YM

Yash Mehta

Also known as: Mehta

Harvard University, Janelia Research Campus, Johns Hopkins University, Nanyang Technological University, University of Freiburg, Birla Institute of Technology & Science Pilani - K K Birla Goa Campus

ORCID iD 0000-0002-9610-7077
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About

I’m currently a PhD student in the Cognitive Science department at Johns Hopkins University, working the intersection of deep learning and neuroscience, and a visiting researcher at Harvard Medical School.

I am very fortunate to have gotten the opportunity to have worked with brilliant researchers along the way. Previously, I was a research engineer at the amazing HHMI Janelia Research Campus, working on inferring synaptic plasticity rules using deep learning with James Fitzgerald and Jan Funke. I have worked on Neural Architecture Search with Prof Frank Hutter (ELLIS Fellow). Previously, I was at the Gatsby Computational Neuroscience Unit at UCL, where I was working on evaluating biologically plausible perturbation-based learning algorithms to train deep networks under
the guidance of Prof Peter Latham (Gatsby, UCL) and Tim Lillicrap (DeepMind). In the past, I’ve also worked on deep learning-based personality detection from text with Prof Erik Cambria (NTU Singapore).

I thoroughly enjoy coding and working on hard algorithmic problems.

Employment

  • Harvard University Visiting PhD student
    2023 - 2024
  • Janelia Research Campus Research Engineer 2
    2022 - 2023
  • University of Freiburg Research Engineer
    2020 - 2021
  • Gatsby Computational Neuroscience Unit Research Intern
    2019 - 2019

Education

  • Johns Hopkins University PhD student
    2023 - Present
  • Nanyang Technological University
    2017 - 2018
  • Birla Institute of Technology & Science Pilani - K K Birla Goa Campus B.E. Hons
    2014 - 2018

Projects & Funding

Projects & funding information is unavailable.

Publications (9)

  • Model-based inference of synaptic plasticity rules
    2023 DOI: 10.1101/2023.12.11.571103
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  • Future-generation personality prediction from digital footprints
    Future Generation Computer Systems 2022
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  • NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy
    International Conference on Learning Representations (ICLR) 2022
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  • On the Limitations of Perturbation-Based Methods for Training Deep Neural Networks
    arxiv 2022
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  • Towards Biologically Plausible Convolutional Networks
    Advances in Neural Information Processing Systems (NeurIPS) 2021
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  • Bottom-Up and Top-Down: Predicting Personality with Psycholinguistic and Language Model Features
    2020 IEEE International Conference on Data Mining (ICDM) 2020
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  • Multitask learning for emotion and personality detection
    Neurocomputing 2020
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  • Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles
    ACL WiNLP Workshop 2020
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  • Recent Trends in Deep Learning Based Personality Detection
    Artificial Intelligence Review 2019 DOI: 10.1007/s10462-019-09770-z
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