Happy Right Here: PhD Student Using Prestigious Fellowship to Continue Research at WPI

This spring, Stephen Price, a PhD student with a B.S. and M.S. from WPI’s Computer Science Department, was named a recipient of the prestigious National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP). While receiving the NSF Fellowship is a significant achievement, Price’s research journey began almost four years prior, when he first stepped foot on WPI’s campus. 

Price arrived at WPI in August of 2020 at the height of the Covid-19 pandemic. With limited social interaction and strict protocols, his goal was simple: to graduate with a bachelor’s degree in computer science so he could transition to a full-time industry job as soon as possible. After discussing this with his academic advisor, Professor Rodica Neamtu, she convinced him to try to expand his education and perspective by attending a few research meetings for the Data-Driven Material Science (DDMS) lab under the supervision of Professor Rodica Neamtu and Professor Danielle Cote.  

Established only the year before, DDMS was founded by the two professors to serve at the cross-section of machine learning and material science with undergraduate and graduate students pursuing degrees in computer science and material science. The lab's mission is to harness cutting-edge machine learning techniques to solve complex problems in material science, such as predicting material behavior at various compositions and automating characterization processes or optimizing compositions for improved durability. Under the guidance of Professors Neamtu and Cote, students at DDMS undertake diverse projects, ranging from developing predictive algorithms for material degradation to automating the analysis of microscopic material structures.

After joining the lab in the Spring of his first academic year, it did not take long for Price to start making visible contributions. Tasked originally with reproducing a recently published paper to kick-start his research skills, he not only reproduced the results of this paper, but also identified areas where the paper could be improved further. Using computer vision to analyze and segment scanning electron microscope (SEM) micrographs, Price developed algorithms that accelerated data labeling by 75%, thus addressing one of the largest bottlenecks in machine learning, and improving performance across a broader range of materials.

Impressed by his work, Cote and Neamtu hired Price as a research intern over the summer of 2021 to continue this research work. Upon completion of the internship, Price submitted his work to IMMI, a peer-reviewed materials science journal. By the start of his sophomore year, Price had a first author publication, True testimony to his academic and research skills and perseverance.

More important than getting published, this was a turning point in his academic and professional career. He was enthralled with research and rather than simply focusing on graduating as soon as possible, Price chose to pursue his passions in researching the interdisciplinary applications of machine learning for material science. By the summer of of his sophomore year, Price traveled alone to Paris to present another research paper at ICPRAI 2022, marking his first conference, first international conference, and first conference presentation. 

Since then, Stephen has published a total of seven papers with four more under review. As a sophomore, Price was awarded the Two Towers prize, an award traditionally given to WPI’s top junior that best exemplifies a combined proficiency in the theoretical and practical union that is at the heart of the WPI educational tradition. Shortly after, WPI’s Computer Science Department nominated him for the Computer Research Association’s Outstanding Undergraduate Researcher award, where he was awarded honorable mention, placing in the top 60 for undergraduate computer science researchers in North America. 

Alongside these very impressive achievements, Price was still able to graduate with his bachelor’s degree in three years, and earning the Salisbury prize, the highest award given to graduating students from each department. However, instead of going directly to industry as he originally planned, Price chose to pursue his Master’s degree co-advised by Professors Neamtu and Cote, which he earned in December of 2023. 

Interested in continuing his work using machine learning to optimize materials and manufacturing conditions and receiving a research assistant position from Professor Cote, Price chose to stay at WPI to pursue his Ph.D. With the support and guidance of Prof. Cote and Neamtu, Price spent the next few months building his NSF GRFP application, including a detailed proposal for his future work. After many months of waiting, Price was named a recipient of the NSF GRFP, the only recipient from WPI in 2024. 

The oldest award of its kind under the NSF, the Graduate Research Fellowship Program, or GRFP, aims to ensure the quality, vitality, and diversity of the scientific and engineering workforce of the U.S. Past GRFP include Nobel Prize winners, former U.S. Secretary of Energy Steven Chu, and Google founder Sergey Brin. The three-year fellowship includes an annual stipend and cost-of-education allowance. Thirty-two WPI graduate students have been named NSF graduate fellows since 1996. It’s also the second straight year Cote has
had a GRFP fellow; her PhD student, Caroline Dowling, received the fellowship last
year. 

Four years since first stepping onto WPI’s campus, Price is poised to use this award to embark on his Ph.D., devising novel ways for using machine learning to improve material characterization and optimize manufacturing capabilities. As he moves forward, Price pushes the boundaries of not only computer science, but also material science, winning multiple material science awards, including Best Student Poster for Additive Manufacturing at TMS 2024 and the winning the Student Speaking Symposium (S3) from ASM internation.  

With the support of Profs. Neamtu and Cote, Price continues to search for new opportunities to use his fellowship to expand his research in innovative ways, bridging the gap between machine learning and material science, and seeking to develop advancements that will shape the future of both fields.