The convergence of spatial transcriptomics and high-resolution digital pathology imaging has revolutionized our understanding of tissue architecture and disease biology. This talk explores the integration of these powerful modalities, enabling a spatially resolved analysis that bridges engineering with basic and translational science. By harnessing generative AI, we can now simulate virtual cells and tissue systems, offering unprecedented opportunities for in silico experimentation and discovery. This synergistic approach not only deepens our insights into the complex interplay of molecular and morphological data but also paves the way for transformative advancements in precision medicine and therapeutic development.

In this talk, I will present a brief overview of the work that myself and others have done to discover disease-relevant biomarkers in the T-cell receptor repertoires from patients. I will detail why immune repertoires have such a large potential to change the way we diagnose diseases, and what hurdles still need to be overcome for them to be applied in daily clinical use. A primary focus of the talk will be the recent AI solutions that have been developed to overcome the intrinsic uniqueness of these repertoires, and how they have lead to novel disease insights and solutions for therapies and diagnostics.

Just as large language models (LLMs) transform human language into computational understanding, nature has already encoded a deeper logic—an immune language composed of receptor sequences, co-occurrence patterns, and network structures shaped by evolution. This language does not predict words. It predicts survival.

We present AIDeN—the Adaptive Immune Defensive Network—a biologically trained model that functions as a consensus reference for the adaptive immune system, much like the reference genome transformed genomics. Built from population-scale immune repertoire data and designed with network-based AI logic, AIDeN captures the structural memory of immunity across individuals.

Instead of isolated biomarkers, AIDeN learns how receptor “nodetypes” co-occur in patterns—called “linklets”—that define immune grammar. Each repertoire becomes a network fingerprint; deviations from the consensus reveal early dysfunction—before disease is clinically evident.

For pharmaceutical development, AIDeN offers new tools to enhance trial design and accelerate drug discovery. It enables:

• Identification of responders based on immune network fitness,
• Discovery of new immunological targets through structural deviation mapping,
• Faster, quantitative endpoints by tracking immune rewiring over time.

AIDeN shifts the paradigm from symptom-based classification to network-informed precision. It supports diagnostics, monitoring, and therapy optimization by evaluating immune system integrity at scale.

Where LLMs decode culture, AIDeN decodes survival. It is the immune system’s own model—built by nature, revealed by data, and ready to guide the future of precision immunology and immune-guided therapeutics.

In this presentation, I will provide an overview of our antibody discovery platform, highlighting how it integrates multiple computational technologies to streamline and optimize the discovery process. We have developed a suite of digital tools spanning traditional bioinformatics techniques, high-throughput data analysis workflows, and advanced ML/AI-driven models to address the complex challenges inherent in antibody screening and selection. By employing these methods, we analyze and refine candidate pools, prioritizing promising antibody clones based on structural, functional, and biological parameters. Our digitally assisted screening improves and accelerates the identification of top-performing clones and offers deeper insights into their underlying mechanisms of action. While we have made significant progress in improving selection accuracy and reducing development timelines, several challenges remain, particularly in data quality, model interpretability, and the ongoing evolution of AI methodologies. To tackle these challenges, we foster close collaborations among scientists, data scientists, and computational biologists, enabling us to continuously refine our toolbox and adapt to emerging needs. We are currently exploring novel paradigms such as generative models and active learning to further expand antibody space exploration. Through this presentation, I aim to share insights from our experiences building and deploying digital solutions for antibody discovery, highlight some of the key breakthroughs achieved, and outline the opportunities that still lie ahead.

In this presentation, I will cover my work integrating machine learning with protein and T cell engineering to advance therapeutic development. I’ll discuss how computational strategies like deep learning models trained on antibody libraries enable in silico prediction of antigen specificity, dramatically accelerating antibody optimization. I’ll highlight approaches to overcome data limitations, including meta-learning frameworks for noisy datasets and nucleotide augmentation to expand training diversity. During the COVID-19 pandemic, these principles guided the creation of deep mutational learning systems to forecast SARS-CoV-2 evolution, informing antibody and vaccine design against variants like Omicron. I’ll also share our work on engineered solutions for T cell therapies, including TCR-Engine—a high-throughput platform to design tumor-targeting receptors—and CAR T cell systems that fine-tune activation thresholds using signaling domain libraries. Together, these examples demonstrate how machine learning and synthetic immunology converge to solve challenges in infectious disease and cancer immunotherapy, from accelerating antibody discovery to enabling safer, more potent cell therapies.