Talk Title: Information in images for drug discovery: image-based profiling

Start Time: 9:40 AM PDT
Speaker: Inti Zlobec, Professor, University of Bern.

Abstract: Cell images contain a vast amount of quantifiable information about the status of the cell: for example, whether it is diseased, whether it is responding to a drug treatment, or whether a pathway has been disrupted by a genetic mutation. We extract hundreds of features of cells from images. Just like transcriptional profiling, the similarities and differences in the patterns of extracted features reveal connections among diseases, drugs, and genes. Improving this pipeline is an active area of research, from feature extraction to batch correction to quality control to assessing similarities. We are harvesting similarities in image-based profiles to identify, at a single-cell level, how diseases, drugs, and genes affect cells, which can uncover small molecules’ mechanism of action, discover gene functions, predict assay outcomes, discover disease-associated phenotypes, identify the functional impact of disease-associated alleles, and find novel therapeutic candidates. As part of the JUMP-Cell Painting Consortium (Joint Undertaking for Morphological Profiling-Cell Painting) we are aiming to establish experimental and computational best practices for image-based profiling (https://jump-cellpainting.broadinstitute.org/results) and produce the world’s largest public Cell Painting gene/compound image resource, with 140,000 perturbations in five replicates, to be released November 2022. With these data and new technologies like Pooled Cell Painting and variants of the assay like LipocyteProfiler and CardioProfiler, we hope to bring drug discovery-accelerating applications to practice.

Speaker Bio: Inti Zlobec holds the position of Professor (Extraordinarius) of Digital Pathology at the Institute of Pathology, University of Bern, Switzerland. She graduated with a PhD degree in Experimental Pathology, from McGill University, Montreal, Canada in 2007 before completing a post-doctoral fellowship at the Institute of Pathology, University Hospital Basel, where she conducted tissue-based research in the field of colorectal cancer using biostatistical models. After habilitating in 2010, she received a position at the Institute of Pathology, University of Bern, where she established and led the Translational Research Unit (TRU) and later the Tissue Bank Bern (TBB). Inti became Associate Professor in 2014. Now, she leads an inter-disciplinary research group of students and researchers using artificial intelligence and machine learning as tools to study pathology images along with other data types to discover and validate novel prognostic and predictive biomarkers for colorectal cancer patients. Inti is a member of the Executive Team of the Center for Artificial Intelligence in Medicine (CAIM) of the University of Bern, Co-Founder and President of the Swiss Consortium for Digital Pathology (SDiPath), Chair of the European Society of Pathology (ESP) Working Group Digital and Computational Pathology and Board Member of the European Society of Digital and Integrative Pathology (ESDIP).

Talk Title: The AI revolution in multimodal radiology informatics

Start Time: 9:20 AM CDT

Speaker: Ron Summers, Senior Investigator, National Institute of Health

Abstract: Deep learning has enabled sophisticated AI analysis of radiology images, including CT, MRI, ultrasound, and radiography. Multimodal AI takes this sophistication to a new level. The incorporation of both clinical text and radiology images into multimodal models enables even more accurate predictions and expands the number of clinical use cases. Incorporation of multimodal imaging data sets is the next logical step. In this presentation, I will explore some of the latest developments in multimodal radiology AI with the goal to improve patient health.

Speaker Bio: Dr. Summers received the BA degree in physics and the MD and PhD degrees in Medicine/Anatomy and Cell Biology from the University of Pennsylvania. He completed a medical internship at the Presbyterian-University of Pennsylvania Hospital, Philadelphia, PA, a radiology residency at the University of Michigan, Ann Arbor, MI, and an MRI fellowship at Duke University, Durham, NC. In 1994, he joined the Radiology and Imaging Sciences Department at the NIH Clinical Center in Bethesda, MD. where he is now a tenured Senior Investigator and Staff Radiologist. He is a Fellow of the Society of Abdominal Radiologists and of the American Institute for Medical and Biological Engineering (AIMBE). He directs the Imaging Biomarkers and Computer-Aided Diagnosis (CAD) Laboratory and is former and founding Chief of the NIH Clinical Image Processing Service. In 2000, he received the Presidential Early Career Award for Scientists and Engineers, presented by Dr. Neal Lane, President Clinton's science advisor. In 2012, he received the NIH Director's Award, presented by NIH Director Dr. Francis Collins. In 2017, he received the NIH Clinical Center Director's Award. He has co-authored over 500 journal, review and conference proceedings articles and is a co-inventor on 14 patents. He is a member of the editorial boards of the Journal of Medical Imaging, Radiology: Artificial Intelligence and Academic Radiology and a past member of the editorial board of Radiology. He is a program committee member of the Computer-aided Diagnosis section of the annual SPIE Medical Imaging conference and was co-chair of the entire conference in 2018 and 2019. He was Program Co-Chair of the 2018 IEEE ISBI symposium.

