Launched in October 2020, the MIT and Accenture Convergence Initiative for Industry and Technology underscores the ways during which industry and technology can collaborate to spur innovation. The five-year initiative goals to realize its mission through research, education, and fellowships. To that end, Accenture has once more awarded five annual fellowships to MIT graduate students working on research in industry and technology convergence who’re underrepresented, including by race, ethnicity, and gender.

This 12 months’s Accenture Fellows work across research areas including telemonitoring, human-computer interactions, operations research, AI-mediated socialization, and chemical transformations. Their research covers a big selection of projects, including designing low-power processing hardware for telehealth applications; applying machine learning to streamline and improve business operations; improving mental health care through artificial intelligence; and using machine learning to know the environmental and health consequences of complex chemical reactions.

As a part of the appliance process, student nominations were invited from each unit inside the School of Engineering, in addition to from the Institute’s 4 other schools and the MIT Schwarzman College of Computing. Five exceptional students were chosen as fellows for the initiative’s third 12 months.

Drew Buzzell is a doctoral candidate in electrical engineering and computer science whose research concerns telemonitoring, a fast-growing sphere of telehealth during which information is collected through internet-of-things (IoT) connected devices and transmitted to the cloud. Currently, the high volume of knowledge involved in telemonitoring — and the time and energy costs of processing it — make data evaluation difficult. Buzzell’s work is concentrated on edge computing, a latest computing architecture that seeks to handle these challenges by managing data closer to the source, in a distributed network of IoT devices. Buzzell earned his BS in physics and engineering science and his MS in engineering science from the Pennsylvania State University.

Mengying (Cathy) Fang is a master’s student within the MIT School of Architecture and Planning. Her research focuses on augmented reality and virtual reality platforms. Fang is developing novel sensors and machine components that mix computation, materials science, and engineering. Moving forward, she’s going to explore topics including soft robotics techniques that could possibly be integrated with clothes and wearable devices and haptic feedback with a view to develop interactions with digital objects. Fang earned a BS in mechanical engineering and human-computer interaction from Carnegie Mellon University.

Xiaoyue Gong is a doctoral candidate in operations research on the MIT Sloan School of Management. Her research goals to harness the facility of machine learning and data science to scale back inefficiencies within the operation of companies, organizations, and society. With the support of an Accenture Fellowship, Gong seeks to seek out solutions to operational problems by designing reinforcement learning methods and other machine learning techniques to embedded operational problems. Gong earned a BS in honors mathematics and interactive media arts from Recent York University.

Ruby Liu is a doctoral candidate within the Medical Engineering and Medical Physics program, a part of the Harvard-MIT Program in Health Sciences and Technology. Their research addresses the growing pandemic of loneliness amongst older adults, which results in poor health outcomes and presents particularly high risks for historically marginalized people, including members of the LGBTQ+ community and other people of color. Liu is designing a network of interconnected AI agents that foster connections between user and agent, offering mental health care while strengthening and facilitating human-human connections. Liu received a BS in biomedical engineering from Johns Hopkins University.

Joules Provenzano is a doctoral candidate in chemical engineering. Their work integrates machine learning and liquid chromatography-high resolution mass spectrometry (LC-HRMS) to enhance our understanding of complex chemical reactions within the environment. As an Accenture Fellow, Provenzano will construct upon recent advances in machine learning and LC-HRMS, including novel algorithms for processing real, experimental HR-MS data and latest approaches in extracting structure-transformation rules and kinetics. Their research could speed the pace of discovery within the chemical sciences and advantages industries including oil and gas, pharmaceuticals, and agriculture. Provenzano earned a BS in chemical engineering and international and global studies from the Rochester Institute of Technology.