Launched in October 2020, the MIT-Accenture Industry and Technology Convergence Initiative highlights ways that industry and technology can collaborate to spur innovation. The five-year initiative aims to achieve its mission through research, education, and scholarship. To that end, Accenture has once again awarded five annual scholarships to MIT graduate students working in research in industry and technological convergence who are underrepresented, including by race, ethnicity, and gender.
This year’s Accenture Fellows work in research areas including telemonitoring, human-computer interactions, operations research, AI-mediated socialization and chemical transformations. His research covers a wide range of projects, including the design of low-power processing hardware for telehealth applications; apply machine learning to streamline and improve business operations; improve mental health care through artificial intelligence; and the use of machine learning to understand the health and environmental consequences of complex chemical reactions.
As part of the application process, students from each unit within the School of Engineering, as well as from the Institute’s four other schools and from MIT’s Schwarzman School of Computing, were invited to nominate. Five exceptional students were selected as fellows for the initiative’s third year.
Drew Buzzell is an electrical and computer engineering doctoral candidate whose research concerns telemonitoring, a rapidly growing sphere of telehealth in which information is collected through Internet of Things (IoT) connected devices and transmitted to the cloud Currently, the high volume of information involved in telemonitoring, and the time and energy costs to process it, make data analysis difficult. Buzzell’s work focuses on edge computing, a new computing architecture that seeks to address these challenges by managing data closer to the source, in a distributed network of IoT devices. Buzzell earned his BS in Physics and Engineering Sciences and his MS in Engineering Sciences from Pennsylvania State University.
Mengying (Cathy) Fang is a master’s student at MIT’s School of Architecture and Planning. Her research focuses on augmented reality and virtual reality platforms. Fang is developing new sensors and machine components that combine computing, materials science, and engineering. In the future, she will explore topics including soft robotics techniques that could be integrated with clothing and wearable devices, and haptic feedback to develop interactions with digital objects. Fang earned a bachelor’s degree in mechanical engineering and human-computer interaction from Carnegie Mellon University.
Xiaoyue Gong is a doctoral candidate in operations research at the MIT Sloan School of Management. Her research aims to harness the power of machine learning and data science to reduce inefficiencies in the operation of businesses, organizations, and society. Supported by a fellowship from Accenture, Gong seeks to find solutions to operational problems by designing reinforcement learning methods and other machine learning techniques for embedded operational problems. Gong earned a BS in Mathematics with honors and Interactive Media Arts from New York University.
Ruby Liu is a PhD candidate in the Medical Engineering and Medical Physics program, part of the Harvard-MIT Program in Health Sciences and Technology. Her research addresses the growing pandemic of loneliness among older adults, which leads to poor health outcomes and poses particularly high risks for historically marginalized people, including members of the LGBTQ+ community and people of color. Liu is designing a network of interconnected AI agents that foster user-agent connections, delivering mental health care while strengthening and facilitating human-to-human connections. Liu received a BS in biomedical engineering from Johns Hopkins University.
Joules Provenzano is a PhD candidate in chemical engineering. His work integrates machine learning and liquid chromatography-high-performance mass spectrometry (LC-HRMS) to improve our understanding of complex chemical reactions in the environment. As an Accenture fellow, Provenzano will build on recent advances in machine learning and LC-HRMS, including novel algorithms for processing real and experimental HR-MS data and new approaches in rule extraction and structure transformation kinetics. His research could accelerate the pace of discovery in the chemical sciences and benefit industries such as oil and gas, pharmaceuticals and agriculture. Provenzano earned a bachelor’s degree in chemical engineering and international and global studies from the Rochester Institute of Technology.
