Find Academic Supervisors

Eugene KennedyView profile

Professor

• Educational Research Methodology
• Psychometrics
• School Effectiveness
• +3 more

Eugene Kennedy is a Professor in the Department of Educational Research at Louisiana State University's College of Human Sciences & Education. Holding a PhD in Educational Research Methodology from the University of South Carolina (1990), Kennedy's career spans academic research, state education departments, and technical consulting roles. His work focuses on educational effectiveness, psychometrics, and technology integration in education. Bachelor's: Sociology, Benedict College (1977) Master's: Educational Statistics & Measurement, University of Iowa (1982) PhD: Educational Research, University of South Carolina (1990) Kennedy's research interests include educational research methodology , psychometrics , school effectiveness , STEM education , data-driven decision making , and learning analytics . His recent publications demonstrate increasing focus on AI's role in education and data-driven strategies for improving educational outcomes. His academic work shows trends in educational technology integration , quantitative research methods , and STEM equity initiatives . Key themes include using data mining for educational insights, technology tools for school leadership, and innovative teaching strategies to improve student outcomes. Kennedy has participated in grants including: Co-PI for Evaluation proposal for the EBRPS American Recovery and Reinvestment Act Program (2010-2011) PI for Longitudinal Study with University of Louisiana Lafayette (2008-2009) Contact: ekennedy@lsu.edu | 221 Peabody Hall | 225-578-2193

Yongkai WuView profile

Assistant Professor

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

Dr. Yongkai Wu is an Assistant Professor in the Department of Electrical and Computer Engineering at Clemson University, where he focuses on advancing Responsible AI , Causal Inference , and Machine Learning . His research addresses fairness, trustworthiness, and transparency in AI systems through causal modeling and has been published in top-tier venues like AAAI, NeurIPS, and KDD. Education: Ph.D. in Computer Science (2020) and M.S. in Computer Science (2018) from the University of Arkansas; B.Eng. in Electronic Engineering (2014) from Tsinghua University. Dr. Wu’s research spans Responsible AI and Causal Inference , with applications in healthcare, computer vision, and cybersecurity. He explores Causal Fairness in non-IID settings, Responsible LLMs , and Robust Learning via hyperspectral data. His work integrates ethics into AI/ML systems, ensuring equitable outcomes in dynamic environments. His recent articles highlight trends in Fairness through causal inference, Explainable AI in healthcare, and Efficient LLMs . Collaborations with institutions like the University of Maryland and Prisma Health underscore real-world impact. Scientific Awards: Best Paper Award (SIGKDD'25), travel awards from SBP-BRiMS, IJCAI, KDD, and NeurIPS. Dr. Wu’s grants include NSF , SC EPSCoR , Prisma Health , and United States Army CCDC funding for projects on Responsible AI in Healthcare , Hyperspectral AI , and Robust Learning . He mentors students through summer programs and directed research, emphasizing hands-on experience with Python, PyTorch, and ethical AI frameworks.

Shane DawsonView profile

Professor

• Learning Analytics
• Educational Data Mining
• Curriculum Development
• +2 more

Shane Dawson is the Executive Dean of UniSA Education Futures and Professor of Learning Analytics at the University of South Australia. His work bridges social network analysis and learner interaction data to enhance teaching quality and educational outcomes. Affiliation : University of South Australia Research Focus : Learning Analytics, Curriculum Mapping, K-12 Decision-Making Systems, and AI in Education Recent Research Trends : Shane’s 2025 publications emphasize generative AI for curriculum analytics, ethical considerations in K-12 dashboards, and longitudinal graduate attribute monitoring. His articles often integrate psychometric models, social network tools, and open-source software like OVAL and SNAPP . Advising and Collaboration : As a co-developer of key learning analytics tools and a supervisor for research students, he collaborates globally with institutions such as Johns Hopkins University and Shahid Beheshti University of Medical Sciences. Labs and Teams : Shane leads UniSA’s Teaching Innovation Unit and is a founding member of the Society for Learning Analytics Research , driving institutional and international initiatives in educational technology.

