Find Academic Supervisors

• Artificial Intelligence
• Machine Learning
• Data Science
• +3 more

Dr. Atie Kia is a Lecturer in Computing Technologies at the School of Science, Computing and Emerging Technologies, Swinburne University of Technology. Her work bridges artificial intelligence, sustainable development, and inclusive education, with a focus on impactful research and innovative teaching. PhD in Engineering (Machine Learning and Data Science) from the University of Melbourne Master of IT (Network & Security) from Deakin University Bachelor of Applied Computing from Southern Cross University Her research applies AI and data science to social good, particularly sustainable development and SDG prioritization. She has developed frameworks for SDG resource allocation, presented to the United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM), and focuses on inclusive education for neurodiverse learners. Dr. Kia has published in top-ranked journals, including the Best Journal Paper Award (IEGRC 2023) , and her work on SDG prioritization has garnered significant citations. Her research spans machine learning, policy analysis, and systems thinking. Teaching Excellence Acknowledgment (2019) Student Experience Award (2020) Star Award for Student Experience, Accountability & Value Creation (2022) Recipient of multiple academic honors at Deakin University She has supervised over 80 student IT projects (36 Master’s and 50 Bachelor’s) and collaborates with industry partners on real-world challenges. Her teaching spans software development, cybersecurity, data science, and game programming.

• Business Analytics
• Computer Information Systems
• Predictive Analytics
• +5 more

Richard V. McCarthy is a Professor of Business Analytics and Information Systems and Associate Dean for the School of Business at Quinnipiac University. His work bridges academia and industry through predictive analytics, data management, and virtual team pedagogy. BS, Central Connecticut State University MBA, Western New England College DBA, Nova Southeastern University Dr. McCarthy's research focuses on healthcare analytics, financial fraud detection, and data science pedagogy. He applies machine learning to prevent post-surgical falls, combat money laundering, and enhance educational strategies. His publications emphasize predictive modeling techniques, including decision trees, neural networks, and regression analysis, across healthcare, finance, and information systems domains. Computer Educator of the Year, International Organization for Computer Information Systems (2019)

• Machine Learning
• Computer Science
• Engineering
• +7 more

David Côté is an academic researcher with significant contributions to the application of machine learning and artificial intelligence in network operations and optical communications. His work focuses on automating network decision-making, fault localization, and predictive maintenance using advanced AI techniques. He has collaborated with researchers like S. Shirmohammadi and Ayşe Rumeysa Mohammed. Research Interests Machine learning for network anomaly detection Reinforcement learning in network automation Deep learning for video QoE forecasting Optical network monitoring Stateless action recommenders Publications Overview His research spans from 2018 to 2023, showcasing applications of supervised, unsupervised, and deep learning in real-world network environments. Key areas include network automation, fault detection, and predictive analytics.

• Artificial Intelligence
• Machine Learning
• Data Science
• +4 more

Laura E. Brown is Associate Professor of Computer Science at Michigan Technological University, also serving as Associate Dean for Data Science Initiatives in the College of Computing and Director of the university’s M.S. and B.S. Data Science programs. She is a core member of the Institute of Computing and Cybersystems (ICC), the Ecosystem Science Center (ESC), and the Center for Agile Interconnected Microgrids (AIM). Education Ph.D., Biomedical Informatics — Vanderbilt University M.S., Biomedical Informatics — Vanderbilt University M.S.E., Electrical Engineering & Computer Science — University of Michigan B.S., Engineering — Swarthmore College Research Interests Laura’s scholarship spans artificial intelligence, machine learning, and data science, with theoretical advances tightly coupled to high-impact applications in energy systems (microgrids, power analytics), health and biomedical informatics, and computer science pedagogy. She is passionate about translating algorithmic innovations into real-world deployments that improve sustainability, clinical decision-making, and equitable education. A second major strand of her work centers on educational innovation: developing automated code-critiquing systems, studying their influence on student self-efficacy—especially for women in engineering—and leading campus-wide initiatives such as Carpentries workshops and ICPC programming competitions. Publication Trends Brown’s 2018–2025 publication record reveals sustained productivity in three overlapping domains: (1) AI/ML methodology—hardware prefetching, neural architectures, transfer learning; (2) energy analytics—forecasting coincident peak load, solar irradiance, battery degradation; and (3) computing education—automated feedback, gender studies, curriculum design. Recent emphasis has shifted toward educational technology, with multiple 2023–2025 papers on code critiquers and their psychological impact. Research Funding & Grants NSF Revolution through Evolution: $699 K, PI (2015–2019) DoD Distributed Agent-Based Management of Agile Microgrids: ~$1 M, co-PI (2013–2017) NSF Adaptive Memory Resource Management in a Data Center: $299 K, PI (2014–2017) NSF Microdevice for Rapid Blood Typing without Reagents: $300 K, co-PI (2016–2019) Google Explore CS Research Workshops: $18 K PI / $35 K co-PI (2018–2020) Student Engagement & Leadership Laura co-advises Women in Computing Science (WiCS) , organizes Michigan Tech’s ICPC regional site, and has led Google-sponsored undergraduate research workshops, Summer Youth Programs, and multiple Carpentries training events.

