- Coached/Mentored 450+ learners through Data Science and Business Analytics (DSBA) and Data Science on Cloud (DSC) programs- Collaborated with academics team to deliver over 300+ hours of teaching in 150+ sessions with rating of 4.84/5

- Worked with more than 10 small/medium sized companies from various domains to build scalable MVPs using advanced analytics, supervised/unsupervised learning, NLP and computer vision algorithms.- Processed datasets with more than a million rows and developed ETL pipelines to ingestnecessary data and deliver using REST APIs.- Generated creative open-source learning modules using interactive visualizations whichgarnered more than 500 views by high school students/teachers on Callysto.- Organized and delivered presentations, workshops, hackathons and data-science fellowships.- Worked within a team of software developers and data scientists in an agile environment.

- Developed a purely data-fueled optimization algorithm consisting time-series models which showed 19% improvement in net present value (NPV) by allocating an optimal amount of steam to different well pads.- Designed a proactive steam allocation workflow that can learn the effect of steam injection pattern on heavy oil recovery with 80% accuracy by using nonlinear machine learning models.- Proposed and proved the concept of "Self-Operating SAGD Reservoirs" using SARSA - a reinforcement learning algorithm - which improved the NPV by at least 30%.- Assisted in developing a multi-faceted data science team and mentored them to create novel reservoir operations workflows.- Facilitated collaborative research with groups from other universities and industry partners.- Authored/co-authored $ 1.2 million funding proposal and research articles.

- Conducted tutorial sessions on how to model conventional/unconventional oil recovery processes (Water Flooding, Chemical Flooding, SAGD, ES–SAGD) in CMG- Provided inputs on shortcomings of numerical models, solved validation and run–time errors in an advanced reservoir simulation course

- Created supervised learning models and integrate them in data assimilation workflows using Python, MATLAB, R, C++ and petroleum softwares such as CMG, Petrel, PVTi, Eclipse, and GSLib.- Applied data mining and machine learning algorithms such as: o Multi-Dimensional Scaling, clustering - to analyze bivariate/multivariate relationships among variables o Principal Component Analysis (PCA) - for decorrelation and data dimension reduction o Variable aggregation, Bayesian Updating, Multi-Point Statistics - for spatial prediction of variables o Global/local optimization algorithms - to improve reservoir management at multiple time scales