Faculty & Research

Brij Disa Centre for Data Science and Artificial Intelligence

About the Centre

The Brij Disa Centre for Data Science and Artificial Intelligence (CDSA) at the Indian Institute of Management Ahmedabad (IIMA) provides a common platform to faculty, scholars, and practitioners for conducting and disseminating cutting-edge research on data analytics and artificial intelligence that offers solutions applicable to business, governance, and policy.
Besides generating action oriented insights, CDSA is also responsible for dissemination of the knowledge generated to a wider audience both within and outside the realm of the Institute. Seminars, workshops, and conferences are regular activities at the Centre, which are conducted to reach out to and engage with stakeholders.
The Centre aims to forge synergistic and collaborative relationships between scholars and practitioners in dataintensive organizations, besides undertaking case-based research to understand the current industry practice and develop case studies for classroom teaching.
Furthermore, through its collaboration with the industry, CDSA takes up challenging consulting projects of considerable practical importance. These projects are targeted at providing an opportunity for students to participate in projects that aim at outcomes that can further benefit the organisation and the business, at large.
A key offering from the Centre is the Annual Report, which would provide a holistic view of the Data Science and Artificial Intelligence industry, identify challenges and gaps, gauge scope of the industry and offer plausible solutions that can be utilised by the industry and policy makers.

Centre Activities

Common platform for faculty, scholars, and industry

Build partnerships to undertake collaborative research and jointly organise workshops

Enable policymakers with reports on trends and progression of analytic tools, techniques and other resources

Share knowledge through seminars, fireside chats, workshops, etc. on research and applications of topical interest

Facilitate businesses by connecting them with researchers to solve challenging business problems

Research Areas

The Centre forges synergistic and collaborative relationships between scholars and practitioners in data intensive organizations, besides undertaking case-based research to understand the current industry practice and develop case studies for classroom teaching.

Centre's Research Dissemination

Projects

Financial networks from big data: A multivariate time series based approach

Prof. Anindya Chakrabarti

Financial markets exhibit non-trivial comovement and dependency structure. The standard approach in the finance literature is to consider the market in its aggregate form. A more recent 'data'-oriented approach emphasizes a more granular decomposition of the market so that the aggregate dynamics can be broken down into contributions arising from individual assets. This leads to two analytical problems. First, one has to necessarily deal with a large amount of data such that the process scales with the volume of data (large N and large T where T>>N). Two, analyzing such a large volume of data requires toolkits which are at the intersection of econometrics and machine learning. In this project, the goal is to construct large scale financial networks based on multivariate time series data to capture the dynamics of the system. The main idea is to provide an algorithmic approach to convert time series into networks such that the properties of time series are also inherited by the resulting network. The spectral structure of the comovement network is known to capture, at least partially, the booms and busts in the markets. Here, we take up two specific problems. One, how reliably does the spectral structure reflect the system for the case where T~N. Two, a large chunk of the literature on networks construction depends on bivariate modelling which is subject to failure due to multiple hypothesis testing. Therefore, an imminent question is how to construct a network with a direct multivariate model.

Can an AI Coach Help You Lose More Weight Than a Human Coach: Empirical Evidence From a Mobile Fitness Tracking App

Prof. Anuj Kapoor

Artificial intelligence(AI) assisted tools are increasingly being used in health care contexts to provide advice and motivation. But whether AI can be a good or even better substitute for human involvement in these contexts is an open question. We provide empirical evidence to answer this question specifically in the context of fitness tracking mobile applications (apps). In addition to facilitating the tracking of activity and food intake, such apps provide advice and motivation in the form of targeted messages to their consumers, and this can be done through human coaches or an AI coach. An AI coach allows these apps to scale their offerings to a larger number of consumers, available on demand to consumers, and potentially more finely targeted by leveraging vast amounts of data. On the other hand, human coaches might be better placed to show empathy, and consumers might also feel more accountable to humans. We compare human and AI coaches on their effectiveness in helping consumers achieve their weight-loss goals. Our empirical analysis is in the context of a large-scale mobile app that offers consumers different levels of subscription plans with human and AI coaches respectively, and specifically compares adopters of the two kinds of plans on their weight loss and goal achievement. We address the potential self-selection in plans by employing a matching-based approach. We find, for our sample of almost 65000 consumers that human-based plans do better than those in AI-based plans in helping them achieve their goals, but that this differs by consumer characteristics including age, gender and body mass index (BMI).

