Machine Learning Engineer

Koosha Sadeghi Email: *.*******@***.***

PhD j Computer Engineering Mobile: +1-480-***-****

Website: kooshasadeghi.weebly.com

SUMMARY

I Creative and passionate engineer with 8+ years hands-on experience in developing machine/deep learning applications with strong background in advanced mathematics and statistical data analysis. EDUCATION

Arizona State University (ASU) j PhD in Computer Engineering j GPA: 3.92 2013 - Present TECHNICAL SKILLS

Programming Languages: Java, MATLAB, Python, C/C++, Lua, JavaScript, & Assembly.

Expertise: Machine/Deep Learning, Data Mining, Signal/Image Processing, High Performance Computing, Applied Optimization, Statistical Data Analysis, & Mobile Computing.

Tools & Technologies: Android Studio, TensorFlow, Keras, SciPy, scikit-learn, NumPy, pandas, Matplotlib, SQLite, MySQL, AWS, Apache Spark, GitHub, Latex, & UML. WORK EXPERIENCE

Ciye.co, Albany, CA, USA j DSP & ML Engineer 2019 - Present I Wireless Monitoring of Swimmers Activities Based on Physical Activity Signals B Internet-of-Things: Collect physical activity signals from goggles built-in sensors, reduce noise using digital filters, & recognize swimmers activities (e.g. resting, swimming, swimming style, lap counts, & traveled distance) using machine learning classifiers on micro-controllers.

Arizona State University, Tempe, AZ, USA j Impact Lab j Research Assistant 2013 - 2019 I SafeDrive: Mobile Driver Safety Application

B Mobile Computing: Develop a mobile application for collecting/processing brain signals from drivers through sensor interfacing to detect mental fatigue and avoid accident by providing real-time feedback j Implemented by Android Studio. B Data Mining: Extract features from brain signals/images using Fourier Transform, Discrete-Wavelet Transform, Auto- Regression, & Principal/Independent Component Analysis j Implemented by MATLAB & Python. B Machine Learning: Classify/predict brain signals/images to recognize/predict mental states using Dynamic Time Warping, Multivariate Gaussian Mixture Model, Markov Process Model, Logistic Regression, Decision Tree, Na ve Bayes Classi er, Support Vector Machine (SVM), & Convolutional Neural Network (CNN) j Implemented by MATLAB, Java, & Python. B Edge/Cloud Computing: Utilize cloudlets/cloud servers to o oad machine learning from mobile devices through Socket Programming/Amazon WebSocket APIs j Implemented by Android Studio, Java, MATLAB, MySQL, & AWS. B Optimization: Optimize deep learning hyper-parameters for higher security using Particle Swarm/Bayesian Optimization, Genetic Algorithm, Simulated Annealing, & Generative Adversarial Network (GAN) j Implemented by MATLAB.

Azad University, Tehran, Iran j Cognitive Informatics Lab j Research Director 2009 - 2012 I Pervasive Smart-Home System for Remote Health-Care B Pervasive Computing: Design a smart-home to collect subjects data using seamless sensors for diagnosing major depressive disorder by applying Markov chain/Bayesian network models j Modeled & Implemented by UML, MATLAB, & Simulink. SELECTED PUBLICATIONS (MORE THAN 150 CITATIONS IN GOOGLE SCHOLAR)

A System-Driven Taxonomy of Attacks and Defenses in Adversarial Machine Learning IEEE Transactions on ETCI 2020

An Analytical Framework for Security-Tuning of AI Applications Under Attack AI Testing 2019

Optimization of Brain-Mobile Interface Applications Using Internet-of-Things HiPC 2016

SafeDrive: An Autonomous Driver Safety Application in Aware Cities PerCom 2016 PATENTS

Framework for Security Strength and Performance Analysis of ML Based Biometric Systems (US20180300487A1) 2018

Brain-Mobile Interface Optimization using Internet-of-Things (US20180189678A1) 2018 AWARDS

University Graduate Fellowship (ASU j $15K) 2013, 2014, 2015, 2016, 2017, & 2018

Contact this candidate