Posts classified under: BME

My research focuses on applied and theoretical machine learning for its application to biomedical sciences. I am interested in methods for the responsible use of machine learning in biomedical imaging, including the development algorithms that are robust, interpretable and fair. Moreover, my work leverages data-driven priors for biomedical image processing and computer vision problems, such as detection, classification, segmentation and image reconstruction and estimation. My contributions typically involve by the deployment of parsimonious priors for tasks in medical imaging, both analytically and in a data-driven manner, enabling the regularization of otherwise ill-posed problems. My group is interested in the development of interpretable machine learning predictors, which could be used for the discovery of biomarkers for disease prognosis and treatment response.

My research focuses on the development and application of engineered biomaterials and human stem cell/tissue engineering technologies, including microfabricated tissues such as organ chips, organoids, and bio-printed tissues, for disease modeling, drug development, and precision medicine. By integrating AI and digital organ twin models with experimental human mini-organ twin models, we aim to develop more predictive human preclinical models for drug discovery and precision medicine. Additionally, my work integrates state-of-the-art multi-scale biomanufacturing techniques with advanced 3D tissue-engineered models of human disease, incorporating biosensors and AI/ML-enabled biosystems for clinically relevant functional analyses. The ultimate goal of my research is to better understand complex human disease biology in response to microenvironmental cues in normal, aging, and disease states, gaining new mechanistic insights into the control of cell-tissue structure and function, and developing multi-scale regenerative technologies for improving human health. I believe these efforts directly support TTEC’s mission to advance transformative technologies in the area of translational tissue engineering.”

The Cahan Lab is a hybrid computational/experimental group that invents computational tools that distill omics data down to specific, testable hypotheses in the contexts of stem cell biology, developmental biology, and cell engineering. Most of our computational efforts are ‘single cell’ or spatial in nature, and thus this central part of our research fits with the ‘Single-cell/Spatial Transcriptomics’ theme of TTEC. Examples of computational platforms that we have created are 1. machine learning tools that measure the extent to which engineered cell populations reflect their natural counterparts, and 2. algorithms that predict the impact of cellular perturbations on cell engineered fidelity. Both of these applications can help to create and evaluate iPSC-derived disease models, which is another TTEC theme. Finally, we use these and other tools to uncover how cell lineages of the synovial joint emerge during development with the long term goal of leveraging this knowledge to engineer cells for regenerative medicine. This long-term goal aligns well with TTEC’s Tissue Engineering & Biomaterials Pillar and Healthy Aging theme.

Immunotherapy relies on the manipulation of the immune system to induce a potent and durable attack on diseased cells. T cells play an integral role in this by directing immune responses against infected or cancerous cells. My laboratory uses biomaterials to induce natural T cell responses for personalized cancer immunotherapy. This includes development of nanoparticle-based artificial antigen presenting cells (aAPC) that activate tumor-specific T cells targeting multiple tumor-specific targets. These tumor-specific T cells can be reintroduced in a process called adoptive cell transfer (ACT), resulting in persistent anti-tumor activity with immunologic memory. With recent advances the our lab has made aAPC that can also be used to transfer genetic material, such as CAR constructs to T cells. Additionally, using biocompatible platforms, we have synthesized an artificial lymph node (aLN) capable of activating T cells in vivo.

Collectively, our interests’ and tools are synergistic with the “Foundational Pillars” and “Cross-Cutting” themes in TTEC’s strategic plan for “Adaptive Therapeutics”, which make him an excellent fit for TTEC.