Tse-Hsun (Peter) Chen is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University, Montreal. He leads the Software PErformance, Analysis, and Reliability (SPEAR) lab, focusing on improving software quality through log analysis, AIOps, and mining software repositories. His research collaborates with companies like Microsoft, BlackBerry, and Ericsson. Education: PhD, MSc, and BSc in Computer Science from Queen's University and the University of British Columbia. Awards include the Gina Cody Research Award (2021) and recognition as one of the world's most active software engineering researchers (JSS study). Research interests include software testing, DevOps, and leveraging LLMs for SE tasks. Recent work emphasizes log parsing with LLMs (e.g., LibreLog) and fault localization. Graduates from his lab hold academic positions at institutions like York University and DePaul University. Teaching includes courses on software verification, testing, and process management. Active in program committees for ICSE, FSE, and MSR. Over 50 publications in top venues like TSE, ICSE, and FSE.
Yann-Gaël Guéhéneuc is a Professor at Concordia University's Department of Computer Science and Software Engineering. He leads the Ptidej Team, focusing on software engineering methodologies, IoT systems, and game engine architecture analysis. His research emphasizes static/dynamic analyses, service-oriented architectures, and machine learning design patterns. Current Affiliations: Concordia University (Full-time Professor) Ptidiej Team Lead Research Interests: Specializes in IoT system testing, microservices architecture, game engine design patterns, and anti-pattern detection in multi-language systems. His work bridges theoretical software engineering principles with practical industrial applications, particularly in legacy system modernization and machine learning system design. Recent Trends in Publications: Focuses on IoT testing methodologies, machine learning architecture patterns, and service-oriented system transformations. His 2025 works advance IoT system taxonomy and game engine analysis techniques. Advising: Supervises MASc and PhD programs in Software Engineering and Computer Science Labs/Teams: Ptidej Team develops software tools for system analysis (e.g., Magnet, SyDRA)
Neil D. B. Bruce is an Associate Professor in the School of Computer Science at the University of Guelph, Canada. His research focuses on computer vision, deep learning, and computational neuroscience, with a strong emphasis on visual saliency, neural networks, and semantic segmentation. He holds a BSc in Computer Science & Pure Math from the University of Guelph, an MASc in Systems Design Engineering from the University of Waterloo, and a PhD in Computer Science from York University. Prior to Guelph, he held academic positions at Ryerson University and the University of Manitoba. Dr. Bruce leads the Vision Lab, exploring topics like attention mechanisms, image processing, and AI-driven solutions for visual computing challenges. His work bridges theoretical models with practical applications, including real-world gaze behavior analysis and exposure blending techniques. Key contributions include saliency prediction frameworks (e.g., AIM model) and semantic segmentation networks (EML-Net, Iterative Gating Networks). His research also intersects with interdisciplinary fields such as neuroscience and healthcare informatics, as seen in recent studies on avian influenza outbreak detection using social media data. Teaching highlights include courses in neural networks, data science, and machine learning. He actively supervises graduate and undergraduate research projects, emphasizing computational methods and AI innovation.
Rachel Stern is an Assistant Professor of Visual Arts at Union College, specializing in photography and interdisciplinary artistic practice. With an MFA from Columbia University and a BFA from RISD, she explores the intersections of desire, visual culture, and critical theory through complex photographic tableaux, installations, and sculptures. Stern's work interrogates authenticity in art, employing kitsch aesthetics and mass-produced materials to challenge traditional visual paradigms. Education MFA, Columbia University BFA, Rhode Island School of Design Skowhegan School of Painting and Sculpture Her recent exhibitions—including Lover's Eye (2025) and One Must Not Look At Anything (2023)—demonstrate a thematic focus on translating literary and philosophical concepts into visual media. Stern's practice synthesizes classical techniques like chiaroscuro with contemporary critiques of desirability and cultural exclusion, particularly through a queer lens. The artist's work engages with translation across multiple dimensions: between media (painting, sculpture, and photography), languages (Oscar Wilde's Salome and Voltaire's Candide), and societal frameworks (fatphobia and kitsch materiality). This approach creates liminal spaces that interrogate the viewer's role in perpetuating or challenging visual norms. While no scientific awards are explicitly documented in the provided texts, Stern's exhibitions at venues like A Hug From The Art World and Ortega y Gasset Projects, along with critical essays in publications like Matte Editions, establish her influence in contemporary visual arts discourse. Rachel Stern maintains an active studio practice and curatorial engagement, demonstrated through her 2020 solo exhibition This Terrestrial Paradise which examined post-pandemic proactivity through allegorical garden imagery. Her work continues to evolve through new projects like Pavilion for Pygmalion (2025), which reclaims classical myths through queer perspectives.
