Ava Amini
Formerly Ava Soleimany

I am a Senior Researcher at Microsoft Research in Cambridge, MA. I completed my PhD in Biophysics at Harvard University, where I worked with Sangeeta Bhatia at the Koch Institute for Integrative Cancer Research and was supported by the NSF Graduate Research Fellowship.

I received my Bachelor of Science in Computer Science and Molecular Biology from MIT, where I conducted research with Tim Lu and was recognized as a Henry Ford II Scholar and with the AMITA Senior Academic Award.

Email  /  CV  /  Google Scholar  /  Twitter

• [April 2023] TEDx MIT, Invited Talk.
• [April 2023] Boston University Center for Computing & Data Sciences, Invited Talk.
• [March 2023] Marble Center for Cancer Nanomedicine, Keynote Talk.
• [December 2022] MILA AI Helps Ukraine Conference, Keynote Talk.
• [October 2022] Microsoft Research Summit, Invited Talk.
• [July 2022] ICML ReALML Workshop, Keynote Talk.
• [June 2022] Advanced Regenerative Manufacturing Institute (ARMI) Annual Meeting, Invited Talk.
• [Apr. 2022] Flagship Pioneering, Flagship AI Talks Series.
• [Mar. 2022] Broad Institute of MIT and Harvard, Special Seminar.
• [Mar. 2022] Massachusetts Institute of Technology (MIT), Special Seminar in Electrical Engineering and Computer Science.
• [Mar. 2022] Harvard University, Seminar in Department of Biomedical Informatics.
• [Mar. 2022] Dana Farber Cancer Institute, Seminar in Department of Data Science.
• [Feb. 2022] UC Berkeley and UCSF, Seminar in Program in Computational Precision Health.
• [Feb. 2022] University of Pennsylvania, Seminar in Department of Bioengineering.
• [Feb. 2022] Columbia University, Seminar in Department of Biomedical Engineering.

My research focuses on engineering new technologies for precision medicine. I have developed new methods in machine learning and statistics, bioengineering, and nanotechnology and leveraged these approaches to create new diagnostic and therapeutic biotechnologies and to achieve new insights into cancer.

We assess deep learning-based uncertainty quantification methods on protein sequence-function prediction tasks.

We discover "sanctuaries" within lymph nodes that contain low proteolytic activity and act as a safe haven for vaccines, and demonstrate that this heterogeneity can be exploited to enhance vaccine-induced antibody response.

We develop an intravenous priming agent that is given prior to a blood draw to increase the abundance of cell-free DNA in circulation, improving the sensitivity of liquid biopsy diagnostic assays.

We present Folding Diffusion: a diffusion model for generating protein structures that is inspired by the biophysics of protein folding.

We develop a self-supervised masked language model for biosynthetic gene clusters in bacteria, and leverage it for natural product classification.

We build Protease Activity Analysis (PAA), a Python software package with a collection of data analytic and machine learning tools for analyzing protease activity data.

We develop a sensor-based, ML-driven system to diagnose pneumonia and classify its etiology, using machine learning to classify directly from molecular barcodes.

We establish a sensor-based, ML-driven diagnostic for noninvasive, real-time monitoring of disease in a preclinical model of lymphangioleiomyomatosis (LAM), a rare lung disease.

We design an easily applicable, non-invasive formulation to deliver diagnostic nanosensors through the skin, enabling a sustained release diagnostic monitoring system for detecting thrombosis.

We discover that protease activity and antigen breakdown are spatially heterogenous in lymph nodes, and that this spatially-compartmentalized antigen proteolysis can be exploited to enhance vaccine-induced antibody response.

We engineer an integrated set of methods for measuring specific enzyme activities across the organismal, tissue, and cellular scales, and unify these methods into a methodological hierarchy that powers new biological insights into cancer.

We design a sense-and-respond system that integrates a synthetic gene circuit and nanotechnology detection tools for tumor-specific expression of heterologous biomarkers.

A fast, scalable approach for uncertainty quantification in neural networks enables uncertainty-aware molecular property prediction, accelerated property optimization, and guided virtual screening.

We engineer a new class of enzyme activity probes that can be applied to fresh-frozen tissue sections to spatially localize protease activty, enabling new insights into the biology of protease dysregulation.

We develop a novel algorithm for fast, scalable uncertainty quantification in highly complex, non-linear neural networks trained for regression tasks.

We optimize the immunogenicity of peptide-based antitumor vaccines in mice by tuning their pharmacokinetics via fusion of the peptide epitopes to protein carriers.

Review detailing how integrating techniques from multiple disciplines has developed engineered diagnostics that are selectively activated in disease states, highlighting their potential to realize the goals of precision medicine.

We couple protease-responsive nanoparticle sensors with machine learning to engineer a sensitive and specific urinary test for lung cancer detection.

Magnetic nanoparticles that display genetically encoded targeting peptides to promote tumor accumulation and enhance MRI contrast.

By leveraging the unique properties of catalytic nanomaterials, we develop a simple color-change urine test for detection of cancer in mice.

Convolutional neural networks for automated segmentation and uncertainty estimation of microscopy images of malaria infection.

Engineered microrobots that use magnetism to push drug-delivery nanoparticles out of blood vessels and into diseased tissue.

Generalizable algorithm for mitigating hidden biases within training data, by leveraging learned latent distributions to adaptively re-weight the importance of certain data points while training.

Estimating uncertainty in neural networks for end-to-end control by exploiting feature map correlations during training.

Programming biological state machines that enable cells to remember and respond to a series of events.

In addition to research, I am passionate about education and leadership, and strive to help and empower others to excel in their own pursuits.

I am an organizer and lecturer for Introduction to Deep Learning (6.S191), MIT’s official introductory course on deep learning foundations and applications. Together with Alexander Amini, I have organized and developed all aspects of the course, including developing the curriculum, teaching the lectures, creating software labs, and collaborating with industry sponsors. All materials can be found online on the course website.

I am a co-founder and director for Momentum AI, an outreach program that teaches AI and machine learning to under-resourced and under-served high school students from the greater Boston area. Our two-week capston program is a free, project-based deep dive into AI and is held on MIT's campus.