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 lead AI curriculum for UB's Center for Intelligent Finance & Fintech and serve as the center's Project Manager, building out its AI program for graduate and executive students. I also teach graduate machine learning courses as adjunct faculty and host AI Office Hours, an open community of 3,100+ learners, and I'm completing my Ph.D. in Computer Science at UB.

Curriculum Vitae Courses Contact me
Portrait of Jue Guo
Buffalo, New York
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Students taught
490+
Courses led
5
Thesis students mentored
20+
AI Office Hours members
3,100+

About

Research-grade rigor, taught to be used.

I like the moment a hard idea finally clicks, and most of my work is about making that happen for people who will actually use machine learning: my graduate students at UB, professionals in finance, and the curious crowd that shows up to AI Office Hours.

My own research asks how machines can learn from connected data, such as networks of people, companies, or transactions, and how AI and large language models can be put to good use in finance. I’m completing my Ph.D. with Prof. A. Erdem Sariyüce.

In class, I like to work through the math first, turn it into code we run together, and then see whether the result holds up. You’ll find the same habit in my course notes and writing below.

You can see that approach in my courses

Teaching

Graduate courses at the University at Buffalo

Every course I teach comes with complete lecture notes, open to anyone: the math worked through step by step, with code you can run. The courses build on one another, so if you're new, start with the first.

All courses →

Jue Guo with his CSE 676 Deep Learning class at the University at Buffalo
With my CSE 676 Deep Learning class at the University at Buffalo.

5 courses · 63 modules of open notes

  1. IEAS 51010 modules Basics of Artificial Intelligence Open the notes
  2. IICSE 474/57414 modules Intro to Machine Learning Open the notes
  3. IIICSE 455/55510 modules Intro to Pattern Recognition Open the notes
  4. IVCSE 67620 modules Deep Learning Open the notes
  5. VCSE 431/5319 modules Algorithm Analysis and Design Open the notes

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

Outside the classroom, I write about how these ideas actually work

Writing

Notes on how AI works

Explainers on the ideas behind modern AI, from the mathematics of learning to the models and systems built on it. Each piece works from first principles and runs its own code.

All writing →

Recent writing

  1. Large language models

    An Introduction to Large Language Models

    Chapter 1 of Hands-On Large Language Models, animated step by step: from bag-of-words and word2vec to attention, the Transformer, BERT and GPT, and generating a first text.

  2. Agentic systems

    Introduction: what is an AI agent?

    The chapter that scaffolds the book: a reasoning language model, augmented with memory, tools, and planning, acting inside a system.

  3. Mathematics of AI

    Probability, likelihood, and where loss functions come from

    Why a model should describe a distribution rather than a single answer, how likelihood turns that distribution into a score for the parameters, and how maximizing it produces the loss you train with.

Prefer to learn live, with others? Join AI Office Hours

Community

AI Office Hours

An open, no-prerequisites learning community of 3,100+ 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

Also on YouTube LinkedIn Group Substack.

Upcoming sessions

  1. Probability and Machine Learning

    Sat, Nov 7 · 3:00 PM EST · Online

    Why do we train classifiers on cross-entropy? Last time we showed that MSE isn't an arbitrary rule. It falls out of assuming Gaussian noise on a continuous target.

    RSVP on Meetup →

  2. Let's talk AI

    Sat, Dec 19 · 2:00 PM EST · Online

    The first AI Office Hours runs like a class — relaxed but structured. I'll walk through where AI actually is in 2026: what LLMs, image models, and agents really do, what they don't, and how to read past the hype.

    RSVP on Meetup →

  3. Running LLMs Locally — A Six-Week Workshop

    Tue, Jan 12, 2027 · 7:00 PM EST · Online

    This workshop series is extremely hands on and interactive. A dedicated teaching assistant and I will answer your questions within 24 hrs. No API keys. No per-token bills. No data leaving your machine.

    RSVP on Meetup →

  4. 2027 AWS AI Practitioner Bootcamp

    Mon, Aug 2, 2027 · 5:00 PM EDT · Online

    A live, 4-week bootcamp built to prepare you for the AWS Certified AI Practitioner exam (AIF-C01) — AWS's foundational credential covering AI/ML fundamentals, generative AI, foundation models, responsible AI, and security.

    RSVP on Meetup →

All upcoming sessions on Meetup →

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.

For organizations, the same teaching comes as programs through CIFF

Jue Guo at the University at Buffalo

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.

  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.

A program of University at Buffalo Center for Intelligent Finance and Fintech, School of Management

Discuss a program →CIFF Programs →

Learning on your own? These are the books I point students to

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.

Before you go

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