Find Academic Supervisors

• RF Engineering
• Medical Imaging (PET/CT)
• Prosthetics Analysis
• +3 more

Jeroen De Geeter is a Researcher at the Faculty of Engineering Sciences, Vrije Universiteit Brussel, specializing in Electronics and Informatics. His roles include Scientific Employee and Electronics and Computer Science Doctorate. He is affiliated with the VUB's tauCAMTM research group and holds an ORCID identifier: 0000-0001-5373-3505 . Education: Doctorate in Electronics and Computer Science Research Interests: RF engineering and instrumentation Medical imaging innovations in PET/CT and radiation correction Prosthetics analysis and metal artifact reduction in diagnostics Signal processing for modulated systems Environmental health impacts on reproductive biology Recent Research Trends: Focus on interdisciplinary applications combining electrical engineering with medical diagnostics, including advancements in automatic ranging for RF experiments and artifact reduction in clinical imaging. His work bridges signal processing techniques with clinical challenges like prosthetic-induced imaging artifacts. Awards: No specific awards listed, but maintains an h-index of 29 with impactful publications in IEEE Transactions and EJNMMI Phys. Grants & Labs: Collaborates on projects involving VUB's tauCAMTM imaging systems and participates in conferences on multivariate function decoupling. Research teams include collaborations on PET/CT attenuation correction and cigarette smoking's reproductive effects.

Shanghua GaoView profile

Research Fellow

• Foundation Models
• Artificial Intelligence
• Computer Vision
• +5 more

Shanghua Gao serves as a Research Fellow at Harvard Medical School's Department of Biomedical Informatics, Harvard University, where he develops foundation models and universal representation learning methods to advance artificial general intelligence across computer vision, biomedical applications, and time series analysis. His research focuses on creating generalizable AI techniques including AI agents, generative modeling, and self-supervised learning frameworks. Gao emphasizes resource-efficient architectures that maintain high performance while minimizing computational demands, with notable contributions in salient object detection and biomedical AI systems. His work bridges theoretical innovation and practical deployment, with several projects transitioning into real-world applications. Analysis of his recent publications reveals a strategic shift toward foundation models for biomedical reasoning (e.g., TxAgent for therapeutic applications) and unified time-series frameworks (UniTS), while maintaining expertise in efficient computer vision systems. His research consistently prioritizes model generalizability across domains, evident in cross-disciplinary applications from medical diagnostics to creative AI systems.

Giovanna Maria DimitriView profile

Assistant Professor

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +7 more

Giovanna Maria Dimitri is a tenure-track Assistant Professor in Artificial Intelligence at the Università degli Studi di Milano (Statale), with strong affiliations at the University of Siena (DIISM) and the University of Cambridge. She conducts research in deep learning, machine learning, and AI applications across domains such as computer vision, bioinformatics, climate modeling, and healthcare. She also lectures in Business Intelligence at the University of Siena and serves as a Guest Lecturer in Data Science at the University of Cambridge's Institute of Continuing Education. PhD in Artificial Intelligence, University of Cambridge MPhil in Advanced Computer Science (Distinction), University of Cambridge Master's and Bachelor's in Computer and Automation Engineering (110/110 cum laude), University of Siena Her research spans foundational AI models and their applications in diverse fields including neuroscience, environmental science, and medical diagnostics. She develops methodologies in deep learning, graph neural networks, and multimodal data analysis, with a growing interest in sustainable AI and ethical implications of machine learning systems. Her recent publications reflect a strong trend in applying AI to real-world challenges: from detecting synthetic images and analyzing brain signals to modeling climate impacts and assessing sustainable development goals. Her work bridges theoretical innovation with practical implementation across healthcare, environmental monitoring, and digital humanities. Scientific Awards and Editorial Roles: Ai-Net Fellows Scholarship (DAAD, 2023) Associate Editor, Neurocomputing (Elsevier) Associate Editor, IEEE Transactions on Technology and Society (since May 2024) Giovanna has extensive experience in teaching, supervising, and science communication. She has delivered seminars at top institutions like the University of Cambridge and has been featured in Italian media for her AI expertise. She mentors students and collaborates internationally, including with Prof. Gemma Roig at the DAAD-sponsored Ai-Net program. She is actively involved in academic service, conference organizing, and public engagement, contributing to both technical and societal aspects of AI advancement. She is a life member of Clare Hall College, University of Cambridge, and maintains active research collaborations across Europe. Her lab and research group focus on developing robust, interpretable, and sustainable AI systems, with ongoing projects in medical AI, climate informatics, and ethical machine learning.

