Find Academic Supervisors

Asta ZelenkauskaiteView profile

Professor

• Social Media Research
• User-Generated Content Analysis
• Emergent Practices Online
• +3 more

Asta Zelenkauskaite is a Professor of Communication and Graduate Faculty Member in the Department of Communication at Drexel University. She is affiliated with the Center for Science, Technology, and Society. Her research focuses on social media dynamics, misinformation, and digital communication practices, employing mixed-methods approaches to analyze emergent online behaviors. She holds a PhD in Mass Communication from Indiana University (2012). Education: PhD in Mass Communication, Indiana University, 2012 Research Interests: Social media research and user-generated content analysis Emergent online practices and ideological influence Disinformation, inauthentic behaviors, and post-truth challenges Macro- and micro-level studies of digital media landscapes Recent Work Trends: Dr. Zelenkauskaite’s recent publications emphasize AI’s role in content analysis, disinformation mitigation strategies, and cross-cultural climate change narratives. Her work bridges social science, information science, and linguistics to address societal challenges like vaccine hesitancy and digital misinformation. Awards/Grants: No specific awards listed, but her research has been supported by interdisciplinary grants focusing on AI ethics and digital media governance. Labs/Teams: Active in Drexel’s Center for Science, Technology, and Society, collaborating on projects addressing technology’s societal impacts. Her work often involves international partnerships, particularly in Eastern European contexts like Lithuania.

• Data Science
• Large Language Models
• Business Analytics
• +5 more

Denghui Zhang is an Assistant Professor in the School of Business at Stevens Institute of Technology. His research focuses on data science, large language models (LLMs), and business analytics, with particular emphasis on applications in financial systems, knowledge graphs, and spatio-temporal prediction. He is a member of the Stevens Institute for Artificial Intelligence and has held academic roles including reviewer positions for prestigious journals like Nature Communications and conferences such as AAAI and SIGKDD. Dr. Zhang holds a PhD in Information Systems from Rutgers University (2023) and an MS in Computer Science from the University of Chinese Academy of Sciences (2018). His educational background bridges computer science and business analytics, enabling his cross-disciplinary research. His research explores cutting-edge topics like federated learning optimization for LLMs, theory-of-mind reasoning mechanisms, and ethical AI governance. Notable contributions include turbulence forecasting models, traffic prediction frameworks, and venture capital investment strategies leveraging reinforcement learning. Dr. Zhang has received prestigious recognitions including the ICIS 2023 Best Student Paper Award and AAAI-23 Student Scholar distinction. His work frequently addresses practical challenges in AI ethics, financial decision-making systems, and scalable machine learning architectures. He actively contributes to academic communities through program committee roles for top conferences and has pioneered novel methodologies in multi-agent financial systems and graph neural network design.

Pawan BudhwarView profile

Professor

• Strategic Human Resource Management
• Expatriation
• Employee-Employer Relations
• +4 more

Pawan Budhwar is the 50th Anniversary Professor of International HRM at Aston Business School, Aston University. He holds senior administrative roles including Associate Deputy Vice Chancellor International and Head of School. His research focuses on Strategic Human Resource Management, Expatriation, Employee-Employer Relations in Emerging Markets, and Cross-Cultural Management. Budhwar has extensive editorial leadership roles, including Co-Editor-in-Chief of the Human Resource Management Journal and former Co-Editor of the British Journal of Management . Education: PhD in Comparative Human Resource Management (Manchester Business School, 1998) MPhil in Industrial Sociology (M.D. University, 1990) MBA and BA (M.D. University, 1986-1984) Research Interests: Integration of AI and HRM practices Ethical implications of algorithmic management Global talent management in emerging markets Sustainability and circular economy in organizational contexts Awards & Affiliations: Fellowships: Academy of Social Sciences, Higher Education Academy Membership: Academy of International Business, Academy of Management (USA), British Academy of Management Editorial boards: Human Resource Management Journal , British Journal of Management Grants & Collaboration: Leadership in global research networks Over 300 publications including 150+ peer-reviewed articles Supervised 21 doctoral candidates Labs/Teams: Active in Aston India Centre for Applied Research and cross-border collaboration initiatives.

