جست‌وجوی استاد و پژوهشگر

Scott L. Diamondمشاهده پروفایل

استاد

• Mechanobiology
• Thrombolysis
• Coagulation
• +۱۰ مورد دیگر

Scott L. Diamond is the Arthur E. Humphrey Professor of Chemical and Biomolecular Engineering and Bioengineering at the University of Pennsylvania's School of Engineering and Applied Sciences. He serves as Director of the Penn Center for Molecular Discovery, Director of the Penn Biotechnology Masters Program (one of the largest in the country with over 130 students), and Associate Director of the Institute for Medicine and Engineering (IME). His laboratory is located in the Roy and Diana Vagelos Laboratories at 3340 Smith Walk, 1020 Vagelos Research Laboratories, Philadelphia, PA. Diamond's research spans multiple interconnected fields in blood biology and biotechnology. His work focuses on mechanobiology, thrombolysis, coagulation, bioadhesion, gene therapy, drug/device development, proteomics, drug discovery, systems biology, and microfluidics. His laboratory has developed numerous specialized microfluidic devices for studying blood clotting under various flow conditions, including 8-channel devices for high-throughput clotting assays, side-view devices for clot structure analysis, stenosis devices for high shear clotting assays, and impingement-post devices for studying von Willebrand factor fibers. Diamond's research group has pioneered approaches to model and predict blood function using systems biology principles. His team has developed computational models that integrate reaction-transport phenomena with platelet signaling networks to predict thrombus formation under flow. These models have enabled the development of 'virtual blood' computer simulations that can predict the effectiveness of anticoagulation drugs for individual patients, contributing significantly to personalized medicine approaches in hemostasis and thrombosis. His extensive publication record demonstrates a consistent focus on understanding the fundamental mechanisms of blood clot formation and dissolution. Recent work has emphasized microfluidic approaches for point-of-care diagnostics, patient-specific modeling of platelet function, and the development of novel therapeutic strategies for thrombotic disorders. His research bridges engineering principles with clinical hematology to address significant challenges in cardiovascular medicine. NSF National Young Investigator Award NIH FIRST Award American Heart Association Established Investigator Award AIChE Allan P. Colburn Award George Heilmeier Excellence in Research Award Elected Fellow of the Biomedical Engineering Society (BMES) Diamond has secured significant research funding, including a $2.8 million NIH grant for 'Blood Systems Biology' and a $9.5 million NIH grant for the Penn Center for Molecular Discovery. His laboratory has developed numerous microfluidic devices for blood analysis and has collaborated extensively with clinicians and industry partners. Diamond has served on advisory committees for NSF, NIH, AHA, and NASA, and has consulted extensively for industry and government. With over 180 publications and patents, his work has significantly advanced the understanding of blood clotting mechanisms and the development of diagnostic and therapeutic approaches for thrombotic disorders.

Garnet K. Chanمشاهده پروفایل

استاد

• Theoretical Chemistry
• Condensed Matter Physics
• Quantum Information Theory
• +۴ مورد دیگر

Garnet K. Chan is the Bren Professor of Chemistry and Director of the Rudolph A. Marcus Center for Theoretical Chemistry at the California Institute of Technology. He received his B.S. from the University of Cambridge in 1996 and his M.A. and Ph.D. from the University of Cambridge in 2000. Dr. Chan's research lies at the interface of theoretical chemistry, condensed matter physics, and quantum information theory, focusing on quantum many-particle phenomena and the numerical methods to simulate them. His group has developed numerous methodologies including density matrix renormalization and tensor network algorithms, canonical transformation-based down-foldings, local quantum chemistry methods, quantum embeddings, and new quantum Monte Carlo algorithms. His work addresses problems that appear naively exponentially hard but where understanding of physics, particularly entanglement structure, allows for calculations of polynomial cost. Analysis of his recent publications reveals a strong focus on quantum simulation techniques, particularly tensor network methods applied to strongly correlated systems. His research spans fundamental theoretical developments to practical applications in quantum computing, molecular simulation, and materials science, with increasing integration of machine learning techniques and GPU acceleration in computational chemistry frameworks. Dr. Chan leads an active research group at Caltech dedicated to simulating chemical and physical systems at the level of many-particle quantum mechanics. His group has welcomed numerous researchers including Kasra Hejazi, Zuxin Jin, Zhihao Cui, Ke Liao, Henrik Larsson, and Wenyuan Liu. He teaches courses in Physical Chemistry (Ch 21 abc) and Advanced Quantum Chemistry (Ch 225), contributing significantly to theoretical chemistry education at Caltech.

