Mathias Niepert is a Professor at the Institute for Artificial Intelligence within the Faculty of Computer Science, Electrical Engineering and Information Technology at the University of Stuttgart. His research focuses on advancing machine learning techniques with applications in scientific computing, graph neural networks, and medical imaging. He is particularly known for contributions to physics-informed neural networks, equivariant models, and graph learning frameworks. Key research areas include: Scientific Machine Learning for PDEs and molecular modeling Graph neural networks and their theoretical limitations Medical vision-language models and multimodal learning Efficient neural network architectures (transformers, FNOs) Domain knowledge integration in deep learning His work often bridges theoretical foundations with practical applications, as evidenced by extensive publications (2018–2025) on topics like adaptive message passing, equivariant networks, and medical imaging systems. He has contributed to benchmark development through initiatives like PDEBench and pioneered methods for equivariant diffusion models and molecular representation learning. His current projects emphasize: Improving generalization in Fourier Neural Operators Addressing oversmoothing in graph networks Combining physics principles with neural architectures Medical AI applications through multimodal fusion
جستجوی استاد راهنمای دانشگاهی
برای مسیر پژوهشیتان، استاد راهنما، استاد دانشگاه و پژوهشگر مناسب را پیدا کنید و با آنها ارتباط بگیرید.
نمایش ۱-۱۵ از ۳۹ استاد راهنما
اطلاعات پروفایلها از صفحات عمومی دانشگاهها گرفته شده و ممکن است بهروز نباشد.
Prof. Dr. Matthias Rarey is a computer scientist and Professor at the University of Hamburg's Center for Bioinformatics. He holds a Ph.D. in Computer Science from the University of Bonn (1996) and has been leading the Algorithmic Molecular Design working group since 2002. His research focuses on molecular design algorithms, cheminformatics tools, and 3D bioinformatics. Co-founder of BioSolveIT GmbH Former cheminformatics group leader at Fraunhofer SCAI Former researcher at SmithKline Beecham and Roche Bioscience Head of Helmholtz Data Science Graduate School DASHH Director of Center for Data and Computing in Natural Science (CDCS) Research interests span algorithmic molecular design, cheminformatics, structure-based drug discovery, and machine learning applications in bioactivity prediction. His group developed widely used tools like FlexX, PoseView, and SpaceLight for molecular modeling and fragment space analysis. Recent publications focus on geometric pattern matching in protein-ligand interfaces, combinatorial fragment space encoding, adverse drug reaction network analysis, and efficient shape-based virtual screening. The work emphasizes scalable algorithms for billion-sized compound libraries and integration of machine learning with traditional cheminformatics approaches. Scientific awards include: GMD Award 1996 (Best Dissertation) GMD Award 2000 (Best Project) NRW Wissenschaftspreis 2002 Corwin Hansch Award 2005 Emerging Technologies Award 2011 Norddeutscher Wissenschaftspreis 2020 Academic leadership roles: Founding director of Center for Bioinformatics Co-founder of M.Sc. Bioinformatics and B.Sc. Computing in Science programs Chair of doctoral committee at Faculty of Computer Science Member of EMBL-EBI's Molecular and Cellular Structure advisory board Former Associate Editor of Journal of Chemical Information and Modeling
Ambuj K. Singh is a Professor in the Department of Computer Science at the University of California, Santa Barbara . With over 278 publications since 1987, his work spans graph neural networks, social network dynamics, and interdisciplinary applications in neuroimaging and cheminformatics. Key collaborations with researchers like Sourav Medya, Arlei Silva, and Francesco Bullo Contributions to network design, opinion dynamics, and interpretable AI His research integrates machine learning with graph theory , addressing problems in community detection , influence limitation , and explanation generation . Recent work focuses on counterfactual explainers and molecular graph analysis . He has contributed to venues like KDD, NeurIPS, WWW, and ICLR, often exploring temporal networks and polarized embeddings .
