CV

To see a more detailed version of my CV, click on the pdf icon on the right side of this page.

Contact

Preferred Name Daniel
Email ikadebi@utexas.edu

Education

  • 2023 - Now
    Ph.D. in Computer Science
    University of Texas at Austin (UT Austin)
    • Advisor: Kristen Grauman
    • Research Interests: Robotics, Computer Vision, Representation Learning, Program Synthesis
  • 2022 - 2023
    M.Eng. in Electrical Engineering and Computer Science
    Massachusetts Institute of Technology (MIT)
  • 2018 - 2022
    S.B. in Computer Science and Engineering
    Massachusetts Institute of Technology (MIT)
    • GPA: 4.8/5.0
    • Groups: MIT InterVarsity, Black Student Union, African Students Association

Publications

  • May 2024
    AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers
    Jake Grigsby, Justin Sasek, Samyak Parajuli, Daniel Adebi, Amy Zhang, Yuke Zhu Conference on Neural Information Processing Systems (NeurIPS), 2024

Research Experience

  • 2024 - Present
    UT Austin Computer Vision Group
    Graduate Researcher
    • Working under Prof. Kristen Grauman.
  • 2023 - 2024
    Machine Intelligence through Decision Making and Interaction Lab
    Graduate Researcher
    • Worked under Prof. Amy Zhang in the MIDI Lab developing better representations for training robots and other reinforcement learning agents to perform downstream tasks.
  • 2022 - 2023
    MIT Environmental Solutions Initiative (M.Eng. Thesis)
    Graduate Research Assistant
    • Designed and implemented various machine learning models to analyze and predict landslide susceptibility from LiDAR and satellite data.
  • 2022
    MIT Computer Science and Artificial Intelligence Laboratory
    Graduate Research Assistant at Kellis Lab
    • Experimented with using graph variational autoencoders to learn representations for personalized Bayesian Networks in computational biology settings.
  • 2021 - 2022
    MIT Computer Science and Artificial Intelligence Laboratory
    Undergraduate Researcher at Distributed Robotics Laboratory
    • Developed and trained transformer-based deep reinforcement learning models to teach a fixed-wing drone how to fly in a virtual environment while accomplishing subgoals.
  • 2021 - 2022
    MIT Media Lab
    Undergraduate Research Assistant in Camera Culture Group
    • Studied the effectiveness of graph neural network-based simulations to build more effective forecasting systems.

Industry Experience

  • 2023
    IBM Research
    AI Research Scientist Intern
    • Used reinforcement learning to fine-tune large language models to play the word game Taboo. Worked on the Trustworthy AI team under Kush Varshney.
  • 2022
    Google
    Software Engineering Intern
    • Experimented with using state-of-the-art NLP models for call transcript summarization.
  • 2021
    Google
    Software Engineering Intern
    • Created an offline pipeline for the Google Lens team to extract labels from random forest decision tree models.
  • 2020
    Google
    Student Training in Engineering Program (STEP) Intern
    • Contributed to a web application designed to help people learn various, user-chosen topics efficiently.
  • 2019
    IBM
    Software Engineering Intern
    • Developed a program that created and maintained product representations that sellers use to sell to IBM Clients.

Teaching Experience

  • 2024
    CS 373 - Software Engineering (UT Austin)
    Graduate Teaching Assistant
  • 2023
    CS 371P - Object-Oriented Programming (UT Austin)
    Graduate Teaching Assistant
  • 2020 - 2021
    6.046 - Design and Analysis of Algorithms (MIT)
    Grader and Tutor
  • 2020
    6.036 - Introduction to Machine Learning (MIT)
    Lab Assistant

Honors and Awards

Skills

  • Programming
    • Python, C++, C#, Java, Terminal
  • Software
    • PyTorch, Gym, Tensorflow, Scikit-Learn, NumPy, Linux, Unity, Git