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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
• +3 more

Shasha Chong is an Assistant Professor of Chemistry at the California Institute of Technology and a Ronald and JoAnne Willens Scholar. She earned her B.S. from the University of Science & Technology of China (2008) and Ph.D. from Harvard University (2014). Her research bridges chemistry, physics, and biology to investigate the molecular mechanisms of cellular processes, focusing on intrinsically disordered regions (IDRs) in transcription proteins. Research Focus: IDRs in transcriptional regulation, cancer biology, liquid-liquid phase separation, and single-molecule imaging techniques. Grants & Awards: CCE Innovation Award (2024), ALSF Innovation Grant, Mallinckrodt Research Grant, Margaret E. Early Medical Research Trust Grant. Collaborations: Caltech-City of Hope Biomedical Research Initiative Grant (2025). Teaching: Co-instructor for courses like Biochemistry Laboratory (Ch 11) and Advanced Topics in Biochemistry (BMB/Bi/Ch 174). Labs & Teams: Leads the Chong Laboratory at Caltech, focusing on interdisciplinary approaches combining single-molecule imaging, genome editing, and bioinformatics.

• Transcriptional Regulation
• Intrinsically Disordered Proteins
• Single-Molecule Biophysics
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Vadim Cherezov, the Ester Dornsife Chair in Biological Sciences and Professor at the University of Southern California (USC), leads groundbreaking research in membrane protein structure and function. Affiliated with the Bridge Institute, Department of Chemistry, and Michelson Center for Convergent Bioscience, his work focuses on GPCRs, ion channels, and transporters—critical targets for drug discovery. His team leverages advanced techniques like Lipidic Cubic Phase (LCP) and Serial Femtosecond Crystallography (SFX) at XFEL facilities to solve high-resolution structures under physiological conditions. Institutional Affiliations: Bridge Institute, USC Michelson Center, Department of Chemistry, Department of Pharmacology and Pharmaceutical Sciences. Key Collaborations: Katritch Lab, Kuhn Lab, NIH, European XFEL. His research explores the role of lipids in modulating GPCR function, addressing diseases like Alzheimer’s, diabetes, and cancer. By solving the structure of the A 2A adenosine receptor via sulfur SAD phasing at XFEL, Cherezov’s lab demonstrated de novo phasing without heavy atoms. This breakthrough enables structural studies of previously intractable membrane proteins. Scientific Awards & Grants: NIH R01 GM108635, U54 GM094618, U54 GM094599, R01 GM095583 Science Signaling Breakthroughs of the Year (2014) Cherezov mentors a dynamic team, including postdocs (e.g., Dong-Gyun Kim), graduate students (e.g., Behnaz Davoudinasab), and alumni (e.g., Benjamin Stauch at Eli Lilly, Nairie Michaelian at Genentech). His lab’s publications span Nature , Science , and Cell , with recent work on Science Advances (2025) addressing ABEL-FRET for GPCR dynamics.

• Membrane Protein Structure and Function
• G Protein-Coupled Receptors (GPCRs)
• Lipid-Protein Interactions
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Professor David Grainger is a faculty member at the University of Birmingham's School of Biosciences, specializing in Molecular Microbiology. He leads the Grainger Lab, focusing on bacterial chromosome biology, pathogenicity, and antibiotic resistance. His research integrates high-throughput techniques and single-molecule analysis to study gene regulation and bacterial pathogenesis. Education: PhD (2004), PGCE (2000), BSc (1999) in Biochemistry from the University of Birmingham. Affiliations: Part of the Institute of Microbiology and Infection (IMI), collaborating with experts in genomics, proteomics, and structural biology. Research Interests: Deciphering chromosome biology of pathogenic bacteria, including transcriptional regulation, toxin production control, and antibiotic resistance pathways. Utilizes cutting-edge methods like Hi-C for 3D chromatin analysis and single-molecule microscopy. Recent Articles: Focused on transposon capture mechanisms, bacterial promoter diversity, and quorum sensing signaling. Highlights include studies on Salmonella regulons and Vibrio cholerae biofilm suppression. Awards: Wellcome Trust Career Development Fellowship (2008), Runner-up in 'Science Snaps' competition for scientific communication. Grants: Career Development Fellowship-funded establishment of his research group at the University of Warwick (2008). Labs/Teams: Grainger Lab at the University of Birmingham, part of the IMI network. Engages in public science outreach via Twitter and lab website.

