Data Science brings Infosys, Stanford Univ closer

BANGALORE, INDIA: Infosys has announced a strategic relationship with the Institute for Computational & Mathematical Engineering (ICME), Stanford University.

Through this arrangement, Infosys will work with ICME to develop curriculum in Data Science and Analytics focused on real-world problem areas and will undertake joint research using Data Science to find solutions to key industry issues.

Infosys and ICME will design curriculum tailored to meet industry-specific needs for Data Science and Analytics. These modules will draw upon identified needs of customers drawn from Infosys’ engagement with enterprises across industries, as the announcement pointed.

Infosys added it will also encourage more students from Stanford to participate in its globally renowned internship program, InStep, and explore career opportunities with the company. Dr. Vishal Sikka, Chief Executive Officer and Managing Director, Infosys shared, “Rapid advances in computing and technology have placed many powerful algorithms only a few clicks away. Gaining meaningful insights from large volumes of data has however become harder, as the deluge of data continues to outpace the gains in technology. Our clients inform us that their great challenge is to fuse domain and business knowledge with algorithms and machine learning. I am confident that our relationship with Stanford will strengthen the foundations of data science for our clients and for Infosys through training on cutting edge technologies and algorithms. We look forward to conducting meaningful research with world-class researchers on issues of most importance to our clients and their business of today and tomorrow."

Commenting here Prof. Margot Gerritsen, Director, Institute for Computational and Mathematical Engineering, Stanford University, said, “We are grateful for Infosys’s support of the Institute for Computational and Mathematical Engineering (ICME). Our institute develops computational and mathematical models to solve complex problems, ranging from optimization problems to flow physics, from financial mathematics to the geosciences, from uncertainty quantification to recommender systems and machine learning.”