Find Academic Supervisors

• Artificial Intelligence
• Data Science
• Machine Learning
• +7 more

Wang Zhaoxia is an Associate Professor of Computer Science (Practice) at the School of Computing and Information Systems, Singapore Management University. Her expertise lies in Artificial Intelligence, Data Science, and Machine Learning with a focus on Natural Language Processing (NLP), Sentiment Analysis, and Knowledge Graphs. She holds a PhD from Nankai University (2004). Research Interests: Her work spans AI-driven solutions for sentiment analysis, maritime risk management, audio-to-music conversion, and social media analytics. Recent projects include leveraging Large Language Models (LLMs) for tasks like logical reasoning, translation optimization, and risk assessment. Publications: Recent work highlights include developing metrics for LLM performance (RAP), emotion analysis in images, and automated music transcription. Her research demonstrates cross-disciplinary applications of AI in fields like finance (ICO success prediction) and health (disease surveillance). Awards: No awards explicitly listed Advising: Supervises 3 PhD/Master students including HUANG Donghao and Nicole Anne Hui Ying TEO Contact: zxwang@smu.edu.sg | +65 68261347

Pei WangView profile

Associate Professor

• Artificial Intelligence
• Artificial General Intelligence
• Non-Axiomatic Reasoning
• +3 more

Pei Wang is an Associate Professor in the Department of Computer and Information Sciences at Temple University's College of Science & Technology. His research focuses on Artificial General Intelligence (AGI), Non-Axiomatic Reasoning System (NARS), and cognitive systems. He holds a Ph.D. in Computer Science and Cognitive Science from Indiana University (1995), and has contributed to foundational AI theories through works like Rigid Flexibility: The Logic of Intelligence (2006) and Non-Axiomatic Logic: A Model of Intelligent Reasoning (2013). Education: Ph.D., Indiana University (1995); M.S./B.S., Peking University (1986/1983) Research interests include AGI architecture design, formal logic for reasoning under uncertainty, and the integration of perception and cognition. His work emphasizes general-purpose intelligence systems capable of adaptive learning and decision-making in dynamic environments. Recent publications explore neurosymbolic systems, consciousness modeling, and causal inference frameworks. Notable contributions include co-editing the Journal of Artificial General Intelligence and leading the OpenNARS project, an open-source AGI implementation. Awards include the Jet Propulsion Laboratory Summer Faculty Research Program (2013–2014) and the Outstanding Dissertation Award from Indiana University (1996).

Iris Xiaohong QuanView profile

Professor

• Entrepreneurship
• Technology Management
• Innovation Management
• +5 more

Dr. Iris Xiaohong Quan is a Full Professor of entrepreneurship at the Lucas College and Graduate School of Business, San José State University. She holds a PhD in Regional Planning from the University of California - Berkeley and serves as Director for the Center for Global R&D and Innovation (GLORAD) Silicon Valley. Dr. Quan's research focuses on entrepreneurship, technology and innovation management, with particular expertise in global R&D strategies, business model innovation, and digital health. Her work examines how firms navigate innovation ecosystems across different cultural and regulatory contexts, with a special focus on Chinese companies and their internationalization strategies. Her recent publications demonstrate a strong trajectory in artificial intelligence business ecosystems, open innovation models, and healthcare technology innovation. She has contributed significantly to understanding how organizations leverage external knowledge sources and develop context-specific innovation approaches in global markets. Best Paper Publication Award from IEEE TEM in 2020 for "Cross-National Complementarity of Technology Push, Demand Pull, and Manufacturing Push Policies: The Case of Photovoltaics" Dr. Quan serves on the editorial boards of Technovation and IEEE Transactions on Engineering Management. She is actively involved with the Silicon Valley Center for Entrepreneurship as a steering committee member and has judged numerous business plan competitions. Additionally, she brings practical experience as a part-time investment partner at TSVC, an early-stage deep tech-focused venture capital firm. Her leadership extends to directing the Center for Global R&D and Innovation (GLORAD) Silicon Valley, which serves as a hub for research on global innovation practices and cross-border technology transfer.

