Category: Projects

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NITI for States for State Support Mission

NITI for States is an initiative under NITI Aayog that aims to strengthen the capacity of Indian states and union territories to drive effective governance, improve development outcomes, and accelerate progress toward national development goals. The State Support Mission (SSM) acts as a collaborative platform that offers technical assistance, data-driven decision-making support, institutional strengthening, and policy advisory services tailored to each state’s unique development context. It promotes evidence-based planning, innovative best practices, and state-level implementation frameworks aligned with national priorities. The SSM has several institutional components to facilitate implementation including designating Lead Knowledge Institutes (LKI) to provide domain and technical support.

IIIT-B has been nominated as a Lead Knowledge Institute under State Support Mission to promote evidence-based policy interventions using data science. Engagement with LKIs includes providing knowledge support to various SSM initiatives through research studies, organising workshops, trainings, seminars or any other technical support as required by NITI Aayog for the implementation of the mission.

Intervention Dashboard

Uttar Pradesh 2023-24 Secondary Dropout Rate

People

Faculty

Research Scholars

  • Jaskirat Singh Sanghera

Consultants

  • Rajesh Ramamoorthy – Program Manager
  • Pooja Bassin – Lead Researcher
  • Abraham GK – Consultant Engineer
  • Dr. Mukund Raj – Consultant Advisor

Project Interns

  • Pratheeksha Rao
  • Kushala B
  • Sai Gana Amruth Kasturi

Project Elective Students

  • Bhavil Sharma
  • Samarjeet Sanjay Wankhade

Activity

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Parichaya

Parichaya project aims at capturing indigenous oral traditional knowledge about sandalwood in rural communities and making it available through an interface that can enable users to interact with audio content to support broader cultural awareness, decision-making, cultivation practices and promote community involvement.

Objectives

Sandalwood plays an important role in Indian cultural, religious, and therapeutic practices. It is extremely important to capture relevant indigenous knowledge in rural communities about the tree from aging populations, support preservation and renew cultivation efforts. In addition to this, the verbal knowledge transferred through multiple generations in rural communities is largely uncodified. The project aims at shaping initial ontologies for a knowledge base about sandalwood.

The Parichaya application contains two interfaces;

  1. The first interface enables browsing the content using frequent keywords and their context words, representing critical aspects of information in the corpus and providing a good viewpoint of the content
  2. The second interface supports question-answering, where a user can post a question, get the summary answer, and listen to the audio contents with answers to the question.

Funding Agency

Mphasis F1 foundation

Demos

Parichaya interface : http://103.156.19.244:33404/
(username: guest, password: guest123)

Parichaya demo video :

Publications

Sharath Srivatsa, Aparna M, Samarth P, Malavika V, and Srinath Srinivasa. 2025. Parichaya: Rural Colloquial Knowledge AI Interface. In Proceedings of the 8th Joint International Conference on Data Science & Management of Data (12th ACM IKDD CODS and 30th COMAD) (CODS-COMAD ’25). Association for Computing Machinery, New York, NY, USA. [to appear]

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ScenariosDB

Autonomous Vehicles (AVs) are expected to have the potential to impact urban mobility by providing increased safety, reducing traffic congestion, mitigating accidents and reducing emissions. Since AVs operate with little or no human intervention, it is very essential to perceive the external world and understand different objects and their relationships in the scene, and respond appropriately. For doing this effectively, AVs need to be trained on a variety of traffic situations and appropriate responses to them. Behavior of vehicular traffic varies widely from one part of the world to another. An AV trained for traffic conditions in one part of the world may not be effective, or worse, even be risky in some other part of the world. Autonomous driving in developing countries like India is extremely challenging mainly due to unstructured driving environment which includes diverse traffic participants, erratic driving patterns and improper road infrastructure. So, there is a need for collecting or creating Indian driving datasets, understanding complex Indian traffic behavior and identifying various events in the scene. Event detection is critical for autonomous driving systems to enable vehicles to perceive and interpret their surroundings accurately as well as to make informed decisions. The main goal of our work is to understand heterogeneous and complex driving scenario in India from autonomous driving viewpoint by identifying disparate events and describe the scene using natural language descriptions to aid semantic scenario search

Funding Agency:

Siemens Technologies and Services Private Limited (STSPL)

Publications:

Bhoomika, A. P., Srinath Srinivasa, Vijaya Sarathi Indla, and Saikat Mukherjee. “Vector Based Semantic Scenario Search for Vehicular Traffic.” In International Conference on Big Data Analytics, pp. 160-171. Cham: Springer Nature Switzerland, 2023.

