A Probabilistic Modeling Approach to CRISPR-Cas9
Lavington, Jonathan, et al. "A Probabilistic Modeling Approach to CRISPR-Cas9"
CV
Education
- BS in Applied Math, University of Colorado Boulder, 2017
- MS in Applied Math, University of Colorado Boulder, 2018
- PhD in Computer Science, University of British Columbia Fall 2018 - Present
Work experience
- Summer 2020-Present: Researcher
- Inverted AI
- Supervisor: Adam Scibior
- Summer 2019: Applied Scientist Intern
- Amazon
- Supervisor: Amber Roy Chowdhury
- Summer 2017: Data Scientist Intern
- Seagate Technology
- Supervisor: Michael Renella
- Spring 2017: Data Scientist Intern
- Seagate Technology
- Supervisor: Michael Renella
- Summer 2016: Analytic Partners
- Analytic Partners
- Supervisor: Michael Leichman
Teaching experience
- 2018/2019: Teaching Assistant
- University of British Columbia, Vancouver
- Course: Introduction to Machine Learning
- 2017/2018: Teaching Assistant
- University of British Columbia, Vancouver
- Course: Calculus 3, Precalculus
- 2017/2018: Learning Assistant
- University of Colorado, Boulder
- Course: Applied Probability, Matrix Methods, Numerical Analysis
Skills
- Programming Languages: MATLAB, R, Python, Clojure, Erlang, Hive/Oracle SQL, C/C++
- Software Packages: Tensorflow, PyTorch, Theano, Keras, Networkx, Pandas, Scikitlearn, Scipy, Numpy, RShiny, Pyspark, SparkR
- Software & Tools: LaTeX, Tableau, Mathematica, Db Visualizer, Alteryx
- Database Systems: Hadoop, Oracle
- Operating Systems: Linux, OSX, Windows
- GitHub: https://github.com/WilderLavington
Publications
An Empirical Study of Non-Uniform Sampling in Off-Policy Reinforcement Learning for Continuous Control
Nicholas, Ioannidis, et al. "An Empirical Study of Non-Uniform Sampling in Off-Policy Reinforcement Learning for Continuous Control" NuerIPS Workshop 2021.
Robust Asymmetric Learning in POMDPs
Warrington, Andrew, et al. "Robust Asymmetric Learning in POMDPs." ICML 2021.
A Closer Look at Gradient Estimators with Reinforcement Learning as Inference
Lavington, Jonathan, et al. "A Closer Look at Gradient Estimators with Reinforcement Learning as Inference."
Improved Policy Optimization for Online Imitation Learning
Lavington, Jonathan, et al. "Improved Policy Optimization for Online Imitation Learning." CoLLAs 2022.
Vehicle Type Specific Waypoint Generation
Liu, Yunpeng, et al. "Vehicle Type Specific Waypoint Generation." IROS 2022.
Critic Sequential Monte Carlo
Lioutas, Vasileios, et al. "Critic Sequential Monte Carlo." ICLR 2023.
Target-based Surrogates for Stochastic Optimization
Lavington, Jonathan, et al. "Target-based Surrogates for Stochastic Optimization." ICML 2023.
Noise Is Not the Main Factor Behind the Gap Between Sgd and Adam on Transformers, But Sign Descent Might Be
Kunstner, Frederik, et al. "Noise Is Not the Main Factor Behind the Gap Between Sgd and Adam on Transformers, But Sign Descent Might Be"
A Diffusion-Model of Joint Interactive Navigation
Niedoba, Matthew, et al. "A Diffusion-Model of Joint Interactive Navigation." NuerIPS 2023.
Conditional Permutation Invariant Flows
Berend, Zwartsenberg, et al. "Conditional Permutation Invariant Flows." TMLR 2023.
Analyzing and Improving Greedy 2-Coordinate Updates for Equality-Constrained Optimization via Steepest Descent in the 1-Norm
Ramesh, Amrutha, et al. "Analyzing and Improving Greedy 2-Coordinate Updates for Equality-Constrained Optimization via Steepest Descent in the 1-Norm" Arxiv 2023.
Video Killed the HD-Map: Predicting Driving Behavior Directly From Drone Images
Liu, Yunpeng, et al. "Video Killed the HD-Map: Predicting Driving Behavior Directly From Drone Images" IEEE-ITSC 2023.
Video Killed the HD-Map: Predicting Driving Behavior Directly From Drone Images
Setareh, Dabiri, et al. "Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models" CVPR 2023 Workshop 2023.
Talks
Robust Asymmetric Learning in POMDPs
Talk at Internation Conference on Machine Learning, Vienna, Austria
Target-based Surrogates for Stochastic Optimization
Talk at Internation Conference on Machine Learning, O'ahu, Hawaii