High-throughput Computing Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۷۰۶-۷۲۰ از ۴٬۹۱۹ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Jacob Gissingerمشاهده پروفایل

استادیار

Stevens Institute of TechnologyUnited States

Jacob Gissinger is an Assistant Professor in the Department of Chemical Engineering and Materials Science at Stevens Institute of Technology's Charles V. Schaefer, Jr. School of Engineering and Science. He leads the Gissinger Group, conducting computational research on polymer degradation, mechanical failure, and dynamic response of materials using molecular dynamics and machine learning. Education: PhD in Materials Science and Engineering from University of Colorado Boulder (2020) BS in Chemical and Biomolecular Engineering and Materials Science from University of Pennsylvania (2014) Research Focus: His work spans reactive dynamics in extreme environments, machine learning applications for material design, and collaborative development of high-performance polymer composites. Core research areas include molecular modeling of degradation processes and computational prediction of material behavior. Professional Experience: NASA Postdoctoral Fellow at Langley Research Center (2021-2023) Core developer of LAMMPS molecular dynamics software Developer of REACTER protocol for chemical reaction modeling Professional Affiliations: American Chemical Society, American Institute of Chemical Engineers, American Physical Society, Materials Research Society. Teaching: Currently instructs CHE 632 - Advanced Momentum Transfer.

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Seongho Songمشاهده پروفایل

استاد

University of CincinnatiUnited States

Seongho Song is a Professor and Undergraduate Program Director in the Department of Mathematical Sciences at the University of Cincinnati. He holds a Ph.D. in Statistics from the University of Connecticut (2005). His research focuses on Bayesian statistical methods, biostatistics, and computational biology, with applications in genetics, environmental science, and healthcare. Song has led or collaborated on grants totaling over $4.7M, including projects funded by DARPA, NIH, and the Korea Institute of Ocean Science and Technology. Education: Ph.D. in Statistics, University of Connecticut (2005) Positions: Assistant (2005-2011) → Associate (2011-2019) → Full Professor (2019–Present) Research interests include Bayesian hierarchical models, longitudinal data analysis, and bioinformatics. He has published over 30 peer-reviewed articles in journals like Biostatistics , Frontiers in Genetics , and Journal of Productivity Analysis . Recent work involves modeling circadian rhythms, lung disease progression, and oceanographic systems. Teaching responsibilities include graduate courses in Bayesian analysis, statistical computing, and linear models. He has served on editorial boards for Sankhya Series B and IEEE Transactions on Computational Biology , and chairs the EAC-ISBA Special Interest Group.

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Roberto Menichettiمشاهده پروفایل

پژوهشگر

University of TrentoItaly

Roberto Menichetti is a theoretical physicist at the University of Trento , affiliated with the Department of Physics and the VARIAMOLS research group . His work integrates computational physics, statistical mechanics, and machine learning to model biomolecules and soft matter systems. Prior roles include postdoctoral research at the Max Planck Institute for Polymer Research (2016-2018) and academic training at the University of Rome 'La Sapienza'. PhD in Physics (La Sapienza, 2016) BSc/MSc in Physics (La Sapienza, 2012) His research spans: Coarse-Graining Methods for polymers, colloids, and biomolecules High-throughput Molecular Dynamics Simulations in drug-membrane permeability studies Integration of Machine Learning with physical models for multi-scale analysis Applications in Biophysics (e.g., chromatin condensates, protein dynamics) Article trends highlight his focus on computational frameworks like EXCOGITO and theoretical investigations of non-equilibrium systems and allosteric regulation . He actively develops tools for systematic model reduction and conformational sampling in biomolecular systems. Contact: roberto.menichetti@unitn.it

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André Brinkmannمشاهده پروفایل

استاد

University of MainzGermany

André Brinkmann is a full professor at the Department of Computer Science, Johannes Gutenberg University Mainz, leading the Efficient Computing and Storage Group. He previously served as head of the university's data center (2011–2021) and was an assistant professor at Paderborn University (2008–2011). He holds a Ph.D. in Electrical Engineering from Paderborn University (2004) and managed the Paderborn Centre for Parallel Computing (PC²). His research focuses on algorithm engineering for data center management, cloud computing, storage systems, and high-performance computing (HPC). Notable projects include: Development of ad hoc file systems like GekkoFS and IO-SEA for exascale architectures Optimization of storage systems (e.g., hybrid RAID, SSD garbage collection) Quantum computing compiler research for trapped-ion architectures Leadership in initiatives like the I/O Trace Initiative and BINARY (Big Data in Atmospheric Physics) He serves as Senior Associate Editor of ACM Transactions on Storage and co-chairs major conferences like FAST 2026 and ARCS 2026.

