Ejung Moon is a Group Leader in Radiation Biology and the Tumour Microenvironment at the Department of Oncology, University of Oxford's Medical Sciences Division. Her research focuses on hypoxia-driven tumor progression and radiation response mechanisms. Education: PhD in Pharmacology and Cancer Biology from Duke University Training: Postdoctoral work with Amato Giaccia at Stanford University Research Interests: Elucidating how hypoxia-induced MAFF protein regulates tumor cell invasion, metastasis, and radiation resistance through antioxidant response pathways. Current work explores MAFF dimerization dynamics and metabolic reprogramming in hypoxic tumors. Scientific Contributions: Identified MAFF's role in radiation-induced antioxidant gene regulation (2021, Nature Communications ). Recent studies investigate iron metabolism's impact on FLASH radiotherapy effects. Scientific Awards: Breast Cancer Research Program (BCRP) predoctoral fellowship Laboratory & Collaborations: Moon Lab collaborates with Oxford Cancer and NHS Cancer and Haematology Centre. Key partnerships include Stanford University's radiobiology research groups.
Jennifer Ross is a Professor of Physics and Associate Dean for Creativity, Scholarship, and Research at the College of Arts & Sciences of Syracuse University . As a biophysicist, she investigates how cells organize their interiors through self-assembly and active matter principles, focusing on the microtubule cytoskeleton and enzyme-driven systems using single-molecule imaging . Her research bridges fundamental physics with biological organization . Education: Ph.D. in Physics, University of California, Santa Barbara (2004) B.A. in Physics and Mathematics, Wellesley College (2000) Research Focus: Self-organization of cytoskeletal networks Active matter dynamics in biological systems Motor protein interactions and cargo transport Programming circadian materials via biomolecular systems Microtubule severing mechanisms Recent Article Trends: 2025 studies explore kinesin-driven cytoskeletal composites, urease-DNA origami engineering, and crosslinker-regulated network mechanics 2024-2023 work examines ionic strength effects on microtubules, programmable circadian materials, and motor-cargo dynamics Earlier studies analyze actin-microtubule composites, liquid crystal phase control, and severing enzyme mechanisms Scientific Awards: Fellow of the American Physical Society (APS) and American Association for the Advancement of Science (AAAS) Cottrell Scholar (2025) and STAR Award Margaret Oakley Dayhoff Award (Biophysical Society) Grants: Leads multiple NSF, Sloan Foundation, and Research Corporation grants for projects like "Energy and Entropy Sculpting" and "Explorations: SUPER-Tech SHIP" . Teaching: Offers courses in experimental physics, microscopy, and biophysics, including a globally adopted hands-on microscope-building curriculum. Lab: Heads the Bio-Active Matter Lab , studying how cells harness noisy systems for autonomous organization.
Arman Cohan is an Assistant Professor in the Department of Computer Science at Yale University, where he leads the Yale NLP Lab since its founding in January 2023. His research spans natural language processing and machine learning with emphasis on language modeling, representation learning, retrieval systems, and specialized domain applications including scientific discovery and AI for science. His primary research interests include: Natural Language Processing Machine Learning Large Language Models Information Retrieval AI for Science Scientific Problem-Solving Recent publications (2025) demonstrate intense focus on evaluating and advancing LLM capabilities across multimodal reasoning, scientific claim verification, financial domain applications, and biological modeling. The lab consistently produces high-impact work accepted at top-tier conferences including ACL, EMNLP, and ICLR, with 11 papers at ACL 2025 alone. Scientific awards include: Best Paper Award at AI4Research Workshop (IJCAI 2024) Outstanding Paper Award at EACL 2023 Best Paper Award at ACL 2024 for Olmo language model research Professor Cohan actively advises PhD students including Kaili Liu, Jacob Dunefsky, Alan Li, Yilun Zhao, and has graduated researchers such as Linyong Nan (now at Zoom) and Ansong Ni (now at Meta). The lab maintains strong industry partnerships and receives substantial research funding as evidenced by its prolific output and conference presence. The Yale NLP Lab hosts the annual New England NLP Workshop and regularly features speakers from leading institutions including Meta AI, Allen Institute for AI, and DeepMind, fostering a collaborative environment for advancing NLP research.
