جستجوی استاد راهنمای دانشگاهی

Stefano Ermon is an Associate Professor in the Department of Computer Science at Stanford University, affiliated with the Artificial Intelligence Laboratory and a Senior Fellow at the Woods Institute for the Environment. His research focuses on advancing machine learning and generative AI techniques to address societal and environmental challenges, including computational sustainability, geospatial analysis, and climate science. He holds a Ph.D. from Cornell University (2015). Education: Ph.D. in Computer Science, Cornell University (2015). Research Interests: Ermon’s work bridges foundational machine learning (e.g., diffusion models, generative AI, and optimization) with applications in sustainability, geospatial analysis (via satellite imagery), and earth observation systems. Notable contributions include predicting poverty using satellite data and developing scalable methods for molecule generation. Articles Trends: His recent work emphasizes diffusion models for generative tasks (e.g., text-to-image, molecule design), geospatial AI (e.g., environmental monitoring), and ethical AI (e.g., bias mitigation in LLMs). He also explores applications in robotics and scientific computing. Awards: He has received prestigious awards, including the ICML 2024 Best Paper Award, Sloan Research Fellowship, Microsoft Research Faculty Fellowship, and the IJCAI Computers and Thought Award. Advising and Grants: Ermon teaches courses like Probabilistic Graphical Models (CS228) and has secured grants from NSF, ONR, AFOSR, and private foundations. His lab develops tools for climate science and sustainable development. Labs/Teams: Leads the Stanford AI Lab group focused on computational sustainability and generative AI, collaborating with institutions like the Woods Institute for environmental applications.

• Machine Learning
• Generative AI
• Computational Sustainability
• +۷ مورد دیگر

Yingyan (Celine) Lin is an Associate Professor in the School of Computer Science at Georgia Institute of Technology, leading the Efficient and Intelligent Computing (EIC) Lab. Her work focuses on cross-layer innovations in machine learning systems, from algorithms to chip design, aiming to advance green AI and ubiquitous machine learning. She holds a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (2017). Research interests include efficient machine learning, neural rendering (e.g., NeRF), hardware-software co-design for AI acceleration, and graph neural networks. Her lab has pioneered projects like RTML and 3DML, funded by NSF, NIH, DARPA, and industry partners (Qualcomm, Intel, Meta). Awards: NSF CAREER Award (2021), ACM SIGDA Outstanding Young Faculty (2022), Meta Faculty Research Award (2022) Grants: Multi-university projects funded by NSF, NIH, DARPA, SRC, ONR, and industry Recognition: First-place wins at DAC 2022 and TinyML Design Contest 2022, IEEE Micro Top Pick 2023 Her research bridges algorithmic innovation with hardware implementation, emphasizing energy efficiency and real-time performance for applications in AR/VR, computer vision, and neuro-symbolic AI systems.

• Efficient Machine Learning
• Cross-Layer Innovations
• Green AI
• +۷ مورد دیگر

Dr. Peichen Zhong is an Assistant Professor in the Department of Materials Science and Engineering at the National University of Singapore (NUS). He leads the Applied Machine Learning and Materials Modeling (AM³) Group, focused on advancing computational methods for clean energy technologies. His research integrates machine learning with atomistic simulations to tackle challenges in battery materials, disordered materials, and sustainable energy systems. Education: B.S. in Physics from University of Science and Technology of China (2018); Ph.D. in Materials Science from UC Berkeley (2023, advised by Prof. Gerbrand Ceder); Postdoctoral training at Lawrence Berkeley National Lab and BIDMaP, co-advised by Persson, Cheng, and Krishnapriyan. Research Interests: Computational modeling of battery cathodes/electrolytes, AI-driven interatomic potentials, statistical mechanics in disordered materials, and generative models for scientific discovery. Key areas include Li/Na-ion batteries, solid-state reactions, and sustainable energy materials. Awards: BIDMaP Emerging Scholar Fellowship (UC Berkeley CDSS, 202?), 2023 Rising Stars in Materials Science (CMU/MIT/Stanford). Labs/Teams: The AM³ Group at NUS MSE focuses on interdisciplinary research combining theory, computation, and AI4Science. Current openings include PhD students and postdoctoral researchers.