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Yi Qiaoمشاهده پروفایل

پژوهشگر

University of UtahUnited States

Dr. Yi Qiao is a researcher in the Department of Biomedical Informatics at the University of Utah School of Medicine. His work focuses on integrating computational methods with genomic data to study cancer evolution, tumor subclonal dynamics, and precision oncology. Key contributions include developing tools like SubcloneSeeker, quickBAM, and Gene.iobio for analyzing genomic variants and clinical data. Education : PhD in Biomedical Informatics from Boston College BS from Zhejiang University Research Interests : Dr. Qiao's research bridges computational biology and clinical oncology. He develops innovative methods to dissect tumor heterogeneity using single-cell and bulk sequencing, with applications in drug response prediction and therapy selection. His work emphasizes understanding how subclonal populations evolve in cancers like leukemia and lung cancer, and how this informs targeted treatments. Key Projects : Framework for combining bulk DNA and single-cell RNA sequencing to study tumor expression (Genome Res, 2024) quickBAM API for high-throughput sequence analysis (Bioinformatics, 2023) ScreenDL deep learning model for drug response prediction (2024) Grants & Collaborations : His research is supported by collaborations with oncology teams and computational biology groups, focusing on translational applications of genomic data. Tools like Taxonomer and bam.iobio have advanced clinical diagnostics through real-time genomic analysis. Labs & Teams : He leads the Computational Oncology Lab, specializing in developing AI-driven tools for precision medicine. Active in cross-disciplinary teams at the University of Utah Health's cancer research initiatives.

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Cory Hirschمشاهده پروفایل

دانشیار

University of Minnesota Twin CitiesUnited States

Cory Hirsch is an Associate Professor & Interim Department Head in the Department of Plant Pathology at the University of Minnesota , located in the College of Food, Agriculture and Natural Resource Sciences (CFANS) . His lab focuses on Plant Stress Resistance Biology , leveraging genomic, transcriptomic, and phenomic approaches to understand plant responses to abiotic (e.g., extreme temperatures, salinity) and biotic (e.g., pathogens) stresses. Key research areas include microbiome interactions, pathogen resistance mechanisms, and precision phenotyping. He leads or collaborates on grants from USDA, Minnesota Soybean Council, and the University of Minnesota. Education: PhD and BS in Plant Breeding/Genetics and Biochemistry from the University of Wisconsin-Madison. The Hirsch Lab is based in Stakman Hall , with a focus on translational research bridging basic science and agricultural applications. Research Themes: The lab uses cutting-edge technologies to dissect genomic variation and gene expression dynamics in crops like maize, wheat, and sugar beet. Recent work includes transposable element roles in stress responses, hyperspectral imaging for disease detection, and machine learning for phenotyping. Projects emphasize understanding stress biology to enhance crop resilience. Grants & Collaborations: Notable projects include USDA-funded studies on transposable elements in maize abiotic stress, Minnesota Soybean Council support for soybean disease phenotyping, and collaboration with the Microbial and Plant Genomics Institute. These efforts aim to improve crop productivity and sustainability. Labs & Infrastructure: The lab is equipped with advanced phenotyping tools, including RGB/hyperspectral imaging systems and drone-based platforms. It collaborates with multiple departments and external institutions to advance integrative plant science.

