I am a research scientist at Apple. My research spans across multiple directions, including nerural rendering, generative models for sequences, and computational imaging and displays. My research has been incorporated into various Apple products, e.g., Siri, Scribble on iPad, QuickPath keyboard on iOS and watchOS. My current research interests are broadly in generative models, computational photography, and novel sensors.
Before joining Apple in 2020, I received my PhD from the ECE department of Carnegie Mellon University, where I worked with professor Vijayakumar Bhagavatula and professor Aswin Sankaranarayanan on computational photography and displays, computer vision, and machine learning. I received my M.S and B.S degrees in electrical engineering from National Taiwan University working with professor Tian-Li Yu, and I worked with Dr. Yu-Chiang Frank Wang on image processing as a research assistant.
Accepted by Optics Express, 2016. (paper)
Accepted by ACM Genetic and Evolutionary Computation Conference (GECCO) 2011 (pdf).
The earth movers' distance measures the distance between two sets of points or histograms by solving the transportation problem. Suppose there are cargoes to transport from source nodes to target nodes. Given the weights of these cargoes and the distances between all source-target pairs. The earth movers' distance calculates the minimum cost to transport these cargoes. However, the earth movers' distance ignores the dependencies among these cargoes or points, and related source points may be transported to unrelated target points. We tackle the transportation problem with cargo dependencies and provide more accurate distance measurement when data dependencies exist. We use the proposed more accurate distance metric to improve computer vision applications, including image retrieval, color transfer.
The simple genetic algorithm can be easily paralleled, because all of its operators are local and not dependent on the whole population of candidate solutions. However, when it comes to model building genetic algorithms, identifying variable dependencies from candidate solutions is not a local operator and needs to use information from the whole population. This makes paralleled model building genetic algorithms difficult. In this work, we implement a distributed model building genetic algorithm, in which the model building process is paralleled by exchanging the distribution of each node's population.
We mine association rules from the population of candidate solutions. These association rules are used to preserve high-fitness subsolutions.
We implement a LEGO NXT robot, which can solve the Rubik's cube all by itself, including scanning the cube and preforming actions to the cube.
We realize a FPGA, which detects the pitch and tempo of a music. This work earned the outstanding paper of the InnovateAsia FPGA Workshop and Design Contest 2009.
We realize a circuit, which detects electromyography (EMG) signals and uses the signals to control a toy car :D
We build an iPhone app, in which users can take photos of store's signboards to retrieve information about these stores. We used SIFT as a feature to perform the recognition.
We design a rule driven agent for the Wumpus world. We study the process of how the agent adapts its belief toward its rules when encounters different situations.
We design a two-level self organizing map to rocognize facial expressions. This algorithm characterizes these expressions into both high-level and low-level terms.
We design and implement the circuit of a smart drug box, which detects whether the drugs have been taken in order to notify patients.