• Mathematical Optimization
• Algorithmic Intelligence
• Energy Systems Modeling
• +۵ مورد دیگر

Thorsten Koch serves as Head of the Department of Applied Algorithmic Intelligence Methods within the Division of Mathematical Algorithmic Intelligence at Zuse Institute Berlin (ZIB). His research spans mathematical optimization, energy systems modeling, quantum computing applications, and scientometrics. Koch leads significant research projects including FAN (focusing on AI in scholarly communication), UNSEEN (energy scenarios), HPO-NAVI (research software visibility), and Multi-Energy Models for European Energy System Planning. Koch's research interests center on developing advanced optimization algorithms for complex systems, particularly in energy networks and scientific data analysis. His work bridges theoretical mathematics with practical applications in gas network optimization, wind farm design, portfolio management, and quantum computing. He has pioneered methods for large-scale mixed-integer programming, scenario generation, and the integration of machine learning with traditional optimization techniques. His recent publications demonstrate growing emphasis on quantum optimization, scientometrics, and the application of AI to scientific communication infrastructure. His publication trends reveal a strategic expansion from traditional mathematical optimization into quantum computing applications and scientific data infrastructure. Recent work shows increasing collaboration across disciplines - connecting energy systems analysis with financial modeling, integrating machine learning with optimization solvers, and applying computational methods to scientometrics. The 15 most recent articles highlight three major thrusts: quantum optimization (33%), energy systems modeling (27%), and scientific data infrastructure (40%), reflecting his leadership in both theoretical algorithm development and practical implementation for societal challenges. Koch actively contributes to research infrastructure through leadership roles in projects like KOBV (Berlin-Brandenburg Cooperative Library Network), HDC (Humanities Data Centre), and CIB (future library networks). His work on the DeepGreen initiative focuses on establishing legally secure workflows for implementing open-access components in scientific publication licensing agreements, demonstrating his commitment to open science principles and research data management.

Sven Pankeمشاهده پروفایل

استاد

• Bioprocess Engineering
• Synthetic Biology
• Fermentation Technology
• +۳ مورد دیگر

Prof. Dr. Sven Panke is a Full Professor and Head of the Department of Biosystems Science and Engineering at ETH Zürich. His research focuses on bioprocess engineering, synthetic biology, and enzymatic process development. Key areas include miniaturized bioreactor systems, microbial engineering for novel metabolite production, and high-throughput screening methodologies. Education: Studied Biotechnology at TU Braunschweig, with postgraduate research at the German National Research Center for Biotechnology and ETH Zurich. Transitioned from industry (DSM) to academia in 2001 as an Assistant Professor, progressing to Associate Professor (2007-2009) before leading the BSS department. Research interests emphasize directed evolution of enzymes, metabolic pathway engineering, and systems biology approaches to optimize microbial production systems. Current projects include bio-indigo synthesis, antimicrobial peptide discovery, and synthetic biology tools for cellular engineering. Labs/Teams: Leads the Bioprocess Engineering Lab at ETH Zurich, collaborating on projects like the E. coli import system design and γ-glutamyltransferase engineering. Active in developing microfluidics platforms for parallel reaction analysis. Grants/Advising: Funded by initiatives in sustainable biomanufacturing and synthetic biology. Supervises graduate students in bioprocess design and microbial systems engineering.

