Speaking

I regularly speak about AI agents, building and scaling AI/ML projects, and how people and AI systems actually work together. Here’s where you can find my recent talks and appearances.

Highlights

[Talk] Beyond the AI hype: Building AI systems that actually work

A discussion with Chicago Data & AI on the strengths and limitations of modern AI agents, why successful AI adoption requires more than just plugging in an LLM, and the workflows, guardrails, infrastructure, and human oversight needed to make AI useful in practice.

[Talk] Silicon Societies: What happens when millions of AI agents use the Internet alongside us?

A talk given to the PyData Chicago community, on multi-agent systems, how well AI agents simulate human behaviors and beliefs, and where things might be headed as we increasingly rely on AI agents to work, shop, call, and interact with others on our behalf.

• YouTube link
• Event link
• Presentation slides

[Talk] Using LLMs for social science (beyond ChatGPT)

A talk given to the Kellogg community, discussing practical tools, tips, and techniques for integrating AI tools into everyday research workflows. Discussed prompt optimizations, human-in-the-loop workflows, and how to use tools like Claude Code and Cursor.

• Presentation Slides
• Demo code

[Podcast] AI’s effect on the Future of Civil Discourse

A discussion with the Fair Point podcast, on AI’s effect on the future of civil discourse and polarization. Spotify link

Interested in having me speak? Reach out at markptorres1 [at] gmail.com or schedule a meeting via my Calendly link.

Teaching

In addition to this, I also enjoy teaching. I spend a lot of my free time TAing, tutoring, and volunteering around teaching. I enjoy helping someone approach something that’s as intimidating as math or AI and coming away feeling like they’ve got a mental model for how to apply it in their own life.

A snapshot of some of my teaching includes:

• Fall 2026: Reinforcement Learning (University of Texas, Austin, Master’s in CS program)
• Spring 2026: Deep Learning (University of Texas, Austin, Master’s in CS program)
• Fall 2025: Deep Learning (University of Texas, Austin, Master’s in CS program)
• Spring 2025: Deep Learning (University of Texas, Austin, Master’s in CS program)
• Fall 2024: Natural Language Processing (University of Texas, Austin, Master’s in CS program)
• Spring 2020: Advanced Multivariate Statistics (Yale University, Master’s of Statistics program)
• Fall 2019: Medical Statistics (Yale University, Master’s of Public Health program)

In addition to this, I’ve had affiliations with the following groups and programs:

• Inspirit AI: Inspirit AI Inspirit AI Scholars is an artificial intelligence program for high school students, developed and taught by Stanford and MIT alumni and graduate students. I was part of the initial 2020 cohort, and had a profile written about me.
• Wyzant
• Varsity Tutors