Aug 27 - AI, ML, and Computer Vision Meetup

Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Date, Time, and Location

Aug 27, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom!

Robust Concept Protection against Diffusion-Based Image Editing and Personalization

Diffusion-based image editing and personalization models have made it increasingly easy to manipulate and replicate visual concepts from only a few reference images. However, existing protection methods often overfit to a single attack model and fail to generalize across diverse editing pipelines.

In this presentation, I will discuss recent advances in concept protection for generative AI systems, focusing on targeted perturbation strategies and style-sensitive diffusion representations. I will also present experimental findings across multiple editing and fine-tuning scenarios, highlighting the challenges of robustness, transferability, and imperceptibility in practical protection settings. Finally, I will discuss open problems and future directions toward trustworthy generative content ownership.

About the Speaker

Qiuyu Tang is a Ph.D. student in Computer Science and Engineering at Lehigh University. Her research focuses on trustworthy AI, media forensics, and robust protection methods against diffusion-based image editing and personalization systems. Her recent work explores concept protection, style safeguarding, semantic image manipulation, and generative AI robustness. She has contributed to multiple publications in computer vision and AI safety, including research on diffusion model protection and manipulation detection, and has also served as a conference workshop organizer.

From Pixels to the Planet: Building Scalable and Grounded AI for Science

AI has demonstrated a lot of new possibilities, from drafting emails to image editing and generation. The efficacy of AI models is largely built upon a standard machine learning pipeline, where data is fed into models to get representations and predictions, and the performance is evaluated with controlled benchmarks and metrics. However, the mismatch arises when we try to transit this pipeline to the interaction with the real world and use AI for scientific discovery. Beyond close-set decisions, scientists want to discover new categories and propose new hypotheses. In this talk, I will share how I address the challenges of AI for science from the perspectives of data-centric methods and interpretability approaches.

About the Speaker

Jianyang Gu is a postdoctoral scholar at The Ohio State University. His research focuses on using data-centric methods to build scalable and interpretable foundation models for science.

Beyond the Barn: Non-Invasive Acidosis Detection in Dairy Cattle Through Multimodal Gas Emission Intelligence

Rumen acidosis silently costs the global dairy industry billions annually and compromises animal welfare, yet current detection methods remain invasive, delayed, and impractical at scale. Our lab has pioneered a fundamentally new approach: capturing and analyzing exhaled CO₂ and CH₄ gas emission patterns through synchronized RGB-thermal imaging, turning every breath into a diagnostic signal. We developed DualGasNet, a dual-stream deep learning architecture with cross-attention fusion that detects acidosis non-invasively and in real time, achieving state-of-the-art accuracy on a first-of-its-kind livestock gas emission dataset we constructed from scratch.

To push toward explainable, farm-ready AI, we integrate vision-language models — CLIP and LLaVA-1.5 — enabling zero-shot diagnostic reasoning that bridges the gap between deep learning predictions and actionable veterinary insight. This talk will walk through the full pipeline from custom dataset creation to multimodal fusion to VLM-powered interpretation, offering the audience a compelling case study in how computer vision can solve high-impact, real-world problems outside traditional benchmarks.

About the Speaker

Taminul Islam is a Doctoral Research Fellow and PhD candidate at Southern Illinois University Carbondale with 40+ publications, 740+ citations, and an h-index of 16 — with publications in CVPR 2026, WACV 2026 (Oral), ICCV 2025, and Nature Scientific Reports, including a Highly Cited Paper for 2024–25.

Building Real-World Computer Vision Systems with Voxel51

This talk will explore practical workflows for building, evaluating, and improving modern computer vision systems. We’ll dive into real-world approaches to dataset curation, model analysis, multimodal AI workflows, and production-ready vision pipelines using open-source technologies.

The session is designed for engineers, researchers, and AI practitioners looking to better understand how teams are developing and scaling computer vision applications today. Expect practical demos, technical insights, and discussions around the evolving AI tooling ecosystem.

About the Speaker

Daniel Gural is an expert in Physical AI and has been working in the field for over 8 years. Working across healthcare he has experience in both operating use case as well as using Visual AI as an aid in psychology applications as well.