Prince Grover has recently joined Manifold as a Machine Learning Engineer. In this capacity, he will devise and implement deployable machine learning pipelines for clients and internal projects.
Prince's previous and current projects span a range of computer vision, IoT, time series, and natural language processing domains, making him a go-to team member for machine learning and deep learning projects. He is also well versed in exploratory data analysis using statistical profiling tools and data visualization; the results of this analysis are often instrumental to determining appropriate feature engineering and machine learning approaches.
Prior to Manifold, Prince was a Senior Business Analyst at Altisource, where he provided data-driven recommendations for business process and operations improvement, such as analyzing tenant application data to revise underwriting criteria, and recommending key strategies to increase the occupancy rate. He has also been a Consultant at Capgemini, where he was involved in almost every aspect of various data science projects and provided end-to-end data-driven solutions for clients. This included data wrangling, data cleaning, feature engineering, predictive modeling, and building statistical models—including regression, clustering, and classification—using R, as well as building dashboards with R.
We are delighted to welcome Prince as a full-time team member after getting to know his work during the course of his Master's in Data Science program at the University of San Francisco (USF). He worked with our team and other interns to develop a multi-camera-based person-tracking system using state-of-the-art computer vision techniques. We know he will continue to grow continuously his software engineering, machine learning, and public speaking skills.
“Working at Manifold is helping me become better programmer and machine learning engineer every day,” he says.
Prince is passionate about deeply understanding the various approaches to machine learning, and sharing that depth of understanding with others. He is a contributor to public codebases and libraries and an avid Medium blogger with 1.8k followers. Some of his more recent posts include:
- (2018) Evolution of Object Detection and Localization Algorithms
- (2018) 5 Regression Loss Functions All Machine Learners Should Know
- (2018) Applications of Matrix Decompositions for Machine Learning
- (2017) Intuitive Interpretation of Random Forest
- (2017) Gradient Boosting from scratch
- (2017) Various Implementations of Collaborative Filtering
Prince earned his undergraduate degree in Geophysical Technology from the Indian Institute of Technology (Roorkee), and a Master of Science degree in Analytics/Data Science from the University of San Francisco (USF).