Find Academic Supervisors

• Software Engineering
• Refactoring Techniques
• Technical Debt Management
• +4 more

Christian Newman is an Associate Professor in the Department of Software Engineering at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT). He serves as the Graduate Program Director and has expertise in software engineering methodologies, refactoring techniques, and source code analysis. His research focuses on improving code quality, developer practices, and automated documentation. Education: Newman holds a BS, MS, and Ph.D. from Kent State University. His academic background aligns with his current research in software engineering and empirical studies. Research Interests: His work emphasizes identifier naming standards, technical debt management, refactoring strategies, and code reuse. He explores how developers perceive and implement refactoring tools, as well as the role of large language models (LLMs) in programming education and code generation. Publications: Newman's recent work includes studies on identifier semantics, part-of-speech tagging for code analysis, and the performance of LLMs in introductory programming tasks. His research often combines empirical studies with tool development, such as SATDBailiff for technical debt tracking and TSDetect for test smell detection. Teaching & Advising: He teaches courses like SWEN-250 (Personal Software Engineering), SWEN-331 (Engineering Secure Software), and graduate-level thesis supervision. His courses emphasize secure development, software design principles, and team-based projects. Tools & Contributions: Newman has developed tools like srcSlice (static slicing), srcType (type resolution), and SCALAR (identifier analysis). These tools support software evolution, code comprehension, and empirical research in the field.

• Artificial Intelligence
• Image Processing
• Electrical and Electronic Engineering
• +4 more

Professor Maja Pantic is a Professor of Affective & Behavioural Computing at the Department of Computing, Faculty of Engineering, Imperial College London. Her research focuses on artificial intelligence, image processing, and audio-visual speech recognition. She leads projects in multimodal systems, including facial analysis, emotion recognition, and speech-driven animation. Affiliations include the AI for Healthcare initiative, the Artificial Intelligence Network, and the Machine Learning Network. Her work addresses challenges in real-time speech enhancement, cross-modal learning, and synthetic data generation. Recent publications emphasize advancements in audiovisual speech synthesis, lip-reading, and emotion-aware systems. She has contributed to datasets like KAN-AV and SEWA DB, advancing research in face analysis and affective computing.

Soteris DemetriouView profile

Senior Lecturer

• Computer System Security
• Cyber-Physical Systems Security
• Machine Learning Security
• +3 more

Soteris Demetriou is a Senior Lecturer of Computer Systems Security at Imperial College London's Department of Computing, within the Faculty of Engineering. He leads the Applications, Platforms, and Systems Security (APSS) Research Lab and directs the Academic Centre of Excellence in Cyber Security Research (ACE-CSR). His research focuses on securing mobile, IoT, and cyber-physical systems through techniques like explainable AI, reverse engineering, and trusted computing. Notable contributions include tools for privacy preservation in machine learning models, detection of LiDAR spoofing attacks, and securing Android's middleware. Education: PhD and MSc in Computer Science (University of Illinois at Urbana-Champaign), Diploma in Electrical and Computer Engineering (University of Patras). Research Interests: Mobile/IoT security, AI security, trusted computing, and vulnerability analysis. Key areas include privacy in generative models, adversarial attacks on autonomous systems, and large-scale distributed systems. Publications: Over 50 peer-reviewed papers in top venues like NDSS, CCS, and SOSP. Recent work addresses privacy in speech generation, LiDAR security for autonomous vehicles, and hyperscale serverless architectures at Meta. Awards: Distinguished Paper Award at NDSS 2018, Best Paper at SafeThings 2024, and multiple travel grants. Served on technical committees for PETS, CCS, and AutoSec. Grants & Collaborations: SPRITE+ grant for Bio-IoT security, collaboration with Meta on distributed systems, and leadership in ACE-CSR. Labs: APSS Lab focuses on systems and AI security, with interdisciplinary projects in healthcare and autonomous systems.

