Curriculum Vitae

Education

• Ph.D. in Biomedical Engineering, Northwestern University, Chicago, USA Expected April 2026

• M.S.E. in Electrical Engineering (Machine Learning), University of Pennsylvania, Philadelphia, USA
  August 2017 – May 2019

• B.Tech. in Electrical and Electronics Engineering, Vellore Institute of Technology, India
  June 2013 – May 2017

Relevant coursework: Signal Processing, Machine Learning, Data Mining, Deep Learning for Biomedical Imaging, Computational Neuroscience, Brain–Machine Interfaces, Dynamical Systems

Research Experience

• Decoding speech from neural signals (PhD thesis) Northwestern University 2020 – Present
  • Analyzed large-scale intracranial electrophysiology datasets (ECoG) from human neurosurgical recordings to study speech-related neural activity.
  • Built end-to-end pipelines for processing and aggregating neural, behavioral, and audio data across subjects and recording sessions.
  • Generated statistical analyses and visualizations to characterize neural dynamics and evaluate model performance across experimental conditions.
  • Developed machine learning models (SVM, XGBoost, contrastive learning) to decode speech-related signals and investigate phoneme-level and sequence-level representations.
  • Evaluated variability across sessions and participants, focusing on robustness and generalization in clinical datasets.
  • Supported intraoperative data collection in operating room environments, working with clinicians and engineers to ensure high-quality recordings.
  • Presented results to interdisciplinary teams, translating analytical findings into insights that informed experimental design and research direction.
• Classification of imagined movements from neural signals
  • Applied feature engineering, signal processing, and machine learning methods to classify imagined limb movements.
  • Achieved significant accuracy using ensemble classifiers including XGBoost and Random Forest.
• Reinforcement learning based rule for optimizing neural networks
  • Contributed to the design and testing of a reinforcement learning based algorithm for learning in Neural networks.
  • Conducted experiments with TensorFlow to optimize deep learning architectures including ANNs and CNNs for improved performance.
• Wheelchair control via visually evoked potentials (EEG)
  • Acquired EEG during visual stimulation to elicit steady-state visual evoked potentials.
  • Implemented correlation-based decoding to generate wheelchair control signals.

Work Experience

• Research Engineer, Feinberg School of Medicine, Northwestern University May 2019 – June 2020
  • Contributed to developing a multi-layered perceptron classifier to detect sleep stages from heart rate data, aimed to improve learning in stroke survivors by monitoring sleep patterns.
  • Analyzed EMG and neuromodulation (TMS) datasets from human studies, quantifying neural and motor responses across experimental conditions.
  • Developed data processing and visualization workflows for physiological time-series data in clinical research settings.
  • Investigated mechanisms of recovery in stroke survivors by developing a real-time electromyography (EMG) acquisition interface and a custom game using Python.
  • Implemented deep learning-based pose estimation (OpenPose) algorithm to track recovery outcomes in stroke survivors.

Publications

First-Author

Restoring communication with speech brain–computer interfaces

Decoding speech intent from non-frontal cortical areas

Other Publications

Wearable Myoelectric Interface for Neurorehabilitation (MINT) to Recover Arm Function: a Randomized Controlled Trial

Insights from Implementing Wearable Technologies for Stroke Rehabilitation Outside Research Laboratories

Aspects of speech production represented in inferior frontal gyrus

High-gamma activity is coupled to low-gamma oscillations in precentral cortices and modulates with movement and speech

Cortical sites critical to language function act as connectors between language subnetworks

Decoding speech intent from non-frontal cortical areas

Myoelectric Interface for Neurorehabilitation (MINT) Conditioning Improves Arm Function in Severely Impaired Chronic Stroke Survivors

Wearable myoelectric interface enables high-dose, home-based training in severely impaired chronic stroke survivors

Learning to solve the credit assignment problem

Investigation of multiple frequency recognition from single-channel steady-state visual evoked potential for efficient brain computer interfaces application

Talks

Neuronal-level representations of speech production in inferior frontal gyrus

January 01, 2025

Conference Talk at BCI Society Meeting,

Decoding speech intent from non-frontal cortical areas

February 01, 2024

Research Talk at Research in Progress, Department of Neurology, Chicago, IL

Teaching

• Graduate Teaching Assistant for Machine learning for Biomedical applications, Northwestern University March 2023 – May 2023
  • Organized office hours to help students with projects and clarify questions about concepts and code in Python.
• Graduate Teaching Assistant for Data Mining, University of Pennsylvania August 2018 – December 2018
  • Designed projects for a class of 80 students. Interacted with students on a weekly basis to clarify concepts taught in class.

Honors and Awards

• Recipient of Travel Award, Brain Computer Interface Society, Belgium, June 2023

Technical Skills