Find Academic Supervisors

Xinghua LuView profile

Professor

• Biomedical Informatics
• Translational Bioinformatics
• Systems Biology
• +5 more

Dr. Xinghua Lu is a Professor in the Department of Biomedical Informatics at the University of Pittsburgh. He specializes in computational methods for identifying signaling pathways in biological processes and diseases, and statistical methods for knowledge extraction from biomedical literature. His work focuses on translational bioinformatics, systems biology, and natural language processing for text mining. Education: PhD from University of Connecticut Health Center Research Interests: Dr. Lu develops computational models to simulate biological signaling systems and applies latent variable models to analyze genomic and transcriptomic data. His recent projects include establishing a Center/Institute in Translational Bioinformatics and advancing NLP techniques for biomedical text analysis. Publications: His research emphasizes genomic alteration analysis, causal discovery from big data, and integrative modeling of gene expression. Key themes include cancer mechanism identification, systems biology applications, and machine learning-driven biomedical informatics. Affiliations: Department of Biomedical Informatics School of Computing and Information McGowan Institute for Regenerative Medicine (implied via research context)

Diana WeinholdView profile

Associate Professor

• Development Economics
• Environmental Economics
• Applied Econometrics
• +3 more

Diana Weinhold is an Associate Professor of Development Economics at the London School of Economics and Political Science (LSE), Department of International Development. She also directs the doctoral research programme in International Development. Her work focuses on land use, environmental economics, applied econometrics, and the socioeconomic impacts of deforestation in tropical regions like the Amazon and Bolivia. Education: PhD in Economics from the University of California and BSc in Economics with Math from the University of Wisconsin. She has held academic positions at Vanderbilt University, Cambridge University (as external PhD examiner), and various research institutes globally, including the Inter-American Development Bank and IPEA in Brazil. Research emphasizes hybrid models analyzing land-use policy trade-offs, noise pollution’s health effects, and deforestation’s socioeconomic consequences. She collaborates with international organizations like NEMESIS and the Institute for Applied Economics Research (IPEA). Diana also develops interactive conservation simulation tools, such as the Conservation Strategies Game, to study contested environmental decision-making. Her articles span topics like urban noise health impacts, deforestation carbon emissions, and biodiversity spending. She advises Ph.D. students on topics ranging from land reform in Southern Africa to historical economic development.

• Energy-Efficient Computing
• High-Performance Computing
• Compiler Techniques
• +4 more

Kanishkan Vadivel is a Researcher in the Electronic Systems group at Eindhoven University of Technology (TU/e), specializing in energy-efficient hardware architectures and compiler-based code-generation techniques. He holds a Master’s degree in Embedded Systems from TU/e (2017) and a Bachelor’s from Coimbatore Institute of Technology (India). Prior to academia, he worked in embedded systems at Tata Engineering and Arm Ltd. Research Interests : His work focuses on computation-in-memory architectures using resistive devices, optimal code generation for CGRA (Coarse-Grained Reconfigurable Architecture), and high-performance computing. Key projects include the MNEMOSENE initiative and development of the CIM-SIM simulator for computation-in-memory systems. Awards : HiPEAC collaboration grant (2019) Advising & Grants : His research is supported by grants focused on neuromorphic processors and edge-AI hardware. He collaborates on projects like NEUROKIT2E for embedded deep learning systems. Labs/Teams : Active member of TU/e’s Electronic Systems Center and Efficient Stream Processing Lab, contributing to neuromorphic and energy-efficient computing initiatives.

