Yash Mehta

Yash Mehta

Research Engineer

AutoML Lab, Freiburg


Hi! I’m currently a research engineer working on challenging neural architecture search research under the supervision of Prof Frank Hutter (ELLIS Fellow). Here, I am also working on advancing the state of the art in deep learning for EEG data prediction. Previously, I was a researcher 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) and Tim Lillicrap (DeepMind).

In the past, I did my research thesis on deep learning-based personality detection from text with Prof Erik Cambria at NTU Singapore. Before getting into research, I was working as a software developer at Amazon.

I thoroughly enjoy coding and working on hard algorithmic problems. My interests lie at the intersection of deep learning and neuroscience.

Download my resumé.


Machine Learning Lab
Research Engineer
Oct 2020 – Present Freiburg, Germany
Supervisor, Frank Hutter. Working on neural architecture search (NAS) applied to EEG data, development of NASLib - a modular, extensible and easy to use NAS library.
Gatsby Computational Neuroscience Unit, UCL
Research Staff
Jan 2019 – Feb 2020 London, UK
Supervisors, Timothy Lillicrap (DeepMind) and Peter Latham (Gatsby). Two papers under review at NeurIPS'21 on biologically plausible learning algorithms.
Software Development Engineer
Jun 2018 – Dec 2018 Bangalore, India
Full-time developer part of the Amazon Music team. Quit this job to get into research.
SenticTeam, Nanyang Technological University
Research Intern
Jan 2018 – Jun 2018 Singapore
Supervisor, Erik Cambria (NTU Singapore). Ongoing collaboration resulting in 4 published papers on deep learning based personality prediction.


Google Scholar

On the Limitations of Perturbation-Based Methods for Training Deep Networks
Towards Biologically Plausible Convolutional Networks
Multitask Learning for Emotion and Personality Detection
Bottom-up and top-down: Predicting personality with psycholinguistic and language model features
Personality trait detection using bagged svm over bert word embedding ensembles
Recent trends in deep learning based personality detection