Julianna Han
Senior Staff Scientist, Ward Lab The Scripps Research Institute
Julianna Han is a Senior Staff Scientist in the Ward Lab at Scripps Research, where she leads research efforts focused on antibody discovery, engineering, and structural characterization to advance next-generation therapeutics. Since joining Scripps Research in 2018 as a postdoctoral scholar, she has progressed through roles of Staff Scientist and Senior Staff Scientist, reflecting her significant scientific contributions and leadership within the laboratory. Julianna earned her PhD through research in the Manicassamy Lab at the University of Chicago, investigating immunology and host-pathogen interactions. With extensive experience spanning immunology, structural biology, and protein engineering, she is dedicated to translating fundamental biological insights into innovative therapeutic strategies.
Seminars
- Advancing microfluidic sample preparation and multiplexed cryo-EM workflows to increase sample-processing capacity and enable the analysis of multiple antibody–antigen specificities from a single sample
- Applying cryo-EM for polyclonal epitope mapping to rapidly resolve heterogeneous antibody responses, identify distinct antigenic specificities and reduce the need for extensive downstream monoclonal antibody screening
- Automating cryo-EM data collection and processing workflows to accelerate structure determination, standardize analysis and generate actionable outputs across larger numbers of samples
As cryo-EM becomes increasingly important for structure-based drug discovery, the field is facing a fundamental throughput challenge. Long data collection times, limited microscope access, difficult sample preparation, computationally intensive analysis, and manual structure building can restrict the number of structures that can be delivered to discovery teams. Join structural biology leaders, cryo-EM specialists, and technology developers as they discuss how to scale cryo-EM workflows to meet the growing demands of modern drug discovery by:
- Overcoming access and throughput limitations, examining how companies are managing constrained microscope capacity, external facilities, CRO access, and the need to generate sufficient structures to support increasingly large discovery pipelines
- Improving sample preparation and data quality, discussing how grid development, alternative sample preparation approaches, additives, and better understanding of sample-specific behaviour can reduce failed experiments and increase the probability of obtaining high-quality structures
- Automating the cryo-EM workflow, exploring how advances in instrumentation, AI-enabled data processing, automated structure building, and improved computational infrastructure can reduce manual intervention and increase the number of actionable structures delivered to drug discovery teams