High-frequency trading: Measuring latency from big data

Prof. Anirban Banerjee

Over the last decade, the Indian market has seen significant growth in algorithmic trading and more specifically, high-frequency trading (HFT) activity. During this period, we have witnessed a significant change in the trading landscape as presently close to half of the trading volume in the stock exchanges is contributed by algorithms. This rise has not always been smooth as there have been calls for regulations to restrict algorithmic trading activity due to the fear of probable market manipulation.

Latency is considered one of the most important market parameters for HFTs. Using a large novel dataset of order and trade level data from the NSE, we would like to inspect how the latency in the Indian market has changed and if that has caused any shift in the way HFTs operate. We would also like to observe how the different market quality parameters have evolved over this time.

Causes, Symptoms and Consequences of Sociocultural polarization

Prof. Samrat Gupta

The Information and Communication Technology (ICT) provides users unparalleled access to information from around the globe. In spite of demographic differences, people can communicate, express and evolve their opinions on topics ranging from politics to culture. The wide-ranging information exchange on digital media can lead to two scenarios viz. formation of public sphere or formation of echo chambers. While the public sphere, which promotes greater diversity, is a well-researched domain, substantially less research has been conducted on echo chambers in relation to socio-cultural events. Despite the huge affirmative impact of socio-cultural events on society, the proliferation of controversies around them and reinforcement through echo chambers is collectively having malefic effects on societies. Recent controversies around socio-cultural products such as movies, painting, books, cartoons, etc. resulted in serious outcomes. For example, Indian movie Padmavat brought polarization of public perception which further reinforced through echo chambers and escalated into widespread agitations. It led to mass destruction of property and human suffering during agitation. We believe this represents a mounting problem for society, one that is likely to intensify in the era of social media. Thus, understanding the causes, symptoms and consequences of socio-cultural polarization is critical and would be valuable for developing interventions to reduce unhealthy societal and organizational polarisations.

Employee Reviews - A Text Mining Perspective

Prof. Adrija Majumdar

With the emergence of web 2.0, there is a deluge of online text. Technologies like online communities, social media, crowd funding platforms have further contributed to the volume of content. From the firm’s perspective, understanding consumers’ sentiment from the text is of supreme importance. The literature on online reviews has predominantly focused on ascertaining consumer sentiment of a firm’s products and services. We extend this stream of research and focus on analyzing reviews that employees post regarding their organizations. The study will seek to identify different dimensions that employees highlight in their reviews and their association with overall job satisfaction. We further wish to understand if employees’ perception of the firms also impacts the firm’s performance. The unstructured and noisy nature of the text data often poses significant challenges for organizations in leveraging them for decision making. We will employ text mining methods and techniques to quantitatively analyse the large dataset of employee reviews. The research will have implications for both theory and practice.

When A Machine Knows When You Are Happy (vs. Upset)

Prof. Hyokjin Kwak

Artificial intelligence - the creation of human intelligence and beyond - has significantly altered the very nature of our modern business practice. The fascination towards non-living entities ‘thinking and acting’ with human intelligence is still as fresh and exciting as it was in 1956 - the year AI was coined. However, the latest trend on the role of AI has been gradually shifted from analytical brains (intelligence) to social brains (emotion). That is, this trend puts more emphasis on anthropomorphic AI (e.g., humanoid AI robots). In fact, this aspect of ‘humanization (anthropomorphization)' is not new to the field of branding in the marketplace. Marketers strategically imbues nonhuman entities with humanlike emotions, intentions, motivations, and characteristics. In general, prior research on anthropomorphism shows a positive effect of downstream consequences on consumer evaluations when the brand “interacts” with consumers. Hence, this research project attempts to further investigate how “emotion (or affective)” AI can help brand practitioners engage their customers more with their anthropomorphic brands. A machine should be more effective in communicating brand information with consumers when the machine knows how to interact with consumers’ emotional state (e.g., through their face and body).