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +4 more

Prof Noel O'Connor is a Full Professor at Dublin City University's School of Electronic Engineering, specializing in cutting-edge research at the intersection of artificial intelligence (AI), medical imaging, robotics, and smart city technologies. His work spans applications such as cardiac MRI reconstruction, robotic manipulation using reinforcement learning, and the development of the Smart DCU Digital Twin for autism-friendly university environments. Research interests include AI-driven medical diagnostics, multimodal data fusion, and adaptive systems. His contributions to cardiac MRI reconstruction and transformer-based medical imaging analysis reflect a strong focus on healthcare innovation. He also explores ethical AI practices to reduce social bias in foundation models. Recent work emphasizes smart infrastructure projects, such as optimizing parking recommendations for electric vehicles and enhancing accessibility through digital twin frameworks. His research often integrates real-time sensor data and multi-agent systems to address complex urban challenges. No scientific awards are listed. Collaborations include the ASU-DCU International Research Program on Sensors and Machine Learning. Advising details and grant information are not explicitly provided.

Shaun M. DoughertyView profile

Professor

• Education Policy Analysis
• Career and Technical Education (CTE)
• Causal Program Evaluation
• +7 more

Shaun M. Dougherty is a Professor and Department Chair of Measurement, Evaluation, Statistics, and Assessment (MESA) at Boston College's Lynch School of Education & Human Development. He directs the Catholic Education Research Initiative and serves as a Strategic Data Project (SDP) Faculty Adviser at Harvard University's Center for Education Policy Research. His work bridges academia and policy, focusing on equity and effectiveness in career and technical education (CTE), educational accountability systems, and regression discontinuity methodologies. Dougherty holds an Ed.D. in Quantitative Policy Analysis from Harvard University. Education: Ed.D., Harvard University, Quantitative Policy Analysis Research Interests: Dougherty's research emphasizes education policy analysis , causal program evaluation , and CTE program impacts . He examines how CTE can address human capital development while addressing systemic inequities related to race, class, gender, and disability. His work integrates advanced statistical methods (e.g., regression discontinuity designs) with policy implementation studies in K-12 systems and postsecondary transitions. Grants & Awards: PI: $647,499 grant from Institute for Education Sciences (2020–2025) Co-PI: $1.7 million grant from Institute for Education Science (2022–2026) Co-PI: $250,000 grant from Institute for Education Sciences (2019–2021) His awards include the Outstanding Reviewer (2017, 2020) and Outstanding Research Paper (2020) from leading journals. Labs & Teams: Leads the MESA department's research initiatives and collaborates with the Catholic Education Research Initiative and Harvard's SDP program. His work engages with states and large districts on applied policy analysis, including Massachusetts, Michigan, and Connecticut.

Christopher Thomas RyanView profile

Associate Professor

• Optimization
• Game Theory
• Theoretical Economics
• +7 more

Christopher Thomas Ryan is an Associate Professor in the Operations and Logistics Division at the UBC Sauder School of Business. He holds a PhD and BA from UBC in Management Science and Mathematics, respectively. His research spans optimization theory, game theory, theoretical economics, video game design, and pedagogy for business education. He has held positions at the University of Chicago Booth School of Business and is affiliated with the Colibri Learning Foundation. Education: BA (Mathematics) and PhD (Management Science) from UBC; BA (Sociology and Economics) from University of Guelph. He has authored numerous textbooks and case studies, including works on research careers, operations management, and teaching methodologies. His work integrates human-centric approaches to operations and education systems. Research Interests include small business operations, education operations, theoretical economics, and the design of video games. He has published in top journals like Management Science, Econometrica, and Mathematical Programming. His grants include SSHRC and NSERC awards. Ryan advises students across multiple institutions and has mentored prominent faculty in marketing, operations, and MIS fields. Teaching focuses on core operations management for MBA programs and pedagogical innovation. He has developed cases on AI in education, early childhood development, and small business sustainability. Ryan’s work bridges academic research with practical applications in business and education sectors.

