جستجوی استاد راهنمای دانشگاهی

• Data Science
• Computational Social Science
• Misinformation
• +۳ مورد دیگر

Meeyoung Cha is a Professor at KAIST and Scientific Director of the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. Her research focuses on Data Science for Humanity, encompassing computational social science, misinformation dynamics, and human-machine interaction. She holds a PhD in Computer Science from KAIST (2008) and previously served as Chief Investigator at the Institute for Basic Science and Visiting Professor at Facebook. Her work addresses societal challenges such as poverty mapping, fraud detection, and AI ethics. Key achievements include best paper awards and recognition like the Hong Jin-Ki Creator Award (2024) and Test-of-Time Awards (ACM IMC 2022, AAAI ICWSM 2020). Research interests span AI ethics, social media analysis, and interdisciplinary applications of machine learning. Notable projects include modeling climate risks via satellite imagery and analyzing chatbot interactions' societal impacts. She leads the MPI-SP's Data Science for Humanity Group, mentoring over 20 students across PhD and postdoc programs. Education: PhD in Computer Science (KAIST, 2008) Affiliations: MPI-SP (Germany), KAIST Key Awards: Hong Jin-Ki Creator Award, Korean Young Information Scientist Award, Test-of-Time Awards Her publications bridge computational methods with societal issues, including climate modeling, protein engineering, and algorithmic fairness. Current projects explore geospatial AI for economic development and ethical AI design frameworks.

Franziska Mathis-Ullrichمشاهده پروفایل

استاد

• Surgical Robotics
• Medical Robotics
• Artificial Intelligence in Medicine
• +۳ مورد دیگر

Prof. Dr. Franziska Mathis-Ullrich is a Professor at Friedrich-Alexander-University Erlangen-Nuremberg (FAU) leading the Surgical Planning and Robotic Cognition Lab (SPARC) in the Department of Artificial Intelligence in Biomedical Engineering. Previously, she was an Assistant Professor at Karlsruhe Institute of Technology (KIT) from 2019 to 2023. Her research focuses on minimally invasive robotic systems, soft robotics, and embedded machine learning for surgical applications. She holds a PhD in Microrobotics from ETH Zurich (2017), with earlier degrees from the same institution. Education: B.Sc. and M.Sc. in Mechanical Engineering and Robotics (ETH Zurich, 2009–2012) Ph.D. in Microrobotics (ETH Zurich, 2017) Research Interests: Minimally invasive medical robotics, soft robotic systems, AI-driven surgical assistance, microrobotics, and robot-assisted surgery. Her work emphasizes translating robotics innovations into clinical applications through interdisciplinary collaboration. Key Awards: IEEE ICRA Best Paper Award in Medical Robotics (2014) IEEE BioRob Best Student Paper Award (2016) ICRA Microassembly Challenge First Prize (2014 & 2015) Forbes 30 under 30 (2017) Grants & Projects: Leading a Bavarian State Ministry-funded project on endometriosis diagnostics (€3M). Active in multidisciplinary collaborations with Erlangen University Hospital. Serves as Vice-President of the German Society for Computer- and Robot-assisted Surgery (CURAC). Labs & Teams: Directs the SPARC Lab, which develops cognitive robotic systems for surgical planning and execution. Collaborates with institutions like Max Planck, Fraunhofer, and Helmholtz.

• Computational Chemistry
• Computer-Aided Drug Design
• Cheminformatics
• +۱۲ مورد دیگر

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.

Gerhard Wolberمشاهده پروفایل

استاد

• Computational Drug Design
• G-Protein Coupled Receptors (GPCRs)
• Cytochrome P450 Enzymes
• +۳ مورد دیگر

Professor Gerhard Wolber leads the Molecular Drug Design research group at the Institute of Pharmacy , Freie Universitaet Berlin. His work focuses on computational approaches to drug discovery, with expertise in G-protein coupled receptors (GPCRs) , cytochrome P450 enzymes , Toll-like receptors , and viral protease inhibitors . He supervises a team of 13 PhD candidates 3 researchers 2 Master's students engaged in projects ranging from calcium channel blockers to CYP enzyme modulators for cancer therapy. Recent publications highlight his lab's contributions to pan-coronavirus drug discovery, TLR8 antagonism, and calcium channel inhibition. The team employs advanced methodologies including Molecular dynamics simulations Fragment-based de novo design Bayesian neural networks DFT calculations 3D pharmacophore modeling to bridge computational predictions with experimental validation. Notable projects include Virtual screening for TREM2-targeted glioblastoma therapeutics Allosteric communication path analysis via MDPath Immune checkpoint inhibitors for cancer immunotherapy Biased GPCR ligand development demonstrating a multidisciplinary approach to contemporary drug design challenges.