Jean Van Den Elsenمشاهده پروفایل

استاد

• Structural Biochemistry
• Protein Homeostasis
• Immune Evasion Mechanisms
• +۵ مورد دیگر

Prof Jean Van Den Elsen is a Professor of Biochemistry at the University of Bath , affiliated with the Department of Life Sciences and several research centers including the Centre for Sustainable Chemical Technologies (CSCT) , Centre for Therapeutic Innovation , and Milner Centre for Evolution . They are actively accepting doctoral students and leading multidisciplinary projects at the intersection of structural biology and immunology. Education: Not explicitly mentioned Appointments: University of Bath (current), with collaborations across Chemistry, Biology & Biochemistry departments Research Focus: The laboratory investigates protein structural biology in two major areas: Complement System Interactions: Studying how pathogenic microbes like Staphylococcus aureus evade host immunity through complement system manipulation , with structural analysis of proteins such as C3d and C5 complexes. This work directly informs vaccine design and autoimmune disease treatment . Glycation Mechanisms: Developing diagnostic tools for detecting non-enzymatic sugar modifications linked to diabetes , Alzheimer's , and aging, with commercial partnerships like Abcam . Article Trends: Recent publications demonstrate: Structural analysis of complement evasion proteins (2022-2025) Engineering knob domains as miniature antibodies (2023) Evolutionary studies on cooperative behavior (2021-2025) Advancements in peptide synthesis platforms (2022-2023) Applications of AI protein folding (2020) Scientific Contributions: ORCID: 0000-0002-0367-1956 BBSRC Funding recipient (2023) 100+ citations across 76 research outputs Student Mentorship: Directly supervising 4 current PhD students and 1 postdoc, with a track record of mentoring 11 previous students including Daphne Jackson Fellows and MSCA researchers. Collaborations: Maintains active partnerships with Dr. Tony James (Chemistry), Dr. Rob Williams (Biochemistry), and industry partners UCB Celltech and Porton Biopharma Ltd. Their work contributes to UN Sustainable Development Goals 3 (Good Health), 12 (Responsible Consumption), and 13 (Climate Action).

Martin Rohdeمشاهده پروفایل

استاد

• Nuclear Reactor Technology
• Molten Salt Reactors
• Transport Phenomena
• +۵ مورد دیگر

Dr. Martin Rohde is a Professor and Group Leader at the Radiation Science & Technology department within the Faculty of Applied Sciences at Delft University of Technology (TU Delft) in the Netherlands. He leads the Transport Phenomena & Nuclear Applications research group, focusing on advanced nuclear reactor technologies, particularly molten salt reactors, and their associated transport phenomena. Professor Rohde's research interests span across several critical areas in nuclear engineering and fluid dynamics. His work primarily focuses on understanding transport phenomena in nuclear applications, with particular emphasis on molten salt reactors for sustainable and safe nuclear power generation, innovative production techniques of medical isotopes, and advanced energy storage systems like flow batteries. His research group actively investigates complex physical phenomena occurring under extreme conditions such as high pressures, high temperatures, and interactions with radioactive processes. His publication record demonstrates a strong focus on computational methods for nuclear applications, particularly the Lattice Boltzmann Method (LBM), which is used to model fluid flow, heat transfer, and phase change phenomena in nuclear systems. Recent work has concentrated on freezing and melting processes in molten salt reactors, microfluidic separation techniques for medical isotopes, and advanced modeling of flow batteries. His research shows a clear progression toward increasingly sophisticated numerical methods applied to real-world nuclear engineering challenges. Professor Rohde has secured significant funding through multiple European Commission projects including ENDURANCE, MIMOSA, and ReZilient, demonstrating the international recognition of his research. He has supervised numerous PhD and MSc students, many of whom have gone on to complete theses on topics related to molten salt reactors, microfluidics, and flow battery technology. His research group includes several technicians, post-doctoral researchers, and PhD candidates working collaboratively on cutting-edge nuclear technology. The Transport Phenomena & Nuclear Applications laboratory operates several specialized facilities including the ESPRESSO facility for measuring melting and solidification under convective boundaries, and experimental setups for studying molten salt behavior, microfluidic purification, and flow battery technology. The group maintains strong collaborations with international partners including TRIUMF (Canada), NRG, and URENCO (The Netherlands).