Swiss Federal Institute of Technology in LausanneSwitzerland
Didier Trono is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Laboratory of Virology and Genetics (LVG) within the School of Life Sciences. Formerly, from 2004 to 2012, he served as the founding dean of EPFL's Faculty of Life Sciences, orchestrating its development and growth during this formative period. Trono received his medical education at the University of Geneva, followed by clinical training in pathology, internal medicine, and infectious diseases in Geneva and at Massachusetts General Hospital in Boston. His scientific career began at the Whitehead Institute of MIT, and in 1990 he was recruited by the Salk Institute of San Diego to launch an AIDS research center. After seven years in the United States, he returned to Europe and eventually joined EPFL. Dr. Trono's research has evolved significantly over his career. Initially focusing on virus-host interactions, he studied pathogens like HIV and Hepatitis B virus, creating HIV-derived genetic transfer tools that are now successfully used in gene therapy. For approximately the last fifteen years, his research has centered on epigenetics, particularly exploring the impact of retroelements and their control mechanisms on development and physiology of higher organisms, including humans. His laboratory investigates how transposable elements and KRAB zinc finger proteins regulate gene expression, with important implications for understanding cancer biology and developing new diagnostic and therapeutic approaches. Analysis of his recent publications (2022-2024) reveals a strong focus on transposable elements, KRAB zinc finger proteins, and their roles in gene regulation and cancer. His work combines molecular biology, genomics, and bioinformatics approaches to understand how these ancient viral remnants have been co-opted by the host genome to regulate development and cellular functions. The research spans basic molecular mechanisms to potential clinical applications in cancer diagnosis and therapy, with some recent work also addressing SARS-CoV-2 and immune responses. Throughout his career, Professor Trono has mentored numerous PhD students, including Bojkowska Karolina, Brandão Sanches Vong Martins Filipe Amândio, Bulliard Yannick, Coluccio Andrea, Corsinotti Andrea, Coudray Alexandre, De Tribolet-Hardy Jonas Caspar, and Dorschel Iris Arianna. His laboratory has received significant funding to support research at the intersection of virology, genetics, and epigenetics, contributing to EPFL's reputation as a leading institution in life sciences research. The Trono Laboratory continues to be at the forefront of research on retroelements and their regulatory mechanisms, maintaining a vibrant research environment that bridges fundamental biological questions with potential medical applications, particularly in cancer research and precision medicine.
Massachusetts Institute of TechnologyUnited States
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.
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).
Harris H. Wang is an Associate Professor in the Department of Systems Biology and Department of Pathology and Cell Biology at Columbia University's Vagelos College of Physicians and Surgeons, where he also serves as Interim Chair of Systems Biology. He is affiliated with the Center for Computational Biology and Bioinformatics (C2B2) and the Integrated Program in Cellular, Molecular and Biomedical Studies (CMBS). B.S., Physics and Mathematics, MIT Ph.D., Biophysics, Harvard University Dr. Wang's research lies at the intersection of systems and synthetic biology, focusing on developing foundational technologies for genome engineering, microbiome manipulation, and synthetic genomics. His lab pioneers methods such as MAGE, MAGIC, CAST, and CAMII to enable high-throughput genetic manipulation, in situ microbiome engineering, and AI-driven microbial culturomics. Key research themes include understanding microbial community dynamics, engineering cellular memory systems, designing biocontained genetic circuits, and applying synthetic biology to human health challenges in personalized medicine and infectious disease. His recent publications reveal a strong trend in spatial and functional metagenomics, CRISPR-based microbiome editing, and synthetic biology tools for data storage and genetic stability. The articles span high-impact journals like Nature , Science , and Nature Biotechnology , reflecting his leadership in developing scalable, programmable biological systems. Scientific Awards: NIH Director’s Early Independence Award Forbes 30 Under 30 in Science Sloan Research Fellowship NSF CAREER Award ONR Young Investigator Award Burroughs Wellcome Fund PATH Award Schaefer Scholar Blavatnik National Award Vilcek Prize PECASE Dr. Wang has advised numerous PhD and postdoctoral researchers, many of whom have gone on to independent scientific careers. His lab is supported by major grants from NIH, NSF, DARPA, DOE, and foundations including the Bill & Melinda Gates Foundation and CZ Biohub NY. He is actively involved in educational initiatives, including organizing Columbia’s iGEM team and the Cold Spring Harbor Laboratory Synthetic Biology course. The Wang Lab is based at the Columbia University Irving Medical Center and is part of national consortia such as the Engineering Biology Research Consortium (EBRC) and the Genome Project-Write (GP-Write) initiative. The lab develops and applies cutting-edge technologies in automation, machine learning, and synthetic biology to engineer microbiomes for applications in medicine, global health, and climate change.
