Find Academic Supervisors

Vladimir VlassovView profile

Professor

• Machine Learning
• Data-intensive computing
• Cloud computing
• +7 more

Dr. Vladimir Vlassov is a full Professor in Computer Systems at the Division of Software and Computer Systems (SCS) , Department of Computer Science (CS) , School of Electrical Engineering and Computer Science (EECS) , KTH Royal Institute of Technology , Stockholm, Sweden. He leads the AVA project in ALEC2, an AI-powered system for mental health care. He is a member of the Distributed Computing research group (DC@KTH) . Education & Roles: Holds a PhD and is a member of ACM and IEEE. Previously visited MIT (1998) and UMass Amherst (2004). Teaches courses on Data Mining , Distributed Systems , and Concurrent Programming . Research Interests: Focus on scalable AI, Cloud computing, distributed systems, and NLP for mental health. Projects include ExtremeEarth (Copernicus data analytics) and EMJD-DC (distributed computing PhD program). Grants & Projects: Principal Investigator in ALEC2 (adaptive mental health care) and ExtremeEarth (EU H2020). Led EU projects like ENCORE (manycore systems) and PaPP (embedded systems). Labs & Teams: Directs the Distributed Computing group, contributing to Hopsworks (machine learning feature store) and Maggy (hyperparameter optimization).

Lu TangView profile

Professor

• Health Communication
• Artificial Intelligence Ethics
• Social Media Analysis
• +4 more

Dr. Lu Tang is a Professor of Communication and Director of the Data Justice Lab at Texas A&M University’s Department of Communication and Journalism. She holds a doctorate from the Annenberg School for Communication at the University of Southern California. Her research focuses on the ethical and social justice implications of emerging technologies in health communication, including AI, chatbots, and virtual reality. She uses computational methods like natural language processing and social network analysis to study health information dissemination on social media, with an emphasis on minority health and cultural contexts. Education: Ph.D., Annenberg School for Communication, University of Southern California Research Interests: Dr. Tang’s work spans three key areas: 1) Ethical integration of AI in healthcare and health promotion; 2) Analysis of health misinformation and information diffusion on platforms like YouTube and Twitter; and 3) Cultural dimensions of health communication among minority populations. Her research is funded by the NIH, Cancer Prevention and Research Institute of Texas, and Robert Woods Johnson Foundation. Grants & Funding: NIH Cancer Prevention and Research Institute of Texas Robert Woods Johnson Foundation Labs & Teams: Director of the Data Justice Lab , affiliated with the Texas A&M Institute of Data Science.

Yannis KonstasView profile

Associate Professor

• Natural Language Processing (NLP)
• Artificial Intelligence (AI)
• Natural Language Generation (NLG)
• +3 more

Yannis Konstas is an Associate Professor (Reader in the UK system) at Heriot-Watt University’s School of Mathematical & Computer Sciences, Department of Computer Science. He co-leads the Safe and Secure AI for Robotics (SAIR) theme at the National Robotarium. His research focuses on Responsible, Safe, and Explainable AI applications using NLP, including Gender-Based Violence (GBV) detection via LLMs, Social Behavior Modeling, and Vision-Language-Action (VLA) models for robotics. He holds a PhD from the University of Edinburgh (2013), supervised by Mirella Lapata, and has held postdoctoral roles at the University of Washington and the University of Edinburgh. Research interests include Natural Language Generation (NLG), representations, modeling, and evaluation across domains like math problems and dialogue systems. His work spans Psycholinguistics, semantics, and Information Retrieval. He actively explores new fields, such as LLM interpretability and instruction-driven robotic manipulation. He accepts PhD students and collaborates on interdisciplinary projects, including regulatory compliance digitalization and embodied AI for egocentric video understanding. Education: PhD in Computer Science (2013), University of Edinburgh; Postdoctoral Researcher at University of Washington (2015–2017) and University of Edinburgh.

Jonathan KummerfeldView profile

Senior Lecturer

• Natural Language Processing
• Artificial Intelligence
• Human-Computer Interaction
• +4 more