Models of implied volatility and information content of option prices

Prof. Sobhesh Kumar Agarwalla and Prof. Vineet Virmani

The proposed research project on modeling implied volatility (IV) and understanding the information content of option prices is part of our larger research agenda on studying ways to quantify uncertainty in financial markets, focusing on India. Traders in options markets do not usually quote option prices, but the volatility implied by them. IV is that volatility input to the famous Black-Scholes option pricing formula such that the Black-Scholes prices match the market price of the options. It has been observed that IV is not a constant but varies systematically with strike/delta and expiration date. The shape of the observed relationship between implied volatility and strike is called volatility smile or skew. In this project, we plan to explore various ways of modeling the dynamics of volatility smile using variants of state-space models and the Kalman Filter.

Hiring for the Future – A People Analytics Approach

Prof. Aditya Christopher Moses

The future of work is a critical aspect for many organizations. A 2020 report by the World Economic Forum suggests that among the various challenges faced by organization one of the most critical areas is skill gaps. They argue that skill gaps continue to remain high as in-demand skills across jobs change in the short term. The top skills and skill groups which employers see as rising in prominence in the lead up to 2025 include groups such as critical thinking and analysis as well as problem-solving, and skills in self-management such as active learning, resilience, stress tolerance and flexibility. On average, companies estimate that around 40% of workers will require reskilling of six months or less and 94% of business leaders report that they expect employees to pick up new skills on the job, a sharp uptake from 65% in 2018.

The changing nature of work and the exponential technology development imply that employees need to constantly re-skill and up-skill. In the current environment, while knowledge can be accessed via multiple sources the behaviours to develop oneself become more important. What behaviours will organizations require for ensuring they have a workforce that can reskill and upskill exponentially? This will be the primary area of research for this study.

Using a data-driven approach, this study uses surveys and NLP to understand which behavioural traits enable re-skilling at pace. We will employ text-mining methods and techniques to identify behavioural traits that help in re-skilling. The insights from this will be further validated and tested using a survey instrument administered to a large sample of individuals.

An iterative gradient-based bilevel approach for hyperparameter tuning in machine learning

Prof. Ankur Sinha

Hyperparameter tuning in the area of machine learning is often achieved using naive techniques, such as random search and grid search that only lead to an approximate set of hyperparameters. Although techniques such as Bayesian optimization perform an intelligent search on the domain of hyperparameters, it does not guarantee an optimal solution. A major drawback of most of these approaches is that as the number of hyperparameters increases, the search domain increases exponentially, thereby increasing the computational cost and making the approaches slow. The hyperparameter optimization problem is inherently a bilevel optimization task, and there exist studies that have attempted bilevel solution methodologies for solving this problem. These techniques often assume a unique set of weights that minimizes the loss on the training set. Such an assumption is violated by deep learning architectures. Our study is on gradient-based bilevel optimization method for solving the hyperparameter optimization problem. The method is general and can be easily applied to any class of machine learning algorithms that involve continuous hyperparameters.

Multi-period Facility Interdiction Problem

Prof. Sachin Jayaswal

We propose to study a multiperiod interdiction problem, in which the leader (attacker) with a limited interdiction budget decides the sequence of facilities to interdict (destroy) over time so as to inflict the maximum cumulative damage to the follower. The follower's objective is to serve a given set of demand points from the surviving subset of facilities the minimum cumulative cost across all periods. for this, his decisions include the assignments of demand nodes to the surviving facilities and the allocation of his limited budget to the revival of interdicted facilities and the protection of the surviving facilities against their interdiction in the future periods. The multi-period version of the problem, which is the focus of the proposed study, presents additional complexity due to the leader's interdiction decisions constrained by the follower's protection decisions. The objective of the proposed study is to design efficient exact solution methods for this challenging bilevel integer program.

Data-driven auction design: A computational approach

Prof. Jeevant Rampal

Auctions are often used to sell property rights for liquor licenses, spectrum licenses, land and mineral rights, and construction projects etc. This project investigates potential improvements in these auctions using a computational data-driven approach. The first part of this project will be to collect primary data of the participants and their choices in auctions. Subsequently, using the game-theoretic properties of the chosen auction design, we will computationally estimate the true (unobservable) value distribution across players of the object(s) being auctioned (e.g., liquor licenses). The estimation method used will be non-parametric “distance minimization” between the observed out-of-sample distribution of bids, and the predicted out-of-sample distribution of bids using optimally calibrated parameter values. E.g., Athey, Levin, and Seira (QJE 2011) use their estimated model to make comparative static predictions and test that for fit against data from timber auctions.