Ignasi Josep Soler PoquetView profile

Research Fellow

• Solar Physics
• Machine Learning/Deep Learning
• Observational Astronomy
• +2 more

Ignasi Josep Soler Poquet is a Doctoral Research Fellow at the Rosseland Centre for Solar Physics, University of Oslo. His research focuses on solar physics and computational science, with a strong emphasis on applying machine learning techniques to analyze solar observations. **Education**: Bachelor's in Physics from University of Valencia (specializing in astrophysics/theoretical physics) Master's in Advanced Physics (University of Valencia, focusing on supernovae and simulations) **Research Interests**: Development of deep learning models for detecting solar phenomena like Ellerman bombs Integration of Python-based tools for observational astronomy Computational analysis of solar dynamics using instruments like SST and SDO **Publications Trends**: His recent work demonstrates a clear trajectory in leveraging neural networks and deep learning to interpret solar observational data, with a focus on automated detection systems for transient solar events. **Advising & Grants**: No specific grants or advisees listed, though his PhD work is institutionally supported through the Rosseland Centre.

Florian ReitzView profile

Researcher

• Computer Science
• Database Systems
• Information Retrieval
• +5 more

Florian Reitz is a Scientific Staff member at Schloss Dagstuhl - Leibniz Center for Informatics, specializing in the dblp computer science bibliography. His current role involves enhancing bibliographic data infrastructure and managing projects like SmartER. He holds a PhD from the University of Trier (2018), focusing on historical metadata analysis. Previously, he was a research assistant in the Databases and Information Systems group under Prof. Bernd Walter. His work centers on metadata quality, author disambiguation, and leveraging historical corrections for test collections. Research interests include information retrieval, database systems, and digital libraries. He contributes to projects like Scalable Author Disambiguation and has co-authored numerous publications on topics such as LLM applications, knowledge graphs, and FAIR data principles. Contact: florian.reitz@dagstuhl.de.

Han van der AaView profile

Professor

• Process Mining
• Business Process Management
• Natural Language Processing
• +3 more

Han van der Aa is a Professor at the Faculty of Computer Science, associated with the Research Group Workflow Systems and Technology. His research focuses on advancing Process Mining, Business Process Management, and Natural Language Processing. He leads projects like the 2025 initiative on Semantic Process Discovery from User Interaction Logs, funded until 2027. With over 82 publications and 11 awards, including Best Paper Awards at CAISE 2023 and ICPM 2024, his work bridges theoretical contributions with practical applications. Notable projects include tools like AgentSimulator for data-driven process simulation and the PET dataset for process extraction from text. His research emphasizes semantic-aware process mining, privacy-preserving techniques, and integration with AI/ML. Key publications address concept drift detection, multi-agent systems, and large language model applications in process analysis. Awards highlight his impact on both academic and applied process mining fields.

Shijie ZhouView profile

Assistant Professor

• Cardiac Electrophysiology
• AI in Biomedical Engineering
• Clinical Translational Research
• +3 more

Shijie Zhou is an Assistant Professor of Biomedical Engineering at Worcester Polytechnic Institute (WPI), previously serving as an Assistant Professor at Miami University, Ohio. He holds a PhD from Dalhousie University and completed a postdoctoral fellowship at Johns Hopkins University. His research focuses on cardiac electrophysiology and biomedical engineering, particularly in developing AI-driven 3D computational heart models for personalized diagnostics and therapies. Dr. Zhou’s work bridges clinical practice and research, emphasizing ventricular tachycardia localization and translational studies to improve patient outcomes. He leads the Computational Clinical Cardiac Electrophysiology Laboratory (CCCEL), which has secured grants including the 2025 American Heart Association Innovative Project Award and National Heart, Lung, and Blood Institute R15 Grant. His lab relocated to WPI in 2023, focusing on clinical applications of virtual-heart technologies. Education PhD in Biomedical Engineering, Dalhousie University, Canada (2018) Postdoctoral Research, Biomedical Engineering, Johns Hopkins University (2018–2020) Research Interests Dr. Zhou’s research integrates computational modeling with clinical data to address arrhythmia mechanisms. Key areas include: AI-based ventricular tachycardia localization using ECG and imaging fusion Personalized heart digital twins for guiding ablation therapies Non-invasive ECG imaging techniques (ECGI) Fat infiltration’s role in post-infarct arrhythmogenesis Professional Recognition Fellow of the Heart Rhythm Society Fellow of the European Society of Cardiology Awarded multiple grants including AHA Innovative Project Award (2025), AHA Career Development Award (2025), and NHLBI R15 Grant (2025) Lab Activities CCCEL develops real-time systems for arrhythmia origin localization, including the RAPID-VT platform. Current projects aim to enhance clinical decision-making through automated algorithms and patient-specific models.

