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Prateek Srivastava, Ph.D. is listed as Senior AI/ML Scientist at SLB l Ex-Amazon | Generative AI, LLMs at SLB, based in San Francisco Bay Area, United States. AeroLeads shows a work email signal at amazon.com and a matched LinkedIn profile for Prateek Srivastava, Ph.D..
Prateek Srivastava, Ph.D. previously worked as Senior Al/ML Scientist at Slb and Applied Scientist at Amazon. Prateek Srivastava, Ph.D. holds Doctor Of Philosophy (Ph.D.), Machine Learning, Operations Research from The University Of Texas At Austin.
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I am an Applied Scientist on the Marketplace Science team at Amazon. My research interests are broadly at the intersection of machine learning, statistics, convex optimization, and reinforcement learning. I completed my Ph.D. in machine learning and operations research from the University of Texas at Austin, where my dissertation work was focused on developing robust solution approaches for problems arising in clustering and data-driven decision-making. I completed my Master's degree in operations research, also from UT Austin. As part of my master's thesis work, I developed a novel integer programming/game-theory based strategic prioritization framework for airline scheduling. Prior to joining UT, I obtained my Bachelor's degree in engineering from BITS Pilani, India.Previously, I have worked with the Machine Learning team at Mathworks (May-August 2018), Operations Research team at Sabre Airline Solutions (Jan-May 2017), Optimization and Uncertainty Modeling team at Schlumberger (May-August 2016), and Data Science team at JP Morgan Chase (Jan-Jun 2013).
Listed skills include Operations Research, Python, Matlab, Optimization, and 22 others.
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Houston, Texas, Us
Part of the Generative AI (NLP and Computer Vision) research team at the SLB Technology and Innovation Center. Research Focus: Domain Adaptation, Pre-training and Instruction fine-tuning of LLMs & VLMs, Retrieval Augmented Generation, Multimodal learning, Evaluation and Benchmarking
Seattle, Wa, Us
At Amazon, I work on developing large-scale machine learning and optimization models for a wide variety of business problems in Amazon retail, which include improving delivery promises for third-party sellers on Amazon Marketplace, building dynamic pricing models for Amazon’s transportation business, improving product discovery for Amazon’s private brands business, etc. My work also involves building end-to-end ML pipelines using AWS services (Glue, S3, Redshift, Lambda, Sagemaker, etc.) leveraging terabytes of data to deploy these models in production.
Austin, Tx, Us
(1) Developed a novel algorithm for the joint outlier detection and kernel clustering problem based on semidefinite programming and spectral relaxations. Conducted several experiments on simulated and real-world datasets to demonstrate the superiority of the algorithm in terms of both accuracy as well as scalability. Under a Sub-Gaussian mixture model assumption, derived statistical guarantees on theoretical error rates for the algorithm. Proposed a robust dimensionality reduction procedure to extend analysis to high dimensional settings. Presented the work at International Conference on Continuous Optimization (ICCOPT)-2019, INFORMS Annual Meeting 2018 and 2019. Paper was published in the prestigious Operations Research journal. Work received an honorable mention in the 2020 INFORMS Computing Society (ICS) Best Student Paper Competition. This award is given annually to the best student paper at the interface of computing and operations research. Our paper was judged to be amongst the top four entries submitted for the award. (2) Developed a formulation for the data-driven stochastic optimization problem with side information based on the Nadaraya-Watson kernel regression estimator. Proposed a variance-based regularization scheme solvable as a convex optimization problem to avoid overfitting issues. Demonstrated the effectiveness of this scheme on simulated and real-world instances of portfolio optimization and inventory control problems with improvements of up to 30% in out-of-sample performancesPresented the work at INFORMS Annual Meeting 2020. Paper currently under revision in the Operations Research journal.
Austin, Tx, Us
Worked as a Teaching Assistant for graduate level course in Optimization under Uncertainty as well as undergraduate level courses in Engineering Finance, Statistical Modeling, and Biostatistics.
Natick, Ma, Us
Prototyped a novel classification algorithm for MATLAB machine learning toolbox, based on binning and high-dimensional sparse feature representation. Investigated its performance on large-scale real-world datasets and demonstrated it to be comparable to tree-based ensemble methods like boosted decision trees, random forests, etc. and superior to logistic regression and SVMs.
Southlake, Texas, Us
Developed a framework for the long-term fleet planning problem based on mixed integer programming (MIP); the formulated mathematical model determined optimal decisions for buying, leasing and retiring aircrafts taking into consideration the operational constraints and future growth goals of airlines. Developed a GUI using Tkinter/Matplotlib and implemented the MIP model using Pyomo/Pandas in Python.
Houston, Texas, Us
Developed commercial C++ code for PIPESIM — a fluid-flow simulation software — to improve its prediction accuracy using data gathered in real-time; resulting changes led to an increase of 15% in operational profits. Formulated a regression model to solve the non-convex inverse optimization problem of data-driven parameter estimation. Developed proxy models based on Support Vector Regression and Multiple Kernel Learning to reduce computation times.
Austin, Tx, Us
Master's Thesis : A Strategic Prioritization Approach to Airline Scheduling during DisruptionsDeveloped a mixed integer programming model and simulated for historical flight data in Python to investigate its congestion cost benefits during disruptions.Presented the work at the INFORMS Annual Meeting 2015 in Philadelphia and Amazon Graduate Research Symposium 2015 in Seattle.
Houston, Texas, Us
Worked with NRG eVgo to identify hotspots for electric vehicle charging stations in major cities of Texas and California. Developed forecasting models based on deep learning and time series techniques such as ARIMA and dynamic regression using Python and MongoDB to estimate the effects of consumer demographics and built-environment factors on the demand of charging stations.
New York, Ny, Us
Work focused on improving the processing efficiency of automated financial transaction systems in the Asia-Pacific region. Developed a maximum margin-based classifier algorithm to automatically rectify errors in financial transactions. Implemented enhancements that increased the accuracy of error detection in existing systems from 89% to 92%.
Blacksburg, Va, Us
Project: A Production Planning Model with Competitive Pricing for Remanufacturing Firms Developed a mixed integer programming (MIP) formulation of a production planning model for firms engaged in remanufacturing and producing multiple products over a finite time horizon. Incorporated competitive pricing in the model after conducting a literature review of around 30 research papers on pricing and product differentiation; then, implemented it in OPL on CPLEX Optimization Studio
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Prateek Srivastava, Ph.D. works for SLB.
Prateek Srivastava, Ph.D. is listed as Senior AI/ML Scientist at SLB l Ex-Amazon | Generative AI, LLMs at SLB.
AeroLeads has found 1 work email signal at @amazon.com for Prateek Srivastava, Ph.D. at SLB.
Prateek Srivastava, Ph.D. is based in San Francisco Bay Area, United States while working with SLB.
Prateek Srivastava, Ph.D. has worked for Slb, Amazon, The University Of Texas At Austin, Mathworks, and Sabre Airline Solutions.
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Prateek Srivastava, Ph.D. holds Doctor Of Philosophy (Ph.D.), Machine Learning, Operations Research from The University Of Texas At Austin.
Prateek Srivastava, Ph.D. is listed with skills including Operations Research, Python, Matlab, Optimization, Statistical Data Analysis, R, Gams, and Data Mining.
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