I am currently working as a scientist on the ML-AI Research team at Tessera Therapeutics, designing language models and Bayesian models for nucleotide sequence design. I also lead the company’s machine learning education program.
- Developed Bayesian approximation machine learning algorithm to design RNAs, enabling up to a 10x reduction in cost and 2x in time for screening platform
- Designed language models and imbalanced learners for DNA manufacturing optimization, with reduction in manufacturing cancellation rates by 59%
- Managed CO-OP employee, and taught an ‘Intro to Machine Learning’ series for company employees, with 50+ attendees per session
Graduate Researcher and Short-term Postdoc
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MITAug 2016 - Oct 2022
My thesis research focused research focused on developing Bayesian nonparametric machine learning models for neurodegenerative diseases.
- Developed Bayesian nonparametric model (Mixture of Gaussian Processes with monotonic biases) for timeseries clinical data, and released open-source python package
- Characterized ALS biomarkers using longitudinal metabolomics data
- Analyzed multi-modal genomic, proteomic, and transcriptomic data
- Mentored and designed research projects for 5 undergrads and 2 masters students
Modeling & Simulations Intern
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NovartisFeb 2021 - May 2021
I interned with the Modeling & Simulations team at Novartis Institutes for BioMedical Research (NIBR).
- Developed ordinary differential equation (ODE) models for CAR-T therapies
- Analyzed clonal heterogeneity of CAR-Ts using single-cell transcriptomics
Software Engineer Intern
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IBMJune 2015 - Aug 2015
I interned with the IBM Cloud Security team, where I contributed to cloud container services.
- Tested cloud container services, directly contributing to the release of IBM cloud services
- Developed APIs for Bluemix and Openstack and created prototype for user interface
- Worked as part of a collaborative team and was integrated with full-time employees