From Graphics to AI: Constraints, Projects, Careers
Join us for an inspiring one-hour session where an industry practitioner shares how neural 3D graphics—the intersection of computer graphics and deep learning—is reshaping careers and creating opportunities across industry. Hear directly about real projects, technical decisions, and the career path that led there.
Speaker: Yifei Li, Researcher at MIT Computer Science and Artificial Intelligence Laboratory
Yifei Li is a researcher in MIT CSAIL’s Computational Design and Fabrication Group. She holds a B.S. in Computer Science with a minor in Machine Learning from Carnegie Mellon University. Her research focuses on computer graphics, leveraging techniques from differentiable physical simulation, computational design, and machine learning. She has interned at Meta Reality Labs, NVIDIA, Facebook AI Research, Google, and Activision & Blizzard. Her work has been published in top conferences and journals, including SIGGRAPH, CVPR, and ACM Transactions on Graphics. She is a recipient of the MIT Stata Family Presidential Fellowship.
What You'll Hear:
The Speaker's Journey & Expertise
How they moved into graphics, vision, or AI—and what shaped those decisions
Key inflection points: projects, mentors, or technologies that accelerated their growth
How industry work differs from academia or early-career roles
Real-World Projects & Impact
A project they've shipped (or are shipping) using neural 3D techniques
Technical challenges they solved: What broke? What surprised them? How did they debug at scale?
From concept to production: timeline, team structure, resource constraints, and trade-offs
How NeRF, Gaussian Splatting, or physics-informed neural representations solved a real business problem
Metrics that matter in industry: rendering speed, memory efficiency, asset quality, user experience
The Technical Landscape (Through Their Lens)
Why positional encoding and volumetric rendering matter—and when they don't
The evolution they've witnessed: from traditional pipelines to learned representations
Tools and frameworks they use daily (PyTorch, CUDA, custom kernels, etc.)
When to use neural methods vs. classical graphics—and the hybrid approaches they've found work best
Career & Opportunity
Where this field is heading: emerging roles and skill gaps in industry
What companies are looking for (and what they wish more candidates knew)
How to position yourself: what projects, skills, or side work matter most
Paths forward: research, graphics engineering, generative AI, VR/AR, game development, visual effects
Why This Matters
You're not just learning a field—you're learning from someone doing the work. Industry moves fast, priorities shift, and the skills that compound are often invisible in textbooks. This session connects technical depth with lived experience: how real constraints shape decisions, how careers actually build momentum, and where the opportunities are opening up.
Who Should Attend
Students exploring graphics, vision, robotics, or generative AI and wondering "what does this actually look like in industry?"
Those curious about career paths: How do you get from Foundation coursework to impactful projects?
Future graphics programmers, rendering engineers, AI research engineers, or VR/AR developers interested in learning from someone in the trenches
Anyone interested in understanding the gap between "learning the field" and "shipping products"