Find Academic Supervisors

Gabriela MinigoView profile

Researcher

• Immunology
• Microbiology
• Malaria Research
• +5 more

Gabriela Minigo serves as an Honorary Fellow in Global and Tropical Health and Associate Dean - Learning and Teaching within the Faculty of Health. Her academic profile demonstrates active engagement in malaria immunology research with a recent expansion into circadian rhythm and metabolic health investigations. Dr. Minigo completed her PhD at the University of Melbourne (awarded March 31, 2007) and earned a Master of Clinical Epidemiology from the University of Newcastle (awarded December 8, 2017). Her educational background provides a strong foundation in both basic science and clinical epidemiology. Her primary research focuses on immunological responses to malaria, with particular expertise in T follicular helper cells, dendritic cell function, and regulatory T cells in Plasmodium falciparum and Plasmodium vivax infections. Analysis of her research fingerprint reveals significant contributions to Plasmodium falciparum Immunology and Microbiology (100%), Dendritic Cell Immunology and Microbiology (45%), Regulatory T Cell Immunology and Microbiology (38%), and other related fields. In recent years, she has expanded her research to investigate the relationship between photoperiod, circadian rhythm, and metabolic health, as evidenced by her 2024 publications on these topics. Dr. Minigo's scholarly output shows an evolving research trajectory from fundamental malaria immunology toward interdisciplinary investigations connecting circadian biology with metabolic health. Her recent publications (2022-2024) demonstrate continued strong contributions to malaria immunology while incorporating new dimensions related to light exposure, sleep patterns, and glucose control. She has secured research funding for multiple projects including 'Rainmaker start up- Does exposure to artificial light alter circadian rhythm and glucose control in Australian adults? A pilot study' (2023-2026) where she serves as Co-Investigator, and 'Effect of Extended Photoperiod Exposure on Circadian Rhythm and Glucose Control' (2024-present) where she is Principal Investigator. Previously, she contributed as Chief Investigator C to 'Manipulating T follicular helper cells to improve human malaria vaccines' (2020-2023). As Associate Dean for Learning and Teaching, Dr. Minigo holds significant educational leadership responsibilities within the Faculty of Health. She is registered to supervise postgraduate research, indicating her active role in mentoring graduate students in immunology and tropical medicine research.

Rajendra AcharyaView profile

Professor

• Artificial Intelligence
• Healthcare
• Machine Learning
• +5 more

Rajendra Acharya is a Professor (Artificial Intelligence in Health) at the University of Southern Queensland's School of Mathematics, Physics and Computing. He holds qualifications including BEng, MTech, two PhDs, and a DSc. His research focuses on AI applications in healthcare, pattern recognition, and medical diagnostics, with notable contributions to EEG analysis, deep learning, and disease detection. Awards include multiple Research.com Leader Awards in Computer Science for Australia and Singapore (2022–2025). His work spans over 650 publications, with high-impact studies on automated disease diagnosis via AI, including COVID-19 detection using X-rays and EEG-based seizure detection. His research interests integrate machine learning, signal processing, and healthcare technologies. He collaborates internationally and advises on AI-driven health solutions. No student list provided; however, his extensive supervision is implied through his research output.

Miguel NacentaView profile

Professor

• Human-Computer Interaction
• Information Visualization
• Infotypography
• +5 more

Miguel Nacenta is a Professor in the Department of Computer Science at the University of Victoria (UVic), Canada, and a founding member of the Victoria Interactive eXperiences with Information (VIXI) research group. Previously affiliated with the University of St Andrews (UK), his work bridges Human-Computer Interaction (HCI), Information Visualization, and Cognitive Science. He specializes in designing interactive systems that enhance human cognition, with a focus on Infotypography (using typography to encode data), collaborative problem-solving tools, and perceptual input/output devices. Research Interests: His key areas include cognitive augmentation, visualization techniques for complex tasks, multi-display environments, and tools for constraint problem-solving. Notable projects include the WriteReason tool for essay writing, InfoTypography studies on perceptual typographic parameters, and Solvi for visual constraint modeling. Grants & Collaborations: He collaborates internationally, including with the University of St Andrews on PhD scholarship programs. His work is supported by grants focusing on HCI innovations and accessibility. He actively mentors students (e.g., Adam Binks, Johannes Lang) and supervises postdoctoral researchers. Affiliations: Member of the VIXI group,他曾是St Andrews计算机科学学院的教授, 并参与多个学术服务活动, including conference program committees and journal reviews. Labs & Teams: Leads the VIXI lab at UVic, focusing on interactive technologies for cognitive tasks. Collaborates with industry partners on projects like TypoCartographer for infoTypographic maps and HaptiQ for accessible graph exploration.

