Biography

I am a Ph.D student at Boston University Computer Science Department Cyber Physical Lab, focusing on heterogeneis heterogeneous System-on-Chip development, and tackling on multi-core real-time scheduling problems. My daily research equips me with confidence to interact with both software and hardware (even design my own hardware!). I also take interests in artificial intelligence, which enables me to implement machine learning algorithm to solve practical problems. I affiliate with Bio-imaging Informatic Lab at Boston Univeristy Medical School, where my machine learning code could really put into use.

Download my resumé.

Interests
  • Cyber Physical System
  • Internet of Things
  • Artificial Intelligence
  • FPGA
Education
  • PhD in Computer Science, 2021-

    Boston University

  • MS in Artificial Intelligence, 2019-2021

    Boston Univeristy

  • BSc in Physics, 2013-2017

    Univeristy of Wisconsin Madison

Skills

Machine Learning | Embedded System

Python

100%

C/C++

80%

OCaml

70%

Java

60%

Bash

70%

Verilog

40%

aarch64

30%

Experience

 
 
 
 
 
Boston University Cyber Physical Lab
Research Fellow
Sep 2020 – Present Massachusetts

Multiple Processor System-on-Chip (MPSoC) has become ubiquitous. Albeit powerful, the complex interaction between various components pose a threat to tasks that require strict timeliness behaviors (a.k.a hard real-time tasks). The timeliness has to be met to prevent distrastrous consequences. For example, the deployment of the safety air bag on cars has to be just in time. Too late or too early would invalid such systems. In fact, hard real-time tasks exist for many industries, automotive, avionic, home appliances, medical, telecommunication, aerospace and military applications. Our vision is to develop a system capable of being self-aware of the time progress of running applications, so that regulation strategies can be effective applied dynamically, to truly unleash the power of modern computing platforms.

  • Keywords: Real-Time Operating System (RTOS); MPSoC; Internet of Things (IoT)
 
 
 
 
 
Boston University Bio-Imaging & Information Lab
Research Assistant & Peer Mentor
Jan 2020 – Sep 2022 Massachusetts

Magnetic Resonance Imaging (MRI) generates a huge volume of data reflecting brain structure. I worked closely with the research group, and developed various machine learning frameworks to analyze the data. I also mentored other undergraduates who take an interest in both machine learning and brain imaging.

  • keywords: Machine Learning; Deep Learning; Computer Vision; Data Science; Optimization; MRI; Brain Imaging

Publications

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(2022). Association of Diabetes and Hypertension With Brain Structural Integrity and Cognition in the Boston Puerto Rican Health Study Cohort.

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(2020). Neuroimaging Markers for Studying Gulf-War Illness: Single-Subject Level Analytical Method Based on Machine Learning.

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