Hi there! I’m Emily F. Gorcenski, and this is my personal website. Before going further, please note that the opinions share on this site belong only to me and I do not speak 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. On this page, you’ll find a brief CV, bio, and other information that you might care about or need.
I am a data scientist and engineer by training and a social justice activist by passion. Occasionally, I write about politics, software, and politics-in-software. I am also a regular conference speaker.
I studied Aeronautical Engineering and Mathematics at Rensselaer Polytechnic Institute in Troy, NY. My degree is in Mathematics, and my focus is on Numerical Analysis and Uncertainty Quantification. My undergraduate research fellowship was done at the Princeton Plasma Physics Laboratory, and I spent the first part of my career in research and development before shifting to data science and engineering. I have worked in a wide range of technical fields, from aerospace control systems, signal processing, video game development, occupational therapy for rehabilitation, and more. Some of my work is summarized in my CV below. Presently, I work as a data science and data engineering consultant for Thoughtworks Germany, where I help clients build high-quality data-driven, intelligent software applications efficiently.
In addition to my regular professional work, I also sit on the advisory boards of the Prosecution Project at Miami University in Ohio (Dr. Michael Loadenthal), and on the Youth Equity and Sexuality Laboratory at Suffolk University (Dr. Mimi Arbeit), two research labs studying political violence. My experience in contemporary right-wing violence is driven in large part by my personal experiences as a survivor of neo-Nazi terrorism in my home city of Charlottesville, Virginia, when terrorists came to the Unite the Right neo-Nazi hate rally intent on doing violence. As part of my efforts to understand this violence, I created First Vigil (offline but returning soon), a database of criminal cases involving hate crimes, right-wing anti-government militancy, and white supremacist violence. For that work, I’ve been given accolades; among them, I was named as one of 2018’s most influential feminists by Bitch Magazine.
My research into the far-right has been part of several stories. My research, footage, and experiences were part of Documenting Hate, a documentary by PBS Frontline and ProPublica that won an Emmy in 2019. The Peabody and Emmy Award-winning documentary, Charlottesville: Race and Terror focused on neo-Nazi Christopher Cantwell, who days later became internationally-known as “The Crying Nazi” after learning I had sworn a felony warrant against him. I was (briefly) depicted as a fictionalized character in American Horror Story: Cult, and I have been the focus of several news stories, including a front-page feature in the Washington Post, a profile in Motherboard, a profile in The Guardian, and a profile in Die Tageszeitung (German).
From time-to-time, I turn my research and experience into published analysis and opinion, some of which are linked in the Publications section of my CV below. I am available as a guest on TV appearances and have appeared on programs from TODAY to Al Jazeera’s The Stream. Please email me at the above email address if you would like me to appear on your segment. I am also available for conference talks. For previous talks, please see the Conferences section below. I am currently offering talks in the following topic areas:
Thanks for reading!
August 2018 — Present
ThoughtWorks is a global technology consultancy that helps its clients excel at software development. With offices in 17 countries and over 11,000 employees, Thoughtworks is a recognized leader in software engineering, Agile, and other technology best practices.
I am a Principal Consultant, Data Scientist, and serve as Head of Data and AI for Thoughtworks Germany and Thoughtworks Europe. In my role, I advise clients in a variety of technology development efforts in the data science space. These efforts can include: consulting on data mesh transformations, 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. My unique experience in technology transition means I am well-suited for both advisory and delivery roles in the data science space. Some of my accomplishments with ThoughtWorks include:
September 2016 — May 2018
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.
I worked as Senior Data Scientist, where I analyzed customer behavior, including app usage spending/saving behavior, customer satisfaction, and fraud detection and mitigation. Additionally, I served as mentor for six other data scientists with varying levels of experience and skills. Key accomplishments include:
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.
I served Barron Associates as a Research Engineer. Using my background in computational mathematics, I worked on interdisciplinary teams with world-class domain experts to help demonstrate and prove complex algorithms and ideas in real-world environments. In addition, I 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 my 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.
(forthcoming events in italics)
(forthcoming events in italics)
Posted: 12.12.2018
Built: 21.12.2024
Updated: 07.08.2023
Hash: d6d37f2
Words: 2033
Estimated Reading Time: 11 minutes