About

I am currently pursuing a Ph.D. in Computer Science at Cornell University. I am broadly interested in the intersection of machine learning, deep learning, and natural language processing. I am currently working on adapting diffusion to language generation. You can view my publications here.

I previously graduated from Carnegie Mellon University with an M.S. in Language Technologies. While there, I utilized textual information to develop knowledge graph completion models with greater robustness to sparsity under the advisement of Dr. Carolyn Rosè. My studies were supported by Research Fellowships from the university.

I completed my undergraduate studies at Texas A&M University and received a B.S. in Computer Science and a Minor in Mathematics. During my undergraduate studies, I was fortunate to be supported as a Craig and Galen Brown Foundation Scholar. While there, I conducted research in Clinical NLP with Dr. Bobak Mortazavi. Specifically, I worked on mining structured information from clinical notes for adverse outcome prediction and on improving the clinical coherence of abstractive medical report generation.

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News

[December 2022]	New preprint out titled "Latent Diffusion for Language Generation". We demonstrate that diffusion can augment the generative capabilities of a pre-trained language model. Check it out here.
[October 2022]	Paper accepted at EMNLP 2022 that explores how to best extract and utilize textual entity representations from language models for knowledge graph completion.
[August 2022]	I began pursuing my Ph.D. in Computer Science at Cornell University! I was fortunate to receive a University Fellowship to support my studies.
[August 2022]	I graduated from Carnegie Mellon University with an M.S. in Language Technologies.
[August 2021]	I was awarded a Research Fellowship from Carnegie Mellon University to support my graduate studies.
[June 2021]	I participated in the shared task organized by the DialDoc Workshop at ACL-IJCNLP 2021 on identifying grounding information in a reference document to aid a conversational agent. You can check out our system description here.
[May 2021]	Paper accepted at ACL-IJCNLP 2021 on developing a knowledge graph completion pipeline that leverages textual information for improved robustness to sparsity. You can check it out here.
[September 2020]	Paper accepted at Findings of EMNLP 2020 on generating clinically coherent medical reports from chest x-rays. You can check it out here.
[August 2020]	I was awarded a Research Fellowship from Carnegie Mellon University to support my graduate studies.
[June 2020]	Paper accepted at MLHC 2020 on extracting problem lists from clinical notes for the prediction of adverse outcomes. You can check it out here.
[May 2020]	I will be interning with Facebook Search for the summer.
[May 2020]	I graduated from Texas A&M University with a B.S. in Computer Science and a Minor in Mathematics. I'm excited to continue my education as a Master's student at Carnegie Mellon University next Fall!
[April 2020]	Paper on extracting problem lists from clinical notes accepted at CHIL 2020 as a workshop spotlight.
[December 2019]	Presented my paper at the 2019 NeurIPS Machine Learning for Health (ML4H) Workshop on predicting ICU readmission and mortality from clinical notes.
[May 2019]	I will be interning with Facebook for the summer working with the Notification Ranking team.
[May 2019]	My undergraduate thesis on predicting ICU readmission using clinical notes was recognized as an Oustanding Undergraduate Honors Thesis by the computer science department.