Ryan Sander is an Image Scientist with the United States Government. He recently finished his Master's of Engineering in Artificial Intelligence from the Massachusetts Institute of Technology, advised by Professor Daniela Rus and Professor Sertac Karaman in MIT's CSAIL Distributed Robotics Laboratory. Ryan's Master's research focused primarily on investigating and applying novel deep reinforcement learning algorithms to autonomous vehicles. Prior to his Master's, Ryan completed his Bachelor's of Science in Electrical Engineering and Computer Science and Mathematical Economics from the Massachusetts Institute of Technology in 2020.
Outside of research, Ryan has had the chance to work as a teaching assistant for MIT's 6.801/6.866: Machine Vision course under Professor Berthold K.P. Horn (Fall 2020), as well as pursue graduate coursework in reinforcement learning, computer vision for autonomous vehicles, and probabilistic programming. In his free time, Ryan enjoys running and biking, as well as lifting weights and listening to some good old heavy metal.
Thesis: Interpolated Experience Replay for Improved Sample Efficiency of Model-Free Deep Reinforcement Learning Algorithms
BS Electrical Engineering and Computer Science, Mathematical Economics, 2020
Deep Learning Theory Summer School, 2021
6.s191: Introduction to Deep Learning | Massachusetts Institute of Technology | January 2021
Course covering the fundamentals of deep learning, including deep neural networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Variational Autoencoders (VAEs), Deep Reinforcement Learning, and Generative Adversarial Neural Networks (GANs). The course is structured with both theoretical and hands-on components. More information can be found here.
6.801/6.866: Machine Vision | Massachusetts Institute of Technology | Sep - Dec 2020
Computer vision course focused on geometric computer vision algorithms, such as edge detection, time to contact, motion estimation, camera calibration, and recovery of 3D structure from 2D imagery.
6.036: Intro to Machine Learning | Massachusetts Institute of Technology | Sep - Dec 2018
Introductory machine learning course that primarily covers classical and novel topics in supervised learning. Topics include SVM, regression, neural networks, CNNs, RNNs, reinforcement learning, recommendation systems, nearest neighors, and decision trees.
6.004: Computation Structures | Massachusetts Institute of Technology | Feb - May 2018
Introductory computer architecture course that primarily covers MOSFET structures, computational primitives such as adders and registers, pipelining, parallelism, operating systems, and Assembly.
MIT Global Startup Labs | Jan 2020 | Montevideo, Uruguay
Collaboratively developed an AWS EC2 Python module for managing cloud computing resources for our students. Developed lessons and tutorials for computer vision and Python machine learning packages, such as PyTorch, TensorFlow, and OpenCV.
MIT Global Teaching Labs | Jan 2019 | Amman, Jordan
Introduced students in Amman, Jordan to the Python programming language through collaborative exercises and interactive instruction.
Machine Learning and Python Tutor
Wyzant | March 2020 - present | Remote
Tutoring students online in machine learning, Python, artificial intelligence, and econometrics. I have over 400 hours of tutoring experience, as well as 115 5.0/5.0 ratings. You can find my profile here, though depending on the number of students I'm tutoring at the time, it may not be available. Linked below is a codebase I've created for tutoring.
Machine Learning Mentor
Polygence | February 2021 - present | Remote
As a mentor with Polygence, I advise and mentor high school students with semester-long machine learning technical projects of their choice. You can find my profile here.
Numerade | April 2020 - present | Remote
Developing videos for solutions to textbook problems in linear algebra, probability and statistics, and pre-calculus. You can find my profile here.
Webmaster Committee Chair
Eta Kappa Nu, Beta Theta Chapter
Leading a project for integrating and iterating on a collaborative filtering-based class recommendation system for students at MIT. Additionally, led an effort to develop documentation for our website codebase to optimize the webmaster transition process.
Community Service Chair
Tau Beta Pi, Mass Beta Chapter
Providing community service mentorship for students to help them create meaningful impact in the local community. Past projects have included: Habitat for Humanity, Red Sox Green Team, and Girls Who Code.
Tech Showcase co-Director
MIT Energy Conference
Organized a two-hour technology showcase featuring 40 energy technology companies at the 2019 MIT Energy Conference.
MIT Undergraduate Energy Club
Leading a team of 50 members to organize events and create opportunities in energy education and professional development. Past events have included: Working the Energy Transition forum, MIT Energy Career Fair, and energy social mixers.
Community Service and Philanthropy Chair
Kappa Sigma, Gamma Pi Chapter
Organized community service and philanthropy events for my chapter. Past projects have included: Military Heroes Campaign fundraiser, career fair t-shirt drive, and food pantry and homeless dinner service programs.