جست‌وجوی استاد و پژوهشگر

Pengyi Yangمشاهده پروفایل

دانشیار

• Computational Systems Biology
• Stem Cell Biology
• Bioinformatics
• +۴ مورد دیگر

Pengyi Yang is an Associate Professor and University of Sydney Robinson Fellow at the School of Mathematics & Statistics, University of Sydney. He leads the Computational Systems Biology group at the Charles Perkins Centre and holds a conjoint appointment as Unit Head of Computational Systems Biology at the Children's Medical Research Institute (CMRI). His research focuses on computational approaches to understand trans-regulatory networks in stem cells and their applications in regenerative medicine. Yang holds a Ph.D. and has been recognized with awards such as the National Stem Cell Foundation Metcalf Prize (2021). His research spans computational systems biology, machine learning for bioinformatics, and spatial/single-cell omics analysis. Key projects include modeling pluripotency transitions, developing stem cell-derived organoids, and creating computational tools for phosphoproteomics and multi-omics integration. Collaborations include international initiatives like the Laboratory of Data Discovery for Health (InnoHK). Yang advises multiple PhD students and leads grants on topics like stem cell-derived brain organoids and embryonic development modeling. His lab develops tools like Cepo, PhosR, and CiteFuse for omics data analysis. He teaches data science and molecular systems biology at the University of Sydney.

• GPU Computing
• Parallel Computing
• Domain-Specific Languages
• +۵ مورد دیگر

Prof. Dr.-Ing. Richard Membarth is a Research Professor for System-on-a-Chip and AI at the Edge Computing at Technische Hochschule Ingolstadt (THI). He is affiliated with the Hardware-Software Co-Design group and holds a secondary position at the German Research Center for Artificial Intelligence (DFKI) Saarbrücken. Co-creator of DSL frameworks like AnyDSL and Hipacc Key contributor to MetaDL (AI metaprogramming) and PRIME (predictive rendering) His research bridges GPU computing , domain-specific languages , and compiler technology , with recent work on Vulkan SPIR-V compilation and device-driven SpMV algorithms . Notable awards include the HiPEAC Paper Award (2018) and multiple Best Paper Awards for his compiler frameworks.

• Genomics
• Computational Biology
• Cell Biology
• +۵ مورد دیگر

Gioele La Manno is an Assistant Professor (tenure track) at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Life Sciences (SV) and the Department of Life Sciences Engineering. He leads the Unité du Prof. La Manno (UPLAMANNO) and holds roles in teaching and doctoral education within the SSV-ENS and Programme doctoral Biologie computationnelle et quantitative . His research focuses on single-cell genomics, cellular heterogeneity, and systems biology approaches to study developmental and neural systems. La Manno’s educational background includes a strong foundation in computational and quantitative biology, reflected in his teaching of courses like Biological data science I: statistical learning and Scientific literature analysis in neuroscience . His lab explores interdisciplinary methods combining genomics, epigenomics, and machine learning to dissect cell fate decisions and tissue organization. He actively contributes to doctoral programs, supervising over a dozen PhD students and advising theses on topics ranging from nervous system development to drug sensitivity profiling. His work bridges basic research with translational applications, such as clinically compliant cell cryopreservation for regenerative medicine. La Manno collaborates across EPFL’s Life Sciences Engineering ecosystem, leveraging state-of-the-art technologies like mass spectrometry imaging and single-cell RNA velocity modeling. His lab’s recent studies address lipidome dynamics, glial diversity in the CNS, and the molecular regulation of midbrain dopaminergic neurons.

Jing Zhangمشاهده پروفایل

استادیار

• Genomics
• Computational Biology
• Biomedical Informatics
• +۶ مورد دیگر

Dr. Jing Zhang is an Assistant Professor in the Department of Computer Science at the University of California, Irvine (UCI), affiliated with the Donald Bren School of Information and Computer Sciences. She holds a Ph.D. in Electrical Engineering and Molecular/Computational Biology from the University of Southern California (2012) and completed postdoctoral training in Computational Biology at Yale University. Her research focuses on developing computational methods to unravel gene regulation mechanisms and link genetic variations to diseases, particularly in noncoding regions of the genome. She has contributed extensively to the ENCODE project, co-authoring pivotal studies in Nature and producing over 5,900 experimental datasets. Dr. Zhang’s work bridges engineering, mathematics, and biology, with applications in precision medicine for cancers and psychiatric disorders. She emphasizes the importance of noncoding DNA in disease causation and has pioneered tools like EN-TEx and scENCORE to analyze epigenomes and regulatory elements. Her lab actively seeks to recruit Ph.D. students, postdocs, and interns to advance genomic technologies. Key research areas include single-cell and spatial transcriptomics, gene regulatory networks, and computational methods for multi-omics data integration. Despite pandemic-related challenges, she maintains strong collaborations and teaches courses in bioinformatics. Her future goals include expanding lab interactions and applying computational models to predict disease susceptibility and treatment responses.

