Wireless Communications Application Developer

********@***.*** Soundarya Venkatesan 949-***-****

Education

University of California Irvine, CA

M.S. in Electrical Engineering, GPA: 3.45/4.0 Sep 2023 – Dec 2025

Concentrations: Systems and Wireless Communications

Coursework: Digital Communications, Probability and Random Processes, Wireless Communications, Artificial Intelligence, Digital Signal Processing, Information theory,Data Privacy, Computer and Communication Networks Work Experience

Deloitte USI India

Web Application Developer Aug 2021 – Jun 2023

Developed features for an Engagement Financial Management Tool using .NET Framework, SQL Server, and AngularJS

Collaborated with global teams to optimize application performance and deployment processes.

Received Spot Award for significant contributions to software development. Thesis Work

Optimizing Sensing Performance of Wireless Sensor Drone Networks : In Progress

Designing hybrid non-smooth control barrier functions to optimize sensing and communication in wireless drone sensor networks

Implementing drone dynamics and control barrier functions for optimized network performance Projects

Advanced Filtering for Time Series Data:

Designed and implemented white noise filtering methods using linear and non-linear (Volterra kernel) approaches.

Created neural network-based denoising filters for signal and music data.

Explored adaptive filtering techniques for signal processing using RNN and LSTM techniques. MATLAB Simulations for Wireless Communication Systems and DSP Techniques:

Compared precoding techniques for Rayleigh fading channels, compensating for phase rotations

Simulated Ergodic capacity for spatially correlated MIMO channels with Kronecker model using waterfilling power allocation algorithms.

Optimized Symbol Error Rate (SER) for MRC, MRT, and EGT techniques.

Performed DSP techniques including upsampling, downsampling,FIR/IIR filter designing and Adaptive filtering Channel Estimation using Conv2DLSTM Networks:

Developed Conv2DLSTM-based channel estimation models for time-series data in wireless systems

Simulated 5G standard-compliant channels referencing Nvidia’s Sionna library

Achieved a Mean Squared Error (MSE) of 0.0138, showcasing the novelty of Conv2DLSTM models in comparison with traditional channel estimation techniques.

Differential Privacy using Mutual Information:

Applied mutual information concepts to time-series databases to measure privacy-utility tradeoff in E-Differential Privacy mechanisms .

Achieved significant improvement using Correlated Laplace Mechanism. Skills

Programming Languages: Python, Java, C++, C, MATLAB Technologies: TensorFlow, Keras, SQL Server, .NET Framework Wireless Communication Tools and Standards: Channel Modeling, 5G, LTE, Sionna Library, OFDM, MIMO Involvement

United Nations Development Programme (UNDP) UN Volunteering: Worked as Research Support for Automated Analysis of Sustainable Development Goals for UNDP Istanbul International for Private sector in Development in collaboration with SDG AI Lab.

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