جست‌وجوی استاد و پژوهشگر

Katherine Herbertمشاهده پروفایل

استاد

• Bioinformatics
• Data Sciences in Scientific Data Sets
• Computer Science Education
• +۶ مورد دیگر

Dr. Katherine Herbert is a Professor in the School of Computing at Montclair State University, specializing in bioinformatics, database systems, and data science education. She holds a BS from Saint Peter's College, and MS and PhD from New Jersey Institute of Technology. Her research focuses on data quality, data cleaning, and integration within scientific datasets, with a strong emphasis on bioinformatics and cheminformatics. She leads multiple NSF-funded initiatives including space weather forecasting using machine learning (ANSWERS Project) and interdisciplinary data science training for educators (DATA3 RET Site). Dr. Herbert has secured over $1.2M in grants, collaborating on projects like Cyberinfrastructure-Enabled Machine Learning for Space Weather and Opening Pathways in Chemistry. She has authored 47+ peer-reviewed publications, including work on K-12 computer science integration and solar weather STEM education. Her teaching spans undergraduate and graduate courses in databases, data mining, and interdisciplinary computing. She advises multiple NSF-funded programs targeting STEM equity, including Pathways to STEM Degrees for underrepresented students. Labs/Teams: Active in Montclair's Data Science and Cyberinfrastructure Research Group, leading collaborations with NASA-funded space weather initiatives and regional STEM education networks.

• 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

Jakob Lykke Andersenمشاهده پروفایل

دانشیار

• Algorithms
• Graph Transformation
• Chemical Reaction Networks
• +۷ مورد دیگر

Jakob Lykke Andersen is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he conducts research in algorithms with applications in cheminformatics and complex systems. He also holds a former external appointment as a Research Fellow at the Tokyo Institute of Technology (2015–2017). Research Interests: His work lies at the intersection of computer science and theoretical chemistry, focusing on algorithmic methods for analyzing chemical reaction networks. He employs hypergraphs, mixed-integer linear programming, and probabilistic models to study metabolic pathways, reaction databases, and prebiotic systems. His research emphasizes computational efficiency, formal modeling, and software implementation. Publication Trends: His recent publications (2019–2025) show a consistent focus on graph-based modeling of chemical systems, rule extraction from reaction databases, thermodynamic feasibility, and stochastic analysis. These works appear in high-impact journals in cheminformatics, bioinformatics, and complex systems, reflecting strong interdisciplinary collaboration. Scientific Contributions: While no specific awards are listed, his sustained research output and leadership in funded projects highlight significant contributions to algorithmic cheminformatics. Grants and Projects: He is actively involved in two major ongoing research projects: (1) Software Infrastructures for Teaching at Scale (funded by Innovation Fund Denmark, 2022–2025), and (2) DIREC (Danish Research Center for Digital Economy, 2020–2025), indicating active engagement in both educational technology and core computational research. Advising and Outreach: While no students are listed, he participates in academic advising through project supervision. He contributes to public discourse through media appearances on topics such as mathematics in health (e.g., intestinal system modeling in obesity) and educational well-being. Labs and Teams: He collaborates with interdisciplinary teams, including researchers from bioinformatics, chemistry, and computer science, particularly through projects involving Merkle, Flamm, Fagerberg, and Stadler. His work is associated with algorithmic cheminformatics and software framework development groups at SDU.