Andrea Volkamer is a computational chemist and active principal investigator in the field of computer-aided drug design (CADD), with a focus on kinase targets, druggability prediction, and machine learning applications. She has published extensively in journals such as the Journal of Chemical Information and Modeling and Journal of Medicinal Chemistry , with recent work up to 2025 indicating an ongoing academic research program. Her research group, referred to as 'volkamerlab,' develops open-source tools including DoGSite, KiSSim, KinFragLib, and TeachOpenCADD, which are widely used in both academic and industrial drug discovery settings. Her research interests span computational drug discovery , structural bioinformatics , kinase inhibitor design , off-target and polypharmacology prediction , and educational platforms for CADD . She emphasizes open science and reproducibility, particularly through the TeachOpenCADD initiative, which provides interactive Jupyter Notebooks and KNIME workflows for teaching cheminformatics concepts. The 15 most recent publications reflect a strong trend toward integrating machine learning and deep learning (e.g., transformers, graph neural networks) with structure-based methods such as molecular docking, free energy calculations, and binding site comparison. Her work increasingly addresses real-world challenges in drug discovery, including kinase mutation resistance, selectivity optimization, and in vivo toxicity prediction using conformal and hybrid models. Scientific Contributions and Awards: Development of key computational tools: DoGSite, KiSSim, KinFragLib, TeachOpenCADD. Leadership in open-source and open-education initiatives in cheminformatics. Active publication record in top-tier journals with interdisciplinary impact. Advising and Grants: While specific student names and grant details are not mentioned in the provided text, her role as a corresponding author on numerous publications and the existence of a dedicated research lab ('volkamerlab') imply that she mentors students and postdoctoral researchers. She likely secures competitive funding to support her research in computational drug discovery and method development. Labs and Teams: She leads the Volkamer Lab ('volkamerlab'), which focuses on developing and applying computational methods for drug discovery. The lab collaborates with both academic and pharmaceutical partners and emphasizes open-source software development and educational outreach.
Zhang Yangمشاهده پروفایل
دانشیار
- Artificial Intelligence in Biomedical Imaging
- Deep Learning for Omics Data Analysis
- Pathogen Detection and Diagnostics
- +۷ مورد دیگر
Zhang Yang is an Associate Professor at the School of Medical Engineering, Harbin Institute of Technology (Shenzhen), with a joint appointment as Visiting Professor at the University of Tokyo starting in July 2024. He holds a PhD from the University of Cambridge's Department of Pathology and an M.Phil. from the University of Hong Kong's HKU-Pasteur Research Center. Previously, he served as an Assistant Professor at Harbin Institute of Technology (Shenzhen) from September 2015 to December 2020. His research integrates computational and experimental approaches to address challenges in pathogen and cancer research. On the computational side, his work focuses on developing AI-powered microscopic imaging systems, applying deep learning to analyze multi-omics data (including proteins, DNA, miRNAs, LncRNAs, and mRNAs), and utilizing deep learning in cheminformatics for drug discovery. On the experimental side, his laboratory combines imaging, high-throughput sequencing, mass spectrometry, and chemical biology to understand disease mechanisms at the molecular level. His publication record demonstrates significant impact, with over 50 SCI-indexed papers in high-impact journals including Nature Communications, Briefings in Bioinformatics, Bioinformatics, Analytical Chemistry, and Trends in Biotechnology. His work has been cited by prestigious journals such as Nature Reviews Methods Primers and Nature Communications, with three ESI highly cited papers. His research spans multiple interdisciplinary fields, combining artificial intelligence with biomedical applications to advance diagnostic and therapeutic approaches. World's Top 2% Scientists 2021 Fellow of the Royal Society of Biology Three ESI Highly Cited Papers Five authorized national invention patents As an academic leader, he serves as Associate Editor for BMC Biology and Frontiers in Microbiology, Academic Editor for PLOS Genetics, Editorial Board Member for Communications Biology, and Guest Editor for a Special Issue on AI in analytical chemistry in Trends in Analytical Chemistry. His laboratory actively collaborates with international institutions, with graduates pursuing further studies at Hong Kong Chinese University, Hong Kong University of Science and Technology, Hong Kong Polytechnic University, Macau University, and the University of New South Wales. He teaches Introduction to Modern Biology for undergraduates and Bioanalytical Chemistry for graduate students.