Professor Maria Craig is a distinguished academic and researcher at the University of New South Wales, Faculty of Medicine & Health, specializing in childhood diabetes research. She holds a prominent position as a Professor with extensive contributions to the field of pediatric endocrinology and diabetes, particularly focusing on type 1 diabetes in children. Professor Craig's educational background includes: MB BS from the University of Melbourne MMedSc(ClinEpid) from the University of Newcastle PhD from the University of Sydney FRACP (Fellow of the Royal Australasian College of Physicians) Professor Craig's research primarily focuses on childhood diabetes, with special emphasis on prediction and prevention of type 1 diabetes. She has a significant interest in the association between viruses and type 1 diabetes, collaborating with the Virology Research Group at Prince of Wales Hospital (POWH). Together with Professor Bill Rawlinson, she leads the viral theme for the multicentre ENDIA study (endia.org.au). As principal investigator for the CoRD trial, she is conducting a world-first phase 1 study using autologous cord blood for prevention of type 1 diabetes in children with islet autoimmunity. Additionally, she serves as principal investigator for the Australasian Diabetes Data Network (ADDN). Her research portfolio also encompasses the epidemiology of various forms of childhood diabetes (type 1, type 2, cystic fibrosis related diabetes and monogenic diabetes) and diabetes complications, in collaboration with Professor Kim Donaghue at the Children's Hospital at Westmead. Professor Craig's extensive publication record, including over 361 journal articles, demonstrates her leadership in advancing our understanding of childhood diabetes. Her recent work shows increasing focus on early detection methods, risk prediction models, technological interventions for diabetes management, and the complex interplay between viral infections and autoimmune diabetes development. She has been instrumental in developing clinical practice guidelines through her role as co-editor of the International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines. Professor Craig has received numerous prestigious awards recognizing her contributions to pediatric endocrinology and diabetes research: Australian Paediatric Endocrine Group Young Investigator's Award (1997) Asia Pacific Paediatric Endocrine Society Clinical Teaching Award (2008) Lifetime Honorary Member, Caring and Living as Neighbours (2013) Australian Diabetes Society Jeff Flack Diabetes Data Award (2019) Australian Paediatric Endocrine Group Norman Wettenhall Award for Research and Innovation (2019) Throughout her career, Professor Craig has demonstrated exceptional leadership in professional societies, having served as former president/treasurer of the Australasian Paediatric Endocrine Group (APEG) and currently as Scientific Convenor of the Asia Pacific Paediatric Endocrine Society Fellows school. Her work with the ENDIA study and Australasian Diabetes Data Network represents significant collaborative research efforts involving multiple institutions across Australia and internationally. Her principal investigator roles for major studies indicate substantial research funding support. Professor Craig leads several important research initiatives including the ENDIA study, the CoRD trial, and the Australasian Diabetes Data Network. These programs involve multidisciplinary teams of researchers, clinicians, and support staff working collaboratively to advance understanding and treatment of childhood diabetes. Her work at the intersection of virology and diabetes represents a unique and innovative approach to understanding the environmental triggers of type 1 diabetes.