Albert H. Titusمشاهده پروفایل

استاد

• Wearable Sensors
• Biosensors
• Bioinstrumentation
• +۵ مورد دیگر

Albert H. Titus is a Professor in the Department of Biomedical Engineering and an Adjunct Professor in the Department of Electrical Engineering at the University at Buffalo, State University of New York. He serves as Associate Vice President for Regulatory Support in the Office of the Vice President for Research and Economic Development. His research focuses on analog VLSI design for neuromorphic visual processing, biosensors, wearable devices, optoelectronic systems, and neural networks. Education: PhD in Electrical and Computer Engineering, Georgia Institute of Technology (1997) MS in Electrical Engineering, University at Buffalo (1991) BS in Electrical Engineering, University at Buffalo (1989) Research Interests: His work spans wearable and implantable sensors, bioinstrumentation, neural network-based visual processing, analog VLSI implementations, optoelectronics, and electronic packaging. He pioneered CMOS-based neuromorphic systems and developed patented technologies for glare sensing and RF power calorimetry. Publication Trends: His recent articles emphasize CMOS-integrated sensors, machine learning for bioimpedance analysis, implantable medical devices, and xerogel-based optical biosensors. These works bridge biomedical engineering and microelectronics. Scientific Recognition: He is a Fellow of the National Academy of Inventors and has received the SUNY Chancellor’s Award for Excellence in Service (2017), NSF CAREER award, and Western New York Inventor of the Year (2010). His inventions include a patented low-power glare sensor (U.S. Patent 7,586,079) featured in Popular Science’s 2011 Top Ten Inventions. Academic Leadership: As a faculty member, he has supervised nearly 20 PhD and over 40 MS students, while teaching courses in circuits, IC design, sensors, and signal processing across electrical and biomedical engineering disciplines.

• Materials Science and Engineering
• Additive Manufacturing
• Materials Informatics
• +۷ مورد دیگر

S. Mohadeseh Taheri-Mousavi is an Assistant Professor in the Department of Materials Science and Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. She joined CMU in September 2022 after postdoctoral appointments at MIT and Brown University. Her research is supported by major grants from NASA STRI, DARPA, the Army Research Laboratory, and the Naval Nuclear Laboratory, and she is affiliated with the NextManufacturing Center and the Wilton E. Scott Institute for Energy Innovation. Her educational background includes a Ph.D. from EPFL, Switzerland, and M.Sc. and B.Sc. degrees from Sharif University of Technology, Iran. She was awarded both early and advanced Swiss National Science Foundation fellowships during her postdoctoral studies. Taheri-Mousavi’s research focuses on the intersection of materials science, mechanical engineering, and computer science. She develops multi-scale computational models and AI-driven frameworks—such as AlloyGPT and generative AI agents—to design next-generation structural alloys, particularly for additive manufacturing and extreme environments. Her work emphasizes materials sustainability, industrial decarbonization, and uncertainty quantification in alloy design. The integration of machine learning with Integrated Computational Materials Engineering (ICME) and CALPHAD methods enables rapid exploration of high-dimensional composition and processing spaces. Her recent publications (2023–2025) show a strong trend toward AI/ML applications in alloy discovery, hydrogen embrittlement modeling, and high-temperature aluminum and tungsten alloys. These works reflect a deep commitment to accelerating materials innovation through human-AI collaboration and smart experimental validation. Her scientific honors include prestigious Swiss National Science Foundation fellowships. She has also received seed funding from the Scott Institute for Energy Innovation to study hydrogen embrittlement. She advises a dynamic team of doctoral students and a postdoctoral researcher, working on topics including hydrogen embrittlement, generative AI for welding, and gradient alloys. Her research is funded by high-impact grants from NASA, DARPA, the Army, and the Naval Nuclear Laboratory, supporting transformative projects in structural alloy design. She leads the Taheri-Mousavi Group, which operates within CMU’s Materials Characterization Facility and the NextManufacturing Center. The group focuses on developing novel AI-integrated computational frameworks to guide efficient and intelligent experimentation in alloy development.

