June 02, 2021
Amazon Names Five Graduate Research Fellows as Part of New SCS Collaboration
Nil-Jana Akpinar, Natalia Lombardi de Oliveria, Divyansh Kaushik, Minji Yoon and Emre Yolcu have been named Amazon Graduate Research Fellows. (Photo courtesy of Amazon Science.)
By Aaron AupperleeMedia Inquiries
- Senior Director of Media Relations
Five Carnegie Mellon University students with ties to the School of Computer Science were selected for the inaugural Amazon Graduate Research Fellows Program.
Amazon and CMU established the program to further the company's commitment to supporting promising researchers across academia. In recent years, the company has collaborated with several major universities to help amplify the work being done by master's and Ph.D. students.
The five fellows are Nil-Jana Akpinar, Natalia Lombardi de Oliveria, Divyansh Kaushik, Emre Yolcu and Minji Yoon.
The program supports graduate students engaged in scientific research in automated reasoning, computer vision, robotics, language technology, machine learning, operations research and data science. Fellows will also be invited to interview for a science internship at Amazon.
"Each fellow was selected based on their academic excellence and potential to achieve big things in their chosen fields," said Alexa Smola, Amazon Web Services vice president and distinguished scientist. "We reviewed their research proposals to make sure they're doing really great work. They are the real stars here. We're supplying some funding, but they are performing the actual research."
Yoon and Yolcu are pursuing doctoral degrees in the Computer Science Department. Yoon is working on automating and democratizing graph mining. Yolcu has made contributions to the complexity of proof systems that reason about symmetries, with publications appearing in SAT and NeurIPS.
Akpinar and de Oliveria are pursuing doctoral degrees through a joint program in the Machine Learning Department and the Department of Statistics and Data Science. Akpinar's work focuses on bias auditing in algorithmic systems and shows how differential victim crime reporting rates can lead to biased outcomes of predictive policing algorithms. De Oliveria studies estimating generalizations, known as optimism in classical statistics terms. Her work examines the difference between the test and training performance of a predictive algorithm.
Read more about the Amazon Graduate Research Fellows Program and the recipients in this blog post on the Amazon Science site.
For More Information