Dr. Jonathan Kummerfeld is a Senior Lecturer at the University of Sydney's School of Computer Science, specializing in Natural Language Processing (NLP) and AI collaboration. He holds a PhD from UC Berkeley and a B.Sc. from the University of Sydney, with postdoctoral research at the University of Michigan. His work focuses on enabling computers to collaborate effectively with humans through NLP, including interactive systems, dialogue, and crowdsourcing. Education: PhD in Computer Science, UC Berkeley (advised by Dan Klein), 2016 B.Sc. in Computer Science, University of Sydney (advised by James Curran), 2010 Postdoctoral Researcher at University of Michigan (2016–2021) Research Interests: Interactive Executable Semantic Parsing NLP Data Science for Messy Real-World Data AI Collaboration in Games and Dialogue His recent work explores empathetic AI systems, alignment algorithms, and AI-resilient interfaces. Over 50 publications span venues like ACL, NAACL, and ICML. Grants & Advising: CHIRON (DARPA): AI for recognizing opponent narratives Google/Amazon grants for annotation tools and dialogue systems Advising PhD student Kent O'Sullivan His lab develops tools like SLATE and contributes to dialogue corpora. Labs/Teams: Leads the Sydney NLP Lab, focusing on human-AI collaboration and scalable NLP systems.

Isak SamstenView profile

Senior Lecturer

• Data Science
• Machine Learning
• Time Series Analysis
• +3 more

Isak Samsten is a Senior Lecturer at Stockholm University's Department of Computer and Systems Sciences (DSV), specializing in data science and machine learning. He leads research in temporal machine learning, counterfactual explanations, and interdisciplinary applications in healthcare and environmental science. His work includes developing the wildboar Python module for time series analysis. Current research projects focus on AI for insurance fraud detection and environmental remediation. Samsten is affiliated with the Data Science Research Group, which bridges algorithmic innovation with practical decision-making. He holds an ORCID identifier (0000-0002-3056-6801) and is active in publishing influential papers on topics like time series classification, ESG performance prediction, and clinical decision support systems. Education: Unspecified in text (assumed doctoral degree given academic rank) Affiliations: DSV, Stockholm University; Data Science Research Group Research Interests: Time series analysis, interpretable machine learning, healthcare informatics, environmental sustainability metrics, and AI ethics. Key contributions include shapelet-based classification methods (e.g., Castor algorithm) and counterfactual explanation frameworks (e.g., Glacier system). Grants & Awards: None explicitly listed in provided text. Labs/Teams: Leads the Data Science Research Group, collaborating on projects like AI to detect unclear insurance claims and Toxicity guided inverse design of materials .

Manuel MoralesView profile

Associate Professor

• Financial Mathematics
• Actuarial Science
• Machine Learning Applications in Finance
• +5 more

Manuel Morales is an Associate Professor in the Department of Mathematics and Statistics at the University of Montreal since 2005. He holds a Ph.D. in Mathematics (2003) from Concordia University, an M.Sc. in Statistics (2000) from Concordia, and a B.Sc. in Mathematics (1996) from the National Autonomous University of Mexico. His research focuses on Financial and Actuarial Mathematics, particularly in Ruin Theory, Lévy processes, and High-Frequency Finance. He leads applied projects integrating Machine Learning in Banking and ESG Investment, and has pioneered AI governance frameworks at the National Bank of Canada as their Chief AI Scientist. Education: Ph.D. Mathematics, Concordia University, 2003 M.Sc. Statistics, Concordia University, 2000 B.Sc. Mathematics, National Autonomous University of Mexico, 1996 Research Interests: His work spans theoretical and applied directions, including non-Gaussian option pricing, regime-switching models, Limit Order Book dynamics simulation, and AI applications in finance. He emphasizes responsible investment and ESG factors through alternative data analysis. Advising & Partnerships: Supervises Master’s/Ph.D. students in Insurance and Financial Mathematics. Leads the FinML Network (since 2018) and collaborates with industry partners like the National Bank of Canada on AI-driven financial projects. His grants and contracts enable applied research in high-frequency market surveillance and model governance. Labs & Teams: Directs the FinML Network and oversees the National Bank’s AI initiatives, focusing on AI governance and algorithmic trading strategies.

• Natural Language Processing
• Sociophonetics
• Social Meaning Analysis
• +5 more

