Find Academic Supervisors

Wei-Yin LohView profile

Professor

• Machine Learning
• Classification and Regression Trees
• Data Analysis
• +4 more

Wei-Yin Loh is a Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. His primary research focuses on classification and regression trees, with applications in machine learning, healthcare, and engineering. He has contributed extensively to the development of algorithms and methodologies in tree-based modeling, including the GUIDE software package. Dr. Loh's work spans theoretical advancements and practical implementations, addressing challenges such as missing data imputation, subgroup identification in precision medicine, and variable importance assessment. His research integrates statistical rigor with real-world problems in public health, federal agency performance analysis, and environmental modeling. Key contributions include studies on tobacco use prediction, cardiovascular health modeling, and pandemic-related mortality analysis. He has also explored applications in engineering, such as pavement thickness analysis and project delivery system evaluation. His publications emphasize interpretable models with strong predictive power, balancing complexity and practical utility. Awards and grants are not explicitly listed in the provided text, though his prolific publication record underscores his academic impact. Advising activities and student mentorship are not detailed here, but his research collaborations likely involve training the next generation of statisticians and data scientists.

• Robotics
• Autonomous Systems
• Data Fusion
• +7 more

Hugh Durrant-Whyte is a Professor at the University of Sydney and Director of the Centre for Translational Data Science. He holds a BSc (Eng) from the University of London, an MSE, and a PhD from the University of Pennsylvania. Previously, he served as CEO of National ICT Australia (NICTA) and Director of the Australian Centre for Field Robotics (ACFR). His research focuses on robotics, autonomous systems, and data fusion, with over 350 publications and four successful startups. Education: BSc (Eng) in Engineering, University of London MSE in Robotics, University of Pennsylvania PhD in Robotics, University of Pennsylvania Affiliations: Director, Centre for Translational Data Science Former CEO, NICTA (2010-2014) Former Director, ACFR (1995-2010) His research interests span robotics, autonomous systems, and sensor networks. Notable contributions include foundational work on SLAM (Simultaneous Localization and Mapping) and decentralized data fusion. He has pioneered applications in mining automation, autonomous vehicles, and environmental modeling. His work emphasizes practical real-world systems, with projects like autonomous straddle carriers for container terminals and terrain mapping for mining operations. Awards & Honors: NSW Scientist of the Year (2010) Fellow of the Royal Society (FRS), Australian Academy of Science (FAA), and IEEE (FIEEE) Recipient of multiple IEEE Best Paper awards Grants & Labs: Leadership in securing multi-million-dollar grants for robotics and data science initiatives Centre for Translational Data Science: Focuses on translating data science into real-world impact He advises on numerous government and industry projects, bridging academic research with industrial applications. His research teams have developed influential algorithms for autonomous navigation and multi-agent systems.

• Hardware Security
• Side-Channel Attacks
• Cryptographic Circuits
• +3 more

Darshana Jayasinghe is a Postdoctoral Research Associate at the School of Electrical and Information Engineering (EIE) , University of Sydney. He holds a PhD from the University of New South Wales (UNSW), completed in 2017, and worked as a Research Associate there until December 2022. His research focuses on hardware security, particularly side-channel analysis attacks and countermeasures. Education: PhD, University of New South Wales (2017) Research Interests include: Side-channel analysis attacks (Power Analysis, EM Attacks, Fault Injection) Countermeasures (Balancing, Random Execution, Masking) On-chip sensors for FPGA monitoring FPGA reliability under power fluctuations Publication Trends reveal expertise in: Hardware Security (15/15 articles) FPGA Vulnerabilities (10/15 articles) Cryptographic Countermeasures (12/15 articles) Sensor Development (5/15 articles)