• Bacterial Chromosome Biology
• Pathogenicity
• Antibiotic Resistance Mechanisms
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Frank L. H. Brown is a Professor at the University of California, Santa Barbara with joint appointments in the Department of Physics and Department of Chemistry and Biochemistry. His research focuses on theoretical and computational approaches to understanding biomembrane dynamics and related biophysical phenomena, situated within the College of Letters and Science. Dr. Brown's research interests span the interface between physical chemistry and biophysics. He employs a variety of theoretical tools including statistical mechanics , hydrodynamics , elasticity theory , and quantum mechanics to study complex biological systems. His work particularly emphasizes the dynamics and structure of biomembranes and the interpretation of various spectroscopy experiments including single molecule fluorescence, neutron spin echo, and flicker spectroscopy. Analysis of his publication record reveals a consistent focus on computational modeling of lipid bilayers, membrane proteins, and related phenomena, with particular emphasis on developing novel theoretical frameworks for understanding membrane behavior across multiple scales. Dr. Brown leads an active research group that includes current members Ehsan Noruzifar (Postdoctoral Researcher) and Sean Cray (Graduate Student). His former group members include numerous successful scientists such as Grace Brannigan, Brian Camley, Lawrence Lin, and Max Watson who completed their graduate studies under his supervision, along with several postdoctoral researchers. His research has been supported by funding that enables theoretical and computational investigations of biomembrane systems. The Brown Research Group operates at the intersection of physics, chemistry, and biology, with facilities connected to the Biomolecular Sciences & Engineering Program and the California NanoSystems Institute (CNSI) at UCSB. Their work combines advanced computational techniques with theoretical physics to address fundamental questions about soft and living matter systems, particularly at biological interfaces.

• Theoretical Biophysics
• Physical Chemistry
• Statistical Mechanics
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Markus Heinonen is an Academy Research Fellow at Aalto University's Department of Computer Science within the School of Science. His academic position is tied to Harri Lähdesmäki's Professorship, focusing on probabilistic machine learning. He holds a Doctoral degree in Engineering and Technology from the University of Helsinki (2013). His research integrates probabilistic modeling , deep learning , and differential equations , with applications in computational biology, drug discovery, and biophysics. Key themes include Gaussian processes, Bayesian inference, generative models, and their use in understanding complex biological systems like immune cell behavior (e.g., T cell receptor analysis in aplastic anemia) and molecular design. He leads major projects such as the Deep Learning with Differential Equations initiative (2020–2025), exploring continuous-time models and physics-informed neural networks. His work bridges theory and application, evidenced by collaborations in diffusion models , optimal transport , and single-cell analysis . Publications span over 65 peer-reviewed outputs, with recent emphases on robust neural network training, multi-target molecular prediction, and interpretable drug design frameworks. His research contributes to UN Sustainable Development Goal 3 (Good Health) through advancements in disease modeling and therapeutic development. He has undertaken visiting research roles at the University of California, San Francisco (2017) and Telecom ParisTech (2013–2014). Media highlights include recognition for work on TCR-epitope prediction and AI-driven enzyme engineering.

