Key Insights from the Second TRACE-TB Webinar

The second TRACE-TB webinar took place on 25 January, and showcased how AI and ML can be used to effectively triage and diagnose infectious diseases.
The TRACE-TB project hosted a two-part webinar series that showcased how data science, artificial intelligence, and machine learning can be utilised to bolster responses to infectious diseases in India.

The TRACE-TB project, supported by USAID and implemented by Wadhwani AI, hosted the second webinar of its two-part series on 25 January 2022. This webinar looked at the ways in which AI and ML tools can be leveraged for the screening, triaging, and diagnosis of infectious diseases, and the panel of speakers comprised experts from WHO, USAID, IIIT, the Stop TB Partnership, and acclaimed AI/ML researchers.

Webinar 2: Key Insights

In the session moderated by Dr. Kirankumar Rade from WHO India, the panellists elaborated on how AI and ML tools were instrumental in combating COVID-19 worldwide. Unprecedented levels of multi-stakeholder collaboration have enabled data-sharing efforts and these early strides are essential for AI-based solutions that can effectively leverage large, inclusive, historical, and real-time datasets. Rapid adoption of novel technology will aid in accelerating and building resilient health systems.

The following are a few key takeaways from the webinar.

“The need of the hour is to detect the suspected infection cases at an early stage, and rapidly, so that positive cases can be isolated and further transmission minimised. However, many parts of the world lack adequate testing and case-finding resources. Ramping this up is both expensive and time-consuming, particularly in remote and rural areas. There are two main objectives of any public health system pertaining to infectious diseases and pandemics: the way in which the spread can be minimised and how lives can be saved. The effective utilisation of resources continues to be critical with each new wave and COVID-19 variant. It is crucial to devise a simple,  non-invasive triaging method that allows the most probable suspected cases to be prioritised for testing infectious diseases and detecting patients at an early stage.”

– Dr. Reuben Swamickan, Division Chief for Tuberculosis and Infectious Diseases, USAID India

“We developed an AI algorithm that predicts the likelihood of the COVID-19 infection. The solution can be scaled to any number of patients over any geography without any cost. It is non-invasive and the results can be known instantly. We transformed the cough signals and other audio signals to a spectrogram and then classified them using TabNet, a type of neural network, to subsequently predict whether or not a patient is COVID-positive.”

– Mr. Jigar Doshi, Senior ML Scientist and Research Manager, Wadhwani AI

“The tool that we have developed is an AI tool based on X-rays and CT scans that is used to detect TB, COVID-19, and other infectious diseases. Our primary focus is on X-rays. Both X-rays and CT scans have been used significantly in the pandemic from these imaging modalities. However, these are secondary to the RT-PCR test, which is a gold standard in the diagnosis of COVID-19. Chest imaging is usually not recommended as a routine screening method. Nonetheless, it becomes a tool of choice for assessing the severity of lung involvement, disease progression, and prognostication of COVID-19.”

– Dr. Amit Kharat, Co-founder, DeepTek, and Professor of Radiology

“From a government standpoint, it will be very important to have an in-house initiative that can establish a sequencing-based surveillance system for important pathogens, and share the data to enable nationwide disease surveillance. Wadhwani AI can play a key role here.”

– Dr. Tavpritesh Sethi, Associate Professor of Computational Biology, IIIT Delhi

“To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia, and lung pathologies) were chosen in our study. For tuberculosis, we considered over 6,400 blood transcriptomes derived from 127 clinical studies, as well as 95,000 chest X-ray images. The study showed that Swarm Learning classifiers outperform those developed at individual sites. Additionally, Swarm Learning completely fulfils local confidentiality regulations by design.”

– Dr. Sreenivas A. Nair, Senior Advisor, Stop TB Partnership Secretariat

  • Wadhwani AI

    We are an independent and nonprofit institute developing multiple AI-based solutions in healthcare and agriculture, to bring about sustainable social impact at scale through the use of artificial intelligence.

ML Engineer


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.


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


  • 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


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.  


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. 


  • 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.