Raymond T. NgView profile

Professor

• Data Mining
• Text Mining
• Health Informatics
• +5 more

Raymond T. Ng is a Professor of Computer Science at the University of British Columbia (UBC) and serves as Director of the Data Science Institute . In addition, he is the part-time Chief Informatics Officer at the PROOF Centre of Excellence for the Prevention of Organ Failures located at St Paul’s Hospital. Since 2016 he has held the prestigious Canada Research Chair in Data Science and Analytics. Education B.Sc. (Hons.) Computer Science, University of British Columbia, 1986 M.Math. Computer Science, University of Waterloo, 1988 Ph.D. Computer Science, University of Maryland, College Park, 1992 Research Interests Professor Ng’s research lies at the intersection of data mining , text mining , health informatics , sensor analytics , and databases . Over the past decade he has focused on two major domains: Genomics & Biomarker Discovery: Developing multi-omics biomarker panels for heart, lung and kidney transplant rejection and COPD exacerbations using transcriptomics, proteomics and metabolomics data. Natural Language Processing: Mining and summarizing conversational text such as emails, blogs and meeting transcripts to generate structured metadata and actionable insights. Scientific Awards Canada Research Chair in Data Science and Analytics (2016-2026) Best Paper Award, ACM SIGMOD 2004 Best Paper Award, ACM SIGKDD 2001 Selected among Best Papers of VLDB ’99 & ’98 Governor General’s Gold Medal, UBC (1986) Research Funding & Leadership Since joining UBC in 1992, Professor Ng has continuously secured major peer-reviewed funding from NSERC, CIHR, Genome Canada, CFI, MITACS and industry partners (Google, IBM, SAP). He leads or co-leads several large-scale initiatives: HEARTBiT multi-marker blood test for cardiac transplant rejection (CIHR 2018-2021) MERIDIAN ocean acoustic data infrastructure (CFI 2018-2021) Pan-Canadian Early Detection of Lung Cancer (Terry Fox 2018-2021) Business Intelligence Network (NSERC 2009-2014) Multiple Genome Canada programs on biomarker translation (2004-2018) Laboratories & Teams Professor Ng directs the Data Science Institute and works closely with the Natural Language Processing Research Group . At the PROOF Centre he heads a multidisciplinary team of statisticians, computer scientists and clinicians advancing computational biomarker pipelines from discovery to clinical implementation.

Ben SwiftView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Computational Art & Music
• +4 more

Dr. Ben Swift is a Senior Lecturer at the School of Cybernetics, ANU, specializing in AI, computational art, and cybernetics. He leads the Cybernetic Studio, an interdisciplinary collective exploring cybernetic systems through hardware/software/people collaborations. As a livecoding artist, he performs globally and co-founded the ANU Laptop Ensemble. His research spans generative AI, open-source tools like Extempore, and UX design. Education: PhD in Computer Science (ANU) Projects: Australia's Digital Economy (2022), The Augmented Web (2019) Research focuses on AI creativity, biofeedback interfaces, and computational music. His work bridges technical innovation with artistic expression, evident in projects like TSPNet and adversarial camera systems. Key contributions include Extempore’s development and studies in live coding disruption. Awards unspecified but recognized internationally for interdisciplinary impact.

Peter BearmanView profile

Professor

• Social Network Analysis
• Historical Sociology
• Health and Population
• +3 more

Peter Bearman serves as Director of the Incite Institute and holds the Jonathan R. Cole Professorship of Sociology at Columbia University, where he maintains an active research and teaching profile within the Department of Sociology. His office is located at Knox Hall on Columbia's Morningside Heights campus. Bearman specializes in social network analysis and historical sociology, with significant contributions to health/population studies and qualitative methodology. His research increasingly integrates computational approaches for text and narrative analysis, examining phenomena ranging from historical religious discourse to modern social conflict dynamics. This interdisciplinary work bridges classical sociological theory with contemporary methodological innovation. Recent publications reveal a trajectory toward computational social science applications, particularly in semantic network analysis of historical texts and neural correlates of social behavior. His work consistently connects micro-level interactions with macro-scale social structures across diverse contexts including religious history, workplace conflict, and collective violence. His distinguished scientific honors include: Kohli Prize for Sociology (2025) for contributions to movements, networks, health, and science Golden Goose Award (2016) for the National Longitudinal Study of Adolescent Health Election to the National Academy of Medicine (2019) Membership in the American Academy of Arts and Sciences Membership in the National Academy of Sciences Bearman co-designed the landmark National Longitudinal Study of Adolescent Health, demonstrating sustained leadership in major federally-funded research initiatives. As Director of the Incite Institute, he oversees interdisciplinary research teams and co-edits Columbia University Press's Middle Range and Oral History book series, actively shaping scholarly discourse through mentorship and publication curation. The Incite Institute functions as Bearman's primary research hub, facilitating collaborations that advance innovative methodologies in social science while connecting historical analysis with contemporary network theory applications.

