Find Academic Supervisors

• Machine Learning
• Healthcare Analytics
• Agricultural Technology
• +3 more

Dr. Yu Zhang is a Researcher at the School of Computer Science, University of Sheffield, affiliated with the Pervasive Computing research group. His work focuses on advanced machine learning techniques applied to healthcare and agricultural domains. He specializes in multi-task learning, spatio-temporal data analysis, and tensor methods. Key research areas include Alzheimer’s disease progression prediction using neuroimaging data and precision fertilization management through sustainable farming systems. His contributions bridge computational methods with real-world applications in healthcare analytics and environmental sustainability. Dr. Zhang’s research integrates temporal relation graphs, adaptive control systems, and interpretable AI frameworks to address complex challenges in disease modeling and agricultural optimization. He explores innovations like continuous incremental learning and randomized feature learning to enhance predictive accuracy and scalability. His work emphasizes practical applications, such as the ParallelFarm system for carbon-neutral farming and frameworks for medical decision-making based on MRI data analysis. His research trends show a strong focus on multi-task learning frameworks that adapt to temporal dynamics, with applications spanning healthcare biomarker identification, resource-efficient farming, and robust algorithm design. Collaborations likely involve interdisciplinary teams in computer science, neuroscience, and environmental science. Future directions may include expanding these methods to other healthcare domains and environmental sustainability initiatives. No scientific awards are explicitly mentioned in the provided text. Advising and grant details are not listed here, but his affiliations suggest involvement in collaborative research projects within the School of Computer Science. His work is rooted in the Pervasive Computing group, emphasizing embedded systems and AI-driven solutions for real-world problems.

Nick O. JaenssonView profile

Assistant Professor

• Non-Newtonian Fluid Mechanics
• Rheology
• Uncertainty Quantification
• +3 more

Nick O. Jaensson is an Assistant Professor (tenure track) in the Processing and Performance of Materials group at Eindhoven University of Technology (TU/e). He holds affiliations with the ICMS and is part of the Mechanical Engineering department. His research focuses on numerical methods for soft materials, including complex fluids like suspensions, emulsions, and polymeric liquids. He collaborates with industry on optimizing processes such as microfluidics and material processing. Academic Background: Master's in Biomedical Engineering (2012) and PhD in Polymer Technology (2016), both from TU/e. Postdoc at ETH Zürich (2018) before joining TU/e in 2020. Research Interests: Non-Newtonian fluid mechanics, interfacial rheology, uncertainty quantification, and physics-informed machine learning. His work bridges fundamental insights into material microstructure and industrial applications. Award: Walters Prize (2018) for contributions to non-Newtonian fluid mechanics. Teaching & Courses: Advanced computational continuum mechanics, interfacial transport phenomena, and design/programming principles. Supervised 19 students (no names listed). Projects: Leads the DAMOCLES project (2021–2025), focusing on data-augmented modeling for engineering systems. Labs/Teams: Head of the Processing and Performance of Materials group, collaborating with experimental and computational teams.

Gonzalo ArceView profile

Professor

• Computational Imaging
• Signal Processing on Graphs
• Machine Learning
• +7 more

Dr. Gonzalo Arce is the Charles Black Evans Professor and JPMorgan-Chase Senior Faculty Fellow at the University of Delaware. His research spans computational imaging, signal processing on graphs, and machine learning, with applications in biomedicine, Earth science, and lithography. He leads interdisciplinary projects in computational compressive lidar, spectral tomography, and hypergraph neural networks, collaborating globally with institutions in Finland, China, and beyond. Dr. Arce has held the Nokia-Fulbright Distinguished Chair and is a Fellow of IEEE, SPIE, and the National Academy of Inventors. His research focuses on advancing imaging technologies through compressive sensing, generative AI, and hypergraph structures. Notable contributions include SpectralCam (low-cost spectral imaging) and super-resolution techniques for satellite LiDAR. He advises on patent litigation involving signal/image processing and QR codes. With over 800 publications and 30 patents, he actively contributes to the Data Science Institute and IFSAN at UD. Awards: National Academy of Inventors Fellow, IEEE Life Fellow, SPIE Fellow, AAIA Fellow Teaching: Courses on machine learning and deep learning in imaging Labs/Teams: Collaborates with global research groups in computational imaging and financial analytics

