@@ -19,4 +19,4 @@ social: true # includes social icons at the bottom of the page
 Hi, welcome to my personal page! I am a research scientist at [Waabi](http://waabi.ai), led by [Professor Raquel Urtasun](http://www.cs.toronto.edu/~urtasun/), working on the next generation of autonomous driving technology. Specifically, I work on **AI-based traffic-model simulation** to improve and validate the autonomy stack. Before this position, I completed my Ph.D in machine learning at [Georgia Institute of Technology](https://ml.gatech.edu/) under the supervision of [Professor Chris Rozell](https://siplab.gatech.edu/rozell.html).
 
 ##### Thesis Research
-My research applied geometric models to deep learning to generate new views of data to improve performance with limited labelled data. To achieve this, I would use techniques like **sparse coding**, **manifold learning**, and **self-supervised learning**. I am interested in building large foundation models with sparse intermediary layers (i.e., only a few elements of each activation non-zero) for interpretability, robustness, and compute efficiency. One approach I have developed uses **variational inference** to reach similar performance as unrolled optimization routines, like [FISTA](http://www.stat.cmu.edu/~ryantibs/convexopt/lectures/prox-grad.pdf), with a single neural network forward pass.
+My research applied geometric models to deep learning to generate new views of data to improve performance with limited labels. To achieve this, I would use techniques like **sparse coding**, **manifold learning**, and **self-supervised learning**. I am interested in building large foundation models with sparse intermediary layers (i.e., only a few elements of each activation non-zero) for interpretability, robustness, and compute efficiency. One approach I have developed uses **variational inference** to reach similar performance as unrolled optimization routines, like [FISTA](http://www.stat.cmu.edu/~ryantibs/convexopt/lectures/prox-grad.pdf), with a single neural network forward pass.