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.

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Recent News and Invited Talks
  • [July 2022] ICML ReALML Workshop, Invited 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.
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Research and Publications

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.

Formerly Ava Soleimany. *Denotes co-first authorship
Deep self-supervised learning for biosynthetic gene cluster detection and product classification
Carolina Rios-Martinez, Nicholas Bhattacharya, Ava P. Amini, Lorin Crawford, Kevin K. Yang
bioRxiv, 2022

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

Protease Activity Analysis: A toolkit for analyzing enzyme activity data
Ava P. Soleimany*, Carmen Martin-Alonso*, Melodi Anahtar*, Cathy S. Wang, Sangeeta N. Bhatia
ACS Omega, 2022
pdf / code

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.

Host protease activity classifies pneumonia etiology
Melodi Anahtar, Leslie W. Chan, Henry Ko, Aditya Rao, Ava P. Soleimany, Purvesh Khatri, Sangeeta N. Bhatia
PNAS, 2022
press / pdf / code

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

Protease activity sensors enable real-time treatment response monitoring in lymphangioleiomyomatosis
Jesse D. Kirkpatrick, Ava P. Soleimany, Jaideep S. Dudani, Heng-Jia Liu, Hilaire C. Lam, Carmen Priolo, Elizabeth P. Henske, Sangeeta N. Bhatia
European Respiratory Journal, 2022

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.

Benchmarking uncertainty quantification for protein engineering
Kevin P. Greenman, Ava P. Soleimany, Kevin K. Yang
ICLR Workshop on Machine Learning for Drug Discovery, 2022

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

Ionic liquid-mediated transdermal delivery of thrombosis-detecting nanosensors
Ahmet Bekdemir, Eden E.L. Tanner, Jesse D. Kirkpatrick, Ava P. Soleimany, Samir Mitragotri, Sangeeta N. Bhatia
Advanced Healthcare Materials, 2022
pdf / supplement

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.

Spatially regulated protease activity in lymph nodes renders B cell follicles a sanctuary for retention of intact antigens
Aereas Aung, Ang Cui, Ava P. Soleimany, Maurice Bukenya, Heya Lee, Christopher A. Cottrell, Murillo Silva, Jesse D. Kirkpatrick, Parastoo Amlashi, Tanaka Remba, Shuhao Xiao, Leah Michelle Froehle, Wuhbet Abraham, Josetta Adams, Heikyung Suh, Phillip Huyett, Douglas S. Kwon, Nir Hacohen, William R. Schief, Sangeeta N. Bhatia, Darrell J. Irvine
bioRxiv preprint, 2021
pdf / supplement

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.

Multiscale profiling of enzyme activity in cancer
Ava P. Soleimany*, Jesse D. Kirkpatrick*, Cathy S. Wang, Alex M. Jaeger, Susan Su, Santiago Naranjo, Qian Zhong, Christina M. Cabana, Tyler Jacks, Sangeeta N. Bhatia
bioRxiv preprint, 2021
pdf / supplement

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.

Synthetic circuit-driven expression of heterologous enzymes for disease detection
Jiang He*, Lior Nissim*, Ava P. Soleimany*, Adina Binder-Nissim, Heather E. Fleming, Timothy K. Lu, Sangeeta N. Bhatia
ACS Synthetic Biology, 2021
pdf / supplement

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

Evidential deep learning for guided molecular property prediction and discovery
Ava P. Soleimany*, Alexander Amini*, Samuel Goldman*, Daniela Rus, Sangeeta N. Bhatia, Connor W. Coley
ACS Central Science, 2021
pdf / supplement

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

Activatable zymography probes enable in situ localization of protease dysregulation in cancer
Ava P. Soleimany*, Jesse D. Kirkpatrick*, Susan Su, Jaideep S. Dudani, Qian Zhong, Ahmet Bekdemir, Sangeeta N. Bhatia
Cancer Research, 2021
pdf / supplement

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.