• Digital Circuits and Systems
• Energy Efficiency
• Smart Cities
• +۱۳ مورد دیگر

Enrico Macii is a Full Professor at the Politecnico di Torino, affiliated with the Interuniversity Department of Regional and Urban Studies and Planning (DIST) and the Department of Control and Computer Engineering (DAUIN). He leads the Electronic Design Automation (EDA) research group and holds key roles as Scientific Advisor for the Politecnico-STMicroelectronics partnership and Scientific Contact for the European Chips Joint Undertaking. Research Interests: His work spans digital circuits and systems, energy efficiency, smart cities, Industry 4.0, and smart manufacturing. He focuses on embedded and cyber-physical systems, low-power design, neuromorphic computing, AIoT, and sustainable urban development. Recent Publications: His recent research demonstrates strong trends in edge AI, neuromorphic computing, and smart energy systems. Articles highlight innovations in low-power hardware acceleration, federated learning, physics-informed AI, and digital twin applications for urban and industrial systems. There is a clear emphasis on deploying AI efficiently on constrained devices and integrating physical models with machine learning. J. William Fullbright Fellowship (1993) Best paper award IEEE European Design Automation Conference (1996) Best paper award ACM/IEEE Great Lakes Symposium on VLSI (2008) DAC Service Award (2014) IEEE Fellow (2006) DATE Fellow (2014) Advising and Grants: He has supervised over 25 PhD students in computer engineering, AI, and urban systems. His research is funded by major EU programs (Horizon 2020, PNRR, KDT JU), national (PRIN, FAR), and regional grants, as well as industrial contracts with STMicroelectronics, Michelin, and Cefriel. He leads numerous high-impact projects in smart manufacturing, energy efficiency, and digital twins. Labs and Teams: He is a core member of the EDA Group, an interdepartmental research team at Politecnico di Torino focusing on VLSI-CAD, bioinformatics, smart cities, and Industry 4.0. He also contributes to IAM@PoliTo (Integrated Additive Manufacturing) and leads multiple EU and national research consortia.

Michel Sannerمشاهده پروفایل

استاد

• Molecular Interactions
• Molecular Graphics
• Component-Based Software
• +۳ مورد دیگر

MICHEL SANNER is a Professor of Molecular Biology at the Department of Integrative Structural and Computational Biology at Scripps Research. He holds a PhD in Computer Science from the University of Haute Alsace, France (1992). His research focuses on computational methods for molecular interactions, molecular graphics, and component-based software development. Notable contributions include the AutoDock suite (for molecular docking), PMV (a molecular visualization environment), and Vision (a visual programming tool). His research group develops tools like AutoDock CrankPep for peptide docking and F2Dock for protein-protein interactions. These tools are widely used in drug discovery and structural biology. His work emphasizes software engineering principles to create adaptable computational pipelines for analyzing macromolecular structures and simulating interactions. Publications span topics like peptide-docking methodologies, ligand-binding site prediction, and GPU-accelerated docking algorithms. His articles highlight advancements in computational methods for understanding protein-ligand interactions, with applications in anticoagulant research and HIV/FIV protease inhibition. Collaborations include work with Arthur J. Olson and David S. Goodsell on docking methodologies.

• Graph Representation Learning
• Deep Learning
• Reinforcement Learning
• +۷ مورد دیگر