• Machine Learning
• Data Mining
• Graph Neural Networks
• +5 more

Hanghang Tong is a Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. He also holds affiliations with the National Center for Supercomputing Applications (NCSA) and the Center for Digital Agriculture. His research interests span machine learning, data mining, graph theory, and ethical AI, with a focus on network analysis, fairness, and large-scale data systems. Tong has been recognized with prestigious awards including the IEEE Fellow (2022), ACM Distinguished Member (2020), and NSF CAREER Award (2019). His recent work explores topics like LLM safety, time series analysis, and network alignment through tools like PLANETALIGN. Tong actively contributes to open-source libraries and benchmarks, advancing reproducibility and ethical standards in AI. Education : Not explicitly listed in provided texts. His research emphasizes practical applications of AI in climate science, healthcare, and social networks. Tong's work often addresses challenges in model fairness, scalability, and cross-domain adaptation. He collaborates widely, contributing to both foundational theory and real-world systems. Key projects include CLIMB (class-imbalanced learning benchmark) and Saffron-1 (LLM safety assurance). Tong’s lab at NCSA and Siebel School drives innovation in AI-driven data science and computational methods.

Geert Jan BiesselsView profile

Professor

• Vascular Cognitive Impairment (VCI)
• Small Vessel Disease (SVD)
• Neuroimaging (MRI)
• +3 more

Geert Jan Biessels is a Full Professor at University Medical Center Utrecht (UMC Utrecht), affiliated with the strategic program Brain Circulatory Health. His research focuses on vascular cognitive impairment (VCI) and small vessel disease (SVD), leveraging advanced MRI techniques like 7T and 3T imaging to improve diagnosis and treatment. He leads initiatives such as the META VCI MAP consortium and the ZOOM@SVDs study, exploring lesion-symptom mapping and small vessel function. He is actively involved in global networks like STRIVE and HARNESS, and has pioneered methods for detecting cerebral microinfarcts in vivo. Key research areas include MRI biomarker development, cognitive impact of vascular lesions, and translational strategies for personalized medicine. His work spans clinical trials (e.g., COGNIKET on ketogenic supplements for MCI) and interdisciplinary collaborations in neurology, cardiology, and AI-driven diagnostics. Awards: 2015 European Stroke Organisation Senior Investigator Award, 1999 Neurodiab Prize, and Royal Society Fellowship. Advisory Roles: Chair of the COGNIKET trial steering committee. Lab/Teams: META VCI MAP (lesion-symptom mapping), ZOOM@SVDs (small vessel structure/function).

Maria SteinView profile

Senior Lecturer

• Clinical Psychology
• Addiction Medicine
• Neuroscience
• +3 more

Maria Stein is a Senior Lecturer and Senior Researcher at the Department for Clinical Psychology and Psychotherapy, University Clinics UPD, affiliated with the University of Bern (UniBE). She leads the Research Group Stein, focusing on clinical psychology, addiction neuroscience, and cognitive behavioral interventions. Her work integrates neuroimaging (fMRI, EEG) and psychophysiological methods to study alcohol use disorders, suicide risk, and contextual inhibition mechanisms. Her research emphasizes the neurobiological underpinnings of addiction, including craving, relapse prevention, and the role of brain networks like the thalamocortical and amygdala pathways. She has pioneered alcohol-specific inhibition training interventions, demonstrated through randomized controlled trials (e.g., INTRA trial). Her studies also explore implicit cognitive markers of suicide vulnerability, such as attentional biases and behavioral impulse deficits. Key collaborations include the Swiss Alcoholism Therapy Research (ATF) network and interdisciplinary teams across psychiatry, neuroscience, and clinical psychology. She has mentored several PhD students, including Hallie Batschelet, Raphaela Tschümperlin, and Kristina Rohde, contributing to over 68 peer-reviewed publications. Her work bridges basic neuroscience with clinical applications, aiming to improve diagnostic tools and therapeutic outcomes in addiction and mental health.

• Neuroimaging
• Biomedical Engineering
• Alzheimer’s Disease
• +4 more

Yonggang Shi is a Professor of Neurology and Biomedical Engineering at the University of Southern California. His research focuses on neuroimaging techniques, particularly diffusion MRI harmonization, brain connectivity analysis, and applications in Alzheimer’s disease and retinal imaging. He leads the NICR Group, advancing methods for connectome imaging and disease biomarker development. His work integrates machine learning with medical imaging to address challenges like distortion correction in MRI and tau pathology visualization. Key areas include retinal vessel analysis for neurodegenerative disease prediction and cortical thickness changes post-vision restoration. Shi’s contributions span both technical innovations (e.g., fiber orientation modeling) and clinical applications (e.g., Alzheimer’s subtyping). Publications highlight advancements in tractography, neuroimaging harmonization, and retinal OCT angiography. His research underscores interdisciplinary approaches bridging engineering and clinical neuroscience.

