I am a second-year Computer Sceince Ph.D. student at the University of Maryland, advised by Soheil Feizi.
My research focus centers around Image Generative Models and Vision-Language Models. Currently, I am working on projects that aim to enhance the compositional abilities of Generative Models, such as Stable Diffusion. Additionally, I am working on enhancing image editing capabilities in Text-to-Image Generative Models
In the past, my research has spanned various domains, including Deep Learning Robustness, 3D Vision, and the application of Machine Learning to address neuroscientific challenges, such as improving the performance of seizure detection devices.
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Our paper demonstrates that text-to-image generative models often fail at accurately composing attributes and relationships due to sub-optimal text conditioning by the CLIP text-encoder, and we show that significant compositional improvements can be achieved by fine-tuning a simple linear projection on CLIP's representation space.
Our approach enhances seizure detection in epilepsy patients by converting raw EEG data into informative embeddings, achieving state-of-the-art classification results with an event-based sensitivity of 100% and specificity of 99% on the CHB-MIT scalp EEG dataset.
This research offers a data-centric approach to enhance machine learning model robustness against imperceptible adversarial perturbations by detecting and removing challenging samples during training, resulting in improved adversarial training.
PhytoOracle (PO) introduces modular, scalable pipelines for processing large volumes of phenomics data, improving efficiency, enabling data fusion, and supporting multi-system trait extraction, with broad applicability across species and various data sources, including drone data.
Our study identifies vulnerabilities in current out-of-distribution (OOD) detection methods and presents the Adversarially Trained Discriminator (ATD) as a robust alternative with improved performance.