• Materials Science
• Machine Learning
• Computational Modeling
• +۵ مورد دیگر

Tommi Jaakkola is the Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology. He received his MSc in theoretical physics from Helsinki University of Technology in 1992 and his PhD from MIT in computational neuroscience in 1997. After completing a postdoctoral position in computational molecular biology as a DOE/Sloan fellow at UCSC, he joined the MIT EECS faculty in 1998. His research advances how machines can learn, predict or control, and do so at scale in an efficient, principled, and interpretable manner. His work in machine learning extends from foundational theory to modern applications, focusing especially on statistical inference and estimation tasks that lie at the heart of complex learning problems. He designs new methods, theory and algorithms to automate the use and generation of semi-structured data such as natural language text, images, molecules, or strategies. Jaakkola applies and develops algorithms to solve multi-faceted recommender, retrieval, or inferential tasks (particularly in biomedical contexts), design and optimize molecules or reactions for drug design, and model strategic, game theoretic interactions. His recent work heavily focuses on diffusion models, protein structure prediction, molecular design, and generative AI, with significant publications in top conferences including ICML, NeurIPS, and ICLR. His scientific contributions span multiple disciplines with significant impact in both theoretical machine learning and practical applications in computational biology and chemistry, including notable work on antibiotic discovery published in Cell. Current advisees: Julia Balla, Bowen Jing, Hannes Stärk, Peter Holderrieth, Chenyu Wang Recent graduates: Gabriele Corso (Boltz PBC), Ezra Erives (DE Shaw), Jason Yim (Xaira) Jaakkola maintains an active research program through MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Institute for Data, Systems, and Society (IDSS), with his office located in the Stata Center (32-G470). His work bridges theoretical machine learning with practical applications, making significant contributions to both the academic field and potential real-world impact in healthcare and drug discovery.

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +۷ مورد دیگر

Ole Winther is Professor in High dimensional biological data analysis/Machine learning at the Department of Biology, University of Copenhagen and Professor in Data science and complexity at DTU Compute, Technical University of Denmark. He serves as CRO and co-founder of raffle.ai, CTO and co-founder of FindZebra, Head of ELLIS Unit Copenhagen, and co-PI of the Machine Learning for Life Science Center. His research spans Bioinformatics , Machine Learning , and AI for Science , focusing on applying deep learning to biological sequence analysis, latent variable models, and medical NLP. Winther's work develops predictive and generative models for bioinformatics, with significant contributions to protein localization tools (SignalP, DeepLoc, DeepTMHMM), single-cell genomics, and novel deep learning architectures like variational autoencoders and diffusion models. Analysis of Winther's recent publications (2023-2025) reveals a strong trend toward integrating protein language models with traditional bioinformatics approaches and applying diffusion models to scientific problems. His work bridges theoretical machine learning advancements with practical applications in biology and medicine, particularly in protein sequence analysis, medical search engines, and scientific simulation acceleration. Winther currently supervises a diverse research group including Panagiotis Antoniadis, Rachael M. DeVries, Jun Wang, Beatrix M. G. Nielsen, Felix G. Teufel, Irene R. Rodriguez, Anders Christensen, and Christopher Heje Grønbech. His former students have established successful careers at institutions including Google, Apple, and various startups, with notable alumni like Casper Sønderby (Google Brain) and Søren Sønderby (Apple). He leads significant research initiatives including the ELLIS Unit Copenhagen and the Machine Learning for Life Science Center, while maintaining active industry partnerships through his co-founded companies raffle.ai (enterprise search using NLP) and FindZebra (search engine for rare diseases). His teaching includes Deep Learning courses at both DTU (02456) and University of Copenhagen (NDAK24002U).

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

Hauke Smidt is a Personal Professor at Wageningen University & Research, affiliated with the Molecular Ecology group within the Faculty of Veterinary Medicine and Animal Sciences. His research focuses on microbial ecology, particularly gut microbiota dynamics, antibiotic resistance, environmental microbiology, and the One Health framework. Smidt has supervised over 50 PhD students and contributed to 519 publications, including studies on microbiome resilience, fermented food impacts, and bioremediation strategies. Education and affiliations include leadership roles in projects addressing antimicrobial resistance, pig health, and drinking water microbiology. His work spans collaborations across Europe and Asia, with notable contributions to understanding microbial community responses to environmental stressors and dietary interventions. Smidt is actively involved in public outreach, as seen in media contributions discussing microbiome research and health benefits. Key projects include aquifer treatment for drinking water, microbiome simulation models, and combating plant pathogens. His awards and recognition are not explicitly listed, but his extensive publication record and project leadership highlight significant academic impact.