• Structural Bioinformatics
• Data Visualization
• Protein Structure
• +۴ مورد دیگر

David Hoksza is an Associate Professor at the Department of Software Engineering, Faculty of Mathematics and Physics, Charles University in Prague. His work bridges bioinformatics and computational biology through innovative algorithm and tool development. University: Charles University School: Faculty of Mathematics and Physics Department: Department of Software Engineering His research focuses on structural bioinformatics and data visualization, particularly in protein and RNA structure analysis. He has also contributed to cheminformatics (ligand-based virtual screening), computational genomics (MinION data analysis), and systems biology (molecular network visualization). David has led major projects including the P2Rank framework for ligand-binding site prediction, R2DT for RNA secondary structure visualization, and the Genomics 2 Proteins portal for linking genetic data to protein structures. His publications span high-impact journals like Nucleic Acids Research , Bioinformatics , and Nature Methods . Recent articles highlight his expertise in RNA structure prediction, protein-ligand interactions, and neurodevelopmental disorder analysis. His work emphasizes template-based modeling, machine learning, and web-based tool development. Scientific Awards David actively teaches courses on data visualization and bioinformatics algorithms, with a focus on practical implementation using technologies like D3.js, Tableau, and Python. He contributes to open-source software and maintains a GitHub repository for his tools.

• Artificial Intelligence
• Reinforcement Learning
• Bioinformatics
• +۴ مورد دیگر

Leander Schietgat is a postdoctoral researcher at Vrije Universiteit Brussel (VUB) working within the Informatics and Applied Informatics department. He is affiliated with the Federated Labs AI and Robotics research group and holds an ORCID identifier (0000-0002-2097-0974). His research spans multiple interdisciplinary domains where artificial intelligence intersects with practical applications. Dr. Schietgat's research interests focus on applying machine learning techniques to diverse domains. His work in reinforcement learning addresses real-world manufacturing challenges through job shop scheduling algorithms. In bioinformatics, he investigates metagenomics and haplotype analysis for microbial communities. His research also extends to computational toxicology, developing methods for toxicophore detection and mutagenicity prediction. This interdisciplinary approach demonstrates his ability to adapt AI methodologies across different scientific domains. His publication record shows consistent output with four research articles between 2021-2023, demonstrating expertise in both theoretical machine learning frameworks and practical implementations. The research trends indicate a strong focus on applying explainable AI to trustworthy control systems, with particular emphasis on reinforcement learning applications in manufacturing and biological systems. Dr. Schietgat is actively involved in multiple research projects including: CTRLxAI=T(H)RUST (2022-2026): CTRL schemes merged with eXplainable AI for t(h)rustworthy control of physical dynamic systems IOF GEAR: Artificial Intelligence Lab (2022-2026) TAILOR: Foundations of Trustworthy AI (2020-2024) ESF project: A people-oriented AI training through serious game experiences (2019-2021) RAINboW: Responsible AI for a smart connected world (2019-2022) He is scheduled to participate in the event 'Europe's Vision on Responsible AI: Research, Practice, Policy' as an organizer in April 2025, indicating his active engagement with the broader AI policy community.

Angelo Freiمشاهده پروفایل

مدرس

• Metalloantibiotics
• Antimicrobial Resistance
• Machine Learning
• +۳ مورد دیگر

Dr. Angelo Frei is Lecturer in Inorganic Chemistry at the University of York, leading research on metal-based antimicrobials to address antibiotic resistance. His group develops machine learning approaches and combinatorial chemistry to explore metalloantibiotics and elucidate their mechanisms of action. Current investigations focus on metal complex photosensitizers for antimicrobial photodynamic therapy and resistance avoidance. The Frei Lab employs high-throughput screening and computational methods to navigate chemical space efficiently. Research has secured JPIAMR funding for metalloantifungal development and explores ruthenium complexes against mycobacteria. Collaborative projects span multiple countries with focus on preclinical validation of metal-based therapeutics.

