John Doe PHONE NUMBER | EMAIL | GITHUB Education UNIVERSITY Master’s of Science, Expected Graduation Dec 2020, 3.98 GPA · Major in Computer Science, Minors in Bioinformatics and Biology Experience LARGE MOBILE GAMING COMPANY (UNICORN) AR Platform Engineering Intern May 2019 - August 2019 · Developed high-performance, highly parallelized systems in modern C++ using the actor model. · Utilized high-speed message passing in-process and over direct TCP connections using ZeroMQ, and added real-time dataset streaming over direct TCP to 3D visualization tool. · Developed performant systems for low-latency (approximately real-time) processing of image data from multiple devices on a central server and communicating the results of processing to each client for on-line, distributed SLAM algorithms. · Designed and led implementation of prototype location and visual metric-based data query system with client-side and server-side caching of large data blobs and metadata. · Optimized computer vision algorithm implementation, achieving a ~20x speedup using multithreading and caching techniques. MEDIUM STARTUP Software Engineering Intern May 2018 - August 2018 · Maintained and extended genetic information retrieval service for large genomic datasets and organismal data for bespoke research needs using Swagger, Akka-Http, Circe, Quill-Postgres, and AWS S3 in Scala; utilized Kubernetes and Docker for deployment of service. · Developed end-to-end, scalable, cloud-based system for standardized bioinformatic analysis of sequencing data, including automated retrieval of sequencing data from AWS S3, automatic scheduling of new analysis jobs from parameters specified in external ELN repository, fault-tolerant running of new analysis jobs on Kubernetes infrastructure with restarts, and web-based tracking of job status. UNIVERSITY MEDICAL SCHOOL Student Researcher August 2016 - December 2018 · Developed software for estimating the parameters of a thermodynamic model to data consisting of RNA-seq experiments on wild-type and transcription factor-deletion strains and Calling Cards (CC) experiments on a set of transcription factors using the Newton-Raphson method and genetic algorithms to approximate complex interactions. · Developed software in Python and Java using htslib and other high-throughput sequencing libraries to detect transposon-mediated modification of regulatory elements in human embryonic stem cells. Publications · Prevalence and genetic variants of G6PD deficiency among two Malagasy populations living in Plasmodium vivax-endemic areas. Malaria Journal, 16(1), 139. Skills & Abilities · Scala, Java, Python, C, C++, and Mathematica development experience in various environments, including Windows, OSX, and GNU/Linux. · Cloud computing platform experience with AWS, including S3, EC2, ElasticBeanstalk, and DynamoDB, as well as DigitalOcean. · RESTful service design in Flask and Akka-Http and deployment with Kubernetes. · Asynchronous message passing using ZeroMQ/nng for fast network/in-process communication. Personal Projects · Developed user-friendly application in Java and Scala to track lengths of microtubules in fluorescent microscopy data. · Developed application for analysis of smart city data, including noise level, temperature, foot and car traffic, humidity, and other metrics. Also developed heatmap-based visualization website to display data. · Implemented Sea-Thru, a method for removing backscatter and light attenuation from underwater photos using a known range map in Python, and extended method to use monocular depth estimation methods. · Implemented a pipeline for artificial depth-defocus (e.g. “portrait mode”) in C++ from semi-aligned stereo images. · Derived and implemented a Bayesian method for single-nucleotide polymorphism detection in next-generation sequencing data using prior data from the 1000 Genomes Project. · Implemented a web-based, platform-agnostic augmented reality protein model viewer using AR.js and three.js. · Designed and implemented an actor system in C++ for high-speed asynchronous message passing. · Implemented a system for SMS-based triaging of COVID-19 cases based on hospital capacity and facility level. This project won HackAtHome and COVID-19 Global Hackathon 1.0 hackathons. · Developed an Android app and Flask backend hosted on DigitalOcean to anonymously cross-check user movements with movements of individuals with confirmed cases. · Used Bayesian optimization to optimize parameters of the CRF-as-RNN semantic segmentation neural network, leaving to a 2.6% increase in accuracy on a validation set.