• Emerging memory and storage technologies
• Embedded systems
• Cyber-Physical Systems (CPS)
• +5 more

Dr. Yiran Chen is the John Cocke Distinguished Professor at Duke University's Department of Electrical and Computer Engineering, leading the NSF AI Institute for Edge Computing (Athena) and the Duke Center for Computational Evolutionary Intelligence (DCEI). A global leader in neuromorphic computing, emerging memory systems, and edge AI, he holds prestigious roles including IEEE Fellow and Editor-in-Chief of IEEE Transactions on Circuits and Systems for AI. His research spans machine learning accelerators, security-hardened hardware, and co-design of EDA tools with LLMs. With over 700 publications and 96 patents, he has been awarded 15 paper awards and 17 nominations, including rare Technical Achievement Awards from IEEE societies. He advises over 60 PhD students and 4 postdocs, many of whom hold academic positions worldwide. His work bridges academia and industry, contributing to startups and venture capital through his board roles. Education: B.S. (Tsinghua, 1998) → M.S. (Tsinghua, 2001) → Ph.D. (Purdue, 2005). Career path: Assistant/Associate Professor at University of Pittsburgh (2010–2014) → Duke since 2014. Awards include the ACM SIGDA Outstanding New Faculty Award (2014), NSF CAREER Award (2013), and the Stansell Family Distinguished Research Award (2022). Research focuses on innovations in: (1) Non-volatile memory architectures for AI acceleration, (2) Hardware-software co-design for edge computing, (3) Security in neuromorphic systems, and (4) Large-scale ML for EDA. His group pioneered ReRAM-based accelerators like ReBNN and MARC, and introduced novel edge AI frameworks like Ecco and Prosperity. These works address scalability, energy efficiency, and real-time performance challenges. Key initiatives include the NSF IUCRC for Alternative Sustainable & Intelligent Computing (ASIC), advancing sustainable computing through novel materials and architectures. His leadership in standard-setting bodies like the IEEE Circuits and Systems Society ensures cutting-edge research translates into industry practices. Grants: Lead PIs for multiple NSF AI Institutes and industry partnerships. Labs: Directs the Athena Institute and DCEI, fostering collaboration between academia and industry. Current projects include quantum computing placement algorithms (QPlacer), federated learning frameworks (FedGPT), and neuro-symbolic architectures.

ANDRES BASELGA FRAGAView profile

Professor

• Biodiversity Conservation
• Biogeography
• Community Ecology
• +7 more

Andrés Baselga Fraga is a Professor at the University of Santiago de Compostela, affiliated with the Department of Zoology, Genetics and Physical Anthropology within the Faculty of Biology. He leads the BiBiCI research group (Biodiversity, Biogeography and Integrative Conservation) and is associated with the Center for Interdisciplinary Research in Environmental Technologies (CRETUS). His doctoral work (2002) focused on Chrysomelidae beetles in Galicia, supervised by Dr. Francisco Novoa Docet. Education: PhD in Biology, University of Santiago de Compostela (2002) Thesis: 'Study of the Chrysomelidae (Coleoptera) of Galicia' Research Interests: His work centers on biodiversity patterns, biogeographical processes, and community ecology, with a focus on spatial scaling of phylogenetic diversity, climate change impacts, and dispersal limitation effects. He explores these through macroecological approaches, integrating genetic, taxonomic, and environmental data. His studies often address beetle communities, particularly Chrysomelidae, and their responses to environmental changes. Article Trends: Recent publications analyze biodiversity value through phylogenetic uniqueness, climate stability's role in species abundance-genetic diversity congruence, and dispersal mechanisms shaping community turnover. He also examines spatial and temporal shifts in montane ecosystems and develops novel statistical methods for assessing distance-decay relationships. Awards: No specific academic awards noted in the provided texts. Grants & Advising: No grant details or student advisees explicitly listed. His research has been supported through institutional affiliations like CRETUS. Labs/Teams: Leads the BiBiCI group and collaborates with CRETUS, focusing on interdisciplinary environmental research.