Tim Oatesمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +۷ مورد دیگر

Dr. Tim Oates is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County . His research spans machine learning, artificial intelligence, and brain-machine interfaces, with a focus on weakly supervised methods, human-in-the-loop reinforcement learning, and grounded policy development for robotics. Ph.D., Computer Science, University of Massachusetts, Amherst, 2000 M.S., Computer Science, University of Massachusetts, Amherst, 1997 B.S., Computer Science and Electrical Engineering, 1989 Current research threads include: Developing non-invasive brain injury severity assessment via medical time series Modeling human brain development through computational frameworks Designing algorithms for autonomous robotic learning Recent publications highlight AI security mechanisms (backdoor detection via tensor decomposition, matrix factorization) Medical applications (3D artery reconstruction, skin lesion diagnosis, EEG denoising) Neuro-symbolic integration (holographic representations, language-guided reinforcement learning) Mathematical reasoning (schema-based problem solving, subitizing algorithms) Contact: oates@cs.umbc.edu | Office: 336 Information Technology and Engineering (ITE) Building

Aswin Gnanaskandanمشاهده پروفایل

استادیار

• Computational Fluid Dynamics
• Multiphase Flows
• Biomedical Acoustics
• +۳ مورد دیگر

Prof. Aswin Gnanaskandan is an Assistant Professor in the Department of Mechanical & Materials Engineering at Worcester Polytechnic Institute (WPI), where he joined in August 2020. He directs the Computational Multiphase Transport Laboratory, focusing on developing high-fidelity models for multiphase flows with applications in engineering and biomedical fields. His research is funded by NSF, Office of Naval Research, NIH, and the Center for Advanced Research in Drying. Education: PhD, Aerospace Engineering & Mechanics, University of Minnesota (2015) MS, Aerospace Engineering & Mechanics, University of Minnesota (2012) BS, Aeronautical Engineering, Madras Institute of Technology (2006) Research Interests: Computational Fluid Dynamics (CFD), Multiphase Flow Modeling, Biomedical Acoustics, High-Performance Computing, and applications in underwater transportation, propulsion, and biomedical acoustics. His work bridges fundamental fluid mechanics with real-world challenges in energy, health, and environmental systems. Recent Research Trends: His articles focus on microbubble-enhanced ultrasound therapy, cavitation dynamics in propulsion systems, and multiphase flow modeling across scales. Key themes include improving thermal ablation precision in medical treatments and optimizing industrial processes like spray drying through advanced numerical techniques. Awards: Excellence in Research Award (WPI, 2024) NSF Engineering Research Initiation Award (2023) James Nichols Heald Research Award (WPI, 2022) Teaching & Advising: Teaches undergraduate/graduate courses in Fluid Mechanics, Thermodynamics, and Numerical Methods. Advises multiple Major Qualifying Projects and fosters interdisciplinary collaboration through lab activities. His lab actively engages with industry and academic partners on projects like HIFU therapy and sustainable energy solutions. Labs & Teams: Leads the Computational Multiphase Transport Lab, which collaborates on projects involving CFD solver development (MFC 5.0), exascale computing, and biomedical acoustics. Aligns research with UN Sustainable Development Goals (SDG 7, 9, 13).

