Apple picks Moshe
Moshe Shenfeld, a computer science PhD candidate at Hebrew University of Jerusalem’s Rachel and Selim Benin School of Engineering and Computer Science has been selected as an Apple Scholar in AI/Machine Learning for 2022.
Moshe Shenfeld
henfeld is one of only 15 awardees worldwide, with another Israeli recipient from Tel Aviv University. The PhD fellowship in Machine Learning and AI was created by Apple “to celebrate the contributions of students pursuing cutting-edge fundamental and applied machine learning research worldwide”.
Currently, Shenfeld is researching privacy-preserving machine learning under the supervision of HU Professor Katrina Ligett. His PhD focuses on differential privacy and its relation to adaptive data analysis and machine learning. This is a relatively new area in computer science that explores how sensitive information with research value—such as data from hospital records—can be made accessible to researchers while also ensuring the privacy of the individuals involved. With the help of his Apple mentor, Shenfeld hopes to create more effective barriers to data breaches, and to move privacy protection from the hands of corporations into the hands of users, so that users will not have to place their trust in external companies.
Prior to Hebrew University, Shenfeld studied at Yeshivat Hebron. He received his high school equivalency degree at the age of 24, after serving in the army. Until he decided to go back to for a PhD, Shenfeld had a successful career at Mobileye. “Being able to leave a job in hi-tech to pursue a PhD is a real privilege. Getting an Apple Scholarship made it possible. Apple has the largest differential privacy research group in the world and is unique because they put their findings into action–it’s not just theoretical. I’m excited to do an internship there this summer or next, depending on the coronavirus.”
Each Apple Scholar will receive support for their research and academic travel and a coveted two-year mentorship with an Apple researcher in their field.