Holly BraniganView profile

Professor

• Language Production
• Dialogue
• Language Development
• +6 more

Holly Branigan is a Professor of Psychology of Language and Cognition at the University of Edinburgh, affiliated with the School of Philosophy, Psychology and Language Sciences. Her research focuses on language production, dialogue, child language development, and bilingualism. She holds a Personal Chair and has led numerous grants including projects on conversational alignment in children with autism, language experience effects on development, and syntactic priming in dialogue. Her teaching includes undergraduate courses on Psychology of Language and postgraduate modules on Dialogue. She supervises PhD students in topics like syntactic processing, bilingualism, and interactional linguistics. Branigan has secured over £10M in research funding from bodies like the ESRC and EU, leading projects such as 'How does language experience support language development?' (2018–2023) and 'AThEME: Advancing European Multilingual Experience' (2014–2019). Her work explores how speakers adapt language based on social context, interlocutor traits, and cognitive factors. Key achievements include a 7-year ESRC fellowship (1999–2006), and recognition for contributions to understanding syntactic processing mechanisms. She currently leads research on child language imitation in social contexts and bilingual syntactic representation.

Scott A. HaleView profile

Associate Professor

• Human-Computer Interaction (HCI)
• Machine Learning
• Natural Language Processing (NLP)
• +7 more

Dr Scott A. Hale is an Associate Professor and Senior Research Fellow at the Oxford Internet Institute (OII), University of Oxford, and a Fellow of the Alan Turing Institute. His work bridges computer science and social sciences, focusing on equitable information access, multilingual online dynamics, and misinformation mitigation. He holds degrees in Computer Science, Mathematics, and Spanish from Eckerd College, followed by a DPhil (PhD) in Social Data Science from the OII. Hale’s research has been supported by grants from UK Research and Innovation, the US National Science Foundation, and organizations like the Omidyar Network and the Alan Turing Institute. Key Roles: Programme on AI, Government & Policy; Director of Research at Meedan; Co-Director of the Social Data Science MSc Research Focus: Misinformation, multilingual systems, social media impact, and AI ethics Education: Eckerd College (BS), OII (MSc, DPhil). His DPhil explored social media design’s role in cross-language information sharing. Recent projects include the Digital Good Network and AI alignment studies. Articles highlight trends in multilingual misinformation detection, LLM cultural biases, and hate speech dynamics. Hale’s work bridges technical innovation with social science rigor to address global digital challenges. Awards: Alan Turing Institute Fellowship, recognition in Oxford’s Teaching Excellence Awards. Grants: Over 20 funding sources including DSO National Laboratories and Meta.

• Machine Learning
• Reinforcement Learning
• Sample-efficient Generalization
• +5 more

Dr. Yangchen Pan is a Departmental Lecturer in Machine Learning at the University of Oxford's Department of Engineering Science. His research focuses on achieving sample-efficient generalization in machine learning, particularly in settings involving distribution shifts (e.g., adversarial learning, domain adaptation) and adaptive capabilities like offline/online reinforcement learning and continual learning. He has contributed to foundational work in reinforcement learning, robustness, and risk-averse optimization. Education and Academic Background: While specific degree details are not explicitly listed, his academic trajectory includes roles at leading institutions such as the University of Alberta (PhD, 2017-2020), where he collaborated with prominent researchers like Martha White and Amir-massoud Farahmand. Additional affiliations include teaching roles at the University of Waterloo, University of Toronto, and Indiana University. Research Interests: Pan's work spans machine learning theory and applications, with emphasis on scalable algorithms for complex decision-making systems. Key areas include adversarial robustness, offline reinforcement learning, and mitigating distribution shifts in real-world deployments. His recent contributions explore risk-aware policy optimization and novel approaches to sample efficiency. Professional Service: Pan serves on the program committees of top conferences like NeurIPS, ICML, and ICLR. He has reviewed for journals including the Journal of Machine Learning Research and Transactions on Machine Learning Research. Teaching: Pan teaches advanced courses in optimization, machine learning, and AI at the University of Oxford, including C25 Optimization and AI/ML with Python. He has also taught at the University of Alberta and Indiana University. Labs/Teams: While no specific lab name is mentioned, his research is closely tied to Oxford's Engineering Science department and collaborative projects with institutions like the ZERO Institute (as seen in event leadership at IMAD2025).