Maia AngelovaView profile

Professor

• Artificial Intelligence in Healthcare
• Data-Driven Models
• Machine Learning
• +7 more

Professor Maia Angelova is a leading academic in data science and mathematical physics at Aston University's Aston Digital Futures Institute (ADFI) and College of Engineering and Physical Sciences. Her research focuses on interdisciplinary AI applications in healthcare, including precision medicine, chronic disease modeling, and athlete performance analytics. She previously held roles as Professor of Data Analytics at Deakin University (2017–2023) and Professor of Mathematical Physics at Northumbria University (1997–2016), with early experience as a College Lecturer at Oxford University (1991–1996). Education: PhD, MSc, and BSc in Physics from Sofia University 'St. Kliment Ohridski'. Research interests span AI-driven healthcare solutions, dynamical systems modeling, and sports performance analysis. Her work addresses sleep disorders, diabetes management, chronic pain, and athlete performance using advanced machine learning and data analytics. She has secured over £5M in research funding and supervised over 30 PhD students and postdoctoral researchers. Awards include Fellowship of The Institute of Physics. Professional memberships include The London Mathematical Society, Australian Mathematical Society, and Complex Systems Society. Key achievements include founding the Data to Intelligence research centre (2018–2020) and leading large-scale interdisciplinary projects. Current initiatives focus on precision healthcare through AI integration in clinical decision-making systems.

Mahdi S. HosseiniView profile

Assistant Professor

• Deep Learning
• Computer Vision
• Computational Pathology
• +3 more

Mahdi S. Hosseini is an Assistant Professor in the Department of Computer Science and Software Engineering at Concordia University and a faculty member of the Applied AI Institute. He holds a PhD from the University of Toronto (2016) and completed a postdoctoral fellowship at UofT, supported by MITACS-Elevate and NSERC fellowships. His research focuses on advancing deep learning and computer vision for computational pathology and healthcare technologies, aiming to develop AI tools for clinical diagnosis. He currently supervises graduate students and has published over 30 papers and two patents. Education: PhD in Electrical and Computer Engineering from the University of Toronto (2016), postdoctoral training at UofT collaborating with Huron Digital Pathology Inc. (Waterloo, Ontario). Research interests include deep learning, computer vision, computational pathology, medical imaging, and AI ethics (P4AI project). His work emphasizes developing explainable AI systems for clinical pathology, biomarker discovery, and efficient learning algorithms. Professional service includes serving as Area Chair for NeurIPS 2023, CVPR 2023-2024, and ECCV 2024. He reviews grants for CIHR, NSERC, and serves on program committees for key conferences (ICCV, CVPR, NeurIPS). Teaching includes courses on applied AI, machine learning, and deep learning for computational pathology at both graduate and undergraduate levels. Awards: MITACS-Elevate Fellowship (postdoc), NSERC Research Funding (2016-2017). His work has led to patents in diagnostic systems and has collaborated with hospitals and pathologists to advance clinical applications. Labs/Teams: Active collaborations with the Applied AI Institute at Concordia, Huron Digital Pathology, and healthcare institutions. Research emphasizes interdisciplinary approaches between computer science and clinical medicine.

