Big Data

Peter Arensburger guides one graduate and two undergraduate students in data-driven research in invertebrates genetics. His student biology team employs coding and programming on projects that includes discovering genome sequences in spiders that are responsible for creating silk, which aids in recreating the strong material for use in society, such as lightweight, bulletproof vests. One graduate student is comparing bird genomes to see if certain sequences have the same function in other bird genomes.

“Once you get a basic understanding of computer science, you become very valuable in graduate school because they see somebody they don’t have to train in how to use these programs. Most biology programs are turning to these methods in collecting huge amounts of data and analyzing it. Biology programs are desperate for people who can take all this data and make sense of it.” - Arensburger

Research
Published Work

Scordato studies the effects of long-term human settlement on the evolution of birds in Southeast Asia and Oceania. She uses high-performance computing (HPC) to assemble short pieces of DNA into entire genomes. This approach allows her to search genomes for evidence of adaptation to human environments and reconstruct evolutionary histories. Her research has broad applications for predicting how species may respond and adapt to human activity.

Scordato uses the R programming language to introduce basic computational skills in her upper-level genetics courses. These tools expand the breadth of knowledge that a biologist can uncover and are essential skills for a career working with big data.

Polycentric Story Scordato's research: Study Finds Bird Evolution Shaped by Tibetan Plateau

Lange and Schatschneider are researching organic molecular crystal properties to uncover new organic semiconductors which can be used in optoelectronic devices such as solar cells and LEDs. The organic materials will serve as a less expensive alternative to conventional inorganic semiconductors such as silicon and germanium.

“Big data actually starts when you have several hundred million or a billion records – it starts when the data is too big to be stored in the memory of one machine. Computer clusters can share the work on these problem in different setups.” – Lange

Hao Ji dedicates his research to improving artificial intelligence and deep-learning systems by developing efficient and improved algorithms. Computer science students gain industry-level programming and experience that they can apply in their careers.

“If you have an intelligence application, but you want to build a model that is intelligent or more powerful, you need more data and more computational power to handle the data processing. It can help gain knowledge from the data and we can use that to gain insight and make decisions. Data is key.” – Ji

Cal Poly Pomona Provost's Teacher-Scholar Award recipient (2018, 2019)
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National Science Foundation Grant $212,680

Transportation and traffic data are used in big data analysis for facilitating the movement of people and goods every second in society. GPS systems, which offer the quickest or best route to the user’s destination and map out traffic conditions, use information gathered by sensors set every half mile on highways to maintain up to date information.

Xudong Jia helped develop early versions of those programs, including Regional Integration of Intelligent Transportation Systems (RIITS). He also helped create the GPS system that jumpstarted the Bronco Express shuttle network at Cal Poly Pomona. The system informs riders on when to expect their shuttle. Although he is not involved in the current timetable systems for Bronco Express, the information is open and available for other programmers to develop better applications.

Webpage
Bronco Digital Magazine: Transportation Power