Find Academic Supervisors

Nan YeView profile

Senior Lecturer

• Machine Learning
• Statistics
• Optimization
• +4 more

Dr. Nan Ye is a Senior Lecturer in Statistics and Data Science at the University of Queensland's School of Mathematics and Physics. His research focuses on machine learning, statistics, and optimization, with contributions to sequential decision making, weakly supervised learning, and probabilistic graphical models. He holds a PhD in Computer Science from the National University of Singapore (NUS) and double first-class honors in Computer Science and Applied Mathematics from NUS. Previously, he held postdoctoral positions at QUT, UC Berkeley, and NUS. Dr. Ye teaches advanced courses such as STAT3007/7007 Deep Learning, covering topics from foundational machine learning to state-of-the-art deep learning architectures and applications. His work has been published in top venues like NeurIPS, ICML, and UAI, earning awards including the IJCAI-JAIR Best Paper Prize (2022) and UAI Best Student Paper Award (2014). His research interests span theoretical and applied machine learning, including reinforcement learning, optimization algorithms, and their applications in fields like healthcare and environmental science. He actively supervises students in these areas and collaborates on interdisciplinary projects. Dr. Ye's academic profile includes extensive contributions to open-source tools and educational materials, reflecting his commitment to advancing both research and pedagogy in data science.

Juho RousuView profile

Professor

• Machine Learning
• Metabolomics
• Biomedicine
• +6 more

Juho Rousu is a Professor in the Department of Computer Science at Aalto University. His research focuses on developing machine learning methods for computational and data science, with a strong emphasis on kernel methods, structured prediction, and applications in metabolomics, biomedicine, and synthetic biology. He leads efforts in multi-view learning, sparsity-driven models, and optimization techniques for complex data analysis. Key research areas include drug combination prediction, metabolite identification, and biomarker discovery through canonical correlation analysis. His work integrates chemical reaction analysis, graph learning, and deep learning approaches to tackle challenges in systems biology and precision medicine. Recent projects involve scalable methods for drug synergy modeling and computational tools like MassSpecGym for molecular discovery. Rousu’s methodologies often bridge algorithmic innovation with real-world applications, such as improving protein production in biotechnology and optimizing metabolic flux analysis. His contributions span from foundational machine learning theory to practical software tools like CamOptimus and SIRIUS 4. He collaborates across disciplines to address challenges in environmental microbiology, clinical diagnostics, and systems pharmacology. His research has been applied to critical areas like tuberculosis drug efficacy prediction and metabolic network reconstruction, demonstrating impact in both academic and industrial contexts. Despite no listed awards here, his extensive publication record reflects sustained leadership in computational science and its biomedical applications.

Daniel WorkView profile

Professor

• Transportation Cyber-Physical Systems
• Traffic Estimation & Control
• Connected & Autonomous Vehicles
• +5 more

Daniel Work is a Chancellor Faculty Fellow and Professor of Civil and Environmental Engineering, Computer Science, and the Institute for Software Integrated Systems at Vanderbilt University. He directs the Work Research Group, focusing on transportation cyber-physical systems (CPS), traffic flow modeling, and autonomous vehicle technologies. His work includes pioneering methods to eliminate phantom traffic jams using automated vehicles and leading the I-24 MOTION project, a large-scale testbed for connected and autonomous vehicles. Education: Ph.D. in Systems Engineering, UC Berkeley (2010) MS in Civil and Environmental Engineering, UC Berkeley (2007) B.S. in Civil and Environmental Engineering, Ohio State University (2006) Research Interests: Autonomous vehicle control and traffic wave smoothing Data analytics for transportation systems Freight rail and urban mobility optimization Human-in-the-loop CPS applications Awards: 2018 NAE Gilbreth Lectureship 2014 NSF CAREER Award IEEE ITS Society Best Dissertation (2011) Featured in Scientific American and ABC's Good Morning America Labs & Projects: I-24 MOTION Testbed: 17-mile freeway instrumented with 300+ cameras CIRCLES Consortium: Mixed-autonomy traffic control AI Decision Support System for Integrated Corridor Management

