Improving scientific reproducibility and making life easier for Data Scientists, one figure at a time.

Providing Machine Learning & bioinformatics expertise to early-stage Biotechnology companies.

* Leading a team of seven software engineers, bioinformaticians and data scientists building tools for next-generation cancer sequencing and personalized cancer care, as well as supporting computational efforts within the larger Pathology department* Overseeing the engineering of multiple clinical software systems* Leading the training & deployment of multiple Machine Learning models into clinical practice, including a somatic variant reporting model that's been in clinical use since 2017* Kickstarting and leading data science within the department; building databases, tools and systems that combine clinical, molecular and imaging data in order to enable discovery and analysis, with the goal of developing clinical decision support systems that improve patient outcomes* Supporting Digital Pathology efforts, including building software tools and computational methods for whole slide image analysis/deep learning* Leading the effort to migrate clinical genomics analysis to the Microsoft Azure cloud, including feasibility assessment, architecture design, patient privacy & InfoSec compliance, as well as cost analysis; collaborating with the Partners Cloud Steering Committee and the Azure team at Microsoft

Leading analytics to develop a blood test for Autism Spectrum Disorders. Responsibilities include design and execution of experiments, development of a cloud Machine Learning pipeline and communication of key results to members of the executive and scientific teams.

Part of a startup developing a blood based test to enable earlier detection of Autism Spectrum Disorders using next-generation sequencing technology.

Technology lead on the DARPA DCAPS program, coordinating efforts to develop algorithms capable of detecting and labeling fine-grained psychological distress signals in multimodal communications. My responsibilities span research in Machine Learning and Natural Language Processing (see publications below) and the development of software prototypes in collaboration with a team of software engineers. I also pursue opportunities for technology transition by presenting BBN's research to government officials and at conferences.

Implementing distributed data processing algorithms with Hadoop, Hive and HBase.

My research focuses on applying machine learning techniques to automatic compiler-enabled optimization of computer programs.

I worked as a software development intern in the Natural Language Group (NLG). I designed and implemented, from scratch, a fully functional Windows Phone 7 version of the group's project management software. I wrote both client- and server-side code that integrated with NLG's prototype Natural Language components.

I was a software engineering intern in the Real Time System group, where I worked on a highly scalable bidding system for Internet advertising. I designed and developed a Geolocation library that could sustain 2 million queries/second with negligible synchronization overhead – 5 times the throughput of the popular MaxMind library. I also contributed to the remote update functionality of the company's flagship product and assisted in Quality Assurance, helping to reach the Beta milestone.

I assisted in prototype development of a highly parallel behavioral targeting engine. My work focused on architecture and concurrency design.

I helped develop Linux-based control software for multi-projector displays. My work focused on improving the automatic calibration process and adding a remote management interface. I overhauled the original architecture for better scalability and maintainability.