Dr. Rui Dai is an Associate Professor in the Department of Computer Science at the University of Cincinnati's College of Engineering and Applied Science. Her research focuses on wireless sensor networks, multimedia communications, and video analytics for healthcare and surveillance applications. She directs multiple NSF and NIST-funded projects on perceptual-quality-aware video systems. Research interests include quality-of-experience optimization for video analytics, compressed domain feature extraction, and edge computing frameworks for intelligent surveillance. Recent work develops deep feature compression techniques, multi-camera fall detection systems, and quality-aware video distribution strategies for 5G networks. Publications demonstrate consistent innovation in video processing for resource-constrained environments, with applications spanning healthcare monitoring, public safety networks, and embedded vision systems. Current projects investigate metaverse communication challenges for 6G networks and PHP vulnerability detection through hybrid static-fuzzing analysis.
Casey O'Callaghan is a Professor of Philosophy and Director of the Philosophy-Neuroscience-Psychology (PNP) Program at Washington University. He holds a PhD from Princeton University and has authored over 50 publications. His research focuses on philosophical questions about perception, particularly auditory perception and multisensory integration. Key themes include the nature of perceptual objects, crossmodal interactions, and how perception shapes understanding of reality. Recent work emphasizes speech perception and multisensory consciousness, culminating in his 2019 monograph A Multisensory Philosophy of Perception . He co-edited Sounds and Perception: New Philosophical Essays (2010) and has published widely in journals like Philosophical Quarterly . His research bridges philosophy of mind, metaphysics, and cognitive science, addressing topics like perceptual constancy, sensory modality taxonomy, and multisensory evidence integration. Education: PhD in Philosophy from Princeton University. Research Interests: Philosophy of Mind, Perception, Metaphysics, Auditory Perception, Multisensory Integration, Speech Perception, Sensory Modality Taxonomy, Perceptual Justification, and Crossmodal Binding. Professional Activities: Director of PNP Program, Editor of Sounds and Perception , and frequent contributor to interdisciplinary discussions on perception and consciousness.
Dr. Walid Morsi Ibrahim is a Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University, Faculty of Engineering and Applied Science. His expertise includes Smart Grid design, power systems analysis, and signal processing. He holds a PhD from Dalhousie University (2009), with prior academic positions including Adjunct Professor at the University of Waterloo and Assistant Professor roles at Ontario Tech University and the University of New Brunswick. Education: PhD (2009, Dalhousie), MSc (2002, Suez Canal University), BSc (1998, Suez Canal University) His research focuses on Smart Grid technologies, including signal processing for power systems, automation, and distributed energy resources. Notable contributions include work on electric vehicle integration, transformer aging analysis, and nonintrusive load monitoring using wavelet transforms and machine learning. He has authored over 50 publications in top-tier IEEE conferences and journals. Recent articles highlight his work on electric vehicle load forecasting during the pandemic, fault detection in photovoltaic systems, and transactive energy frameworks for prosumers. Awards include the IEEE PEDG 2010 Best Paper Award and multiple poster awards at Ontario Tech University. Dr. Ibrahim teaches courses on smart grid fundamentals, power systems operation, and information technology for engineers. He has secured grants for research on smart grid monitoring systems and holds patents related to energy management solutions. His work bridges theoretical advancements with practical grid applications, emphasizing resilience and sustainability.
John Guttag is the Dugald C. Jackson Professor in Electrical Engineering and Computer Science at MIT. His work focuses on AI-driven healthcare solutions, biomedical systems, and advanced computer vision applications. He leads research in medical image analysis, machine learning reliability, and healthcare equity. Guttag's contributions include innovative frameworks like MultiMorph and Scale-Space Hypernetworks, addressing challenges in medical imaging and clinical decision-making. Affiliations: MIT Electrical Engineering & Computer Science Department (EECS) Research emphasizes AI for healthcare, particularly in segmentation, predictive analytics, and ethical algorithm design. Notable projects include real-time fraud detection systems and studies on racial disparities in clinical risk scores. His work bridges computer science with clinical practice through tools like Voxelmorph for medical image registration and ScribblePrompt for interactive biomedical segmentation. Recent publications highlight advancements in uncertainty-aware AI, contrastive learning, and scalable medical data processing. Guttag’s methodologies prioritize practical clinical applications, aiming to improve diagnostics and healthcare workflows. His lab develops open-source tools and frameworks that enhance accessibility to advanced medical imaging technologies.