• Bioinformatics
• Machine Learning in Healthcare
• Cancer Genomics
• +۳ مورد دیگر

Tuğba Süzek is an Associate Professor in the Department of Computer Engineering at Muğla Sıtkı Koçman University, Turkey. She holds a PhD in Bioinformatics from George Mason University (2012), an MSc from Johns Hopkins University (2000), and a BSc from Middle East Technical University (1997). Academic Background: BSc: Computer Engineering, Middle East Technical University (1993-1997) MSc: Bioinformatics, Johns Hopkins University (1998-2000) PhD: Bioinformatics, George Mason University (2003-2012) Research Interests center on bioinformatics , machine learning in healthcare , cancer genomics , transcriptomics , and molecular data analysis . She develops tools like TCGAnalyzeR for pan-cancer cohort discovery and CompCorona for coronavirus transcriptome analysis. Recent Articles span lung cancer pathogenesis, orthodontic malocclusion prediction, microbiome-host disease associations, and AI-driven diagnostic tools. Her work bridges cheminformatics and bioinformatics for drug repurposing. Scientific Awards ORISE Fellowship (2000) Patent Holder: System for Early Diagnosis of Skeleton Syndromes (2024) Advising and Grants include supervising student theses on AI in orthodontics and molecular cancer research . She leads EU-funded projects like Intraperitoneal immune modulation and TÜBİTAK grants for drug repurposing and neurodegenerative disease analysis .

• Molecular Docking
• Virtual Screening
• Ligand Discovery
• +۷ مورد دیگر

John Irwin is an Adjunct Professor in the School of Pharmacy at the University of California, San Francisco , specializing in computational methods for drug discovery. His research focuses on molecular docking , virtual screening , and ligand discovery , with a strong emphasis on structure-based drug design and chemical informatics . Irwin's work includes developing and applying tools like ZINC Database and DOCK Blaster for large-scale compound screening. He has contributed to understanding protein-ligand interactions in targets such as opioid receptors, CFTR, and SARS-CoV-2 proteins, advancing non-covalent and covalent drug development . His publications (2025-2022) span Chemistry , Medical Informatics , and Computational Biology , with key subfields: GPCR Modulation , Neurodegenerative Disease Targets , Virtual Screening Libraries , Protein Structure Analysis , Pharmacology , and Drug Repurposing . Irwin collaborates extensively with researchers in Pharmaceutical Chemistry and Structural Biology , and his projects often involve NIH-funded grants (R01GM133836, R01GM071896).

Lily Cheungمشاهده پروفایل

دانشیار

• Systems Biology
• Genetically Encoded Biosensors
• Quantitative Fluorescence Microscopy
• +۸ مورد دیگر

Lily Cheung is an Associate Professor at the Georgia Institute of Technology , affiliated with the College of Engineering and the School of Chemical and Biomolecular Engineering . She is also the Roy C. and Polly B. Sheffield Faculty Fellow. Her work integrates computational methods with experimental approaches in plant biology, focusing on the interplay between metabolic and gene regulatory networks to advance agricultural biotechnology. BS, Chemical Engineering, Rutgers University, 2008 PhD, Chemical Engineering, Princeton University, 2013 Her research bridges systems biology, synthetic biology, and computational modeling to study plant physiology and development. She specializes in designing biomolecular sensors, quantitative fluorescence microscopy, and predictive models to decode how metabolic and genetic interactions govern plant growth. Lily’s publications emphasize systems-level approaches to plant biology, with recurring themes in gene regulatory networks , sugar transport , computational modeling , and plant-microbe interactions . Recent work (2025) applies hybrid modeling to lignin bioconversion, while earlier studies (2024-2015) explore transporter specificity, root microbiome dynamics, and developmental signaling. Scientific awards include: NSF NPGI Postdoctoral Fellowship in Biology NSF CAREER Award Human Frontier Science Program Early Career Award Lily’s lab develops quantitative tools for plant systems biology , with a focus on metabolic network interactions and predictive agricultural models . She previously held postdoctoral positions at the Carnegie Institution for Science and conducted foundational work at Rutgers University and Princeton University .