Rob Voigt Assistant Professor of Linguistics and courtesy faculty in Computer Science at Northwestern University, affiliated with the Institute for Policy Research and Cognitive Science Program. Specializes in computational linguistics, focusing on natural language processing (NLP) applied to social science questions including policing, mental health, and immigration. Director of the Linguistic Mechanisms Lab, exploring how language reflects and shapes societal structures. Education PhD in Linguistics (2019), Stanford University Postdoctoral Scholar (2019-2020), Stanford University M.A. in East Asian Studies (2013), Stanford University B.A. in Chinese (2008), Vassar College Research Interests Combines computational methods with sociolinguistics to study: Police-community interaction through body-worn camera data Linguistic markers of procedural justice and racial disparities Language in marginalized communities and social movements LLM applications to clinical language analysis (autism spectrum disorders) Historical discourse analysis of immigration policies Recent Work Recent projects include analyzing 140 years of political immigration rhetoric (PNAS 2022), evaluating officer communication training via bodycam NLP (PNAS Nexus 2024), and developing stereotype analysis pipelines (EMNLP 2024). Active in policy-relevant research through grants on police training (NIJ) and mental health language analysis (NIH). Awards & Recognition Holds Cozzarelli Prize (2017) for groundbreaking policing research and Cialdini Prize for field methods innovation. Recipient of interdisciplinary fellowships from Stanford and NIH grants totaling $8M+. Teaching Current courses include Text Processing for Linguists . Known for ungrading pedagogy emphasizing intrinsic motivation and collaborative learning. Develops open-access computational linguistics curricula using Unix/Python. Labs & Collaborations Linguistic Mechanisms Lab focuses on socially responsible NLP. Collaborates with criminologists, cognitive scientists, and sociologists on projects like the Media Accountability Project (racial disparities in gun violence reporting).

Naveed MahmudView profile

Assistant Professor

• Quantum Computing
• Hybrid Quantum-Classical Systems
• Machine Learning Applications in Quantum Contexts
• +3 more

Naveed Mahmud is an Assistant Professor at the Department of Electrical Engineering and Computer Science, Florida Institute of Technology. He specializes in quantum computing, hybrid quantum-classical systems, and reconfigurable computing architectures. His research focuses on optimizing quantum algorithms, data encoding/decoding techniques, and secure communications using quantum technologies. Research interests include quantum-classical integration, algorithm emulation on high-performance reconfigurable computers, and applications of quantum computing in pattern recognition and cryptography. Key areas of exploration are hybrid quantum-classical machine learning, quantum wavelet transforms, and securing free-space optical communications with quantum key distribution. His recent work emphasizes scalability and efficiency in quantum computing frameworks, including frameworks like QASM-to-HLS for quantum circuit acceleration, and decoherence-optimized quantum circuits. Articles highlight advancements in quantum data decoding, algorithm emulation, and secure communication systems. No scientific awards or formal advisees are listed. His profile includes links to ORCID, Google Scholar, and ResearchGate for further details on publications and collaborations.

Uchenna Daniel AniView profile

Lecturer

• Cyber Security
• Industrial Control Systems (ICS)
• Internet of Things (IoT)
• +6 more

Dr. Uchenna Daniel Ani is a Lecturer in Cyber Security at the School of Computer Science and Mathematics, Keele University. He leads the MSc Cyber Security programme and chairs the School Taught Programme Ethics Committee. Previously, he was a Senior Research Fellow at the PETRAS National Centre of Excellence for IoT Systems Cybersecurity at University College London (UCL), focusing on cybersecurity in critical infrastructure systems. Dr. Ani holds a PhD in Industrial Control System Cybersecurity from Cranfield University, UK, and degrees from the University of Bedfordshire. His research emphasizes socio-technical security analysis, cybersecurity risk assessment, and the intersection of technical solutions with human and policy factors. Key projects include 'Analytical Lenses for Internet of Things Threats' and 'Modelling for Socio-Technical Security,' collaborating with transport and water infrastructure organizations. His research interests span ICS/IoT Cybersecurity, security modelling/simulations, human-factor security, and digital forensics. He actively contributes to cybersecurity policy and standards development, advising on IoT-enabled infrastructure resilience. Dr. Ani is a Fellow of the Higher Education Academy and member of IET/ACM. Teaching includes modules on Human Factors of Cyber Security, Cryptography, and Cloud Computing. He advocates STEM education through mentoring and public engagement, including roles as a STEM Ambassador and IET Young Professional Ambassador.

