Find Academic Supervisors

Megan CourtmanView profile

Research Fellow

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +6 more

Megan Courtman is a Research Fellow at the Peninsula Medical School, University of Plymouth, specializing in applying machine learning to medical imaging for healthcare solutions. Her research focuses on explainable AI models for diagnostics, including stroke prediction and Parkinson’s disease detection in MRI/CT scans. She holds a PhD in Health Data Science (2024) and an MSc in Data Science & Business Analytics (2020), both from the University of Plymouth, alongside a BA in History from the University of York (2014). Education: Megan’s academic journey includes PhD: Health Data Science, University of Plymouth (2020–2024) MSc: Data Science & Business Analytics, University of Plymouth (2019–2020) BA: History, University of York (2011–2014) Her research interests integrate computational methods with clinical applications, emphasizing interpretability through explainable AI. Key areas include medical imaging classification, neural network development, and leveraging NLP for pathology analysis in scan descriptions. Her work aligns with UN Sustainable Development Goals targeting health and well-being. Megan’s publications and conference contributions reflect a growing emphasis on AI-driven medical diagnostics, particularly in neurological conditions. She has explored MRI-based detection of Parkinson’s and related disorders, CT aneurysm clip identification for imaging safety, and NLP techniques for preprocessing medical data. Awards and Recognition: 2024 Health and Care Research Conference poster prize 2023 University of Plymouth Three Minute Thesis winner 2022 Association of British Neurologists award for top poster Advising & Grants:Megan has secured grants from the MRC and EPSRC for healthcare intervention development and has led interdisciplinary projects. While no formal advisees are listed, her collaborative projects involve mentoring roles within research teams. Notable grants include MRC funding for stroke prediction AI (2024–2026) EPSRC PhD studentship (2020–2024) Affiliations: She is part of the SUSTAIN South West , Big Data group , and Applied Parkinson’s Research Group , alongside professional memberships in the European Stroke Organisation, International Parkinson and Movement Disorder Society, and British Institute of Radiology.

Ziyi WangView profile

Senior Lecturer

• Multivariate Statistics
• Artificial Intelligence
• Machine Learning
• +4 more

Ziyi Wang is a Senior Instructor in Business Analytics at the CU Denver Business School, University of Colorado Denver. His industry background includes executive roles in marketing analytics and technology entrepreneurship, having co-founded and sold two technology companies. He holds patents in data science applications for homeland security and marketing analytics. Research interests span multivariate statistics, artificial intelligence, machine learning, business analytics, media mix modeling, large language models, and cloud technologies. His publications focus on analytical chemistry, chemometrics, and statistical modeling techniques.

Markus WolfienView profile

Researcher

• RNA-Seq Data Analysis
• Workflow Development
• Omics Data Integration
• +3 more

Dr. Markus Wolfien is a Researcher in Systems Biology and Bioinformatics at the University of Rostock's Faculty of Computer Science and Electrical Engineering. His research develops computational workflows for analyzing biological data, particularly RNA-Seq datasets. Research focuses on integrating omics data through customizable analysis pipelines using tools like Galaxy, Conda, and Docker. Key applications include: Stem cell research and cardiac regeneration Machine learning for biomedical data Workflow optimization for NGS data Epigenomic analysis in cancer Recent publications demonstrate cross-disciplinary approaches combining bioinformatics, machine learning, and systems biology. Current projects include developing deep learning methods for sequencing data and creating accessible tools for cancer genomics research.

• Resilient Infrastructure Systems
• Disaster Management
• Smart Cities
• +5 more

Mohammad Ilbeigi is an Assistant Professor in the Department of Civil, Environmental and Ocean Engineering at Stevens Institute of Technology (2019–present), previously serving at Bowling Green State University (2017–2019). He holds a PhD and MS in Construction Engineering and Management from Georgia Tech (2017), and earlier degrees from Sharif University of Technology and the University of Tehran. His research focuses on resilient infrastructure systems, disaster management, smart cities, data-driven uncertainty analysis, and pedagogical innovations in construction engineering education. He has led NSF-funded projects totaling over $700K, including AI-enhanced educational systems and post-disaster damage assessment technologies. Dr. Ilbeigi serves on academic committees such as the Civil Engineering Program Committee and Teaching and Learning Excellence Committee. He has received awards including the 2021 Siemens Tech Sustainability Challenge and the 2017 ASCE Thomas Fitch Rowland Prize. His work spans 100+ peer-reviewed publications across journals like IEEE Transactions and ASCE series, with a focus on smart infrastructure systems and pedagogical innovations. He teaches courses in construction project management, infrastructure sustainability, and automation in construction. His research bridges engineering education, AI applications, and disaster resilience, with recent work emphasizing equitable energy access and gamified learning strategies.