• Machine Learning
• Probabilistic Modeling
• Bayesian Inference
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Matthew Lakin is an Associate Professor with tenure in the Department of Computer Science at the University of New Mexico, with a courtesy appointment in the Department of Chemical & Biological Engineering. He is affiliated with the UNM Center for Biomedical Engineering and the School of Engineering, and collaborates extensively with the UNM Health Sciences Center and external institutions. Education: Ph.D., Computer Science, University of Cambridge, 2010 M.A. (Cantab), University of Cambridge, 2009 B.A. (Hons), Computer Science, University of Cambridge, 2005 Dr. Lakin's research focuses on molecular computing, DNA nanotechnology, synthetic biology, and formal verification of biomolecular circuits. He develops computational models and experimental systems for programmable biological devices, especially using heterochiral DNA to enhance stability in living cells. His work spans software tools for biodesign and experimental validation in mammalian systems, with applications in nanomedicine and biosensing. The recent publications highlight a strong trend in engineering robust, intelligent biomolecular systems. His work integrates machine learning concepts into chemical reaction networks, advances geometric modeling of DNA systems, and pioneers L-DNA-based circuits for intracellular applications. The research spans theoretical foundations, software tools, and wet-lab experimentation, emphasizing interdisciplinary innovation. Scientific Awards: Presidential Early Career Award for Scientists and Engineers (PECASE), 2025 NSF CAREER Award, 2021 UNM School of Engineering Junior Faculty Research Excellence Award, 2021 Multiple student awards under his mentorship, including the Outstanding Graduate Student Award and DNA28 Best Student Presentation recognition Dr. Lakin has advised numerous graduate and undergraduate students, including Ph.D. graduates in Biomedical Engineering and Computer Science. He leads major funded projects such as the NSF CAREER grant on heterochiral molecular computing, an EPSCoR Research Fellowship, and a $3M NSF grant on heavy metal biosensing in collaboration with Native American communities. He is also PI on multiple NSF grants related to synthetic cells and nucleic acid technologies. He directs the Lakin Lab for Programmable Biology, which operates within the Department of Computer Science and collaborates with Chemical & Biological Engineering and the Center for Biomedical Engineering. The lab emphasizes both computational modeling and experimental molecular biology, and runs an NSF-funded biotechnology summer camp in partnership with ¡Explora! science museum to strengthen STEM education in New Mexico.

• Molecular Computing
• DNA Nanotechnology
• Synthetic Biology
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André Bardow is a Full Professor at the Department of Mechanical and Process Engineering, ETH Zürich. His research focuses on energy systems optimization, life cycle assessment, computer-aided molecular design, and CO2 capture/utilization. Professor (ETH Zürich, 2020–present) Head of Institute of Technical Thermodynamics (RWTH Aachen University, 2010–2020) Visiting Professor (University of California, Santa Barbara, 2015/16) Part-time Director (Forschungszentrum Jülich, 2017–2022) Associate Professor (TU Delft, 2007–2010) Research Interests: His work spans energy and process systems engineering, with emphasis on sustainable technologies. Key areas include: Computer-aided molecular and process design Machine learning for chemical engineering Carbon capture and utilization (CCU) Life cycle assessment (LCA) of industrial processes Thermo-economic modeling of energy systems Multiphase equilibrium analysis Publication Trends: Recent articles focus on integrating machine learning with process design, optimizing CO2 capture in steel production, and advancing electrochemical cooling technologies. Subfields include sustainable plastics, ORC working fluids, and solvent mixture design. Scientific Awards: Fellow of the Royal Chemical Society Recent Innovative Contribution Award (EFCE, 2019) PSE Model-Based Innovation Prize (2018) Covestro Science Award (first recipient) Arnold-Eucken-Award (VDI-GVC) Highly Cited Researcher (Clarivate, 2024) Advising and Grants: Professor Bardow mentors students in process optimization and leads projects like Systemic expansion of territorial CIRCULAR Ecosystems for end-of-life FOAM (Grant 101036854, EC).

• Energy systems optimization
• Conceptual process design
• Life cycle assessment
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Christoph Bostedt holds dual appointments as a Professor of Physical Chemistry at the Ecole Polytechnique Fédérale de Lausanne (EPFL) and as Head of the Laboratory for Synchrotron Radiation and Femtochemistry (LSF) at the Paul Scherrer Institut (PSI). He leads strategic operations for the LSF, managing five research groups and overseeing four beamlines at the Swiss Light Source and the Alvra Endstation at SwissFEL. His research focuses on ultrafast x-ray science, including single-shot imaging, non-linear x-ray spectroscopy, and femtosecond pump-probe techniques. He collaborates globally on initiatives like the Athos project, aiming to advance ultrafast x-ray technologies. Bostedt has over 150 publications and is a Fellow of the American Physical Society, recipient of the Röntgen Prize. Education: Ph.D. from the University of Hamburg with research at Lawrence Livermore and Berkeley National Laboratories. Prior roles include leadership at Argonne National Laboratory and SLAC National Accelerator Laboratory. Research Interests: Single-particle imaging and coherent diffraction X-ray free-electron laser applications Ultrafast dynamics in nanoparticles and molecular systems Non-linear x-ray spectroscopy Time-resolved x-ray pump-probe methods Awards: Fellow of the American Physical Society Röntgen Prize (University of Giessen) Labs & Projects: Spearheads the Athos beamline project at SwissFEL, developing the Maloja endstation for ultrafast x-ray studies. Oversees the Laboratory for Femtochemistry and collaborates on advanced imaging techniques for nanoscale science.

