About

Xin Huang is an Associate Professor in the Department of Neuroscience at the University of Wisconsin–Madison. His research focuses on understanding the neural mechanisms underlying visual perception and visually guided behavior, emphasizing the importance of vision in human activity and subjective sensory experience. His laboratory investigates how visual information is represented and processed by neurons across various brain areas, exploring hierarchical and parallel structures, and how neural representations transform from sensory to cognitive and motor functions as information flows deeper into the brain. His work aims to address key questions related to perceptual organization, including how the visual system integrates multiple stimulus features to form perceptual wholes, segregates objects from backgrounds, and the role of selective attention in these processes. Additionally, his research seeks to understand the neural basis of visual awareness, principles of population neural coding, and the circuit mechanisms underlying neural computations. To achieve these goals, his lab employs integrated approaches such as electrophysiology, psychophysics, computational modeling, behavioral tasks, calcium imaging, and optogenetics. Students interested in these research areas are encouraged to apply to the relevant Ph.D. programs at UW–Madison.

Research topics

• Machine Learning
• Computer Science
• Chemistry
• Artificial Intelligence
• Pharmacology
• Computational chemistry
• Biochemistry
• Immunology
• Internal medicine
• Virology
• Medicine

Selected publications

• Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning

  JACS Au · 2021 · 127 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  Markov state models (MSMs) based on molecular dynamics (MD) simulations are routinely employed to study protein folding, however, their application to functional conformational changes of biomolecules is still limited. In the past few years, the field of computational chemistry has experienced a surge of advancements stemming from machine learning algorithms, and MSMs have not been left out. Unlike global processes, such as protein folding, the application of MSMs to functional conformational changes is challenging because they mostly consist of localized structural transitions. Therefore, it is critical to properly select a subset of structural features that can describe the slowest dynamics of these functional conformational changes. To address this challenge, we recommend several automatic feature selection methods such as Spectral-OASIS. To identify states in MSMs, the chosen features can be subject to dimensionality reduction methods such as TICA or deep learning based VAMPNets to project MD conformations onto a few collective variables for subsequent clustering. Another challenge for the application of MSMs to the study of functional conformational changes is the ability to comprehend their biophysical mechanisms, as MSMs built for these processes often require a large number of states. We recommend the recently developed quasi-MSMs (qMSMs) to address this issue. Compared to MSMs, qMSMs encode the non-Markovian dynamics via the generalized master equation and can significantly reduce the number of states. As a result, qMSMs can be built with a handful of states to facilitate the interpretation of functional conformational changes. In the wake of machine learning, we believe that the rapid advancement in the MSM methodology will lead to their wider application in studying functional conformational changes of biomolecules.

  DOI

• Remdesivir, lopinavir, emetine, and homoharringtonine inhibit SARS-CoV-2 replication in vitro

  Antiviral Research · 2020 · 969 citations

  • Pharmacology
  • Virology
  • Medicine
  DOI

Frequent coauthors

• Ben Zhong Tang

  Chinese University of Hong Kong, Shenzhen

  171 shared

• Lu Zhang

  Chinese Academy of Sciences

  121 shared

• Siqin Cao

  Madison Group (United States)

  118 shared

• Lizhe Zhu

  Chinese University of Hong Kong, Shenzhen

  118 shared

• Ilona Christy Unarta

  University of Wisconsin–Madison

  117 shared

• Jacky W. Y. Lam

  Hong Kong University of Science and Technology

  105 shared

• Xiaoyan Zheng

  Ministry of Industry and Information Technology

  105 shared

• Fu Kit Sheong

  Hong Kong University of Science and Technology

  103 shared

Education

• Ph.D., Neuroscience

  University of Wisconsin–Madison

  2005

• M.S., Neuroscience

  University of Wisconsin–Madison

  2001

• B.S., Biological Sciences

  University of Science and Technology of China

  1998

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