Wenzhuo ZhouView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +16 more

Wenzhuo Zhou is an Assistant Professor in the Department of Statistics at the University of California, Irvine (UCI), affiliated with the Donald Bren School of Information & Computer Sciences. His research bridges machine learning theory and practice, focusing on reinforcement learning, deep representation learning, and large language models. He emphasizes developing efficient, reliable AI algorithms to address challenges in healthcare, finance, and robotics. Research Interests: Statistical foundations of learning algorithms Sample efficiency and model generalization Alignment of AI models with human preferences Applications in healthcare (e.g., cancer, diabetes, Alzheimer’s), finance, and robotics Collaborations involve domain experts in medical research, semantic search, and financial systems. His team adapts existing methods and develops new pipelines for real-world problem-solving. No scientific awards or grants are explicitly listed in the provided text. Advising details are mentioned but without specific student names. The Center for Statistical Consulting is part of his professional environment.

• Artificial Intelligence
• Electronic Commerce
• Intelligent Agents and Multi-Agent Systems
• +6 more

Thomas Tran is a Full Professor at the School of Electrical Engineering and Computer Science (EECS) at the University of Ottawa. He holds a Ph.D. in Computer Science from the University of Waterloo (2004) and a B.Sc. (Double Major in Mathematics and Computer Science) from Brandon University (1999). His research focuses on Artificial Intelligence, Electronic Commerce, Multi-Agent Systems, Trust and Reputation Modeling, and Recommender Systems. He has published over 80 refereed papers and supervised 27 graduate students (4 PhDs and 23 Masters). Education: Ph.D. in Computer Science, University of Waterloo (2004) B.Sc. (Double Major in Mathematics and Computer Science), Brandon University (1999) Research Interests: AI Applications in E-Commerce and Mobile Business Trust Establishment Models in Multi-Agent Systems Recommender Systems and Deep Learning Clinical Data Analysis for Hidradenitis Suppurativa Awards: Governor General's Gold Medal (2004) NSERC Postgraduate Scholarships (PGS A/B) AAAI Doctoral Consortium Participant (2002) Advising and Grants: Supervised 27 graduate students Recipient of multiple research grants (details unspecified) Labs/Teams: Active in AI and E-Commerce research groups within EECS.

Ziquan LiuView profile

Lecturer

• Reliable Machine Learning
• Uncertainty Quantification
• Computer Vision
• +4 more

Dr. Ziquan Liu is a Lecturer (Teaching & Research) at Queen Mary University of London's School of Electronic Engineering and Computer Science, affiliated with the Centre for Multimodal AI. He holds a PhD from City University of Hong Kong (2023) and dual B.Sc./B.Eng. degrees from Beihang University (2017). His research focuses on trustworthy machine learning, adversarial robustness, and uncertainty quantification in foundation models. He has served as a reviewer for top conferences like NeurIPS, ICLR, and CVPR, earning an Outstanding Reviewer Award in 2021. His teaching includes modules on machine learning for visual data analysis and principles of machine learning. He supervises PhD students in AI safety and reliability, with notable work on conformal prediction, adversarial attacks, and multimodal learning. His research outputs span top venues such as ICML, CVPR, and NeurIPS, addressing challenges in algorithmic fairness, model certification, and cross-modal alignment.