• Free-Electron Lasers
• Ultrafast X-ray Spectroscopy
• Nanoparticle Dynamics
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Kathrin Lang is a Full Professor at the Department of Chemistry and Applied Biosciences, ETH Zurich, and Head of the Organic Chemistry Laboratory. Her research focuses on chemical biology, particularly the development of tools for genetic code expansion to incorporate non-canonical amino acids into proteins and advance bioorthogonal chemistries for studying biological processes. Keywords: Genetic Code Expansion, Bioorthogonal Chemistry, Protein Engineering, Ubiquitylation Networks, Post-Translational Modifications. Lang’s work emphasizes proximity-triggered crosslinking reactions, bioorthogonal labeling, and in vivo chemistries to address challenges in protein interaction mapping and structural elucidation. Her group’s recent publications highlight methodologies for dual protein labeling, deciphering ubiquitin code, and enhancing cycloaddition reactivity. Current projects include exploring cyclopropene-fused dibenzocyclooctynes for improved labeling and investigating methylated lysine as a conformational regulator in Hsp90. Funding sources include the ERC (Ubl-tool), DFG (SFB1035, SPP1926), and ETH Zurich. She contributes to education through courses like Genetic Code Expansion for Studying Posttranslational Modifications and Chemical Biology and Synthetic Biochemistry . Collaborative efforts span structural biology, microbiology, and synthetic biochemistry, with applications in ubiquitin research and cellular imaging.

• Chemical Biology
• Protein Engineering
• Genetic Code Expansion
• +3 more

Xi Ling is an Associate Professor in the Department of Chemistry and Materials Science & Engineering at Boston University. They lead the Ling Group, which focuses on the fundamental science and applications of nanomaterials, particularly 2D van der Waals materials. Their research integrates synthesis, characterization via advanced spectroscopy, and device development for energy conversion and chemical sensing. The group utilizes facilities at the Photonics Center for cutting-edge materials analysis. Education: B.A. in Chemistry (Lanzhou University, 2007); Ph.D. in Physical Chemistry (Peking University, 2012). Research emphasizes interdisciplinary approaches to synthesize novel 2D crystals, investigate their physical properties through Raman and photoluminescence spectroscopy, and engineer flexible, transparent devices. Recent publications highlight innovations in strain engineering, ferroelectricity modulation, and exciton dynamics in materials like NiPS3 and GaSe. Students gain expertise applicable to academia and industry roles in semiconductor manufacturing, materials engineering, and instrumentation. The group’s work bridges foundational science and practical applications, addressing challenges in nanoelectronics and sustainable energy technologies.

• Nanomaterials
• 2D Materials Synthesis
• Advanced Spectroscopy Techniques
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David Patterson is an Associate Professor in the Department of Physics at the University of California Santa Barbara (UCSB). His research focuses on extending atomic, molecular, and optical (AMO) physics tools to prepare polyatomic molecules in single quantum states for the first time. Key areas of interest include quantum information systems, high precision spectroscopy, parity violation in molecular spectra, and single-molecule chemical/chiral analysis. Developed single molecule inelastic recoil spectroscopy for ultraprecise analysis Created non-destructive single molecule infrared spectroscopy (2022 breakthrough) Engineered a "ping-pong" double-well ion trap for Sr+ atoms and molecular ions Collaborates with the Schlemmer group (Cologne) on Doppler-limited spectroscopy The group's work aims to achieve unprecedented resolution (2-3 orders of magnitude improvement) in polyatomic molecule spectroscopy, addressing fundamental questions in nanoanalytics.

