2025Manuscript
Model, dataset & draft
Fine-tuning LLMs for batch prompting
Fine-tuned language models that answer batches of questions in one inference pass.
2019–2024 / Austin, TX
UT Austin
Computer science / BS & MS
A foundation in computer science, artificial intelligence and machine learning.
- BS in Computer Science, 2019–2023.
- MS in Computer Science, 2022–2024.
- Research spanning factuality evaluation, robot imitation learning and efficient language-model prompting.
Related work
2024Publication
EMNLP 2024
Detecting factual errors with DEEP
Ensembling prompt judgments to detect factual inconsistencies in generated summaries.
2024Repository
Research project
Mastering Leduc Hold’em
Comparing reinforcement learning and counterfactual regret minimization in imperfect-information poker.
2023Repository
Zero-shot & multi-task
Shared context. Multiple answers.
Give a language model one document and several questions or requested outputs. Return the answers together in one inference pass.
2023Manuscript
Research project
Factuality detection with AlignScore and LLMs
Context decomposition and prompt-based evaluation of factual consistency.
2023Repository
2023Manuscript
Research project
Temporal grounding in egocentric video
Contrastive learning, hard negatives and temporal reordering for video-language representations.
2022Repository
Reinforcement Learning 2022
Learning from mixed-quality demonstrations
Reformulating robot imitation learning as a sequence of returns, states, and actions.
2022Repository
Research project
Zero-shot controllable text generation
Topical control through contrasting expert language models and self-organizing structures.
2021Repository
Research project
Adversarial attacks on question answering
Modified beam search for adversarial triggers against ELECTRA question answering.
2019–2020Research period
Research project
Deep reinforcement learning for wells
Planning well paths through 3D subsurface data with actor-critic methods.