DAVID LOPEZ VILARIÑOView profile

Professor

• Computer Architecture
• Computer Vision
• FPGA Acceleration
• +3 more

David Lopez Vilariño is a **Professor** at the **University of Santiago de Compostela**, affiliated with the **Department of Electronics and Computing** within the **Faculty of Physics**. He earned his PhD in 2001 with a thesis titled *"Active contours at the pixel level: design and implementation on cellular network architectures,"* advised by Dr. Diego Cabello Ferrer. His research focuses on **Computer Architecture**, **FPGA Acceleration**, **LiDAR Data Analysis**, and **Embedded Systems**, with notable contributions to LiDAR-based applications in urban planning, infrastructure monitoring, and medical imaging. He is part of the **ARQCOMP (Computer Architecture)** and **Artificial Vision** research groups. His work spans topics such as high-performance computing, parallel processing, and hardware optimization for vision-capable systems. Key projects include developing FPGA-based solutions for real-time video surveillance, retinal vessel analysis, and autonomous navigation systems. Publications emphasize **LiDAR data processing**, including algorithms for road detection, power line characterization, and 3D point cloud analysis. He also pioneered tools like the *Open Lidar Visualizer and Analyser* for 3D stereoscopic visualization. His expertise bridges hardware design and software development, particularly in leveraging FPGAs for embedded vision systems. No scientific awards or grants are explicitly listed, but his prolific publication record highlights sustained innovation in computer vision and geospatial technologies. His research team collaborates on projects involving manycore systems, GPU acceleration, and reconfigurable computing architectures.

António Palma Dos ReisView profile

Professor

• Digital Transformation
• Information Systems
• Robotic Process Automation (RPA)
• +7 more

António Palma Dos Reis is a Professor at the ISEG School of Economics and Management at the University of Lisbon, specializing in Management Science and Information Systems. He holds a PhD in Management Science from the University of Wisconsin-Milwaukee (1996), along with MS (1993) and BS (1989) degrees in Management and Information Systems. His professional roles include leadership positions such as Dean and President of the ADVANCE research unit. His research focuses on digital transformation, robotic process automation (RPA), blockchain applications, and technology adoption in organizations. Notable studies include analyzing RPA's value for enterprises and blockchain's role in online gambling. He has advised over 30 master’s students on topics like cloud security, RPA implementation, and AI-driven decision systems. Reis has served on the scientific council of the Open University of Lisbon and contributed to institutional governance as a member of ISEG’s Senate. His work bridges theoretical frameworks (e.g., TOE model) with practical applications in procurement systems, mobile business, and financial decision-making. His publications span 30+ years, covering strategic performance analysis, e-procurement systems, and collaborative decision-making. Current research emphasizes organizational ambidexterity and post-AI societal impacts, reflecting a dual focus on technological innovation and managerial strategy.

Stephen RamseyView profile

Associate Professor

• Computational Systems Biology
• Bioinformatics
• Precision Medicine
• +6 more

Stephen Ramsey, an Associate Professor at Oregon State University, holds dual appointments in the School of Electrical Engineering and Computer Science (College of Engineering) and the Department of Biomedical Sciences (Carlson College of Veterinary Medicine). With a PhD in Physics from the University of Maryland, his postdoctoral training in computational genomics at the University of Washington, and professional experience at the Institute for Systems Biology and Center for Infectious Disease Research, Ramsey bridges computational methods with biomedical applications. Education : Ph.D., Physics, University of Maryland; M.S., Physics, University of Maryland; Sc.B., Mathematical Physics, Brown University Ramsey specializes in computational systems biology , focusing on bioinformatics , biomedical knowledge graphs , and precision medicine . His research integrates machine learning , gene regulatory network modeling , and multi-omics data analysis to address challenges in rare disease diagnostics , drug monitoring , and inflammatory disease mechanisms . Current work includes AI-driven biomedical translation and electrochemical biosensor development for non-invasive diagnostics . Recent publications highlight knowledge graph applications in translational biomedicine , causal network inference in clinical-environmental data integration , and cross-species cancer transcriptomics . His team develops tools like RTX-KG2 and PloverDB to standardize biomedical data sharing and semantic reasoning . Scientific Awards : 2019 Zoetis Award (Carlson College of Veterinary Medicine) 2016 NSF CAREER Award 2016 PhRMA New Investigator Award 2010 NIH K25 Mentored Quantitative Research Award Ramsey advises in computational biology courses (CS 446/546) and contributes to biomedical AI through projects like mediKanren for rare disease diagnostics . His NSF-funded research explores gene expression noise and regulatory network dynamics , while NIH and PhRMA grants support his translational medicine initiatives. He leads the Ramsey Laboratory , which develops graph-based reasoning tools for biomedical data translation and multi-omics integration . The lab's work spans comparative oncology models, electrochemical biosensors , and knowledge graph infrastructure for clinical decision support .

