Boston AI, Machine Learning and Computer Vision Meetup

Nov 09, 2026
9:00 AM - 10:00 AM PST
Online. Register for the Zoom!

Frontier multimodal models handle familiar concepts well and struggle on niche ones: sorting drill-core samples into 35 geological classes, or telling adhesion from scarring on a ceramic substrate. Our study across 11 specialized visual datasets in manufacturing inspection, geology, and medical imaging found that a few labeled examples in the prompt can close much of that gap, and it also found where that stops working.

What you’ll learn:

• Niche versus hard: how in-context examples supply domain knowledge a model is missing, and how to tell a niche task from a genuinely hard one
• What the study measured: on an industrial defect task, one labeled example per class raised macro-F1 from 34% to 59% with no fine-tuning, while on other tasks trained specialists pulled ahead at 5 to 10 examples per class
• Building a few-shot labeling task: define classes, pick reference examples, preview predictions on a small batch, and refine the instructions
• Finding the crossover point: when to stop prompting a foundation model and train a specialist on bootstrapped labels
• Running it on your own data: attendees get free GPU compute from Nebius to try the workflow after the session

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

Agentic RAG: Beyond Retrieve-and-Generate

Retrieval-Augmented Generation (RAG) has become the default pattern for grounding large language models in external knowledge, but most implementations still follow a rigid retrieve-once, generate-once pipeline — one that struggles with multi-hop questions, ambiguous queries, and knowing when its own retrieved context is insufficient. This talk introduces Agentic RAG, where retrieval is treated as an action within an agent's reasoning loop rather than a fixed upstream step.

We'll examine query decomposition and routing for breaking complex questions into targeted sub-queries, self-reflective and corrective retrieval loops that let an agent judge and re-query its own results, and tool-orchestration patterns (via MCP) that let retrieval sit alongside other agent actions like database lookups and API calls. Using a live architecture — evolving a standard RAG chatbot into an agentic, MCP-connected system — we'll walk through what changes in design, and where these systems introduce new failure modes: grounding drift, latency and cost from repeated retrieval loops, and cases where a simpler RAG pipeline still wins.

Attendees will leave with a practical framework for deciding when the added complexity of agentic RAG is worth it, and a set of design patterns for building it correctly.

About the Speaker

Balaji Venkatasubramaniyar is a Technical Lead at Wisdom Infotech, leading a 15+ person engineering team delivering enterprise solutions. With 13+ years of experience in enterprise software and insurance technology, he specializes in agentic AI systems, RAG architectures, and vector databases.

GeoAI for the Physical World: Earth Observation, Foundation Models, and Urban Digital Twins

Earth observation provides a unique form of computer vision for understanding the physical world at city to continental scales. In this talk, I will show how satellite imagery, geospatial data, machine learning, and foundation-model representations can be combined to characterize urban environments and environmental conditions.

I will present UrbanScope Open, an open GeoAI digital-twin prototype integrating Earth observation with 3D buildings, vegetation, land-surface temperature, air quality, noise, population, and other urban data. I will also share lessons from my research using geospatial foundation-model embeddings for environmental prediction across Europe.

The talk will discuss how these approaches can contribute to increasingly multimodal AI systems capable of reasoning about real-world environments.

About the Speaker

Cesar Alvarez is a researcher at the University of Augsburg working at the intersection of GeoAI, Earth observation, remote sensing, and environmental intelligence. His research applies machine learning, computer vision, and geospatial foundation models to problems including urban environments, climate risk, air quality, and agriculture.

Can agents get curious?

Most AI agents are good at answering a question once we tell them exactly what to look for. The harder problem is building agents that can explore a complex dataset autonomously: generating hypotheses, deciding which analyses are worth running, allocating additional compute when evidence is ambiguous, and knowing when they have enough evidence to stop.

In this talk, I’ll show an architecture for autonomous research agents that combines structured knowledge, iterative tool use, and explicit evidence tracking to turn open-ended questions into a sequence of testable investigations. I’ll discuss practical lessons from building and evaluating these systems, including why more test-time compute does not automatically produce better research and how provenance and evaluation can make long-running agents more reliable.

I’ll close with a live example of an agent exploring a dataset, revising its hypotheses, and choosing what to investigate next.

About the Speaker

Srivatsa P is a member of technical staff at Sigma Computing, where he works on AI agents that reason over complex enterprise data. Previously, he worked on machine learning at Apple and conducted research at the MIT Media Lab; outside of traditional ML, he has also worked on mapping coral reefs through underwater imaging, which sparked an enduring interest in how intelligent systems make sense of messy real-world data.

Does Cross-Validation Work in Deep Learning?

Cross-validation is a standard in hyperparameter optimization in classical ML, but is generally considered computationally prohibitive and unnecessary in deep learning. We investigated whether CV produces 1. better DL models 2. better estimates of future performance of said models. We focused on medical image classification as an important deep learning application domain.

Our results show that cross-validation consistently and materially reduces the gap between development and independent testing error for relatively small training datasets (up to about 1000 images). The test performance improvement was generally modest. We discuss the implications for deep-learning practitioners, including whether the increased computational cost of cross-validation is justified.

About the Speaker

Ljubomir Buturovic is an AI scientist and biotech executive with extensive experience in clinical diagnostics, including work at Inflammatix and Pathwork Diagnostics. His current research focuses on deep-learning image classification, infectious disease adjudication using LLMs, and development of AI models for helping critically ill patients.

Seats are limited, Pre-registration is mandatory.

Date, Time and Location

Nov 12, 2026
5:30 PM - 8:30 PM
Northeastern University
140 Fenway, Room 378
Boston, MA 02115

Meetup Agenda

5:30–6:30 PM

• Networking, food, drinks, and lightning talks

Workshop Agenda
This hands-on session uses DROID, a real-world robotics dataset loaded into FiftyOne as a native multimodal MCAP recording, and YOLO11n, fine-tuned live during the session.

6:30–7:30 PM

• Welcome + framing: from raw robot logs to a trained detector
• Explore a real DROID robotics recording in FiftyOne's native multimodal MCAP viewer: camera, proprioception, and language on one synced timeline, no ROS install required
• Curate: extract and browse frames from the recording, filter and deduplicate
• Compute embeddings on the curated frames; explore via similarity search and embeddings visualization (via Nebius Serverless AI Jobs)

7:30–8:00 PM

• Auto-label: open-vocabulary detection to generate bounding boxes for the robot gripper and target objects
• Train: fine-tune a YOLO11n detector on the auto-labeled frames (via Nebius Serverless AI Jobs)
• Evaluate results and close the loop: view predictions back on the original MCAP timeline

8:00–8:30 PM

• What else Nebius offers: Token Factory walkthrough — chat/vision models, fine-tuning, credits

Nov 19, 2026
9:00 AM - 10:00 AM PST
Online. Register for the Zoom!

Robot learning data doesn't look like the data that trains most vision-language models. It's temporal, low-dimensional in the parts that matter most, embodiment-specific, and expensive to collect, and increasingly, it's shipped in Hugging Face's LeRobot format. But packaging robot data consistently isn't the same as being able to see it: episodes hide broken metadata, silently dropped sensor modalities, and mismatched schemas between robots that look identical on paper.

Starting from the core differences that make robot data hard (temporal structure, proprioception, action grounding, embodiment heterogeneity, data scarcity), we'll move into live debugging: a metadata bug that broke multiple dataset imports, sensor streams that vanish without an error, and two robots running an identical benchmark whose data is structurally incompatible. By the end, you'll know what to check before you trust a LeRobot dataset, and how to actually look inside one.