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Deep Learning Performance Architect at Nvidia Corporation
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Stanford University
Stanford Center for Professional Development (Non-degree), Computer Architecture
January 1, 2017 – January 1, 2017
University of Pennsylvania
MSE, Bioengineering
January 1, 2011 – January 1, 2013
Birla Institute of Technology and Science, Pilani
B.E.(Hons.), Electrical and Electronics Engineering
January 1, 2006 – January 1, 2010
NVIDIA
Senior Deep Learning Performance Architect
August 1, 2023 – Present
Intel Corporation
Deep Learning Engineer
November 1, 2017 – August 1, 2023
Santa Clara
Oracle
Hardware/Software Engineer, Machine Learning
November 1, 2016 – October 1, 2017
Oracle
Hardware Engineer, Physical Design
July 1, 2013 – October 1, 2017
University of Pennsylvania
Summer Research Assistant, Litt Lab
May 1, 2012 – August 1, 2012
Greater Philadelphia
LSI Corporation
ASIC Development/Design engineer 1
July 1, 2010 – July 1, 2011
Pune/Pimpri-Chinchwad Area
Texas Instruments
Intern
July 1, 2009 – December 1, 2009
Bengaluru, Karnataka, India
OpenWave - Brain Computer Interface for controlling the brightness of indoor lighting
February 1, 2013 – Present
People's choice award-winning project at Pennhacks 2013 - Implemented a BCI using Neurosky's Mindband headset and Neurosky Developer API's - Modulated brightness of a light bulb using properties of EEG by gauging the user's level of attention
The effect of subdural electrode implantation on stability and reliability on features of iEEG signals
January 1, 2013 – May 1, 2013
Masters thesis project: - Examining various features of EEG to search for quantifiable changes in their stability over long periods of time after electrode implantation - Using signal processing techniques over MATLAB to filter out features in large amounts of EEG data
Predicting finger flexion movements from intracranial EEG recordings
May 1, 2012 – Present
Project for course Brain Computer Interfaces (BE 521) - The data and tasks are derivatives of the 4th International Brain Computer Interfaces Compe- tition - Used various signal processing (zero-clamping, smoothing) and machine learning techniques (Linear Regression, PLSR) to generate a model to predict finger flexion movements from EEG recording of three subjects (data was provided)
Design of headstage circuit board for chronic neural recordings from rats
March 1, 2012 – December 1, 2012
- Implemented a PCB design for head-stage interface to be used with high-density multichannel multiplexing flexible electrodes using Cadence Capture Schematic and OrCAD tools - The system will be used for continuous chronic EEG recordings from rats.
Cultural Fit Analysis
The candidate's career trajectory shows a strong focus on hardware and deep learning, which is a significant pivot from the target role of 'Data Analyst'. While there are foundational data analysis skills from academic projects (signal processing, linear regression), the recent professional experience is heavily skewed towards deep learning architecture and hardware optimization. This suggests a potential mismatch with a pure Data Analyst role, which typically requires strong SQL, data visualization, A/B testing, and business intelligence skills, none of which are explicitly mentioned or demonstrated in recent roles. The project diversity is strong within the bioengineering and hardware domains, but less so in traditional data analysis.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted problems. Experience at large corporations suggests familiarity with structured environments. However, specific soft skills like leadership, teamwork, or problem-solving are not explicitly detailed in the provided data.