Yildiz YilmazView profile

Associate Professor

• Survival Analysis
• Statistical Genetics
• Genetic Epidemiology
• +4 more

Yildiz Yilmaz serves as Associate Professor of Statistics and Deputy Head of Statistics at Memorial University of Newfoundland's Department of Mathematics and Statistics within the Faculty of Science. Holding a PhD from the University of Waterloo, she maintains an active research program at the intersection of statistical methodology and biomedical applications. Education PhD in Statistics, University of Waterloo (2009) MSc in Statistics, Middle East Technical University (2004) MSc in Computer Engineering, Middle East Technical University (2004) BSc in Statistics with minor in Computer Engineering, Middle East Technical University (2002) Her research focuses on developing advanced statistical methods for survival analysis, genetic epidemiology, and causal inference. Key programs include: (1) novel methods for genome-wide prognosis studies of time-to-event phenotypes, (2) genetic association methods using joint/directional models of multiple phenotypes, (3) evaluation of response-dependent sampling designs, and (4) models for multivariate survival times. Her work addresses critical challenges in biomedical research through innovative statistical theory. Recent publications demonstrate consistent focus on colorectal cancer genomics, statistical genetics methodology, and survival analysis applications. Trends show increasing emphasis on multi-phenotype analysis, SNP interaction networks, and time-varying effects in genetic association studies, with significant contributions to copula-based joint modeling approaches. Research Support Natural Sciences and Engineering Research Council (NSERC) Canadian Statistical Sciences Institute (CANSSI) Faculty of Medicine, Memorial University Research and Development Corporation (RDC) of Newfoundland and Labrador Dr. Yilmaz actively mentors students across multiple programs including PhD, MSc, MAS, and undergraduate levels. Her current research group comprises seven students working on statistical genetics and survival analysis projects, building on a track record of successfully supervising numerous graduate students and post-doctoral fellows since 2013.

Maysson IbrahimView profile

Senior Lecturer

• Bioinformatics
• Genetic Data Analysis
• Pathway Enrichment
• +3 more

Maysson Ibrahim is a Senior Lecturer in Computing at the School of Computing, University of Buckingham, where she joined in 2020 as a part-time Lecturer in Computer Science while continuing her research role at the University of Oxford. She holds a PhD in Bioinformatics from the Buckingham Institute for Translational Medicine (2013) and a BEng in Software Engineering and Information Systems. PhD in Bioinformatics, University of Buckingham (2013) BEng in Software Engineering and Information Systems Her research focuses on bioinformatics, genetic data analysis, and machine learning applications in big data. She has developed computational tools for pathway enrichment analysis and biomarkers identification using gene expression data. At Oxford, she leads bioinformatics work on large-scale biobanks (e.g., UK Biobank) and clinical trials like REVEAL, SHARP, and THRIVE to decode genetic determinants of complex diseases, particularly cardiovascular conditions. Her recent publications emphasize pathway-based algorithms, disease classification, and the integration of gene network analysis with machine learning techniques. Despite no explicit scientific awards listed, her contributions span academic roles in teaching statistics and data science modules at Buckingham and leading analytical pipelines for major clinical studies. She teaches modules such as Data Exploration and Visualisation and Systems and Tools for Data Science in the MSc Applied Data Science programme.