Hugo BuddelmeijerView profile

Researcher

• Astronomy
• Astrophysics
• Scientific Visualization
• +6 more

Hugo Buddelmeijer is an active Researcher at the Faculty of Science and Engineering , University of Groningen , affiliated with the Kapteyn Astronomical Institute . He specializes in astronomical data management, scientific visualization, and cosmological surveys. Research Interests : Galaxy evolution, photometric redshifts, data lineage, and visualization techniques for large-scale surveys like KiDS and Euclid. Technical Expertise : Development of simulation tools (e.g., ScopeSim), data catalog systems, and query-driven visualization frameworks. Contributions : Key participant in the KiDS and Euclid missions, focusing on optical/near-infrared imaging and dark matter analysis. His work emphasizes integrating machine learning with astronomical data. Collaborations : Extensive international partnerships with institutions like the European Southern Observatory and projects involving Very Large Telescope observations.

Nilüfer YURTAYView profile

Professor

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

Prof. Dr. Nilüfer YURTAY is a distinguished faculty member at Sakarya University's Faculty of Computer and Information Sciences, Department of Computer Engineering. Her academic career spans over three decades, beginning as a Research Assistant in 1992 and advancing to her current position as Professor. Current Courses: Computer Engineering Design, Optimization, Medical Statistics and Medical Informatics, Graduation Projects, Data Mining Applications, and Specialization Areas Previously Taught: Discrete Operational Structures, Data Mining, Applied Engineering Experience Training Professor YURTAY's research spans multiple domains of computer science with a focus on practical applications. Her work demonstrates consistent excellence in machine learning, data mining, and artificial intelligence, particularly applied to medical informatics, traffic safety analysis, and assistive technologies for visually impaired individuals. She has made significant contributions to feature selection algorithms, metaheuristic optimization techniques, and medical diagnosis systems. Her publication record reveals a strong trajectory of high-impact research, with recent works focusing on real-time object detection (AYOLO), traffic accident analysis using decision trees, binary chaotic optimization algorithms, and voice-assisted systems for visually impaired students. These publications appear in reputable journals including IEEE ACCESS, Applied Sciences, and Engineering Science and Technology. Professor YURTAY has supervised numerous graduate students, guiding research in data mining applications in healthcare, Turkish text-to-speech synthesis, pattern recognition in bioinformatics, and demand prediction for ATMs. Her academic leadership extends to developing educational resources, including her textbook 'Ayrık İşlemsel Yapılar' (Discrete Operational Structures). Her research group maintains active projects in medical informatics, traffic safety analysis, and assistive technologies, reflecting her commitment to solving real-world problems through computational methods. Professor YURTAY's work bridges theoretical computer science with practical applications that address societal challenges in healthcare, transportation, and accessibility.

