Industry Positions & Projects

Applied Research Scientist II, Microsoft, Oct 2017 - present

Project: A Machine Learning Approach for Optimizing Azure Cloud Resource Allocation  

  • The objective of this project is to determine the right Azure SQL DB-tier sizing per customer
    with the goal of  efficient resource utilization & COGS Savings.

  • Collected telemetry data on attributes such as DTU usage, number of active users, slow
    batch queries, etc for all our customers (order of 1500). 

  • Analyzed the current DTU usage along with all the other contributing factors and determined
    the optimal tier assignment per customer using R-language. 

  • Identified customers whom their current DB-tier assignment is not a good representative of 
    their usage and provided downsizing recommendations on a regular basis.

  • Downsizing effort is being executed in cycles. So far by downsizing our recommended candidates, we have gained a COGS savings of order of $2M per year.

  • By moving our customers to service fabric, current efforts are focused on clustering our customers in elastic pools in an efficient manner based on their usage history. 

Project: Payment Predictor

 

  • The objective of this project is to integrate Payment Predictor into Dynamics 365 for
    Finance and Operations (F&O) product. This enables an F&O user to check the likelihood
    of her invoices being paid on-time while navigating through the product and to better
    manage his/her cash flow.

  • I investigated the problem domain, relevant machine learning methodologies, and performed
    predictive analytics in R-language on the synthetic data provided by Finance
    team. I also
    worked on the featurization script in Python for the process of integrating
    Power AI into platform. 

Project: Customers Usage Analyses 

  • The objective was to better understand our customers' usage of F&O Entity Store & Power BI Embedded and pro-actively improved customer's experience. 

  • I performed and bi-weekly reported exploratory and predictive analyses of telemetry data.

Research Scientist Intern, Amazon CoreML team, June-Sep 2017

Project: Informative Prior Using Empirical Bayes

  • The objective of my internship project was to address two existent challenges in MS and OM
    applications; the absence of informative prior and inability to control parameter learning rates. 

  • Proposed and investigated building informative prior using empirical Bayes approach in a
    Generalized Linear Model.

  • Applied our method to a standard optimization problem, as well as an online combinatorial
    optimization problem in a contextual bandit setting implemented 
    in an Amazon production system.

  • Both during simulations and live experiments, our method showed marked improvements, especially in
    cases of small traffic.

  • Our approach can be applied to any problem instance modeled as a Bayesian framework.

                        

Product Intelligence Manager Intern, Microsoft, June-Sep 2015

Project: Predicting Customer's Business processes

  • The objective of this project was to predict a business process a customer is engaged in by
    navigating his/her actions through Dynamics 365 for Finance and Operations product. 

  • Investigated various machine learning methodologies to tackle the problem. Applied and
    implemented Hidden Markov Model in R-language as our final solution.

  • Obtained over 90% accuracy on predicting customer’s behavior using D365 in F&O. 

Data Scientist Intern, Microsoft, June-Sep 2014

Project: Heat Map of Search Issues in Life Cycle Services

  • Built a heat map of search intent and topic identification for tools provided by Life Cycle
    Services (LCS) in F&O product. 

  • Implemented and applied unsupervised topic clustering techniques to LCS search data logged
    internally. Analyzed and provided preliminary classification results. 

  • Refined and labeled topic clusters based on more scaled data. Provided Topic to business topics
    index map (business process, issues, etc).