Simon LindgrenView profile

Professor

• Digital Sociology
• Political Science
• Social Media Analysis
• +5 more

Simon Lindgren is a Professor of Sociology and Director of DIGSUM (Digital Societies and Media) at Umeå University's Department of Sociology. His research focuses on politics, power, and resistance in the context of digital technologies, employing methods like critical discourse analysis, computational text analysis, and social network analysis. He leads interdisciplinary research on social dimensions of digital technology, including AI ethics, algorithmic governance, and digital activism. Key research areas include algorithmic politics, social media mobilization, critical AI studies, and data activism. Lindgren has directed projects such as 'Programmable Politics,' exploring algorithmic automation in civil society, and '#NeverForget vs. #NeverHappened,' analyzing Holocaust commemoration on social media. His work bridges sociology with computational methods, emphasizing the sociotechnical implications of digital tools. Recent publications address bot activity in progressive movements, class dynamics in AI research, and mental health discourse on Instagram. Lindgren has secured grants for AI ethics projects and co-authored works like the Handbook of Critical Studies of Artificial Intelligence . His research further explores digital care for aging populations and misinformation during emergencies. Lindgren coordinates the 'AI and Society' research group and has advised numerous collaborative projects at Umeå University. His work frequently intersects with policy analysis, aiming to inform governance of emerging digital technologies while critiquing techno-solutionist approaches.

Paolo RotaView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Multimodal AI
• +9 more

Paolo Rota is a tenure-track Assistant Professor at the University of Trento, affiliated with the Department of Information Engineering and Computer Science (DISI) and the Center for Mind/Brain Sciences (CIMeC). His research lies at the intersection of computer vision, machine learning, and multimodal AI, with a strong emphasis on vision-language models and activity recognition. His research interests include zero-shot action recognition, temporal action localization, open-world recognition, and person image synthesis. He explores how large multimodal models can be leveraged for practical applications in video analytics and industrial AI, often developing training-free or source-free adaptation methods that improve model generalization. Recent publications show a consistent trend in utilizing large vision-language models (e.g., CLIP, LMMs) for tasks such as image classification, domain adaptation, and action recognition, emphasizing simplicity, zero-shot capabilities, and real-world applicability. His work frequently appears in top venues including CVPR, NeurIPS, ICCV, and ICIAP. He actively mentors PhD students including Benedetta Liberatori, Jiaqi Liu, Yan Shu, Shiyao Xu, and Alessandro Conti, often co-advising with faculty such as Elisa Ricci and Nicu Sebe. He also contributes to teaching, including delivering lectures on machine learning for the MSc in Data Science program. He co-founded Mountain Maps, a startup using AI to enhance outdoor navigation and mountain exploration. His work bridges academic research and practical innovation, aiming to increase the real-world impact of AI systems.

V.S. SubrahmanianView profile

Professor

• Artificial Intelligence
• Cybersecurity
• Machine Learning
• +3 more

V.S. Subrahmanian is the Walter P. Murphy Professor of Computer Science at Northwestern University and a Faculty Fellow at the Northwestern Buffett Institute for Global Affairs. His research focuses on AI-driven solutions for security challenges, including forecasting terror attacks, preventing poaching, and analyzing social media threats. Education includes a PhD and MS in Computer Science from Syracuse University (NY) and an MSc (Tech.) from Birla Institute of Technology and Science (India). Research interests span AI applications in cybersecurity, predictive modeling for security policy, and machine learning for geospatial/social network analysis. Recent work addresses deepfakes, banking crisis forecasts, and airline profit optimization. Notable publications include works on Android malware detection, adversarial attack defenses, and geospatial conflict analysis. His research has influenced international security policies and industry practices. No scientific awards are listed in the provided text. Advising/grants information appears incomplete in the source material. Active involvement in multidisciplinary teams tackling global security challenges is implied through his institutional affiliations.

• Information Systems Methodologies
• Artificial Intelligence
• Agent Systems
• +8 more

Prof Ghassan Beydoun is a Professor and Head of Discipline (Information Systems) at the School of Computer Science, University of Technology Sydney (UTS). He leads the Information Systems discipline and is affiliated with the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS). His research focuses on AI-driven systems, agent-based modelling, ontologies, and disaster management, with notable contributions to knowledge graphs, enterprise architecture, and IoT applications. Beydoun actively supervises Masters and PhD students in these domains. His research interests span metamodelling, agent systems, and AI applications in disaster management (e.g., flood, landslide, and earthquake risk assessment), health systems, and smart infrastructure. He has pioneered frameworks for reproducible machine learning solutions, digital identity systems, and cloud migration strategies. Beydoun’s work integrates interdisciplinary methods, such as bibliometric analysis for journal evolution and XAI for spatial hazard prediction. Recent publications highlight his expertise in AI for climate-induced hazard modelling, agent-based knowledge transfer mechanisms, and metaverse applications in education. His funded projects include AI-powered circular economy initiatives, smart beach safety systems, and health data querying frameworks. Beydoun collaborates with industry partners like CSIRO, Capsicum Business Architects, and Data Zoo, translating research into practical solutions for enterprise architecture, cybersecurity, and public health.