Cai YtsmaView profile

Research Fellow

• Machine learning
• Applications in health
• Data engineering and data science
• +4 more

Cai Ytsma is a Research Fellow and Health Data Scientist specializing in Clinical Epidemiology at University College London. They also hold an Affiliate position at Mount Holyoke College in the United States since September 2019 and serve as a Collaborator on the NASA VERITAS Science Team since June 2023. Ms. Ytsma's research spans multiple interdisciplinary domains with a focus on: Machine learning applications in health data analysis Electronic health records (EHR) and phenotyping for genetic associations Analytical spectrometry including Laser-Induced Breakdown Spectroscopy (LIBS), X-ray fluorescence, and absorption Planetary geology and data analysis for space missions Statistical data science and software engineering for data curation and analysis Ms. Ytsma's recent publications demonstrate a strong interdisciplinary approach, bridging health data science with planetary science. Their work shows expertise in developing computational frameworks for disease phenotyping in large datasets like the UK Biobank while simultaneously advancing analytical techniques for geological and planetary materials. A notable trend is the application of machine learning across both medical and geological domains, particularly in improving accuracy of spectroscopic measurements and disease prediction models. While specific scientific awards are not mentioned in the available information, Ms. Ytsma's work has gained significant attention, with publications picked up by numerous news outlets and cited across various platforms. Ms. Ytsma actively mentors students across disciplines in data science principles despite not having formal teaching responsibilities. They founded Cai Consulting in September 2019, providing data science consultation for diverse research projects. As a Collaborator on the NASA VERITAS Science Team (2023-2026), they contribute to planetary science missions, particularly in calibration and data verification for Venus exploration.

• Bayesian statistics
• Uncertainty quantification
• Expert elicitation
• +2 more

Jeremy Oakley is Professor of Statistics and Head of the School of Mathematical and Physical Sciences at the University of Sheffield. His work spans Bayesian statistics, uncertainty quantification for complex computer models, expert elicitation of probability distributions, and health-economic applications. Research Interests Bayesian statistics and inference Uncertainty quantification (UQ) for computer models Expert elicitation of probability distributions Health-economic modelling He co-developed the Sheffield Elicitation Framework (SHELF) , a widely-used set of protocols and software tools for structured expert judgement. Teaching & Supervision Professor Oakley teaches undergraduate and postgraduate statistics modules and supervises PhD students within SoMaS and in collaboration with other departments, focusing on UQ and expert-elicitation topics. Contact Email: j.oakley@sheffield.ac.uk

Melanie SchallerView profile

Researcher

• Machine Learning
• Anomaly Detection
• Graph Signal Processing
• +4 more

Dr. Melanie Schaller is a researcher at the Institute of Information Processing, Leibniz Universität Hannover. Her work bridges machine learning with engineering applications in biomedical and civil infrastructure domains. PhD in Cyber-Physical Systems for Intracranial Pressure Monitoring (University of Würzburg, 2023) Specializes in end-to-end integration of engineering knowledge into ML models Research focuses on anomaly detection in multivariate time-series and graph signal processing . Recent projects like P-BIM and KI@FlowChief demonstrate applications in structural behavior analysis and water distribution network optimization. Publications emphasize sensor network applications and adaptive learning systems. Dataset contributions include detailed pulsating waterjet cutting experiments and smart beehive monitoring , with methodologies for incremental learning and audio signal classification. Technical reports highlight expertise in heterogeneous graph neural networks and signal processing for material transitions.