Jürgen Hesserمشاهده پروفایل

استاد

• Medical Imaging
• Computational Oncology
• Inverse Problems in Science
• +۷ مورد دیگر

Jürgen Hesser is a Professor at the Mannheim Medical Faculty , Heidelberg University, specializing in Experimental Radiotherapy and Medical Imaging . His research focuses on solving inverse problems in imaging, particularly for CT reconstruction , brachytherapy planning , and low-dose imaging . Current affiliations: Clinic for Radiotherapy and Radiooncology, Mannheim University Hospital Collaborative ties: Interdisciplinary Center for Scientific Computing (IWR) and Center for Bioinformatics (ZITI) at Heidelberg University Research interests center on anisotropic total variation techniques for medical and industrial applications, including MR-guided interventions and real-time radiation therapy . His work has led to a 1000x speed improvement in brachytherapy planning algorithms. Recent publications highlight expertise in image reconstruction (CT/X-ray), noise optimization , machine learning for cancer classification, and big data management solutions. His methods are applied to both clinical and industrial imaging challenges. Additional contributions include scientific data infrastructure development and variance stabilization techniques for medical sensors. The research group maintains strong interdisciplinary links with Physics, Mathematics, and Computer Science faculties.

Karin Nachbagauerمشاهده پروفایل

استاد

• Multibody System Dynamics
• Numerical Mathematics
• Inverse Dynamics
• +۳ مورد دیگر

Karin Nachbagauer is a Professor of Applied Mathematics at the University of Applied Sciences Upper Austria, affiliated with the Faculty for Engineering's Mechanical Engineering Department. She holds a Hans Fischer Fellowship at the TUM Institute for Advanced Study (since 2020). Her research focuses on multibody system dynamics, numerical mathematics, optimal control, and inverse dynamics, with applications in mechanical engineering and robotics. She earned her PhD in Engineering Sciences (2012) and Diploma in Industrial Mathematics (2009) from Johannes Kepler University Linz. Notable awards include the 2020 Best Paper Award for optimal control research and 2019 Excellence in Teaching Award. Her work emphasizes adjoint gradient methods for optimization problems, parameter identification in multibody systems, and time-optimal control applications. Current projects include the VRoboCoop initiative for human-robot collaboration and IOMMS for innovative optimization in multibody systems. Publications span journals like Journal of Computational and Nonlinear Dynamics and Multibody System Dynamics , with over 80 peer-reviewed articles. She actively participates in international conferences and serves on editorial boards.

• Electrocatalysis
• CO2 Reduction
• Energy Conversion Devices
• +۳ مورد دیگر

Kazuyuki Iwase serves as Associate Professor at Tohoku University's Institute of Multidisciplinary Research for Advanced Materials since April 2025, following progressive appointments as Senior Assistant Professor (2023-2025) and Assistant Professor (2019-2023). His academic journey includes postdoctoral research at Paul Scherrer Institute (Switzerland) and multiple JSPS Research Fellowships. He maintains active collaborations with prominent researchers including Prof. Itaru Honma and Prof. Takaaki Tomai. Dr. Iwase's research focuses on electrocatalysis for sustainable energy conversion, specializing in carbon dioxide reduction reaction (CO2RR) and oxygen evolution reaction (OER) systems. His work spans nanomaterials engineering, electrocatalyst design, and device integration for renewable energy applications. Key methodologies include supercritical hydrothermal processing, mechanical alloying, and machine learning optimization of electrochemical systems. His publication record demonstrates consistent high-impact output with 36 accepted articles through 2025, featuring 11 as corresponding author and 15 as first/equal-first author. Recent work explores manganese nanospinels for OER, Ag-Sn intermetallics for CO2RR, and machine learning approaches for reaction optimization, showing strong interdisciplinary connections between materials science, electrochemistry, and sustainable engineering. The 5th Symposium for The Core Research Clusters for Materials Science and Spintronics Poster Award (2021) Student Presentation Award, Chemical Society of Japan (2016) International Exchange Support Award, Electrochemical Society of Japan (2016) SIEMME Best Oral Presentation Award (2014) Dr. Iwase has secured significant research funding as Principal Investigator, including a JST PRESTO grant (¥40,000,000) for CO2 conversion research and multiple JSPS Grants-in-Aid totaling over ¥59,000,000. His academic service includes peer review for prestigious journals including Angewandte Chemie and Nature Sustainability. He maintains active international engagement through invited lectures in Japan, India, and Switzerland, focusing on nanomaterials for electrocatalysis.

