جستجوی استاد راهنمای دانشگاهی

Olga Wodo is an Associate Professor in the Department of Materials Design and Innovation at the University at Buffalo, part of the School of Engineering and Applied Sciences. Her research focuses on materials informatics, high-performance computing for materials design, and computational mechanics. Education: PhD in Mechanical Engineering (2003–2008), Czestochowa University of Technology, Poland Erasmus Programme (2002), Universita degli Studi Roma Tre, Italy MSc in Mechanical Engineering (1998–2003), Czestochowa University of Technology, Poland Her work spans topics such as microstructure analysis, additive manufacturing, and organic photovoltaics. She has developed tools like GraSPI for graph-based morphology quantification and applied active learning to optimize structure–property mapping. Recent publications highlight her contributions to defect identification in materials, phase diagram modeling, and accelerated discovery of processing–microstructure–property relationships. Dr. Wodo’s research emphasizes data-driven approaches and computational modeling, with applications in renewable energy technologies and sustainable materials. She maintains active collaborations and contributes to the development of open-source software for materials analysis.

• Materials Informatics
• High-Performance Computing for Materials Design
• Microstructure Analysis
• +۳ مورد دیگر

Jiang Zhe is a Professor in the Mechanical Engineering/Biomedical Engineering department at the University of Akron . With a career spanning over two decades, Dr. Zhe has made significant contributions to micro/nano sensor technology, biosensors, and machine health monitoring. He joined the University in 2003 after industry experience at Fitel Technologies and Advanced Micro Sensors, and was recognized as a Fellow of the American Society of Mechanical Engineers. Ph.D., Columbia University (2002) M. Phil., Columbia University (2000) B.S., Northwestern Polytechnic University (1993) Dr. Zhe’s research focuses on Micro and Nano Sensors , Lab-on-a-Chip , and Microfluidic Devices . His work integrates Artificial Neural Networks and Biosensors for applications in Machine Health Monitoring and Biomolecular Detection . Recent publications highlight advancements in Viscoelastic Fluids , Surface Charge Analysis , and Deep Learning for Sensor Arrays . Dr. Zhe has secured 11 funded NSF grants and received prestigious awards including the ASME Lewis F. Moody Award and the College of Engineering Outstanding Researcher Award . He leads a research group at the University of Akron’s MEMS Laboratory, where he develops cutting-edge technologies for particle manipulation, fluid analysis, and biomedical diagnostics. His laboratory specializes in Microfluidic Resistive Pulse Sensors , Inductive Sensors , and Wearable Devices , with applications in automotive systems, healthcare diagnostics, and environmental monitoring. Dr. Zhe’s interdisciplinary approach bridges mechanical engineering, biomedical science, and machine learning to solve complex challenges in sensor design and fluid dynamics.

• Micro and Nano Sensors
• Biosensors and Bioinstrumentation
• Lab on a Chip
• +۳ مورد دیگر

Leon Eyrich Jessen is an Associate Professor and Groupleader at the Department of Health Technology, Technical University of Denmark (DTU). He leads the Computational Autoimmunity research group, focusing on computational immunology with a particular emphasis on understanding disruptions in central tolerance and the mechanisms that allow the immune system to differentiate between self and non-self. His work aims to uncover immuno-cellular processes associated with autoimmune diseases through single-cell technology and machine learning-based bioinformatics tools. Dr. Jessen's research spans several key areas including Bioinformatics, Immunology, Machine Learning/AI, Data Science, and T-cell Biology. His work specifically addresses Computational Autoimmunity, Bayesian Modeling, and Molecular Evolution. He employs single-cell technology to investigate autoimmune disease mechanisms, with applications in psoriatic arthritis, diabetes, and other autoimmune conditions. His group develops computational methods for T cell receptor analysis, epitope prediction, and spatial transcriptomics. His recent publications demonstrate a strong focus on computational immunology, with key contributions in single-cell RNA sequencing, T cell receptor analysis, and spatial transcriptomics. His work bridges computational methods with immunological applications, particularly in autoimmune disease research. The research shows a consistent pattern of developing and applying machine learning algorithms to complex immunological data, with significant contributions to understanding T cell biology and autoimmune mechanisms. Dr. Jessen actively supervises PhD students including Drachmann, C., Montemurro, A., Povlsen, H. R., and Schaap-Johansen, A.-L. on projects related to single-cell analysis, immunoinformatics, and deep learning methods for immunotherapy. He is also the creator and instructor of "R for Bio Data Science," which is the third largest course at his department, demonstrating his commitment to education and training the next generation of bioinformaticians. He leads the Computational Autoimmunity group which focuses on developing, applying, and serving machine learning-based bioinformatics tools. The group works at the intersection of computational methods and immunological research, with particular emphasis on single-cell technologies and their application to autoimmune disease mechanisms.

