Computing Science Graduate Student Receives Helmut and Hugo Eppich Family Scholarship
By: Karen Shen
SFU computing science PhD candidate Mugilian Mariappan recently received the Helmut and Hugo Eppich Family Scholarship to support his research in developing tools and techniques to enable real-time analytics on large graphs. Established in 2013, the Helmut and Hugo Eppich Family Scholarship provides financial support to a graduate student whose research is relevant to the area of Intelligent Systems.
Mugilan works with tremendous amounts of rich, complex data interactions and relationships which are represented by networks or graphs. He gained a passion for building next-generation scalable graph analytics solutions from working on “cutting-edge” research on graph analytics during the early stages of his PhD.
While researching concentrations for his thesis, Mugilan says he was fascinated by SFU computing science professor Keval Vora’s research in “developing elegant and theoretically provable solutions to the complex challenge in big data analytics systems.” This inspired Mugilan to pursue his thesis at SFU in the Parallel and Distributed Computing Lab (PDCL) under the guidance and supervision of professor Vora.
Mugilan researches on developing techniques and tools to efficiently handle massive changes on large graphs, and to enable real-time analytics with provable guarantees. Important practical problems are often modelled as analysis over graph data. This is evident across various domains, including security (e.g., thread/attack detection), bioinformatics (e.g., drug discovery, protein interaction), finance (e.g., credit card fraud detection) and social network analysis (e.g., friendship recommendations). The fast changing nature of information (e.g., billions of payment transactions per minute) makes it challenging for data scientists and practitioners to analyze dynamic graph information and learn about interesting trends or anomalies in real-time.
Mugilan built the GraphBolt System which “empowers domain experts to run computationally expensive analytics on large, fast-changing dynamic graphs.” His research has significantly pushed the practical boundaries of scalability when it comes to the rate at which graph data can be processed.
One of Mugilan’s highlights at SFU is receiving the Best Poster Award at the 2019 CS Research Day. He says that he is eternally grateful for the constant support and encouragement from his advisor, his lab-mates, friends, and family during his research journey.
Receiving the Helmut and Hugo Eppich Family scholarship motivates Mugilan to continue to strive for excellence in his studies and research. He advises future students applying to similar scholarships to, “diligently work on their research and publishing at top-tier venues to bolster their scholarship applications.”