Carolin Müllerمشاهده پروفایل
استادیار
Carolin Müller is a Juniorprofessor for the Theory of Electronically Excited States at the Friedrich-Alexander University Erlangen-Nuremberg since November 2023. Previously, she was a Feodor Lynen Postdoctoral Researcher at the University of Luxembourg (June 2022-October 2023) and a Postdoctoral Researcher at Friedrich Schiller University Jena (March 2021-May 2022). Dr. Müller received her B.Sc. (2016) and M.Sc. (2018) in Chemistry from Friedrich Schiller University Jena, followed by her Ph.D. (Dr. rer. nat) in 2021 from the same institution. Her doctoral research focused on "Towards Operando Spectroscopy of Supramolecular Photocatalysts – A Case Study on Ru-dppz-derived Systems" under the supervision of Prof. B. Dietzek-Ivanšić. Dr. Müller's research focuses on the theoretical understanding of photoinduced processes in molecules and materials. Her group (CPC Group) investigates electron transfer processes, isomerization reactions, and excited-state dynamics with the goal of controlling and optimizing light-driven processes for increased reactivity and efficiency. Her work combines computational chemistry, spectroscopy, and machine learning approaches, specifically utilizing methods like TD-DFT, CASSCF, molecular/quantum dynamics, and cheminformatics techniques including SVD, MCR, and global/target lifetime analysis. Her recent publications demonstrate a strong interdisciplinary approach spanning computational chemistry, spectroscopy, and machine learning. Key themes include nonadiabatic molecular dynamics, excited-state simulations, photoswitch design, photocatalysis, and the development of computational tools like KiMoPack for kinetic modeling. Her work often bridges theoretical predictions with experimental validation through close collaboration with spectroscopy research groups. Feodor Lynen Research Fellowship (Alexander von Humboldt Foundation) Thuringian Research Award 2023 for Applied Research Albert-Weller Award (German Chemical Society) Dissertation Award (Faculty of Chemistry and Earth Sciences) FCI Kekulé PhD fellowship As a Juniorprofessor, Dr. Müller leads the CPC Group at FAU, where she mentors students in computational chemistry research. She has developed expertise in combining spectroscopic techniques (resonance Raman, transient absorption, and time-resolved emission spectroscopy) with computational methods and cheminformatics approaches. She also actively contributes to the scientific community through service roles including co-organizing the ESTML 2023 Workshop and serving as an active member in the yPC organization of the German Bunsen Society. Dr. Müller is actively developing the CPC Group research program at the Computer Chemistry Center, focusing on light-induced physical processes and chemical reactions. Her group combines quantum chemistry, chemoinformatics, and experimental spectroscopy to reveal mechanisms behind photoinduced phenomena and optimize light-driven processes.
Prof. Dr. Ulrich Schatzschneider leads the Laboratory of Bioinorganic Chemistry and Inorganic Chemical Biology at the University of Würzburg's Faculty of Chemistry and Pharmacy. His research spans interdisciplinary projects at the intersection of chemistry, biology, and medicine. Research focuses on bioorthogonal "Click" reactions Development of bioactive metal complexes Design of CO-releasing molecules Application of mass spectrometry and DFT calculations Integration of cheminformatics and machine learning The international team includes members from six continents, with recent participants from Germany, Afghanistan, Egypt, Cameroon, China, Iran, Serbia, South Africa, and Spain. Prospective students must apply with transcripts and follow strict deadlines for thesis projects.