• Bioinformatics
• Immunology
• Machine Learning
• +۶ مورد دیگر

Prof. Dr. Sandra Pohl is a full Professor at the University Medical Center Hamburg-Eppendorf (UKE), affiliated with the Department of Osteology and Biomechanics. Her research focuses on lysosomal storage disorders, particularly mucolipidosis types II and III, and their systemic effects on bone, cartilage, kidney and the central nervous system. She employs mouse models, human genetic studies and translational approaches to dissect disease mechanisms and develop therapeutic strategies. Education & Training No specific educational details provided in the source material; only the academic title "Prof. Dr. (PhD)" is listed. Research Interests Prof. Pohl’s work centers on: Molecular pathology of mucolipidosis type II (I-cell disease) and type III (pseudo-Hurler polydystrophy). GlcNAc-1-phosphotransferase biology and its role in lysosomal enzyme targeting. Skeletal manifestations of lysosomal storage disorders, including osteopenia, cartilage degeneration and dysostosis multiplex. Neurocognitive outcomes and CNS imaging in mucolipidosis patients. Enzyme replacement therapy design and evaluation in pre-clinical mouse models. Proteostasis, autophagy and ubiquitin-proteasome system interactions in disease pathogenesis. Publication Trends Between 2020 and 2024, Prof. Pohl has published extensively on the intersection of lysosomal dysfunction and skeletal/renal/neurological pathology. Her recent articles emphasize: Genotype–phenotype correlations in GNPTAB/GNPTG variants. Translational mouse models recapitulating human mucolipidosis. Multi-omics profiling (proteomics, imaging, metabolomics) to uncover disease biomarkers. Interdisciplinary studies linking basic cell biology to clinical manifestations. Research Support & Collaborations Prof. Pohl currently leads or co-leads the following initiatives: SFB 877, Teilprojekt B03: “Proteolysis-ver” (exact title abbreviated in source) – a collaborative research center funded by the German Research Foundation (DFG). Cross-institutional consortia involving nephrologists, neurologists, geneticists and imaging specialists across Germany and internationally. Laboratory & Core Facilities Her team utilizes state-of-the-art core facilities provided by UKE, including: Transgenic mouse core Advanced imaging and microscopy Mass-spectrometry-based proteomics High-throughput sequencing and bioinformatics platforms

• Lysosomal storage disorders
• Mucolipidosis type II and III
• Osteogenesis imperfecta
• +۵ مورد دیگر

Perdita Barran is a Professor of Mass Spectrometry at the University of Manchester’s Manchester Institute of Biotechnology. She specializes in advanced mass spectrometry applications, including protein dynamics, metabolomics, and biocatalysis. Her research focuses on structural biology, ion mobility-mass spectrometry, and translational diagnostics for diseases like Parkinson’s and post-COVID-19 conditions. She leads or co-leads 16 active research projects, including studies on SARS-CoV-2, biotherapeutic manufacturing, and synthetic biology. Education: Not explicitly listed in provided text. Affiliations: Manchester Institute of Biotechnology, University of Manchester. Research interests span metabolomics (e.g., sebum biomarkers for Parkinson’s), viral protein characterization (e.g., SARS-CoV-2 nucleocapsid), and analytical methods for biomanufacturing. Recent projects include developing diagnostic tools for long-term health outcomes post-COVID-19 and optimizing CHO cell cultures for antibody production. Her work integrates computational modeling with experimental techniques to elucidate molecular structures and dynamics. Articles highlight advancements in mass spectrometry for high-throughput screening, protein conformational studies, and biomarker discovery. Awards and grants (not detailed) support her interdisciplinary research. Supervises 40+ students across projects, emphasizing training in cutting-edge analytical techniques. Key collaborations involve synthetic biology, photocatalysis, and material science applications. Labs and teams: Active at the Manchester Institute of Biotechnology, collaborating with interdisciplinary groups on bioanalytical chemistry and biotechnology.

