Machine Learning Software Engineer
PhD in Electrical Engineering
San Jose, California
Recent PhD grad working in machine learning, computer vision, and data science with research and application skills in neural networks, feature selection, forecasting, using statistical, computer vision, and deep learning packages.
I worked at the Image Processing and Neural Networks Lab at UTA on machine learning, computer vision, data mining, signal processing, embedded systems, and Linux. I’m also a Software Carpentry certified instructor, and trained in flipped classroom pedagogy and research mentoring.
- PhD (Electrical Engineering) University of Texas at Arlington, Arlington, Texas. December 2016.
Dissertation available at: https://uta-ir.tdl.org/uta-ir/bitstream/handle/10106/26419/RAWAT-DISSERTATION-2016.pdf
- MS-Thesis (Electrical Engineering) University of Texas at Arlington, Arlington, Texas. December 2009.
MS-Thesis available at: https://uta-ir.tdl.org/uta-ir/bitstream/handle/10106/2062/Rawat_uta_2502M_10503.pdf
- Bachelor of Technology (Electronics and Communication Engineering) Guru Gobind Singh Indraprastha University, New Delhi, India. May 2007.
Spring 2014 – December 2016
My dissertation topic is near optimal feature selection from data (link). I also work on developing neural network training algorithms and applying neural networks to computer vision and biomedical applications such as license plate reading systems and cancer cell detection.
Summer 2013 – EE3318 Discrete Signals and Systems
- EE3417 Linear Systems (Fall 2013)
- EE5350/EE4318 Digital Signal Processing (Spring 2013, Fall 2012, Fall 2011, Fall 2010, Fall 2009, Fall 2008)
- EE4314 Control Systems (Spring 2013)
- EE4330 Fundamentals of Telecommunication Systems (Spring 2012)
- EE5356 Digital Image Processing (Spring 2011)
- EE6314 Advanced Embedded Microcontrollers (Spring 2010)
- EE4328 Microcontrollers (Spring 2009)
List of Publications
- PhD Dissertation: “Feature Selection Using an Extended Piecewise Linear Orthonormal Floating Search,” PhD Dissertation, available: https://uta-ir.tdl.org/uta-ir/bitstream/handle/10106/26419/RAWAT-DISSERTATION-2016.pdf
- Stochastic Programming for Interdisciplinary Pain Management (2017)
- Second Order Training of a Smoothed Piecewise Linear Network, Rawat, R. & Manry, M.T. Neural Process Lett (2017). doi:10.1007/s11063-017-9618-2
- Minimizing Validation Error With Respect to Network Size and Number of Training Epochs
- Multi-variable Neural Network Forecasting Using Two Stage Feature Selection
- Optimal output gain algorithm for feed-forward network training
- Analysis and improvement of multiple optimal learning factors for feed-forward networks
- A Preliminary Investigation of Human Frontal Cortex Under Noxious Thermal Stimulation Over the Temporomandibular Joint Using Functional Near Infrared Spectroscopy
- Investigation of human frontal cortex under noxious thermal stimulation of temporo-mandibular joint using functional near infrared spectroscopy
- An Efficient Piecewise Linear Network (MS Thesis)
- Second order design of multiclass kernel machines
- edX Verified BerkeleyX CS190.1x: Scalable Machine Learning
- The Data Scientist’s Toolbox
- Machine Learning
- R Programming
- Cluster Analysis in Data Mining
- Software Carpentry Instructor
- Research Mentor Training
- Introduction to Mobile Application Development using Android
List of Journals worked as a Reviewer