About IDeAL
The Interdisciplinary Decision Sciences & Analytics Lab (IDeAL) at the Indian Institute of Management Visakhapatnam is a Centre of Excellence focused on academic research, industrial and government consultancy, product development, and training. We support interdisciplinary research through innovations in artificial intelligence, machine learning, and data science, with an emphasis on mathematical optimization to address complex challenges faced by India and other emerging economies.
We invite researchers and scholars to submit their original research work to IDeAL working paper series. The papers can fall under intersection of any field of data science with areas like Health Care, Smart Cities Development, Sustainable Supply Chain Management, Behavioral Finance, Environmental Risk Assessment, Human-Computer Interaction in Decision Support Systems, Biotechnology Policy and Management, Cognitive Neuroscience in Business Strategy, Energy-Economics, Global Public Health Decision Making, Educational Data Mining and Analytics, Resource Management, etc. Researchers can visit IDeAL Working Paper Series page to submit their papers.
Thrust Areas
Health Care
With data science and optimization models, the public-health system could be transformed into a high-quality, quick-response service-delivery system. Data analytics, AI, and optimization-based solutions have great potential in bringing efficiency and effectiveness to service delivery.
By harnessing historical PHC data and machine learning, we contibute to the advancements predictive healthcare. Our solutions include web based telemedicine and emergency care finding for maternal, infant and neonatal care.
Natural Resource Management
IDeAL will enable researchers to apply decision science and data models against real-world challenges such as water storage, biodiversity loss, and mineral resource extraction. Managing natural resources faces the central problem of how data is exploited to build predictive and integrated models for sustainable decisions amid uncertainty.
We leverage decision science and advanced analytics to support sustainable resource management with data driven models predict water storage needs, asses biodiversity loss, and optimise mineral extraction by revealing patterns between natural resources.
Computational Public Policy
The data science approach is valuable for public servants and policy, pushing people to defy conjecture, consider counterfactuals, reason about complex patterns, and question missing information. It makes people skeptical of narratives that, while emotionally powerful, are not good sources for comprehensive policies.
We apply compute science and mathematics to tackle public policy challenges. Our work focuses on analysing data rigorously to create evidence-based, transparent and effective policies.
Cyber-Physical Systems
Cyber-Physical Systems (CPS) are a compilation of computing and communication units connecting the cyberworld with the environment. CPS spans applications with enormous societal impact and economic benefit. CPS is already used in medical devices (pacemakers, insulin pumps), infrastructure (surveillance and control), manufacturing, and transportation (airplanes, air traffic control, rail).
Our research in CPS involves integrating computing and communicating devices with physical systems. By developing solutions for real-time monitoring and control, Advances in CPS will lead to systems that significantly surpass the capabilities of today’s simple embedded systems.
Collaborate With Us
Corporations & Government Organizations
Collaborate with us on short term and long term joint research projects.
Students
Explore various internship opportunities in Inter-disciplinary research areas to use your existing skills on entirely new areas.
Researchers
Submit your research processes to IDeAL Working Paper Series and collaborate towards inter-disciplinary research.
Tools We Work With
Machine Learning and Data Analytics
Most of the data oriented research is carried out with help of open-source tools which include python programming language. Researchers at IDeAL use various libraries to extract new patterns and insights from data. NumPy and Pandas libraries are used to perform data processing, data cleaning, and data reshaping. Scikit-learn library which consists of various machine learning techniques predefined is used to build a machine learning model on the data.
Mathematical Programming and Optimization
While predictive modelling and machine learning models help in understanding and exploring information required to solve a problem, the actual data problem needs to be solved using optimization techniques using integer, linear and non-linear programming techniques. R software consists of a wide range of data and statistical libraries which are useful in performing experimental analysis like data envelopment analysis, structural equation modelling, etc. A Mathematical Programming Language (AMPL) is used to perform optimization using customized mathematical programs using solvers. MATLAB is also used to perform optimization and generate visualizations on the results.
Data Visualizations and Decision Support Systems
The solutions developed at IDeAL needs to reach users and require utilization of proper visualization techniques and platform to host the applications. Matplotlib, seaborn, and plotly are three popular plotting libraries that are used in visualizing the results of data analysis. Flask is a framework in python that is used to create and deploy web applications. Researchers at IDeAL use combination of Flask framework with one of the relevant plotting libraries to build scalable decision support systems which aid decision makers with accurate findings.