Machine Learning

**Agenda :** * 4.30 to 4.45 PM ET: Food and Networking * 4.45 to 5.30 PM ET: "Practical Applications of Model Context Protocol (MCP) (Feb 2026 Edition)" Hello Everyone! Please join us for our Feb 10th edition of the AI-ML MeetUp. **Please note this is an in-person meeting and light refreshments/food will be provided.** **Title:** Practical Applications of Model Context Protocol (MCP) (Feb 2026 Edition) **Description:** This session explores how the Model Context Protocol (MCP) can be used to connect AI models with real-world tools and data. Attendees will learn practical patterns for implementing MCP in production workflows and applications. Through examples and use cases, the session highlights how MCP enables more capable, context-aware AI systems. **Location:** The meeting will be hosted in the Applied Information Sciences ( AIS ) office in Reston, at 11440 Commerce Park Dr # 600 · Reston, VA. The location is also right off the Silver Line metro's Wiehle-Reston Metro Station. **Parking:** Parking is paid and can be validated at the AIS office reception. We will meet in Room Lake Anne. We hope to see you all there!!!!

**Agenda :** * 4.30 to 4.45 PM ET: Food and Networking * 4.45 to 5.30 PM ET: "Practical Applications of Model Context Protocol (MCP) (Feb 2026 Edition)" Hello Everyone! Please join us for our Feb 10th edition of the AI-ML MeetUp. **Please note this is an in-person meeting and light refreshments/food will be provided.** **Title:** Practical Applications of Model Context Protocol (MCP) (Feb 2026 Edition) **Description:** This session explores how the Model Context Protocol (MCP) can be used to connect AI models with real-world tools and data. Attendees will learn practical patterns for implementing MCP in production workflows and applications. Through examples and use cases, the session highlights how MCP enables more capable, context-aware AI systems. **Location:** The meeting will be hosted in the Applied Information Sciences ( AIS ) office in Reston, at 11440 Commerce Park Dr # 600 · Reston, VA. The location is also right off the Silver Line metro's Wiehle-Reston Metro Station. **Parking:** Parking is paid and can be validated at the AIS office reception. We will meet in Room Lake Anne. We hope to see you all there!!!!

Join us for another session of our study group as we cover the book Domain-Specific Small Language Models. In this session, we will cover Chapter 1 Large Language Models and maybe even start Chapter 2 Tuning for a Specific Domain. This isn't just a lecture! Come ready to ask questions, share insights, and code along. Whether you're a beginner or have some experience, this is the perfect opportunity to continue to learn together. If you plan to work with the code on your own laptop during the session, try and download the code from here https://github.com/virtualramblas/Domain-Specific-Small-Language-Models/ ahead of time. We will be meeting at the North County Governmental Center this time that is across from the Reston public library.

We will be meeting at Starbucks to learn together. Come with an online class you're already going through or an interest and we will try to connect you with a course where you can learn it. Already have a skill you want to contribute to a Kaggle Datascience competition? We will work on these too! Laptop required :)

Title: Geometric Machine Learning with Symmetries and Graphs: Applications in Molecular Problems Date: Jan 31 2026 Noon - 14:00 EST Abstract: For geometric problems, symmetry equivariant machine learning has emerged as the modus operandi. In physics, symmetry is treated as the north star, the guiding principle for problem characterization and method optimization. Geometric features are inherently compatible with continuous Lie groups (and therefore symmetries), leading to continuous parametrization and successful training methodologies. The general mapping of a classical embedding and a quantum mechanical embedding will be discussed in terms of group theoretical concepts. In the context of embeddings, graph embedding is also a powerful method for machine learning problems taking better care of inter-relations among nodal variables. Classical and quantum neural networks with different subsets of the concepts mentioned will be defined and learning attempted with. The dataset under study is geometric molecular data (from quantum mechanical models) with varying geometric complexity. Performance comparison will include accuracy metrics, generalizability metrics, and.general remarks about expressivity. Bio: Dr Saumya Biswas completed a Master's degree in electrical engineering from University of California Riverside in 2016, a PhD in Physics from University of Oregon in 2021. He has been serving as a PostDoc at University of Maryland from 2022. He is also active in the technology subspace, working with the companies Apsidal LLC and Sapienly for AI and quantum information innovations. Moderators: Dr. Sebastian Zajac, member of QPoland, Dr. Pawel Gora, CEO of Quantum AI Foundation

HYBRID EVENT, VIRTUAL SPEAKER In this talk, Sean Hoar will explore the transformative journey of AI’s role in modern software engineering, from rapid prototyping (“Vibe Coding”) to strategic AI integration by skilled practitioners. It clarifies the distinctions between AI-assisted, AI-enabled, and practitioner-led approaches, offering insights into how developers can effectively adapt to and harness AI tools. The presentation also highlights recent advancements and practical strategies for leveraging AI in daily workflows and long-term development.