• Microbiology
• Environmental Microbiology
• Gut Microbiota
• +۴ مورد دیگر

Howard A. Stone is the Donald R. Dixon '69 and Elizabeth W. Dixon Professor and Neil A. Omenn '68 University Professor in the Department of Mechanical and Aerospace Engineering at Princeton University's School of Engineering and Applied Science. He leads the Complex Fluids Group, conducting interdisciplinary research at the intersection of engineering, physics, chemistry, and biology. Dr. Stone received his B.S. in Chemical Engineering from UC Davis (1982) and Ph.D. from Caltech (1988). After a postdoctoral year at Cambridge University, he joined Harvard University's faculty in 1989, where he became the Vicky Joseph Professor of Engineering and Applied Mathematics before moving to Princeton in 2009. His research focuses on fluid dynamics phenomena across multiple scales, with particular emphasis on microfluidics, complex fluids, and biomechanics . His group investigates multiphase flows, colloidal systems, bio-inspired fluid phenomena, and physicochemical hydrodynamics. Recent work spans from fundamental studies of thin film drainage and droplet dynamics to applications in biological systems including blood flow, bacterial transport, and biomolecular condensates. The Complex Fluids Group employs experimental, theoretical, and computational approaches, often collaborating with industry partners on applications from medical devices to industrial processes. Analysis of his recent publications reveals a continued expansion into biological applications of fluid dynamics, with increasing focus on cellular mechanics, biomolecular condensates, and pathological hemodynamics, while maintaining strong contributions to fundamental fluid mechanics in complex systems. His work consistently bridges theoretical insights with practical applications across multiple disciplines. Major honors include: Election to the National Academy of Engineering (2009) Election to the National Academy of Sciences (2014) APS Fluid Dynamics Prize (2016) G.K. Batchelor Prize in Fluid Dynamics (2008) NSF Presidential Young Investigator Award Professor Stone has advised numerous PhD students through their Final Public Oral examinations, with recent graduates working on topics spanning microfluidics, bacterial transport, and complex fluid phenomena. His research has been supported by diverse funding sources including NSF, NIH, and industry partnerships. The Complex Fluids Group maintains state-of-the-art experimental facilities in the Engineering Quadrangle, featuring specialized equipment for microfluidics, rheology, and interfacial phenomena investigations. The group actively collaborates with researchers across Princeton and globally, maintaining strong connections to both academic and industrial partners working on fluid-related challenges.

• Fluid Dynamics
• Microfluidics
• Complex Fluids
• +۷ مورد دیگر

Thomas Walz, PhD, is a Professor at The Rockefeller University and Head of the Laboratory of Molecular Electron Microscopy. Previously, he held positions as Assistant, Associate, and Professor at Harvard Medical School (1999–2015) and was an Investigator at the Howard Hughes Medical Institute (2008–2015). He earned his PhD and BS in biophysics from the University of Basel, Switzerland, and completed postdoctoral research at the University of Sheffield. Walz completed his education at the Biozentrum, University of Basel, Switzerland, where he received his Diploma in Biophysics (1992) and PhD in Biophysics (1996). He furthered his training as a postdoctoral researcher at the University of Sheffield (1996–1999). His research focuses on understanding membrane-related processes and the structural biology of membrane proteins in lipid environments. Utilizing cryo-electron microscopy and nanodisc technology, he investigates how lipid bilayers influence membrane protein structure and function. Key areas include mechanosensitive channels, T-cell receptor dynamics, and telomere maintenance mechanisms. Collaborations with the de Lange lab explore the CST-Polα/primase complex's role in telomere regulation. Walz has been recognized with the Genzyme Award for Outstanding Achievement in Biomedical Sciences (2004) and continues to contribute to advancements in structural biology and membrane protein research. While specific student advisees are not listed, Walz actively mentors through his roles in the David Rockefeller Graduate Program and Tri-Institutional programs. His research is supported by grants and institutional funding, though specific grants are not detailed here. He directs the Laboratory of Molecular Electron Microscopy at Rockefeller, a hub for innovative structural biology and membrane protein studies. The lab collaborates widely, integrating cryo-EM with electrophysiology and molecular dynamics simulations.

