On June 8, AI+X GTC hosted an industry workshop at the intersection of computer graphics, machine learning, and real-world careers. The session was designed for international students who want to understand not just what to study — but how technical skills actually land in industry, and what the path from classroom to cutting-edge research looks like from the inside.
Our speaker was Yifei Li, a PhD graduate in Computer Science from MIT, where her research sits at the intersection of graphics, differentiable simulation, and physical AI. Before that, she completed her Bachelor's at Carnegie Mellon University — one of the top CS programs in the world.
Over the course of her academic career, she completed seven internships at companies including Activision Blizzard, Google, Meta Reality Lab, Boston Dynamics AI Institute, and NVIDIA. Each one took her somewhere different: game engines, augmented reality, digital humans, robotics. She wasn't chasing trends. She was following the problems.
This workshop was her telling us what she learned.
Chapter 1: The First Internship — Graphics Is Engineering
"Graphics means making things very fast to run in real time, writing code that other engineers can use, optimizing performance to its extreme."
When Yifei was a sophomore at Carnegie Mellon, she spent a summer writing code that made grass move.
Not metaphorically. Real grass — thousands of individually generated grass blades, swaying with wind, shifting in detail as the camera moved — all rendered in real time across the massive Battle Royale map of Call of Duty: Black Ops 4. It was her first internship, at Activision Blizzard. By the end of it, her work had shipped on every console and mobile platform the game touched.
Before that summer, she thought graphics was mostly about visual algorithms. After it, she understood something different: graphics in industry means performance, systems thinking, and code that survives a production environment. The grass looked beautiful. But she spent most of those months optimizing it.
Chapter 2: Google Maps — Making Virtual Objects Feel Real
"Small realism errors can break immersion. There are actually many things that could go wrong."
Her next stop was Google Maps, on the augmented reality navigation team. The challenge: when virtual information widgets float in front of real buildings on your phone screen, how do you make them look like they actually belong there?
The answer was the sun. By knowing a user's GPS coordinates and exact time, you can calculate the sun's position and render accurate shadows on virtual objects — shadows that match the real world around them. Yifei built that lighting estimation pipeline. It had to work in sunshine, in overcast weather, across different cities, at any time of day. The engineering problem wasn't the idea. It was the robustness.
Chapter 3: Meta Reality Lab — The Research Project That Went Viral
"At the beginning, this was framed as a research project. I never imagined it would actually be used."
Graduate school shifted Yifei's work from product-focused engineering to open-ended research. At Meta Reality Lab, she tackled an unlikely problem: teaching an AI to animate hand-drawn humanoid characters — the lopsided, expressive, anatomically approximate kind that children draw.
The system had to detect a skeleton in an image that looked nothing like a real body, reconstruct the character's geometry, and apply real motion sequences to it. Nobody expected it to become a product.
It did. Meta released it as a public web demo. Millions of people used it. Those users generated the data the team needed to keep improving the model. The lesson Yifei took: the distance between a research idea and something people love is sometimes shorter than you think.
Chapter 4: The Bigger Picture — Riding Industry Shifts
"The skills underneath stayed the same. What they were applied to kept changing."
One of the most valuable parts of Yifei's talk wasn't about any single project. It was the pattern across all of them.
She entered the industry in 2018, when games were the natural home for graphics talent. By 2020, the focus was shifting to AI. By 2021, every major tech company was building toward the metaverse — digital humans, virtual avatars, physics simulation fast enough to feel real. By 2024, the wave was robotics.
Yifei followed each current. Her later internships at Boston Dynamics AI Institute and NVIDIA put her inside the physical world: designing robotic gripper shapes in simulation, optimizing finger geometry for manipulation, working on simulation-supervised visual reasoning for humanoid robots.
The skills — geometry, simulation, differentiable physics, rendering — stayed constant. Their application just kept expanding.
Chapter 5: Why Graphics Belongs Everywhere Now
"Graphics tells us what structure should look like. That's exactly what modern AI needs."
By the end of the session, the picture was clear: computer graphics isn't a niche skill. It's a language for describing the physical world.
Geometry. Material properties. Light. Motion. Contact. Force. These are the building blocks graphics has always worked with — and they're exactly what modern AI systems need when they try to reason about the real world. Whether that's a robot learning to pick up an object, a simulation training a neural network, or a differentiable physics engine that lets you optimize a design by describing what you want it to do.
The problems worth working on, Yifei said, are the ones where you have to convert messy real-world input into something structured and controllable. That's what graphics has always done.
Chapter 6: Q&A — What the Interview Actually Looks Like
"For research roles, it's more about a match on the skills. They want to make sure you have the right skills for the problem."
The session closed with questions from attendees. The one that generated the most detail: how do engineering and research interviews actually differ?
Engineering interviews test coding, systems design, and behavioral fit. Research interviews are a different shape: less focused on distributed systems or databases, more focused on whether your specific background matches the specific problem the team is working on. Expect a presentation round where you walk through your own research in depth — they want to see how you think.
It's a small distinction that changes how you prepare, and how you choose what to apply for.
Join the GTC
AI+X GTC hosts sessions like this regularly — researchers, engineers, and builders sharing what the work actually looks like from the inside. It's free, open to all international students, and built around one idea: the earlier you understand what's happening at the frontier, the better positioned you are when you get there.
If you want to go deeper — BlendED's NVIDIA PBL puts students inside the exact space Yifei described: visual computing, simulation, and AI systems that reason about the physical world. Learn more about the NVIDIA PBL →
Yifei Li is a researcher at MIT working at the intersection of computer graphics, differentiable simulation, and physical AI. Her work spans cloth simulation, fluid dynamics, robotic manipulation, and digital humans.