Grants

Energy metabolism and bipolar disorder: From early detection, to diagnosis and effective treatment

Oxygenating the brain with laser stimulation therapy to improve cognitive function in bipolar disorder

CURE-Bench: Universal Benchmark for All-Disease Drug Repurposing

Drug repurposing holds the potential to expand the utility of existing drugs and thus, therapeutic options for patients across a variety of diseases. This project aims to develop foundation artificial intelligence models and an accompanying evaluation framework to benchmark models across diseases to promote the development, evaluation, and widespread use of artificial intelligence models to identify clinically-relevant drug repurposing hits.

Biswas Center for Transformative Computational Cancer Biology

Personalized medicine informs and subsequently tailors disease prevention, diagnosis, and treatment efforts based on an understanding of an individuals unique biology. This project aims to support personalized diagnosis and treatment for colorectal and skin cancers through the development of machine learning models trained to predict how a patientís genetic mutation can alter tumor cell biology and evaluate the effectiveness of candidate immunotherapies.

The MAIDA Initiative: Democratizing Global Medical Imaging Data Sharing for Safer and Fairer AI

Datasets representative of diverse patient populations enables the development and deployment of reliable, equitable, and inclusive artificial intelligence tools. This project aims to collect medical imaging data, notably chest X-rays and chest CT images, from a variety of clinical settings to facilitate the use of artificial intelligence to analyze images and improve diagnosis and treatment.

A Chatbot Assistant for Genetic Diagnosis and Interpretation of Common and Rare Cardiovascular Diseases

In the clinical diagnostic workflow, the impact of genomic mutations is unknown in many cardiovascular diseases. This project leverages machine learning models to develop an artificial intelligence chatbot interfaced with genomic knowledge to improve the diagnostic process and accelerate the diagnosis of patients with cardiovascular diseases.

AI for Cancer Genomic Medicine: Circuitry, Treatment, Personalization

Enhancing biomarker selection, de novo drug synthesis, and drug repurposing are essential for identifying and generating more effective therapeutics. By integrating single-cell and spatial sequencing with machine learning approaches, this project aims to predict target genes to inform the development of precision therapeutics in cancer, neuroscience, and metabolic disorders.

Enhancing Fas-mediated Bystander Killing of Follicular Lymphoma to prevent post-immunotherapy antigen escape.

This project investigates Fas signaling pathways and their regulators in follicular lymphoma to identify actionable targets that enhance T cell Bystander Killing. The goal is to develop novel or repurposed combination therapies to overcome antigen escape and improve the effectiveness of T-cell-based immunotherapies.

Dual CD19-CAR-T approaches to counteract antigen escape and tumor microenvironment immune suppression in FL

This project develops dual CD19-based CAR T cell therapies targeting antigens such as BCMA, CD70, and FR? to overcome tumor escape and immunosuppression in follicular lymphoma. The research aims to evaluate the safety and efficacy of these novel CAR T candidates in preclinical models to advance them toward clinical trials.

Utilizing EZH2 Inhibition to Improve Immune Therapies in Follicular Lymphoma

This project aims to investigate how EZH2 inhibitors can enhance the effectiveness of immunotherapies like bispecific antibodies and CAR T-cell therapies in follicular lymphoma (FL). It seeks to understand the impact of EZH2 inhibition on lymphoma cells, the tumor microenvironment, and immune responses to inform rational drug combinations that improve patient outcomes.

Towards an Optimized CAR T Cell Therapy for Follicular Lymphoma

This project investigates the disrupted HVEM-BTLA immune checkpoint pathway in follicular lymphoma to develop a novel therapeutic approach. By evaluating the effects of targeted nanobody inhibitors on the tumor microenvironment and immune response, the team aims to restore anti-tumor immunity and improve long-term treatment outcomes for patients.

Elucidating the Immunometabolism of Sarcoidosis

This research examines how metabolic changes in macrophages and circulating monocytes contribute to early granuloma formation in sarcoidosis. By analyzing gene expression and metabolism in patient-derived skin biopsies and blood cells, the study aims to uncover key metabolic drivers of inflammation and identify potential therapeutic targets to modulate macrophage function.