Profiles

Yasin Silva

About

Yasin (Yas) N. Silva is an associate professor in the Computer Science department at Loyola University (Chicago). He received his doctorate (2010) and master's degree (2006) in computer science from Purdue University and his bachelor's degree in computer engineering from the Pontifical Catholic University of Peru (2001). Before joining Loyola in 2022, Dr. Silva worked as assistant and associate professor at Arizona State University since 2010. Other work experiences include internships at IBM Research and Microsoft Research.

Dr. Silva’s research focuses on innovative ways to analyze and process data. More specifically, he has been working in the areas of social media analysis, online misbehavior detection, social computing, cyberbullying detection in social networks, big data, similarity-aware data analysis, scalable database systems, and fairness and transparency in AI. He has published many papers in top tier conference proceedings and journals in computer science such as ACM SIGMOD, VLDB, IEEE ICDE, WSDM, SIAM SDM, IJCAI, IEEE TKDE, and the VLDB Journal. Dr. Silva has presented at many national and international conferences and has also been invited as keynote speaker and presenter by prominent data analytics research groups including the ones at the University of Zurich, Switzerland, and the University of Salzburg, Austria. He has served as program committee member in conferences like SIGMOD, ICDE and BigData and is a member of the ACM Special Interest Group on the Management of Data (SIGMOD), the ACM Special Interest Group on Computer Science Education (SIGCSE), and the Institute of Electrical and Electronics Engineers (IEEE). Dr. Silva received the Motorola Scholarship for Entrepreneurship and was also inducted into Upsilon Pi Epsilon, the International Honor Society for the Computer Sciences.

Research Interests

Dr. Silva’s research focuses on innovative ways to analyze and process data. His specific areas of interest include: social media analysis, online misbehavior detection, social computing, cyberbullying detection in social networks, big data, similarity-aware data analysis, scalable database systems, and fairness and transparency in AI.