Alexander ArtikisView profile

Associate Professor

• Artificial Intelligence
• Distributed Systems
• Complex Event Recognition
• +4 more

Dr. Alexander Artikis is an Associate Professor of Artificial Intelligence at the University of Piraeus and a Research Associate at the National Centre for Scientific Research (NCSR) "Demokritos". He leads the Complex Event Recognition (CER) group , focusing on symbolic and probabilistic approaches to event recognition and forecasting. University of Piraeus (2025–present) NCSR Demokritos (2017–present) Complex Event Recognition Group (2017–present) His research spans Artificial Intelligence and Distributed Systems , with a focus on: Complex Event Recognition (CER) : Developing logic-based systems for detecting events in real-time data streams Event Calculus : Creating probabilistic and incremental versions for runtime reasoning Multi-Agent Systems : Modeling norm-governed interactions Maritime Informatics : Applying CER to vessel trajectory analysis and fleet management Key publications reveal trends in: Neuro-symbolic forecasting models combining deep learning and logic-based reasoning Symbolic automata with memory for pattern detection Online learning techniques for dynamic event rule generation Tensor-based formalizations for efficient temporal reasoning Handling uncertainty in real-time maritime data streams Optimizing memory usage for scalable stream processing He contributes to open-source tools like RTEC (Run-Time Event Calculus) and holds a European patent on complex event forecasting. His work addresses challenges in: Proactive decision-making systems Knowledge Graph consistency Hybrid human-machine discovery of movement patterns Big Data analytics for time-critical applications

Jeffrey R. BishopView profile

Professor

• Psychopharmacology
• Pharmacogenomics
• Pharmacogenetics
• +7 more

Jeffrey R. Bishop is a Professor in the Department of Experimental and Clinical Pharmacology at the University of Minnesota , with an Adjunct Associate Professor appointment in the Department of Psychiatry & Behavioral Sciences. His work spans psychopharmacology, pharmacogenomics, and precision medicine, focusing on psychiatric disorders. Education : PharmD, University of Iowa College of Pharmacy; MS in Clinical Investigation, NIH K30 Program; BA in Biology, Luther College. Dr. Bishop’s research integrates pharmacogenetics , epigenetics , and proteomics to address challenges in psychiatric treatment. His projects include pharmacogenomics-enabled healthcare implementation and epigenetic studies in PTSD. Recent publications highlight trends in schizophrenia neuroanatomy , genomic associations in psychosis , and AI-driven dietary supplement information systems . These works reflect his interdisciplinary focus on mental health and precision medicine. Scientific Awards Board Certified Psychiatric Pharmacist (BCPP) Fellow of the American College of Clinical Pharmacy (FCCP) Dr. Bishop leads grants like Epigenetics of PTSD Treatment Response and collaborates on statewide and global initiatives such as the Minnesota Precision Medicine Collaborative and Pharmacogenomics Global Research Network . His team works on implementing individualized prescribing systems and advancing equity in underserved populations.

Kavita BalaView profile

Professor

• Computer Vision
• Artificial Intelligence
• Computer Graphics
• +6 more

Kavita Bala is the 17th Provost of Cornell University and a Professor of Computer Science. She previously served as the inaugural Dean of the Cornell Ann S. Bowers College of Computing and Information Science, leading its transition to a degree-granting college by 2025, and as Chair of Cornell’s Department of Computer Science. Her academic leadership includes expanding faculty, establishing research programs like the Bowers CIS Undergraduate Research Experience (BURE), and securing a new research facility for computing and information science. Education: B.Tech (IIT Bombay), M.S. and Ph.D. (MIT, Computer Science) Bala’s research focuses on computer vision, artificial intelligence, and computer graphics , with groundbreaking work in material and style recognition using deep learning. Her innovations in crowdsourced training data and differentiable rendering have advanced visual search technologies and translucent material modeling, powering her startup GrokStyle. She pioneered AI techniques applied to environmental monitoring through projects like MONITRS and AllClear , addressing Earth observation challenges. Her scientific awards include: American Academy of Arts and Sciences (2025) SIGGRAPH Computer Graphics Achievement Award (2020) IIT Bombay Distinguished Alumnus Award (2021) ACM Fellow (2019) SIGGRAPH Academy Fellow (2020) As Provost, Bala drives strategic initiatives like the Cornell AI Initiative , creating interdisciplinary minors in AI and AI in Society, and establishing the Schmidt AI in Science postdoctoral program. She co-chaired a task force for generative AI guidelines in education.

Hua XuView profile

Professor

• Clinical Natural Language Processing
• Biomedical Informatics
• Large Language Models for Biomedicine
• +3 more

