including declarative networking for security management, privacy-preserving federated learning, and hybrid neuro-symbolic approaches for robust reasoning. Her current work focuses on developing interpretable AI systems through neuro-symbolic integration, creating frameworks that combine neural networks with symbolic reasoning for explainable decision-making. Recent publications explore rule learning from knowledge graphs, transformer-based world models, and formal methods for representation learning. Professor Russo teaches courses on Logic-Based Learning and AI Applications, and has received the Google PhD Fellowship for her research contributions. She mentors numerous PhD students in areas spanning theoretical foundations and practical applications of computational logic and machine learning.
