The Algorithmic Apprentice Returns — AI-Powered Research Methods, Hands On
The sequel, by popular demand. 350+ packed it last year — we're back with LLMs, agentic systems, and ML for causal inference. No coding required.
Chair: Sheen S. Levine (UT Dallas & Columbia)
Speakers: Navid Asgari (Fordham) Natalie Carlson (U. Penn.) Shinjinee Chattopadhyay (Illinois) Aharon Cohen Mohliver (London Business School) Qian Cecilia Gu (Georgia State) Bekhzod Khoshimov (NYU Abu Dhabi) Pankaj Kumar (Virginia Tech) Milan Miric (USC) Ehsan Valavi (MIT) Dawei Wang (Hong Kong) Xiaoning Wang (UT Dallas)
Last year's Algorithmic Apprentice attracted over 350 participants and 12 speakers in a four-hour session that was, by all accounts, not enough time. We return with an updated program showcasing research-grade AI tools — large language models, multimodal models, and agentic systems — applied to questions that matter to organizational scholars.
The format is hands-on: short conceptual talks, live demonstrations, and roundtable-based paper development where you work with panelists on your own project. You will explore supervised and unsupervised ML for classification and pattern discovery, ML-enhanced causal inference, deep learning for measuring complex constructs, and LLM-based coding and simulation. Throughout, we emphasize what separates rigorous AI-enabled research from the merely impressive: evaluation, reproducibility, and ethical governance.
No coding background required. You will leave with an AI-enabled research design tailored to your own work.
📅 Sat Aug 1 · 8:00–12:00 · Sheraton, Freedom Ballroom 🔗 All sessions → https://l.sslevine.com/AoM2026
------------------------------
Sheen S. Levine, PhD
https://twitter.com/sslevine------------------------------