Nelly ElsayedView profile

Assistant Professor

• Applied AI
• Machine Learning
• Healthcare Informatics
• +7 more

Dr. Nelly Elsayed is an Assistant Professor at the School of Information Technology, University of Cincinnati. She leads the Applied Machine Learning and Intelligence (AMLI) Lab, focusing on AI, cybersecurity, healthcare informatics, and smart technologies. Her research emphasizes practical applications in IoT security, neural networks, and predictive modeling. Her work includes grants from the National Science Foundation, Procter & Gamble, and federal agencies, totaling over $600k. Notable projects include IoT security frameworks, mental health monitoring systems, and cyberbullying detection. Dr. Elsayed has received multiple recognitions, including the Golden Apple Award for Teaching and UCAADA Advising Excellence. She actively serves on technical committees for IEEE and ACM conferences, contributing to academic service and outreach. Research Interests: Applied AI & Machine Learning Cybersecurity for IoT/Healthcare Deep Learning Models (LSTM, CNN) Data Science & Pattern Recognition Smart Technologies for Accessibility Recent Research Trends: Her publications emphasize hybrid models (e.g., LSTM-CNN) for intrusion detection, cybersecurity frameworks, and AI-driven solutions for mental health and elderly care.

Tristan MillerView profile

Assistant Professor

• Computational Linguistics
• Natural Language Processing
• Lexical Semantics
• +4 more

Tristan Miller is an Assistant Professor in the Department of Computer Science at the University of Manitoba, leading the Computational Linguistics at Manitoba (CLAM) Lab. His research focuses on computational linguistics, natural language processing, and humor analysis. He holds academic affiliations with institutions like the Austrian Research Institute for Artificial Intelligence (OFAI) and has contributed to projects like the JOKER Lab at CLEF. His work emphasizes lexical semantics, historical online corpora, and the computational detection/translation of wordplay. He organizes workshops on computational humor (CHum, JOKER) and serves as an editor for journals like Humor and European Journal of Humour Research . Key contributions include the JOKER corpus for multilingual wordplay analysis, tools like PunCAT for pun translation, and LaTeX classes for proposal formatting (Heria, SSHRC-Insight). His research bridges theoretical linguistics with practical NLP applications. He has held roles such as Senior Area Co-Chair at EMNLP 2025 and Faculty Advisor at ACL 2025. His funded projects include the FWF-funded "Computational PUN-DERSTANDING" and Horizon Europe proposal development initiatives.

Jeff GilchristView profile

Academic

• Artificial Intelligence
• Health Data
• Neonatal Intensive Care
• +3 more

Dr. Jeff Gilchrist is an Adjunct Research Professor at Carleton University's Faculty of Engineering and Design since 2013. He holds a Ph.D. in Systems and Computer Engineering from Carleton University. He currently works as a Senior AI Data Scientist at the Children’s Hospital of Eastern Ontario (CHEO) Research Institute, collaborating on machine learning applications in healthcare. His research focuses on predicting medical outcomes for neonatal intensive care unit (NICU) patients using AI, analyzing real-time patient data to develop clinical decision support systems. Key projects include 'Applying Data Preprocessing Methods to Predict Premature Birth' (2018) and 'Developing an Automated Clinical Trending Tool for the NICU' (2018). His work integrates machine learning with health data to improve care for infants and supports evidence-based decision-making for medical teams and parents. Dr. Gilchrist collaborates with BORN Ontario and contributes to precision child and youth mental health initiatives.

Vana KalogerakiView profile

Professor

• Distributed Systems
• Real-Time Systems
• Participatory Sensing
• +8 more

Prof. Vana Kalogeraki is a Professor leading the Distributed and Real-Time Systems research group at Athens University of Economics and Business. Previously, she held faculty positions at the University of California, Riverside, and was a Research Scientist at Hewlett-Packard Labs. She earned her PhD from UC Santa Barbara in 2000, with earlier degrees from the University of Crete. Her research focuses on distributed systems, real-time computing, participatory sensing, and edge computing. She has published over 100 papers and co-authored the CORBA Dynamic Scheduling Standard. She has received notable awards including the Marie Curie Fellowship and best paper recognitions at IPDPS and SAINT conferences. Her work is supported by grants from the ERC, EU, NSF, and industry partners like SUN and Nokia. She has organized major conferences and serves on editorial boards for journals such as Transactions on Mobile Computing and Peer-to-Peer Networking. Education: PhD in Computer Science, University of California, Santa Barbara (2000) M.S. in Computer Science, University of Crete (Greece) B.S. in Computer Science, University of Crete (Greece) Research Interests: Prof. Kalogeraki explores distributed systems, real-time processing, and edge computing applications. Her work emphasizes resource management, fault-tolerance, and sensor network middleware. Recent projects include serverless computing optimizations, privacy-preserving mobile cloud frameworks, and anomaly detection in IoT environments. She also contributes to diversity initiatives in database conferences. Awards and Honors: Marie Curie Fellowship Best Paper Awards at IPDPS 2009 and SAINT 2008 Best Student Paper Award at SAINT 2011 UC Regents Fellowship Award ERC Starting Independent Researcher Grant Grants and Collaborations: Her research is funded by the European Union (including Thalis and Aristeia grants), NSF, and industry partnerships. She has led projects on edge AI, serverless pipelines, and participatory sensing systems. Labs and Teams: She directs the Distributed and Real-Time Systems Lab at Athens University, focusing on real-time distributed applications and edge computing innovations.

