As a proven Data Science leader, I consistently deliver data-driven products with applications to a variety of industries such as energy, environment, mobility, and IoT. I have both contributed to and led cross-functional teams throughout the entire data science lifecycle from ideation to development to operationalization, ensuring that data products translate into tangible business outcomes. In my current role as Data Science Team Lead at sonnen, I drive strategic initiatives in residential renewable energy, fostering a culture of innovation and delivering measurable results.
: austinctodd
austin@austinctodd.com
sonnen
2021 - present • Berlin, DE
National Renewable Energy Laboratory
2019 - 2021 • Golden, CO
MeteoGroup
2017 - 2018 • Berlin, DE
Telekom Innovation Labs/TU Berlin
2015 - 2016
• Berlin, DE
NC State University
2013 - 2015
• Raleigh, NC
Florida State University Center for Ocean-Atmospheric Prediction Studies
2006 - 2013 • Tallahassee, FL
PhD, Oceanography • 2013 My dissertation work investigated the circulation of coastal ocean waters in the Northeast Gulf of Mexico. The work used an understanding of ocean physics to understand how the larvae of gag grouper could be transported from deep waters to shallow coastal waters. I used numerical modeling approaches with a high-performance computing system. My primary advisors for this work were Dr. Eric P. Chassignet and Dr. Steven L. Morey.
B.S., Meteorology (cum laude) • 2007
B.S., Mathematics (cum laude) • 2007
Fields, M. J., M. Optis, J. Perr-Sauer,
Perr-Sauer, J., M. Optis, J.M. Fields, N. Bodini, J.C.Y. Lee,
Berres, A. T. LaClair, C. Wang, H. Xu, S. Ravulaparthy,
R. He,
D.M. McVeigh, D.B. Eggleston,
Johansen, C.,
TU Berlin Report: http://www.snet.tu-berlin.de/...
Telekom Press Release: http://www.telekom.com/media/company/321718
ELTE Press Release: http://www.elte.hu/...
Hír TV (in Hungarian language, 09:15-11:58): http://aszt.inf.elte.hu/...
NC State Press Release: https://news.ncsu.edu/2015/07/shipwreck-2015/
CNN Report: http://edition.cnn.com/...
NBC Report: http://www.nbcnews.com/news/...
Washington Post: http://www.washingtonpost.com/news/...
Boston Globe: http://www.bostonglobe.com/news/...
Feature: http://www.flseagrant.org/studentwebmap/
https://news.fsu.edu/student-stars/2010/06/01/austin-todd/
Internet of Services Lab
Technische Universität Berlin, Winter 2015/2016 and Summer 2016
MEA642: Observational Methods and Data Analysis in Marine Physics
NC State University, Spring 2014
OCE4017: Issues in Environmental Science
Florida State University, Spring 2013
Stars Middle School Career Day
Tallahassee, FL, May 2012
Antarctic Research Facility Tour
Florida State University, July and December 2011
Young Scholars Program
Florida State University, Summer 2009
OCE4017: Issues in Environmental Science
Florida State University, Fall 2008
I have been part of a group of researchers at NREL who are building up computer vision as a core competency at the lab, with a large focus on transportation research projects. Currently the largest focus is implementing some deep learning algorithms for counting cars and trucks from existing traffic camera infrastructure. These counts are being used to improve traffic volume and energy consumption estimates for the city of Chattanooga.
Computer Vision, Neural Networks, Transportation
The Eagle supercomputer at NREL is the most energy efficient supercomputer in the world. However, we want to make the system performance even more optimal. Collaborating with Hewlett Packard Enterprise, we are implementing algorithms to detect anomolous hardware activity and to perform power-aware job scheduling in an effor to further increase the energy efficiency of the system.
AI, HPC, Energy, Machine Learning
Traffic congestion is not only an inconvenience to many of us, but also a major source of pollution and unnecessary energy consumption. In this project, my team at NREL is collaborating with scientists and developers at Oak Ridge National Lab to develop a situational awareness tool for the City of Chattanooga to monitor the latest traffic situation and to optimize traffic mitigation strategies in near real time through simulation and machine learning. Our optimized control strategies aim to reduce energy consumption by 20%.
Transportation, Energy, Simulation
When a new wind plant is being considered for construction, a necessary first step is to assess the potential long-term power generation capabilities and sources of uncertainty. However, there are considerable differences in how consultants assess the potential performance considerations of a new wind plant. In this project, our team at NREL is developing a set of open source tools to drive standardization in the industry and to identify areas of improvement in these pre-construction estimates so that wind plant operators can maximize their expected profit margins.
Energy, Renewables, Open source
Distribution network operators (DNOs) face enormous pressure to properly prepare for widespread power outage events, which are mostly due to big storms. In this project, MeteoGroup partnered with DNOs in the UK to develop advanced machine learning models that leverage our weather data and forecasts by predicting the expected volume of weather-related network faults. This product is currently being developed for operational use.
Energy, Machine Learning, Data Science
In this research project I created a sample dashboard for monitoring data from a simulated sensor. The dashboard allows the user to generate new data, test how different linear model fitting techniques change with degrees of freedom, and to investigate the limitations of using certain linear models for predicting future sensor values.
IoT, Anomaly Detection, Trend Analysis
Ski resorts depend on a reliable amount of artificially-generated snow for their daily wintertime operations. However, the quality and quantity of artifical snow generation can vary significantly with the ambient weather. Together with the Research & Innovation division of MeteoGroup and TechnoAlpin, we demonstrated our ability to save more than 100€ per snow cannon per day by using hyper-local weather forecasts to optimize snow generation times.
Weather Forecasting, Optimization, Snow CannonsAll cars since the mid-1990s have been equipped with sensors to measure the vehicle's 'diagnostics. Modern vehicles record information from significantly more vehicles. I am actively working with data from connected cars and from simulated vehicles to identify features about the roads from driver-generated data. The above animation shows data from cars in Ann Arbor, MI and demonstrates how windhshield wiper activity compares to weather radar intensity.
Connected Cars, Transportation, Big Data