Romain Lopez is an Assistant Professor of Computer Science and Biology at New York University, with a joint appointment in the Courant Institute of Mathematical Sciences and the Department of Biology. He will be joining NYU in September 2025, bringing expertise at the intersection of machine learning and computational biology. Prior to joining NYU, he was a Postdoctoral Fellow at Genentech and Stanford Medicine from 2021 to 2025, working with Jonathan Pritchard and Aviv Regev. Dr. Lopez received his educational training at prestigious institutions: PhD in Computer Science (2021) from the University of California, Berkeley, advised by Mike Jordan and Nir Yosef M.S. in Applied Mathematics (2016) from École polytechnique, Palaiseau, France Dr. Lopez's research focuses on developing machine learning methods to understand biological systems at the cellular level. His work bridges computational techniques with biological applications, particularly in single-cell and spatial omics analysis. He pioneered probabilistic approaches for single-cell analysis with scVI and co-developed scvi-tools, now widely adopted tools in the field. His research spans deep generative models, causal inference, perturbation modeling, and representation learning for biological data. His publication record demonstrates a consistent trajectory of innovation in computational biology, with recent work focusing on spatial biology, disentangled representations of cellular perturbations, and causal modeling of cellular responses. He has made significant contributions to the field of single-cell analysis, developing methods that help scientists interpret complex cellular data and predict how cells respond to various perturbations. Dr. Lopez has received numerous honors and awards for his research: Best Paper Award from the ICML Workshop on AI for Science (2024) Best Paper Award Honorable Mention from the AAAI Conference on Artificial Intelligence (2021) Best Student Poster Award from the ICML Workshop on Computational Biology (2019) UC Berkeley EECS Departmental Graduate Fellowship (2016) Carnot Foundation Fellowship (2016) Monahan Foundation Fellowship (2016) French National Defence Medal, Bronze Echelon (2014) At NYU, Dr. Lopez will lead the Biological Machine Learning group, which develops probabilistic machine learning methods to uncover biological mechanisms governing cellular behavior and disease. His lab focuses on creating tools that transform complex cellular data into biological insights, with applications in understanding cancer, immune responses, and fundamental cellular processes. His work has significant implications for precision medicine and drug discovery.
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.
California Institute of Technology (Caltech)United States
Richard M. Murray is the Thomas E. and Doris Everhart Professor of Control and Dynamical Systems and Bioengineering at the California Institute of Technology (Caltech). He holds a B.S. from Caltech (1985), M.S. from UC Berkeley (1988), and Ph.D. from UC Berkeley (1990). He has served in academic roles from Assistant Professor (1991–1997) to his current endowed professorship. He chaired the Engineering and Applied Science division (2000–2005) and Biology and Biological Engineering (2020–2024). His research focuses on feedback control in biological and autonomous systems, synthetic cells, and networked control systems. Collaborators include experts in robotics, synthetic biology, and systems biology. Key awards include the IEEE Control Systems Award and election to the National Academy of Engineering. His educational contributions span courses on control systems, robotics, and bioengineering. Current research projects include the Developer Cell initiative (Sloan Foundation), layered testing for autonomous systems (AFOSR), and microbiome-based environmental solutions (CHARMME, ARO). He advises numerous graduate students and postdocs, with notable alumni in academia and industry. Labs include facilities in Keck and Steele laboratories at Caltech. His work bridges control theory, synthetic biology, and autonomous systems to address societal challenges like environmental monitoring and safe autonomy.
Memorial Sloan Kettering Cancer CenterUnited States
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.
Jonas Fischer is the head of the Explainable Machine Learning group at the Max Planck Institute for Informatics, Department of Computer Vision and Machine Learning. His research focuses on interpreting complex machine learning models, particularly in genomics and healthcare, aiming to enhance robustness and alignment with human decision-making. Prior to his role at MPI, he was a postdoctoral fellow at Harvard University's Department of Biostatistics, where he worked on interpretable models for gene regulatory systems in cancer. Education: PhD in Computer Science from Saarland University (2022), with a thesis titled More than the sum of its parts , exploring the intersection of pattern mining and deep learning. He has contributed to advancing methods in neural network pruning, federated learning, and low-dimensional embeddings (e.g., dtSNE, Mercat). His work bridges computational biology, data mining, and machine learning, with applications in DNA methylation analysis, graph-based differential networks, and biomedical informatics. Key research areas include: (1) Explainable AI and neural network interpretability, (2) Biomedical applications of machine learning (e.g., gene regulatory networks, cancer genomics), (3) Low-dimensional embeddings and visualization techniques, (4) Federated learning for privacy-preserving collaborative models, and (5) Pattern mining for error analysis in NLP and classification tasks. Publications span top venues like NeurIPS, ICLR, Bioinformatics, and Genome Biology. His group develops tools such as BONOBO for omics data integration and node2vec2rank for scalable graph analysis. He actively collaborates with biomedical researchers to address challenges in data-driven healthcare and precision medicine.