• Membrane Biology
• Structural Biology
• Cryo-electron Microscopy
• +۵ مورد دیگر

Dr. Jimeng Sun is a Health Innovation Professor at the Siebel School of Computing and Data Science and Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign. Co-founder of Keiji AI , he leads groundbreaking research at the intersection of artificial intelligence and healthcare, actively deploying clinical AI systems and developing frameworks like PyHealth and Therapeutics Data Commons . His research spans four major areas: Clinical AI Systems : Developing interpretable models (e.g., RETAIN) for patient similarity, temporal event prediction, medication recommendation, and clinical outcome forecasting Drug Discovery : Creating molecular optimization frameworks, drug-target interaction models, and AI-driven platforms Clinical Trials : Pioneering patient-trial matching, outcome prediction, and optimization frameworks using deep learning and graph neural networks Biosignal Analysis : Advancing sleep staging, seizure classification, and automated EEG/Cardiac monitoring systems With over 500 top-tier publications (including in Nature , NEJM AI , and leading AI conferences) and an h-index of 99, his work has been recognized with the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare award. He maintains active collaborations with institutions like Massachusetts General Hospital , Medidata Solutions , and OSF Healthcare . His recent publications reveal a strong focus on: Reinforcement learning applications in medical data analysis Large language model adaptation for clinical tasks Knowledge graph integration with AI systems Synthetic data generation for healthcare Multi-modal learning in clinical contexts Explainable AI for medical applications Dr. Sun's lab ( Sunlab ) emphasizes practical impact over theoretical work, actively collaborating with hospitals and healthtech companies. He welcomes contributions from clinicians, researchers, and industry partners through initiatives like his AI for Health webinar series .

• Artificial Intelligence
• Healthcare Analytics
• Drug Discovery
• +۵ مورد دیگر

Adam Runions is a researcher in the Department of Computer Science at the University of Calgary, leading the MPG Partner Group in computational analysis of leaf development through collaborative work with Miltos Tsiantis. His group is embedded in the Graphics Cluster, focusing on interdisciplinary problems at the intersection of computer science and developmental biology. University of Calgary - Department of Computer Science MPG Partner Group (2022) Graphics Cluster affiliation His research explores computational modeling and analysis of plant form and development across multiple scales, integrating geometric modeling, physically-based simulation, and computer-aided design. Key themes include plant morphogenesis, self-organization of natural forms, and cross-disciplinary applications in computer graphics and animation. Recent publications emphasize plant development (leaf shape, bark patterning), mathematical modeling (auxin-driven patterning), and geometric techniques (subdivision surfaces, PUPs). Collaborations span institutions like the Max Planck Institute for Plant Breeding Research. Scientific Awards Marie Sklodowska-Curie Fellowship Best Paper Award (International Conference on Cyberworlds 2015) Best Student Paper Award (Computer Graphics International 2011) The group actively recruits BSc, MSc, and PhD students with backgrounds in computer science and mathematics for projects on plant form simulation and digital content creation. Research integrates evolutionary biology, biomechanical modeling, and computational techniques.

• Computational Modeling
• Developmental Biology
• Computer Science
• +۴ مورد دیگر

