About Me

A selfie of Emily

Hi there! I’m Emily F. Gorcenski, and this is my personal website. Before going further, please note that I am highly opinionated, but those strong opinions belong only to me and I am not speaking for my employer or any other organization unless stated otherwise. I am a Data Scientist by profession, a mathematician and engineer by training, and an activist by passion. I am somewhat of a digital nomad, though I am from Charlottesville, and the events of 2017 in Charlottesville have strongly informed my activism. On this page, you’ll find my CV, conference bio, and other information that you might care about or need.

Contact Information

Conference Bio

Emily has over ten years of experience in scientific computing and engineering research and development. She has a background in mathematical analysis, with a focus on probability theory and numerical analysis. She is currently working in Python, though she has a background that includes C#/.Net, Unity3D, SQL, and MATLAB. In addition, she has experience in statistics and experimental design, and has served as Principal Investigator in clinical research projects. An advocate for social justice, Emily is an activist and survivor of the 2017 Charlottesville neo-Nazi attacks. She was named as one of 2018’s most influential feminists by Bitch Magazine for her work in shining a light on far-right violence with her First Vigil project.

Curriculum Vitae

Experience

Lead Consultant, Data Scientist: [Redacted], Berlin, Germany
August 2018 — Present

[Employer redacted for safety reasons].

Emily is a Lead Consultant and Data Scientist. In her role, she advises clients in a variety of technology development efforts in the data science space. These efforts can include: designing effective data platforms to empower faster data analytics and data science, data research and exploration, and implementing agile workflows and continuous delivery for machine learning applications. Emily’s unique experience in technology transition means she is well-suited for both advisory and delivery roles in the data science space. Some of her accomplishments with [Redacted] include:

  • Designing and building a location analytics platform for land use analysis;
  • Designing workshops for teaching continuous delivery and agile development methodologies in a data science space;
  • Training project and product managers on effective ways to utilize data and data scientists on cross-functional teams.

Senior Data Scientist: Simple, Portland, USA
September 2016 — May 2017

Simple Financial Technical Corporation is a personal finance company acting as a technology-first organization. The core product is a consumer checking account with an app-first design. The company offered web, iOS, Android, and mobile-web apps to allow customers to manage finances, interact with support, and plan budgets. The company operated no physical branches.

Emily worked as Senior Data Scientist, where she analyzed customer behavior, including app usage spending/saving behavior, customer satisfaction, and fraud detection and mitigation. Additionally, she served as mentor for six other data scientists with varying levels of experience and skills. Key accomplishments include:

  • Building a generalized genetic algorithm framework for optimizing risk models;
  • Implementing and maintaining a knowledge repository for insight documentation;
  • Developing and implementing an improved fraud-detection model with rapid deployment needs;
  • Designing experiments to quantify customer behavior and assess feature feasibility.

Research Engineer: Barron Associates, Inc., Charlottesville, USA
April 2008 — May 2016

Barron Associates, Inc. is a small research engineering firm specializing in real-time control systems, simulation, and mathematical modeling in the aerospace, automotive, and biotechnology fields. Predominantly working in technology transition—the complex space between core research and technology implementation—Barron Associates helped move technologies and methodologies from university laboratories to production environments within industry and government.

Emily served Barron Associates as a Research Engineer. Using her background in computational mathematics, she worked on interdisciplinary teams with world-class domain experts to help demonstrate and prove complex algorithms and ideas in real-world environments. In addition, she wrote winning grant proposals for new work, led multi-center teams, and presented the company’s work and vision before industry and academic professionals. A sample of her projects is presented below.

Intelligent Prognostics for Vehicle Maintenance Planning

Objective: Using engine, powertrain, and vehicle telemetry, dynamically detect degraded performance to schedule preventative maintenance and allow for greater variability in maintenance schedules.

Approach: Using MATLAB and Simulink, developed a high-fidelity model of engine and vehicle dynamics. Applied modeling approach to large-scale real-world datasets to identify performance and detect failures. Used inverse methods to correct for sensor bias and noise. Applied Kalman filtering techniques for fault detection.

Neural Networks for Low-Resolution Image Classification

Objective: Develop a method for identifying humans in variable sea-state conditions using low-resolution radar images.

Approach: With the requirements of running on real-time embedded hardware, developed a feature extraction pipeline based on Hough Transformations to extract image data. Built a multiclass classification algorithm using polynomial neural networks. Trained and validated data in a real-world environment using a functional radar platform.

Wearable Health Tracker for Lower-Limb Amputees

Objective: Develop a wearable IoT health monitor for lower-Limb combat amputees capable of assessing physical health in people with complex medical needs.

Approach: Designed algorithms for a photoplethysmography-based health monitor capable of being embedded within gel prosthetics linings. Ensured algorithms would be functional in atypical conditions e.g. tissue ossification, where commercial health trackers generally fail.

Image Analysis for Automated Corrosion Mapping

Objective: Develop software capable of identifying corrosion pits in laser profilometry scans of nickel-based superalloys corroded at high temperature with sulfur-based salts.

Approach: Used regularization methods to detect pits while preserving surface geometries where convolutional methods would typically fail. Built algorithms for automating volumetric measurement and classifying inclusions to detect conditions that would lead to adverse stress concentrations. Designed software package to output results to commercially-available failure prediction tools.

Skills
  • Programming: Python, C#, MATLAB, FORTRAN, C, SQL
  • Python-specific Competencies: Python 3.x, jupyter, pandas, numpy/scipy, keras
  • Cloud Competencies: AWS (Elastic Beanstalk, Route53, S3, Redshift, Lambda), Google Cloud (Firebase, Cloud Storage, Load Balancer, Kubernetes Engine, Compute Engine, Cloud Functions)
  • Tools: Visual Studio .NET/VSCode, Anaconda, git, Jira, Trac, Trello, Chartio, Tableau
Languages
  • English: Native Fluency (American)
  • German: Learning (approximately A1)
  • Spanish: Reading/writing competency, some conversational
  • Thai: Learning
  • Turkish: Learning
Education
  • BS Mathematics (applied and computational), 2007, Rensselaer Polytechnic Institute, Troy, NY.
  • MA (In Progress) Mathematics (analysis), University of Virginia, Charlottesville, VA.
Publications
Selected Conferences
  • SRECon EMEA 2018, “SRE for Good: Engineering Intersections between Operations and Social Activism” (keynote), with Liz Fong-Jones (Google), August 2018, Düsseldorf
  • Mozfest 2017, “Debunking Fake News and Fake Science” (keynote), with Sarah Jeong (New York Times), October 2017, London https://www.youtube.com/watch?v=TXL4SfXH5zM
  • Open Source Bridge 2017, “Fake Science: Sad!” (keynote), June 2017, Portland
  • PyData Berlin 2018, “Going Full Stack with Data Science,” July 2018, Berlin https://www.youtube.com/watch?v=huqpXMNFD54
  • PyData Berlin 2017, “Polynomial Chaos, a Technique for Modeling Uncertainty,” July 2017, Berlin https://www.youtube.com/watch?v=Z-Qio-n6yPc