Suzana LoshkovskaView profile

Professor

• Programming
• Visualization
• Human-Computer Interaction
• +3 more

Suzana Loshkovska, PhD , is a Full Professor at the Faculty of Computer Science and Engineering of Ss. Cyril and Methodius University in Skopje , Macedonia. Since 1992 she has held continuous academic appointments, interrupted only by a visiting-researcher stay at the Academy of Sciences of Lower Austria (1993–1994) where she designed a medical information system for Krems Hospital. Education : BSc (1989) and MSc (1992) in Computer Science and Automation, Faculty of Electrical Engineering, Skopje PhD (1995) in Technical Sciences, Technical University of Wien, Austria Research Interests span programming, visualization, human–computer interaction, virtual reality, medical imaging, and modelling/visualization of medical processes. Her work integrates advanced algorithmic techniques—such as hierarchical multi-label classification, support-vector machines, predictive clustering trees, and Bayesian networks—into practical medical and multimedia applications. Across more than one hundred peer-reviewed publications, her recent articles concentrate on content-based medical image retrieval , annotation of medical images , visual concept detection , and interactive medical systems . These contributions collectively advance computer-aided diagnosis, clinical decision support, and efficient management of large-scale medical data sets. Scientific Awards & Recognition : Member, Editorial Board, INFORMATICA (international journal, Slovenia) Projects & Funding : Principal investigator or participant in bilateral and EU-funded projects including SEE-ERA.NET Plus, TEMPUS DL@WEB, TEMPUS M.Sc. E-Learning, and multiple Macedonian Ministry of Science grants covering medical imaging, environmental modelling, distance learning, and electromagnetic-effects visualisation. While no specific research group or laboratory name is provided, her sustained involvement in national and international projects indicates active leadership within the university’s medical-informatics and multimedia-research community.

Justin KernView profile

Associate Professor

• Educational Measurement
• Psychological Measurement
• Item Response Theory (IRT)
• +6 more

Justin Kern is an Associate Professor in the Department of Educational Psychology at the University of Illinois, Urbana-Champaign . He earned his Ph.D. in Quantitative Psychology (2017) and holds additional graduate degrees in Psychology and Statistics. His research focuses on educational and psychological measurement with emphasis on item response theory (IRT) , Bayesian statistical methods , and multivariate data analysis . Research Themes Item Response Theory (IRT) and extensions Computerized Adaptive Testing (CAT) Four-parameter IRT models Response time modeling Measurement invariance Bayesian estimation techniques Recent Publications His recent work (2025-2020) spans topics in educational measurement , psychological testing , and family behavior analysis . Key trends include: Development of IRT-based frameworks for health survey data Investigations into extreme response styles using IRTree models Advancements in four-parameter IRT modeling Applications of Bayesian methods in test construction Analysis of father involvement in early intervention Collaborations Kern collaborates across disciplines with researchers in psychology , statistics , healthcare informatics , and special education . His work has been supported by grants from the Institute for Education Sciences and NICHD .

Ana Belén Gil GonzálezView profile

Associate Professor

• Artificial Intelligence
• Educational Technology
• Data Mining
• +4 more

Ana Belén Gil González is an Associate Professor in the Department of Computer Science and Automation at the University of Salamanca, Spain. She is affiliated with multiple research groups including BISITE (Bioinformatics, Intelligent Computer Systems and Educational Technology), CAUSAL (Academic Culture, Heritage and Social Memory), and MIDA (Data Mining). Her work spans across artificial intelligence, educational technology, blockchain applications, and intelligent systems. Dr. Gil González earned her PhD from the University of Salamanca in 2011 with a thesis on "Intelligent Recovery of Digital Educational Content," supervised by Dr. Juan Manuel Corchado Rodríguez and Dr. Sara Rodríguez González. Her research interests focus on applying artificial intelligence and data mining techniques to solve real-world problems. She has made significant contributions to educational technology, developing intelligent systems for digital content retrieval and personalized learning. In healthcare, she has worked on AI applications for medical diagnosis, including breast cancer prediction and Papanicolaou test analysis. Her work in blockchain technology addresses applications in supply chain management and smart cities with a focus on privacy preservation. Additionally, she has explored therapeutic robotics through projects like i-Teddy, which uses doll therapy to improve human interaction. Analysis of her recent publications (2022-2024) reveals a strong interdisciplinary approach with work spanning healthcare AI, educational technology, blockchain applications, and therapeutic robotics. Her research shows increasing focus on explainable AI in medical applications, privacy-preserving technologies for smart cities, and innovative approaches to educational technology. She maintains an active collaboration network across multiple research groups at the University of Salamanca. Dr. Gil González is actively involved in mentoring through her teaching and research supervision. Her extensive publication record across diverse domains suggests successful funding acquisition for her research projects. She is a key member of the BISITE research group, which focuses on bioinformatics, intelligent computer systems, and educational technology. Her work with the CAUSAL group explores academic culture and social memory, while her involvement with MIDA centers on data mining applications. These collaborative environments provide rich interdisciplinary contexts for her research across technology, education, and social applications.