Associate Professor Leow Wee Kheng is affiliated with the Department of Computer Science at the School of Computing, National University of Singapore . His career spans over three decades with expertise in medical image analysis , computer vision , and surgical simulation . Ph.D. in Computer Science, University of Texas at Austin (1994) M.Sc. in Computer Science, National University of Singapore (1989) B.Sc. in Computer Science, National University of Singapore (1985) His research focuses on medical image analysis for craniofacial surgery and stroke diagnosis, 3D modeling of anatomical structures, and computer vision techniques like robust PCA and texture analysis . Recent work includes knee joint motion modeling and forearm rotation simulation for clinical applications. Key trends in his 2017-2025 publications involve skull reconstruction algorithms , multi-objective optimization for digital media, subject-specific biomechanical modeling , and low-rank decomposition techniques in visual computing. Collaborations include institutions like Singapore General Hospital and National Taiwan University Hospital . Scientific Awards : CAIP 2017 Best Paper Award 2007 Andrew P. Sage Best Transactions Paper Award Faculty Teaching Excellence Award (AY2015/16) Annual Teaching Excellence Award (AY2015/16) He has mentored numerous students in medical imaging , computer vision , and biomedical modeling . Current and former advisees include Chen Ying , Vineta Lum Lai Fun , and Long Huizhong (Ph.D.).
Dr Elliot J. Crowley is a Senior Lecturer in Electronics and Electrical Engineering at the University of Edinburgh, serving as Discipline Programme Manager. He co-leads the Bayesian and Neural Systems research group. His research focuses on simplifying machine learning, automated ML, low-resource deep learning, and engineering applications. He holds an MEng in Engineering Science and a DPhil (PhD) from the University of Oxford, with postdoctoral experience at Edinburgh's School of Informatics. He leads the EPSRC New Investigator Award and participates in the dAIEdge Horizon Network. Notable contributions include foundational work in neural architecture search (NAS), probabilistic methods for model efficiency, and applications in computer vision. His courses, such as the Data Analysis and Machine Learning module, emphasize practical Python-based learning for engineering students. Key awards include an EPSRC grant and recognition through distinguished papers at ASPLOS 2021. His team includes current PhD students (Linus Ericsson, Miguel Espinosa) and former advisees (Chenhongyi Yang at Meta, Jack Turner at Qualcomm). Research spans from NAS algorithms to ethical machine learning practices, with a focus on bridging theoretical advances and real-world engineering challenges.
Miroslaw Bober is Professor of Video Processing at the University of Surrey, where he joined in 2011. He leads the Visual Media Analysis team within the Centre for Vision, Speech and Signal Processing (CVSSP) in the School of Computer Science and Electronic Engineering. His extensive industry experience includes 15 years as General Manager of the Mitsubishi Electric R&D Centre Europe and Head of Research for its Visual & Sensing Division. BSc and MSc in Electrical Engineering from AGH University of Science and Technology, Krakow, Poland (1990) MSc in Machine Intelligence with distinction from Surrey University (1991) PhD in Computer Vision from Surrey University (1995) Professor Bober's research focuses on novel techniques in signal processing, computer vision and machine learning with applications in industry, healthcare, big-data and security. His expertise particularly lies in image and video analysis and retrieval, including visual search, object recognition, and analysis of motion, shape and texture. His algorithms for shape analysis, image/video fingerprinting, and visual search are considered world-leading and have been selected for ISO International standards within MPEG, with applications used by organizations like the Metropolitan Police. His recent publication trends show a strong focus on hybrid network architectures, scene graph generation, medical imaging applications, and augmented reality publishing systems. His work spans both theoretical advancements in computer vision and practical implementations addressing real-world challenges in media, healthcare, and security domains. The research demonstrates a consistent pattern of bridging academic innovation with industrial applications, particularly in visual search technology and media analysis. Presidential Award for strengthening the TV business in Japan via innovative 'Visual Navigation' content access technology (2010) Mitsubishi Best Invention Award for Image Signature Technology (2008) Professor Bober serves as Programme Director for the MSc in Multimedia Signal Processing and Communications and holds various teaching and mentoring roles. He has secured over 30 research and industrial grants totaling more than £16M, including the BRIDGET FP-7 project (5.28 M€) as coordinator and PI, and the CODAM project (£1.05 M) as PI. His work with the BBC, Huawei, and other industry partners demonstrates strong industry-academia collaboration. As chair of MPEG technical work on Compact Descriptors for Visual Search (CDVS) and Compact Descriptors for Video Analysis (CDVA), Professor Bober leads international standardization efforts. His Visual Media Analysis team develops cutting-edge visual search and media analysis algorithms with applications across broadcast, security, and healthcare domains.