Dr. Krishnan Mahesh is a Professor at the University of Michigan with joint appointments in Mechanical Engineering and Naval Architecture and Marine Engineering. He serves as Director of the Center for Naval Research and Education and leads the Computational Fluids Laboratory, where he develops advanced numerical methods for simulating multi-physics turbulent flows. Education: Ph.D. (1996), M.S. (1990) from Stanford University, B.Tech (1989) from IIT Bombay Leadership: Director, Center for Naval Research and Education (2022-present) His research focuses on high-fidelity simulations of turbulent flows with applications in marine propulsors, multiphase systems, cavitation, hydroacoustics, superhydrophobic surfaces, biofouling, fluid-structure interaction, and flow stability. His group develops the MPCUGLES software for unstructured grid simulations on parallel computing platforms. Recent work examines cavitation dynamics , tip vortex flows , and roughness-induced transition in complex marine and aerospace systems. His 15 most recent articles demonstrate expertise in LES/DNS of multi-physics flows, with emphasis on marine propulsion, bubble collapse, and turbulent noise prediction. Scientific Honors: 2021 AIAA Best Paper Award 2018 Fulbright Scholar 2017 Marine Propulsors Symposium Best Paper 2011 APS Fellow 2010 Taylor Award for Distinguished Research He mentors numerous graduate students and postdoctoral fellows, with collaborative projects spanning jet in crossflow analysis, gas turbine simulations, and shock-turbulence interactions. His research receives funding from ONR, NSF, and international naval programs.

• High-fidelity simulation of turbulent flows
• Multi-physics fluid dynamics
• Computational Fluid Dynamics (CFD)
• +۱۰ مورد دیگر

Wojciech Matusik is a Professor of Electrical Engineering and Computer Science at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Computational Design and Fabrication Group and is a member of the Computer Graphics Group. His research spans computer graphics, robotics, and AI-driven manufacturing, with a focus on computational design, tactile sensing, and material science. Matusik holds a PhD in Computer Science from MIT (2003), an MS from MIT (2001), and a BS from UC Berkeley (1997). His work includes groundbreaking projects like differentiable cloth simulation (DiffCloth), AI-enhanced molecular design, and tactile sensing gloves. He has received prestigious awards such as the MIT TR35 (2004), DARPA Young Faculty Award (2012), and Ruth and Joel Spira Teaching Award (2014). Matusik teaches courses on computer graphics, machine learning, and computational fabrication at MIT. Key research themes include: Robotics: Robotic assembly, tactile interaction, and soft robotics Graphics: 3D holography, procedural material generation Manufacturing: Additive fabrication, topology optimization His recent articles explore AI-driven molecular synthesis, holographic displays, and tactile-enabled VR systems. Matusik collaborates on open-source tools like the WiReSens tactile platform and Simit language for sparse systems.

• Computer Graphics
• Computational Design and Fabrication
• Computer Vision
• +۷ مورد دیگر

Dr. Gloria Milena Monsalve Bravo is an Advanced Queensland Industry Research Fellow and lecturer at The University of Queensland's School of Chemical Engineering, where she develops novel multiscale simulation techniques combining molecular simulations with macroscopic physics-based modeling to solve complex energy and environmental problems. Her interdisciplinary work bridges applied mathematics and engineering to improve understanding of phenomena in complex systems across chemical, biomedical, and ecological applications. Her research focuses on: Multiscale simulation techniques for complex systems Molecular simulations coupled with macroscopic modeling Gas permeation and separation in mixed-matrix membranes Uncertainty and sensitivity analysis in mathematical models Applied mathematics for engineering problems Dr. Monsalve Bravo's publication record demonstrates a strong trajectory in membrane technology and computational modeling. Her recent work has advanced understanding of gas transport in novel membrane materials, particularly mixed-matrix membranes, with applications in carbon capture and hydrogen storage. She has made significant contributions to theoretical frameworks for modeling permeation in finite-sized composite systems and developed Bayesian approaches for analyzing parameter uncertainty in sorption predictions. Her research bridges fundamental science with practical applications in energy and environmental engineering. Her scientific contributions have been recognized through research funding including: ARC Research Hub for Value-Added Processing of Underutilised Carbon Wastes (2024-2029) Tailor-made composite membranes for greenhouse gas capture (2023-2026) through Advance Queensland Industry Research Fellowships Dr. Monsalve Bravo actively mentors PhD students on cutting-edge projects related to membrane technology, catalyst development, and waste conversion. She collaborates extensively across disciplines, as evidenced by her diverse publication record spanning chemical engineering, materials science, and environmental applications.

