Owen Oertell — Resume

Education

Cornell University

2022 — Current

B.S. in Computer Science.

Georgia Institute of Technology

2021 — 2022

Full Time High School Dual Enrollment

Teaching:

  • TA for CS 4789 (Introduction to Reinforcement Learning), Spring 2024
  • TA for CS 4820 (Introduction to Analysis of Algorithms), Fall 2023
  • TA for CS 4820 (Introduction to Analysis of Algorithms), Spring 2023

Graduate-level technical coursework:

  • Lattices (CS 6802)
  • Foundations of Reinforcement Learning (CS 6789)
  • Advanced Programming Languages (CS 6110)
  • Analysis of Algorithms (CS 6820)
  • Complexity Theory (CS 6810)
  • Analysis (MATH 6110)
  • Computational Foundations of Machine Learning (GT CX 4803)

Undergraduate technical coursework:

  • Compilers (CS 4120)
  • Introduction to Analysis of Algorithms (CS 4820)
  • Discrete Mathematics (GT MATH 2603)
  • Differential Equations (GT MATH 2552)
  • Multivariable Calculus (GT MATH 2551)
  • Linear Algebra (GT MATH 1554)

Professional Experience

Software Engineering Intern at NVIDIA

August 2024 – December 2024 • Santa Clara, CA

  • AI + Compilers

Software Engineering Intern at DRW Holdings

June 2024 – August 2024 • Chicago, IL

  • Commodities desk

Undergraduate Researcher at Sun Lab, Cornell University

September 2022 – Current • Ithaca, NY

  • Researching reinforcement learning theory and algorithms.
  • Developing and implementing algorithms for reinforcement learning for diffusion models.

Undergraduate Researcher at Cornell University Artificial Intelligence

September 2022 – Current • Ithaca, NY

  • Participating in reading groups for Reinforcement Learning, Computer Vision, and MLSystems.
  • Developing novel techniques for active learning for taxonomy expansion via coarsened Shannon entropy with Prof. Emaad Manzoor.

Lab Researcher at Dickson Lab, Georgia Institute of Technology

May 2020 – August 2022 • Atlanta, GA

  • Adapted C code from bacterial genome to the human genome for novel copy number variation detection algorithm.
  • Reduced memory consumption by 300GB while maintaining speed via parallelization and low-level C programming.
  • Assisted in development of efficient blood assay technique for bacterial infection identification.
  • Increased data gathering speed by 4x by writing code to use multiple cameras in parallel with single camera port.
  • Co-authored low budget blood assay technique paper; submission for publication in process.

Head of Engineering at Y STEM and Chess Inc. 501(c)(3)

April 2020 – July 2022 • Boise, ID

  • Managed 30 undergraduate and professional SWEs.
  • Led development of website: YStemAndChess.com to provide free mentoring of underprivileged children from around the world and expand Y STEM and Chess to tutor more than 800 children.
  • Interviewed and hired interns and full time developers.
  • Engineered and implemented scalable microservice architecture designs to minimize cost.
  • Developed real-time chess pairing and mentoring system.
  • Implemented recording storage system allowing parents and students to review lessons.

Projects

PrepByAI

July 2021 • Atlanta, GA

  • Led development of website: PrepByAI.com, a free ACT preparation site.
  • Built machine learning model to identify needs and suggest questions to improve performance using term frequency–inverse document frequency and k-means clustering.
  • Over 500 regular users and 9,000+ questions answered to date.

Publications

REBEL: Reinforcement Learning via Regressing Relative Rewards
Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, J. Andrew Bagnell, Jason D. Lee, Wen Sun. Preprint. [paper]
RL for Consistency Models: Reward Guided Text-to-Image Generation with Fast Inference
Owen Oertell, Jonathan Daniel Chang, Yiyi Zhang, Kianté Brantley, and Wen Sun. Preprint. [paper]
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning
Kaiwen Wang, Owen Oertell, Alekh Agarwal, Nathan Kallus, and Wen Sun. To appear in ICML 2024. [paper]
Dataset Reset Policy Optimization for RLHF
Jonathan Daniel Chang, Wenhao Zhan, Owen Oertell, Kianté Brantley, Dipendra Misra, Jason D. Lee, and Wen Sun. Preprint. [paper]
A Kernel Method Approach to Orbital Debris Blast Point Determination
Jackson Kulik, Owen Oertell, and Dmitry Savransky. American Institute of Aeronautics and Astronautics 2024. [paper]
Overdetermined Eigenvector Approach to Passive Angles-Only Relative Orbit Determination
Jackson Kulik, Owen Oertell, and Dmitry Savransky. Journal of Guidance, Control, and Dynamics 2023. [paper]

Awards and Honors

  • Chamblee High School Magnet Salutatorian.
  • Technology Student Association National Competition (each category 500+ submissions):
    • 2nd in software development for DataManager project.
    • Top 25 for data science for pulsar star detection deep neural network.
  • Georgia Science and Engineering Fair Award for Novel Application of Document Distance for CNV Detection.
  • National Merit Scholarship Recipient.
  • Presidential Scholar Semi-finalist.

Technical Skills

  • Languages: C (OpenACC, OpenMP), Python C++, Java, SQL, C#, JavaScript, Ruby, HTML/CSS
  • Developer Tools: Jupyter Notebooks, Git, Docker, Kubernetes, VS Code, Amazon AWS, VIM, Makefiles
  • Libraries: TensorFlow, Valgrind, Numpy, Pandas, OpenCV, Pillow, Matplotlib
  • Frameworks: React.js, Angular.js, Electron.js, Node.js, Express.js, Svelte, Tailwind.css, .NET core
  • Databases: PostgreSQL, MongoDB

You can also view my resume as a pdf here.