Finally, to analyse which auction design would have best met the various aims of the auction designer, we will use the calibrated model, parameters, and the estimated valuations of the bidders. In particular, using these we will simulate the revenue, efficiency, and other metrics of importance for different auction designs. In addition to the use of simulation described above, to analyse alternate auction designs, we will use simulations of variations of the estimated model, parameters (like risk aversion, budgets etc.), and value distributions to analyse the different rates with which different auction designs can meet the various possible aims of the auction designer.

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Advisory Board

Anand Deshpande

Persistent Systems

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Sanjiv Das

Santa Clara University

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Sunil Gupta

Harvard Business School

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Tiziana Di Matteo

Kings College London

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Members

Name  University
Sobhesh Kumar Agarwalla IIM Ahmedabad
Cheong Siew Ann Nanyang Technological University
Anirban Banerjee IIM Ahmedabad
Arindam Banerjee IIM Ahmedabad
Dhiman Bhadra IIM Ahmedabad
Indranil Bose IIM Ahmedabad
Anindya S. Chakrabarti IIM Ahmedabad
Swanand Deodhar IIM Ahmedabad
Anil Deolalikar UC Riverside
Samrat Gupta IIM Ahmedabad
Sachin Jayaswal IIM Ahmedabad
Anuj Kapoor IIM Ahmedabad
Hyokjin Kwak IIM Ahmedabad
Andrea Lodi École Polytechnique de Montréal
Thomas Lux Kiel University
Tanmoy Majilla IIM Ahmedabad
Adrija Majumdar IIM Ahmedabad
Pekka Malo Aalto University School of Business
Sheri Markose University of Essex
Mohsen Mohaghegh IIM Ahmedabad
M P Ram Mohan IIM Ahmedabad
Aditya Christopher Moses IIM Ahmedabad
Soumya Mukhopadhyay IIM Ahmedabad
Sundaravalli Narayanaswami IIM Ahmedabad
Viswanath Pingali IIM Ahmedabad
Sudha Ram University of Arizona
Jeevant Rampal IIM Ahmedabad
Neelkant Rawal Wells Fargo
Sriram Sankaranarayanan IIM Ahmedabad
Suprateek Sarker University of Virginia
Pankaj Setia IIM Ahmedabad
Avinash Sharma IIIT Hyderabad
Peng Shi Wisconsin School of Business
Hemant Kumar Singh University of New South Wales
Pranav Singh IIM Ahmedabad
Ankur Sinha IIM Ahmedabad
Sitabhra Sinha The Institute of Mathematical Science, Chennai
Chetan Soman IIM Ahmedabad
Karthik Sriram IIM Ahmedabad
Anish Sugathan IIM Ahmedabad
Abhishek Tripathi Perfios
Arvind Tripathi University of Auckland Business School
Ellapulli Vasudevan IIM Ahmedabad
Sanjay Verma IIM Ahmedabad

Team

Name  Designation
Present Members
Debjit Ghatak Centre Head
Neaketa Chawla Post-doctoral Research Associate
Kulvinder Kaur Post-doctoral Research Associate
Satender Pre-doctoral Research Associate
Nebu Varghese Pre-doctoral Research Associate
Vaishnav Garg Pre-doctoral Research Associate
Rahul Sharma Pre-doctoral Research Associate
Sayantan Pramanick Pre-doctoral Research Associate
Anjali Nair Centre Secretary

 

Past Members

Kushal Bhalla Post-doctoral Research Associate
Arnab Chakrabarti Post-doctoral Research Associate
Vani Dwivedi Pandya Pre-doctoral Research Associate
Prince Roy Pre-doctoral Research Associate
Shivam Kumar Pre-doctoral Research Associate
Viswash Mehta Pre-doctoral Research Associate
Saswot Nayak Pre-doctoral Research Associate

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