Kyle Hanquistمشاهده پروفایل

استادیار

• Hypersonics
• Aerothermodynamics
• Nonequilibrium Flows
• +۱۱ مورد دیگر

Kyle Hanquist is an Assistant Professor in the Department of Aerospace and Mechanical Engineering at the University of Arizona, where he is also a member of the Graduate Faculty. He directs the Computational Hypersonics and Nonequilibrium Laboratory (CHANL), focusing on advanced simulation techniques for high-speed flows. His academic journey includes a PhD and MSE in Aerospace Engineering from the University of Michigan and a BSE in Mechanical Engineering from the University of Nebraska. PhD, Aerospace Engineering, University of Michigan, Ann Arbor MSE, Aerospace Engineering, University of Michigan, Ann Arbor BSE, Mechanical Engineering, University of Nebraska, Lincoln Dr. Hanquist's research centers on hypersonics, aerothermodynamics, and nonequilibrium flows , with strong emphasis on computational fluid dynamics , low-temperature plasmas , and thermal management systems . His work involves modeling complex physical phenomena such as electron transpiration cooling, plasma-assisted flow control, and high-temperature gas effects in reentry environments. He also investigates molecular gas dynamics and finite-rate chemistry in extreme conditions. His recent publications reveal a strong trend in computational modeling of hypersonic boundary layers , plasma sheaths , and shock-tube validation of thermochemical models . The interdisciplinary nature of his work spans aerospace engineering, plasma physics, and materials response under extreme thermal loads. Much of his research integrates multi-physics simulations to address fluid-thermal-structural interactions critical for next-generation hypersonic vehicles. Dr. Hanquist has received several scientific honors, including: 2020 AIAA Plasmadynamics and Lasers Best Paper Award Editor's Choice, AIP Publishing - Physics of Fluids (Summer I 2020) Featured Article, AIP Publishing - Physics of Fluids (Summer I 2021) Frontiers in Physics – Plasma Physics (Spring 2020) As an advisor and lab director, he mentors graduate students in computational hypersonics and collaborates with institutions like NASA and the University of Michigan. His research is supported by grants from aerospace and defense agencies, though specific funding sources are not listed. He teaches courses in fluid mechanics, numerical methods, and nonequilibrium flows, contributing to both undergraduate and graduate education. He leads the Computational Hypersonics and Nonequilibrium Laboratory (CHANL) , which develops and applies high-fidelity simulation tools for hypersonic applications. The lab focuses on kinetic modeling, plasma interactions, and optimization of thermal protection systems, often using massively parallel CFD codes and multi-fidelity surrogate models.

Stephen Pankavichمشاهده پروفایل

استاد

• Partial Differential Equations
• Kinetic Theory
• Mathematical Biology
• +۴ مورد دیگر

Stephen Pankavich is a Professor and Department Head in the Department of Applied Mathematics and Statistics at the Colorado School of Mines. He holds a PhD in Mathematical Sciences from Carnegie Mellon University, with research focused on partial differential equations, kinetic theory, and mathematical biology. His work bridges theoretical analysis and computational methods, addressing challenges in plasma dynamics, epidemiological modeling, and multiscale systems. Education: PhD, Mathematical Sciences, Carnegie Mellon University (2005) MS, Mathematical Sciences, Carnegie Mellon University (2001) BS, Mathematical Sciences, Carnegie Mellon University (2000) Research interests include the analytical and numerical study of collisionless plasmas, HIV dynamics, and epidemiological models. He has received awards such as the W.M. Keck Mentorship Award and the Colorado School of Mines Alumni Teaching Award. His articles explore topics like plasma decay rates, HIV therapy models, and particle-tracking algorithms. He has advised over 20 graduate and undergraduate students, contributing to impactful research in applied mathematics and computational science.