• Mass Spectrometry
• Biocatalysts
• Protein Dynamics
• +۷ مورد دیگر

Dr. Jean-Charles Stinville is an Assistant Professor in the Materials Science and Engineering Department at the University of Illinois at Urbana-Champaign (UIUC), where he also holds appointments in the Materials Research Laboratory. Previously, he served as a Specialist (2019-2021) and Associate Specialist (2015-2019) at the University of California Santa Barbara (UCSB). His research focuses on the mechanical and environmental performance of metallic materials for extreme applications, including cryogenic and high-temperature environments. He leads experimental developments in in-situ SEM mechanical characterization and materials informatics. Education: Ph.D., Solid Mechanics, Materials Science and Structures Mechanics (Ecole Nationale Supérieure de Mécanique et Aerotechnique / University of Poitiers, 2010) M.Sc., Materials Science and Mechanical Engineering (ISAE-ENSMA / University of Poitiers, 2006) M.Eng., Aeronautics, Computer Science, Materials Science, and Mechanics (ISAE-ENSMA, 2006) Research Interests: Materials Informatics Cryogenic Materials In-situ Characterization Deformation Processes High-/Low-Temperature Materials Sustainable Metallurgy Recent Research Trends: His work emphasizes high-throughput experimental methods and machine learning to predict material behavior. Recent studies focus on plastic localization in nickel-based superalloys, cryogenic performance, and microstructural heterogeneity analysis. Awards: 2024 National Science Foundation Career Award 2024 Kent D. Peaslee Junior Faculty Award 2024 Stanford/Elsevier World's Top 2% Scientists Teaching: Teaches courses including MSE 529 (Hard Materials), MSE 598 (Materials Informatics), and MSE 443 (Design of Engineering Alloys). Recognized with student excellence awards in 2023 and 2024. Labs/Teams: Leads the Stinville Research Group, focusing on experimental and computational approaches to materials design. Collaborates with UCSB and institutions globally via projects like AdvancedExperiment (YouTube) and ResearchGate.

• Materials Informatics
• Cryogenic Materials
• Deformation Processes
• +۳ مورد دیگر

Christoph Ziegenhain is an Assistant Professor at the Karolinska Institute , affiliated with the Department of Medical Biochemistry and Biophysics . He leads the Ziegenhain Lab , which focuses on molecular processes in human cells, particularly the control of alternative splicing in transcribed mRNAs. His group integrates novel single-cell sequencing techniques with computational biology to advance understanding in these areas. Position: Assistant Professor Institution: Karolinska Institute Department: Medical Biochemistry and Biophysics Lab: Ziegenhain Lab Christoph Ziegenhain's research spans Bioinformatics , Computational Biology , and Single-Cell RNA Sequencing . His work emphasizes transcriptional dynamics , alternative splicing , and the development of high-throughput sequencing methods like Smart-seq3xpress and Prime-seq . Current projects analyze gene expression noise, allelic imbalance, and the role of long noncoding RNAs in regulation. The Ziegenhain Lab's recent publications demonstrate expertise in single-cell transcriptomics , multi-omics profiling , and method development for RNA sequencing. Key subfields include transcriptional bursting , redox signaling , epithelial homeostasis , and hematopoietic stem cell differentiation . Swedish Research Council Grant (2023–2026): "Decoding the interplay of regulation and stochasticity in alternative mRNA splicing" Lab members include doctoral students Shiyao Chen and Constantin Diekmann , research assistants Cleo Damon , Zhouhui Qi , and Lukas Schertler , and affiliated researchers such as Gandolfo Gennaro and Ursa Zevnik . Collaborations with institutions like New York University and Stanford University highlight his interdisciplinary approach.

