Ava Soleimany

I am a PhD student in the Harvard Biophysics program and at MIT, where I work with Sangeeta Bhatia at the Koch Institute for Integrative Cancer Research and am supported by the NSF Graduate Research Fellowship.

Previously, I completed my Bachelor of Science in Computer Science and Molecular Biology at the Massachusetts Institute of Technology (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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Research and Publications

My PhD research focuses on engineering novel diagnostics for the early detection of cancer. My work leverages tools from nanotechnology, machine learning and statistics, chemical biology, and bioengineering to create new diagnostic and therapeutic biotechnologies.

*Denotes co-first authorship
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
pdf

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
supplement

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

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.

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.

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.

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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