CMU-10605 Java Project

Machine Learning with Large Data Sets focus on applying machine learning techniques to large data sets, distributed machine learning, with approximation to the precision but faster speed on modeling training and processing

Common tricks used involve approximation and usage of distributed framework such as MapReduce, etc.

ApproxPageRank Java Gist

I worked on data mining and search engine. For the Question And Answer project in NLP, we used Solr to index the documents and perform our search based on Solr engine, which greatly improved our performance and relavance.
Also, I am proficient in SQL, Pig, Python to do data processing, and ETL process, etc.

I took Distributed System in Autumn, 2014. Combining 15-213, 6.828 and 6.033, the course gives me new perspective on computer systems. And we are working on implementing distributed frameworks such as Migratable Process and RMI (To be available soon).

Front-end is usually overlooked by others. However, I created dashboards in my last two internships and delivered product-level webpages utilizing the exisiting frameworks, making it easily accessible.
I also worked on mobile apps. We launched the LunchBox prototype allowing users to submit lunchbox orders via mobile apps. They appeared in both Android and iOS versions. We helped solve real-world problems.