CV
Education
- Ph.D in EECS (Theoretical Machine Learning), University of Michigan, Ann Arbor ‘17
- M.S. in EECS, University of Michigan, Ann Arbor, ‘14
- B.Tech. in EECS, Indian Institute of Technology, Delhi, ‘12
Work experience
- Data Scientist, AI lab 3M Corporate Research Systems Lab (Oct’17 – Oct‘20):
- Projects focused on multiple ML projects focusing on data analytics, computer vision, generative adversarial networks, reinforcement learning, time series analysis
- Developed web deployment pipeline using AWS Sagemaker for multiple projects
- Research throughput: Applied for 18 patents
- Data Scientist, General Data Science lab, Oral Care Solutions Division (Jan’20-Oct’20):
- Projects focussed on 3D geometry, computational imaging, and Geometric Deep Learning within the domain of Digital Orthodontics
- Developed ML pipelines for research and development using Azure cloud infrastructure
- Research throughput: Applied for 4 patents
- Graduate Research Assistant, EECS Department University of Michigan Ann Arbor:
- Applications of recent results from Random Matrix Theory on Low Rank Subspace Estimation problems in the Multi Modal setting.
- Collaborated with network scientists from Army Research Lab and MIT Lincoln lab.
- Taught EECS301:Probability and Statistics to Undergraduate students.
Skills
Machine Learning:
- 2D/3D Computer Vision
- Time Series Analysis/ Forecasting
- Dictionary Learning, Low Rank Subspace Estimation
- Optimization
Programming Languages:
- Python (proficient):
- ML frameworks: Tensorflow (1.x and 2.x), PyTorch, scikit-learn
- Scientific computing: numpy, pandas, scipy, scikit-optimize, OpenCV, statsmodels
- Visualization: matplotlib, seaborn, plotly, dash
- Cloud infrastructure: AzureML SDK, AWS sagemaker, boto3
- R (intermediate):
- ML frameworks: caret
- Tidyverse: dplyr, tidyr, purr
- Visualization: ggplot2, R shiny
- MATLAB(intermediate), GNU Octave (intermediate), C++ (learning)
Miscellaneous Technologies:
Publications
Honors and awards:
