Oct 15 - 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.

Time, Place and Location

Oct 15, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom!

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.

Where Should Your Model Live? A Framework for Tiering Computer Vision Deployments

Where should a computer vision model actually run - on-device, near the edge, or in the cloud? It's a decision that looks simple until requirements like latency, cost, connectivity, and update cadence start pulling in different directions, often revealing themselves only after deployment.

Drawing on hands-on experience developing and deploying CV models across Hailo, Nvidia, Qualcomm and AWS platforms, this talk introduces a practical framework for tiering computer vision deployments based on real project requirements and constraints. Discussion will include what changes at each tier - from development to deployment to monitoring and update strategy - with relevant industry examples.

Attendees will leave with a set of questions or a framework they can use to place their own CV projects into the right tier.

About the Speaker

Ajaykumaar Sivacoumare is an AI Software Engineer specializing in computer vision and edge AI, with production experience developing and deploying CV models across Nvidia, Hailo, Qualcomm and AWS-based platforms.

From 2D Slices to 3D Tumors: Lightweight Volumetric Detection Without Heavy 3D Networks

Slice-wise 2D detectors are fast and scalable, but they struggle to produce reliable 3D bounding boxes from volumetric medical data. This talk presents YOLO-PVC, a lightweight post-processing framework that consolidates slice-wise YOLO detections into coherent 3D bounding boxes using percentile-based geometric aggregation and a minimal MLP calibration module.

This talk demonstrates consistent improvements in volumetric IoU across three liver tumor categories i.e., HCC, CCA, and Mixed, without requiring dense 3D annotations or memory-intensive architectures. The talk covers the clinical motivation, the technical approach, and practical lessons from deploying computer vision on real hospital MRI data.

About the Speaker

Talha Waqas is a second-year PhD student at ESME Research Lab, Paris and LISSI, Université Paris-Est, working on computer vision applied to medical imaging, with a focus on tumor classification, detection, and segmentation in multi-phase liver MRI.

The Two-Loop Architecture for Voice AI

Building responsive voice AI requires balancing latency with intelligence. This talk introduces a practical architecture that separates real-time conversation from asynchronous reasoning, enabling richer interactions without slowing the user experience. The session covers reusable design patterns drawn from production-inspired conversational AI systems.

About the Speaker

Abhinav Tushar is an ML engineer and researcher specializing in Conversational AI and Speech Technology.