• Software Security
• Software Testing
• Program Analysis
• +2 more

Dr. Seongmin Lee is a researcher at the Max Planck Institute for Security and Privacy, specializing in software security and program analysis. Their work bridges theoretical and practical aspects of software testing, with a particular focus on automated testing techniques, dependency modeling, and genetic improvement. Research interests include: Software Security Software Testing and Fuzzing Program Analysis and Slicing Machine Learning Applications in Software Engineering Genetic Algorithms for Code Optimization Statistical and Causal Analysis of Code Behavior Recent publications (2016–2025) demonstrate a trajectory from foundational work on GPU parameter optimization to cutting-edge research on LLM-driven regression testing. Key trends include: Statistical modeling of software behavior Machine learning for bug classification and optimization Approximate analysis techniques for scalability Advancements in greybox fuzzing and coverage prediction Application of causal inference to mutation testing

Isabel Valera MartinezView profile

Professor

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

Isabel Valera Martínez is a full Professor of Machine Learning at the Department of Computer Science, Saarland University, and an Adjunct Faculty at the Max Planck Institute for Software Systems (MPI-SWS). She holds a PhD in Machine Learning from the University Carlos III of Madrid and has held prestigious fellowships including the Humboldt Postdoctoral Fellowship and the Minerva Fast Track Fellowship from the Max Planck Society. Her research focuses on developing interpretable, robust, and fair machine learning methods, with applications in healthcare, social systems, and consequential decision-making like hiring and loan approvals. Education: PhD in Machine Learning (2014) and MSc in Multimedia and Communications (2012) from University Carlos III of Madrid; Master’s work at Leibniz University Hannover (2009); Telecommunications Engineering from the Technical University of Cartagena (2009). Research Interests: Fairness in AI, Bayesian nonparametric models, robust machine learning, and applications in healthcare and social systems. She co-leads the ELLIS Robust Machine Learning program and the Saarbrücken AI & Machine Learning (SAM) Unit. Key Publications: Contributions to fair classification frameworks, automatic discovery of data types, and modeling psychiatric comorbidity. Notable venues include AAAI, NIPS/NeurIPS, JMLR, and ICML. Awards: Humboldt Fellowship, Minerva Fellowship, Best Paper Honorable Mention at WWW 2017. Labs & Collaborations: Active in the ELLIS Society and MPI-SWS, focusing on interdisciplinary AI applications.

Ryan T. WhiteView profile

Associate Professor

• Deep Learning
• Machine Learning
• Artificial Intelligence
• +11 more

Ryan T. White is an Associate Professor at Florida Institute of Technology in the Department of Mathematics and Systems Engineering within the College of Engineering and Science. He serves as Director of the NEural TransmissionS (NETS) Lab, focusing on deep learning, computer vision, and data science. He is also an Affiliate Faculty member in Electrical Engineering and Computer Science. Ph.D. in Applied Mathematics (2015) from Florida Tech His research bridges deep learning and computer vision with applications in autonomous satellite operations , physics-informed neural networks for biomedical and geoscience problems, and NLP in aerospace domains. Projects include real-time edge computing , stochastic process analysis , and generative AI for synthetic data. The NETS Lab he directs has produced 15+ recent publications in conferences like IEEE Aerospace, AIAA SCITECH, and AAS/AIAA, with funding from the U.S. Space Force, Air Force Research Lab, and NSF. His teaching spans graduate/undergraduate courses in deep learning , machine learning , probability , and honors calculus . Current advisees include Ph.D. candidates and M.S. students working on topics like 3D object detection , information-theoretic neural analysis , and geophysical signal processing . The lab’s scientific contributions include real-time satellite feature detection , physics-guided neural networks for blood flow modeling, and entropy-based visual explanations for AI interpretability. Collaborations span Georgia Tech , Mulitscale Cardiovascular Fluids Laboratory , and Engage-AI for global development projects analyzing UNDP Sustainable Development Goals.

Christos DiouView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Healthcare Applications
• +3 more

Christos Diou is an Associate Professor of Artificial Intelligence and Machine Learning at the Department of Informatics and Telematics, Harokopio University of Athens, Greece. His academic career spans over 15 years of participation in national and international research projects, with a focus on machine learning algorithms, domain generalization, causal inference, and bias mitigation. He earned a BSc and Ph.D. in Electrical and Computer Engineering from Aristotle University of Thessaloniki. His research emphasizes the application of machine learning to healthcare, addressing challenges such as visual bias mitigation, causal effect estimation from observational data, and fairness-aware representation learning. Notable projects include REBECCA and RELEVIUM , both EU-funded, and MELIORA , targeting lifestyle interventions for breast cancer risk reduction. He has published extensively in top-tier venues like IEEE TPAMI, CVPR, and ICCV. Christos is a leading voice in AI ethics and healthcare innovation, with over 150 publications and best paper awards at IEEE Big Data Service 2023 and AIAI 2022. His work includes developing platforms like Effector for feature effects and Beam for behavior studies. He collaborates with institutions such as Karolinska Institutet and CERTH/ITI, and his students include PhD candidates Ioannis Sarridis and Aristotelis Ballas.