John NewellView profile

Professor

• Biostatistics
• Clinical Trials
• Sports Analytics
• +4 more

John Newell is a statistician and the inaugural Professor of Biostatistics at the School of Mathematical and Statistical Sciences at the National University of Galway. He serves as a Funded Investigator at the Insight Centre and leads research collaborations with elite athlete organizations and industry partners across the globe. Research Focus: Biostatistics, Sports Analytics, Translational Statistics Expertise: Statistical modeling, computational inference, design and analysis of medical device trials, statistical learning decision trees His work bridges classical and computational statistical approaches to enhance clinical trial design and athlete performance monitoring, with applications in multidisciplinary translation of statistical analyses. He has collaborated with prestigious institutions and sports organizations, including the Premier League, NFL, NBA, and Aspire Academy, and co-authored the textbook Statistics for Sports and Exercise Scientists . Scientific Awards Fulbright Scholar John has developed the widely used R package 'Dynom,' which has seen over 45,000 downloads, with its 2019 PLOS ONE paper ranking in the top 10% most cited.

Samir BhattView profile

Professor

• Statistics
• Machine Learning
• Public Health
• +5 more

Samir Bhatt is a Professor of Machine Learning and Public Health at the University of Copenhagen's Faculty of Health and Medical Sciences, Department of Public Health, Section for Health Data Science and AI. He also holds a position as Professor of Statistics and Public Health at Imperial College London since 2016. His work focuses on developing mathematical, statistical, and computer science tools to address critical questions in human health. His educational background includes a DPhil in Statistical Genetics from the University of Oxford (2010), an MPhil in Computational Biology from the University of Cambridge (2006), and a BEng in Chemical and Bioprocess Engineering from the University of Bath (2005). Professor Bhatt's research spans the intersection of statistics, machine learning, and public health with particular emphasis on infectious disease modeling. His primary research areas include Bayesian inference, genomic epidemiology, and kernel methods applied to health data. His work bridges theoretical statistical approaches with practical public health applications, particularly in disease surveillance and outbreak response. The integration of AI with traditional epidemiological methods represents a key innovation in his research program. Analysis of his recent publications reveals a strong focus on applying advanced computational methods to pressing public health challenges. His work demonstrates consistent innovation in developing AI-driven approaches for infectious disease modeling, genomic surveillance, and survival analysis. Notable themes include the application of graph neural networks to epidemiological data, development of interpretable AI tools for public health decision-making, and sophisticated modeling of disease transmission dynamics across multiple pathogens including malaria, cholera, and respiratory viruses. Professor Bhatt has published extensively with over 107 research outputs to date. His work has received significant attention, with multiple publications covered by news outlets, referenced on social media platforms, and read by researchers on academic platforms like Mendeley. His research on AI for infectious disease modeling published in Nature demonstrates the high impact of his work in both academic and policy spheres. His research group at the University of Copenhagen appears to focus on developing computational tools for health data science, with particular emphasis on creating analytical frameworks that can be rapidly deployed during disease outbreaks. The development of GRAPEVNE (Graphical Analytical Pipeline Development Environment for Infectious Diseases) represents one such effort to create accessible tools for public health practitioners.

• Cryptography
• Secure Computation
• Privacy-Preserving Machine Learning
• +3 more

Hiraku Morita serves as a Research Fellow in the Department of Computer Science at the University of Copenhagen, actively contributing to the Machine Learning research group. His work bridges theoretical cryptography with practical secure computation applications, focusing on protocol efficiency and privacy preservation in data analysis. His research interests prominently feature Cryptography , Secure Multi-Party Computation , and Privacy-Preserving Machine Learning , with specialized expertise in card-based cryptographic systems and secret-sharing methodologies. He investigates foundational protocols for secure data processing while optimizing computational rounds and resource constraints in privacy-sensitive environments. Recent publications reveal a distinct trajectory toward practical cryptographic implementations for machine learning tasks, particularly in developing constant-round evaluation techniques for decision trees and novel card-based gate protocols. These contributions address critical challenges in balancing security guarantees with computational feasibility in distributed settings. As a core member of the University of Copenhagen's Machine Learning research group, Dr. Morita collaborates extensively with international researchers including Takaaki Mizuki, Koji Tozawa, and A. Mitrokotsa, advancing the field through both theoretical innovations and applied cryptographic solutions.

