About

Graduate Student & Graduate Research Assistant

Performance and Resource Optimization in Networks (PROTON) Lab, Arizona State University

Interests

Machine Learning Signal Processing Communication Networks Deep Learning

City

Tempe, Arizona, United States

Email

debaleena82001@gmail.com

I am a Master's student in the School of Electrical, Computer and Energy Engineering at Arizona State University and a member of the Performance and Resource Optimization in Networks (PROTON Lab), advised by Dr. Eirini Eleni Tsiropoulou. I received my B.Tech. in Electronics & Communication Engineering from IIIT Guwahati.

I am a researcher with a strong interest in applied machine learning, having worked on EEG-based seizure classification and network optimization using over-the-air techniques. I am now focusing on leveraging machine learning for fault-tolerant and resilient computing systems, bridging my prior experience with future research directions.

Education

M.S. in Computer Engineering

Arizona State University

Aug 2024 – May 2026 · Tempe, USA

GPA: 4.0 / 4.0

B.Tech in Electronics & Communication Engineering

IIIT Guwahati

Aug 2019 – Jul 2023 · Guwahati, India

GPA: 8.66 / 10

Experience

Graduate Research Associate – PROTON Lab, Arizona State University

May 2025 – Present

  • Conducting thesis research on Over-the-Air Computation using game theory and reinforcement learning for wireless edge networks.
  • Developing Python-based simulations for power control and resource allocation under dynamic channel conditions.

Data Engineer – ZS Associates

Sep 2023 – Jun 2024 · India

  • Built SQL-driven workflows and Excel-based analytics for medical sales data.
  • Optimized ETL pipelines, improving execution time by 30%.
  • Designed quality-check frameworks reducing SQL database error rates by 25%.

Summer Research Fellow – IISc Bangalore

May 2022 – Jul 2022

  • Analyzed EEG signals using time-frequency decomposition and Matching Pursuit algorithm.
  • Developed MATLAB pipelines for preprocessing and visualization of biomedical signals.

Publications

Game-Theoretic Over-the-Air Computation for Socially Coupled Crowdsensing Systems Submitted · 2026

A. Sabyrbek, D. Chakraborty, E. E. Tsiropoulou

IEEE International Conference on Communications (ICC)

Attention-based Deep Learning for Epileptic Seizure Type Detection 2024

A. Shankar, D. Chakraborty, et al.

ASSIC

Seizure Type Detection Using EEG Signals Based on Phase Synchronization and Deep Learning 2023

A. Shankar, D. Chakraborty, et al.

IEEE BSN

Long Short-Term Memory Framework for Seizure Type Classification Using EEG Signals 2023

A. Shankar, D. Chakraborty, et al.

IEEE SPMB

Efficient Hardware Implementation of Cube Architecture using Yavadunam Sutra on FPGA 2021

M. Thakare, P. Yash, D. Chakraborty, B. Jajodia

IEEE MWSCAS

Skills

Python SQL AWS Docker MLflow TensorFlow PyTorch Signal Processing

Projects

End-to-End MLOps Pipeline – YouTube Sentiment Analysis

Python · AWS · NLP · Docker · MLflow · DVC

Built a full MLOps pipeline integrating YouTube API, custom NLP models, and experiment tracking, reducing manual workflow time by ~60%.

Smart Task Prioritizer (LLM-Based Agent)

Python · Streamlit · LangChain

Designed an LLM-powered productivity agent with real-time reasoning and interactive dashboards.

Seizure Type Classification using Deep Learning

TensorFlow · CNN · LSTM · Signal Processing

Developed EEG-based seizure classification models achieving up to 98% accuracy.

Awards

Herbold Graduate Engineering Scholarship Aug 2025

Awarded to high-achieving graduate students (GPA ≥ 3.5) with research interests in computer science and engineering.

Engineering Graduate Fellowship Aug 2024

Competitive fellowship awarded by the Ira A. Fulton Schools of Engineering.

Google Research Week – Computer Vision Track Jan 2023

Selected among 50 students nationwide for an intensive research program by Google Research India.