Andrew P. MichelsonView profile

Assistant Professor

• Machine Learning in Medicine
• Artificial Intelligence in Healthcare
• Lung Transplantation
• +3 more

Andrew P. Michelson, MD is an Assistant Professor of Medicine in the Division of Pulmonary and Critical Care Medicine at Washington University in St. Louis School of Medicine. He holds affiliations with the Institute of Clinical and Translational Sciences (ICTS), Institute for Informatics, Data Science and Biostatistics (I2DB), and the Center for Applied Health Informatics (CAHI). His research focuses on machine learning and artificial intelligence applications in medicine, particularly in lung transplantation, critical care, and clinical informatics. He has contributed to over 36 peer-reviewed publications since 2011, emphasizing predictive modeling for primary graft dysfunction, AI-driven diagnostics for cochlear implants, and sepsis prediction systems. His work bridges clinical practice and advanced computational methods to improve patient outcomes in respiratory and transplant medicine. Affiliations: ICTS, I2DB, CAHI Key Research Areas: Machine learning in healthcare, lung transplantation outcomes, clinical informatics Notable Projects: Development of machine learning models for predicting primary graft dysfunction post-lung transplantation, AI applications in otology and neurotology Michelson's research integrates clinical data with advanced analytics to address critical questions in pulmonology and critical care. His recent work highlights the use of CT imaging and multimodal data to enhance transplant outcomes and improve diagnostic accuracy in otolaryngology. He collaborates widely, contributing to translational research initiatives like the CRITICAL resource for intensive care analytics.

Jeremy BradburyView profile

Associate Professor

• Software Engineering
• Software Quality Assurance
• Testing and Analysis
• +7 more

Jeremy Bradbury is an Associate Professor in the Faculty of Science at Ontario Tech University, where he has served as Undergraduate and Graduate Program Director for Computer Science and as a member of the Board of Governors. His research focuses on software testing, analysis, and quality assurance, with a particular emphasis on human-centered approaches, empirical methodologies, and concurrency issues in software systems. He leads the Software Quality Research Lab (SQRLab) and develops tools like PIE for visualizing software design patterns and GidgetML for adaptive educational gaming. Education: PhD in Computer Science from Queen's University (2007). Research interests include software visualization, open-source software analysis, flaky test detection, and the application of machine learning to software engineering challenges. His work spans both academic contributions (e.g., combinatorial testing frameworks) and practical innovations like the Run, Llama, Run game for computational thinking education. Recent work explores AI-driven bug prediction, data augmentation bias in ML models, and cybersecurity for connected autonomous vehicles. He has organized workshops on testing configurable systems (ToCaMS) and challenges in parallel computing.

• Transportation engineering
• Sustainable Transport and Infrastructures
• Environment, Economy, and Society
• +7 more

Catherine Morency is a Full Professor in the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal, where she holds the Mobility Chair and a Tier-1 Canada Research Chair on Personal Mobility. Her research focuses on transportation engineering, sustainable transport, intermodality, and equity in urban mobility. She is affiliated with multiple research units including the Centre interdisciplinaire de recherche en opérationnalisation du développement durable (CIRODD), Agence de mobilité durable, Société de transport de Montréal (STM), Laboratoire en intelligence des données, Institute for Data Valorization (IVADO), and CIRRELT. Her work integrates modeling interactions between transportation modes, analyzing spatial-temporal data, and evaluating sustainability metrics. She advocates for reducing car dependency through ridesharing, carsharing, and active modes, while emphasizing equity in transport planning. Recent publications explore driving cycle construction, metro network vulnerability, e-bike route optimization, and GHG emission modeling. 2021 - Prix d'excellence en recherche et innovation, Polytechnique Montréal 2019 - Prix Antoine Grégoire, ATUQ 2014 - Prix du président, AQTr 2005 - Prix de la francophonie, France Morency supervises numerous Ph.D. and Master's students, with completed theses spanning topics like car ownership modeling, bike-sharing demand prediction, transit network design, and impact assessments of shared mobility. She actively contributes to public discourse on Quebec transport policy, particularly critiquing car-centric projects and advocating for prioritization of public transit, equity, and data-driven decision-making.

Michael RobertsView profile

Associate Professor

• Image Processing
• Machine Learning
• Medical Imaging
• +3 more

Dr. Michael Roberts is a Principal Research Associate (equivalent to Reader/Associate Professor) at the University of Cambridge's Department of Applied Mathematics and Theoretical Physics (DAMTP) and Department of Medicine. He leads the BloodCounts! consortium and the algorithm development team for the global AIX-COVNET collaboration. His interdisciplinary research focuses on variational methods for image processing, machine learning for medical imaging, and addressing reproducibility challenges in AI-driven science. Education: PhD in Mathematics, University of Liverpool (2019) MSc in Mathematics with Honors, Durham University (2015) Research Interests: Image segmentation and registration Machine learning applications in medical imaging (e.g., lung diseases, cardiovascular imaging) Scientific integrity and reproducibility in AI Collaborations with clinicians, computer scientists, and applied mathematicians Key Projects: BloodCounts!: Developing AI tools for blood cell analysis AIX-COVNET: Global collaboration for AI-driven COVID-19 imaging analysis Publications Highlight Trends: His work emphasizes improving AI reliability in medical contexts, addressing challenges like image quality assessment, artifact correction, and mitigating biases in machine learning models. Recent studies include OCT plaque analysis, chest radiograph quality control, and consensus-based guidelines for machine learning research (ReFORMS). Grants and Labs: Leads major interdisciplinary initiatives and collaborates with institutions like AstraZeneca. Active in Cambridge Image Analysis (CIA) group.

