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Shreyas Mahimkar

Mr. Shreyas Mahimkar

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  • Data Scientist

Data Scientist at Liveramp

Scholar9 Profile ID

S9-082024-1505863

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About

Shreyas Mahimkar is a skilled Data Scientist at LiveRamp, with extensive experience in the consumer electronics industry. He excels in Python, Java, Apache Spark, and database management, holding a Master's Degree in Computer Science from Northeastern University. Shreyas has previously worked at Data Plus Math and TiVo, where he developed data pipelines, predictive models, and clustering methods to enhance TV viewership insights. His projects include predicting crime locations using big data and developing a web crawler. He has also interned at Novartis, contributing to big data analysis with Spark. Shreyas's background combines strong technical skills with practical experience in data science.

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skill

Skills

Experience

Experience

LiveRamp

Jul-2019 to Present
Data Plus Math was acquired by LiveRamp in July 2019
Education

Education

Northeastern University

M.SC in Computer Science

Passout Year: 2016
Courses: Algorithms, Information Retrieval, Fundamentals of Artificial Intelligence, Programming Design Paradigm, Parallel Data Processing in Map Reduce. Projects:Fighting Crime with Big Data (Hadoop, Map Reduce) Web Crawler in java, PACMAN UC Berkeley project Technologies used: Java, Python, Elastic Map Reduce, AWS, HBase
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Publication

Publication

Publication

Role in Research Journals

Projects

Projects

How Mobility Informs Epidemic Dynamics Districts Sierra Leone

Nov-2014 to Present
Question: How does mobility inform epidemic dynamics on the district level in Sierra Leone Data Used/Wrangled/Processed: Sierra Leone Roads; Sub-national infection dataset, Sierra Leone; general demography Sierra Leone (CIA World Factbook); NEJM article for certain parameters Analysis Approach: SEIR Model with Metapopulation Model Approach (Multi-compartments / SEIR for each district and flows between); Preliminary Training & Testing; Graphs/Visuals; GIS; Findings/Limitations: Estimated ~Mobility Weights between Districts; Tested November Predictions Compared to November Observations Cumulative Case Numbers with preliminary measurement of error (need to extend to p-values? need to measure cross-validation by district &/or time); Predicted December Possible Applications: 1) Predict Epidemic Dynamics In Future Over Time & Space (By District); 2) Use Trained, Validated, Tested Model + all current data mobility + case data to predict optimal locations/expansions of mobility checkpoints/restrictions (locations to test/treat/isolate/restrict-movement/etc.) in order to stem epidemic
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Seminar

Conference/Seminar/STTP/FDP/Symposium/Workshop

Certificates

Certificates

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Issued on:

Apr 2018

Issued By:

Coursera

Event:

DeepLearning.AI

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Membership

Membership

Invited Position

Invited Position

Honours & Awards

Honours & Awards

ROBO SOCCER EVENT in GENESIS 08
Awarded by:

Watumull

 Year: 2008
Doctoral and Master Thesis Guided

Doctoral and Master Thesis Guided

Patent

Patent

Academic Identity

Authors

No Any Co Author

Following

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