Dr. Hua Xu is the Robert T. McCluskey Professor of Biomedical Informatics and Data Science at Yale School of Medicine. He serves as Vice Chair for Research and Development in the Department of Biomedical Informatics and Data Science and as Assistant Dean for Biomedical Informatics at Yale School of Medicine. Dr. Xu leads the Clinical NLP Lab and is Chair of the NLP working group at the Observational Health Data Sciences and Informatics (OHDSI) program. Dr. Xu received his PhD in Biomedical Informatics from Columbia University, an MS in Computer Science from New Jersey Institute of Technology, and a BS in Biochemistry from Nanjing University. Dr. Xu is a renowned researcher in clinical natural language processing (NLP), having developed novel algorithms for important clinical NLP tasks such as entity recognition and relation extraction. His work has been top-ranked in over a dozen international biomedical NLP challenges. He has developed CLAMP, a comprehensive clinical NLP toolkit that has been successfully commercialized and adopted by hundreds of healthcare organizations worldwide. His research focuses on applying NLP technologies to diverse clinical and translational studies to accelerate clinical evidence generation using electronic health records data. Recently, he has been utilizing NLP to harmonize metadata of biomedical digital objects to promote FAIR principles in biomedicine, and his lab is actively working on developing large language models (LLMs) for diverse biomedical applications. Dr. Xu's recent publications demonstrate a clear trend toward leveraging large language models for biomedical applications. His work spans from benchmarking LLMs for clinical NLP tasks to developing specialized architectures like BiomedRAG (retrieval augmented LLMs for biomedicine). His research addresses critical healthcare challenges including adverse event extraction, oncology clinical trial analysis, and EHR-based association studies, showing how NLP can bridge the gap between unstructured clinical text and actionable medical insights. Dr. Xu's lab has achieved top rankings in numerous NLP challenges, including multiple #1 positions in i2b2 Temporal information extraction, SemEval Disease-modifier extraction, BioCREATIVE Chemical-induced disease extraction, and other prestigious competitions. His contributions to clinical NLP have significantly advanced the field's ability to extract meaningful information from complex medical texts. As the leader of the Clinical NLP Lab at Yale, Dr. Xu oversees research that forms a complete ecosystem: developing novel NLP methods, building robust software tools, and applying these technologies to clinical and translational research. His lab's work closes the loop between methodological innovation and practical healthcare applications, ensuring that advances in NLP directly benefit patient care and medical research.

Kai HobergView profile

Professor

• Supply Chain Analytics
• Inventory Modeling
• Digital Transformation in Supply Chains
• +7 more

Prof. Dr. Kai Hoberg is a Professor of Supply Chain and Operations Strategy at Kühne Logistics University (KLU) since 2017, where he also served as Department Head of the Operations and Technology Department from 2017 to 2023. Prior to joining KLU as an Associate Professor in 2012, he was an Assistant Professor at the University of Cologne (2010–2012) and a strategy consultant at Booz & Company (2006–2010). His research interests span supply chain analytics and technology integration inventory modeling for intermittent demand digital transformation in operations management additive manufacturing in after-sales services human-machine interaction in forecasting pharmaceutical supply chain challenges IoT-enabled vendor-managed inventory behavioral aspects of operations . Recent publications highlight empirical studies leveraging machine learning for semiconductor order fulfillment, typologies for additive manufacturing adoption, and process mining applications in SCM. His work frequently combines theoretical modeling with real-world validation, including partnerships with firms in food manufacturing, postal services, and medical devices. He earned a PhD in Supply Chain Management from Münster University (2006) and a Diplom in Industrial Engineering from Paderborn University and Monash University. He has held visiting scholar roles at institutions like Cornell, NUS, Oxford, and Stellenbosch.

Yu ZhangView profile

Lecturer

• Text Mining
• Information Management
• Social Computing
• +12 more

Dr. Yu Zhang is a Lecturer of Data Science at the School of Business, UNSW Canberra. His academic career focuses on text mining, knowledge and information management, social computing, and bibliometric analysis, with interdisciplinary applications in areas such as sustainable logistics, supply chain management, and net-zero energy solutions. Fields of Interest: Text Mining, Information Management, Social Computing, Bibliometric Analysis, Machine Learning for Information Systems, Heterogeneous Network Analysis, Data Mining for Asset Management, Sustainable Logistics, Supply Chain Management, Net-zero Energy in Green Buildings, and Transportation. Grants: Served as CI in projects like "Online health monitoring in Li-ion batteries via trustworthy AI" (ACT Government, $1.22M) and "Delivering net-zero energy buildings" (TRaCE Lab to Market, $1.05M). Awards: Best Paper Award (Runner-up) at ADMA 2024 and Excellent Paper Award at ICEBE 2024. Teaching: Coordinated courses in Data Analytics, Workforce Planning Research, Business Capstone, and Logistics Intelligence with Big Data Analysis. Supervision: Guided research on topics like federated learning for healthcare fraud detection, blockchain-based carbon offset management, and tier-based supply chain visibility. His publications span materials science and photovoltaic technologies, with a focus on thin-film solar cells and defect passivation methods. For collaboration or supervision inquiries, contact him at m.yuzhang@unsw.edu.au .

