P.D. Soros Fellowship for New Americans


Helen Zhou, 2019

PhD, Carnegie Mellon University

Helen Zhou is an immigrant from Canada

Fellowship awarded to support work towards a PhD in Machine Learning at Carnegie Mellon University

Helen Zhou was born in Winnipeg, Canada after her parents left China for her father to pursue doctorate studies in Canada. When she was six, her family moved to Canton, Michigan. Following along in her parents’ arduous journey, she gained a deep sense of purpose, optimism, and perseverance.

As an undergraduate at MIT, Helen was captivated by computer science’s blend of practicality and mathematical rigor. Working with advisors at the MIT Media Lab, she pursued machine learning research in a variety of domains—from uncovering insights embedded in social networks, to making virtual reality technologies more accessible. To better understand machine learning problems relevant at the industry scale, Helen also completed internships at Google and Amazon Search (A9).

After graduating with a BS in computer science and electrical engineering, Helen began tackling challenges that arise when machine learning is applied to healthcare. She joined the MIT Clinical Machine Learning group where she learned from both experts in machine learning and in medicine. Her master’s thesis work on predicting antibiotic resistance from electronic medical records inspires much of her current research interests.

Now, as a machine learning PhD student at Carnegie Mellon University, Helen works on problems at the intersection of machine learning and healthcare, such as personalization, interpretability for human-in-the-loop learning and synthesizing heterogeneous data from multiple modalities. Throughout her academic career, she hopes to develop methods that will allow scientists to continually shine new light on aspects of healthcare and medicine that are not well understood. Inspired by her personal experiences, her long-term research goal is to develop intelligent personal healthcare assistants which are widely accessible and trusted.

  • MEng Artificial Intelligence | Massachusetts Institute of Technology (MIT) 2018
  • BS Electrical Engineering and Computer Science | Massachusetts Institute of Technology (MIT) 2017
  • PhD Machine Learning | Carnegie Mellon University
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