• Machine Learning
• Natural Language Processing
• Medical Informatics
• +3 more

Stefan Riezler is a full professor of Statistical Natural Language Processing at Heidelberg University's Department of Computational Linguistics (since 2010), affiliated with the Faculty of Mathematics and Computer Science. Prior to this, he worked in Silicon Valley at Xerox PARC and Google Research. He holds a PhD in Computational Linguistics from the University of Tübingen (1998) and conducted postdoctoral research at Brown University (1999). His research spans machine learning, NLP, and medical informatics, focusing on interactive statistical learning. He co-leads the Interdisciplinary Center for Scientific Computing (IWR) and serves on the editorial boards of Computational Linguistics and Transactions of the Association for Computational Linguistics . Key research areas include neural machine translation, healthcare AI (e.g., sepsis prediction), data augmentation, and reproducibility in ML. He develops tools like JoeyNMT and explores ethical challenges in clinical machine learning. Notable recent work includes advancements in time series analysis, multimodal interfaces (e.g., NLMaps for OpenStreetMap), and ethical frameworks addressing validity in healthcare ML. His publications emphasize practical applications of NLP in healthcare, speech translation, and cross-lingual systems. Grants and collaborations include interdisciplinary projects on medical data science and training next-gen NLP researchers. He actively contributes to open-source toolkits and reproducible research practices.

Hesham ElSawyView profile

Assistant Professor

• Wireless Networks
• Internet of Things (IoT)
• Federated Learning
• +3 more

Hesham ElSawy is an Assistant Professor in the Department of Systems and Networks at the School of Computing, Faculty of Arts and Science, Queen's University. His work focuses on advancing next-generation wireless systems, with particular emphasis on federated learning, IoT networks, and edge computing. He is affiliated with Ingenuity Labs Research Institute, Queen's University, and contributes to interdisciplinary research at the intersection of communication theory and network optimization. His research interests span stochastic geometry modeling, energy-efficient protocols for massive IoT deployments, and resilient federated learning frameworks. ElSawy explores novel paradigms in aerial wireless networks, UAV-assisted communication, and network security through percolation theory applications. Recent publications highlight his contributions to system-level analysis of parallel computing at extreme edges, UAV-enabled federated learning architectures, and energy-as-a-service models for RF-powered networks. His work emphasizes practical implementations and large-scale network validation. ElSawy holds no listed awards or grants in the provided text but maintains active collaborations through Ingenuity Labs. His research addresses critical challenges in 5G/6G networks, including latency optimization, resource allocation, and heterogeneous network integration.

• Agricultural Economics
• Food Policy
• Policy Analysis
• +3 more

Prof. Zoltán Károly Lakner is a Professor at the Hungarian University of Agriculture and Life Sciences, affiliated with the Department of Agricultural and Food Economics. His research focuses on applying dynamic systems theory, econometrics, and artificial intelligence to analyze food supply networks, agricultural policy, and socio-economic systems in Africa and Central Asia. Key areas include big-data modeling of complex systems, game-theory applications, and open-source intelligence tools in innovation policy. He leads the SafeConsume H2020 project, developing strategies to mitigate foodborne illness risks through consumer education and prototype tools. His work spans food policy analysis, climate change impacts on agriculture, and ethical food consumption behaviors. Education details not explicitly provided in text. Research interests emphasize quantitative methods in agricultural economics, econometrics-driven policy analysis, and interdisciplinary approaches to global food challenges. Recent publications explore bioenergy expansion effects, water tax policy implications, and post-Paris Agreement green finance trends. No scientific awards explicitly stated in the text. Grants include the Horizon 2020 SafeConsume project. Active in advising and mentoring, though specific student names are not listed. Affiliated with the Szént István Campus in Gödöllő, contributing to institutional research and policy initiatives.