Jochen GarckeView profile

Professor

• Numerical Methods in Science and Technology
• Machine Learning
• Sparse Grids
• +5 more

Prof. Dr. Jochen Garcke is a faculty member at the Institute for Numerical Simulation, University of Bonn, with a dual affiliation at Fraunhofer SCAI's Department of Numerical Data-Based Prediction. His work bridges numerical simulation and machine learning, focusing on high-dimensional problems, sparse grids, and optimal control. Key research themes: Sparse grids, machine learning for simulations, reinforcement learning, uncertainty quantification Teaching includes courses on Numerical Methods in Science and Technology and Scientific Computing , emphasizing practical machine learning applications. Recent publications explore hybrid models combining data-driven and physics-based approaches in automotive engineering, wind turbines, and geoscientific modeling. His group employs adaptive sparse grids, graph algorithms, and spectral methods to tackle challenges in crash simulations, fluctuating renewable energy systems, and turbulent flow analysis. Collaborations span Fraunhofer SCAI and industry 4.0 initiatives.

Jieh HsiangView profile

Professor

• Automated Reasoning
• Programming Language Logic
• Artificial Intelligence
• +5 more

Jieh Hsiang is a Distinguished Professor at National Taiwan University , with affiliations in the Department of Computer Science and Information Engineering, the Digital Archives and Automatic Inference Laboratory, and the Digital Humanities Research Center. He holds concurrent roles at the Institute of Information Science, Academia Sinica, and the Higher Education Research & Development Office, National Taiwan University. Education PhD in Computer Science, University of Illinois at Urbana-Champaign (1979–1982) BS in Mathematics, National Taiwan University (1972–1976) Research Interests Hsiang's work spans automated reasoning , digital libraries , digital humanities , and information retrieval . His research focuses on integrating computational methods with cultural heritage preservation , particularly through tools like DocuSky and databases such as the Taiwan Historical Digital Library . He explores AI applications in patent analysis , historical text mining , and semantic relationships in legal documents . Recent Trends in Publications His recent articles highlight advancements in BERT and GPT-2 fine-tuning for patent classification , LARGE language models for legal automation , and GIS-based analysis of historical archives . Themes include digital preservation , AI-driven legal text analysis , and cross-disciplinary computational tools for humanities scholars. Scientific Awards 2019 Ministry of Science and Technology Distinguished Research Fellow 2009 National Taiwan University Outstanding In-House Service Award 2008 Chinese Library Association Special Contribution Award 2006 IEEE Test-of-Time Award 1997 & 1999 National Science Council Outstanding Research Award 1997 Ministry of Education Outstanding Industrial-Academic Collaboration Award 1998–2001 Founder and First Chair of IFIP WG1.6 Labs and Collaborations Hsiang leads the Digital Archive and Automatic Inference Laboratory , developing platforms like DocuSky for digital humanities, Taiwan Historical Digital Library , and QGIS Cloud Maps for spatial analysis. His team collaborates internationally on projects involving historical document digitization , patent automation , and cross-domain knowledge integration .

Peng GaoView profile

Assistant Professor

• Cybersecurity
• Network Security
• AI for Security
• +7 more

Peng Gao is an Assistant Professor in the Department of Computer Science at Virginia Tech. He is affiliated with the Virginia Tech Security & Intelligence Lab and holds a Ph.D. in Electrical Engineering from Princeton University. Prior to his faculty position, he was a postdoctoral researcher at UC Berkeley and held research internships at Microsoft Research, Facebook, and NEC Laboratories America. Education includes a B.Eng. from Shanghai Jiao Tong University (2009-2013), M.A. and Ph.D. from Princeton (2013-2019), and an exchange program at the University of Hong Kong (2012). Key roles include Technical Program Committee memberships for conferences like IEEE S&P, USENIX Security, and CCS. Research focuses on Cybersecurity (APT prevention, network security with P4/eBPF) AI applications (LLMs for security, AI safety) Systems security (attack investigation, threat intelligence) AI for science (molecular/materials prediction) Notable awards include the 2022 Amazon-VT Initiative Faculty Award, CCI Fellowships, and multiple best paper nominations. His lab has received grants from CCI, NSF, and industry partners like Google Cloud and Cisco. Teaching includes courses on Principles of Computer Security (CS 4264) and Blockchain Technologies (CS 5594). He advises ~20 students across Ph.D., MS, and undergraduate levels.