Suining HeView profile

Assistant Professor

• Cyber-Physical Systems
• Smart & Connected Communities
• Human-Centered Computing
• +10 more

Suining He is an Assistant Professor at the University of Connecticut (UConn)'s School of Computing, leading the Ubiquitous and Urban Computing Lab since 2019. Previously, he was a postdoctoral research fellow at the University of Michigan's Real-Time Computing Lab (2016–2019). He holds a Ph.D. in Computer Science from the Hong Kong University of Science and Technology (2016) and a B.Eng. in Mechanical Design from Huazhong University of Science and Technology (2012). His research focuses on Cyber-Physical Systems (CPS), Smart & Connected Communities, Human-Centered Computing, and Urban Computing Cyberinfrastructure, with emphasis on mobility, equity, and AI-driven solutions. He has received prestigious awards including the NSF CAREER Award (2023), Google Research Scholar Program Award (2021), and recognition as a Stanford Top 2% Scientist (2020–2024). His work spans interdisciplinary grants from NSF, USDA, Google, NVIDIA, and industry partners. Recent publications explore autonomous driving simulation, equity-aware mobility prediction, and urban crowd activity modeling. Teaching excellence is reflected in his 2020 UConn Provost Award. He advises on reinforcement learning, CPS, and mobile computing, with openings for 2025/2026 PhD students. His lab collaborates on socially-conscious AI, privacy-preserving learning, and location-based services with industrial impact.

LING Chun KaiView profile

Assistant Professor

• Artificial Intelligence
• Decision Making & Planning
• Machine Learning
• +13 more

LING Chun Kai is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His research focuses on multiagent systems, computational game theory, and machine learning applications in adversarial real-world domains like cybersecurity and logistics. Educational background includes a PhD in Computer Science (2017-2023) from Carnegie Mellon University and a First Class BEng in Computer Engineering (2015) from NUS. Previously, he was a Postdoctoral Research Scientist at Columbia University. Current research interests span computational game theory, machine learning for multi-agent systems, equilibrium characterization in imperfect information settings, and applications in network security, logistics, and recreational games. Key methodological contributions include scalable algorithms for game solving, differentiable game solvers, and copula-based statistical modeling. Recent publications focus on attacker-defender graph games, language negotiation agents, and modeling games with incomplete information. Collaborations include researchers from Columbia University, Carnegie Mellon, and institutions working on GameSec, AAAI, Neurips, and ICML venues. Scientific Awards: IJCAI 2018 Distinguished Paper Award GameSec 2023 Best Paper Award GameSec 2024 Best Paper Award Singapore Teaching and Academic Research Talent Scheme (2024) Teaching includes courses on AI Planning and Decision Making (CS4246, CS5446) and Advanced Topics in Artificial Intelligence (CS6208).

• Trustworthy Artificial Intelligence
• Privacy-Preserving Systems
• Adversarial Robustness
• +4 more

Mathias Lécuyer is an Assistant Professor in the Computer Science Department at the University of British Columbia (UBC) , part of the Faculty of Science . His research focuses on trustworthy AI systems, with emphasis on privacy (differential privacy), adversarial robustness, and causal machine learning. He leads the Systopia Lab and collaborates with groups such as UBC S&P, TrustML, and CAIDA. Education: PhD in Computer Science from Columbia University (2019), MSc from Columbia University (2013) and École Polytechnique (2011). Research Interests: Ensuring rigorous guarantees for AI systems via differential privacy, robustness against adversarial attacks, and causal inference. His work spans theoretical foundations and practical implementations in areas like privacy-preserving systems, federated learning, and transparent machine learning. Awards: SOSP Distinguished Artifact Honorable Mention (2024), Google Research Award (2022), Bourse Carnot Fellowship (2011), and UBC’s top teaching evaluations (2021). Advising & Service: Supervised over 20 students (PhD, MSc, undergrad) across privacy and machine learning topics. Serves on PCs for top conferences (OSDI, S&P, NeurIPS) and organizes workshops like TrustML @ UBC. Actively mentors high school students in ML research through outreach programs. Labs/Teams: Leads the Systopia Lab at UBC, focusing on AI safety and privacy. Collaborates with MSR, Google, and industry partners on practical system implementations.

