collaborators

6 papers

cs.CV2026

CoLA: Cross-Modal Low-rank Adaptation for Multimodal Downstream Tasks

Wish Suharitdamrong, Tony Alex, Muhammad Awais +1

Foundation models have revolutionized AI, but adapting them efficiently for multimodal tasks, particularly in dual-stream architectures composed of unimodal encoders, such as DINO…

cs.AI2026

From Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs

Wish Suharitdamrong, Muhammad Awais, Xiatian Zhu +1

Multimodal Large Language Models (MLLMs) can listen and see, but how do audio and visual signals actually travel through the network to shape an answer? Despite their growing role…

cs.SD2026

PAL: Probing Audio Encoders via LLMs -- Audio Information Transfer into LLMs

Tony Alex, Wish Suharitdamrong, Sara Atito +4

Integration of audio perception into large language models (LLMs) is an emerging research area for enabling machine listening applications, yet efficient transfer of rich audio sem…

q-bio.QM2025

Few-Label Multimodal Modeling of SNP Variants and ECG Phenotypes Using Large Language Models for Cardiovascular Risk Stratification

Niranjana Arun Menon, Yulong Li, Iqra Farooq +3

Cardiovascular disease (CVD) risk stratification remains a major challenge due to its multifactorial nature and limited availability of high-quality labeled datasets. While genomic…

cs.LG2025

How Effectively Can Large Language Models Connect SNP Variants and ECG Phenotypes for Cardiovascular Risk Prediction?

Niranjana Arun Menon, Iqra Farooq, Yulong Li +4

Cardiovascular disease (CVD) prediction remains a tremendous challenge due to its multifactorial etiology and global burden of morbidity and mortality. Despite the growing availabi…

cs.SD2025

SSLAM: Enhancing Self-Supervised Models with Audio Mixtures for Polyphonic Soundscapes

Tony Alex, Sara Ahmed, Armin Mustafa +2

Self-supervised pre-trained audio networks have seen widespread adoption in real-world systems, particularly in multi-modal large language models. These networks are often employed…