Paolo GorgiView profile

Associate Professor

• Score-driven time series models
• Stochastic processes
• Forecasting economic variables
• +3 more

Dr. Paolo Gorgi is an Associate Professor at the Department of Econometrics and Data Science in the School of Business and Economics at Vrije Universiteit Amsterdam. He holds a PhD in Statistics and Econometrics from the Joint Degree Program of the University of Padova and Vrije Universiteit Amsterdam (2017). His research focuses on score-driven time series models, stochastic processes, forecasting economic variables, and statistical inference in non-linear dynamic models. He is also a Partner at ACMetric B.V. since 2021. His work contributes to UN Sustainable Development Goals related to economic growth and sustainable cities. Key research interests include developing novel methodologies for time series analysis, particularly in handling non-linear dynamics and high-dimensional data. He has authored over 36 publications, including influential works on score-driven models, copula-based count models, and robust statistical estimation techniques. Teaching responsibilities include courses on Data Science Methods and Financial Econometrics . His research network spans collaborations with institutions globally, focusing on econometric applications in finance, macroeconomics, and sports analytics. He has supervised two PhD theses and actively contributes to the academic community through editorial roles and conference participation.

Insung KongView profile

Researcher

• Machine Learning
• Statistics
• Probability Theory
• +1 more

Insung Kong is a researcher specializing in machine learning and statistics, with a focus on theoretical foundations. His work addresses key challenges in algorithmic fairness, representation learning, and neural network theory. In 2025, he has published five research articles in prestigious venues including Transactions on Machine Learning Research and IEEE Transactions on Pattern Analysis and Machine Intelligence. His primary research areas include machine learning, statistical learning theory, probability theory, and optimization theory. He investigates topics such as fairness in AI, expressivity of deep networks, nonparametric regression, and integral probability metrics. His approach combines rigorous mathematical analysis with practical machine learning applications, particularly in developing theoretically grounded fairness frameworks and analyzing neural network architectures. Kong's recent publications reveal a strong emphasis on fairness and theoretical guarantees in machine learning. He has developed novel methods for fair representation learning using integral probability metrics and analyzed the expressivity of deep Heaviside networks. His work also explores tensor product neural networks for functional ANOVA models, contributing to interpretable machine learning while establishing convergence rates and VC dimension bounds.

Apurva GandhiView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Apurva Gandhi is a Researcher at Carnegie Mellon University , affiliated with the School of Computer Science and the Computer Science Department. Their research focuses on Artificial Intelligence with applications in program synthesis , agentic tasks , and handwritten content analysis . Research Interests : Artificial Intelligence Machine Learning Natural Language Processing Program Synthesis Publications highlight expertise in web agents , AI-driven database systems , deepfake detection , and sequence modeling for cybersecurity . Contact : apurvag@andrew.cmu.edu

Ankur ParikhView profile

Adjunct Assistant Professor

• Natural Language Processing
• Machine Learning
• Spectral Learning
• +3 more

Ankur Parikh is a staff research scientist at Google NYC and an adjunct assistant professor at New York University. His research focuses on natural language processing, machine learning, and spectral learning methods with applications to graphical models and biological networks. He holds a PhD from Carnegie Mellon University’s Machine Learning Department (2014), advised by Eric Xing, and an undergraduate degree in Computer Science and Applied Math from Princeton University. His work bridges theoretical advancements in machine learning with practical applications, including decomposable attention models for NLP, kernel graphical models, and network analysis of biological systems. Notable contributions include the TREEGL algorithm for gene network inference and spectral methods for latent structure discovery. He has been recognized with best paper awards at EMNLP and ISMB conferences. Education: PhD, Machine Learning, Carnegie Mellon University (2014) BS, Computer Science & Applied Math, Princeton University Parikh has taught advanced courses on spectral learning and probabilistic graphical models, including guest lectures at CMU and tutorials at Microsoft Research. His research is supported by an NSF Graduate Fellowship and has led to open-source implementations like the PLRE language modeling toolkit. Current affiliations include NYU’s machine learning community and Google’s NYC research team, with ongoing collaborations in NLP, computational biology, and statistical network analysis.