Somil BansalView profile

Assistant Professor

• Autonomous Systems
• Robotics
• Control Theory
• +3 more

Somil Bansal is an Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University, part of the School of Engineering. Previously, he served as an Assistant Professor in the Electrical and Computer Engineering (ECE) department at the University of Southern California. He holds a B.Tech. from IIT Kanpur, an MS, and a Ph.D. from UC Berkeley’s EECS department. Research Focus: Development of mathematical tools and algorithms for safety-critical autonomous systems, emphasizing learning-enabled systems’ safety. Key Collaborations: Waymo, Skydio, Google, Boeing, NASA AMES/JPL. Awards: NSF CAREER Award, Eli Jury Award, RSS Pioneer Award, and Outstanding Graduate Instructor Award. His research integrates control theory and machine learning to ensure safety in autonomous systems, focusing on safe learning frameworks, anomaly detection, and real-time safety guarantees. He leads the Safe and Intelligent Autonomy (SIA) Lab, which explores applications in robotics, autonomous driving, and aerospace systems. Teaching: Courses include Introduction to Control Design Techniques and Principles of Safety-Critical Autonomy. He advises doctoral students and supervises research projects in his lab.

Elham KazemiView profile

Professor

• Mathematics Education
• Teacher Education
• Educational Equity
• +6 more

Elham Kazemi is a Professor of Mathematics Education at the University of Washington’s College of Education, holding the Geda and Phil Condit Endowed Chair. She specializes in elementary teacher education, instructional leadership, and equity-focused mathematics pedagogy. Her research emphasizes professional learning communities, student-centered classroom practices, and disrupting inequities in education. Education: PhD in Mathematics Education, UCLA (1999) MA in Educational Psychology, UCLA (1997) BS in Psychology (Elementary Teaching Certificate), Duke University (1992) Research Interests: Kazemi’s work focuses on equitable mathematics instruction, teacher learning, and leadership development. She designs school-wide professional development models (e.g., Math Labs) and studies classroom discourse, student disciplinary identities, and organizational learning. Key projects include the Labs Project (funded by JSMF) and RMLL (NSF-funded leader learning). Publications & Grants: Over 40 peer-reviewed articles, including work in American Educational Research Journal and Journal of Teacher Education . Co-authored influential books like Choral Counting and Counting Collections and Intentional Talk . Recipient of $5.4M+ in grants from NSF, McDonnell Foundation, and others for projects on teacher leadership and equitable instruction. Awards: 2022 AERA Open Article of the Year 2019 Outstanding Faculty Award for Educational Equity 2015 Geda & Phil Condit Professorship Advising & Leadership: Kazemi co-leads initiatives like the Leading Towards Racially Just Mathematics Instruction partnership. She has supervised numerous graduate students and collaborates with schools to implement equity-driven practices. Labs & Teams: Her work includes the Labs Project (designing Learning Labs for mathematics/literacy discussions) and the Mathematics Education Project (MEP), fostering statewide collaboration among educators.

Xiaolei FangView profile

Associate Professor

• Statistical Learning
• Deep Learning
• Federated Learning
• +3 more

Xiaolei Fang is Associate Professor in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University. His research develops advanced statistical learning, deep learning, and optimization methods for industrial applications involving high-dimensional data, with particular focus on condition monitoring, failure prognostics, and system performance optimization. He holds a PhD in Industrial Engineering and MS in Statistics from Georgia Tech. Professor Fang's research integrates machine learning with industrial engineering to solve complex problems in predictive maintenance, quality control, and energy systems. His methodological innovations include federated learning approaches for privacy-preserving prognostics, distributionally robust machine learning models, and tensor-based statistical methods for manufacturing quality diagnostics. He has received multiple prestigious awards including the ISE Outstanding Research Award (2024), Sigma Xi Best PhD Thesis Award (2019), and SAS Data Mining Best Paper Award (2016). His research has been funded by NSF, Cisco Systems, and the US Department of Energy. Professor Fang teaches courses in Quality Design & Control, Statistical Models for Systems Analytics, High-Dimensional Data Analytics, and Optimization Models. He has supervised 9 PhD students to completion and currently advises 7 graduate students working on projects spanning federated learning for prognostics, tensor-based quality control, and machine learning applications in manufacturing and energy systems.