Jingqiong Zhangمشاهده پروفایل

پژوهشگر

• Data Analytics
• Manufacturing Engineering
• Signal Processing
• +۳ مورد دیگر

Jingqiong Zhang is a Research Associate in Data Analytics for Manufacturing at the School of Electrical and Electronic Engineering, University of Sheffield. Her work spans interdisciplinary applications of data analytics in materials science, robotics, and industrial systems. University: University of Sheffield School: School of Electrical and Electronic Engineering Email: Jingqiong.zhang@sheffield.ac.uk Research Interests: Her research focuses on: Advanced data analytics for materials characterization Machine learning in manufacturing systems Signal and image processing for industrial diagnostics Human-robot collaboration safety frameworks Cheminformatics and hyperspectral imaging Optimization of energy and process systems Publication Trends: Recent articles highlight integration of AI/ML with manufacturing (e.g., digital twins for human-robot collaboration), hyperspectral imaging innovations for materials science, and novel sensor applications in energy and chemical processes. Conference papers emphasize practical implementations of her research. Labs & Collaborations: Based in the Amy Johnson Building, she collaborates with researchers like L. Mihaylova, Y. Yan, and C. Rodenburg across multidisciplinary projects in manufacturing analytics and materials science.

Janna Hastingsمشاهده پروفایل

استادیار

• Medical Knowledge Systems
• Artificial Intelligence in Healthcare
• Ontology Development
• +۴ مورد دیگر

Janna Hastings is Assistant Professor for Medical Knowledge and Decision Support at the Institute for Implementation Science in Health Care (Faculty of Medicine) at the University of Zurich since August 2022, while also serving as Vice-Director of the School of Medicine at the University of St. Gallen. Her research focuses on digitalization in clinical contexts, examining how AI-driven knowledge systems reshape clinical practice, professional identity, and doctor-patient relationships. Education: PhD in Computational Biology (University of Cambridge, 2019), part-time MSc in Computer Science (University of South Africa, 2011), MSc in Philosophy (Open University, 2012) Prior Roles: Group Coordinator at European Bioinformatics Institute (2006-2015), Postdoctoral Researcher at Otto-von-Guericke University Magdeburg (2019-2022), Co-Leader of Human Behaviour-Change Project at University College London (2017-2022) Her research spans ontology development for biomedical domains (ChEBI, Human Behaviour Ontology), AI applications in healthcare decision-making, and behavior change interventions. Key projects include: Building ChEBI molecular ontology Developing Human Behaviour-Change Project knowledge system Ontology-driven mental health frameworks LLM applications in radiation oncology Time-series modeling of metabolism in ageing She explores the capabilities and limitations of clinical AI systems, with publications in JMIR and Lancet Digital Health , covering topics like bias prevention in generative AI and proteomic biomarker discovery. Her work bridges biomedical research with implementation science and digital ethics.

Alan Whiteمشاهده پروفایل

استاد

• Plant Cell Biology
• Science Education
• Cell Wall Structure
• +۳ مورد دیگر

Alan R. White is a Professor of Biological Sciences at the University of South Carolina's Department of Biological Sciences, affiliated with the McCausland College of Arts and Sciences. He previously served as Dean of the Thomas Harriot College of Arts and Sciences at East Carolina University (2005–2013) and Dean of the College of Science and Mathematics at North Dakota State University (2000–2005). Dr. White holds a BS in Biology and a PhD in Botany from the University of North Carolina at Chapel Hill. His research focuses on plant cell wall structure and synthesis, alongside contributions to the Virtual Cell project, an educational tool for cell biology. He has actively participated in funded initiatives to improve science education practices, emphasizing technology integration and interdisciplinary approaches. Key areas of research include plant cell biology, virtual learning environments, and STEM education reform. His work spans collaborations in academia, health promotion via sports (e.g., men’s health programs in football clubs), and technology-driven pedagogy. Dr. White has held faculty roles at multiple institutions, reflecting his commitment to both research and educational innovation. Labs/Teams: Core contributor to the Virtual Cell animation collection, a widely used resource for molecular and cellular biology education.

