Hi there! I am a final-year PhD student in the Computer Vision Lab
and InfoLab
in the Department of Computer Science and Engineering at Texas A&M University.
I work closely with Prof. Shu Kong
and Prof. James Caverlee
on Vision-Language Models and Information Retrieval.
Previously, I was fortunate to intern as a System Software Engineer at HPE,
and as a Research Intern at VISA Research.
Mar 2024: I was awarded TAMU CSE department travel grant.
Mar 2024: I received TAMU CSE Graduate Teaching Assistant Excellence Award (1 each year).
Feb 2024: 1 paper on improving VLMs for zero-shot recognition was accepted to CVPR'24.
Research
My research focuses on few-shot recognition with pretrained foundation models, especially Vision-Language Models (VLMs),
for solving challenging fine-grained recognition tasks,
such as recognizing the biological species from image data.
The core challenge in limited labeled data motivates my prior works in retrieving open data and
exploiting unlabeled data to boost few-shot
recognition performance.
In addition, I have broad interest in cyber-physical systems, building efficient multimodal systems for IoT and edge devices,
as well as AI for Science that leverages machine learning to solve crucial healthcare and geoscience problems.
We compile the first Unusual Activities Localization benchmark and propose VLM-LLM framework to improve multimodal
models for better video understanding.
We develop efficient vision system to estimate hyperlocal rainfall from doorbell camera for precision residential irrigation,
saving > 9,000 gallons of water/month.
We build the first end-to-end mobile voice assistant system to assist Emergency Medical Technicians in selecting proper protocols for critical medical intervention.
We develop a feature extraction method for efficient integration of massive 4D seismic data, achieveing 2x error reduction and 6x speedup.
Workshop Papers/Presentations
(* denotes equal contribution)
T. Liu, H. Zhang, S. Parashar, S. Kong. "Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuning." CVPR 2025 Workshop on Computer Vision in the Wild. Nashville, U.S, June 2025.
T. Liu, H. Zhang, S. Parashar, S. Kong. "Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuning." CVPR 2025 Workshop on Fine-Grained Visual Categorization. Nashville, U.S, June 2025.
Y. Yang*, T. Liu*, S. J. Lee, C.-Y. Liao, H. Shao, F. Pasquel, M. B. Weber, E. Keyvanshokooh,
G.-G. P. Garcia. "Development and Fairness Evaluation of CVD Risk Prediction Models for
Patients with Type-2 Diabetes." Society for Medical Decision Making Annual Meeting, Boston,
MA, October 2024. Poster
Y. Yang*, T. Liu*, S. J. Lee, C.-Y. Liao, H. Shao, F. Pasquel, M. B. Weber, E. Keyvanshokooh,
G.-G. P. Garcia. "Survival Modeling for CVD Risk Estimation Among a Diverse Cohort with
Type-2 Diabetes." AI for Health Equity Symposium AIM-AHEAD Annual Meeting, Atlanta,
GA, August 2024.
S. Parashar*, Z. Lin*, T. Liu*, X. Dong, Y. Li, D. Ramanan,
J. Caverlee, and S. Kong, "The Neglected Tails in Vision-Language Models."
ICML 2024 Workshop on Data-centric Machine Learning Research (DMLR):
Datasets for Foundation Models, Vienna, Austria, July 2024.
L. Jin, T. Liu, A. Haroon, R. Stoleru, M. Middleton, Z. Zhu,
T. Chaspari, "Demo: EMSAssist -- An End-to-End Mobile Voice Assistant at the Edge for
Emergency Medical Services." The 21st IEEE International Conference on Mobile Systems, Applications
and Services (MobiSys), 2023, Helsinki, Finland, June 2023.
Teaching Assistance
CSCE670: Information Storage and Retrieval, Spring 2025
CSCE606: Software Engineering, Fall 2023, Fall 2025
CSCE313: Introduction to Computer Systems, Summer 2023