Professor Linda Hogan is a leading ethicist at Trinity College Dublin, holding the position of Professor of Ecumenics in the School of Religion. She has served as Vice-Provost/Chief Academic Officer and Deputy President of Trinity (2011-2016), and previously as Head of the Irish School of Ecumenics (2006-2010). Her work bridges religious studies, human rights, and gender ethics, with global influence through roles like Chair of UNESCO's AI Ethics Expert Committee (2021). Her research focuses on inter-religious ethics, social-political ethics, and gender studies. Notable contributions include analyses of climate vulnerability through a gender lens and ethical frameworks for AI governance. She has authored three monographs, including Keeping Faith with Human Rights (2015), and edited volumes addressing bioethics and theological engagement. Professor Hogan has received prestigious recognitions such as Royal Irish Academy membership (2023), an Honorary Doctorate from Regis College (2022), and International Women's Forum election (2016). She has delivered keynotes at Oxford, Georgetown, and the United Nations, and advises institutions like the Coombe Hospital and Science Gallery. Her advisory roles include the Irish Council for Bioethics and UNESCO, reflecting her commitment to ethics in public policy and global challenges. Ongoing work addresses AI governance, climate justice, and human rights frameworks.
Dr. Christopher Gilliam is an Assistant Professor in Applied Signal Processing at the University of Birmingham's Department of Electronic, Electrical and Systems Engineering. He holds an MEng (1st Class Hons) in Electrical & Electronic Engineering (2008) and a Ph.D. in Signal Processing (2013), both from Imperial College London. Prior to joining Birmingham in 2022, he was a Postdoctoral Fellow at The Chinese University of Hong Kong (2013–2017) and a Research Fellow at RMIT University, Australia (2017–2022). Research Interests: Sensor signal processing, radar imaging, sampling theory, motion estimation, quantum navigation, and medical imaging. Labs: Microwave Integrated Systems Laboratory (MISL). Committees: Member of IEEE Signal Processing Society and APSIPA Technical Committees. His work focuses on advancing signal processing techniques for radar systems, navigation, and medical imaging. Recent research highlights include drone-based SAR imaging, motion correction in MRI, and fusion of classical/quantum sensors for inertial navigation. He is actively supervising PhD students and contributes to projects sponsored by DSTG. Publications span radar SLAM, probabilistic navigation algorithms, and deep learning-driven medical imaging solutions. His research bridges theoretical signal processing with practical applications in autonomous systems and healthcare.
Steve Marron is the Amos Hawley Distinguished Professor of Statistics and Operations Research at the University of North Carolina at Chapel Hill (UNC-CH). He holds a joint appointment in the School of Data Science and Society and is a professor in the Department of Biostatistics at the Gillings School of Global Public Health. Additionally, he serves as an adjunct professor in the Department of Computer Science within the College of Arts & Sciences. His research focuses on statistics, data science, and machine learning, with a particular emphasis on integrating diverse data types such as genomics, imaging, and demographic data. Education: Marron earned an AA from Orange Coast College (1974), BS from University of California, Davis (1977), and PhD from UCLA (1982). He has held faculty positions at UNC-CH since 1982 and Cornell University (2001-2002). His honors include Fellowships from the Institute of Mathematical Statistics and American Statistical Association, and he is a top-cited mathematician (1991-2001). Research interests include object-oriented data analysis, high-dimensional data methods (HDLSS), visualization techniques like SiZer, and statistical methodology for imaging and genomics. Notable contributions include Distance-Weighted Discrimination (DWD), Principal Nested Spheres, and JIVE for data integration. His work has applications in cancer genomics, medical imaging, and bioinformatics. Awards and recognitions include the Amos Hawley Professorship, S. N. Roy Memorial Lectureship, and Saw Swee Hock Visiting Professorship. Marron has advised numerous students and collaborated on NIH-funded grants, including studies on cancer genomics and imaging. His lab focuses on developing statistical tools for complex, multi-source data analysis.
Professor Ferrante Neri is a faculty member at the University of Surrey, holding the positions of Professor of Machine Learning and Artificial Intelligence and Associate Dean (International) for the Faculty of Engineering and Physical Sciences (FEPS). He is affiliated with the Nature Inspired Computing and Engineering Research Group, Surrey Institute for People-Centred AI (PAI), and the Computer Science Research Centre within the School of Computer Science and Electronic Engineering. His research focuses on optimization, explainable AI, and machine learning, with contributions to memetic computing and differential evolution. Since 2010, he has chaired the IEEE Task Force on Memetic Computing. He advises PhD students in topics like dynamic multi-objective optimization and AI-driven applications. His teaching expertise includes mathematical foundations for computer science. He has supervised students such as Aisha E S E Saeid and Pengjin Wu. Notable research areas include evolutionary algorithms, neural architecture search, and applications in robotics and environmental monitoring. Labs and teams include the Nature Inspired Computing group, which explores AI-driven solutions for complex problems. His work bridges theoretical advancements and practical applications in fields like autonomous systems and deep learning.