• Artificial Intelligence
• Natural Language Processing
• Computational Semantics
• +5 more

Luis Espinosa-Anke is a Senior Lecturer at Cardiff University's School of Computer Science and Informatics. His academic journey includes working as a Natural Language Processing (NLP) scientist at Savana Médica, a Madrid-based healthcare AI company, prior to joining Cardiff. He completed his PhD at Pompeu Fabra University in Barcelona while working at Savana. Dr. Espinosa-Anke's research focuses on Artificial Intelligence and NLP, with particular emphasis on meaning representation, computational semantics, multilingual NLP, and computational lexicography. His work spans theoretical and applied aspects of language technology, with applications in healthcare, social media analysis, and multilingual systems. His recent publications reveal a strong trend toward analyzing language model behavior, bias detection in AI systems, and creating resources for semantic analysis. The publications show increasing focus on practical applications of NLP in healthcare, social media, and cross-lingual settings, with notable contributions to datasets like WIKITIDE and 3D-EX that support definition extraction and semantic understanding. laCaixa Fellow Fulbright scholarship recipient Erasmus Mundus program participant Dr. Espinosa-Anke has secured research funding including a Kaggle Open Research grant ($2,000 USD) as PI for the 'Don't Patronize Me!' project, a Snap Inc. grant ($10,000 USD) as CO-I for modeling meaning shift in social media, and a £90,000 Welsh Government grant for English-Welsh bilingual embeddings research. He currently supervises five PhD students working on meaning representations, contextual word embeddings, NLP for healthcare applications, and metaphor identification.

Kanika GoelView profile

Lecturer

• Process-Oriented Data Analytics
• Data Governance
• Data Quality
• +3 more

Dr. Kanika Goel is a Lecturer in the School of Information Systems at Queensland University of Technology (QUT), specializing in Business Process Management (BPM), Data Governance, and Process Analytics. She holds a PhD from QUT and has over 9 years of teaching experience, coordinating programs such as BIT Honours (IN10) and Masters of Philosophy (IN80). Her research focuses on process-oriented data analytics, data quality, and process mining, with industry collaborations spanning health, retail, and asset management sectors. She is a Lean Six Sigma Green Belt certified trainer and a Fellow of the Higher Education Academy (FHEA). Dr. Goel has led several industry-funded projects, emphasizing applied research in data governance, process mining, and process improvement. Notably, she received the Vice-Chancellor's Award for Excellence (2019) for innovative BPM integration in research management systems. Her work bridges academic research and real-world applications, contributing to journals like Business and Information Systems Engineering and IEEE Access . She teaches courses on Business Process Technologies, Modern Data Management, and BPM units in QUT's continuing professional education programs. Her articles explore topics like data imperfections in healthcare systems, process standardization strategies, and privacy risks in NoSQL databases. She advocates for digital literacy and has published on initiatives to build tech-savvy communities. Dr. Goel is also involved in supervising research topics such as prescriptive process analytics and process-data governance patterns.

Charles Raymond SarimuthuView profile

Senior Lecturer

• Power Systems
• Renewable Energy
• Smart Grids
• +3 more

Dr. Charles Raymond Sarimuthu is a Senior Lecturer in the Department of Electrical and Computer Systems Engineering at Monash University Malaysia. He holds a Ph.D. in Electrical Power Engineering from National Energy University, Malaysia. His research focuses on voltage control, power transformers, distribution systems, renewable energy, and smart grids. He actively supervises undergraduate and postgraduate students and has published extensively in high-impact journals such as Electric Power Systems Research and Renewable Energy . Dr. Sarimuthu has collaborated on projects including the Electric Field Imaging (EFI) Sensor for Comprehensive Static Charge Analysis (2024-2025) and the Fundamental study on memristor switching performances (2023-2026). He has also led the Investigation of on-load tap changer transformer operation (2019-2022), demonstrating expertise in grid stability and renewable integration. Teaching commitments include ECE3051 - Electrical Energy Systems , ECE4053 - Electrical Energy-Generation and Supply , and ECE5886/6886 - Smart Grids . He is a member of professional bodies such as the Board of Engineers Malaysia (BEM) and IEEE. His work aligns with UN Sustainable Development Goals related to affordable and clean energy (SDG7) and industry innovation (SDG9). Recent research highlights include game theory approaches for voltage control and adaptive synchronverter technologies for solar power systems.