Jue Guo

Lead, AI Curriculum · UB School of Management

Jue Guo

I research machine learning — and teach it to the people who will use it.

I build the AI curriculum for UB's Center for Intelligent Finance & Fintech, teach graduate machine learning courses as adjunct faculty, and host AI Office Hours, an open community of 2,800+ learners. I'm also completing my Ph.D. in Computer Science at UB.

Curriculum Vitae Courses
Portrait of Jue Guo
Buffalo, New York
Students taught at UB
300+
Graduate courses led
5
Thesis students mentored
20+
AI Office Hours members
2,800+

About

Research-grade rigor, taught to be used.

I lead AI curriculum at the University at Buffalo School of Management, where I’m building the AI program for the Center for Intelligent Finance and Fintech (CIFF) — graduate and executive modules that apply modern machine learning to valuation, trading systems, and model governance, with hands-on Python labs.

I’m also a Ph.D. candidate in Computer Science at UB, advised by Prof. A. Erdem Sariyüce, and adjunct faculty in the School of Engineering and Applied Sciences. My research is in machine learning — graph representation learning, AI in finance, and large language models. I’ve led five graduate courses for more than 300 students and mentored over 20 thesis students.

Outside the classroom I run AI Office Hours, an open community of 2,800+ learners that cuts through the hype to explain how modern AI actually works. I care about bridging research-grade rigor with code that ships.

Research interests

Graph representation learning · AI in finance · Large language models

Currently

  • Lead, AI Curriculum UB School of Management · Center for Intelligent Finance & Fintech (CIFF) since 2026
  • Adjunct Faculty UB School of Engineering & Applied Sciences since 2023
  • Founder & Host AI Office Hours — an open AI learning community since 2026
  • Lead STEM Instructor WhyMaker · in partnership with the New York Power Authority since 2025

Teaching

Graduate courses at the University at Buffalo

Since 2023 I have led five graduate courses for more than 300 students. Full lecture notes, slides, and schedules are online for every course.

All course materials →

Jue Guo with a class of graduate students at the University at Buffalo
With graduate students at the University at Buffalo.

Guest lectures and workshops on deep learning, LLMs, and applied AI are available on request — write to me.

Community

AI Office Hours

An open, no-prerequisites learning community of 2,800+ members, with around a hundred people at each live session. We set the hype aside and explain how modern AI actually works — for beginners and practitioners alike.

RSVP on Meetup Join the Discord

Next session

From Word2Vec to Attention: The Idea Behind Every LLM

Saturday, Oct 24 · 2:00 PM EDT · Online

How does a machine know that “bank” means something different in “river bank” and “bank account”? We go from static word vectors to the attention mechanism inside every modern LLM — intuitively, with no heavy math, and with open Q&A.

Reserve a place →

Session decks

  • 01

    Where AI Actually Is in 2026

    A field overview — foundations, the deep-learning revolution, Transformers, and generative AI — closing with open Q&A.

    Preview the slides

    Click into the slides, then use the arrow keys to navigate.

Replays are on YouTube; write-ups go out on Substack.

For organizations

AI programs through CIFF

Through the Center for Intelligent Finance and Fintech at UB's School of Management, we design and deliver AI programs for financial institutions, nonprofits, and international partners.

Discuss a program →

  1. Executive programs

    For leadership teams who need to make sound AI decisions without becoming engineers.

  2. Technical training

    Machine learning, LLMs, and the evaluation methods that separate a working model from a demo — for analysts and developers.

  3. Custom curriculum

    Built around your data and your workflows, not generic case studies — from valuation and trading systems to model governance.

  4. Applied research

    Research partnerships that apply modern machine learning to real financial decision problems.

For self-learners

Books I recommend

Freely available texts I point students to when they want to go deeper on their own.

  • Dive into Deep Learning

    Zhang, Lipton, Li & Smola

    Interactive textbook with full implementations in PyTorch, MXNet, and JAX. The reference I lean on most in CSE 676.

  • Deep Learning: Foundations and Concepts

    Christopher M. Bishop & Hugh Bishop

    A modern volume bridging classical ML to deep learning — the spiritual successor to PRML.

  • Pattern Recognition and Machine Learning

    Christopher M. Bishop

    The classic graduate ML text (2006). A rigorous foundation in Bayesian methods, kernels, and graphical models — freely available online as a PDF.