• Bayesian Networks
• Artificial Intelligence
• Risk Analysis
• +3 more

Steven Mascaro is a Senior Research Fellow in the Department of Data Science & AI at Monash University. His research focuses on advancing Bayesian network methodologies and their application in complex domains such as public health, biosecurity, and policy analysis. He collaborates on projects like 'Fitting AI technology to complex policy problems' and 'The Development of Novel Bayesian Network and Multi-Criteria Decision Analysis Techniques.' Key research areas include causal modeling for medical decision support, risk assessment frameworks for invasive species, and expert elicitation techniques. Mascaro's work bridges theoretical AI advancements with practical implementations in healthcare and environmental management. His contributions span over 27 peer-reviewed outputs, emphasizing interdisciplinary collaboration. Projects: 2 major research initiatives funded through 2022 Publications: Over 25 articles since 2001, with recent focus on Bayesian networks in pandemic response and clinical diagnostics Expertise: Bayesian network parameterization, causal inference, and decision-support tool development His current efforts prioritize scalable AI solutions for policy challenges and improving diagnostic accuracy through causal modeling.

Kaushik MallikView profile

Assistant Professor

• Controller Synthesis for Dynamical Systems
• Combinatorial Games on Graphs
• Algorithmic Fairness in AI
• +4 more

Kaushik Mallik is an Assistant Professor at the IMDEA Software Institute in Madrid, Spain. His research focuses on formal methods for ensuring safety and trustworthiness of AI systems, combining game theory, control theory, and machine learning. He holds a PhD in Computer Science from the Max Planck Institute for Software Systems (2022), an M.Tech in Systems and Control from IIT Roorkee (2015), and a B.Tech in Electrical Engineering from Meghnad Saha Institute of Technology (2012). His work addresses challenges in controller synthesis for cyber-physical systems, bidding games, and algorithmic fairness monitoring. Education: PhD in Computer Science, Max Planck Institute for Software Systems, Germany (2022) M.Tech in Systems and Control, IIT Roorkee (2015) B.Tech in Electrical Engineering, Meghnad Saha Institute of Technology (2012) Research Interests: Formal synthesis of safe AI systems Algorithmic fairness in decision-making algorithms Bidding games and stochastic control Symbolic control for cyber-physical systems His current projects include runtime fairness monitoring tools and theoretical advancements in auction-based scheduling algorithms. Key Achievements: Winner of the 2023 ETAPS Doctoral Dissertation Award Recipient of the DAAD IIT Master-Sandwich Programme Scholarship Lead developer of tools like Mascot-SDS and BOCoSy for symbolic control Ongoing research explores resilient abstraction-based controllers and fairness watchdog systems for AI applications.

• Machine Learning
• Computer Science
• Medical Informatics
• +7 more

Yan Li is a researcher with extensive contributions across interdisciplinary domains including Machine Learning , Signal Processing , and Environmental Science . Affiliated with institutions such as the University of Southern Queensland , Hebei University , and Shandong University , Li's work spans applications in Medical Informatics , Remote Sensing , and Operations Research . Recent publications highlight expertise in Deep Learning (e.g., hyperspectral classification, image fusion), Stochastic Modeling (e.g., chemotaxis models), and Federated Learning (e.g., vertical federated fuzzy clustering). Collaborative projects include 3D Reconstruction , Smart Grid Security , and Landslide Monitoring using satellite data. Li's 2025 work demonstrates a focus on Medical Imaging (segmentation algorithms with dual-frequency decoupling), AI in Education (ChatGPT adoption), and Industrial IoT (GPU-accelerated vessel trajectory visualization). While no explicit academic rank is provided, their prolific publication record suggests a Researcher role.