Bennett A. LandmanView profile

Professor

• Medical Imaging
• Biomedical Engineering
• Machine Learning
• +6 more

Bennett A. Landman is the Stevenson Chair of Engineering and a Professor of Electrical and Computer Engineering at Vanderbilt University's School of Engineering. He holds additional professorships in Computer Science, Biomedical Engineering, Radiology, Psychiatry, Biomedical Informatics, and Neurology. Landman leads the Vanderbilt Lab for Immersive AI Translation (VALIANT) and has been instrumental in advancing medical image processing and AI-driven personalized medicine. Education: B.S. & M.Eng. in Electrical Engineering and Computer Science from MIT (2001-2002) Ph.D. in Biomedical Engineering from Johns Hopkins University (2008) Research Focus: Landman's work centers on applying image-processing technologies to large-scale medical imaging studies, particularly in neuroimaging and radiology. His research integrates machine learning, big data analytics, and AI to improve diagnostic accuracy and personalized healthcare. Key areas include diffusion MRI harmonization, functional MRI analysis, and automated medical image segmentation. Recent Trends in Articles: Landman's latest publications focus on spatial smoothing techniques in white matter imaging, super-resolution eye atlases, and scalable medical data processing. His work emphasizes cross-disciplinary collaboration and the development of robust preprocessing methods for multi-site datasets. Awards: Fellow of the SPIE (2021) Grants & Advising: Secured grants from NIH, NSF, and DoD. Advised over 340 co-authors across institutions, resulting in 450+ peer-reviewed publications and 24,000+ citations. Served as editor for IEEE Transactions on Medical Imaging and co-chair of SPIE Medical Imaging conferences. Labs & Teams: Director of VALIANT and founding director of Vanderbilt's Center for Computational Imaging. Active in the Vanderbilt Brain Institute and Kennedy Center, advancing interdisciplinary research in surgery, engineering, and neuroscience.

Alf GerischView profile

Senior Lecturer

• Mathematical Modeling
• Numerical Analysis
• Scientific Computing
• +6 more

Dr. Alf Gerisch is a Senior Lecturer at the Department of Mathematics, Technical University of Darmstadt, where he leads the Numerical and Scientific Computing Group. His research focuses on mathematical modeling, numerical simulation, and analysis of biological, material, and natural science processes, particularly in tumor models, cell adhesion, bone fracture healing, and multiscale mechanical properties of tissues. He has organized and contributed to numerous international conferences, including ENUMATH2025 and ECMTB 2024. His work integrates advanced numerical methods like peer time integration schemes and uncertainty quantification, with applications in biomedical systems and soft matter. Education: Ph.D. in Numerical Analysis (2001, University of Halle-Wittenberg), M.Sc. in Applied Mathematics (1997, University of Dundee). Research Interests: Dr. Gerisch advances numerical methods for taxis-diffusion-reaction systems, develops computational frameworks for uncertainty quantification, and models biological processes such as tumor invasion and cell migration. His projects include leadership of the DFG-funded Multiscale structure-functional modeling of musculoskeletal mineralized tissues (2009–2016) and membership in SFB Transregio 154 and 146. Publications: Over 50 peer-reviewed articles, including contributions to journals like Journal of Computational Physics , Bulletin of Mathematical Biology , and Computer Methods in Biomechanics and Biomedical Engineering . Recent work explores nonlocal models of cell adhesion and data-driven approaches for gas networks. Teaching: Offers courses in numerical linear algebra, mathematical biology modeling, scientific programming, and finite element methods. Consultation hours via Zoom during lecture weeks. Grants & Projects: Co-led DAAD-funded research visits to India (2017), and collaborates internationally on biomechanical and computational biology initiatives. Labs/Teams: Active in the Numerical Analysis and Scientific Computing research group, contributing to the Kardos adaptive Finite Element software and the asymptotic homogenization framework for composite materials.