Timo FleckensteinView profile

Professor

• Comparative Social Policy
• Labour Market Policy
• Political Economy
• +3 more

Professor Timo Fleckenstein is a Professor of Social Policy at the London School of Economics and Political Science (LSE), Department of Social Policy. He previously held a Lecturer (Assistant Professor) position after completing his DPhil at the University of Oxford. His research focuses on comparative welfare state analysis, emphasizing Western Europe and East Asia, particularly South Korea. Key areas include labour market policies, family policies, skills development, and political economy dynamics. Teaching responsibilities include undergraduate courses on 'Comparative and International Social Policy' and postgraduate 'Politics of Social Policy', except during sabbatical periods like 2023/24. His expertise spans comparative political economy, policy-making processes, and institutional change, with a regional emphasis on Germany, South Korea, and Japan. Research highlights include analyses of labour market dualisation, welfare reforms, and social investment policies. His work bridges political science and social policy, exploring how institutional frameworks and policy ideas shape welfare systems in diverse contexts. He actively supervises PhD students in areas like comparative political economy, family policy, and labour market reforms. Publications span journals such as World Politics , Comparative Political Studies , and Journal of Contemporary Asia , reflecting his interdisciplinary approach to policy analysis and institutional transformation.

• Zoology
• Entomology
• Applied Entomology
• +13 more

Teresa Bonacci is an Associate Professor in the Department of Biology, Ecology and Earth Sciences at the University of Calabria (UNICAL), where she conducts research and teaches in zoology with a focus on entomology. She is also the Scientific Director of the Zoology Museum within the university's museum system (SIMU). Her academic affiliations include membership in the PhD School of Life Science and Technology and leadership roles in degree program coordination. Laurea in Natural Sciences, University of Calabria PhD in Animal Biology, University of Calabria Socrates/Erasmus Scholarship, University of Kiel, Germany Research Grant (Assegno di ricerca), SSD BIO/05, University of Calabria National Scientific Qualification for Full Professor (I fascia) Her research spans applied and medico-veterinary entomology, forensic entomology, and conservation biology. She investigates saproxylic beetles (e.g., Cucujus spp.), pollinators (bees, flies, beetles), and insect behavior, especially in forensic contexts such as post-mortem interval estimation and myiasis. Her work integrates taxonomy, morphology, ecology, and chemical signaling, often in collaboration with chemists, legal experts, and conservation agencies. She has published extensively in high-impact journals, with over 80 scientific publications and strong citation metrics. The most recent articles reflect a multidisciplinary trend, combining taxonomy, forensic science, biodiversity monitoring, and veterinary entomology. Topics include the discovery of new insect species in Italy, the role of beetles in decomposition, insect damage in cultural heritage, and myiasis in domestic and wild animals. These works highlight her expertise in both fundamental zoological research and practical applications in forensics and conservation. Scientific Contributions: Over 80 scientific publications Scopus h-index: 15; Google Scholar h-index: 20 Key researcher in saproxylic beetle conservation Pioneer in forensic entomology studies in Southern Italy Active collaborator with legal and medical institutions She supervises numerous PhD and master's students and has directed over 90 theses. Her teaching responsibilities include courses in Zoology, Entomology, Forensic Entomology, and Medical-Veterinary Entomology. She is deeply involved in student mentorship, thesis supervision, and academic service, including examination boards and curriculum development. She also leads biodiversity monitoring projects in Calabrian national parks and has contributed to the development of machine learning tools for insect identification. Her research team collaborates with national and international institutions, including the CNR, University of Milan, and forensic pathology units. She leads projects on bioindicators, insect-plant interactions, and the impact of climate change on mountain species. The Zoology Museum under her direction supports both research and public outreach.