Jian Tang is an Assistant Professor at HEC Montreal and the Montreal Institute for Learning Algorithms (MILA), as well as an Associate Professor at the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also affiliated with IVADO (Institut de valorisation des données) as a member. His research spans multiple institutions including collaborations with leading biology labs worldwide and access to extensive computational resources through industry partners. Ph.D. in Computer Science, Peking University (2009-2014) Visiting Ph.D. student, University of Michigan (2011.10-2013.8) B.S. in Mathematics, Beijing Normal University (2005-2009) Professor Tang's research focuses on the intersection of deep learning and graph theory, with particular emphasis on geometric deep learning, knowledge graph reasoning, and applications in drug discovery. His work bridges symbolic and neural approaches to create robust reasoning systems that can handle complex structured data. He has pioneered techniques in graph representation learning that have significantly advanced the field of molecular property prediction and protein design. His publication record shows a clear trajectory toward applying geometric deep learning to biological problems, with a growing emphasis on protein design, molecular conformation generation, and multi-omics analysis. Recent work demonstrates sophisticated integration of 3D geometry with deep learning architectures to model complex biomolecular interactions. Canada CIFAR Artificial Intelligence Chairs (CCAI Chair) Tencent AI Lab Rhino-Bird Gift Fund Amazon Faculty Research Award Microsoft-Mila collaboration grant National Research Council Canada (NRC) Collaborative Research and Development Grant Professor Tang actively mentors doctoral and master's students, with six recent graduates working on cutting-edge topics including graph neural networks for reasoning, protein design, and molecular representation learning. His research is supported by substantial funding from industry partners including Microsoft, Amazon, and Tencent, as well as government agencies like NRC. He collaborates extensively with biology labs worldwide, applying AI to solve real-world biomedical challenges. He leads a research group focused on geometric deep learning for drug discovery, with active projects in protein design using geometric-aware models and large language models for multi-omics analysis. The group has access to thousands of GPUs through industry collaborations, enabling large-scale experiments in molecular simulation and generative modeling.

• Bioinformatics and Computational Biology
• Machine Learning
• Data Science
• +۳ مورد دیگر

Fardina Alam is a Lecturer in the Department of Computer Science at the University of Maryland, College Park. She holds a Ph.D. from George Mason University (2023), where she specialized in Structural Bioinformatics and Machine Learning. Her academic rank reflects UMD's Professional Track Faculty, equivalent to an Assistant Professor of Teaching. Dr. Alam's research focuses on deep learning applications in protein structure prediction, generative AI, and ethical data practices. She has contributed to projects funded by an NSF FET Grant (#1900061) and published in journals like Biomolecules. Awards include the 2023 Outstanding Dissertation Award and 2022 Editor's Choice Article recognition. Education: Ph.D. (Computer Science, George Mason, 2023); M.S. (Computer Science, George Mason, 2019); B.S. (Computer Science and Engineering, Military Institute of Science and Technology, Bangladesh, 2013). Her teaching emphasizes data science ethics and interdisciplinary applications, including a new course for UMD's Data Science minor. Professional roles include Associate Guest Editor at Bioinformatics Advances (2024), Faculty Advisor to the Bangladeshi Graduate Student Association, and Program Co-Chair for the ACM-BCB Computational Structural Bioinformatics Workshop (2023). Research Interests: Structural Bioinformatics, Generative AI, Responsible AI Ethics, Deep Learning, Machine Learning, and Data Science. Her work bridges computational biology and AI, with a focus on equitable data practices and protein structure analysis. Recent projects address challenges in generating physically-realistic protein structures and improving question-answering systems through equitable data strategies. Awards: Recipient of the 2023 Outstanding Dissertation Award (George Mason University), 2023 Best Paper Award, and 2022 Biomolecules Editor's Choice Article. These accolades highlight contributions to protein structure prediction and bioinformatics methodology. Advising & Grants: NSF FET Grant #1900061 supported her computational biology research. She advises the Nobanno graduate student group and chairs workshops in her field. Her teaching and research aim to promote inclusivity, particularly for underrepresented groups in STEM. Labs/Teams: Active contributor to the Computational Biology Lab at George Mason University and collaborates with UMD's interdisciplinary teams on data ethics and AI applications. Her work aligns with UMD's vision for data-driven education and innovation.

• Ontologies and Terminologies
• Neuroinformatics
• Big Data Analytics
• +۳ مورد دیگر

Licong Cui, Ph.D., is an Associate Professor at the McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth). She is also affiliated with the Center for Translational AI Excellence and Applications in Medicine (TEAM-AI) and the Texas Institute for Restorative Neurotechnologies (TIRN). Her research focuses on developing informatics methods to address biomedical data challenges, with expertise in ontologies, neuroinformatics, big data analytics, and clinical text mining. Dr. Cui has authored over 100 peer-reviewed publications and secured grants from NIH and NSF. Her work emphasizes ontology quality assurance, large language model applications in healthcare, and data integration frameworks. Notable contributions include developing the VaxBot-HPV chatbot for vaccine communication and advancing seizure frequency extraction methodologies using LLMs. Her honors include the 2022 AMIA New Investigator Award and 2021 NSF CAREER Award. Current projects involve enhancing NIH Common Data Elements with AI tools and improving EHR-based cohort querying through ontology-driven approaches. She collaborates on initiatives like the National Sleep Research Resource and Vaccine Ontology harmonization efforts.