Estela SuarezView profile

Professor

• High Performance Computing (HPC)
• Heterogeneous HPC Architectures
• Modular Supercomputing Architecture
• +4 more

Estela Suarez is a Professor of High Performance Computing at the University of Bonn's Computer Science Department and holds leadership roles at the Jülich Supercomputing Centre (JSC) in Forschungszentrum Jülich. Her work focuses on advancing modular supercomputing architectures, heterogeneous systems, and application optimization for exascale computing. She currently serves as Chair of the EuroHPC Joint Undertaking's Research and Innovation Advisory Group (RIAG) and leads the Next Generation Architectures and Prototypes research group at JSC. Education includes a PhD in Physics from the University of Geneva (2010) and a Master's in Physics (Astrophysics specialization) from Universidad Complutense de Madrid (2004). She has held senior research positions at JSC since 2010 and has led major EU-funded projects like DEEP, DEEP-ER, and DEEP-SEA. Research interests include HPC system design, hardware-software co-design, and energy-efficient supercomputing architectures. She was awarded the University of Bonn's 2023/2024 teaching award and actively contributes to initiatives like the European Processor Initiative (EPI) and the NUMERIQS consortium. Current projects include optimizing exascale systems for Earth system modeling (IFCES2), AI-driven data analytics (AIDAS), and modular supercomputing software (DEEP-SEA). During her 2024/2025 sabbatical, she is not accepting new students. Her work is published in journals like Geoscientific Model Development and conferences such as ISC and EuroHPC.

Shunxing BaoView profile

Academic

• Medical Imaging
• Machine Learning
• Distributed Computing
• +5 more

Shunxing Bao is a Research Assistant Professor in Electrical and Computer Engineering at Vanderbilt University's School of Engineering. His research focuses on distributed computing, medical imaging, and machine learning applications in healthcare. He holds a Ph.D., M.S., and B.E. in Computer Science and Software Engineering from Vanderbilt University and Huazhong University of Science and Technology. Research Interests: - Distributed computing for big data management in medical imaging - Computational image analysis and digital pathology using machine learning - Medical informatics and scalable data processing pipelines - Medical imaging harmonization and enhancement techniques Notable Contributions: Bao's work addresses challenges in medical imaging workflows, including low-dose CT reconstruction, diffusion MRI analysis, and multiplexed immunofluorescence (MxIF) imaging. He has developed algorithms for image harmonization, segmentation, and bias mitigation in AI systems. His research integrates high-performance computing to enable large-scale medical data analysis. Publications (2025): Focus on advanced imaging techniques, including CycleGAN harmonization, 3D kernel reparameterization, and applications of SAM models in pathology. His work bridges computational methods with clinical needs in neurodegenerative disease prediction and pediatric diagnostics.

Siamak ArdekaniView profile

Senior Lecturer

• Medical Imaging
• Cardiovascular Disease
• Neurological Disorders
• +3 more

Siamak Ardekani is a Senior Lecturer in the Department of Biomedical Engineering and the Department of Medicine at Johns Hopkins University. He holds a PhD in Biomedical Engineering from UCLA (2006), an MD from Shiraz University of Medical Sciences (1993), and an MS in Biomedical Engineering from Drexel University (2000). His research focuses on computational medical imaging, particularly MRI, to develop statistical shape and function models for early disease detection and therapy monitoring in neurological and cardiovascular disorders. His work emphasizes extracting clinically relevant data from medical images to aid in patient risk assessment and treatment efficacy evaluation. Ardekani's contributions include advancements in cardiac MRI analysis, diffusion tensor imaging (DTI), and automated anatomical modeling. He received the Johns Hopkins Discovery Award in 2020 for multidisciplinary research. His affiliations include the Center for Imaging Science at Johns Hopkins. Ardekani’s research spans computational methods for cardiac motion analysis, MRI-based disease phenotyping, and image registration techniques. He collaborates on projects such as the Cardiovascular Research Grid (CVRG), enhancing data sharing and analysis in cardiovascular studies. His work bridges medicine and engineering, with applications in both clinical and preclinical settings.

