TRACE-TB Webinar 2: How to Use AI and ML for the Screening, Triaging, and Diagnosis of Infectious Diseases

The second webinar in the two-part series will take place on 25 January 2022 at 4PM IST. Registrations are now open.
Register now to learn from acclaimed public health officials and AI researchers about how AI- and ML-driven tools and frameworks can be used for the screening, triaging, and diagnosis of infectious diseases.

The first webinar by the TRACE-TB project took place on 18 January. Moderated by Ms. Kachina Chawla from USAID, the stimulating hour-long session saw presentations from a number of public health and AI experts, and highlighted the ways in which public health responses to COVID-19 can be strengthened using predictive modelling. Read more.

How to Use AI and ML for the Screening, Triaging, and Diagnosis of Infectious Diseases

The second TRACE-TB webinar will take place on 25 January 2022 at 4PM IST. As part of the webinar, our panel of speakers will showcase how AI- and ML-driven tools and frameworks can be used for the screening, triaging, and diagnosis of infectious diseases.


WEBINAR 2: PANEL OF SPEAKERS

Dr. Amit Shah
Deputy Director Health Office, USAID India

Dr. Amit Shah is the Deputy Director at USAID, India Health Office. In his current role, he provides strategic leadership to USAID India’s health portfolio comprising more than 20 programs dedicated to improving health outcomes across several high-focus states of India.

In his current role at USAID he works closely with Central Ministries, NITI Aayog, Industry associations (CII/FICCI), professional medical associations, academic institutions, funding agencies, state governments, and civil society organizations towards strengthening India’s health systems.

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

Jigar Doshi has completed his MS from Georgia Institute of Technology. He is interested in applying ML for social impact particularly in the area of agriculture and public health. He spearheads the team responsible for the end-to-end solution development starting from data collection, labelling, model development, and deployment in production. Prior to joining Wadhwani AI, he was the head of machine learning at CrowdAI, a silicon valley startup building large-scale geospatial applications. At CrowdAI, he led several projects that used satellite imagery for disaster mapping, forest fire tracking, and X-View dataset. Previously, he worked at IBM Research, Intel, and Georgia Institute of Technology.

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

Dr. Amit Kharat is a clinical radiologist (MBBS, DMRD, DNB) with over 20 years of experience. He has a PhD in Musculoskeletal Radiology and is a fellow of Indian College of Radiology and Imaging. He has a degree in hospital administration and is also certified from MIT Management School on AI – Implications for Business strategy. He has over 80 publications and is an Ashok Mukherjee Oration Recipient and K M Rai Oration Recipient from IRIA. He is the former Joint Secretary of the Indian Musculoskeletal Society and reviewer for the Indian Journal of Radiology and Imaging and the Indian Journal of Musculoskeletal Radiology as well as the British Journal of Radiology. In 2018, he co-founded the AI Healthcare radiology diagnostics startup, DeepTek.

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

Dr. Tavpritesh Sethi is an Associate Professor of Computational Biology at the Indraprastha Institute of Information Technology and a fellow of the Wellcome Trust/ DBT India Alliance at AIIMS. Dr. Sethi is an editorial board member of PLOS One, Systems Medicine, and the Journal of Genetics. He is a member of the European Association of Systems Medicine and leads the Australasia region for International Association of Systems and Networks Medicine (IASyM).

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

Dr. Sreenivas A Nair (MBBS, MD, DPH) has 20 years of experience working in public health, primarily in tuberculosis care and control. He is the Senior Advisor for the Stop TB Partnership, UNOPS, Geneva, since February 2018. Prior to this, he worked as National Professional Officer-Tuberculosis (NO-C) with the World Health Organization, country office (India) for 8 years.

His areas of expertise include tuberculosis epidemiology, data for action for TB care strategies and policies, urban TB and private sector engagement in TB care, public health interventions for better lung health, and operational and implementation research.

Dr. Kirankumar Rade
National Program Officer – TB (Epidemiologist), WHO India
(Moderator)

Dr. Kirankumar Rade is a medical doctor and a public health professional with two decades of experience working with TB programmes at PHC, district, state, national and international level with UN agencies.

He has designed India’s digital surveillance system (Nikshay) and contributed to mandatory notification policy, Nikshay Poshan Yojana, and DBT schemes, as well as effective novel models for private sector engagement, active case-finding strategies, and differentiated TB care. Additionally, he has designed the first state-level TB prevalence survey, the current National TB prevalence survey, and four national COVID sero-surveys.

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
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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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