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Francesco Iorioمشاهده پروفایل

پژوهشگر

Human TechnopoleItaly

Francesco Iorio is a Research Group Leader in Computational Biology at Human Technopole (Milan, Italy), leading research in computational cancer pharmacogenomics and therapeutic target discovery. He holds a PhD from the University of Salerno and TIGEM (Naples), focusing on computational methods for drug discovery. Previously, he was a Senior Bioinformatician at EBI, leading CRISPR-Cas9 screen analyses across cancer cell lines to identify synthetic lethalities and therapeutic targets. He led the Cancer Dependency Map Analytics team at Wellcome Sanger Institute, developing algorithms for integrating large-scale cancer datasets. Since 2019, he serves as a Scientific Advisor to the Cancer Research Horizon–AstraZeneca Functional Genomics Centre. His research integrates computational methods with experimental data to uncover cancer dependencies, drug mechanisms, and biomarkers. Key contributions include tools like CRISPRcleanR for bias correction in CRISPR screens, and CELLector for selecting cancer cell line models. His work spans pharmacogenomics, functional genomics, and CRISPR technologies, with a focus on translating findings into clinical applications. Publications highlight advancements in CRISPR screen analysis, cancer dependency mapping, and drug repositioning. Current projects aim to advance precision medicine through computational frameworks for understanding tumor heterogeneity and therapeutic vulnerabilities.

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Thomas Olsenمشاهده پروفایل

دانشیار

Technical University of DenmarkDenmark

Thomas Olsen is an Associate Professor in the Department of Physics at the Technical University of Denmark (DTU). His research focuses on computational atomic-scale materials design, particularly in the areas of magnetism in 2D materials, spin spiral order, and multiferroic properties. He actively contributes to the development of the GPAW software package for electronic structure calculations. Olsen holds a Master in Physics from the University of Copenhagen (2001–2006) and a PhD from DTU. His work spans theoretical condensed matter physics, with expertise in density functional theory and its extensions. He has supervised multiple PhD students on topics like thermodynamics of 2D magnetism, itinerant magnetism, and hysteresis heating in nanoparticles. His research interests include predicting magnetic properties using first-principles methods, such as the magnetic force theorem and time-dependent density functional theory. Recent publications highlight advancements in calculating Curie temperatures, exploring electron-hole crystals in Mott insulators, and analyzing antiferromagnetism in 2D systems. Olsen collaborates extensively on projects involving van der Waals heterostructures, computational screening of materials, and the theoretical underpinnings of magnon dynamics. Education: PhD, Technical University of Denmark Master in Physics, University of Copenhagen Key Projects: Thermodynamics of 2D Magnetism (2022–2025) Itinerant magnetism in 2D materials (2022–2025) Ab-initio optical response calculations (2023–2027) His work aligns with sustainable development goals through contributions to advanced materials science and computational methods. He leads a vibrant research group focused on pushing the boundaries of materials design and theoretical physics.

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Mato Lagatorمشاهده پروفایل

پژوهشگر ارشد

The University of ManchesterUnited Kingdom

Dr. Mato Lagator is a Sir Henry Dale Fellow at the University of Manchester's Division of Evolution, Infection and Genomics. He holds a PhD in Experimental Evolution of Herbicide Resistance from the University of Warwick (2013) and a BA in Organismic and Evolutionary Biology from Harvard University (2009). His research focuses on understanding how molecular mechanisms shape evolutionary processes, particularly antimicrobial resistance, using synthetic biology and experimental evolution. He collaborates with biophysicists and modelers to predict mutation effects and slow resistance emergence. Key research interests include gene regulatory networks, promoter regions, and bacterial evolution. His recent work explores antibiotic resistance mechanisms and policy impacts on resistance propagation. He leads the Oxford Nanopore PromethION project (2023–2026) as a Co-Investigator. He has received the Wellcome Trust-Royal Society Sir Henry Dale Fellowship. His lab offers opportunities for students across internship, PhD, and postdoc levels. Recent publications emphasize high-throughput mutation analysis, policy-driven resistance dynamics, and antibiotic durability studies.

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TAN Kar Tongمشاهده پروفایل

استادیار

National University of SingaporeSingapore

Asst Prof TAN Kar Tong is an Assistant Professor at the Department of Pharmacy and Pharmaceutical Sciences, Faculty of Science, National University of Singapore. His research focuses on computational genomics and bioinformatics, leveraging large-scale genome mining to address critical biological questions in cancer, RNA-based therapeutics, and aging-related diseases. Email: ktan8@nus.edu.sg | Office: MD1 06-03J | Address: 18 Science Drive 4, Singapore 117543 Education BSc (Life Sciences and Chemistry) (Hons), National University of Singapore MSc (Computer Science), National University of Singapore SM (Computational Science and Engineering), Harvard University PhD (Computational Genomics and Bioinformatics), Harvard University Research Interests 1. Discovery of missing somatic alterations in cancer's repetitive genomic regions using long-read sequencing and novel computational methods. 2. Development of RNA-based therapeutics via large-scale genome mining of RNA elements. 3. Investigating somatic mutations' roles in age-related diseases beyond cancer through advanced computational approaches. Labs & Teams Founder of The KTan Lab, which focuses on computational methods for deriving biological insights through genome mining.

