External Author: Srujana Merugu

Interpretability of Epidemiological Models: The Curse of Non-Identifiability

Post author By Arun
Post date April 21, 2021

Interpretability of epidemiological models is a key consideration, especially when these models are used in a public health setting. Interpretability is strongly linked to the identifiability of the underlying model parameters, i.e., the ability to estimate parameter values with high confidence given observations. In this paper, we define three separate notions of identifiability that explore the different roles played by the model definition, the loss function, the fitting methodology, and the quality and quantity of data. We define an epidemiological compartmental model framework in which we highlight these non-identifiability issues and their mitigation.

Adaptive COVID-19 Forecasting via Bayesian Optimization

Post author By Arun
Post date January 21, 2021

Accurate forecasts of infections for localized regions are valuable for policy making and medical capacity planning. Existing compartmental and agent-based models for epidemiological forecasting employ static parameter choices and cannot be readily contextualized, while adaptive solutions focus primarily on the reproduction number. The current work proposes a novel model-agnostic Bayesian optimization approach for learning model parameters from observed data that generalizes to multiple application-specific fidelity criteria. Empirical results point to the efficacy of the proposed method with SEIR-like models on COVID-19 case forecasting tasks. A city-level forecasting system based on this method is being used for COVID-19 response in a few impacted Indian cities.

ML Engineer

ROLES AND RESPONSIBILITIES

An ML Engineer at Wadhwani AI will be responsible for building robust machine learning solutions to problems of societal importance; usually under the guidance of senior ML scientists, and in collaboration with dedicated software engineers. To our partners, a Wadhwani AI solution is generally a decision making tool that requires some piece of data to engage. It will be your responsibility to ensure that the information provided using that piece of data is sound. This not only requires robust learned models, but pipelines over which those models can be built, tweaked, tested, and monitored. The following subsections provide details from the perspective of solution design:

Early stage of proof of concept (PoC)

Setup and structure code bases that support an interactive ML experimentation process, as well as quick initial deployments
Develop and maintain toolsets and processes for ensuring the reproducibility of results
Code reviews with other technical team members at various stages of the PoC
Develop, extend, adopt a reliable, colab-like environment for ML

Late PoC

This is early to mid-stage of AI product development

Develop ETL pipelines. These can also be shared and/or owned by data engineers
Setup and maintain feature stores, databases, and data catalogs. Ensuring data veracity and lineage of on-demand pulls
Develop and support model health metrics

Post PoC

Responsibilities during production deployment

Develop and support A/B testing. Setup continuous integration and development (CI/CD) processes and pipelines for models
Develop and support continuous model monitoring
Define and publish service-level agreements (SLAs) for model serving. Such agreements include model latency, throughput, and reliability
L1/L2/L3 support for model debugging
Develop and support model serving environments
Model compression and distillation

We realize this list is broad and extensive. While the ideal candidate has some exposure to each of these topics, we also envision great candidates being experts at some subset. If either of those cases happens to be you, please apply.

DESIRED QUALIFICATIONS

Master’s degree or above in a STEM field. Several years of experience getting their hands dirty applying their craft.

Programming

Expert level Python programmer
Hands-on experience with Python libraries
- Popular neural network libraries
- Popular data science libraries (Pandas, numpy)
Knowledge of systems-level programming. Under the hood knowledge of C or C++
Experience and knowledge of various tools that fit into the model building pipeline. There are several – you should be able to speak to the pluses and minuses of a variety of tools given some challenge within the ML development pipeline
Database concepts; SQL
Experience with cloud platforms is a plus

ML Scientist

ROLES AND RESPONSIBILITIES

As an ML Scientist at Wadhwani AI, you will be responsible for building robust machine learning solutions to problems of societal importance, usually under the guidance of senior ML scientists. You will participate in translating a problem in the social sector to a well-defined AI problem, in the development and execution of algorithms and solutions to the problem, in the successful and scaled deployment of the AI solution, and in defining appropriate metrics to evaluate the effectiveness of the deployed solution.

In order to apply machine learning for social good, you will need to understand user challenges and their context, curate and transform data, train and validate models, run simulations, and broadly derive insights from data. In doing so, you will work in cross-functional teams spanning ML modeling, engineering, product, and domain experts. You will also interface with social sector organizations as appropriate.

REQUIREMENTS

Associate ML scientists will have a strong academic background in a quantitative field (see below) at the Bachelor’s or Master’s level, with project experience in applied machine learning. They will possess demonstrable skills in coding, data mining and analysis, and building and implementing ML or statistical models. Where needed, they will have to learn and adapt to the requirements imposed by real-life, scaled deployments.

Candidates should have excellent communication skills and a willingness to adapt to the challenges of doing applied work for social good.

DESIRED QUALIFICATIONS

B.Tech./B.E./B.S./M.Tech./M.E./M.S./M.Sc. or equivalent in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, Physics, Economics, or a relevant quantitative field. Work experience beyond the terminal degree will determine the appropriate seniority level.
Solid software engineering skills across one or multiple languages including Python, C++, Java.
Interest in applying software engineering practices to ML projects.
Track record of project work in applied machine learning. Experience in applying AI models to concrete real-world problems is a plus.
Strong verbal and written communication skills in English.

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