Maria MoruzView profile

Researcher

• Romanian linguistics
• Philology
• Phraseology
• +3 more

Maria Moruz serves as a Researcher at the Department of Social Sciences and Humanities, Alexandru Ioan Cuza University of Iasi, a position she has held since February 2009. Her academic profile centers on Romanian linguistic research with strong interdisciplinary connections to biblical studies and computational methods. She earned her PhD from Alexandru Ioan Cuza University of Iasi in December 2009 with a dissertation analyzing phraseological units in parallel bilingual corpora, establishing her foundational expertise in comparative linguistics. Maria Moruz's research program integrates traditional philological scholarship with modern computational techniques . Her primary domains include Romanian linguistics , phraseology , and computational linguistics , with significant contributions to Biblical philology through critical editions of historical texts. She examines national specificity in phraseological units while developing linguistic resources for Romanian language processing , bridging historical textual analysis with contemporary NLP applications. Her publication trajectory reveals distinct thematic phases: early computational linguistics work (2004-2012) on Romanian lexical resources and question-answering systems evolved into specialized biblical philology research (2014-present) within the Monumenta linguae Dacoromanorum project. Recent publications focus on Romanian lexicography and biblical marginalia, demonstrating sustained engagement with both digital humanities methodologies and historical textual criticism. Maria Moruz has secured significant research funding as Project Director for a CNCSIS TD grant and served as a POSDRU postdoctoral researcher. Her collaborative work includes membership in the international "Widespread Idioms in Europe and Beyond" project and leadership in multiple Romanian linguistics initiatives. Project Director: CNCSIS TD grant POSDRU Postdoctoral Researcher Collaborator: "Widespread Idioms in Europe and Beyond" project Co-organizer: International Symposium "Explorations in the Romanian and European Biblical Tradition" Founding Member: Romanian Association of Biblical Philology and Hermeneutics She maintains active scholarly infrastructure through editorial work on the Monumenta linguae Dacoromanorum series and organization of specialized symposia, fostering interdisciplinary dialogue between computational linguists, philologists, and biblical scholars.

Marco MagginiView profile

Professor

• machine learning
• neural networks
• kernel machines
• +6 more

Marco Maggini is a Full Professor in the Department of Information Engineering and Mathematics at the University of Siena, a position he has held since joining the university in 1996. His academic career spans over 25 years with foundational expertise in computer engineering and artificial intelligence, focusing on theoretical and applied machine learning research. His educational background includes: Laurea degree (cum laude) in Electronics Engineering from the University of Florence (1991) Ph.D. in Computer Engineering and Control Systems from the University of Florence (1995) Prof. Maggini's research encompasses machine learning, neural networks, kernel machines, and the integration of symbolic and sub-symbolic knowledge systems. He extends these foundations into practical applications including web mining, search engine technology, pattern recognition, natural language processing, and computer vision. This interdisciplinary approach bridges theoretical computer science with real-world implementation challenges across multiple domains. Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on multilingual NLP applications, particularly educational puzzle generation for low-resource languages (Italian, Arabic, Persian, Turkish) using LLMs. His work demonstrates consistent innovation in named entity recognition, commonsense reasoning evaluation, and cross-lingual adaptation techniques. Secondary research threads include medical imaging segmentation, molecular property prediction, and AI security vulnerabilities, reflecting his broad technical mastery across computer vision, bioinformatics, and adversarial machine learning. No specific scientific awards were mentioned in the provided documentation, though his editorial roles indicate peer recognition within the academic community. While student mentoring details are absent from the source material, his position as Full Professor and leadership of SAILab imply active graduate supervision. His extensive publication record (120+ papers) and editorial service suggest significant research grant involvement, though specific funding sources remain undocumented. He directs the Siena Artificial Intelligence Laboratory (SAILab), which serves as an interdisciplinary hub for advancing machine learning theory and applications. The lab's current projects emphasize educational technology, multilingual NLP systems, and the integration of symbolic reasoning with neural architectures, maintaining strong industry and international academic collaborations.

David WagnerView profile

Professor

• Computer Security
• Security for AI (Large Language Models)
• AI for Security
• +7 more

David Wagner is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley's College of Engineering. He holds the Carl J. Penther Chair in Engineering and leads the Wagner Research Group, which focuses on computer security, AI security, and systems security. Education Ph.D., Computer Science, UC Berkeley (2000) M.S., Computer Science, UC Berkeley (1999) A.B., Mathematics, Princeton University (1995) Research Interests Wagner's work spans computer security, with a particular focus on security for AI systems (especially large language models) and security for wearable/smartphone devices . He has extensive experience in wireless security , sensor network security , applied cryptography , and software security , including electronic voting systems and program analysis for security. His research group develops tools like OpenCount for election auditing, AuditBear for voting machine analysis, and PrimeVul for vulnerability detection in code. Scientific Awards ACM SIGSAC Test of Time Award (2022) IEEE Symposium on Security and Privacy Test of Time Award (2020) USENIX Test of Time Award (2019) Google Faculty Research Award (2018) Internet Defense Prize (2017) ACM SIGSAC Outstanding Innovation Award (2016) IBM Pat Goldberg Memorial Best Paper Award (2008) UC Berkeley Distinguished Teaching Award (2006) Sloan Research Fellow (2003) NSF CAREER Award (2001) Advising & Grants Wagner has advised numerous graduate students including Sizhe Chen , Zhanhao Hu , and Dennis Jacob . His research has been supported by grants from NSF (CAREER, ITR), Microsoft, Intel, and the Okawa Foundation. Labs & Teams He co-directs the Berkeley Security Research Group , participates in the ACTION Institute , and leads the KACST-UCB Joint Center on Cybersecurity. His team develops open-source security tools and datasets like DiverseVul for vulnerability detection.

