About Me
Hi! I am currently pursuing graduated with a M.S. in Computer Science at Georgia Institute of Technology, specializing in Machine Learning and Computer Vision . Before that, I earned my dual degree in Computer Science & Business Administration at the University of Southern California.
My M.S. research interests were in object detection, 3D understanding, and deep learning, with an emphasis on perception for autonomy and human-centered Artificial Intelligence. During undergrad I specialized in computer systems and security.
I've been fortunate to live in many places: New York, Los Angeles, Atlanta, Philadelphia, Hong Kong, San Jose, and (currently) Palo Alto. In my spare time, I enjoy hiking outdoors, exploring new places, reading inspirational bios/stories, and indulging in history.
Dual fan setup: --> --> vs. <-- --> vs. --> <-- vs. <-- <--
Software:
For the hardware build, I tried to implement a dual-fan setup, where I arranged the case fan (12mm diameter) and the water cooler's fan (8mm diameter) in a way that maximizes heat exhaust from within the case. I eventually settled with a PUSH/PULL CPU ventilation architecture, where the smaller fan is sandwiched betweeen the inner-side of the case and the CPU cooler's heatsink, while the larger case fan is attached to the outer side of the case. Theoretically this would allow the heatsink fan to suck the hot air into the cooler's fins and the case fan sucking the air out of the cooler's fins. I hoped it result in lower temperature than single fan systems - after trying solutions both with and without the outer case fan, the results verify my hypothesis, differing between 2-5 degrees celsius.
Many machine learning algorithms, including PGM based techniques, has many parameters that needs to be tuned to increase its performance on the task. These parameters can be structure level parameters, i.e. direction of an edge in a Bayesian Network or the choice of a factor function from several possibilities in undirected models; or hyper parameters that are present lower levels of the algorithms. In majority of such algorithms, finding the optimal combination of structure and hyper parameters is an exhausting problem. This is due to mainly two reasons, lack of gradient of the response surface which makes gradient based optimization such as quasi-newton or LBFGS not usable and the CPU extensive function queries which makes the use of evolutionary algorithms impractical. A possible solution for this class of black-box optimization problems is Bayesian Optimization (BO) based on Gaussian Process.
By varying the amount of perturbation, we validate a relation between pixel-salient regions and density of perturbed pixels. We show that the adversarial images generated using MobileNetV2 does not transform well to attack MobileNetV1. We publish an interactive tool for experimenting with and visualising different epsilon values and adversarial attacks in the browser, using TensorFlow.js on top of MobileNetV1. We hope our work provides a more in depth understanding of the generation of adversarial examples and inspire future defenses against gradient based adversarial attacks.
AdVis lets users explore adversarial attacks interactively by reflecting updates to the classification scores of an image as the user changes the epsilon value deployed for the FGSM attack. Users can also visualize the CAM overlay for a specific class by clicking on one of the rows, which is updated in real-time as they continue to tune their desired epsilon values. AdVis currently supports Fast Gradient Sign Method attacks, with future plans to support additional attack vectors against different classification models.
With the advent of recent social media oligarchies, fake news, and extremist groups, division of opinion has received a reputation as the enemy of societal progress. This shouldn’t be the case, though. We create a place where people can anonymously proclaim their opinions on various articles regarding pressing issues. Then, we match people with differing opinions and encourage them to meet and discuss in a productive fashion. We hope to foster a diverse yet highly empathetic community.