A.C.G. (Toine) EgbertsView profile

Professor

• Clinical Pharmacy
• Adverse Drug Effects
• Pharmacoepidemiology
• +3 more

Prof. A.C.G. (Toine) Egberts is a Professor of Clinical Pharmacoepidemiology and Clinical Pharmacy at Utrecht University's Faculty of Medicine. He leads the Department of Pharmacoepidemiology & Clinical Pharmacology and serves as Director of Doctoral Education at the Graduate School of Life Sciences. His career includes roles as Chief Pharmacist at UMC Utrecht and Board Member of Pharmacy Division. He earned a PharmD from the University of Groningen and a PhD from Utrecht University, specializing in clinical epidemiology. Egberts focuses on reducing drug-related harm through research in adverse effects, sustainable pharmacy practices, and optimizing medication use. With over 500 publications, he sits on editorial boards and committees like CCMO and EPHOR. His work bridges drug development and patient outcomes, emphasizing translational research. Research interests include clinical pharmacology, pharmacoepidemiology, drug sustainability ('green pharmacy'), and innovations in prescribing/monitoring. Key projects like the ROAD study address medication waste reduction, published in JAMA Oncology . Awards include the Senior Jan Glerum Award (2016) and Huizinga Penning for clinical pharmacy contributions. Teaching and mentorship are central: guided over 70 PhD students, emphasizing education in hospital pharmacy. Grants and collaborations span clinical trials, adverse event detection, and AI applications in healthcare. Active in Dutch medicines policy through advisory roles and public media engagement.

Zahraa AbdallahView profile

Senior Lecturer

• Machine Learning
• Data Science
• Time Series Analysis
• +6 more

Zahraa Abdallah is a Senior Lecturer at the School of Engineering Mathematics and Technology, University of Bristol. She holds a PhD and BSc in relevant fields. Her research focuses on Machine Learning, Data Science, Time Series Analysis, and their applications in Health Informatics, Neuroscience, and Bioinformatics. She leads projects on wearable technology integration for diabetes management and EEG-based disease classification. Her work emphasizes explainable AI and multimodal approaches. Zahraa is affiliated with the Bristol Doctoral College Initiative (BDFI) as an Academic Co-Director and collaborates with experts like Prof. Raul Santos-Rodriguez. Contact: zahraa.abdallah@bristol.ac.uk | Website: zahraa-abdallah.com Research Interests: Time Series Clustering & Forecasting EEG-based Disease Detection (Parkinson’s, Alzheimer’s) Smartwatch-Driven Healthcare Systems Explainable AI in Biomedical Applications Key Projects: Development of the CSTS benchmark for time series clustering Investigating insulin needs using automated delivery data Gene essentiality classification via graph neural networks Collaborations: Professor Raul Santos-Rodriguez (BDFI) Lucia Marucci (Systems & Engineering Biology)

Andrew M. OlneyView profile

Professor

• Natural Language Processing
• Intelligent Tutoring Systems
• Dialogue Systems
• +5 more

Andrew M. Olney is Professor in both the Institute for Intelligent Systems and Department of Psychology at the University of Memphis. His work bridges artificial intelligence, cognitive science, and education, with a primary focus on natural language interfaces for learning. His educational background includes a Ph.D. in Computer Science from the University of Memphis (2006), an M.S. in Evolutionary and Adaptive Systems from the University of Sussex (2001), and a B.A. in Linguistics with Cognitive Science from University College London (1998). Dr. Olney's research centers on intelligent tutoring systems and natural language processing applications in education. His specific interests include vector space models, dialogue systems, unsupervised grammar induction, and computational representations of meaning. He has made significant contributions to conversational intelligent tutoring systems and task-oriented natural language interfaces, with particular emphasis on dyadic interaction and engagement in learning contexts. Analysis of his recent publications reveals a consistent focus on leveraging AI for educational enhancement, particularly through automated content creation, question generation, and adaptive learning systems. His work spans multiple disciplines including computer science, cognitive science, linguistics, and education, demonstrating strong interdisciplinary integration. Scientific Awards and Recognition: Best Paper Award, Proceedings of Empowering Education with LLMs (2023) Associate Editor, International Journal of Artificial Intelligence in Education (Impact Factor 4.9) Dr. Olney has been actively involved in mentoring graduate students and securing research funding. He has served as Principal Investigator on $6M of federal grants within a total of $18M in federal grant funding. His service includes former editorship of the Journal of Educational Data Mining (2017-2022) and former leadership roles as Director/Associate Director of the Institute for Intelligent Systems (2006-2017). He currently coordinates both the Cognitive Science Graduate Certificate and Undergraduate Minor in Cognitive Science at the University of Memphis. His BrainTrust project, funded by the NSF, addresses the knowledge engineering bottleneck for intelligent tutoring systems through innovative approaches to virtual student simulation and knowledge representation.