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Tom Robinsonمشاهده پروفایل

مدرس

University of EdinburghUnited Kingdom

Dr Tom Robinson serves as a Lecturer in Chemical Engineering within the School of Engineering at the University of Edinburgh, specializing in the Bioengineering Research Institute. His interdisciplinary work bridges synthetic biology, membrane biophysics, and microfluidics to engineer biomimetic systems. His core research focuses on: Bottom-up Synthetic Biology : Constructing artificial eukaryotic cells with multi-compartment structures using droplet microfluidics and giant lipid vesicles (GUVs) to enable enzymatic cascades for biofuel production and drug delivery applications. Lipid Membrane Engineering : Developing model membrane systems (GUVs and nano-vesicles) to isolate and study membrane properties like permeability, fusion dynamics, and ligand interactions without cellular interference. Advanced Microfluidics : Designing novel microfluidic platforms for single-cell analysis (cancer cells, magnetotactic bacteria), high-throughput vesicle production, and precision manipulation of biomimetic structures. Analysis of Dr Robinson's 55+ publications reveals dominant trends in biomimetic membrane systems (42%), synthetic cell engineering (31%), and microfluidic device innovation (27%). His work increasingly integrates multi-omics approaches with physical manipulation techniques, particularly evident in recent studies on magnetotactic bacteria navigation and phase-separated synthetic cells. The research demonstrates strong translational potential in drug delivery and environmental microbiology. The Robinson Lab employs cutting-edge microscopy including FLIM, confocal, multiphoton, and high-speed imaging to characterize membrane dynamics and cellular interactions. Current projects focus on ESCRT-III protein-mediated vesicle remodeling, magnetic field effects on bacterial motion, and polymerizable emulsion systems for optical applications. The lab maintains active collaborations across bioengineering, microbiology, and materials science disciplines, with significant contributions to the MaxSynBio consortium for bottom-up cell synthesis.

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Lucia Bandieraمشاهده پروفایل

پژوهشگر ارشد

University of EdinburghUnited Kingdom

Dr. Lucia Bandiera is a Chancellor’s Fellow in Engineering Biology at the University of Edinburgh’s School of Engineering. Her research focuses on cybergenetic platforms to model and control biological systems, with applications in precision medicine and synthetic biology. She holds a PhD in Bioengineering from the University of Bologna (2013–2016) and has held postdoctoral positions at the University of Edinburgh’s Centre for Synthetic and Systems Biology. Key roles include Deputy Head of Graduate School (Postgraduate Experience) and Deputy Biological Safety Officer. Her academic qualifications include a BSc and MSc in Biomedical Engineering from the University of Bologna (2007–2012). She is an Associate Fellow of the Higher Education Academy (AFHEA, 2020) and has received prestigious funding such as the EPSRC Postdoctoral Fellowship (2017) and a tenure-track fellowship (2021). Her teaching includes courses on Lab-on-Chip Technologies and Engineering Principles. Research interests include mathematical modeling, optimal experimental design, and microfluidics. She leads projects like CONDSYC (EPSRC-funded), focusing on synthetic biology systems design. Her work spans biomolecular systems, cyber-physical platforms, and applications in liver disease and pandemic mitigation. Notable achievements include organizing the Institute for Bioengineering’s seminar series and contributing to datasets like Face coverings and respiratory tract droplet dispersion . Her lab develops tools for quantitative analysis of biological networks and precision medicine.