Tyler Derrمشاهده پروفایل

استادیار

• Data Mining
• Social Network Analysis
• Graph Representation Learning
• +۵ مورد دیگر

Dr. Tyler Derr is an Assistant Professor in the Department of Computer Science at Vanderbilt University, with affiliate roles in the Data Science Institute and the Frist Center for Autism and Innovation. He holds a PhD from Michigan State University (2020) and focuses on data mining, machine learning, and graph neural networks with applications in drug discovery, ethical AI, and neurodiversity research. Education: PhD in Computer Science, Michigan State University (2020) M.S. in Computer Science, Pennsylvania State University B.S. in Computer Science and Mathematics, Pennsylvania State University His research emphasizes interdisciplinary social good, including drug discovery, education equity, and autism innovation. He directs the Network and Data Science (NDS) Lab, mentoring students who have won 50+ awards. Notable contributions include tools like FairNNV for fairness certification and datasets like WelQrate for drug discovery benchmarking. Awards include the NSF CAREER Award (2023), NVIDIA Academic Grant (2024), and Vanderbilt’s Career Catalyst Impact Award (2025). He co-founded the MLoG workshop series and serves on editorial boards for ACM TKDD and IEEE Transactions on Big Data . Grants & Leadership: NSF Grant for student travel to KDD2024 NVIDIA BioNeMo Platform grant for peptide design DOE Computational Science Fellowship advising His work bridges theory and practice, with tutorials on graph ML at AAAI/KDD and keynotes on ethical AI. The NDS Lab hosts interdisciplinary collaborations in computational biology, transportation, and neurodiversity advocacy.

Catia Pesquitaمشاهده پروفایل

دانشیار

• Semantic Web
• Biomedical Ontologies
• Knowledge Graphs
• +۷ مورد دیگر

Catia Pesquita is an Associate Professor in Computer Science at the Faculty of Sciences of the University of Lisbon , where she is also a Senior Researcher at LASIGE and leads the Health and Biomedical Informatics Research Line . With a multidisciplinary background in Biology and Computer Science, she focuses on Artificial Intelligence and Data Science applications in life and health sciences . Her research spans Semantic Web , Biomedical Ontologies , Knowledge Graphs , and Explainable AI , with significant contributions to ontology matching and semantic similarity . Education: PhD in Computer Science - Bioinformatics (2012) MSc in Bioinformatics (2008) Degree in Cell Biology and Biotechnology (2005) Current Projects: KATY (2021-2024): AI-Empowered Personalized Medicine for cancer treatments. BRAINTEASER (2021-2024): AI for ALS and MS disease progression models. Research Outputs: Developed tools like AgreementMakerLight (AML) , KGsim-benchmark , and the Epidemiology Ontology . Over 133 publications with significant citations (32,909 reads, 3,889 citations). Teaching: Lectures advanced topics in Databases , Data Integration , Bioinformatics , and Big Data . Advocacy: Vice-president of Biodata.pt , promoting biological data valorization in Portugal. Actively involved in initiatives to promote computer science careers to young women .

Ernesto Jiménez-Ruizمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Knowledge Graphs
• Semantic Web Technologies
• +۵ مورد دیگر

Dr. Ernesto Jiménez-Ruiz is a Senior Lecturer in Artificial Intelligence and Director of Research at City, University of London. He holds a PhD from Jaume I University, Spain (2010), and has held academic positions since 2010. His research focuses on knowledge graphs, ontology alignment, semantic web technologies, and neuro-symbolic AI. Currently, he teaches modules on semantic web technologies and research methods. He leads the Research Center affiliated with City St George's, University of London. Administrative Roles: Director of Research (2024–present), Senior Tutor for Research (2023–2024) Research Interests: AI, knowledge representation, ontology alignment, semantic integration, and biomedical informatics. His work bridges machine learning and symbolic reasoning, with contributions to ontology matching and knowledge graph embeddings. He supervises PhD students in areas like knowledge graph alignment and neuro-symbolic systems. Awards: SWSA Ten-Year Award (2021) for influential ontology alignment work. He is a Fellow of AdvanceHE (FHEA). Grants & Collaborations: Leads projects on semantic industrial data modeling and collaborates with Samsung Research UK, Bosch, and Statoil. Active in initiatives like the Turing Institute's Knowledge Graph Interest Group.