• Bioinformatics
• Computational Biology
• Single-Cell RNA Sequencing
• +۶ مورد دیگر

Benedetta Bolognesi is a Group Leader at the Institute for Bioengineering of Catalonia (IBEC), focusing on protein phase transitions in health and disease. She previously earned her PhD in Chemistry from the University of Cambridge (UK) under Prof. Chris Dobson, studying amyloid-beta aggregation in Alzheimer's disease. Her postdoctoral work at the Centre for Genomic Regulation (Barcelona, Spain) was supported by a Marie Curie Interdisciplinary Fellowship. Current Role: Group Leader, Protein Phase Transitions in Health and Disease Research Focus: Protein aggregation mechanisms, amyloid formation, deep mutational scanning (DMS), yeast models for neurodegenerative diseases Her research employs DMS to quantify mutation effects on disordered protein domains, particularly in amyloid-beta and prion-like proteins. She investigates liquid condensates and amyloids in neurodegenerative contexts like Alzheimer's and Fragile X syndrome. Recent publications demonstrate her leadership in amyloid nucleation studies using high-throughput methods and interpretable AI models. Collaborations include researchers like Ben Lehner and Mireia Seuma. Her work bridges bioengineering and molecular biology to decode protein aggregation rules. Bolognesi's lab at IBEC explores protein phase transitions through interdisciplinary approaches, integrating yeast systems and computational biology. Key contributions include atlases of variant effects and insights into amyloid transition states.

• Protein Aggregation
• Amyloids
• Neurodegenerative Diseases
• +۷ مورد دیگر

R. Van de Plas is a Researcher at Delft University of Technology (TU Delft) , leading the Team Raf Van de Plas . His work bridges Mass Spectrometry Imaging , data analysis , and chemical imaging applications in biomedical and cultural heritage domains. Recent contributions include molecular atlases of human kidney tissues , advanced MALDI imaging matrices , and automated data workflows for multimodal imaging. His research emphasizes high-resolution spatial analysis , reproducibility in computational methods , and interdisciplinary collaboration with institutions like Vanderbilt University Medical Center and the University of Antwerp. Current projects focus on preserving full spectral data and integrating multiplexed imaging modalities for cellular and tissue-level studies. Outputs include open datasets on platforms like 4TU.ResearchData .

• Mass Spectrometry Imaging
• Data Analysis
• Chemical Imaging for Cultural Heritage
• +۳ مورد دیگر

Adelina Rogowska-Wrzesinska is an Associate Professor at the Department of Biochemistry and Molecular Biology, University of Southern Denmark. Specializing in Biomedical Mass Spectrometry and Systems Biology , her research spans proteomics, oxidative stress, protein oxidation, and 3D cell culture models. Affiliation : Department of Biochemistry and Molecular Biology, University of Southern Denmark Academic Role : Teaching and research leadership Her research interests focus on: Redox biology and protein oxidative modifications 3D spheroid models for disease and toxicity studies Mass spectrometry-based proteomics and lipidomics Oxidative stress in cellular senescence and disease Plant stress responses and proteomics Scientific Trends : Recent publications highlight advancements in 3D culture modeling (NAFLD, genotoxicity), redox proteomics (glutathionylation, disulfide bonds), and multi-omics integration (chromatin proteomics, lipidome mapping). Teaching and Supervision : She has supervised multiple PhD students (e.g., Christina Erika Hagensen, Helle Frandsen) and taught courses like Basic Biochemistry (BMB530) and Fundamental Molecular Biology (BMB504) .

