Hello, my name is Yisong Yue.
I'm a Professor of Computing and Mathematical Sciences here at Caltech.
My research area is machine learning.
I'm most interested in figuring out new ways to bring the theory and practice of machine learning closer together.
What that means is I work on real applications through collaboration with domain experts.
I then work to understand how to extract the way the key characteristics of these applications
and encode them as mathematical objects.
We then build supporting theory and algorithms to leverage these characteristics, and then
show that these new algorithms actually work in practice by translating them back to the real world application.
Within machine learning, I work primarily in two sub areas.
The first is called interactive learning, and the basic idea is that you have machine
learning systems that must interact with its environment, collect data, learn, and adapt on the fly.
The canonical example is a personalized recommender system, where the system must personalize
on the fly from very limited data from the subject that it's personalizing to.
Here at Caltech, I work on those in the context of clinical therapies.
For instance, if we implant an electro-stimulation device inside the body of a paralyzed patient
then we can do personalized electro-stimulation therapy to help the patient regain some limb function.
Of course every patient is unique, their injuries are unique, and their physiology is unique.
So we really need to personalize with very little data on the fly.
Other examples include things like exoskeleton therapies where we want to personalize the
gaits of exoskeletons to maximize the comfort for the subject using the exoskeleton.
Another example that you see in the background of my screen is learning to fly for drones,
where we want to help drones fly more effectively in novel environments where there may be unknown
disturbances or poorly modeled disturbances.
And so that is a form of interactive learning as well.
Finally, interactive learning is useful in AI-driven design.
If you think about things like protein design or nanostructure design and material design,
the typical way to do this process is to run a simulation or run an experiment of a design,
measure the properties of interest of that design such as the thermal stability of the protein.
And then repeat.
Now, of course, collecting that data is very expensive so you want to be very strategic
about how to collect that data and that's where interactive learning can come into play.
A lot of the methods I've been thinking about have been applied to various iterative design
processes with various engineering faculty and research groups here at Caltech.
The other area that I work on is called structured machine learning.
Here the idea is that we want to actually impose some structure on top of blackbox machine learning.
You may have heard of an area of machine learning that's very popular now called deep learning,
and deep learning is popular because it's so powerful.
Unfortunately, it's also a blackbox, which makes it hard to interpret or analyze.
That is very challenging because in many safety critical applications we need safety guarantees
in order to actually certify that this learning system will not behave in a way that it's not supposed to.
That is something that's very important to me and my research agenda.
Other reasons why you want impose structure is machine learning and blackbox learning
include things like improving data efficiency so that the method is less data hungry, making
the methods more interpretable, or making the methods more controllable.
So again, in the background here you see an example of a neural swarm or neural lander where
we actually combine deep learning with control theory to come up with deep-learning-based
controllers that are much more agile than traditional controllers, but also have guaranteed
stability properties which is very cool.
This allows for agile drone control with provable safety guarantees in potentially unsafe conditions.
Other applications I worked on include automated broadcast theory where we'll want to train
a neural net to be able to automatically broadcast let's say a sports game.
We do so by imitating a human expert.
We'll also want to guarantee that the deep learning neural net will behave in way that's
guaranteed to be smooth, because smoothness translates to comfort in the visual experience
for watchers and viewers.
In other applications of sports analytics, we worked on being able to model team sports
behavior, which requires inferring the roles or late intents of the various agents in the team.
We do this with the help of human domain knowledge from our sports analyst collaborators.
The final area that I want to highlight is controllable agent design.
If you think about next generation video games, interactive movies, or even interactive educational systems,
we want to be able to design AI agents that can interact with humans, and do so in
a way that's easily controllable by an experience design or UX design.
It's sort of like a director telling an actor what to do.
The director gives high level commands, like act more happy or act more sad here, and the
actor just knows what to do.
Similarly, we want a way of training AI-based agents that you give it high level commands,
and high level directives, and it knows how to execute the low level commands.
That requires blending a structure onto these black box learning devices.
I'm also very interesting in the theory of machine learning.
Thinking about these two areas of machine learning, the theory that comes to mind include
things like sample complexity, can we analyze how much data is needed to get a machine learning
system working on these applications.
Things like can we actually provably constrain the learning to have certain types of guaranteed
behaviors such as safety or stability.
And finally, how to get machine learning systems that can optimally trade off of multiple criteria
like accurately panning to capture a sporting event, while guaranteeing safety or smoothness.
Fundamentally, there's an algorithmic design principle: How do we properly blend blackbox
machine learning with these domain specific structures in a general framework to get the
best of both worlds.
The power of deep learning with the structural benefits of domain knowledge.
If you're interested in more information please visit my website www.yisongyue.com where we
have all of our papers, talks, blog posts and other things to help you get started with learning more about this area.
Thank you for your attention.