Akitaka MatsuoView profile

Lecturer

• Political Science
• Data Science
• Text Analysis
• +3 more

Akitaka Matsuo is a Lecturer in the Department of Government at the University of Essex. His academic career includes roles as a Postdoctoral Fellow at the Institute for Analytics and Data Science (IADS) and a Research Fellow in Data Science at the London School of Economics and Political Science (LSE). He holds a Ph.D. in Political Science from Rice University (2012). His research focuses on computational social science, political communication, and text analysis, leveraging tools like R packages quanteda and spacyr . Matsuo’s work spans topics such as Brexit discourse, election forecasting, legislative sentiment analysis, and gendered language strategies in campaigns. He actively contributes to open-source software development for text analysis and has published widely on NLP applications in political science. His academic support hours are Tuesdays 3-5 PM (appointment-only). Grants and funding details are not explicitly listed in the provided texts.

• Health Informatics
• Machine Learning
• Artificial Intelligence
• +5 more

Gang Luo is an Assistant Professor in the Department of Biomedical Informatics at the University of Utah's School of Medicine. He holds a PhD in Computer Science from the University of Wisconsin-Madison with a minor in Mathematics. His research focuses on health informatics, machine learning, natural language processing, and database systems, with applications in clinical decision support, medical search engines, and healthcare data analytics. Dr. Luo has over 50 publications and 20+ patents, including inventions like the iMed intelligent medical search engine and systems for progress indication in database operations. His work integrates advanced computing techniques with healthcare challenges to improve patient care and public health outcomes. Education: PhD in Computer Science (University of Wisconsin-Madison), BS in Computer Science (Shanghai Jiaotong University). Research Interests : Health Informatics (software systems, data analytics) Machine Learning & AI for healthcare Data Mining and Databases Medical Search and Information Retrieval Decision Support Systems His patent portfolio includes innovations in database systems, real-time event detection, and medical search tools. Recent work explores computational approaches for asthma management, COPD phenotyping, and predictive modeling in clinical settings. His research emphasizes translating technical advancements into practical healthcare solutions.

Jerzy W. Grzymala-BusseView profile

Professor

• Data mining
• Knowledge discovery
• Machine learning
• +3 more

Jerzy W. Grzymala-Busse is a Professor in the Department of Electrical Engineering and Computer Science at the University of Kansas, where he has made significant contributions to data mining, machine learning, and rough set theory for several decades. Professor Grzymala-Busse's research focuses on data mining, knowledge discovery, machine learning, expert systems, reasoning under uncertainty, and rough set theory . He is the creator of the LERS (Learning from Examples based on Rough Sets) system, which induces rules from examples and classifies new examples using rough set theory. His work connects rough set theory with Dempster-Shafer theory, and he has explored various aspects of data preprocessing including discretization methods and handling missing attribute values. His research has demonstrated that expert systems with rule sets induced by LERS achieve significantly higher positive predictive values (59-93%) compared to traditional methods (17-38%) in medical applications like preterm labor risk assessment. Professor Grzymala-Busse has been actively teaching courses such as EECS 510, EECS 638 (Fundamentals of Expert Systems), EECS 645, EECS 690 (Introduction to Data Mining), EECS 837, and EECS 839. He authored the textbook "Managing Uncertainty in Expert Systems" published by Springer Verlag in 1991 and has supervised numerous graduate students on research spanning classification systems, data reduction techniques, and comparative studies of rule induction algorithms. His LERS system has found practical applications in diverse domains including finance (loan decision-making), medical areas (diagnostic decision support), and military applications (job performance evaluation). Notably, it has been used by NASA Johnson Space Center for developing expert systems for medical decision-making on board the International Space Station, and by the U.S. Environmental Protection Agency for facility compliance under Sections 311, 312, and 313 of Title III, the Emergency Planning and Community Right to Know Act.