Dietmar W. Hutmacherمشاهده پروفایل

استاد

• Biomaterials Engineering
• Regenerative Medicine
• Tissue Engineering
• +۳ مورد دیگر

Professor Dietmar W. Hutmacher is a Hans Fischer Senior Fellow at the Technical University of Munich (TUM) and holds a professorship at Queensland University of Technology. His research focuses on biomaterials engineering, regenerative medicine, and tissue engineering, with a strong emphasis on clinical translation. He pioneered FDA-approved bone scaffolds used in over 400 patients, demonstrating the practical impact of his interdisciplinary work. His expertise spans biomaterials design, biomechanics, and 3D bioprinting, aiming to develop functional tissues and devices for medical applications. Research interests include cartilage regeneration , bone graft engineering , and 3D cell culture systems . His work bridges life sciences, engineering, and clinical practice, addressing challenges in tissue repair, cancer metastasis modeling, and drug development. Notable contributions include humanized xenograft models for cancer-bone interactions and advanced additive manufacturing techniques for customized medical devices. Professor Hutmacher’s publications highlight advancements in bioprinting standards , hydrogel reinforcement , and bone metastasis mechanisms . His awards include the ARC Future Fellowship and recognition from the International College of Fellows in Biomaterials Science. He collaborates extensively, contributing to global initiatives in regenerative medicine and translational research.

• Agricultural Engineering
• Sensor Technology
• Computational Fluid Dynamics
• +۷ مورد دیگر

Qianying Yi is a Postdoctoral Scientist at the Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) in Germany since July 2019, with a focus on Sensor Technology and Modeling . Her academic journey includes a PhD in Engineering (2015-2018) and a Research Assistant role (2018-2019) at Aarhus University , Denmark, and a Master in Pharmaceutical Engineering (2012-2015) and Bachelor in Biological Engineering (2008-2012) in China. PhD in Engineering, Aarhus University, Denmark Master in Pharmaceutical Engineering, Sichuan University, China Bachelor in Biological Engineering, Shandong University of Science and Technology, China Her research spans two distinct domains: (1) Agricultural Engineering , where she specializes in designing natural ventilation systems for livestock buildings, modeling indoor climate and emissions , and optimizing airflow patterns using computational fluid dynamics (CFD) and wind tunnel experiments , and (2) Pharmaceutical Engineering , where her early work focused on virtual screening of enzyme inhibitors for drug discovery applications, particularly targeting NAMPT , EphB4 , and MEK1 . Recent publications highlight her expertise in agricultural ventilation systems , including machine learning-based modeling , CFD simulations for multi-floor pig buildings, and pollutant dispersion mitigation . Earlier works emphasize computational drug discovery through pharmacophore modeling and molecular dynamics. While no explicit awards are listed, her career trajectory reflects interdisciplinary innovation across engineering and bioeconomy sectors.

Steffen Lindertمشاهده پروفایل

استادیار

• Computational Chemistry
• Biomolecular Simulations
• Structure Prediction
• +۱ مورد دیگر

Steffen Lindert is Assistant Professor in Chemistry and Biochemistry at Ohio State University. His research applies computational methods to study biomolecular systems and facilitate drug discovery. He holds a PhD in Chemical and Physical Biology from Vanderbilt University and M.Sc. in Physics from University of Leipzig. Technical expertise includes protein structure prediction, molecular dynamics simulations, and computational drug design. Research develops algorithms for modeling protein interactions and dynamics to enable rational drug design strategies for therapeutic targets.

Jens Meilerمشاهده پروفایل

استاد

• Computational Biology
• Drug Discovery
• Protein Modeling
• +۴ مورد دیگر

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.