• Analytical Chemistry
• Chemometrics
• Multivariate Data Analysis
• +3 more

Marina Cocchi is a Full Professor in the Department of Chemical and Geological Sciences at the University of Modena and Reggio Emilia. She specializes in Analytical Chemistry (CHEM-01/A) and maintains an active teaching schedule for the 2025 academic year across multiple degree programs. Her research interests focus on advanced analytical methodologies including chemometrics, multivariate data analysis, and experimental design. Professor Cocchi's expertise spans sampling theory, quality assurance in analytical laboratories, and application of separation techniques (GC, HPLC) and spectroscopic methods (IR, NIR, Raman) for analysis of complex matrices including food products, fertilizers, and environmental samples. Professor Cocchi maintains regular office hours on Tuesdays (12:00-13:00) and Thursdays (14:30-16:30) at her office located on the first floor of Via Campi 103. Her teaching methodology combines theoretical instruction with hands-on laboratory work, emphasizing practical application of analytical protocols for real-world sample matrices. She utilizes various chemometric software tools including PLS-Toolbox for MATLAB in her instruction and research, with applications spanning quality by design (QbD), process analytical technology (PAT), and multivariate calibration techniques.

• Machine Learning
• Health Informatics
• Transportation Modeling
• +4 more

Jaakko Hollmen serves as a Senior University Lecturer in the Department of Computer Science at Aalto University, affiliated with the Helsinki Institute for Information Technology (HIIT) and the Computer Science Lecturers research group. His interdisciplinary work bridges machine learning with critical applications in healthcare, transportation systems, and environmental science. His research focuses on advanced machine learning methodologies including Bayesian optimization, random forests, and principal component analysis. Key application areas span neonatal healthcare (mortality prediction, brain injury analysis), transportation modeling (activity-based model calibration), and environmental data science (weather-crop relationships, drug-environment interactions). His approach emphasizes practical implementations of complex algorithms for real-world problems. Hollmen's recent publications (2018-2023) reveal a consistent trajectory in developing machine learning solutions for high-dimensional data challenges, with increasing emphasis on medical applications. His work demonstrates strong cross-domain collaboration, particularly with medical researchers at Helsinki University Hospital. His notable recognition includes: Best paper finalist and runner-up award (Computer Track) at the first IEEE Life Sciences Conference (LSC) for research on predicting complications in very low birth weight infants (2017) As a core member of HIIT, Hollmen contributes to Finland's national information technology research infrastructure while maintaining active collaborations with medical and agricultural research groups. His current projects focus on optimizing transportation models and advancing clinical prediction systems through novel machine learning techniques.

ANA HERRERO GUTIERREZView profile

Associate Professor

• Analytical Chemistry
• Chemometrics
• Qualimetrics
• +3 more

Ana Herrero Gutierrez serves as an Associate Professor in the Department of Chemistry at the Faculty of Sciences, University of Burgos, specializing in Analytical Chemistry with emphasis on chemometrics and qualimetrics. Her core research interests include: Analytical Chemistry Chemometrics Qualimetrics Multivariate Analysis Calibration Transfer Voltammetry She leads research within the "Q&C Chemometrics and Qualimetrics" group and actively participates in two doctoral programs: "Avances en Ciencia y Biotecnología Alimentarias" (Advances in Food Science and Biotechnology) and "Química Avanzada" (Advanced Chemistry). Dr. Herrero Gutierrez earned her PhD from the University of Burgos in 1996 with her thesis "Análisis multivariante y transferencia de calibrado en voltamperometría" (Multivariate Analysis and Calibration Transfer in Voltammetry), supervised by Dr. María Cruz Ortiz Fernández. While her profile indicates thesis supervision activities, specific student names and grant details are not provided in the available documentation.

