Samantha Hangsan

Hi, I’m Samantha! I am a full-stack engineer currently at a startup in the fintech space modernizing insurance payments. I formerly worked on Search at Disney Streaming (Disney+ and Star+).

Before I became a software engineer, I received a B.S. in Engineering Geology with a minor in Environmental Engineering at UCLA and interned at Shannon & Wilson, a geotechnical engineering and environmental consultancy firm.

Afterwards, I entered the realm of technology and received a B.S. in Computer Science at CSULB, where I was fortunate to be advised by Jelena Trajkovic.

news

May 27, 2020	Dr. Mehrdad Aliasgari, chair of the CSULB Computer Engineering and Computer Science Department, recognizes Samantha Hangsan as the recipient of the Student Leadership and Excellence Award of the class of 2020.
Sep 4, 2019	Women in Computing Club want to empower women in the field of technology
Aug 30, 2019	Meet the Duo Starting College of Engineering’s Newest Student Club
Mar 27, 2015	Directed United Khmer Students’ 18th Culture Night: Thoun Chey and The Golden Fish

publications

ACM Journals

Prediction Modeling for Application-Specific Communication Architecture Design of Optical NoC

Trajkovic, J., Karimi, S., Hangsan, S., and Zhang, W.

ACM Transactions on Embedded Computing Systems 2022

Abs HTML PDF

Multi-core system-on-chip are becoming state-of-the-art and therefore there is a need for fast and energy-efficient interconnect to take full advantage of the computational capabilities. Integration of silicon photonics with traditional electrical interconnect in Network on Chip (NoC) proposes a promising solution for overcoming the scalability issues of electrical interconnect. In this paper, we derive and evaluate prediction modeling techniques for the design space exploration (DSE) of application-specific communication architectures for Optical Network-on-Chip (ONoC). Our proposed model accurately predicts network packet latency, contention delay, the static and dynamic energy consumption of the network. This work specifically addresses the challenge of accurately estimating performance metrics of the entire design space without having to perform time-consuming and computationally-intensive exhaustive simulations.