• Computational Fluid Dynamics
• Internal Combustion Engines
• Cavitation
• +13 more

Professor Manolis Gavaises is a leading academic in the field of mechanical engineering and computational fluid dynamics at City St George's, University of London, where he holds the position of Professor in the School of Engineering and Mathematical Sciences. He earned his PhD from Imperial College London and has been a faculty member since 2001, progressing to full Professor in 2009. His research is centered on advanced modeling of multi-phase flows, cavitation, and fuel injection systems, with extensive collaborations across Europe and industry partners such as Delphi, Caterpillar, and BP. Education: DIC, Mechanical Engineering, Computational Fluid Dynamics, Imperial College London, 1997 PhD, Mechanical Engineering, Computational Fluid Dynamics, Imperial College London, 1997 Diploma (5 years), Mechanical Engineering, National Technical University of Athens, 1992 His research interests span computational fluid dynamics, cavitation, fuel injection, atomization, high-pressure and supercritical flows, and alternative fuels . He has developed advanced numerical models and experimental techniques, including X-ray phase contrast imaging and high-pressure test rigs. His work integrates fundamental DNS and LES simulations with industrial applications in automotive, marine, aerospace, and medical devices such as heart valves. The recent publications reflect a strong trend toward real-fluid thermodynamic modeling (e.g., PC-SAFT), multi-component fuel behavior, cavitation erosion, and advanced diagnostics . His research increasingly incorporates machine learning and high-fidelity imaging to understand complex flow phenomena across energy, transportation, and biomedical domains. Scientific Awards and Recognitions: Richard Way Prize (1998) Arch T. Collwell Merit Award (1998) Best Oral Paper, SAE World Congress (2006) PE Publication Award, IMechE (2007) Best Presentation Award, Engine Combustion Processes (2009) Fellow, IMechE (2013) Fellow, IMA (2015) As a dedicated mentor, Professor Gavaises has supervised 13 PhDs to completion and currently guides 23 doctoral students. He has secured over €16 million in EU and UK funding, including multiple Horizon 2020 Marie Skłodowska-Curie ITN projects (CAFÉ, HAOS, IPPAD), which support 46 early-career researchers globally. He has created academic opportunities for post-docs and junior faculty, significantly advancing the research profile of his institution. He leads the International Institute of Cavitation Research (IICR), co-founded in 2011 with partners from Loughborough University, TU Delft, and Imperial College, supported by The Lloyd’s Register Foundation. His lab maintains strong experimental capabilities, including a 2000bar pressure flow rig with micro-transparent nozzles and collaborations with Argonne National Laboratory for X-ray imaging.

Ruchit AgrawalView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +7 more

Dr Ruchit Agrawal is an Assistant Professor and Head of CS Outreach at the School of Computer Science, University of Birmingham Dubai. He holds a PhD in Computer Science from Queen Mary University of London and was a postdoctoral research scientist at the University of Oxford, where he worked on Artificial Intelligence for Healthcare. His research is centered on advancing AI systems with applications in healthcare and natural language understanding. Research Interests: Dr Agrawal specializes in Artificial Intelligence, Machine Learning, Natural Language Processing, Deep Learning, and Clinical Machine Learning. His work explores Multimodal Deep Learning and Adaptive and Contextual AI , aiming to develop intelligent systems that can understand and respond to complex, real-world environments, particularly in medical contexts. His research bridges theoretical innovation with practical healthcare applications. His scholarly contributions include numerous publications in leading journals and top-tier international conferences in AI and computer science. The body of his work demonstrates a strong trajectory in healthcare-oriented AI, multimodal data integration, and context-aware learning models. Scientific Awards: Marie-Curie Fellow Dr Agrawal has experience supervising students at the PhD, MSc, and BSc levels, contributing to academic training and research mentorship. While specific grants are not listed, his Marie-Curie Fellowship and postdoctoral position at Oxford indicate a strong record of competitive research funding. He currently leads computer science outreach initiatives at the University of Birmingham Dubai, promoting engagement and education in computing fields. He is affiliated with the School of Computer Science at the University of Birmingham’s Dubai campus, contributing to both research and academic leadership in an international academic environment.