About

Shu Hu is an Assistant Professor at Purdue University, affiliated with the Purdue Polytechnic Institute. He holds a Ph.D. in Computer Science from the University at Buffalo, SUNY, obtained in June 2022, and has a background in Mathematics from the University at Albany, SUNY, as well as a Master of Engineering in Software Engineering from the University of Science and Technology of China. His research interests include Machine Learning, Digital Media Forensics, and Computer Vision. Prior to his current academic position, he served as a Post-Doctoral Fellow in Machine Learning at Carnegie Mellon University from August 2022 to August 2023. Shu Hu has received several honors, including the Outstanding Reviewer Award in Machine Intelligence Research in 2023 and the CSE Best PhD Dissertation award at the University at Buffalo in 2022. His work has contributed to the fields of machine learning and digital media forensics, with a focus on topics such as adversarial attacks, deepfake detection, and loss functions in supervised learning.

Research topics

• Environmental science
• Architectural engineering
• Engineering
• Economics
• Computer Science
• Sociology
• Environmental economics
• Geography
• Business
• Natural resource economics
• Civil engineering
• Environmental resource management
• Mathematics
• Physics
• Ecology
• Statistics

Selected publications

• Modelling building energy consumption in China under different future scenarios

  Energy · 2020 · 177 citations

  • Sociology
  • Architectural engineering
  • Environmental economics
  DOI

• A systematic review of occupant behavior in building energy policy

  Building and Environment · 2020 · 182 citations

  1st authorCorresponding
  • Computer Science
  • Architectural engineering
  • Environmental science
  DOI

• Advances Toward a Net-Zero Global Building Sector

  Annual Review of Environment and Resources · 2020 · 218 citations

  • Natural resource economics
  • Business
  • Architectural engineering

  The building sector is responsible for 39% of process-related greenhouse gas emissions globally, making net- or nearly-zero energy buildings pivotal for reaching climate neutrality. This article reviews recent advances in key options and strategies for converting the building sector to be climate neutral. The evidence from the literature shows it is possible to achieve net- or nearly-zero energy building outcomes across the world in most building types and climates with systems, technologies, and skills that already exist, and at costs that are in the range of conventional buildings. Maximizing energy efficiency for all building energy uses is found as central to net-zero targets. Jurisdictions all over the world, including Brussels, New York, Vancouver, and Tyrol, have innovated visionary policies to catalyze themarket success of such buildings, with more than 7 million square meters of nearly-zero energy buildings erected in China alone in the past few years. Since embodied carbon in building materials can consume up to a half of the remaining 1.5°C carbon budget, this article reviews recent advances to minimize embodied energy and store carbon in building materials.

  DOI

Recent grants

• CAREER: Scalable Manufacturing of Hierarchical Nanostructures by Acoustically Modulated Emulsion Technique for Next Generation Renewable Energy Applications

  NSF · $500k · 2018–2024

• Collaborative Research: Multi-functional and Multi-Material Additive Nanomanufacturing: Acoustic Field-Assisted Stereolithography (AFS)

  NSF · $179k · 2017–2022

• Physics-Based Probabilistic Prognostics for Battery Health Management

  NSF · $385k · 2020–2025

Frequent coauthors

• Yong Ge

  65 shared

• Mengxiao Liu

  Tianjin University of Technology

  47 shared

• Chuanyong Jing

  Chinese Academy of Sciences

  39 shared

• Zhoupeng Ren

  Institute of Geographic Sciences and Natural Resources Research

  30 shared

• Baojiang Jiang

  Heilongjiang University

  26 shared

• Guozhong Cao

  University of Washington

  24 shared

• Yu Hui Lui

  Iowa State University

  23 shared

• Haisheng Chen

  22 shared

Labs

• Purdue Machine Learning and Media Forensics (M2) LabPI

Education

• PhD, Mechanical Engineering

  University of Minnesota Twin Cities

  2014

Awards & honors

• Outstanding Reviewer Award, Machine Intelligence Research, 2…
• CSE Best PhD Dissertation (with a prize of $500), University…

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• Masahiro Murakamih-100
• Zhixiang Chenh-87
• Anthony H Smithh-79
• Clint Chappleh-75
• Laurence B. Leonardh-74