• Proteomics
• Mass Spectrometry
• Oxidative Stress
• +۳ مورد دیگر

Professor David Cairns is a leading academic in clinical trials research at the University of Leeds, Faculty of Medicine and Health, School of Medicine. He serves as Professor of Clinical Trials Research, Deputy Director of the Leeds Cancer Research UK Clinical Trials Unit (CTRU), and Director of Late Phase Trials in the Cancer Division. His work bridges biostatistics and oncology, with a focus on designing and analyzing large-scale confirmatory trials. His educational background includes a PhD and MSc in Statistics from the University of Sheffield and a BSc in Mathematics from the University of Leeds. David Cairns specializes in the design and analysis of cancer clinical trials, particularly in haematological malignancies such as multiple myeloma and chronic lymphocytic leukaemia, as well as solid tumours including colon, gastro-oesophageal, and urological cancers. He is passionate about developing efficient trial designs that incorporate biomarkers to define treatment pathways and enhance personalization. He actively promotes the integration of translational research within clinical trials to improve scientific value and inform future study designs. The 15 most recent articles reflect a strong focus on methodological innovation in clinical trials, including platform trials, non-concurrent controls, biomarker-driven designs, and the integration of real-world data. His work combines statistical rigor with clinical relevance, particularly in oncology settings. His scientific recognition includes the MRC Career Development Award in Biostatistics. He is also actively involved in national research governance through membership in key panels: Fellow of the Royal Statistical Society NCRI Multiple Myeloma Subgroup (Member) NIHR Clinical Trial Fellowship Panel (Member) NIHR Development and Skills Enhancement Fellowship Panel (Member) NIHR Doctoral Research Fellowship Panel (Deputy Chair) Cancer Research UK Expert Review Panel (Member) David Cairns is deeply committed to student education and the training of future clinical researchers. He welcomes PhD applicants interested in clinical trials, biostatistics, and cancer research. He supervises postgraduate research and contributes to the academic development of junior researchers through mentorship and collaborative projects. His leadership roles indicate substantial grant oversight and management of large-scale research initiatives. He is embedded in the Leeds Institute of Clinical Trials Research, a multidisciplinary team dedicated to improving patient outcomes through high-quality trial design and execution. He is affiliated with several research groups and institutes, primarily the Leeds Institute of Clinical Trials Research (Populations) and contributes to national and international efforts in advancing cancer clinical research methodology.

• Clinical Trials
• Biostatistics
• Haematological Oncology
• +۱۲ مورد دیگر

Dr. Joergen Kornfeld is a Research Group Leader at the MRC Laboratory of Molecular Biology in Cambridge, UK, where he leads the 'Connectomics of learned behaviour' group. He also maintains a connection with the Max Planck Institute for Biological Intelligence as a Guest Scientist. Previously, he was a Group Leader at the Max Planck Institute of Neurobiology in Martinsried, Germany (2021-2024). Dr. Kornfeld received his PhD in Biology (summa cum laude) from the Max Planck Institutes of Medical Research and Neurobiology, with research stays at NYU. He completed his Master's in Computational Biology and Bioinformatics at ETH Zurich and his Bachelor's in Molecular Cell Biology at Heidelberg University. Dr. Kornfeld's research focuses on understanding how animals store learned behaviors in their neuronal networks. His work centers on the zebra finch songbird as a model system, investigating how song memories are stored and retrieved from underlying brain circuits. His laboratory employs high-throughput 3D electron microscopy to map brain circuits at synaptic resolution, generating massive datasets that require advanced deep learning techniques for analysis. His team develops and applies state-of-the-art machine learning algorithms to reconstruct neural circuits and infer connectivity patterns. His recent publications demonstrate a clear trajectory toward increasingly sophisticated methods for neural circuit reconstruction and analysis, combining volume electron microscopy with deep learning approaches. The research spans multiple model organisms including zebra finches, mice, and zebrafish, with applications to understanding memory formation, behavioral control, and neural computation. Dr. Kornfeld leads a research team including Caitlin Gillespie, Nelson Medina, and Anastasia Sorokina. His laboratory is at the forefront of developing new methodologies for connectomics that bridge neuroscience, computer science, and engineering. He maintains active collaborations with multiple institutions including MIT, NYU, and the Max Planck Society.

