AI News, ML-India interview series - Dr. Manish Gupta, VP, Xerox Research Center India

ML-India interview series - Dr. Manish Gupta, VP, Xerox Research Center India

As part of the new year’s initiatives on ML India, we promised you that we will be talking to leading researchers and academics in the field of machine learning to understand their work better.

Previously, Manish has served as Managing Director, Technology Division at Goldman Sachs India, and has held various leadership positions with IBM, including that of Director, IBM Research - India.

Manish talks about four areas which Xerox is innovating in - Education, Healthcare, Transportation and the services vertical.

He describes how challenges in each of these domains can be posed as hard computer science and specifically, machine learning problems in areas as diverse as vision, NLP and speech.

He talks of the problem in of automatically preparing the table of contents of a video, parsing equations described in lecture videos, tweaking deep neural networks to create intelligent chat bots and more.

Xerox is organizing a winter school for machine learning and today was its

section of our population is underserved in the most fundamental services like health

intensive education systems to massive online courses where lectures given

learn what content suits you best, what style and pace of lecture delivery

fairly new – we set up shop in 2010 in India.

recent work includes a paper at ACM multimedia, the top conference

creating table of content for online lecture videos.

of the best conferences for showcasing working in human computer interfaces;

list out XRCI’s key areas of focus, they’d be - I

grasp of the kind of specific problems we’re pursuing.

learning and data science to improve the accuracy of these systems.

of the areas we’ve worked on is in predicting which patients get admitted to

The state of the art method of predicting this is based on a scoring system

blood pressure, heart beat and many such body vitals.

by these methods are generally ignored by doctors and nurses given the

high false alarm rates the present systems have.

to do better than the best known results by a significant margin.

these results, our business groups have given us the challenge that of having

99% specificity so that there are at least 3-4 true positives for every false

screened for using mammography, which involves pumping X-rays into the body.

It’s a very painful procedure and additionally, it’s not effective at all in

the early 80s, there were too many false positives in analysing these images.

By applying modern machine learning and image processing techniques, we have

run our algorithms for a larger datasets and a larger class of cancer detection

is a comprehensive suite for a learner to learn rich and relevant content from

We have a product in place currently where virtual agents, as against humans, try

agents and identifies when a human representative ought to intervene and

that it is now possible to analyse these chats in real time, unlike the current

mechanism in which a post-hoc analysis of such logs take place.

successful demonstration over web chats, we are now moving towards replicating similar

speech research, where we shall get a chance to compare our work with the state of

integer linear programming problems, coming up with greedy algorithms to get

third work being informed by our business groups is relatively short term and

your take on startups innovating in the machine learning space?

combine it with our research depth to bring something unique and technically sound

I think there’s a very positive vibe in the startup community regarding machine

I think today’s entrepreneur realizes machine learning helps in

a shining example of guys who have been actually publishing papers at KDD and

machine learning where we have ten teams spending a fully paid two

Our lab is championing 5 faculty research grants of which

were very neat peace of work which were totally new ideas on how

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