Audronė Jakaitienėمشاهده پروفایل

عضو هیئت علمی

• Statistics
• Biostatistics
• Machine Learning
• +۵ مورد دیگر

Prof. Dr. Audronė Jakaitienė is a Senior Researcher and Group Leader at the Interdisciplinary Statistical Research Group of Vilnius University’s Institute of Data Science and Digital Technologies. She supervises doctoral students in Computer Science Engineering (T 007) and Informatics (N 009) , focusing on topics like machine learning in neuro-oncology and genetic variation analysis. Her research spans education systems benchmarking, biomedical statistics, and big data applications in healthcare. Education : Ph.D. in Economics (2010), Vilnius University Teaching : Courses in Nonlinear Statistical Models (Doctoral), Biostatistics (Medicine), and Multivariate Statistics with R (Master’s) Her recent work examines multimorbidity patterns , COVID-19 modeling , and educational effectiveness through international collaborations like the European Joint Program on Rare Diseases and eduBEST project. She employs advanced statistical methods (R packages, DEA, PCA) for analyzing large-scale datasets. Scientific Awards European Social Fund (2018-2022) - EFECTAS project Research Council of Lithuania (2015-2017) - Education System Modeling European Social Fund (2011-2014) - LITGEN project She has advised 4 doctoral students and contributed to 30+ publications, including studies on hypertrophic cardiomyopathy , educational equity , and prediabetes monitoring . Her methodological expertise includes data envelopment analysis and multidimensional scaling.

Zhicheng Jiمشاهده پروفایل

استادیار

• Biostatistics
• Bioinformatics
• Single-cell RNA sequencing
• +۵ مورد دیگر

Zhicheng Ji is an Assistant Professor of Biostatistics & Bioinformatics at Duke University, affiliated with the Division of Integrative Genomics. His research focuses on developing computational methods for single-cell RNA-seq , spatial transcriptomics , and genomic data analysis . He teaches courses like BIOSTAT 824: Case Studies in Biomedical Data Science . Research Highlights : Statistical modeling of microbiome data Machine learning for cell segmentation T cell immunology in cancer Multi-omics data integration Spatially variable gene detection Foundation models for epigenetics Scientific Awards : NIGMS Grant (2024-2029) for spatial transcriptomics NIEHS Grant (2023-2028) on PFAS exposure NSF ERC PreMiEr (2022-2027) NCI Grant (2022-2027) on ferroptosis

• Computer Vision
• Artificial Intelligence
• Pattern Recognition
• +۶ مورد دیگر

Andrey Vladimirovich Savchenko is a prominent researcher and educator in computer vision and artificial intelligence at the National Research University Higher School of Economics (HSE) in Nizhny Novgorod. He holds multiple positions including Professor at the Faculty of Informatics, Mathematics, and Computer Science, Leading Researcher at the Faculty of Computer Science and Institute of Artificial Intelligence and Digital Sciences, and Academic Director of the "Artificial Intelligence and Computer Vision" educational program. His educational background includes: 2016: Doctor of Technical Sciences from Nizhny Novgorod State Technical University 2015: Academic title of Associate Professor 2011: Candidate of Technical Sciences 2008: Specialist degree in Applied Mathematics and Computer Science Savchenko's research focuses on computer vision, pattern recognition, and artificial intelligence, with particular emphasis on facial recognition, emotion analysis, and efficient deep learning algorithms. His work bridges theoretical foundations with practical applications, especially in mobile computing environments where computational resources are limited. He has developed innovative methods for making AI systems more efficient without significant loss in accuracy. His recent publications demonstrate a strong trend toward multimodal analysis, combining visual, audio, and textual data for more robust recognition systems. There's a clear emphasis on making AI systems more efficient, especially for mobile devices, and on developing methods that can work with limited computational resources while maintaining high accuracy. His work spans fundamental research on neural network architectures and practical applications in education, healthcare, and human-computer interaction. Among his notable scientific achievements: Gratitude from the Governor of Nizhny Novgorod region (2022) Multiple gratitude awards from HSE (2021-2022) Best Teacher Award (2018-2019) Leaders of IT Industry Award from NEYMARK IT Campus (2023) Academic Success Bonus at HSE (2011-2013) Savchenko has successfully supervised numerous master's students and currently mentors PhD candidates working on cutting-edge topics like large language models for recommendation systems and document analysis. He has secured significant research funding, including projects with Huawei, Sberbank, and the Russian Science Foundation, totaling millions of rubles. His laboratory focuses on developing efficient algorithms for computer vision and multimodal data analysis. He leads the Laboratory of Theoretical Foundations of Artificial Intelligence Models and has established strong industry partnerships that ensure his research has practical impact. His NVIDIA Deep Learning Institute certification demonstrates his commitment to staying current with the latest AI technologies.