Alumni Conducting Research

Computer Science PhD, University of Illinois at Urbana-Champaign

Through my academic journey, I realized I love learning, and I love learning about learning (both in people and machines). This led me to pursue a PhD in computer science at the University of Illinois at Urbana-Champaign focusing on using deep learning to estimate terrain properties and detect anomalies from robot-terrain interaction sounds.

Last year, I was awarded a NASA Space Technology Graduate Research Opportunities (NSTGRO) fellowship to support my PhD journey, working on using deep learning strategies to estimate terrain properties and detect anomalies from vehicle-terrain interaction sound. As part of this program, I get to do a Visiting Technologist Experience (basically an internship) at a NASA center every year to further my research. I will be collecting data at the NASA Glenn Research Center in Cleveland, Ohio.

My interest in going to grad school stemmed from taking Dr. Michael Wollowski’s Artificial Intelligence class, Dr. Matthew Boutell’s Image Recognition class, and Dr. Yosi Shibberu’s data science courses, where I was exposed to and really enjoyed lab-type data analysis assignments; from there, I did a senior thesis project combining my interests in deep learning and music composition, and coincidentally, now am similarly focusing on deep learning and sound applications in my PhD program.

Computer Science MSE, Princeton University
Professional Development Chair, GradSWE

My research interests broadly span the areas of AI alignment, interpretability, trustworthy AI, probabilistic modeling, and cognitive science. My goal is to create robust and performant AI systems that can learn to understand and follow complex objectives with human guidance, even when these objectives may be difficult or impossible for human operators to specify explicitly. I am currently working on my master’s thesis at Princeton, where I show how aligning the representations learned by machine learning agents with humans can help them learn human values faster, more safely, and in a more generalizable manner. Here is an early version of this work presented at an AAAI workshop.

I became interested in research during my junior year at Rose, when I sought out a research opportunity with a Rose professor ( published paper). I began my master’s degree with the intention to explore machine learning research as an alternative career path to software engineering, and loved it so much that I decided to pursue a PhD. My professors at Rose have been incredibly supportive of my career path in many ways — through offering opportunities to engage with exciting research questions, to mentoring me throughout the graduate school application process, to helping me talk through my decision on where to attend graduate school. 

Human Centered Design & Engineering, PhD, University of Washington (2016) 

His research portfolio investigates the social and technical dynamics of collaborative scientific work, scientific software development, organizations, and growth of open science communities. His academic work has been published in the fields of Computer Supported Cooperative Work (CSCW), Science & Technology Studies, Information Science, and eScience. Currently, Drew conducts user experience research for scientific software tools, workflow systems and user facilities at Lawrence Berkeley National Laboratory. He has also worked at Google Cloud shaping the user experience for global top 50 customers obtaining and monitoring their cloud usage.