Inter-disciplinary Decision Sciences & Analytics Lab Inauguration.
Seminar on Artificial Intelligence: Industrial Manufacturing, Health Systems and COVID-19 by Dr. Arni Srinivasa Rao
On Applications of Machine Learning and Data Science in Inter-disciplinary areas.
Lecture on predictive analytics to students of MBD at Centre of Business Data Analytics at Copenhagen Business School(CBS).
A seminar on the importance of Mathematics and Statistics in today’s era and how they underpin the field of Artificial Intelligence (A.I.).

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The Interdisciplinary Decision Sciences & Analytics Lab (IDeAL) is a Centre of Excellence at the Indian Institute of Management Visakhapatnam. This Lab is dedicated to academic research, industrial/Govt. consultancy, product development and training to support inter-disciplinary research using the innovations of artificial intelligence, machine learning, deep learning, cloud computing, graph analytics, game theory, cyber-physical systems, pervasive computing etc. while keeping Mathematical Optimization and Data Science as the core in the approach of solving the inter-disciplinary problems faced by India and other emerging economies.



Knowledge centre
Knowledge Centre

A space for students, scholars and practitioners to come together for knowledge creation and dissemination.

Knowledge Sharing
Knowledge Sharing

Through seminars and training programmes, a good knowledge sharing for the capacity-building of learners to ensure the sustainability of individuals, teams, and organization.

Consultancy and Advisory
Consultancy and Advisory

Joint research projects with Government and industry for common goals and maximizing the outcome for the stakeholders.


Thrust / Applied Areas:

Health Care
Health care

With data science and optimization models, public-health system could be transformed into a high-quality and quick-response service-delivery system. The data analytics, AI and optimization-based solutions have great potential in bringing efficiency and effectiveness into service-delivery.

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Computational Public Policy
Computational Public Policy

The data science approach is tremendously valuable for public servants and public policy. It pushes people to defy conjecture, consider counterfactuals, reason about complex patterns, and question what an information is missing.

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Natural Resource
Natural resource

The management of all-natural resources faces the common central problem of how data is exploited to build predictive and integrated models that can be used to make sustainable decisions in the presence of uncertainty.

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Cyber-physical Systems
Cyber physical

As computing and communication devices become smaller and cheaper, they can be embedded in objects and structures to interact directly with the physical environment and reach human capabilities. Cyber-Physical Systems (CPS) are a compilation of computing and communication units.

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Technology and Platform


R is ‘GNU S’, a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modeling, statistical tests, time series analysis, classification, clustering, etc.


Python has some libraries that support statistical modeling (Scipy and Numpy), Scikit-learn, a library of machine learning techniques very useful to data scientists. For visualization Matplotlib and Python being very robust languages and ingretable to other enterprise systems.


MATLAB makes data science easy with tools to access and preprocess data, build machine learning and predictive models, and deploy models to enterprise IT systems.


The AMPL modeling language and system is for a comprehensive range of research applications that use optimization as a modeling paradigm.