Vincent WadeView profile

Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +8 more

Professor Vincent Wade is a prominent academic and co-founder of the ADAPT SFI Research Centre, holding the Professorial Chair of Computer Science (established 1990) and a Personal Chair in Artificial Intelligence at Trinity College Dublin's School of Computer Science and Statistics. He co-directs the DREAL Centre for Research Training and leads ADAPT, a globally recognized centre for digital media technology and AI research. His work spans intelligent systems, personalisation, machine learning, and ethical AI applications in healthcare and education. Research interests include AI-driven personalisation, multimodal interaction, knowledge graphs, and ethical considerations in digital technologies. He has published over 350 peer-reviewed papers, earned the prestigious Provost Innovation Award (2018), and holds patents in personalisation technologies. He co-founded EmpowerTheUser, a TCD spin-out focused on simulation-based learning analytics. Major Achievements: 2018 Provost Innovation Award (Trinity College Dublin) 2010 European Language Label Award Fellow of Trinity College Dublin Over 350 scientific publications Key Contributions: Developed the ADELE corpus for social conversation analysis Pioneered cross-site personalisation frameworks Advanced adaptive e-learning systems through platforms like Slicepedia and AMASE His research bridges technical innovation with societal impact, addressing challenges in healthcare, education, and digital ethics.

Janet C.E. WatsonView profile

Professor

• Modern South Arabian languages
• Arabic dialects
• Phonology
• +12 more

Professor Janet C.E. Watson is a leading scholar in Semitic linguistics at the University of Leeds, affiliated with the School of Languages, Cultures and Societies within the Faculty of Arts, Humanities and Cultures. She is the co-director of the Centre for Endangered Languages, Cultures and Ecosystems (CELCE) and leads major international projects on Modern South Arabian languages. Her work bridges linguistics, anthropology, and environmental studies. Her research focuses on the documentation and analysis of endangered languages in southern Arabia, particularly Mehri, Śḥerɛ̄t (Jibbali), Harsusi, and Bathari. She employs instrumental phonetics, acoustic analysis, and community-based methodologies to study phonology, morphology, and the relationship between language and nature. Her fieldwork spans Oman and Yemen, often in collaboration with local scholars and communities. The recent articles reflect a strong trend in laboratory phonology, language documentation, and computational approaches to under-resourced languages. Key themes include emphatic consonants, silent articulations, spatial language, and the integration of traditional ecological knowledge into linguistic analysis. Her work is increasingly interdisciplinary, connecting language with biodiversity, cultural heritage, and digital preservation. Fellow of the British Academy (2013) Leverhulme Trust Research Fellowships (2007–2008, 2010–2011, 2019–2021) British Academy Small Research Grant British Council International Research Fellowship (host, 2023) Professor Watson has supervised numerous PhD students working on Arabic dialects, Mehri, and language revitalization. Her grants include major funding from the Leverhulme Trust, British Academy, AHRC, and King Faisal Centre. She collaborates with institutions in the UK, Oman, Yemen, USA, and Europe. She founded the Language@Leeds Leadership Chair and co-founded CELCE with Prof. Jon Lovett. She leads multimodal documentation projects, including the creation of an Arabic-based script for unwritten languages, children’s e-books in Mehri, and digital archives at ELAR (SOAS). Her team includes local researchers, PhD students, and international collaborators, emphasizing community engagement and capacity building.

Jan PfisterView profile

Researcher

• Natural Language Processing
• Deep Learning
• Sentiment Analysis
• +5 more

Jan Pfister is a researcher at the Chair of Data Science (Informatik X) , affiliated with the Faculty of Mathematics and Computer Science at the University of Würzburg . Holding an M.Sc. in Computer Science from 2021, he contributes to the DMIR group while maintaining the BibSonomy platform. His work bridges Natural Language Processing and Deep Learning , with a focus on structured sentiment analysis and German language modeling. His research explores the integration of Large Language Models with Pointer Networks to extract fine-grained sentiment information. He has developed ModernGBERT , a German-only encoder model, and LLäMmlein , advancing NLP capabilities for the language. Pfister's recent publications highlight trends in encoder-decoder architectures, model-agnostic hallucination detection, and multi-label classification frameworks that refine LLM outputs. He has taught courses including Information Retrieval (SS '22), Text Mining (WS '23), and Selected Topics in Machine Learning (SS'21, WS '21, WS'24). His collaborative projects span medical informatics, human-AI interaction, and social media analysis, often involving interdisciplinary teams and real-world data.