Jiankun Lyuمشاهده پروفایل

استادیار

• Computational Chemistry
• Drug Discovery
• Molecular Docking
• +۷ مورد دیگر

Jiankun Lyu, Ph.D., is an Assistant Professor at The Rockefeller University, affiliated with the Department of Biochemistry, Biophysics, Chemical Biology, and Structural Biology. He leads the EVNIN FAMILY LABORATORY OF COMPUTATIONAL MOLECULAR DISCOVERY, focusing on computational methods to discover novel small-molecule therapeutics. His work integrates molecular docking, virtual screening, and cheminformatics to explore vast chemical spaces for drug discovery. Education: B.E. in Pharmaceutical Engineering, East China University of Science & Technology (2013) Ph.D. in Pharmaceutical Science, East China University of Science & Technology (2019) Postdoctoral Researcher at University of California, San Francisco (2019–2022) Research Interests: The lab develops computational algorithms to screen ultra-large chemical libraries (e.g., 'make-on-demand' platforms) for ligand discovery, with applications in cancer, immunology, and neuroscience. Key projects include optimizing molecular docking techniques, expanding chemical space exploration, and engineering chemogenetic tools using novel chemotypes. Publications: Recent work highlights the use of AlphaFold2 structures to guide ligand discovery and the development of ultra-large library docking platforms. These efforts aim to address challenges in screening billions of molecules efficiently. Awards: Irma T. Hirschl and Monique Weill-Caulier Trust Research Award (2023) Searle Scholar (2024) Lab & Collaborations: The lab collaborates with the David Rockefeller Graduate Program and Tri-Institutional Ph.D. Programs in Chemical Biology and Computational Biology & Medicine. Ongoing projects include experimental validation of understudied protein targets and expanding chemogenetic tools for in vivo studies.

• Molecular Modeling
• Thermodynamics
• Nanomaterials
• +۵ مورد دیگر

Clare McCabe is a Bicentennial Professor at Heriot-Watt University and a Professor at Vanderbilt University's Department of Chemical & Biomolecular Engineering and Chemistry. She holds dual positions across institutions, reflecting her extensive academic leadership and research contributions. Her work focuses on molecular modeling tools to study thermodynamic properties of complex fluids, nanomaterials, and biological systems, with applications in skin health and sustainable materials development. Research interests include advanced simulations (molecular dynamics, Monte Carlo) and theoretical approaches to predict material behavior. Key contributions include the Molecular Simulation Design Framework (MoSDeF) for reproducible simulations and collaborative studies on skin lipid phase behavior. She has been recognized with prestigious fellowships from the American Association for the Advancement of Science, American Institute of Chemical Engineers, and Royal Society of Chemistry. McCabe’s articles span computational methodologies, membrane design, and biological systems, emphasizing interdisciplinary applications like sustainable solvents and skin barrier function. She actively contributes to editorial roles (e.g., Molecular Physics, Chemical Thermodynamics) and organizes workshops promoting FAIR data practices in soft matter simulations.

Toby Kingمشاهده پروفایل

پژوهشگر

• Cheminformatics
• Structure-activity relationships
• Molecular dynamics
• +۳ مورد دیگر

Dr Toby King is a Researcher at the School of Information, Journalism and Communication, University of Sheffield , specializing in cheminformatics and biopharmaceuticals. His work integrates computational modeling, machine learning, and molecular dynamics to improve drug formulation technologies. Research Interests : Cheminformatics Structure-activity relationships Molecular dynamics simulations Biopharmaceutical formulation Multidisciplinary collaboration Biophysics The 2024 article Optimizing Excipient Properties to Prevent Aggregation in Biopharmaceutical Formulations exemplifies his focus on computational approaches in pharmaceutical design, combining QSAR modeling, molecular dynamics, and industrial biotechnology. He contributes to the Cheminformatics group , applying neural networks for de novo compound generation and QSAR modeling in drug discovery.