Johann Gagnon-BartschView profile

Associate Professor

• Causal Inference
• Machine Learning
• Nonparametric Methods
• +7 more

Johann Gagnon-Bartsch is an Associate Professor in the Department of Statistics at the University of Michigan, Ann Arbor, and serves as Director of Undergraduate Programs and Associate Director for Undergraduate Programs. He is affiliated with the Michigan Institute for Data Science (MIDAS) and the Center for Computational Medicine and Bioinformatics. His research focuses on causal inference, machine learning, and nonparametric methods with applications in biological and social sciences. He holds a PhD in Statistics from UC Berkeley (2012), an MS in Statistics from UC Berkeley (2007), and a BS/BA in Math, Physics, and International Relations from Stanford University (2003). Education: PhD in Statistics, UC Berkeley, 2012 MS in Statistics, UC Berkeley, 2007 BS/BA in Math, Physics, and International Relations, Stanford University, 2003 Research Interests: His work spans high-throughput biological data analysis, particularly in addressing systematic errors via negative controls, and integrating experimental/observational data. Recent focuses include causal inference in education experiments, social media analytics for public opinion tracking, and reproducible research workflows. He develops methods to correct for unobserved confounders and laboratory variability in genomic datasets. Grants & Awards: MIDAS Reproducibility Challenge Winner (2020) Thomas R. Ten Have Award (2016) Evelyn Fix Prize (2013) Outstanding Graduate Student Instructor (2010) Teaching & Mentorship: Teaches courses in applied statistics and statistical learning. Mentors numerous PhD, master's, and undergraduate students in areas like genomic data analysis and causal inference. Active in curriculum development and student advising within the Statistics Department. Labs & Collaborations: Leads projects at MIDAS and collaborates with researchers in computational medicine, bioinformatics, and social data science. His lab develops open-source tools like the RUV package for batch effect correction and Docker-based reproducibility workflows.

Margo SeltzerView profile

Professor

• Computer Systems
• Operating Systems
• Database Systems
• +7 more

Margo Seltzer is a Professor and Co-Head of the Department of Computer Science at the University of British Columbia (UBC). She holds the Canada 150 Research Chair in Computer Systems and the Cheriton Family Chair in Computer Science. Her research focuses on computer systems, including operating systems, databases, data provenance, graph analytics, and interpretable machine learning. She is also involved in educational initiatives and has held leadership roles in academia, including serving as Dean of Computer Science and Engineering at Harvard University. Education: PhD in Computer Science from UC Berkeley (1992), AB in Applied Mathematics from Harvard (1983). Professional roles include Chief Technology Officer at Sleepycat Software and architect at Oracle Corporation. She has received prestigious awards such as the ACM Athena Lecturer Award, Killam Teaching Prize, and membership in the National Academy of Engineering. Research Interests: Systems, databases, transaction processing, provenance capture, healthcare informatics, and cybersecurity. Her work emphasizes practical applications of systems research, including optimizing storage, improving operating system performance, and enhancing data security. She has contributed to open-source projects like Berkeley DB and CamFlow provenance system. Key Contributions: Developed systems for provenance-aware storage, sparse decision trees (e.g., GOSDT), and intrusion detection (FRAPpucino). She advocates for reproducible research through tools like Encapsulator and Rclean. Her teaching and mentorship have influenced countless students and junior faculty in computer science. Awards: ACM Athena Lecturer (2023), SIGMOD Systems Award (2020), USENIX Lifetime Achievement Award (2019), and election to the American Academy of Arts and Sciences (2021). Her work bridges theoretical computer science with real-world applications, emphasizing both technical innovation and societal impact.