Team Details:

  • Bhoomika A P (PhD Research Scholar)
  • Prof. Srinath Srinivasa (PI)

Project Students:

  • Vidish Trivedi
  • Sasank Karamsetty
  • Dhanvi Medha Beechu
  • Swetha Murali
  • Ankita Agrawal
  • Somesh Awasthi

Interns:

  • Yashasvi Virani
  • Nayan Radadiya

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SDG 2D Visualization

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IndicNLP

IndicNLP project focuses on building an knowledge management framework for oral community knowledge in low-resource and colloquial Kannada language.

Background

Knowledge in rural communities is largely created, preserved, and is transferred verbally, and it is limited. This information is valuable to these communities, and managing and making it available digitally with state-of-the-art approaches enriches awareness and collective knowledge of people of these communities. The large amounts of data and information produced on the Internet are inaccessible to the population in these rural communities due to factors like lack of infrastructure, connectivity, and limited literacy. Knowledge internal to rural communities is also not conserved and made available in any global Big Data information systems. Artificial Intelligence (AI) technologies such as Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) provide substantial assistance when vast quantities of data, like Big Data, are available to build solutions. In the case of low-resource languages like Kannada and rural colloquial dialects, publicly available corpora are significantly less. Building state-of-the-art AI solutions is challenging in this context, and we address this problem in this work. Knowledge management in rural communities requires a low-cost and efficient approach that social workers can use. Organizations such as Namma Halli Radio have collected an audio corpus of a few hours containing community interactions spoken in colloquial language. We propose an architecture for oral knowledge management for rural communities speaking colloquial Kannada using audio recordings.

Funding Agency

Mphasis F1 foundation

Publications

Aparna, M., Srivatsa, S., Sai Madhavan, G., Dinesh, T.B., Srinivasa, S. (2024). AI-Based Assistance for Management of Oral Community Knowledge in Low-Resource and Colloquial Kannada Language. In: Sachdeva, S., Watanobe, Y. (eds) Big Data Analytics in Astronomy, Science, and Engineering. BDA 2023. Lecture Notes in Computer Science, vol 14516. Springer, Cham. https://doi.org/10.1007/978-3-031-58502-9_1

Sharath Srivatsa, Aparna M, Sai Madhavan G, and Srinath Srinivasa. 2024. Knowledge Management Framework Over Low Resource Indian Colloquial Language Audio Contents. In Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD) (CODS-COMAD ’24). Association for Computing Machinery, New York, NY, USA, 553–557. https://doi.org/10.1145/3632410.3632483 

Aparna M and Srinath Srinivasa. 2023. Active learning for Named Entity Recognition in Kannada. TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.24580582.v1

Media Mentions

Demo

Graama-Kannada Audio Search webapp : http://103.156.19.244:33035/,
(username : guest, password : guest123)

Graama-Kannada demo video:

People

Research Scholars

Project Students

  • Goutham U R
  • Ram Sai Koushik Polisetti
  • Sai Madhavan G
  • Kappagantula Lakshmi Abhigna
  • Manuj Malik
  • Debmalya Sen
  • Vikram Adithya C P
  • Venumula Sai Sumanth Reddy

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Consent Management in Digital Public Infrastructures

The primary aim of our work is to develop a formal framework to manage consent for open-ended data sharing use cases like Digital Public Infrastructures (DPIs). Our framework delves into philosophical, legal and technical aspects of consent to ensure a holistic solution. In this regard, we propose policy-based consent management which resolves consent across 4 key dimensions – ownership, policy compliance, access-control and post-access conditions. The prototype of the work can found at Anumati: Consent Management Service.

Other important aspects of the work include developing an architecture for cross-border data flows in DPIs , developing a consent ontology to understand meaningful consent policies.

Funding Agency

Centre of Data for Public Good (CDPG), IISc

Publications

  1. Ayappane, Balambiga, et al. “Extensible Consent Management Architectures for Data Trusts.” arXiv preprint arXiv:2309.16789 (2023).
  2. Balambiga Ayappane, Rohith Vaidyanathan, Srinath Srinivasa, Santosh Kumar Upadhyaya, and Srinivas Vivek. 2024. Consent Service Architecture for Policy-Based Consent Management in Data Trusts. In 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD) (CODS-COMAD 2024), January 04–07, 2024, Bangalore, India. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3632410
  3. Asilata Karandikar. 2024. What Makes Consent Meaningful? In Companion Publication of the 16th ACM Web Science Conference (Websci Companion ’24). Association for Computing Machinery, New York, NY, USA, 42–46. https://doi.org/10.1145/3630744.3658616

Team Details

  1. Dev Shinde (MS Scholar)
  2. Balambiga Ayappane (Research Associate) 
  3. Rohith Vaidyanathan (MS scholar) 
  4. Prof. V Sridhar (collaborator) 
  5. Asilata Karandikar (collaborating PhD Scholar) 
  6. Prof. Srinivas Vivek (collaborator) 
  7. Santosh Kumar Upadhyaya (collaborating PhD scholar)
  8. Prof. Srinath Srinivasa (PI)
  9. Sachin Poojary (Developer)
  10. Meghana TM (Developer)
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Big Data Analytics for Policy Support

Spotlight Data Story: Intervention Dashboard for Maternal Deaths

The primary focus of the Big Data Analytics for Policy Support project is to develop a sophisticated data management system that consolidates diverse documents from various government departments into a unified access point. This centralized hub serves as a valuable resource for policymakers, domain experts, and data analysts, allowing them to easily search and analyze documents through visual dashboards. The front-facing website of this work can be accessed at Karnataka Data Lake: Policy Research using Big Data Analytics.

