Tomas Singliar I am a principal data scientist at Amazon SCOT - Supply Chain Optimization Technology, working to make our systems more efficient by building in substitution and complementarity effects. Over time I have also worked in: Forecasting - python SDKs Pricing elasticity as an econometric causal effect Machine Learning, especially graphical probability models Action understanding through IRL Data collection and mining, sensor data analytics at 100TB scales Data visualization software General software engineering process and staffing Contact information work: singliar@(my company).com personal: last name at google's public mail service

[Patents and Publications]    [CV - Industry (2019)]   

My Research and Development at Microsoft

Forecasting Python SDKs and services

At Microsoft Cloud AI Platform I, and later my team, created software that modernizes forecasting and pricing with machine learning methods and econometric causality.

We enabled forecasting in Microsoft's automated machine learning platform. AutoML. We generalized AutoML concepts to the peculiarities of time series data. Cross-validation and parallelization need to be time-aware, the models are different, and it matters when data points become known (information sets).

Projects shipped at Microsoft:

• The Azure ML Package for Forecasting ("FTK"), which pivoted to be the underlying technology for forecasting in AutoML. AutoML is part of Azure ML SDK.
  • [azuremlftk @ PyPI]
  • [FTK at Build 2018]
  • Sample Jupyter Notebooks
• Pricing Engine and Solution
• Internal and customer projects in demand forecasting, pricing, and root cause analytics.

Non-experimental near-causal price analytics

Based on exprience from customer engagements, we built a pre-configured solution for pricing analytics. The interesting thing about is the advanced "double-ML" estimation technique that allows it to get decent price elasticity estimates without running explicit experiments. We use hierarchical regularization to provide elasticity estimates at fine grain for data-poor items. Finally, business constraints generate interesting optimization problems in pricing.

OR-like problems

Predictive models around business operations, such as forecasting, on-time delivery, inventory business intelligence. Integration with MS Dynamics data.

Machine learning SME

Subject Matter Experts help other within the company with modeling, machine learning technologies. We give talks and tutorials and organize the ML research community within Microsoft around the MS Journal of Applied Research and the ML and Data Science conference.

My Research and Development at Amazon

Causality from non-experimental data (proprietary)

My research is mostly proprietary. In general terms, I estimate the causal effect of making improvements to the catalog data to the key perfomance metrics such as page views and purchases. This is extraordinarily complex because of the number of subsystems that each affect customer experience, and feed off the catalog data. It is not classical A/B testing for reasons of scalability, we have to make do with observational data. That has all sorts of statistical validity issues that we need to mitigate. We built and maintain a system using a large scale data pipeline feeding an econometric model that does the estimation.

Statistical consulting

Design and analyze experiments to answer business questions, define sampling protocols to deal with wild data exhibiting power-laws distributions, ...

My Research and Development at Boeing

Before Amazon, I was an Advanced Technologist (research scientist in disguise) at Boeing Research and Technology in Bellevue, WA. These are the things I've done there. Due to proprietary nature of the projects, the descriptions are deliberately vague - sorry.

Large-scale sensor data analytics (proprietary)

How does one build self-service predictive data analytics for engineers who are not experts in computing, but rather the system where the data originates?

The XDATA project

Program website.

Building a highly-scalable Bayesian network library based on SMILE with University of Pittsburgh's DSL folks.

Understanding Purposeful Behavior

Using methods of inverse reinforcement learning, computers actively learn from humas to really understand observed behavior (defined as: ascertain and interpret the incentives and beliefs that explain the behavior as rational) of large numbers of agents, creating a ISR data exploitation capability to concentrate analaysts' attention on unusual and suspicious behavior instances, alert and generate explanations of the observations.

Publications:

• Scaling up Inverse Reinforcement Learning through Instructed Feature Construction, Snowbird 2011,
• IMA ML Workshop 2012.
• US Patents pending.

DARPA Bootstrapped Learning (Phases 2 and 3)

The BL program attempts to implement the "Bootstrap Learning Dream", which is to dispense with the need for programming. Instead, the agent (such as a UAV) is taught how to perform the required tasks by somebody who understands the problem, instead of understanding programming.

Agent Executive based on Partial-Order Planning (proprietary)

Wrote a simple executive for an autonomous agent that uses the "repairability" of partial order plans to react in an environment where actions are nearly deterministic (so you can ignore uncertainty in planning), but robust recovery from action or resource failure is essential.

Automatic Derivation of Decision Policies (proprietary)

How to do reinforcement learning when rewards are large but rare. Automating reward shaping.

Computer Vision & Machine Learning applied to Satellite Imagery (proprietary)