Giorgio GilestroView profile

Professor

• Neurobiology
• Sleep Biology
• Animal Neurobiology
• +5 more

Giorgio Gilestro is a Reader in Systems Neurobiology at Imperial College London's Department of Life Sciences, part of the Faculty of Natural Sciences. He holds a PhD in Developmental Biology from the University of Vienna (2006) and completed postdoctoral work in Sleep Biology at the University of Wisconsin-Madison (2009). His research focuses on sleep biology and neurobiology using Drosophila as a model organism, exploring topics such as synaptic plasticity, sleep function evolution, and subconscious experiences during sleep. He has pioneered innovative computational tools like pySolo and Ethoscopes, advancing sleep analysis methodologies. His lab's work intersects genetics, engineering, and bioinformatics to address fundamental questions in systems neurobiology. Research interests include: Evolutionary mechanisms of sleep behavior Neurobiological basis of sleep-wake regulation Development of open-source analytical platforms for behavioral research Subconscious processing during sleep in invertebrate models Gilestro has secured over £2.5M in grants from the BBSRC and mentored seven PhD students, three of whom have established independent research groups. His affiliations include the Centre for Neurotechnology, Grantham Institute, and the Physics of Life initiative. He actively administers the Drosophila-focused Mastodon server (@drosophila.social) to foster scientific communication.

Arash ShaghaghiView profile

Senior Lecturer

• Cybersecurity
• Internet of Things (IoT) Security
• Zero Trust Architecture
• +4 more

Arash Shaghaghi is a Senior Lecturer in Cyber Security and Acting Director of Online Education at the School of Computer Science and Engineering (CSE) at UNSW Sydney. Previously affiliated with RMIT University, Deakin University, Data61 CSIRO, The University of Melbourne, and The University of Texas at Dallas, he holds a PhD in Computer Science and Engineering from UNSW Sydney, an MSc in Information Security from University College London, and a BSc with Distinction from Heriot-Watt University. His research spans cybersecurity with emphasis on IoT security, zero trust architecture, blockchain applications, and posthumous data management. With over 1.7 million AUD in research funding as Lead CI/CI, his work addresses critical challenges in intelligent transport systems, quantum-safe security, and digital legacy solutions. He serves as Associate Editor for the Journal of Ad Hoc Networks and reviews for top conferences including NDSS and IEEE S&P. His 15 most recent publications demonstrate expertise in IoT security frameworks, digital will solutions, encrypted traffic analysis, and vulnerability prediction models. These works appear in premier venues like IEEE TDSC, IEEE S&P, and ACM WWW, reflecting his leadership in solving practical security challenges for emerging technologies. Early Career Academic Excellence Award (Research), UNSW Engineering, 2024 Educational Excellence & Innovation Award, UNSW Engineering, 2023 Heidelberg Laureate Forum Selection (Top 200 young scientists), 2023 Best Demo Award, IEEE LCN Conference, 2020 As an award-winning educator, Shaghaghi leads UNSW's Master of Cyber Security online programs and previously directed Deakin University's Graduate Certificate in Information Technology. His research team includes postdocs, software developers, and 3 current PhD students. He actively engages with media outlets including ABC and The Guardian on cybersecurity topics, particularly digital legacy management and IoT security.

Kathryn EcclesView profile

Associate Professor

• Cultural Heritage
• Digital Humanities
• Museum Studies
• +7 more

Kathryn Eccles is an Associate Professor and Senior Research Fellow at the Oxford Internet Institute (OII), University of Oxford. Her research focuses on digital culture and heritage, examining the impact of immersive technologies, AI, and social media on cultural engagement. She holds a PhD in modern British history (2008) from the Faculty of History at Oxford and has pioneered projects like Cabinet (an object-based learning platform) and Open Cabinet (exploring 3D/AR technologies in museums). Her work also includes the Hashtag Heritage project (using social media to study heritage site engagement) and AI Futures (interrogating AI’s role in cultural production). Key affiliations include the Oxford Research Centre for the Humanities (TORCH), where she led the Digital Humanities Programme (2014–2017), and the Faculty of History. She has been awarded grants from AHRC, Google, JISC, and the Dieter Schwarz Foundation. Her research interests span AI ethics, gender studies, and decolonial practices in digital culture. Notable awards include the AHRC Early Career Fellowship (2012–2013) and the Dieter Schwarz Foundation Project Award (2024). Her teaching includes courses on Cultural Analytics and mentoring students in the MSc in Digital Scholarship. She supervises a vibrant group of doctoral students exploring AI, creativity, and heritage. Current projects include AI is not Photography (examining AI’s impact on cultural production) and collaborations with institutions like the Pitt Rivers Museum and the National Trust. She also leads the GLAM Labs Playful Spaces initiative, using social media data to map visitor engagement.

