Shreya Kadambi
Data AnalystStaff Researcher at Qualcomm AI Research
Qualcomm
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Staff Researcher at Qualcomm AI Research
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
New York University
Master of Science (M.S.), Electrical, Electronics and Communications Engineering
January 1, 2016 – January 1, 2018
Indian Institute of Technology, Madras
Mtech, wireless communications
January 1, 2010 – January 1, 2012
Visvesvaraya Technological University
Bachelor of Technology - BTech, Electrical and Electronics Engineering
N/A – Present
Qualcomm
Staff Researcher
December 1, 2025 – Present
San Diego, California, United States
Qualcomm
Research Engineer
June 1, 2020 – December 1, 2025
San Diego, California, United States
Petuum, Inc.
Machine Learning Engineer
September 1, 2018 – May 1, 2020
Pittsburgh, Pennsylvania
NVIDIA
Deep Learning autonomous vehicles intern
February 1, 2018 – May 1, 2018
New Jersey
New York University - Polytechnic School of Engineering
Graduate Assistant
August 1, 2017 – December 1, 2017
New York University
Graduate Student and Teaching Assistant
January 1, 2017 – May 1, 2017
New York City Metropolitan Area
Intel Corporation
Firmware Engineer
August 1, 2012 – August 1, 2016
Bengaluru, Karnataka, India
Indian Institute of Technology, Madras
Graduate Teaching Assistant
January 1, 2012 – June 1, 2012
Greater Chennai Area
Nash equilibria on graphical games
September 1, 2017 – Present
The project is based on the paper "Nash Propagation for Loopy Graphical Games " . Through this work we explore inference algorithms to compute approximate or epsilon Nash Equilibrium. - Gambit solver to compute actual Nash equilibrium for a graphical game with 100 players . - Compute approximate Nash using Modified Loopy belief propagation for Graphical games - Tested the performance for different graphical structures such as Trees , grids , chordal graphs
Distributed training of neural nets
June 1, 2017 – August 1, 2017
Distributed training of neural nets to reduce the training time taken on huge data sets. Based on the paper 'Large Scale Distributed Deep Networks' which implemented Asynchronous training on multiple GPU's for faster training. The implementation done with model parallelism. Analysis of tradeoffs between latency and training time upon adding more GPUs was done on a shallow network . Parameter averaging and asynchronous gradient descent was implemented on Tensorflow
Master's Thesis : Adversarial examples and Detection of Adversarial attacks
April 1, 2017 – Present
Research work aims to fool a state of art neural network into mis classifying with high confidence. We exploit geometric properties of error surfaces and decision boundaries of deep neural network to build adversarial examples and detect a Targeted attack on the networks. In addition devise statistical quantities used to Identify adversarial inputs Language : Tensorflow
Performance Prediction and Risk Evaluation
January 1, 2017 – May 1, 2017
The Project involved using cognitive principles to evaluate risk in stocks. The idea is to eliminate the need for dependence on statistical quantities that needed huge amount of data and computing power. The project demonstrates the idea of using metrics that need minimum derived quantities We build a real time application with data being downloaded and updated using YAHOO API Presented in NY U research expo ; Paper accept in NYU Abu Dhabi Research competition Language : Python , Flask
Master’s Thesis Channel Shortening Pre-Filter Design and Implementation
September 1, 2011 – April 1, 2012
Implemented and Integrated Channel Shortening Pre-Filter into LTE RX chain Developed an algorithm to find inverse of block Toeplitz matrix using Levinson Durbin and Sherman Woodberry lemma of the order 128 for unit tap constrained MMSE. * Implementation done on ADSP SharC on VDSP simulator.
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
DeepLearning.AI
June 24, 2026 – Present
Sequence Models
DeepLearning.AI
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background is heavily skewed towards research and academic environments (NYU, IIT Madras, Qualcomm, Petuum, NVIDIA). While this demonstrates a strong intellectual curiosity and drive, the transition to a potentially more business-oriented Data Analyst role might require adaptation. The projects show a strong inclination towards theoretical and algorithmic challenges, which aligns with a data-driven culture. The breadth of skills, from wireless communications to deep learning and statistical analysis, indicates a versatile individual. However, the direct alignment with typical 'Data Analyst' responsibilities (e.g., dashboarding, A/B testing, business intelligence) is not explicitly demonstrated in the project descriptions.
Soft Skills & Operational Fit
The candidate's project descriptions indicate strong analytical and problem-solving skills, particularly in complex research environments. Experience as a Teaching Assistant suggests communication and mentorship abilities. The diverse project portfolio implies adaptability and a proactive learning attitude. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and team collaboration is not possible.