Hang Lu is a Professor and holds the Cecil J. "Pete" Silas Chair of Chemical & Biomolecular Engineering at the Georgia Institute of Technology. Dr. Lu also holds a Love Family Professorship and leads the Lµ Fluidics Group, which focuses on engineering microfluidic systems and machine learning tools to address complex questions in neuroscience, developmental biology, and cell biology that are difficult to address with conventional techniques. Dr. Lu's research lies at the intersection of engineering and biology, with primary interests including: Microfluidic systems for high-throughput screens and image-based genetics and genomics Systems biology: large-scale experimentation and data mining Microtechnologies for optical stimulation and optical recording Big data, machine vision, and automation Developmental neurobiology, behavioral neurobiology, and systems neuroscience Cancer biology, immunology, embryonic development, and stem cells Her laboratory engineers microfluidic devices and BioMEMS to study neuroscience, genetics, cancer biology, and biotechnology. These miniaturized Lab-on-a-chip tools operate at scales comparable to biological systems, leveraging unique micro and nano-scale phenomena to gather large-scale quantitative data about complex biological systems. Current projects include Microfluidics for Life Sciences, Optical Neuron Recordings and Manipulations, Machine Learning Tools for Neuroscience, Measuring and Modeling Behavior, and High-throughput, High-content Cell-based Assays. Analysis of Dr. Lu's recent publications (2024-2025) reveals a strong trend toward integrating microfluidics with advanced computational methods: Development of deep learning frameworks for biological image analysis Advanced neuron tracking and functional imaging techniques Non-invasive characterization of 3D organoid cultures Sophisticated neuromechanical modeling of locomotion Microfluidic temperature control systems for in vivo studies Label-free imaging pipelines for neural development Dr. Lu's significant professional honors include: Cecil J. "Pete" Silas Chair of Chemical & Biomolecular Engineering Love Family Professorship The Lµ Fluidics Group actively mentors students and postdocs, currently accepting new postdoctoral researchers. The lab receives substantial funding for interdisciplinary projects at the engineering-biology interface, with research implications spanning fundamental biological understanding to therapeutic development. The group operates within Georgia Tech's School of Chemical & Biomolecular Engineering, with specialized facilities for microfluidic device fabrication, biological experimentation, and advanced imaging, maintaining strong collaborative ties across engineering, neuroscience, and biological disciplines.
Sriram Subramaniam is a Professor in the Department of Biochemistry and Molecular Biology at the University of British Columbia (UBC) and holds the Gobind Khorana Canada Excellence Research Chair in Precision Cancer Drug Design. His research leverages cryo-electron microscopy (cryo-EM) to advance structural biology and drug design, focusing on protein dynamics and therapeutic target identification. Education: PhD in Physical Chemistry (1987) from Stanford University; MSc in Chemistry (1981) from Indian Institute of Technology, Kanpur. Subramaniam's interdisciplinary work combines cryo-EM with computational tools and molecular biology to study protein structures at atomic resolution. His lab has pioneered cryo-EM applications in precision medicine, including mapping small molecule drugs on patient-specific cancer mutants. Recent publications (2024-2022) highlight his contributions to understanding SARS-CoV-2 immune evasion, structural mechanisms of ATPases, and AI integration in structural biology. His research spans viral entry mechanisms, CRISPR systems, and neurodegenerative disease pathways. Scientific Awards: Gobind Khorana Canada Excellence Research Chair NIH Director’s Award for Scientific Excellence Fellow of the Biophysical Society Breakthrough Prize nomination Based at the Djavad Mowafaghian Center for Brain Health, Subramaniam leads the Program in Cryo-EM Guided Drug Design, contributing to over 177 peer-reviewed publications with a career h-index of 58 and citations exceeding 12,340.