• Multiscale Simulation
• Molecular Simulation
• Gas Permeation
• +۷ مورد دیگر

Bradley Olsen is a Professor of Chemical Engineering at the Massachusetts Institute of Technology (MIT), holding the Alexander and I. Michael (1960) Kasser Chair in Chemical Engineering. He is affiliated with MIT's School of Engineering and directs research in the Plastics and the Environment Program. His academic career spans over two decades with numerous prestigious appointments and recognitions. Olsen earned his S.B. from MIT in 2003 followed by a Ph.D. from the University of California Berkeley in 2007. His educational background is complemented by postdoctoral fellowships including NIH and Beckman Institute Postdoctoral Fellowships (2008-2009) and the Hertz Fellowship (2003-2007). Research Interests Professor Olsen's research focuses on designing materials to address important challenges while understanding the fundamental science necessary for materials design. His primary research areas include block copolymers, soft condensed matter physics, protein-based materials, and bioelectronics. His group specializes in polymer networks, protein-polymer conjugates, self-assembly phenomena, and sustainable polymer development. The research has significant implications for biomaterials, sustainable polymers, and advanced materials design. Publication Trends Analysis of Professor Olsen's recent publications reveals a strong focus on polymer network topology, protein-polymer conjugates, and sustainable materials. His work increasingly integrates computational methods with experimental approaches, particularly in polymer characterization and data science applications to materials science. Recent publications show growing emphasis on biodegradable polymers, polymer informatics, and biomedical applications of advanced materials. Scientific Recognition Professor Olsen has received numerous prestigious awards throughout his career, including: American Physical Society (APS) Fellow (2023) Fulbright Amazonia Scholar (2023) Alexander and I. Michael Kasser Chair in Chemical Engineering (2021) ACS Macro Letters/Biomacromolecules/Macromolecules Young Investigator Award (2021) AIChE Owens Corning Early Career Award (2019) American Physical Society Dillon Medal (2018) Alfred P. Sloan Research Fellow in Chemistry (2014) Advising and Funding Professor Olsen has secured significant research funding from multiple federal agencies including NSF, NIH, AFOSR, and DOE. His group has produced numerous high-impact publications across top journals in polymer science, materials science, and chemistry. He has advised multiple graduate students and postdoctoral researchers who have gone on to successful careers in academia and industry. The MIT OGE's Committed to Caring Honor (2019) recognizes his excellence in graduate student mentoring. Research Infrastructure Professor Olsen leads a research group with capabilities spanning polymer synthesis, protein engineering, materials characterization, and computational modeling. His lab maintains strong collaborations with other MIT departments, national laboratories, and international research institutions. The group participates in several interdisciplinary initiatives including the Plastics and the Environment Program and has developed significant data infrastructure for polymer science through projects like CRIPT and BigSMARTS.

• Polymer Science
• Materials Engineering
• Soft Condensed Matter Physics
• +۶ مورد دیگر

Frank L. Brown is a Professor of Chemistry & Biochemistry at the University of California, Santa Barbara, with a joint appointment in Physics and the Biomolecular Sciences & Engineering (BMSE) program. His research focuses on theoretical and computational studies at the interface of physical chemistry and biophysics, particularly biomembrane dynamics and spectroscopy. Dr. Brown received his B.S. in Chemistry and B.A. in Applied Mathematics from UC Berkeley, followed by a Ph.D. in Physical Chemistry from MIT. He has held postdoctoral appointments at UC San Diego and the University of Chicago before joining UCSB in 2001. He is the recipient of prestigious awards including the Alfred P. Sloan Research Fellowship and the Presidential Early Career Award in Science and Engineering. His laboratory employs tools from statistical mechanics, hydrodynamics, and quantum mechanics to study biomembrane structure, dynamics, and interactions with embedded proteins. Key research areas include lipid bilayer fluctuations, membrane protein diffusion, and interpretation of spectroscopic techniques like single-molecule fluorescence and neutron spin echo. Dr. Brown has mentored numerous graduate students and postdoctoral researchers, with notable alumni including Brian Camley, Max Watson, and Golan Bel. His research is supported by grants from agencies such as the National Science Foundation and the Department of Energy. He directs the Brown Research Group, which collaborates with institutions like the CNSI Center for Scientific Computing. His work bridges computational modeling and experimental biophysics, advancing understanding of membrane systems in health and disease.

• Physical Chemistry
• Biophysics
• Biomembranes
• +۳ مورد دیگر