Frederik SimonsView profile

Professor

• Geophysics
• Seismology
• Climate Science
• +4 more

Frederik Simons is a Professor of Geosciences at Princeton University and Associate Chair of the Department of Geosciences. He holds affiliations with the High Meadows Environmental Institute (HMEI), the Program in Applied & Computational Mathematics, and the Andlinger Center for Energy and the Environment. His research focuses on geophysical data analysis, seismic properties of Earth's lithosphere, and planetary processes. He previously held roles including Dusenbury University Preceptor and Hess Post-doctoral Fellow. Simons earned his Ph.D. from MIT and M.Sc. from KU Leuven. Research interests span seismic and mechanical properties of lithospheres, sea-level variations, wavelet analysis, oceanic instrumentation, and planetary magnetic fields. His work emphasizes reproducible research and computational methods. He leads the EarthScope-Oceans consortium deploying oceanic hydrophones for seismic tomography. Teaching emphasizes clarity and student engagement, with a focus on geophysical fundamentals. His publications address topics like GRACE satellite gravity analysis, mantle discontinuities, and full-waveform inversion. He advises graduate students and maintains an active lab group.

Jens H. JensenView profile

Professor

• Magnetic Resonance Imaging (MRI)
• Neurological Disorders
• Neuroimaging Techniques
• +3 more

Dr. Jens H. Jensen is a Professor of Neuroscience at the Medical University of South Carolina (MUSC) and Director of the Center for Biomedical Imaging. He holds a PhD in Physics from Princeton University and previously worked at New York University School of Medicine before joining MUSC in 2011. His research focuses on advanced MRI techniques applied to neurological disorders, including Alzheimer’s disease, epilepsy, stroke, and addiction. His work emphasizes diffusion MRI, brain microstructure analysis, and translational neuroimaging. Education: PhD in Physics, Princeton University Prior affiliation: New York University School of Medicine Research Interests: Dr. Jensen’s lab develops and applies diffusion MRI methods to study brain microstructure in health and disease. Key areas include: Diffusional kurtosis imaging White matter integrity assessment Neuroimaging biomarkers for Alzheimer’s disease Therapeutic monitoring using MRI (e.g., transcranial stimulation) His recent work explores longitudinal changes in mouse models of neurodegeneration and clinical applications in stroke and addiction. Advising & Grants: While specific grant details are not listed here, his prolific publication record suggests sustained federal and institutional funding. He mentors students in physics, engineering, and neuroscience. Labs/Teams: Leads the Center for Biomedical Imaging at MUSC, focusing on cutting-edge neuroimaging technologies and clinical translation.

Lifang HeView profile

Associate Professor

• Machine Learning/Deep Learning
• Tensor Analysis
• Data Mining
• +3 more

Lifang He is an Associate Professor in the Department of Computer Science & Engineering at Lehigh University. She holds a B.S. in Computational Mathematics from Northwest Normal University and a Ph.D. in Computer Science from South China University of Technology. Prior to joining Lehigh, she was a postdoctoral researcher at the University of Pennsylvania’s Perelman School of Medicine and Weill Cornell Medical College. Her research focuses on machine learning/deep learning, data mining, tensor analysis, and biomedical informatics. Notable projects include developing BiomedGPT, an AI model for real-time patient-focused insights, and contributions to structural brain imaging analysis and rare event prediction in materials science. Her work bridges computational methods with healthcare applications, emphasizing multi-modal data fusion and federated learning in medical contexts. Recent publications span topics like self-paced learning for sign language recognition, graph-based brain network analysis, and efficient large language models. She actively participates in symposiums on AI in healthcare and has collaborated on initiatives like the Mountaintop Campus’ interdisciplinary projects.

• Machine Learning
• Remote Sensing
• Signal Processing
• +3 more

Jianbo Liu is a prolific researcher with a focus on interdisciplinary fields spanning machine learning, remote sensing, signal processing, and computer vision. His work emphasizes developing advanced algorithms for applications in environmental monitoring, telecommunications, and data-driven decision-making. Liu collaborates frequently with institutions and researchers in China and internationally, contributing to journals like IEEE Transactions on Wireless Communications , Remote Sensing , and Pattern Recognition . Research Interests: Machine learning frameworks for computer vision tasks, remote sensing data analysis, signal processing in communication systems, and optimization of industrial processes. Key Collaborators: Fu Chen, Jimmy S. J. Ren, Ningyuan Cao, Hongsheng Li, and Boyang Cheng. Publications: Over 138 papers across venues such as CVPR, ICCV, and IEEE journals, demonstrating expertise in neural networks, sensor fusion, and spatiotemporal data analysis. His recent work highlights contributions to physics-informed machine learning, gesture recognition via hierarchical attention networks, and secure communication protocols for satellite systems. Liu’s research bridges theoretical advancements with practical applications in environmental science, healthcare, and smart infrastructure.