July 23 - Chicago AI, ML and Computer Vision Meetup
Details
Join our in-person meetup in Chicago to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.
Pre-registration is mandatory to clear building security.
Date, Time and Location
Jul 23, 2026
5:30 PM - 8:30 PM CT
10 S Riverside Plaza, Suite 800, Chicago, IL 60606
Talks will include:
Teaching Vision-Language Models to Read Spine MRIs
Radiologists face mounting report volumes and rising burnout, creating demand for assistive tools that can draft structured findings directly from imaging. We fine-tuned Qwen2.5-VL with LoRA on roughly 5000 paired lumbar spine MRI studies and reports, training the model to generate spinal level-specific findings evaluated with lexical, semantic, and clinical metrics.
We are extending the work with self-supervised pretraining on an additional 20000 unlabeled studies to build domain-specific backbones for downstream tasks including lumbar spine MRI segmentation and classification. The talk shares current results, challenges faced, and why evaluating structured radiology reports is harder than standard metrics suggest.
About the Speaker
Mattia Perrone is a Research Scientist at Rush University Medical Center, where his work focuses on deep learning, computer vision and vision-language models for medical imaging. He holds a dual Master’s Degree in Biomedical Engineering from Politecnico di Milano and the University of Illinois Chicago.
Improving Efficiency of DNN Stereo Depth Estimation models
Stereo depth estimation is a core perception capability in robotics and autonomous systems, converting rectified stereo image pairs into dense disparity and depth maps. While modern deep stereo methods achieve strong accuracy, state-of-the-art models, especially transformer-based architectures often incur high computational and energy costs, limiting deployment on resource-constrained devices.
This studies stereo depth estimation and proposes an efficiency-oriented modification to a transformer-based stereo pipeline by incorporating Walsh–Hadamard Transform (WHT) operations into the feature extraction stage. Specifically, we experiment with a WHT-based convolutional substitute (WHTConv2D) to reduce multiply-accumulate operations while preserving representational capacity via structured ±1 transforms.
We inferenced classical and neural stereo models on a specific dataset to compare, culminating in an STereo TRansformer (STTR) baseline and a WHTConv2D-enhanced variant. The proposed design achieves an observed 18.33% efficiency improvement relative to the baseline configuration while maintaining competitive long-range disparity accuracy.
About the Speaker
Prathyush Sajith, MS ECE, University of Illinois Chicago. Experienced in building Machine Learning Models - including but not limited to ViTs and LLMs.
Testing AI Systems in Production: Data Quality, Drift, and Model Evaluation
AI systems can pass offline evaluation and still fail in production when real-world data changes, features become stale, labels or feedback signals are incomplete, or model behavior drifts away from expected outcomes. This talk shares practical patterns for testing and evaluating AI systems after deployment, including data quality checks, drift detection, online/offline metric comparison, model monitoring, and rollback analysis.
Using personalization and recommendation systems as examples, we will examine how teams can build evaluation workflows that catch quality issues before users do. Attendees will leave with a practical checklist for making AI-backed systems easier to evaluate, debug, and operate as data changes over time.
About the Speaker
Jayakumar Ramalingam is a Staff Software Engineer and Cloud Architect at SiriusXM with over 16 years of experience building cloud-native platforms, real-time data pipelines, resilient APIs, and AI/ML-enabled applications at production scale.
Building Real-World Computer Vision Systems
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
Harpreet Sahota is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.