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Deep learning for autonomous vehicles
I am a Master's student at University of Southern California majoring in Electrical Engineering with a focus on Signal Processing and Data Science. My interests lie at the intersection of image processing and machine learning. I have worked on projects in different domains such as Deep Learning, Image Processing, Computer Vision and Machine Learning. Currently, I am developing deep neural networks to solve image splicing detection problem at Media Communication Lab, USC. Previously, I was involved in developing real time image processing algorithms at Technophilia. I also worked as a part time software developer at KickWheel to develop a solution for water contamination detection. I enjoy programming in C++, Java and Python. I have implemented and optimized algorithms in machine learning and computer vision. In addition, I have experience in working with deep learning frameworks such as TensorFlow, Caffe, Keras, Lasagne and Theno. Currently, I am seeking opportunities in the field of computer vision and machine learning. I am looking forward to contribute in these fields to help achieve personal and organizational goals. -------------------------------------------------------------------------------------------------------------------------------- Skills: Languages: C, C++, Java, Objective-C, Swift, Python, Lua Tools: OpenCV, TensorFlow, MATLAB, SIMULINK, Photoshop, AutoCAD, Git, GDB, Valgrind, Weka, CMake, ImageJ Operating Systems: Linux (Ubuntu), Windows, UNIX, iOS -------------------------------------------------------------------------------------------------------------------------------- Github: https://github.com/maroofmf
University of Southern California
Master of Science (M.S.), Electrical and Electronics Engineering
January 1, 2015 – May 1, 2017
Sir M Visvesvaraya Institute Of Technology
Bachelor's of Engineering, Electrical and Electronics Engineering
January 1, 2011 – January 1, 2015
NVIDIA
Senior Deep Learning Software Engineer
August 1, 2017 – Present
United States
University of Southern California
Grader
January 1, 2017 – May 1, 2017
University of Southern California
Graduate Research Assistant
January 1, 2017 – May 1, 2017
Technophilia
Intern
January 1, 2013 – May 1, 2013
Bengaluru Area, India
Digital Image Processing
August 1, 2016 – December 1, 2016
- Implemented and contrasted the performance of image de-noising algorithms such as NLM, BM3D, Mean filter, Median filter and Gaussian filter. - Implemented algorithms for dithering, morphological processing, texture segmentation, texture classification using Law's filter, geometrical transformations and image overlay. - Tested the performance of Structured Edge detector and Auto Canny Edge detector. - Implemented SIFT and SURF feature extraction to develop a bag of words model for object classification. - Trained and tested the performance of convolutional neural network on MNIST dataset. Languages/Tools Used: C++, MATLAB, OpenCV
iOS Application for Solving Handwritten Mathematical Expression
January 1, 2016 – May 1, 2016
-Developed an iOS application using Objective – C and Swift to solve handwritten mathematical equations. -Formed dataset consisting of digits and mathematical symbols from MNIST database and preprocessed handwritten data. -Developed a convolutional neural network for character recognition and achieved an accuracy of 98%. -Formulated rules for interpretation system and implemented contextual learning to suppress parsing errors by 30%. - Developed a decision tree using hand crafted features to classify a character as baseline, super-script or sub-script element and achieved an accuracy of 90%. Tools/Languages: OpenCV, Python, Objective-C, Swift, MATLAB, TensorFlow
Information Retrieval and Analysis of Weather Datasets
December 1, 2015 – February 1, 2016
- Designed UI using tkinter package in Python. - Established weather data retrieval for the requested dates on the web using Python. - Formed a local Database to store and manipulate weather data in SQLite database using Python. - Optimized the program by using on-demand cache method and achieved high reduction in initial setup time and running time.
Design and Stabilization of a Quadcopter using PID Controller
January 1, 2015 – May 1, 2015
-Formulated the parameters of the PID controller on MATLAB using Ziegler–Nichols method. -Built a lightweight coil gun to shoot targets within the range of 10 meters. -Scrutinized the trigger circuit design of the coil gun.
All Terrain Bomb Disposal Robot
July 1, 2014 – December 1, 2014
-Collaborated with two group members to engineer a working model of an all terrain bomb disposal robot that could semi-automatically diffuse a bomb. -Scrutinized the design of the robot developed on CATIA. -Formulated optimal motor sizing and battery capacity keeping weight constraints. -Programmed a Raspberry Pi in Python to control the robotic arm movements and locomotion. -Implemented Sockets using Python to facilitate communication between two computers connected to the same wireless network.
Rapid Spectrophotometric Detection of E. Coli Bacterial Contamination in Water
January 1, 2014 – December 1, 2014
- Devised a portable spectrophotometer using Arduino Uno to obtain real time OD values. - Proposed a method to automate the process of counting CFU from images using ImageJ that effectively reduced data acquisition time. - Devised a novel algorithm to detect water contamination based on E. Coli bacteria concentration with an accuracy of 73%.
Cultural Fit Analysis
The candidate's project portfolio is diverse, ranging from iOS app development to robotics and environmental sensing, indicating a broad interest in applying technical skills to various domains. The experience at NVIDIA as a Senior Deep Learning Software Engineer suggests a fit for fast-paced, innovation-driven environments. However, the target role is 'Data Analyst', while the experience is heavily skewed towards Deep Learning and Software Engineering. This might indicate a potential mismatch in day-to-day responsibilities and expectations, requiring further clarification on the candidate's career aspirations for a Data Analyst role.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving aptitude and a collaborative spirit (e.g., 'Collaborated with two group members'). The variety of projects suggests adaptability and a proactive approach to learning and applying new technologies. However, without specific psychometric test results or interview data, a detailed assessment of soft skills and operational fit is limited.