April 23 - Advances in AI at Johns Hopkins University

Join our virtual Meetup to hear talks from researchers at Johns Hopkins University on cutting-edge AI topics.

Date, Time and Location

Apr 23, 2026
9AM PST
Online. Register for the Zoom!

Recent Advancements in Image Generation and Understanding

In this talk, I will provide an overview of my research and then take a closer look at three recent works. Image generation has progressed rapidly in the past decade-evolving from Gaussian Mixture Models (GMMs) to Variational Autoencoders (VAEs), GANs, and more recently diffusion models, which have set new standards for quality. I will begin with DiffNat (TMLR’25), which draws inspiration from a simple yet powerful observation: the kurtosis concentration property of natural images. By incorporating a kurtosis concentration loss together with a perceptual guidance strategy, DiffNat can be plugged directly into existing diffusion pipelines, leading to sharper and more faithful generations across tasks such as personalization, super-resolution, and unconditional synthesis.

Continuing the theme of improving quality under constraints, I will then discuss DuoLoRA (ICCV’25), which tackles the challenge of content–style personalization from just a few examples. DuoLoRA introduces adaptive-rank LoRA merging with cycle-consistency, allowing the model to better disentangle style from content. This not only improves personalization quality but also achieves it with 19× fewer trainable parameters, making it far more efficient than conventional merging strategies.

Finally, I will turn to Cap2Aug (WACV’25), which directly addresses data scarcity. This approach uses captions as a bridge for semantic augmentation, applying cross-modal backtranslation (image → text → image) to generate diverse synthetic samples. By aligning real and synthetic distributions, Cap2Aug boosts both few-shot and long-tail classification performance on multiple benchmarks.

About the Speaker

Aniket Roy is currently a Research Scientist at NEC Labs America. He recently earned a PhD from the Computer Science department at Johns Hopkins University under the guidance of Bloomberg Distinguished Professor Prof. Rama Chellappa.

From Representation Analysis to Data Refinement: Understanding Correlations in Deep Models

This talk examines how deep learning models encode information beyond their intended objectives and how such dependencies influence reliability, fairness, and generalization. Representation-level analysis using mutual information–based expressivity estimation is introduced to quantify the extent to which attributes such as demographics or anatomical structural factors are implicitly captured in learned embeddings, even when they are not explicitly used for supervision. These analyses reveal hierarchical patterns of attribute encoding and highlight how correlated factors emerge across layers. Data attribution techniques are then discussed to identify influential training samples that contribute to model errors and reinforce dependencies that reduce robustness. By auditing the training data through influence estimation, harmful instances can be identified and removed to improve model behavior. Together, these components highlight a unified, data-centric perspective for analyzing and refining correlations in deep models.

About the Speaker

Basudha Pal is a recent PhD graduate from the Electrical and Computer Engineering Department at Johns Hopkins University. Her research lies at the intersection of computer vision and representation learning, focusing on understanding and refining correlations in deep neural network representations for biometric and medical imaging using mutual information analysis, data attribution, and generative modeling to improve robustness, fairness, and reliability in high-stakes AI systems.

Scalable & Precise Histopathology: Next-Gen Deep Learning for Digital Histopathology

Whole slide images (WSIs) present a unique computational challenge in digital pathology, with single images reaching gigapixel resolution, equivalent to 500+ photos stitched together. This talk presents two complementary deep learning solutions for scalable and accurate WSI analysis. First, I introduce a Task-Specific Self-Supervised Learning (TS-SSL) framework that uses spatial-channel attention to learn domain-optimized feature representations, outperforming existing foundation models across multiple cancer classification benchmarks. Second, I present CEMIL, a contextual attention-based MIL framework that leverages instructor-learner knowledge distillation to classify cancer subtypes using only a fraction of WSI patches, achieving state-of-the-art accuracy with significantly reduced computational cost. Together, these methods address critical bottlenecks in generalization and efficiency for clinical-grade computational pathology.

About the Speaker

Tawsifur Rahman is a Ph.D. candidate in Biomedical Engineering at Johns Hopkins University, advised by Prof. Rama Chellappa and Dr. Alex Baras, with research focused on weakly supervised and self-supervised deep learning for computational pathology. He has completed two clinical data science internships at Johnson & Johnson MedTech and has published extensively in venues including Nature Modern Pathology, Nature Digital Medicine, MIDL, and IEEE WACV, accumulating over 8,500 citations and recognition in Stanford's Top 2% Scientists ranking.

Towards trustworthy AI under real world data challenges

The current paradigm of training AI models relies on fundamental assumptions that the data we have is clean, properly annotated, and sufficiently diverse across domains. However, this is not always true for the real world. In practice, data is may be physically corrupt, incompletely annotated, and specific to certain domains. As me move towards large scale general purpose models like LLMs and foundation models, it is even more important to address these data challenges so that we can train trustworthy AI models even with noisy real world data. In this presentation, we discuss some methods to tackle these potential issues.

About the Speaker

Ayush Gupta is a Ph.D. student at the AIEM lab, Johns Hopkins University in the department of Computer Science. He is advised by Prof. Rama Chellappa and is working on problems in Computer Vision and Deep Learning. His research has two focus points - general-purpose vision language models, where he works on multimodal LLMs on tasks like VQA, Video Grounding and LLM interpretability; and on fine-grained computer vision problems, where he works on person re-identification and gait recognition.