Deep evidential regression
Alexander Amini, Wilko Schwarting, Ava Soleimany, Daniela Rus
NeurIPS, 2020
pdf / press

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

Pharmacokinetic tuning of protein-antigen fusions enhances the immunogenicity of T-cell vaccines
Naveen K. Mehta, Roma V. Pradhan, Ava P. Soleimany, Kelly D. Moynihan, Adrienne M. Rothschilds, Noor Momin, Kavya Rakhra, Jordi Mata-Fink, Sangeeta N. Bhatia, K. Dane Wittrup, Darrell J. Irvine
Nature Biomedical Engineering, 2020
pdf / supplement / press

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

Activity-based diagnostics: an emerging paradigm for disease detection and monitoring
Ava P. Soleimany, Sangeeta N. Bhatia
Trends in Molecular Medicine, 2020

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.

Urinary detection of lung cancer in mice via noninvasive pulmonary protease profiling
Jesse D. Kirkpatrick*, Andrew D. Warren*, Ava P. Soleimany*, Peter M. K. Westcott, Justin C. Voog, Carmen Martin-Alonso, Heather E. Fleming, Tuomas Tammela, Tyler Jacks, Sangeeta N. Bhatia
Science Translational Medicine, 2020, *Co-first authors
supplement / press / video

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

Genetic encoding of targeted MRI contrast agents for tumor imaging
Simone Schuerle, Maiko Furubayashi, Ava P. Soleimany, Tinotenda Gwisai, Wei Huang, Christopher A. Voigt, Sangeeta N. Bhatia
ACS Synthetic Biology, 2020

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

Renal clearable catalytic gold nanoclusters for in vivo disease monitoring
Colleen Loynachan*, Ava P. Soleimany*, Jaideep S. Dudani, Yiyang Lin, Adrian Najer, Ahmet Bekdemir, Qu Chen, Sangeeta N. Bhatia, Molly M. Stevens
Nature Nanotechnology, 2019, *Co-first authors, Co-corresponding authors
supplement / data / press / video

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

Image segmentation of liver stage malaria infection with spatial uncertainty sampling
Ava P. Soleimany, Harini Suresh, Jose Javier Gonzalez Ortiz, Divya Shanmugam, Nil Gural, John Guttag, Sangeeta N. Bhatia
ICML Workshop on Computational Biology, 2019  

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

Synthetic and living micropropellers for convection-enhanced nanoparticle transport
Simone Schuerle, Ava P. Soleimany, Tiffany Yeh, Giridhar M. Anand, Moritz Haberli, Heather E. Fleming, Nima Mirkhani, Famin Qiu, Sabine Hauert, Xiaopu Wang, Bradley J. Nelson, Sangeeta N. Bhatia
Science Advances, 2019  
supplement / press / video

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

Uncovering and mitigating algorithmic bias through learned latent structure
Alexander Amini*, Ava P. Soleimany*, Wilko Schwarting, Sangeeta N. Bhatia, Daniela Rus
AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, 2019, *Co-first authors
MIT press / VentureBeat press

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.

Spatial uncertainty sampling for end-to-end control
Alexander Amini, Ava Soleimany, Sertac Karaman, Daniela Rus,
NeurIPS Workshop on Bayesian Deep Learning, 2017  

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

Synthetic recombinase-based state machines in living cells
Nathaniel Roquet, Ava P. Soleimany, Alyssa C. Ferris, Scott Aaronson, Timothy K. Lu
Science, 2016  
supplement / press / blog

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

Teaching and Leadership

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

Introduction to Deep Learning, MIT 6.S191

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.

Co-founder, Momentum AI

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.

Teaching Fellow, Harvard MCB294, Fall 2019, with Nancy Kleckner
mit_teaching Teaching Assistant, MIT 7.05, Spring 2016, with Matt Vander Heiden and Mike Yaffe

Teaching Assistant, MIT 7.05, Spring 2015, with Matt Vander Heiden and Mike Yaffe

Captain, MIT Varsity Women's Tennis, 2014-2016

MIT Varsity Women's Tennis, 2012-2016

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