Lothar Richterمشاهده پروفایل
پژوهشگر
- Bioinformatics
- Machine Learning
- Data Mining
- +۳ مورد دیگر
Dr. Lothar Richter is a researcher at the Technical University of Munich (TUM), affiliated with the Chair for Bioinformatics within the Department of Informatics. He holds a doctoral degree (Dr. rer. nat.) and is actively involved in interdisciplinary research spanning bioinformatics, machine learning, and computational biology. His work integrates advanced data mining techniques with biological applications, particularly in protein structure-function prediction and medical informatics. Research Interests: Bioinformatics & Computational Biology: Development of algorithms for protein structure and function prediction. Machine Learning in Healthcare: Application of radiomics and predictive modeling in oncology. Data Mining Systems: Design of inductive databases and query languages for biological data analysis. His publications demonstrate a consistent focus on leveraging computational methods to address complex biological questions, from predicting protein interactions to modeling drug resistance in HIV. Recent work emphasizes translational applications, such as machine learning-based radiomics for sarcoma diagnosis. Affiliations & Contact: Technical University of Munich - Informatics 12 (Chair for Bioinformatics) Email: lothar.richter@mytum.de Room: 5609.01.061, Boltzmannstr. 3, Garching b. Munich
- Bioinformatics
- Deep Learning
- Biosensors
- +۵ مورد دیگر
Yang Zhang is a Professor at Tianjin University and has held academic positions at the Harbin Institute of Technology (2015-present) and The University of Tokyo (Visiting Professor, 2024). Education : PhD in Pathology from University of Cambridge (2012-2015) MPhil from University of Hong Kong (HKU-Pasteur Research Center, 2009-2011) His research bridges computational biology and experimental microbiology , focusing on: AI-powered microscopic imaging for disease diagnostics Deep learning analysis of multi-omics data (proteins, RNAs, etc.) Development of biosensors for pathogen and cancer detection Research Trends from 2018-2025: Consistent focus on deep learning applications Expanding from parasitology to broader cancer research Integration of electrochemical and optical biosensors Scientific Recognition : Elected Fellow of The Royal Society of Chemistry (2025) Elected Fellow of the Royal Society of Biology (2023) His work combines computational approaches (AI, protein language models) with experimental techniques (high-throughput sequencing, mass spectrometry) to address fundamental questions in disease mechanisms at the molecular level.
Chris Kuennethمشاهده پروفایل
استادیار
Chris Kuenneth is an Assistant Professor at the University of Bayreuth, specializing in Materials Informatics and Polymer Science. His research focuses on AI-driven approaches for materials discovery, with particular emphasis on polymer informatics and computational chemistry. He contributes actively to open-source projects like RDKit, ensuring cross-platform availability of cheminformatics tools. Key areas of expertise include machine learning applications in materials science, data-driven polymer property prediction, and high-throughput screening for novel materials. Developed polyBERT, a chemical language model for polymer informatics. Advances in AI-assisted discovery of battery electrodes and dielectric materials. His work bridges computational methods with practical applications, addressing challenges in sustainable materials and energy storage technologies. Collaborations involve interdisciplinary teams and open-source communities.
Jan Zuberمشاهده پروفایل
پژوهشگر
- Ultra-high-resolution mass spectrometry
- Structural elucidation of organic compounds
- Stability studies of supramolecular complexes
- +۵ مورد دیگر
Jan Zuber is a Researcher at the Institute of Analytical Chemistry within the Faculty of Chemistry, Physics and Biosciences at Technische Universität Bergakademie Freiberg. His work focuses on advanced mass spectrometry techniques for analyzing energy resources and complex organic mixtures. PhD in Chemistry (2015-2018), TU Bergakademie Freiberg Master’s in Chemistry (2012-2015), TU Bergakademie Freiberg Bachelor’s in Chemistry (2009-2012), TU Bergakademie Freiberg Zuber specializes in ultra-high-resolution mass spectrometry (FT-ICR-MS) for structural elucidation of organic compounds in pyrolysis oils, coal, and biomass. He also investigates supramolecular complex stability, polymer impurities, and chromatography-MS coupling for isomer separation. His research spans energy resources, environmental chemistry, and cheminformatics. Recent publications highlight applications of FT-ICR-MS in lignin oligomers, yeast extract analysis, and hydrogel scaffolds. His work often integrates chemometrics, laser desorption, and ionization methods to tackle challenges in energy and material science. Zuber contributes to e-learning development in analytical chemistry modules and collaborates on multidisciplinary projects in chemical structure elucidation and energy material characterization.