• Thermodynamics
• Diffusion
• Phase Transformations
• +۳ مورد دیگر

Powder Metallurgy (MH2100) and has expertise in computational materials science. His research emphasizes predictive modeling of material behavior, including precipitation kinetics, sintering processes, and coating interactions. Notable areas include phase field modeling of discontinuous precipitation, spinodal decomposition in Fe-Cr alloys, and high-entropy alloy design. His studies bridge experimental data with computational tools like the YAPFI phase-field framework. Key themes in his publications span cemented carbides, Co-based entropic alloys, and tool wear mechanisms. He combines CALPHAD thermodynamic modeling with first-principles calculations to address challenges in materials processing and corrosion resistance. His work often addresses industrial applications, such as optimizing machining tools and additive-manufactured superalloys.

Christian Plesslمشاهده پروفایل

استاد

• High-Performance Computing
• Reconfigurable Computing
• FPGA Acceleration
• +۳ مورد دیگر

Prof. Dr. Christian Plessl is a W3 Professor of High-Performance Computing at the Institute of Computer Science, University of Paderborn. He leads the Paderborn Center for Parallel Computing (PC²), a national HPC center within the NHR alliance. His roles include Director of PC², Board Member of the NHR association, and member of the Sonderforschungsbereich 901. Education: PhD (Dr. sc. ETH) in Computer Engineering, ETH Zürich (2006) MSc in Electrical Engineering, ETH Zürich (2001) Postdoc at ETH Zürich (2007–2011) Research Interests: Architecture and tools for high-performance parallel and reconfigurable computing, FPGA acceleration, quantum chemistry, scientific computing, adaptive systems, and energy-efficient HPC solutions. Key projects include EKI-App (FPGA-based neural networks), FPGA4XPCS (X-ray spectroscopy), and HighPerMeshes (unstructured grid frameworks). Publications: Over 100 peer-reviewed works, focusing on FPGA acceleration, HPC frameworks, and quantum computing. Recent trends emphasize energy-efficient neural networks, FPGA-based quantum computing, and scalable HPC algorithms. Awards: Best Paper Awards at HEART 2023, ReConFig 2012/2014 Paderborn University Research Awards (2018, 2009) SEW-EURODRIVE Student Award (2001) Grants & Projects: Principal investigator in DFG, BMBF, and EU-funded projects. Collaborates with AMD/Xilinx, Intel/Altera, and Fujitsu. Leads initiatives like PerficienCC (custom computing) and HighPerMeshes. Labs/Teams: Directs the High-Performance Computing group at PC², focusing on FPGA supercomputing and HPC infrastructure. Active in the NHR alliance for national HPC coordination.

Jonathan Weissmanمشاهده پروفایل

استاد

• Protein Folding
• Systems Biology
• Genomics
• +۶ مورد دیگر

Jonathan Weissman is a Professor of Biology at the Massachusetts Institute of Technology (MIT) and a Member of the Whitehead Institute. He is also an Investigator of the Howard Hughes Medical Institute and the Landon T. Clay Professor of Biology. His research spans protein folding mechanisms, ribosome profiling, CRISPR-based tools (CRISPRi/a), and genetic interaction mapping. Whitehead Institute Member MIT Professor HHMI Investigator Co-founder, Maze Therapeutics & KSQ Therapeutics Research Interests focus on: Protein folding in cellular contexts Endoplasmic reticulum (ER) function and stress responses Genome-wide CRISPR screening for gene regulation High-density genetic interaction maps in mammals Mitochondrial protein targeting and quality control Epigenomic engineering with synthetic tools Scientific Awards include: Protein Society Irving Sigal Young Investigator Award (2004) Raymond & Beverly Sackler Prize (2008) National Academy of Sciences election (2009) NAS Award for Scientific Discovery (2015) Genetics Society of America Ira Herskowitz Award (2020) Labs & Collaborations : Leads the Weissman Lab at MIT/Whitehead Institute, co-leads the Laboratory for Genomic Research with GlaxoSmithKline, and chairs the Stowers Institute Scientific Advisory Board.