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 |
ikadebi@utexas.edu |
Education
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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
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2022 - 2023
M.Eng. in Electrical Engineering and Computer Science
Massachusetts Institute of Technology (MIT)
- Concentration: Artificial Intelligence
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Thesis: "Landslide Susceptibility Prediction Adaptive to Triggering Events"
- Research Group: MIT Environmental Solutions Initiative
- Advisor: John E. Fernández
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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
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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
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2024 - Present
UT Austin Computer Vision Group
Graduate Researcher
- Working under Prof. Kristen Grauman.
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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.
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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.
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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.
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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.
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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
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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.
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2022
Google
Software Engineering Intern
- Experimented with using state-of-the-art NLP models for call transcript summarization.
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2021
Google
Software Engineering Intern
- Created an offline pipeline for the Google Lens team to extract labels from random forest decision tree models.
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2020
Google
Student Training in Engineering Program (STEP) Intern
- Contributed to a web application designed to help people learn various, user-chosen topics efficiently.
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2019
IBM
Software Engineering Intern
- Developed a program that created and maintained product representations that sellers use to sell to IBM Clients.
Teaching Experience
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2024
CS 373 - Software Engineering (UT Austin)
Graduate Teaching Assistant
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2023
CS 371P - Object-Oriented Programming (UT Austin)
Graduate Teaching Assistant
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2020 - 2021
6.046 - Design and Analysis of Algorithms (MIT)
Grader and Tutor
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2020
6.036 - Introduction to Machine Learning (MIT)
Lab Assistant
Honors and Awards
Skills
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Programming
- Python, C++, C#, Java, Terminal
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Software
- PyTorch, Gym, Tensorflow, Scikit-Learn, NumPy, Linux, Unity, Git