Talks
Talks, workshops, panels, and interviews.
Selected Talks
2026
Brown AI Winter School 2026: Reinforcement Learning for Orbital Transfers
A 2.5-hour workshop on training PPO policies for orbital transfers and checking their trajectories and fuel use against a Hohmann baseline.
AI Winter School 2026, Center for the Fundamental Physics of the Universe at Brown University.
Workshop guide and syllabus
2025
Brown AI Winter School 2025: Exploring LLMs and RAG
A 2.5 hour interactive workshop at the 2025 AI Winter School, hosted by the Center for the Fundamental Physics of the Universe at Brown University.
We queried LUX papers and Brown theses using a hosted model and a Llama model running in Colab, then inspected the retrieved passages.
AI Winter School 2025, Center for the Fundamental Physics of the Universe at Brown University.
2021
Using Deep Learning to Detect Abusive Sequences of Member Activity on LinkedIn
A production sequence model for detecting logged-in profile scrapers from request paths and timing, including early results.
Scale Exchange recording hosted on YouTube.
2020
Preventing Abuse Using Unsupervised Learning
A presentation on applying isolation forests and unsupervised ML to prevent abuse at production scale.
Spark+AI Summit 2020 (recording hosted on YouTube).
2019
Fighting Abuse @Scale: Preventing Abuse Using Unsupervised Learning
Using Isolation Forest to find unusual account behavior without labeled training examples.
@Scale Conference.
github.com/linkedin/isolation-forest ·
Original @Scale page
Panels
2024
SXSW 2024 Panel with DARPA: Real or Not - Defending Authenticity in a Digital World
A panel with DARPA on detecting synthetic media and assessing the authenticity of online content.
SXSW and DARPA.
DARPA recap
Podcasts & Interviews
2025
AI Innovators Ep. 10
Interview on applied AI engineering, platform trust, and building practical ML systems at scale.
2022
The Data Scientist Show #035: Detect Online Abuse with AI
Conversation about online abuse detection, machine learning in production, and the path from physics research to staff-level ML engineering.