Engineer Electrical

Neda Parvin, MS

**** ******** ***, *** ****, Clayton, MO, 63105

Phone: 240-***-**** E-Mail: ************@*****.*** Work Permit: Permanent Resident

SKILLS SUMMARY

• Interests: Image and Signal Processing, Computer Vision, Deep Learning, Machine Learning, FPGA Programming, Statistical Analysis, Bio-Medical Signal Processing

• Programming Languages: C++, Matlab, Python, VHDL

• Software Packages: Tensorflow, Amira, Labview, ALTIUM EDUCATION

MS in Electrical and Computer Engineering

Rowan University, Glassboro, NJ. GPA 3.6/4.0

Thesis: Automatic Analysis of Surface Electromyography of Reflexes for Central Nervous System Inhibition During Pregnancy December 2016

BSEE, Azad University, Fasa, Iran June 2006

GRADUATE COURSEWORK (Completed in US)

PROFESSIONAL AND RESEARCH EXPERIENCE

• Engineering Optimization • Digital Speech Processing

• Probability & Mathematical Statistics • Statistical Signal Analysis

• Advanced System Controls • Digital Image & Signal Processing

• Pattern Recognition & Machine Learning • Strategic Engineering Management Electrical Engineer

Kulicke and Soffa, Fort Washington, PA

March 2017- June 2018

• A member of optic team with test applications and developing a new image acquisition pipeline

• Developed firmware (C/C++) for a Shark DSP for a motor overload protection for safety (I2T protection)

• Developed firmware (C/C++) for voice coil motor from three phases in single-sine drive system

• Worked with electronic schematics and performed stress analysis on PCB board Graduate Research Assistant

ECE Research Labs of Rowan University, Glassboro, NJ Sept 2014 - March 2017

• Led a multidisciplinary research effort (including MDs, PhDs and graduate students) with emphasis on 2

HONOR AND AWARD

REFERENCES

signal processing using Matlab to quantify deep tendon reflexes (DTR) in pregnancy to understand the physiology and create normal ranges. This may subsequently be used to try to risk stratify patients who may develop Eclampsia

• Performed analysis of features to boost the performance of speaker identification systems. (Using machine learning and signal processing toolbox in Matlab)

• Worked knowledge of digital signal processing algorithms and methods, including: FFT, IIR, FIR, digital modulation, linear system theory, wavelet transform and empirical mode decomposition. Performed machine learning and statistical analysis methods on big datasets from UCI Machine Learning Repository such as classification, regression, clustering, Bayesian modeling, deep learning, anomaly detection, regression models, hypothesis testing, confidence intervals Network Switching Subsystem Engineer

ZTE Corporation, Shiraz, Iran

September 2006 - August 2013

• Defined traffic and signaling routes based on core plan

• Integrated and tested procedures to ensure mobile switching service (MSS) center and multimedia gateway (MGW) were working properly

• Responsible for problem mitigation, fault analysis and uncovering systems errors

• Performed daily system functional reports and health checks Rowan University College of Engineering Graduate Fellowship 2014-2016 1. Steve Jaeschke

Email: *********@***.***

Staff Engineer at Kulicke and Soffa

2. Mark Soffa

Email: ******@***.***

Engineering Manager at Kulicke and Soffa

3. Nidhal C. Bouaynaya, Ph.D.

Email: *********@*****.***

Associate Professor at Rowan University

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