Reinhold LudwigView profile

Professor

• Electromagnetic and Acoustic Nondestructive Evaluation (NDE)
• MRI RF Coil Design
• Electromagnetic Sensors
• +5 more

Reinhold Ludwig is a Professor and Associate Head in the Department of Electrical & Computer Engineering at Worcester Polytechnic Institute (WPI). He holds a Dipl.-Ing. (MS) from the University of Wuppertal (1983) and a PhD from Colorado State University (1986). His research focuses on electromagnetic and acoustic nondestructive evaluation (NDE), MRI RF coil design, and biomedical imaging systems. As director of the Center for Imaging and Sensing (CIS), he leads projects in energy/media interaction analysis and advanced MRI technology development. Key contributions include anatomically shaped RF coils for MRI systems up to 7T, including full-body, head, and extremity imaging applications. His team has developed NDE sensing technologies and electronic circuits for clinical and pre-clinical MR scanners. Recent work explores ultra-low-power neuromorphic circuits for autonomous vehicles and AI-driven optical defect detection systems. Professional memberships include IEEE, Eta Kappa Nu, and Sigma Xi. Notable grants include a $3M NIH award (2016) for robot-assisted brain cancer treatment research. His lab also works on microwave imaging innovations, ceramic material testing, and embedded computing systems. Current projects emphasize compact neural networks for real-time NDE, non-contact electromagnetic stimulation devices for pain management, and high-resolution MRI coil optimization. Patents include multi-modal RF coils and moisture analysis systems for industrial materials.

• Laser Physics
• Optics and Laser Technology
• Scintillator Materials
• +4 more

Emőke Lőrincz is an Honorary Professor in the Department of Atomic Physics at Budapest University of Technology and Economics (BME). Her research focuses on advanced optical and nuclear technologies, including scintillator materials for medical imaging (PET), laser physics, and holographic data storage. She has contributed to the development of high-resolution PET detectors, digital photon counter systems (SPADnet), and novel optical memory technologies using azobenzene polymers. Her work bridges fundamental material science with applied engineering solutions for biomedical and data storage applications. Key research areas include optimizing light-sharing modules for PET detectors, measuring optical properties of scintillator crystals, and advancing polarization-based holographic storage systems. Her interdisciplinary approach combines experimental validation with computational modeling to enhance detector performance and data density in optical systems. Publications span from 1985 to 2017, reflecting her long-term contributions to optics, nuclear instrumentation, and materials science. Notable collaborations include studies on LYSO:Ce scintillators, SiPM arrays, and phase-modulation techniques for holography. While no specific awards are listed, her extensive publication record underscores her influence in these fields. Her work often addresses practical challenges such as improving detector spatial resolution, optimizing light collection efficiency, and ensuring material durability in extreme conditions. This research has direct applications in medical imaging systems and next-generation optical storage technologies.

