Benjamin Batorsky
Ben is the Lead Data Scientist at GoGuardian, working on text-based ML pipelines for student safety. Previously, he led Data Science teams in academia (Northeastern University, MIT) and industry (ThriveHive). He obtained his Masters in Public Health (MPH) from Johns Hopkins and his PhD in Policy Analysis from the Pardee RAND Graduate School. Since 2014, he has been working in data science for government, academia and industry. His major focus has been on Natural Language Processing (NLP) technology and applications. Throughout his career, he has pursued opportunities to contribute to the larger data science community. He has presented his work at conferences, published articles, taught courses in data science and NLP, and is co-organizer of the Boston chapter of PyData. He also contributes to volunteer projects applying data science tools for public good.
Session
Through the construction of a Deep Research Agent, tutorial participants will learn the fundamental building blocks of LLM-driven applications. Starting with in-context learning and prompt design, we will progress through memory management, tool integration via the Model Context Protocol (MCP), and planning workflows. Participants will build a working agent that can query a Zotero citation library, synthesize literature summaries, and engage in multi-turn research conversations. We will also discuss failure modes, limitations, and the role of such agents in an age of coding assistants.