• Quantum Information Systems
• High Precision Spectroscopy
• Parity Violation in Molecular Spectra
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Maizie Zhou is an Assistant Professor in Biomedical Engineering and Computer Science at Vanderbilt University’s School of Engineering. She holds dual PhDs in Computer Science (Stanford University) and Neuroscience (Wake Forest School of Medicine), with additional degrees from Wake Forest University and Huazhong University of Science and Technology. Her research focuses on computational genomics, bioinformatics, and machine learning applied to problems in cancer genomics, single-cell and spatial transcriptomics, and computational neuroscience. She leads the Zhou Lab, which develops algorithms for structural variant detection, neural circuit analysis, and integrative omics approaches. Recent work includes tools like VolcanoSV and stDyer, and she has received grants from NIH, Vanderbilt Brain Institute, and industry partnerships. Key achievements include VUSE Best Paper Awards, Global Engagement Travel Grants, and mentoring students in prestigious programs like the Provost’s Pathbreaking Discovery Award. Her lab also explores the neural underpinnings of cognitive maturation in primates, combining computational and experimental neuroscience. Education: PhDs in Computer Science (Stanford) and Neuroscience (Wake Forest), MS (Computer Science, Wake Forest), BS (Biotechnology, Huazhong). Research interests span computational genomics (e.g., structural variant detection, haplotype phasing), spatial transcriptomics (clustering, integration), and computational neuroscience (neural circuit dynamics, prefrontal cortex plasticity). Her lab’s tools address challenges in precision medicine, cancer genomics, and understanding adolescent brain development. Recent projects include NIH-funded work on spatial transcriptomics and collaborations with Dr. Meltzer’s lab on cancer genomics. Publications highlight advancements in bioinformatics tools and neural mechanisms, with trends toward multi-omics integration and algorithmic innovation in genomics. Awards include the Global Engagement Travel Grant and CCSB Accelerator Fund. Students under her mentorship have excelled in qualifying exams and travel grants, reflecting her impactful training program.

• Computational Genomics
• Bioinformatics
• Computational Neuroscience
• +6 more

Suliana Manley is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Basic Sciences and the Laboratory of Experimental Biophysics . She also holds teaching and research roles in EPFL's School of Life Sciences and Swiss Plasma Center , focusing on interdisciplinary biophysical studies. Education : PhD in Physics (2004), Harvard University Bachelor's in Physics & Mathematics (1997), Rice University Manley's research centers on super-resolution fluorescence imaging , single-molecule tracking , and quantitative biophysics . Key themes include: Understanding protein assembly dynamics at cellular membranes Elucidating viral assembly mechanisms (e.g., HIV-Gag) Developing 3D imaging algorithms and high-density data reconstruction tools like PALMsiever and FALCON Quantifying nanoscale organization in systems like telomeres and centrioles Her work bridges optical physics , computational image analysis , and cellular biology , with notable Nature and PNAS publications. Collaborations span bioengineering , genetics , and medical research . Scientific Awards : Featured in Nature Methods Research Highlights (3x) Very Important Paper and Cover Article (ChemBioChem, 2012) Postdoctoral Fellow, NIH and MIT Advising & Collaborations : Current PhD students in biophysics, cellular biology, and bioengineering Former students: Anna Archetti, Aleksandr Benke, Andrea Callegari, and others Co-founder of tools for high-density super-resolution microscopy and live-cell imaging Labs & Teams : Leads the Laboratory of Experimental Biophysics at EPFL, integrating physics-based methods into biological questions. The lab focuses on quantitative imaging , computational modeling , and software development for nanoscale analysis.

• Super-resolution Microscopy
• Single-molecule Biophysics
• Protein Assembly
• +5 more

Jian Peng is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. His research focuses on computational biology, machine learning, and their applications to protein structure prediction, drug design, and molecular modeling. He has contributed to advancements in antibody engineering, protein-ligand docking, and generative models for biological systems. Key research areas include: Machine Learning for Molecular Modeling Protein Structure Prediction Antibody and Peptide Design Genomics and Single-Cell Analysis Structure-Based Drug Discovery His work emphasizes integrating deep learning techniques with biological datasets to address challenges in precision medicine, drug development, and systems biology. Notable achievements include developing the FastFold system to accelerate AlphaFold training and pioneering flow-based methods for antibody design. Awards include the Overton Prize (2020), recognizing contributions to computational biology. His research has been published in top journals and conferences, spanning topics from protein mutation prediction to geodesic-based immune complex modeling.

• Machine Learning
• Bioinformatics
• Computational Biology
• +5 more