Speaker: Mackenzie Mathis, Ph.D., Swiss Federal Institute of Technology, Lausanne (EPFL)

Speaker Bio: Prof. Mackenzie Mathis is the Bertarelli Foundation Chair of Integrative Neuroscience and an Assistant Professor at the Swiss Federal Institute of Technology, Lausanne (EPFL). Following the award of her PhD at Harvard University in 2017 with Prof. Naoshige Uchida, she was awarded the prestigious Rowland Fellowship at Harvard to start her independent laboratory (2017-2020). Before starting her group, she worked with Prof. Matthias Bethge at the University of Tübingen in the summer of 2017 with the support of the Women & the Brain Project ALS Fellowship. She is an ELLIS Scholar, a former NSF Graduate Fellow, and her work has been featured in the news at Bloomberg BusinessWeek, Nature, and The Atlantic. She was awarded the FENS EJN Young Investigator Prize 2022. Her lab works on mechanisms underlying adaptive behavior in intelligent systems. Specifically, the laboratory combines machine learning, computer vision, and experimental work in rodents with the combined goal of understanding the neural basis of adaptive motor control.

Speaker: Hoifung Poon, Ph.D., General Manager, Microsoft.

Speaker Bio: Dr. Poon is a General Manager at Microsoft Health Futures where he leads biomedical AI research and incubation, with the overarching goal of structuring medical data to accelerate discovery and improve delivery for precision health. His team and collaborators are among the first to explore large language models (LLMs) in health applications, from foundational research to incubations at large health systems and life science companies, and ultimately to commercialization. Dr. Poon received his B.S. with Distinction in Computer Science from Sun Yat-Sen University in Guangzhou, China, and his PhD in Computer Science and Engineering from the University of Washington in Seattle, specializing in machine learning and natural language processing (NLP). He joined Microsoft Research in 2011.

Speaker: Drew Linsley, Assistant Professor, Brown University.

Speaker Bio: Dr. Linsley is an Assistant Professor (Research) in Computational Neuroscience and AI at Brown University. He previously did a postdoc with Thomas Serre, also at Brown. Before that, he received his PhD at Boston College with Sean MacEvoy, and BA at Hamilton College working with Jonathan Vaughan. He studies biological and artificial vision. He is Co-Founder and CEO at Operant Biopharma whose robotic microscope, powered by artificial intelligence, designs drugs by watching and controlling diseases over time.

Talk Title:Generative Models of Cellular Organization

Start Time: 16:10 PDT
Speaker: Greg Johnson, Ph.D, Research scientist, EvolutionaryScale.

Abstract: Understanding cells as integrated systems is a central challenge in modern biology. While fluorescence microscopy provides high-resolution views of specific subcellular structures, imaging many structures simultaneously or across time remains limited by phototoxicity, spectral overlap, and acquisition cost. We address these limitations by learning joint representations across imaging modalities and subcellular structures. Our approach enables the generation of highly multiplexed, structure-specific visualizations from label-free inputs, bypassing the constraints of fluorescence labeling. We extend this framework to jointly model cell and nuclear morphology alongside subcellular localization, enabling conditional generation of realistic 3D images and the discovery of spatial associations between organelles. By capturing these relationships, our method quantifies structural variability across cells and perturbation conditions. Finally, we outline extensions to incorporate additional data modalities—including perturbation metadata, single-cell transcriptomics, and genetic variation—to enable integrative modeling of cellular state and structure at scale.

Speaker Bio: Dr. Gregory R. Johnson is a research scientist at EvolutionaryScale, developing technology to model biological systems across diverse data types and scales. Previously, he co-founded NewLimit to pursue therapies targeting age-related diseases and built predictive models of cellular organization. His work spans machine learning applications in natural sciences and technology development across academia and industry. He earned his Ph.D. from Carnegie Mellon University, specializing in generative models to understand how cells are organized and respond to perturbations.