Shubhankar Pataskar

adbve9@r.postjobfree.com

+1-415-***-****

San Francisco, CA

github.com/shubhankarpataskar

www.linkedin.com/in/shubhankarp

SUMMARY

• Graduate Research Student Electrical & Computer Engineer with automobile industry experience as a R&D Design Engineer.

• Working on Biometric detection using Deep Machine Learning.

• Looking for opportunities in the field of Embedded & Machine Learning Development. SKILLS

• Languages & Softwares: Python, C, C++, Java, Golang, Ruby, Verilog, Xilinx, Quartus, MATLAB, CATIA, AutoCAD

• Tools & Technologies: Jupyter Notebook, PyTorch, Git, AWS, Linux, PyCharm

• Libraries/Frameworks: TensorFlow, SciKit Learn, NumPy, Pandas, Keras, Matplotlib, Plotly, OpenCV, NLP, Caffe, Theano, OpenVino

• DBMS: PostgreSQL, MongoDB, MySQL, Oracle Database. EXPERIENCE

San Francisco State University Nov 2017 – Present

Research Assistant

• Working on Face detection and recognition integrated on FPGA (Arria 10 Development Board).

• Used different machine learning classifiers on basic datasets (CIFAR-100, ImageNet, Caltech Faces, LFW).

• Tested VGG-16, VGG-19 and Inception model for face detection and recognition. Obtained an accuracy of 93%, with the help of transfer learning concept.

• Bounding boxes implemented for object detection for real time frames, attained an accuracy of 100%. Force Motors Ltd. Sep 2016 – May 2017

R&D (Electronic & Electrical) Design Engineer

• Designed & developed engine & vehicle harnesses, terminal diagrams & rendered drawings in CATIA.

• 3D modelling, Packaging of electrical components, wiring harness 3D routing & worked for electrical integration team.

• Prototype vehicle build support & vehicle performance checking & solved production, service & R&D vehicle’s electrical issues.

• Troubleshooted CAN bus of CNG vehicle and reprogramming.

• Interacted with clients for modification of components according customer requirement and proposal submission.

• Verified and released two Light Commercial Vehicle (LCV) harnesses and interacted with QA for production queries. EDUCATION

• Master’s in Electrical Engineering & Computer Science, San Francisco State University Aug 2017 – Apr 2020

• Bachelor’s in Electrical Engineering (B.E), University of Pune, India Aug 2012 - May 2016 ACADEMIC PROJECTS

• Workout Analyzer (C++) Feb 2019 – May 2019

Used myoelectric sensor placed on the brachii muscle with Ag/AgCl electrodes, collects the Electromyography signals via UART. Data collected with the help of Arduino Nano, interfaced with MATLAB for live graphs. Determined the number of repetitions, duration of the workout and the workout intensity level. Attained an accuracy of 87% (Average age group -23).

• Pacman (Python) Aug 2018 – Dec 2018

Implemented different search algorithms like BFS, DFS, A*, Heuristics, Alpha-Beta Pruning, Expectimax, Q-Learning, Classification, Reinforcement Learning. The implementation of these algorithms made the Pacman reach its goal state more efficiently with the consideration that environment scenarios were altered.

• Projectile Motion analysis and Object Retrieval System (C++) Aug 2018 – Oct 2018 Used TIVA C (TM4C123GH6PM) microcontroller for the computing & calculating the distance of the object to the landing surface. Piezoelectric sensor, interfaced via ADC for weighing. Bluetooth module (UART) communicated the receiver, driven by DC motor controlled by an 8-bit controller (GPIO). An accelerometer and barometer connected via I2C are used to determine the projectile of the object. Interfaced the sensors via MATLAB.

• Convolutional Neural Application Network for Hardware Security (Python) Oct 2017 – Jan 2018 Used CNN to tackle the IC Counterfeit issue. Algorithm tested on well-known datasets, MNIST & CIFAR-10 & attained an accuracy of 99.48% & 78.5% respectively. Algorithm used three layered CNN (Relu) sequential model with a max pooling layer and a softmax layer with a backend of Keras.

PUBLICATIONS

2018 IEEE International Symposium on Technologies for Homeland Security: Nov 2017 Security of Automated Border Control (ABC) using Multi-Spectrum Biometric Authentication System: Challenges & Solutions.

(Under review)

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