• Artificial Intelligence
• Machine Learning
• Data Science
• +2 more

Dongjie Wang is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Kansas, School of Engineering. His research focuses on artificial intelligence, machine learning, and data science applications in bioinformatics, transportation planning, and data-centric AI. Wang's research interests span several key areas: Development of reinforcement learning frameworks for biological pathway analysis and gene selection Generative AI applications in urban transportation systems Multi-modal representation learning for cross-domain retrieval Neuro-symbolic approaches for feature engineering His recent publications demonstrate a consistent focus on developing novel AI methodologies for real-world applications, particularly in transportation systems optimization, biological data analysis, and tabular data processing. The articles collectively show strong emphasis on reinforcement learning architectures, graph neural networks, and multimodal integration techniques. Wang advises graduate students in computer science and leads research in data-centric AI applications. His laboratory focuses on developing scalable machine learning solutions for complex systems.

Dylan MurrayView profile

Assistant Professor

• Structural Biology
• Biochemistry
• Biophysics
• +7 more

Dylan Murray is an Assistant Professor in the Molecular and Cell Biology department at the University of Connecticut. His research focuses on the structural and mechanistic aspects of biopolymer assembly in both functional and pathological contexts. Ph.D. in Molecular Biophysics from Florida State University PRAT Postdoctoral Fellow at the National Institutes of General Medical Sciences, Bethesda MD Research Interests : Dr. Murray's lab investigates molecular assembly mechanisms using magnetic resonance techniques and computational approaches. Key projects include: RNA granule formation and aggregation in neurodegenerative diseases Intermediate filament assembly defects linked to cancers and pediatric disorders Plant cell wall structure for biofuel and drought tolerance engineering Publications span structural biology, neurodegeneration, and plant biophysics. Recent work highlights cryo-EM and solid-state NMR integration to study protein fibrils (2024) and xylan-cellulose interactions (2023). His studies often explore low-complexity domains, phase separation, and amyloid-like aggregation. Scientific Awards : ACS Editor’s Choice Award PRAT Postdoctoral Fellowship Cover Highlight in Cell JACS Spotlight Recognition Labs & Teams : He leads the Murray Lab, which combines experimental and computational methods to study protein and polymer structures. The lab collaborates with interdisciplinary teams in biomedical and plant sciences.

Olga OrmandjievaView profile

Professor

• Software Engineering
• Big Data Quality Modeling
• Software Measurement
• +5 more

Dr. Olga Ormandjieva is a Professor in the Computer Science and Software Engineering Department at Concordia University, Montreal, Canada. She is a member of the Ordre des Ingénieurs du Québec (OIQ) and holds a Ph.D. in Computer Science (2002) and a Master's in Computer Science and Mathematics (1987). Her research focuses on software quality engineering, big data quality modeling, and AI-driven cybersecurity solutions for detecting cyber-predators in social media. She has published over 135 peer-reviewed papers and seven book chapters, and supervised seven doctoral and seventeen master's students. Dr. Ormandjieva's expertise spans measurement in software engineering, empirical software engineering, and formal methods. Her work integrates AI and software engineering best practices to address challenges in big data and cybersecurity. She has received grants from NSERC and other sources, and contributed to academic governance, including CEAB accreditation visits and IEEE leadership roles. Her recent research emphasizes automated cyber-predator detection and big data quality frameworks like MEGA. She teaches software engineering courses and actively engages in curriculum development, emphasizing real-world applications in healthcare and enterprise systems. Dr. Ormandjieva's service includes organizing international conferences, reviewing for top journals, and developing methodologies for mobile health UIs and requirement traceability systems. Her work bridges theory and practice, addressing critical issues in software reliability, user-centric design, and ethical AI applications.

Luiza AntonieView profile

Associate Professor

• Data Mining
• Record Linkage
• Classification
• +4 more

Luiza Antonie is an Associate Professor at the School of Computer Science, University of Guelph. She specializes in data mining methodologies applied to interdisciplinary research, particularly historical demography and economic analysis. Her work bridges computer science with fields like history and economics through projects such as longitudinal census data analysis and studies on gender wage gaps. Her research focuses on record linkage techniques, classification systems, and data integration challenges. Notable collaborations include analyzing 19th-century Canadian economic mobility patterns and creating automated coding systems for historical occupations. She also explores modern workforce trends, including future technical skills demands and accessibility in work-integrated learning. Antonie’s publications span machine learning applications, historical data analysis, and algorithm optimization. Her recent work emphasizes deep learning approaches for product matching and bias evaluation in data linkage processes. She maintains an active Google Scholar page and contributes to international data linkage research initiatives.