Melinda HodkiewiczView profile

Professor

• Maintenance Engineering
• Asset Management
• Data Science
• +6 more

Professor Melinda Hodkiewicz is a leading academic at the University of Western Australia (UWA), affiliated with the School of Engineering's Mechanical Engineering department. Her work focuses on advancing maintenance, asset management, and safety practices through data-driven approaches, particularly in Technical Language Processing (TLP) and industrial ontologies. She has held significant roles such as the BHP Fellow for Engineering for Remote Operations (2015–2022) and leadership in the $8.8M Australian Government-funded Training Centre for Transforming Maintenance through Data Science. Research interests include applying TLP to maintenance and safety texts, ontology development for industrial interoperability, and advancing standards like ISO 23726. Awards include the MESA Medal (Lifetime Achievement in Asset Management) and Fellow of the Australian Academy of Technology and Engineering (ATSE). She has contributed to international standards (ISO 55000 series) and served on boards of organizations like NOPSEMA and MRIWA. Her work aligns with UN SDG 4 (Quality Education) and SDG 9 (Industry, Innovation & Infrastructure). Key contributions include developing the Industrial Ontology Foundry's Maintenance Working Group, creating open-source tools like OpenBoltRF for wind turbine analysis, and advancing semantic quality assurance frameworks for maintenance procedures. Her research bridges technical language processing with industrial applications, enhancing safety and operational efficiency in sectors like mining, energy, and offshore engineering. Education: BA(Hons) in Metallurgy (University of Oxford, 1985), PhD in Engineering (UWA, 2014) Grants: ARC Training Centre for Transforming Maintenance (2019–2025), ARC Hub for Offshore Floating Facilities (2014–2021) Advisory Roles: Standards Australia EL-069 (Automation Systems), ISO TC184/SC4 (Industrial Data)

• Statistics Education
• Educational Psychology
• Voice Perception
• +3 more

Dr. Phil McAleer is a Senior Lecturer at the University of Glasgow's School of Psychology & Neuroscience. His research focuses on improving statistics education in Higher Education, addressing negative attitudes toward statistics, and exploring how personality and emotion are conveyed through voices. He holds a B.Sc. (2002) and Ph.D. (2006) from the University of Glasgow, with postdoctoral work on intent perception and voice neurocognition. He has been a Lecturer since 2012, emphasizing reproducible research practices and teaching via the PsyTeachR initiative. His work spans over 40 publications, including contributions to journals like PLoS ONE , Perception , and Nature Reviews Psychology . He supervises multiple postgraduate students and actively participates in educational conferences and workshops. His research also examines student engagement and feedback strategies, leveraging technologies like MarkR software for enhanced academic feedback. Education: B.Sc. Psychology, University of Glasgow, 2002 Ph.D., University of Glasgow, 2006 (PACo Lab) Research Interests: Statistics education and reproducible practices Voice perception and social impressions Technology-enhanced learning and feedback systems Teaching Contributions: Developed open-source tutorials via PsyTeachR Promotes data skills and reproducibility in curricula Professional Activities: Member of the Voice Neurocognition and PACo labs Presenter at DARTP, TIER, and learning conferences

Gustavo CarneiroView profile

Professor

• Artificial Intelligence
• Machine Learning
• Medical Image Analysis
• +4 more

Professor Gustavo Carneiro is a faculty member at the University of Surrey's School of Computer Science and Electronic Engineering, leading the Centre for Vision, Speech and Signal Processing (CVSSP). His primary role is as Professor of AI and Machine Learning, with a focus on medical image analysis, computer vision, and theoretical machine learning. He holds a PhD from the University of Toronto and has extensive international experience, including roles at Siemens Corporate Research and academic positions in Brazil and Australia. Research interests span applied medical imaging (e.g., mammogram analysis, colonoscopy, and histology) and theoretical advances in deep learning, semi-supervised learning, and robust models under label noise. Notable projects include the People-centred Mammogram Analysis (PecMan) initiative, aiming to improve AI trustworthiness and fairness in clinical settings. Prof. Carneiro has supervised over 30 students and postdocs, including PhD candidates in Adelaide and Surrey. His work bridges academia and industry, with publications in top venues like CVPR, MICCAI, and IEEE Transactions. Awards include Dean’s Commendation for Doctoral Thesis Excellence for multiple students. Key collaborations involve medical institutions and tech partners, addressing challenges in endometriosis diagnosis, Barrett’s esophagus detection, and AI-driven surgical tools. His lab develops open-source datasets and frameworks, such as the tumor hypoxia analysis dataset.