• Climate Science
• Glaciology
• Paleoclimatology
• +3 more

Etienne Legrain serves as an unpaid employee in the Department of Water and Climate at Vrije Universiteit Brussel (VUB), Brussels, Belgium. He leads the FWOTM1276 CAPSULE project (2025-2028), a fundamental research initiative modeling Antarctic Blue Ice areas to extract the oldest climatic reconstructions from ice cores. His research centers on climate science with specialization in glaciology and paleoclimatology. Legrain investigates Antarctic blue ice formations to reconstruct historical climate patterns through ice core analysis, while extending his work to model glacial inception thresholds using positive degree-day methodologies. This integrates field observations with numerical simulations to understand long-term climate dynamics. His 2025 publications reveal a dual focus: analyzing elevation-dependent warming absence in Antarctica through paleoclimate records, and establishing global thresholds for glacial inception. Both studies demonstrate interdisciplinary approaches combining geophysics, climate modeling, and ice core chronology to address critical questions in paleoclimatology and contemporary climate change. Scientific awards: None documented in source material. Legrain currently directs the FWOTM1276 CAPSULE project funded for fundamental research on Antarctic ice cores. No student advisees are mentioned, though his publications indicate extensive international collaboration across six institutional profiles. The research involves significant computational modeling and field data synthesis. His work operates within VUB's Department of Water and Climate research ecosystem, leveraging collaborative networks across European and global Antarctic research teams as evidenced by multi-institutional co-authorship on recent publications.

Zhenqin WuView profile

Assistant Professor

• AI/ML for Computational Biology
• Spatial Omics
• Multi-modal Spatial Biology
• +3 more

Zhenqin Wu is an Assistant Professor at the Department of Computer Science, University of Hong Kong. He holds a PhD from Stanford University (2016–2022) under advisors Prof. James Zou and Dr. Vijay Pande, with research focused on applying AI/ML to computational biology and spatial omics. His work bridges machine learning and spatial biology, developing tools for analyzing spatial proteomics, transcriptomics, and histopathological imaging. Education: PhD in Computer Science, Stanford University (2016–2022) BSc in Chemistry, Peking University Research Interests: AI-driven spatial omics analysis Multimodal data integration in biology Machine learning for medical imaging Computational modeling of cellular microenvironments His recent work highlights include developing the ROSIE AI framework for immunofluorescence generation and the CORAL method for spatial multiomics integration. Articles focus on immunotherapy response prediction, disease biomarker discovery, and interpretable models for tissue structure analysis. Professional Affiliations: Scientific Advisor, Enable Medicine (biotechnology startup) AI Fellow, Genesis Therapeutics He actively mentors PhD/postdoc researchers and collaborates on AI-driven drug discovery projects. His lab focuses on translating spatial omics insights into clinical applications.

• Analytical Chemistry
• Liquid Chromatography Optimization
• Chromatographic Fingerprint Analysis
• +3 more

Jose Antonio Navarro Huerta is a Researcher at the Department of Analytical Chemistry, Universitat de València. He obtained his Doctorate in 2021 with the thesis "Design of strategies for optimisation in liquid chromatography," advised by Dr. José Ramón Torres-Lapasio and Dr. Mª Celia García Alvarez-Coque. His research focuses on advancing analytical chemistry methodologies, particularly in liquid chromatography optimization, retention modeling, and experimental design applications. He specializes in developing strategies for efficient separation methods in LC, including mobile phase optimization, peak capacity estimation, and chromatographic fingerprint analysis of natural extracts and therapeutic proteins. His work trends emphasize improving analytical precision through algorithmic approaches (e.g., BEADS baseline subtraction), validating new column technologies (e.g., ultra-short ion-exchange columns), and applying global modeling techniques to predict chromatographic behaviors. His publications span from fundamental studies on secondary chemical equilibria to applied analyses of herbal extracts and biopharmaceuticals. No scientific awards or grants are explicitly mentioned in the provided information. He has not listed advisees, though his own doctoral training indicates expertise in mentorship within chromatography research. His research actively engages with cutting-edge topics like 2D-LC for polyphenol profiling, micellar LC optimization under extended solvent conditions, and nano-HPLC with monolithic columns. While no specific lab affiliations are noted, his work aligns with modern analytical chemistry infrastructure needs.

