Curriculum Vitae

Ryan Sander



  • Programming skills

    Python, PyTorch, TensorFlow, RLlib, C++, Linux, AWS, ROS, Anaconda, Gen, Docker, MATLAB, Stata, R

  • Computer Science skills

    Reinforcement Learning, Computer Vision, Machine Learning, Generative Models, Graphical Models, Search Algorithms, Data Structures

  • Electrical Engineering skills

    Signal Processing, State Space Control & Estimation, Inference, Electromagnetics, Arduino, Circuits

  • Economics Skills

    Econometrics, Statistics, Real Analysis, Intermediate Microeconomics, Introductory Macroeconomics, Energy Economics

  • Communication and Collaboration Skills

    GitHub, LaTeX, Google Suite, Microsoft Office

  • Project Management and Leadership skills

    DSMs, Critical Path Method, Task Documentation, Note Organization, Google Drive, DropBox, Microsoft Office, Asana

  • Language skills

    English - native | Spanish - basic conversational proficiency, reading and writing


Research Experience

  • Graduate Research Assistant, MIT CSAIL

    Massachusetts Institute of Technology | January 2021 - May 2021

    Developing an interpolated experience replay framework for improving the sample efficiency of model-free reinforcement learning algorithms using Python and RLlib.

  • Research Assistant, MIT CSAIL

    Massachusetts Institute of Technology | August 2019 - December 2020

    Developed a multi-agent reinforcement learning simulation platform using Python, TensorFlow, OpenAI, and Docker. Once it becomes publicly available, our codebase can be found here.

  • Undergraduate Research Assistant

    MIT Photovoltaics Lab | September 2017 - May 2018

    Utilizing MATLAB to write semiconductor physics model scripts, and to iteratively solve for minority excess carrier density for long-timescale degradation measurements. Using Lifetime measurement instruments and degradation testing to determine the cause of light and elevated temperature-induced degradation (LeTID)/ carrier-induced degradation (CID) in Passivated Emitter Rear Contact (PERC) solar cells. You can find the GitHub repository for this code base here.

Industry Experience

  • Lidar Imagery Scientist

    United States Department of Defense | August 2021 - present

    Conducting research on machine learning applications.

  • Data Science and Machine Learning Intern

    Nasdaq | June 2020 - August 2020

    Developed a portfolio optimization framework for time series data using methodologies from Deep Reinforcement Learning, Temporal Convolutional Neural Networks, and clustering and regression methods. Implemented these modules using Python, TensorFlow, Scikit-Learn, and AWS EC2.

  • Lidar Imagery Scientist

    United States Department of Defense | June 2018 - August 2019

    Developed a neural network-based building footprint extraction pipeline using PyTorch and AWS. Presented final projects to agency leadership & at 2018 Lidar Community of Practice Conference.

  • Data Science Intern

    Spacemaker AI | December 2018 - May 2019

    Utilized AWS, Python, & Docker to create, preprocess, & analyze data for energy efficiency modeling. Used DCGAN neural network topologies to generate & evaluate urban planning designs.

  • Electrical Engineering Intern

    Raytheon | May 2017 - August 2017

    Built circuit boards for Spectroscopy-based Explosive Detection System Optimized system power delivery through the use of vector network analyzers & oscilloscopes.

Outside Project Experience

  • Webmaster

    MIT Eta Kappa Nu

    Maintaining codebase and leading team to integrate and iterate upon a collaborative-filtering based class recommendation system. Writing documentation for our codebase to assist future webmasters with troubleshooting. You can find this website at

  • Webmaster

    MIT Tau Beta Pi

    Updating website and adding additional website documentation. Developing features such as FAQ and event ideation pages. You can find this website at

  • Webmaster

    MIT Energy Club

    Updating websites and and domains, and maintaining/building features through Squarespace, Wix, and GoDaddy. You can find our main website at

  • Developer

    "My CS/AI Quick Reference Guide"

    Documenting open-source solutions to problems I have come across in computer science and machine learning. You can find my most updated guide here (Google Docs), as well as on this site here.


  • MEng Artificial Intelligence

    Massachusetts Institute of Technology | September 2020 - May 2021

  • BS Electrical Engineering and Computer Science, Mathematical Economics

    Massachusetts Institute of Technology | September 2016 - May 2020

  • Deep Learning Theory Summer School

    Princeton University | July - August 2021

Relevant Coursework

  • Relevant Graduate Coursework:

    Machine Learning, Visual Navigation for Autonomous Vehicles (VNAV), Computer Vision, Embodied Intelligence, Probabilistic Programming and Artificial Intelligence, Feedback System Design

  • Relevant EECS Undergraduate Coursework:

    Intro to Machine Learning, Inference, Signals & Systems, Robotics: Science and Systems, Circuits and Electronics, Algorithms, Electromagnetics

  • Relevant Economics Undergraduate Coursework:

    Econometrics, Economic Research, Intermediate Microeconomics, Energy Economics, Real Analysis, Computational Science and Engineering

Societies and Membership

  • Academic Societies

    Tau Beta Pi, Eta Kappa Nu, Phi Beta Kappa

    Professional Societies