
Abhishek G. Saxena
Machine Learning @ Oppo US Research Center
Updated 7 months ago
9+
Years Experience
8
Roles
23
Skills
2
Education
About
I am an engineering professional with a graduate degree, a research background and technical expertise in the domain of machine learning and deep learning. Apart from having proficiency in almost all major ML frameworks and libraries, I am also an efficient programmer, capable of delivering production grade code in Python, C++, Matlab and Java. Hardware development is another domain I am skilled at. With deep knowledge and experience in FPGA development, embedded systems design and VLSI design, I understand hardware development lifecycle and can use various CAD tools with proficiency. Owing to my background, I am also adept at deploying algorithms efficiently on the hardware. I bring along with me a thirst to learn new technologies and a 'will get it done' attitude. With strong communication skills and qualities of an efficient team player, I ensure I deliver the best for my team and the organization I work for. Feel free to say a 'Hi' as I am always open to discussions and in connecting with people. *SKILLS* BLOG: https://medium.com/@abhi95.saxena LANGUAGES: Software- Python, C, C++, Java, JavaScript, R Hardware- Verilog, SystemVerilog, VHDL SOFTWARES AND TOOLS: Cadence tools: Virtuoso Schematic/ layout editor Synopsys: STA PrimeTime, IC Compiler, Design Vision, Tetramax, HSPICE, WaveView Xilinx tools: Vivado, ISE Design suite, Vivado HLS Microcontroller dev: Keil, mbed Deep/ Machine learning frameworks: Keras, Tensor Flow, Caffe, Nvidia Digits, PyTorch Computer Vision: OpenCV, MATLAB MISC: Jupyter notebooks, Weka HARDWARE: Nvidia Jetson TX2, Zybo Zynq, STM32F4 Discovery, MSP430, Raspberry Pi, Arduino Mega.
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Experience (8 roles)
5 yrs 11 mos
Current3 roles · Jun 2020 - Present
Senior Software Engineer
CurrentSoftware Engineer III
Machine Learning Engineer II
Software Engineer - Computer Vision & AI
Fields Lab Huawei Project iVision: -> iVision is a high-performance inference engine which helps to efficiently deploy Convolutional Neural Networks (CNNs) on phone's GPU & NPU/ CPU. It minimizes latency and power consumption. -> Contributed to implement fast neural network inference via OpenGL sha...
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Education (2)
The University of Texas at Dallas
2016 - 2018
Grade: 3.802/4
University of Mumbai
2012 - 2016
Grade: ~3.9/4
Skills (23)
Certifications (2)▼
Convolutional Neural Networks
Coursera
Generative AI Overview for Project Managers
Project Management Institute
Publications (1)▼
ShaderNN: A lightweight and efficient inference engine for real-time applications on mobile GPUs
Neurocomputing · Sep 19, 2024
Languages (2)▼
Volunteer Experience (1)▼
Volunteer
Greenthinkerz
Honors & Awards (2)▼
Jonsson School Graduate Scholarship
Associated with The University of Texas at Dallas Competitive, merit-based scholarships awarded to new graduate students
Tech Whiz Quiz Contest, Texas Instruments
Jan 2016
Winner.
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