Vaibhav Thakur

Vaibhav Thakur

Research Assistant

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  • Timeline

  • About me

    PhD Candidate in Experimental and Computational Neuroscience Reverse Engineering Decision-Making Process in Mammals

  • Education

    • Jawahar Navodaya Vidyalaya, Pune

      2005 - 2012
      High School
    • Indian Institute of Science Education and Research (IISER), Pune

      2012 - 2017
      Master of Science - MS Neuroscience; Physics
    • UCLA

      2019 - 2021
      Master of Arts - MA PSYCHOLOGY
    • University of Washington

      2021 -
      Doctor of Philosophy - PhD Neuroscience
  • Experience

    • Indian Institute of Science (IISc)

      May 2016 - Oct 2017
      Research Assistant

      • Lead a study on neural representation of planning and execution of movement kinematics in healthy humans.• Developed electroencephalography (EEG) experiment and analysis pipeline in the lab for the first time.• Disseminated the findings from project at renowned research conferences and thesis• Developed EEG analysis pipeline

    • UCLA

      Nov 2017 - Jan 2022

      • Conducted psychophysical experiments to understand the mechanism of perceptual bias implementation in humans during the decision process.• Collaborated with colleagues to develop experiments on implicit learning of motor sequences and understanding the transfer of learning in novel environments.

      • Graruate Student

        Sept 2019 - Jan 2022
      • Staff Research Associate

        Nov 2017 - Aug 2019
    • University of Washington

      Jan 2022 - now
      Graduate Student Researcher

      • Conduct experiments in people with neurodegenerative disorders, complemented by advanced experiments in mice, utilizing cutting-edge neurotechnology.• Heading the mouse laboratory, managing diverse experiments, and leading a team of research assistants.• Single-handedly developed low-cost distributed system (hardware/software and electronics) for controlled as well as naturalistic neurophysiological experiments with automated training• Applying state-of-the-art algorithms (e.g., Bayesian state-space modeling, RNN) for modeling of the neuronal basis of decision-making.• Sharing findings with a broader audience through compelling talks and impactful research papers.• Trained 24 undergraduate students and technicians in developing experimental techniques and programming skills• Machine learning approaches to mimic the DM process to identify potential brain regions involved in the DM process. Show less

  • Licenses & Certifications