Barath Mohan U

AI Researcher

Bangalore Urban, Karnataka, India5 yrs experience

Key Highlights

3+ years of experience in ML research.
Proven track record with publications in top-tier conferences.
Expertise in building neural networks for diverse applications.

Stackforce AI infers this person is a Research Scientist specializing in Machine Learning and Deep Learning applications.

Contact

Skills

Core Skills

Machine LearningDeep Learning

Other Skills

CC#C++Distributed ComputingFeature EngineeringGoogle BigQueryMathematicsMatlabNeural NetworksPredictionPythonSQLTensorFlow

About

Researcher with 3+ years of experience building artificial neural networks for neuroscience, computer vision, and gaming research. Proven track record in ML research with publications in top-tier conferences. I value a robust, reproducible, falsifiable, yet agile approach to R&D. I love working with highly passionate, motivated, and collaborative teams.

Experience

Microsoft

Applied Scientist II

Jul 2024 – Present · 1 yr 8 mos · Bengaluru, Karnataka, India · On-site

Hike

ML Scientist

Jun 2022 – Jun 2024 · 2 yrs

Deep LearningTensorFlowMachine LearningGoogle BigQuery

Indian institute of science (iisc)

3 roles

Master Thesis

Sep 2021 – May 2022 · 8 mos · Bangalore Urban, Karnataka, India

Developed outlier detection techniques by debiasing generative models like Variational Autoencoders and PixelCNNs.

Deep LearningTensorFlowMachine LearningMatlab

Summer Internship

Jun 2021 – Aug 2021 · 2 mos · Bangalore Urban, Karnataka, India

Guide: Dr. Sridharan Devarajan
Using neural networks to predict the contrast of the stimulus in one hemifield using the SSVEP power and contrast in the other hemifield.
Developing a method based on numerical differentiation to visualize deep neural networks for EEG data.

Deep LearningTensorFlowMachine LearningMatlab

Bachelor Thesis

Sep 2020 – May 2021 · 8 mos · Bangalore Urban, Karnataka, India

Decoding human attention
Guide: Dr. Sridharan Devarajan
Used Convolutional Neural Networks to decode covert spatial attention in humans using SSVEPs.
Prepared a large-scale dataset (129 subjects, 50,000 trials) to combat poor SNR of EEG.
Developed a novel method using subject embeddings to account for the inter-subject variability, allowing us to reach higher accuracies than traditional CNNs and SVMs.
Developed novel neural network architectures which provide interpretability in time, frequency, and spatial domains without compromising performance.

Deep LearningTensorFlowMachine LearningMatlab

Ku leuven

Summer Internship

May 2020 – Aug 2020 · 3 mos · Leuven, Flemish Region, Belgium

EEG Channel Selection for Brain-Computer Interfaces
Guide: Dr. Marc Van Hulle
Worked on a channel selection algorithm for predicting the occurrence of an event related potential called mVEP (motion onset visually evoked potential) using Deep Convolutional Neural Networks.

Deep LearningTensorFlowMachine LearningMatlab

Instrumentation and applied physics, indian institute of science

Summer Internship

May 2019 – Jul 2019 · 2 mos · Bangalore Urban, Karnataka, India

Soft Robotics
Guide: Dr. Sanjiv Sambandan
Worked on building a touch-based sensory system using piezoelectric materials.

Indian institute of science (iisc)

Summer Internship

May 2018 – Jul 2018 · 2 mos · Bangalore Urban, Karnataka, India

Quantum Computation
Guide: Dr. Subroto Mukherjee
Studied the basics of quantum computation (notations, bit representation, Bloch sphere, operations and gates), quantum algorithms and parallelism, with special focus on the Bernstein-Varizani Problem.