• Vibrational Spectroscopy
• Chemometrics
• Analytical Chemistry
• +4 more

Peter de Peinder is a Researcher at the Faculty of Science, Utrecht University , affiliated with the Advanced Research Center Chemical Building Blocks Consortium . His work bridges Chemometrics , Vibrational Spectroscopy , and Analytical Chemistry , focusing on crude oil characterization , biomass conversion , and polymerization processes . Expertise : Vibrational Spectroscopy, Chemometrics, Analytical Chemistry Affiliations : Utrecht University, ARC Chemical Building Blocks Consortium Research Interests : Utilizing ATR-FTIR , NMR , and Raman spectroscopy for chemical analysis, with applications in crude oil residue prediction , technical lignin characterization , and polymer degradation monitoring . His work often integrates multivariate statistical models to extract chemical insights from complex spectral datasets. Publication Trends : Recent articles (2024-2025) emphasize biomass valorization (lignin, humin), plastic waste conversion (polyethylene to di-acids), and in-situ process monitoring via operando spectroscopy . Earlier works include pharmaceutical counterfeit detection and catalyst impregnation studies using MRI . Collaborations : Frequent partnerships with Bert Weckhuysen , Frank de Bruijnincx , and Florentine Meirer in sustainable chemistry projects.

Yurun CaiView profile

Assistant Professor

• Gerontology
• Physical Activity
• Wearable Sensors
• +5 more

Dr. Yurun Cai is a tenure-track Assistant Professor at the University of Pittsburgh School of Nursing , specializing in aging research through her work in the Department of Health and Community Systems . Her research integrates wearable sensor technology and digital biomarkers to advance understanding of aging processes. Develops novel physical activity metrics using accelerometry Focuses on pain-cognition interactions in older adults Conducts clinical trials for neurodegenerative early detection Currently teaching NUR1054 Nursing Care of Older Adults , Dr. Cai leads multidisciplinary research initiatives including her National Institute on Aging R03 project and Jewish Healthcare Foundation collaborations. She serves on editorial boards and review panels for major aging research organizations. 2023 Gerontological Society of America Health Sciences Research Award Principal Investigator on PRIME pilot study Co-investigator in medication safety research Connect with Dr. Cai at yuc199@pitt.edu or via her professional website . She is currently accepting PhD students for mentorship.

• Technical Thermodynamics
• Raman Spectroscopy
• Multicomponent Systems
• +3 more

apl. Prof. Dr.-Ing. Hans-Jürgen Koß is a faculty member at the Chair and Institute of Technical Thermodynamics, RWTH Aachen University. He serves as Vice Head of the institute and Group Leader for Measurement Systems Engineering, focusing on thermodynamic characterization and spectroscopic analysis. Research Interests: His work centers on applying Raman spectroscopy to thermodynamic systems, particularly for Vapor-Liquid Equilibrium characterization Diffusion coefficient modeling in complex liquids Bioprocess monitoring through Indirect Hard Modeling System automation for thermodynamic measurements Recent Publication Trends: The five listed articles (2024-2025) demonstrate his expertise in Raman spectroscopy integration for thermodynamic and bioprocess systems, with emphasis on multicomponent analysis, fluid dynamics, and automated modeling. Contact: hans-juergen.koss@ltt.rwth-aachen.de