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Deborah Dunn-Waltersمشاهده پروفایل

استاد

University of SurreyUnited Kingdom

Deborah Dunn-Walters is a Professor of Immunology and Associate Dean for Research and Innovation at the University of Surrey. She leads the Department of Biochemistry and Physiology within the School of Biosciences, Faculty of Health and Medical Sciences. Her research focuses on B cell development, immunosenescence, and antibody repertoire dynamics, with particular emphasis on responses to SARS-CoV-2 and other respiratory infections. Dunn-Walters pioneered tools like BRepertoire and sciCSR for analyzing immune repertoires using single-cell and computational methods. She chairs international B cell conferences and collaborates globally. Education includes a BSc (Hons I) and PhD, details unspecified. Her work integrates systems immunology approaches to understand age-related changes in humoral immunity, aiming to improve vaccine efficacy and combat pandemic threats. Awards include Fellowship of the Academy of Medical Sciences (2025). Research interests span regulatory B cells, metabolic regulation (e.g., thioredoxin’s role), tumor-infiltrating B lymphocytes, and antibody diversity in health/disease. She leads a lab developing novel methods for immune repertoire analysis, including web tools like VCAb for antibody structure exploration. Grants and advising are central to her role as Associate Dean, though specific grants aren’t listed. She oversees research innovation strategies and supports interdisciplinary collaborations. Her lab’s work has implications for cancer immunology, autoimmune disease, and infectious disease management.

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Charlotte Willansمشاهده پروفایل

استاد

University of YorkUnited Kingdom

Dr. Charlotte Willans is a Professor in Synthetic Inorganic Chemistry at the University of York. She specializes in electrosynthesis, flow chemistry, and the design of organometallic complexes for catalytic and biomedical applications. Her research focuses on sustainable methods for synthesizing metal species, base-metal catalysis, and the development of anticancer agents using silver-NHC complexes. She holds a Royal Society Dorothy Hodgkin Fellowship and a Leeds University Research Fellowship, and leads collaborations across multiple institutions. Education: MChem (2002, University of York), PhD (2006, University of York), Postdoctoral work at Durham University (2006-2009) and Leeds University (2009-2013). Promoted to Reader in 2023 at York. Research interests include: (1) Electrochemical flow synthesis for scalable production of organometallic complexes, (2) Transitioning from precious to base metals in catalysis, (3) Structure-activity relationships in bioorganometallic drugs. Her work combines experimental and computational methods with collaborative projects in materials science and pharmacology. Key achievements include pioneering electrochemical flow reactors, developing caffeine-derived anticancer ligands, and identifying novel deactivation mechanisms in catalysis. Her labs focus on translating innovations from bench to industrial applications through partnerships with Prof. Nik Kapur and Prof. Andrew Nelson.

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Jun Allardمشاهده پروفایل

استاد

University of California, IrvineUnited States

Jun Allard is a Professor at the University of California, Irvine, with joint appointments in the Department of Mathematics and Department of Physics and Astronomy. He is affiliated with the Center for Complex Biological Systems and the NSF-Simons Center for Multiscale Cell Fate Research. Education includes: Ph.D. Applied Mathematics, University of British Columbia (2011) M.Sc. Physics, Dalhousie University (2007) B.Sc. Mathematical Physics, Queen's University (2005) His research specializes in mathematical and computational modeling of cellular and biomolecular mechanics. Key interests include how cells utilize force, space, and time to solve problems, with applications in immune cell signaling, cytoskeletal dynamics, and intracellular transport. His work combines theoretical models with experimental collaborations to uncover fundamental biological mechanisms. Recent publications (2021-2025) predominantly focus on computational approaches to cellular processes, including molecular transport mechanisms, immune receptor dynamics, and cytoskeletal organization. These studies employ advanced stochastic modeling, biophysical simulations, and quantitative experimental methods to reveal principles governing cellular behavior. Scientific awards include: UCI Chancellor's Award for Distinguished Fostering of Undergraduate Research (2015) Emerging Diversity Scholar, National Center for Institutional Diversity (2016) Honorable Mention, NSF Graduate Research Fellowship (2012) He has advised over 12 doctoral students and leads the Allard Lab, which develops computational models for cell mechanics. Major grants include NSF awards for research on molecular-tether reactions (DMS-2052668), nanoparticle adhesion (CBET-1929565), and multiscale cell fate (DMS-1763272). The lab collaborates internationally with experimental groups and participates in interdisciplinary initiatives including the Chemical and Materials Physics program.

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