Petr Popovمشاهده پروفایل

استادیار

• Machine learning for molecular structures
• Computational biomolecular modeling
• Computer-aided drug discovery
• +۳ مورد دیگر

Petr Popov is an Assistant Professor at the Skoltech Center for Molecular and Cellular Biology . His research focuses on applying machine learning and computational methods to solve critical problems in structural bioinformatics, chemoinformatics, and GPCR (G protein-coupled receptor) signaling mechanisms. Core Research Areas: Machine learning for molecular property prediction Computational biomolecular modeling and design Computer-aided drug discovery Mechanistic analysis of biased signaling in opioid receptors Recent Publications (2023–2022): His articles investigate the structural basis of signaling bias in kappa opioid receptors (KOR), including crystal structures of KOR-nalfurafine complexes and molecular dynamics simulations of GPCR transducer coupling. Topics span ligand-specific conformations, arrestin/G protein selectivity, and computational modeling of receptor dynamics. Methodologies: Utilizes atomic-level MD simulations, mutagenesis validation, and structure determination to analyze receptor-ligand interactions. Keywords include structural biology, computational pharmacology, and neuroreceptor engineering.

• Design of Experiments
• Quality Improvement
• Computer Experiments
• +۲ مورد دیگر

William Welch is Professor in the Department of Statistics at the University of British Columbia. Former department head (2003-2008), Welch has held positions at University of Waterloo, University of Illinois, and UBC's business school. His research spans computer-aided design of experiments, quality improvement, computer experiment analysis with engineering applications, statistical methods for drug discovery, and machine learning.

Vlad Cojocaruمشاهده پروفایل

استاد پژوهش

• Computational Structural Biology
• Molecular Simulations
• DNA recognition by transcription factors
• +۴ مورد دیگر

Vlad Cojocaru is a Research Professor and Group Leader of the Computational Structural Biology project group at the Max Planck Institute for Molecular Biomedicine in Münster, Germany. Since September 2018, he has also served as a Group Leader at the Hubrecht Institute of the Royal Netherlands Academy of Arts and Sciences in Utrecht, The Netherlands. His research group applies state-of-the-art computational methods to investigate biological processes from a physico-chemical perspective, with particular focus on stem cell pluripotency mechanisms. Dr. Cojocaru's research centers on understanding how transcription factors recognize and bind to DNA through computational approaches including structural modeling, biomolecular simulations, and molecular docking. His work examines protein-nucleic acid interactions, protein-membrane interactions, and computer-aided drug discovery. Key research areas include DNA recognition by transcription factors involved in combinatorial control of transcription, cooperativity between transcription factors during DNA binding, and the impact of transcription factor folding on DNA recognition. Analysis of Dr. Cojocaru's recent publications reveals a strong emphasis on chromatin structure and transcription factor dynamics, particularly regarding how pioneer factors like OCT4 interact with nucleosomes. His work combines computational modeling with experimental validation to reveal molecular mechanisms difficult to observe through traditional methods, significantly advancing our understanding of stem cell regulation and chromatin dynamics. PLoS Computational Biology 2011 journal cover feature Biochimica et Biophysica Acta 2007 journal cover feature Nucleic Acids Research 2005 journal cover feature Dr. Cojocaru actively mentors students in computational structural biology and receives funding from prestigious sources including the Max Planck Society and the DFG SPP1356 Priority Program. He utilizes significant computational resources from the Max Planck Society and the PRACE EU initiative. His group welcomes Master's and PhD students interested in pursuing computational biology research, with applications directed to him personally. The Computational Structural Biology group, which includes researchers like Jan Huertas, utilizes what Cojocaru terms a 'computational nanoscope' - a suite of molecular modeling and simulation methods enabling visualization of biomolecular dynamics at atomistic resolution. Their work has produced significant insights into chromatin dynamics, nucleosome breathing, histone tail dynamics, and the structural mechanisms of pioneer transcription factor binding to nucleosomes.

• Molecular Simulations
• Digital Pharmacology
• Computational Neuromedicine
• +۵ مورد دیگر

Giulia Rossetti is a University Professor at Forschungszentrum Jülich, Germany, holding dual chairs: the Chair for Theoretical Biophysics and the Chair for Computer-Aided Molecular Medicine. She is based in the Computational Biomedicine department (INM-9) of the Institute of Neuroscience and Medicine (INM), located in Building 16.15, Room 3001 at Wilhelm-Johnen-Straße 52428 Jülich. Her research integrates molecular simulation and data science to revolutionize drug discovery, with core expertise in Molecular Simulations, Digital Pharmacology, and Computational Neuromedicine. She specializes in neuronal membrane proteins, RNA, and intrinsically disordered proteins, targeting central nervous system disorders through computational innovation. As group leader of the Drug Design Hub for Digital Neuropharmacology, she directs cutting-edge research applying advanced computational methods to neuropharmacology. While specific scientific awards, student advisees, and grant details are not provided in source materials, her leadership role indicates active supervision of research teams and project management in computational biomedicine.