- Helping to maximize the probability of the
long-term survival and wellness of humanity and all other life on
Earth (now and throughout the future).
- Climate change is currently the biggest existential threat to humankind,
as well as all other species on Earth. We must rally
to fight against it if we want to save as much life on Earth as possible
from climate disaster and if we want to preserve a bright future for all
- Life on Earth represents the only known life in the universe;
we must protect it at all costs. We must not blink out of
existence just yet.
- Special interest: food and agriculture - we'll always need to eat, and what
we eat is directly related to our wellbeing. In particular, we must
produce healthy and diverse food in a way that is sustainable for the environment,
sustainable from a socioeconomic standpoint, and resilient against climate change.
- Pursuing real-world applications of artificial intelligence and
- Special interest in applying machine learning and large-scale
data as part of a climate tech / ag-tech solution.
- Bringing humanity together as one family by seeing the bigger
picture of our existence and how meaningless our conflicts are in
light of this.
- Breaking down imagined barriers to instead work together
towards a brighter future.
Quick links: LinkedIn, AngelList, GitHub
- Stanford University: M.S., Computer Science (2016)
- Specializations: Artificial Intelligence, Real-World Computing
- UCLA: B.S., Neuroscience (2012)
- Social, worldwide, daily vlogging platform focused on
simplicity and authenticity. Provides a quick and easy way for
people around the world to vlog their daily lives, with the
mission of bringing the world closer together as one family.
- Built using Golang, Scala, PostgreSQL, Redis, React Native,
iOS/Swift/Objective-C, and AWS.
- (This platform has been retired as of 2021.)
- 2D simulation of robot constructing human-livable habitats on
Mars. Modeled as Markov Decision Process (MDP), with multiple
task phases implicitly formulated into transition/reward
functions, allowing naturally solving the sequential phases
without manual phase conditions or explicit instructions.
- Manually modeled state and action spaces, transition and
reward functions, and various “worlds”/difficulties. Applied
varying degrees of model uncertainty (movement, object
interaction, etc.). Used discrete value iteration to solve MDP.
- Successful in simplistic settings, always achieving optimal
policies and performances in minimal time.
- Implemented using Julia, POMDPs.jl, and PyPlot.jl. Individual
project. Dec 2015.
- Social network recommendation system for encouraging
shared-interest, distance-based connections. Used Twitter
network/dataset/API and Google Maps Geocoding API; acquired
network structure and geographical locations for ~57,000 users,
with 1 million connections between them. Generated simulated
user interests vectors.
- New user connection ranking and recommendation using network
structure features, user geolocations and distances, and
simulated interests. Successfully recommended both users with
similar interests and geolocations (“distance-penalizing” mode)
and with similar interests and distant geolocations
- Implemented using Python, Snap.py, and Gephi. Individual
project. Dec 2015.
- 2D real-time puzzle game for Android OS using only Open GL ES
- Available on Google Play Store, built entirely from scratch
with no external game libraries in under 10 days
- Low-level handling of all shape drawing, shape texturing, game
logic, player interaction, cross-thread communication, Android
- Implemented using Android/Java and Open GL ES. Individual
project. Aug 2015.
- Mobile computer-vision/augmented-reality Android application.
Performed real-time hand and finger tracking on a mobile device
camera using only on-device computation. Interface enabled
interaction with several on-screen functions via real-world hand
and finger placement.
- Used HSV-flesh probabilities with per-user/per-session
calibration, CAMSHIFT and convex hull estimation for hand/finger
localization, and Android for the augmented reality on-screen
- Implemented using Android NDK, C++, OpenCV, and Java/Android.
Individual project. May - Jun 2015.
- Machine learning and computer vision system for galaxy
- Used ~140K images from the Sloan Digital Sky Survey to provide
outputs along ~40 continuous-valued morphology categories.
Preprocessing involved cropping, Gaussian filter smoothing,
downsampling, and rotated image copies for rotational
- Used (Dense) SIFT, radii-based spatial descriptors, HSV, and
bag-of-visual-words for features. Used K-nearest neighbors
regression, ridge regression, support vector regression, and
convolutional neural networks for learning. RMSE for eval.
- Implemented using Python, OpenCV, scikit-learn, and Caffe.
Individual project. Feb - Mar 2015.
- Machine learning system to detect exoplanets using NASA Kepler
Space Telescope data and planetary transit detection method
applied to Kepler brightness time series data.
- Noise reduction via sliding-window percentage-change
transformation. Engineered manual features extracted from time
series data, considering properties such as global statistics,
peak width/magnitude/frequency, inter-peak width, etc. Applied
k-means clustering and PCA methods for dimensionality reduction.
- Used k-nearest neighbors, logistic regression, softmax
regression, SVMs, and k-fold cross validation for
classification. Achieved ~85% test classification performance.
- Implemented using Python, NumPy, matplotlib, and LIBSVM.
Individual project. Sep - Dec 2014.
Light Object-Relational Environment (LORE). Provides a simple and
lightweight pseudo-ORM/pseudo-struct-mapping environment for
relational database interaction using Golang.
Simple REST API server for geocoding addresses, implemented in