Hi! I'm a grad student in CSE at UW working with Prof. Steve Seitz and Prof. Ira Kemelmacher in Graphics-Vision group GRAIL. My main interest is computer vision / graphics, but also includes computational photography, machine learning, and optimization. I went to Cornell for undergrad, and had a great pleasure working with Prof. John Hopcroft on social graph algorithms, and later got inspired by Prof. Noah Snavely with his computer vision class. I love hacking, coding, and tackling hard problems, and I think computer vision is fun because I get to see why on earth eigenvector is useful.


I'm looking for highly motivated students to collaborate with. If you'd like to get involved in a research project, please shoot me an email!


The best way to contact me is through email: my first name at gmail. If I don't reply, feel free to ping me. It's likely I lost it in the pile of 10K+ mails.


What Makes Tom Hanks Look Like Tom Hanks

S. Suwajanakorn, S.M. Seitz, I. Kemelmacher-Shlizerman ICCV 2015
Madrona Prize Winner - GeekWire's Innovation of the Year

We reconstruct a controllable model of a person from a large photo collection that captures his or her persona, i.e., physical appearance and behavior. Our system is based on a novel combination of 3D face reconstruction, tracking, alignment, and multi-texture modeling, applied to the puppeteering problem.
Paper Web

Depth from Focus with Your Mobile Phone

S. Suwajanakorn, C. Hernández, S.M. Seitz CVPR 2015
We introduce the first depth from focus (DfF) method capable of handling images from mobile phones and other hand-held cameras. With this technique, we can automatically generate a depth map for every photo you take with your phone.
Paper Supplement Supp-Video Data U.S. Patent

Total Moving Face Reconstruction

S. Suwajanakorn, I. Kemelmacher-Shlizerman, S.M. Seitz ECCV 2014  - Madrona Prize Runner-Up
Our approach takes a single video of a person's face and reconstructs a high detail 3D shape for each video frame. We target videos taken under uncontrolled and uncalibrated imaging conditions.
Paper Web

Illumination-aware Age Progression

I. Kemelmacher-Shlizerman, S. Suwajanakorn, S.M. Seitz CVPR 2014
We present an approach that takes a single photograph of a child as input and automatically produces a series of age-progressed outputs between 1 and 80 years of age, accounting for pose, expression, and illumination.
Paper Web

Extracting the Core Structure of Social Networks Using (α, β)-Communities.

Liaoruo Wang, John Hopcroft, Jing He, Hongyu Liang, Supasorn Suwajanakorn Internet Mathematics, 2013
We present a heuristic algorithm that in practice finds a fundamental community structure and demonstrate that the core structure in social networks is due to underlying social structure rather than high-degree vertices or degree distribution.

Detecting the Structure of Social Networks Using (α,β)-Communities

Jing He, John Hopcroft, Hongyu Liang, Supasorn Suwajanakorn, Liaoruo Wang 8th Workshop on Algorithms and Models for the Web Graph (WAW) 2011
A talk I gave on my algorithm used in the paper (not in WAW).