John P. KerekesView profile

Research Professor

• Remote Sensing
• Imaging Science
• Digital Imaging
• +3 more

John P. Kerekes is a Research Professor at the Chester F. Carlson Center for Imaging Science within the College of Science at Rochester Institute of Technology (RIT) . With a career focused on advancing remote sensing technology, his work spans theoretical modeling, data analysis, and system design for applications in environmental monitoring, agriculture, and medical imaging. Education: BS, MS, and Ph.D. in Electrical Engineering from Purdue University . Research Interests: Dr. Kerekes investigates end-to-end remote sensing systems, emphasizing statistical parametric models to understand sensor performance. His research includes hyperspectral imaging for subpixel target detection, lidar-based surface profiling, atmospheric monitoring, and biomedical applications. Recent projects explore methane detection, urban air quality, and satellite-based crop yield estimation. Article Trends: His publications focus on hyperspectral imaging , lidar systems , and spectral modeling , with subfields like subpixel detection, environmental monitoring, and algorithm development. Collaborative efforts address real-world challenges in agriculture and climate science. Scientific Awards: Jefferson Science Fellowship (2019) Stanford University’s Top 2% of Scientists List (2022) Advising and Grants: He mentors graduate students in imaging science. Funded by the National Geospatial-Intelligence Agency (NGA) with a $1M grant (2021), his team studies spectral remote sensing limits. He also contributed to NASA’s citizen science initiatives.

• Machine Learning
• Cloud Computing
• Fault Diagnosis
• +3 more

Chao Yin is a researcher at Shanghai University , Department of Computer Engineering and Science. His work spans multiple domains including Machine Learning, Cloud Computing, Fault Diagnosis, and Supply Chain Optimization. Key research areas: Machine Learning , Cloud Computing , Quantum Computing , Supply Chain Systems , Network Security Scientific Contributions (2024-2025): Developed heterogeneous graph neural networks for automotive supply chain analysis Created MSDF-VAE cloud-edge fault diagnosis framework using transfer learning Proposed quantum metrology methods with Heisenberg-limited precision Designed LARP pseudonym protocol for V2X communication Optimized fog computing resource scheduling with hybrid metaheuristics Prior Work (2012-2023): Contributed to fluid animation feature preservation from single images Developed label distribution learning for facial age estimation Designed erasure coding storage systems for big data Created multi-agent manufacturing networks in cloud environments

Cindy ChenView profile

Associate Professor

• Spatio-temporal Databases
• Data Mining
• Cloud Computing
• +4 more

Cindy Chen serves as Department Chair and Associate Professor at the Miner School of Computer and Information Sciences within the Kennedy College of Sciences at the University of Massachusetts Lowell. Her academic journey includes a Ph.D. in Computer Science with specialization in Database and Knowledge Base Management from UCLA (2001), an MS in Computer Science from UCLA, and a BS in Space Physics from Peking University. Ph.D.: Computer Science (Database and Knowledge Base Management), University of California at Los Angeles (2001) MS: Computer Science, University of California at Los Angeles BS: Space Physics, Peking University - Beijing, China Her research spans spatio-temporal databases, data mining, cloud computing, and social network analysis. Early work focused on spatio-temporal data models and query languages , evolving into cloud data warehouse optimization and social network mining (particularly online dating behavior), then advancing to high-dimensional indexing , knowledge graph querying , and recent innovations in temporal database granulation using Allan Variance techniques for CAV friction modeling. Current work integrates database systems with autonomous vehicle infrastructure and real-time urban analytics. Her publication timeline reveals a transition from foundational spatio-temporal database systems (1998-2008) to cloud/social applications (2008-2015), then to granulation techniques and CAV systems (2015-present). Key domains include XML databases, skyline queries, social trend discovery, and friction data modeling for autonomous vehicles. Dean's Fellowship (1998) Dean's Fellowship (1999) Dr. Chen has secured significant research funding including the UMASS Transportation Center grant for an Interactive TMC Decision Support Tool (2008) and a UML Healey Grant Award for Spatio-Temporal Knowledge Discovery in Nuclear Physics (2008). Her collaborative projects span transportation systems, nuclear physics applications, and autonomous vehicle infrastructure. She actively advises on database curriculum development and serves as department chair overseeing academic operations.