• Biomedical Informatics
• Data Science
• Operations Research
• +3 more

Jeffrey Zhang is a Research Fellow at the Yale School of Medicine , affiliated with the Department of Biomedical Informatics and Data Science . Supported by an NLM T15 training grant , he works in Dr. Hua Xu's lab , applying large language models to biomedical challenges. Education: PhD in Operations Research and Financial Engineering , Princeton University (2020) BA in Computer Science, Economics, and Mathematics , Yale University (2014) His research spans biomedical informatics , data science , and optimization algorithms , with a focus on machine learning applications in clinical contexts and mental health analysis . Recent publications investigate higher-order Newton methods , computational complexity , and biomedical relation extraction using AI models. Scientific Awards: NLM T15 Training Grant Jeffrey collaborates with Dr. Hua Xu , Dr. Kalpana Raja , and Dr. Qingyu Chen , among others, and contributes to interdisciplinary projects at the intersection of immunology , engineering , and AI .

Sanja BaricView profile

Professor

• Plant Pathology
• Agricultural Sciences
• Molecular Genetics
• +3 more

Sanja Baric is a Professor at the Faculty of Agricultural, Environmental and Food Sciences of Free University of Bozen-Bolzano (unibz), Italy. Her research focuses on plant pathology , particularly chestnut blight and apple postharvest diseases , integrating molecular genetics and agricultural technology to enhance disease detection and management. Current projects address phytoplasma infections in apple trees and fungal pathogens in fruit crops. She teaches courses in plant protection and phytopathology for bachelor’s and master’s programs in Agricultural, Food and Mountain Environmental Sciences. Her publications emphasize genetic diversity of pathogens like Cryphonectria parasitica and Colletotrichum species , alongside innovative diagnostic tools such as knowledge-based Bayesian networks and duplex TaqMan assays . While no specific awards are listed, her work aligns with EU-funded collaborations and interdisciplinary agricultural research .

Amir H GandomiView profile

Professor

• Machine Learning
• Evolutionary Computation
• Global Optimization
• +7 more

Professor Amir H Gandomi is a leading academic in data science and artificial intelligence at the University of Technology Sydney, where he serves as Professor of Data Science at the Data Science Institute within the Faculty of Engineering and Information Technology. An ARC DECRA Fellow with over 450 journal papers and 14 books, his research has garnered more than 70,000 citations with an H-index exceeding 110. Ranked 18th among over 17,000 researchers in Genetic Programming bibliography and 24th in Artificial Intelligence & Image Processing by Stanford University, Prof. Gandomi is recognized as one of the world's most influential scientific minds. His research interests span machine learning, evolutionary computation, global optimization, and big data analytics, with applications across healthcare, structural engineering, environmental science, and cybersecurity. He has developed innovative frameworks like Adaptive Strategy Management for large-scale optimization and Boundary Update methods for constrained optimization problems. His work bridges theoretical advancements with practical implementations in diverse fields including medical diagnostics, renewable energy site selection, and smart infrastructure. Prof. Gandomi's publication portfolio demonstrates consistent high-impact contributions across multiple disciplines, with recent work focusing on AI-driven healthcare solutions, optimization algorithms, and climate modeling. His research shows strong interdisciplinary connections between computer science, engineering, and medical applications, with particular emphasis on practical implementations of theoretical frameworks. The breadth of his work reflects both deep technical expertise and the ability to apply computational methods to solve real-world problems across various domains. 2024 IEEE TCSC Award for Excellence in Scalable Computing (MCR) 2023 Achenbach Medal from Stanford University 2022 Walter L. Huber Prize (highest-level mid-career civil engineering research award) 2025 Sigma Xi Young Investigator Award 6 consecutive years as Clarivate Analytics Highly Cited Researcher AmCham Alliance Award in AI As a dedicated educator and mentor, Prof. Gandomi has supervised numerous research students in evolutionary machine learning, structural health monitoring, and uncertainty-aware AI systems. His funded research projects include Amazon Research Awards for medical report generation, Climate Change AI grants for drought prediction, and Digital Finance CRC projects for cyber threat detection. He leads the Data Science Institute's efforts in developing practical AI solutions while maintaining strong industry partnerships and international collaborations across multiple continents.