Alejandro SchulerView profile

Assistant Professor

• Causal Inference
• Biostatistics
• Machine Learning
• +3 more

Alejandro Schuler is an Assistant Professor at the University of California, Berkeley, specializing in causal inference, biostatistics, and machine learning. He earned his PhD in Biomedical Informatics from Stanford University (2018) and completed a postdoctoral fellowship under Mark van der Laan. His industry experience at Kaiser Permanente’s Division of Research and a health tech startup informs his applied research agenda focused on bridging mathematical formalisms with real-world healthcare challenges. PhD – Biomedical Informatics, Stanford University, 2018 MS – Mechanical Engineering, University of California, Los Angeles, 2013 BS – Mechanical Engineering, University of California, Berkeley, 2012 His research centers on developing statistical methods for causal inference, including the selectively adaptive lasso, prognostic adjustment, and NGBoost. Recent work emphasizes power calculation in randomized trials, robust neural network frameworks, and stochastic intervention models for healthcare and environmental exposure analysis. His publications highlight advancements in targeted maximum likelihood estimation, Riesz regression, and efficient covariate adjustment. These methods aim to improve the accuracy and interpretability of causal effect estimates in complex datasets. Dr. Schuler collaborates with domain experts to translate clinical and public health questions into mathematical models, ensuring his work maintains relevance beyond academia. He is actively involved in methodological development and application to healthcare data, focusing on reducing hospital readmissions and optimizing trial design.

• Evolutionary Psychology
• Personality Psychology
• Developmental Psychology
• +7 more

Marco Del Giudice is an Associate Professor in the Department of Life Sciences at the University of Trieste, Italy, where he conducts interdisciplinary research at the intersection of evolutionary biology, psychology, and development. His work spans multiple domains including personality psychology, developmental psychopathology, attachment theory, and sex differences research. Dr. Del Giudice's research interests focus on the evolution of personality, motivation, and self-regulation, with particular emphasis on attachment styles, evolutionary psychopathology, sex differences in behavior and cognition, middle childhood development, stress mechanisms in development, and developmental plasticity. His approach is characterized by theoretical synthesis and integrative frameworks, including the Adaptive Calibration Model (ACM) of stress responsivity, evolutionary-developmental models of attachment, and a general motivational architecture for personality. More recently, he has expanded into evolutionary immunology. His extensive publication record demonstrates consistent scholarly contributions across multiple disciplines, with recent work focusing on methodological innovations in statistics, evolutionary emotion theory, and critical analyses of research practices in sex differences research. His articles reveal a strong emphasis on theoretical integration, methodological rigor, and challenging conventional wisdom in psychological science. Early Career Award of the Human Behavior and Evolution Society (HBES), 2016 Dr. Del Giudice's work has established him as a leading scholar in evolutionary approaches to psychology, with significant contributions to understanding how evolutionary principles illuminate human behavior, development, and psychopathology. His interdisciplinary approach bridges biological and psychological perspectives while maintaining methodological sophistication.

• Multivariate Statistics
• Tolerance Intervals
• Statistical Inference
• +3 more

Sumona Mondal serves as Professor of Mathematics and Co-Director of the MS Program in Applied Data Science at Clarkson University, where she has been faculty since 2007. She is affiliated with the Coulter School of Engineering & Applied Sciences within the Department of Mathematics, contributing to both theoretical statistics and interdisciplinary applications. Her educational foundation includes a Ph.D. in Statistics from the University of Louisiana at Lafayette, complemented by Bachelor of Science and Master of Science degrees in Statistics from the University of Calcutta, India. Professor Mondal specializes in constructing tolerance regions for multivariate linear models, developing tolerance factors for multivariate normal distributions, and creating statistical methodologies for engineering failure prediction and pollution assessment. Her research extends to bio-engineering, environmental sciences, and physical therapy applications, focusing on robust inferential procedures for complex real-world problems. This work bridges theoretical statistics with practical implementations across diverse scientific domains. Analysis of her recent publications (2021-2025) reveals a strong trend toward interdisciplinary collaboration, particularly in environmental monitoring (air quality sensor networks, PM2.5 analysis), health statistics (rheumatoid arthritis comorbidities, dental caries, HPV vaccination), and emerging fields like robotics acceptance and hate speech detection. Her methodological contributions consistently emphasize statistical rigor while addressing data challenges such as imbalanced datasets, correlated predictors, and low-cost sensor calibration. As Co-Director of the MS Program in Applied Data Science, Professor Mondal actively shapes graduate education and mentors students in statistical methodology development. She maintains significant academic engagement through over 27 national and international conference presentations, demonstrating consistent scholarly productivity and community involvement.