Alexander Haggمشاهده پروفایل
پژوهشگر
Dr. Alexander Hagg is a Researcher at Hochschule Bonn-Rhein-Sieg (H-BRS) in the Department of Engineering and Communication, affiliated with the Institute of Technology, Resource Conservation and Energy Efficiency (TREE). With a PhD from Leiden University, he specializes in evolutionary computation, machine learning, and computer-aided ideation, focusing on applications in climate adaptation, energy efficiency, and resource conservation. His work bridges theoretical research with practical applications across multiple domains including urban planning, computational chemistry, and robotics. PhD in Computer Aided Ideation (2017-2020) - Leiden University Master's in Autonomous Systems (2013-2016) - Bonn-Rhein-Sieg University of Applied Sciences Bachelor's in Computer Science (2009-2013) - Bonn-Rhein-Sieg University of Applied Sciences Dr. Hagg's research centers on efficient computer-aided ideation algorithms that help understand early on what good solutions to complex problems might look like. His primary focus is on quality diversity algorithms, which efficiently create diverse sets of high-performing solutions to inform engineers' intuition. His work spans multiple application domains including urban climate resilience, structural chemistry, robotics, and digital twins for urban planning. He is particularly interested in how AI can serve as a co-designer, helping humans explore and understand complex optimization and data domains. His recent publications reveal a strong trend toward applying evolutionary computation and machine learning to real-world engineering problems. A significant portion of his work focuses on computational chemistry and force-field parameter optimization, where machine learning substitutes expensive molecular dynamics calculations. Another major theme involves quality diversity algorithms applied to urban planning and building design. His research consistently emphasizes practical applications in climate adaptation and resource efficiency, with growing interest in digital twins for urban sustainability. 2023: GECCO Best Paper Award (honourable mention) 2022: ACM SIGEVO Best Dissertation Award (honourable mention) 2017: AFCEA Studienpreis 2017: GECCO Best Student Paper Award (honourable mention) 2016: RoboCup Symposium Best Paper Award Dr. Hagg has led multiple research projects including OpenSKIZZE (open-source tools for climate-adaptive urban development), Digital Twin-4-Multiphysics Lab (DT4MP), and KISs-BiS (AI for elite sports). He teaches courses on evolutionary computation, AI, and machine learning, and has developed workshops on digital twins for urban sustainability. His research is supported by collaborations with institutions including University of Siegen, University College London, University of Leiden, and various Fraunhofer institutes. He leads the Digital Twin-4-Multiphysics Lab (DT4MP) which focuses on urban digital twins and multiphysics twins for industry. Dr. Hagg is also active in several research groups including the Computational Chemistry Working Group at H-BRS and serves as a representative for H-BRS in the GeoIT Round Table NRW. His work often involves interdisciplinary teams spanning computer science, engineering, urban planning, and environmental science.
Jens Meiler is a Professor and Director of the Institute for Drug Development at Leipzig University. He holds a Humboldt Professorship and leads multiple research projects including DFG-funded studies on GPCR modeling and drug discovery. His research integrates computational approaches with experimental validation for therapeutic design. His research interests focus on computer-aided drug discovery, protein structure determination, and development of algorithms for designing small molecule drugs and biologics. Current projects address challenges in cancer, infectious diseases, neurological disorders, and metabolic conditions through computational modeling and machine learning approaches. Dr. Meiler's work has earned him the Humboldt Professorship award. His collaborative projects involve interdisciplinary teams across biochemistry, bioinformatics, and medical physics, working on structure-based drug design for various disease targets.
Dr. Marcel Friedrichs is a Researcher at the Faculty of Engineering , Bielefeld University , Germany. He is affiliated with both the Algorithmic Cheminformatics Group and the Center for Biotechnology (CeBiTec) , contributing to interdisciplinary research at the intersection of computational methods and biomedical systems. Researcher in Algorithmic Cheminformatics Group Center for Biotechnology (CeBiTec) member Focus on graph-based data integration and biomedical networks Research Interests : Computational biology, systems biology, bioinformatics tool development, and medical biochemistry. His work emphasizes automated data integration, modeling of regulatory networks, and analysis of gene-disease comorbidities. Article Trends : Publications span 2018–2025, highlighting graph-based data integration, stem cell differentiation (cardiomyocytes), miRNA databases, clinical trial analysis, and pharmacological network modeling. Key keywords include Computational Biology , Medical Biochemistry , and Graph Theory . Labs & Teams : Algorithmic Cheminformatics Group (Faculty of Engineering) Center for Biotechnology (CeBiTec) Contributor to projects like BioDWH2 and GenCoNet
Kil To Chong is Professor in the Department of Electronics and Information Engineering at Jeonbuk National University, South Korea, with research focus on computational methods in biomedical sciences. He directs the Advanced Electronics and Information Research Center in Jeonju. Research expertise includes: Machine learning for biomolecular discovery Neural network architectures for biological data Computational drug development Bioinformatics algorithm design Recent publications develop: Deep learning models for tumor antigen identification Retrosynthetic pathway prediction networks Anti-inflammatory peptide detection systems Drug synergy prediction methods