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Mphasis Laboratory for Machine Learning and Computational Thinking

We stand at the threshold of a fundamental discontinuity brought about by the confluence of at least two parallel developments viz. (i) an increased ability to sense and produce enormous amounts of data (the digital universe continues to double every 2 years), and (ii) the ability to create extremely large clusters (~200,000 cores) driven by parallel runtimes. These developments, the first of which has been occurring across domains, have enabled machine learning and other data driven approaches to become the paradigm of choice for complex problem solving.

Driverless cars, face recognition and other behavioral biometrics with accuracy good enough to be deployable as a primary border control mechanism, paintings that are generated entirely by a machine to be consistent with a style of painting, and the use of the entire literature of published knowledge to enable truly personalized medicine are a few examples of what has been made possible due to advances in machine learning. Indeed, the results go far beyond convincing, both in terms of accuracy and in terms of the ease of construction of the machine. 

There is now a considerable opportunity to improving life-at-large based on these capabilities. The “Mphasis Laboratory for Machine Learning and Computational Thinking” was established thanks to a generous contribution from Mphasis, with an objective to address these opportunities.

The overall goals of the laboratory are to,

  • Apply machine learning and design thinking to produce world-class papers and compelling proof-of-concepts of systems with the potential for large societal impact
  • Produce experiential pedagogy-based modules that are virtually offered and designed to be broadly accessible by all students of various disciplines. Each module is based on a sequence of hands-on activities that allow a student to reconstruct proof-of-concepts produced in the laboratory.
  • Conduct workshops that create opportunities for collaboration between academicians, practitioners, and policy makers


1. Health and Medicine

Computational methods have the potential to significantly reduce the cost and time required to find drugs with a lower probability of attrition. While many Computer-Aided-Design methods have been used for structure-based-design, such methods have typically failed in predicting physiochemical properties resulting in high attrition rates. Our proposal is based on utilizing advances in Machine Learning, or more specifically in Generative Models, to develop methods that will allow rapid discovery of molecule with desired binding, ADME and toxicological profiles. 

The laboratory is also focused on the use of machine learning for processing multi-modal images to serve as an accurate though low cost, diagnostic aid to pathologists and radiologists.

2. Accessibility

We are committed to using advances in Computer Science to improve the quality of life of persons with disability (PwDs). 

One of our efforts is based on haptic rendering of an image to help those with limited or no visibility understand their surroundings. Specifically, we use a cane mounted camera or head mounted camera and two haptic wristbands to render visual scenes. The camera continually acquires images; advanced resource sensitive machine learning methods identify objects, and haptic feedback is used to “haptically render” an image.

3. Information Curation

Our effort is directed towards using natural language and advanced machine learning approaches to automatically construct a knowledge graph from publicly available information. 

We also use a design thinking motivated approach to create an interface that makes it easy for individuals with varying levels of computer literacy to be able to discover, explore and rapidly understand a larger picture of a given topic. 

4. Core Machine Learning

This category of our work focuses on the core aspects of machine learning. It includes work that involves advanced methods of transfer learning, privacy-preserving machine learning, federated learning, creativity, Fairness, Accountability and Transparency (FAT), and Explainable AI. 

We are particularly interested in black-box methods that allow discovery of bias in empirical models, and in situ and ex-situ methods that allow human understanding of machine learning models.

5. Experiential Pedagogy

A goal of the laboratory is to make its discoveries and methods widely accessible to students globally. We adopt a novel pedagogy and believe that “doing” leads to long term retention and deeper understanding of concepts. Thus, instead of a linear approach to learning in which topics are arranged by subject, we adopt a non-linear approach in which students learn all technologies and methods that are required to achieve a specific goal. We hope to be able to periodically release self-contained modules that describe the overall journey of each completed project in our laboratory. The modules holistically describe the conceptualization, design, implementation, testing, and usability aspects of each projects, and where applicable, includes Jupyter notebooks and other aids to allow students to recreate many aspects of the project. 


Contact Us

For more information and queries about Mphasis Laboratory, contact –

1. Ravi Kothari,

Head of Department – Computer Science,


2. Gurbirender Singh


Study at Ashoka

Study at Ashoka