• Connectomics
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Roger Espasa Sans is a senior academic and researcher at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Informatics of Barcelona (FIB) and the Department of Computer Architecture. He has made significant contributions to computer architecture, particularly in vector processing, GPU design, and performance optimization. Universitat Politècnica de Catalunya (UPC) Faculty of Informatics of Barcelona (FIB) Department of Computer Architecture His research focuses on advanced computer architecture, including vector and superscalar processors, memory systems, instruction-level and data-level parallelism, GPU microarchitecture, and performance modeling. His work bridges theoretical design and practical implementation, especially in high-performance and embedded systems. The recent articles highlight a strong trend in GPU and shader optimization, memory redundancy elimination, and performance modeling. His publications span prestigious venues such as IEEE Microarchitecture Symposium, IEEE HPCA, and The Visual Computer, indicating sustained impact in the field of computer systems and architecture. Premiada (Awarded activity) Roger Espasa Sans has advised several doctoral students, including Manel Fernandez Gomez and Francisca Quintana Domínguez, contributing to the next generation of computer architects. His collaborative network is extensive, involving key figures like Mateo Valero and international institutions. While specific grants are not listed, his prolific output suggests sustained funding. He has contributed to major research groups such as CAP (High Performance Computing Group). He is a core member of the CAP - Grup de Computació d'Altes Prestacions (High Performance Computing Group), and has also been associated with PM - Programming Models and ARCO - Microarchitecture and Compilers. His work often involves simulation frameworks like JINKS and Asim, indicating a strong focus on performance evaluation and modeling.

• Computer Architecture
• Vector Processors
• GPU Microarchitecture
• +۹ مورد دیگر

Dr. Alexander Vekker is an Adjunct Lecturer in the Department of Economics at the University of Pennsylvania, affiliated with the School of Arts and Sciences. He teaches courses in economics and statistics and contributes to academic and applied research. His professional work extends to expert consulting in legal contexts, particularly in employment-related litigation. Research Interests: Dr. Vekker's work focuses on labor economics, especially temporary and contingent workforces, and the application of statistical methods in both economic and biological domains. His interdisciplinary approach bridges social science and genomics, reflecting a unique integration of quantitative techniques across fields. He has contributed to research on labor market dynamics and served as a referee for journals such as the International Journal of Manpower and the Journal of Regional Science . Publication Trends: His scholarly output shows a trajectory from traditional labor economics toward multidisciplinary applications of statistics, notably in genomics. This reflects a growing trend of applying econometric and statistical tools beyond social sciences into life sciences and data-intensive research. Professional Engagement: Dr. Vekker is actively involved in applied economics through Vekker Consulting, LLC, where he provides expert analysis in employment discrimination and wage disputes. He has testified in federal and state courts and participated in mediations, demonstrating significant real-world impact of his expertise. Referee, International Journal of Manpower Referee, Journal of Regional Science Teaching and Affiliation: He is affiliated with the University of Pennsylvania, teaching in the Department of Economics. His email through Wharton (avekker@wharton.upenn.edu) suggests cross-school collaboration, though his primary academic home is in Arts and Sciences.

• Economics
• Labor Economics
• Statistics
• +۵ مورد دیگر

Prof. Dr. Dirk Benndorf serves as Professor of Microbiology at the Department of Applied Biosciences and Process Engineering, Anhalt University of Applied Sciences (since October 2022). He previously held an acting professorship there from 2019-2022 and worked as a research associate at Otto von Guericke University Magdeburg (2007-2022) in collaboration with the Max Planck Institute for Dynamics of Complex Technical Systems. Education: Biochemistry degree at University of Leipzig (1991-1996), PhD in Biochemistry (2000) focusing on xenobiotic resistance mechanisms His research centers on metaproteomic analysis of microbial communities in technical systems (biogas plants, wastewater treatment facilities) and natural environments (gut microbiome, soil). He investigates complex microbial interactions through mass spectrometry and bioinformatics, particularly examining syntrophy, competition, and phage-host relationships in biogas systems, while exploring human microbiome connections to inflammatory bowel disease, colon cancer, and metabolic disorders. Key publication trends include: metaproteomic workflows (2019), microbial community modeling (2016-2017), and environmental stress response studies (2004-2007). His work bridges fundamental microbiome understanding with practical applications for sustainable energy production and medical diagnostics. Teaching emphasizes laboratory skills development, OMICS technologies, and bioinformatics training. He advocates student-driven content exploration and presentation as critical professional skills. His research group employs high-resolution mass spectrometry and MALDI-TOF identification techniques.

• Microbial Communities
• Metaproteomics
• Bioinformatics
• +۳ مورد دیگر