CRA-E’s “Undergraduate Research Highlights” series showcases outstanding research done by undergraduate students at universities and colleges across North America.  It is one of a number of CRA-E’s activities that foster and recognize  talented computing researchers with the goal of increasing the research pipeline, promoting graduate education, and advocating research-based careers.

Each article features the story of a successful undergraduate researcher and offers personal insights into their experiences with finding an advisor, undertaking new research projects, and discovering how research can impact their personal and professional futures. In addition to helping students understand the process of getting involved in research, the articles also serve as a venue for students to pass along advice to others who aspire to become involved in research themselves. Students selected for the research highlights include those receiving recognition in the CRA Outstanding Undergraduate Researcher Award competition.

This series is written and edited by CRA-E Graduate Fellows.


Cutting Through the Noise: Improving Weakly Supervised Machine Learning for Practical Applications


Esteban Safranchik hopes to harness the potential of weakly supervised machine learning to impact fields beyond computer science. Now a PhD student at the University of Washington, Esteban got his start in research as an undergraduate at Brown University. His work was published at the 2020 Association for the Advancement of Artificial Intelligence (AAAI) Conference and is also used by economists and data scientists.

Goodbye Accounting, Hello High-performance Computing


Janaan Lake is living proof that it’s never too late to pursue a career in computing. After working 17 years as a Certified Public Accountant, she decided the time was right to pursue a computer science degree and enrolled at the University of Utah. “Although changing careers in midlife has been more challenging than I anticipated, it has also been more rewarding.” 

Advancing the Theory of Programmable Matter for Swarm Robotics and Multi-Agent Systems


Joseph Briones wants to help robots work together more effectively. While double majoring in Computer Science and Math at Arizona State University (ASU), Joseph has worked towards extending the theory of programmable matter for applications in swarm robotics and multi-agent robot systems. His undergraduate research revolved around the 3D Amoebot model for self-organizing particle systems, a 3D programmable matter simulator. His work also resulted in two publications to the 2018 and 2019 International Symposium on Self-Stabilizing Systems. Currently, he is a computer science PhD student at his alma mater, furthering the research he started as an undergraduate.