• Heavy Ion Collisions
• Quark-Gluon Plasma
• General Relativity
• +4 more

Dr. W. van der Schee is an Assistant Professor at Utrecht University's Faculty of Science, within the Department of Theoretical Physics (ITF). He also holds a staff position at CERN. His research focuses on heavy ion collisions, quark-gluon plasma dynamics, and gravitational wave studies using holographic methods. Key areas include numerical relativity, neutron star equations of state, and tensor networks. He received the NWO Veni award in 2017. Educational background: PhD in Theoretical Physics (cum laude, Utrecht University, 2014), postdoctoral work at MIT (2014-2018) and Utrecht University (2016-2020), supported by a VENI grant. He teaches courses on quantum chromodynamics and particle physics field theory. Research highlights include Bayesian analysis of heavy-ion data using the Trajectum framework, gravitational wave studies from neutron star mergers, and quantum null energy condition explorations. His group includes PhD students Olaf Massen and Maaike Bakker, and postdoc Javier Subils. Ongoing projects involve holographic jet dynamics and neutron star merger simulations. Awards: NWO Veni award (2017). Active in both university and CERN-based collaborations, advancing theoretical frameworks for high-energy and gravitational physics phenomena.

Rodrigo MorenoView profile

Associate Professor

• Medical Imaging
• Biomechanics
• Artificial Intelligence in Healthcare
• +4 more

Rodrigo Moreno is an Associate Professor at the School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH) at KTH Royal Institute of Technology, and a Co-Principal Investigator (Co-PI) in two major research projects: 'Characterization of the mechanical tissue properties of the brain in the developing brain with magnetic resonance elastography' and 'Advanced Magnetic Resonance Elastography for the Brain.' He is also affiliated with Digital Futures, a cross-disciplinary research center focused on developing digital technologies to address societal challenges. His primary research interests include biomedical imaging techniques, biomechanical properties of tissues, and the application of artificial intelligence (AI) in healthcare. Dr. Moreno’s work spans neurodegenerative diseases like Alzheimer’s and Parkinson’s, as well as advancements in medical imaging modalities such as MRI, CT, and ultrasound. His recent projects emphasize precision medicine using AI-powered longitudinal MRI analysis and the mechanical characterization of brain tissue in developmental and disease contexts. He leads collaborations in computational methods like tensor voting and deep learning for medical image processing, segmentation, and generative modeling of biological structures. His contributions include developing algorithms for tractography filtering, synthetic data generation, and robust segmentation of medical images. Dr. Moreno’s research has produced over 50 peer-reviewed articles since 2014, with a focus on advancing imaging technologies and their clinical applications. His work bridges computational engineering, biomedical research, and digital innovation, aiming to improve diagnostics and personalized treatment strategies for complex diseases.

Khanh LuongView profile

Research Fellow

• Machine Learning
• Data Mining
• Multi-Aspect Data Clustering
• +7 more

Dr. Khanh Luong is a Research Fellow at the School of Communication within the Faculty of Creative Industries, Education & Social Justice at Queensland University of Technology (QUT). She holds a PhD in Computer Science specializing in Data Science (2019) from QUT. Her research focuses on multi-aspect data, addressing challenges in high-dimensional, sparse datasets through innovative machine learning and data mining methods. She contributes to real-world applications in diverse domains like text, image, and bioinformatics data. Teaching roles include tutoring in Discrete Mathematics, Programming Principles, and Database Management for undergraduate/postgraduate students. She also designed the online unit 'Introduction to Programming' for the Master of IT program. Research interests span machine learning, data mining, multi-aspect data clustering, dimensionality reduction, and unsupervised learning. She works on the Applied Data Science project at QUT's Centre for Data Science, developing solutions for complex industry datasets. Publications emphasize multi-view clustering, manifold learning, and domain adaptation. Collaborations include roles in organizing the 2021 AusDM conference and reviewing for IEEE journals.

Daniel ParkerView profile

Assistant Professor

• Condensed Matter Physics
• Quantum Materials
• Topological Insulators
• +4 more

Daniel Parker is an Assistant Professor in the Department of Physics at the University of California, San Diego (UCSD). His research focuses on theoretical and computational condensed matter physics, with an emphasis on quantum materials, topological phases, and strongly correlated electron systems. His work explores phenomena such as anomalous Hall effects, fractional Chern insulators, and moiré physics in layered materials like graphene. He employs advanced computational tools, including tensor networks and density matrix renormalization group (DMRG) methods, to study electronic structures and phase transitions. His recent contributions include studies on zero-field quantum Hall states, vortexable bands, and superconductivity in topological lattices. Dr. Parker’s research bridges theoretical frameworks with cutting-edge experimental observations in moiré materials, aiming to uncover new quantum states and their applications. He collaborates on developing computational frameworks like Tensor Network Python (TeNPy) to simulate complex quantum systems. His studies often address fundamental questions in topological matter, quantum transport, and symmetry-breaking phases in low-dimensional systems.

King-Wah ChuView profile

Research Fellow

• Numerical Analysis
• Numerical Linear Algebra
• Control Theory
• +3 more

King-Wah Chu is an Adjunct Senior Research Fellow at the School of Mathematics, Monash University. He has been working in Melbourne, Australia for 30 years. His research focuses on numerical analysis, numerical linear algebra, eigenvalue problems, control theory, and matrix equations. He serves as an Associate Editor for the SIAM Journal on Matrix Analysis and Applications since 2015. Education: Hong Kong and England Affiliations: Monash University, SIAM Journal His research interests include perturbation and numerical solutions of eigenvalue problems, inverse eigenvalue problems, Riccati equations, and large-scale structured computations. He has contributed to the development of algorithms for control systems and tensor computations. Key Projects: Research visit to the International Symposium on Mathematical Theory of Networks & Systems (2000) Advising & Grants: Chu has advised on multiple projects but no specific students are listed. His work involves collaborations on large-scale algebraic Riccati equations and numerical methods. Labs/Teams: Active in the School of Mathematics at Monash University, focusing on numerical linear algebra and control systems.

Yuedong WangView profile

Professor

• Biostatistics
• Computational Statistics
• Machine Learning
• +7 more

Yuedong Wang is a Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara (UCSB), part of the College of Letters and Science. He holds a PhD from the University of Wisconsin-Madison and has prior experience at the University of Michigan. His research focuses on statistical methodologies, including nonparametric and semiparametric models, smoothing splines, machine learning, and biostatistical applications in renal diseases and human circadian rhythms. He is a Fellow of the ASA, IMS, ISI, and RSS, and a member of IBS and ICSA. His work spans over 150 publications, with recent emphasis on modeling dialysis patient outcomes, SARS-CoV-2 transmission in healthcare settings, and statistical computing. Key collaborations include the MONDO initiative for dialysis outcomes research. He has developed R packages like ASSIST for nonlinear mixed-effects models and contributed to smoothing spline theory. His book *Smoothing Splines: Methods and Applications* is a seminal text in the field. Education: BS in Mathematics (USTC), MS in Operations Research (Chinese Academy of Sciences), PhD in Statistics (UW-Madison). Research interests: Machine learning applications, functional data analysis, longitudinal data modeling, and biostatistical methods in nephrology. His articles address topics like tensor product RKHS analysis, nonparametric mixture models for clinical data, and AI-driven predictive analytics for dialysis patients. Awards include recognition for contributions to statistical methodology and its biomedical applications.

Tingting ZhangView profile

Professor

• Neuroimaging Data Analysis
• Human Brain Mapping
• Bayesian Statistics
• +4 more

Dr. Tingting Zhang is a Professor in the Department of Statistics at the University of Pittsburgh's Dietrich School. She holds a Ph.D. in Statistics from Harvard University (2008), an M.S. in Statistics from Harvard (2005), and a B.S. in Mathematics from Peking University (2003). Her research focuses on neuroimaging data analysis, human brain mapping, and Bayesian statistics, with applications to neurological disorders. She collaborates with clinicians and neuroscientists to develop statistical methods for analyzing multimodal brain imaging data, including fMRI and EEG. Her methodological contributions include Bayesian network models, high-dimensional data analysis, and variational inference techniques. Recent work examines functional connectivity across lifespan stages using HCP datasets and epileptic seizure analysis through directional brain networks. She has developed software tools including the Bayesian Modular and Indicator-based Dynamic Directional Model (BMIDDM) and the Bayesian hierarchical model for state-space stochastic block analysis. Dr. Zhang has taught courses in Bayesian statistics, stochastic processes, and experimental design. Her work bridges statistical theory with clinical applications, emphasizing translational research in neuroimaging. She maintains an active research program with over 30 peer-reviewed publications in top journals like Annals of Applied Statistics and NeuroImage.