The other research direction of the project focuses on creating a comprehensive model library, which becomes a crucial asset for policymakers. This library not only aids in making informed decisions based on the current state but also looks ahead to anticipate future scenarios related to policy interventions. The ultimate aim of the Policy Support System (PSS) is to provide policymakers with a powerful tool that addresses current challenges related to Sustainable Development Goals (SDGs) while also proactively navigating the evolving landscape of policy dynamics.

The Architecture Diagram of Big Data Analytics for Policy Support

SDG Intervention Dashboards

Publications

2025

2024

2023

2022

2021

People

Faculty

Research Scholars

Project Associates

Project Students

  • Nithin Raj
  • Omkar Lavangad
  • Phani Tirthala
  • Himesh Krishna Osuri
  • Anish Sai Potluri
  • Ayush Tiwari
  • Sowmith Nandan R
  • Arpitha Malavalli
  • Saiakash Konidena
  • Arjun Dutta
  • Akshay Girish Thite
  • Umang Okate
  • Gaurav Nilesh Tirodkar
  • Jasvin James Manjaly
  • Bhavil Sharma
  • Mallangi Siva Charan Reddy
  • Nimish Gaurish Sinai Khandeparkar
  • Gramya Gupta
  • Manish Reddy Koppula
  • Swadi Korattiparambil
  • Anant Pandey
  • Riya Patidar
  • Anuj Arora

Interns

  • Shreya Subramaniyam
  • Ojas Hegde
  • Greeshma Chandra Shekar
  • Nandan N
  • Sachin Tiwari
  • Siddharth Gautam
  • Sparsh Dalal
  • Rutvi Chavda
  • Mruga Patel

Research Site

Karnataka Data Lake: Policy Research using Big Data Analytics

Media Mentions

https://www.newindianexpress.com/cities/bengaluru/2022/jul/04/bengaluru-to-get-rs-5-crore-data-lake-forbetter-governance-improved-planning-2472616.html

Talks & Videos

  • Big-data-analytics in Astronomy, Science, and Engineering (BASE) 2021
  • MLN-DIVE Lecture Series: Srinath Srinivasa

Title: Diffusion and Perturbation Modeling for Policy Interventions

Date: June 11, 2022

https://psu.mediaspace.kaltura.com/media/MLN-DIVE+lecture+seriesA+Srinath+Srinivasa/1_mm4wvj58

  • Bangalore Tech Summit 16th – 18th November 2022

Miscellaneous

Thesis Reference Document for Intervention Modeling

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Responsible Autonomy

Designing computational models of multi-agent autonomous systems which can act responsibly.

Funding Agency

Machine Intelligence and Robotics (MINRO)

Center for Internet of Ethical Things (CIET)

Publications

  1. Janvi Chhabra, Karthik Sama, Jayati Deshmukh, and Srinath Srinivasa. “Evaluating computational models of ethics for autonomous decision making”. In: AI and Ethics 2024 (Aug. 2024), pp. 1–14. issn: 2730-5961. https://doi.org/10.1007/s43681-024-00532-4 [pdf]
  2. Janvi Chhabra, Jayati Deshmukh and Srinath Srinivasa. 2024. Modelling the Dynamics of Subjective Identity in Allocation Games. In Proceedings of the 2024 International Conference on Autonomous Agents and Multiagent Systems (AAMAS ’24). [link]
  3. Karthik Sama, Jayati Deshmukh and Srinath Srinivasa. 2024. Social Identities and Responsible Agency. In Proceedings of the 2024 International Conference on Autonomous Agents and Multiagent Systems (AAMAS ’24). [link]
  4. Sama Sai Karthik, Jayati Deshmukh, Srinath Srinivasa. 2024. Transcending To Notions. Preprint. https://arxiv.org/abs/2401.12159 [link]
  5. Janvi Chhabra, Jayati Deshmukh, Srinath Srinivasa. 2024. Modelling the Dynamics of Identity and Fairness in Ultimatum Game. Preprint. https://arxiv.org/abs/2401.11881 [link]
  6. Jayati Deshmukh, Nikitha Adivi, and Srinath Srinivasa. 2023. Resolving the Dilemma of Responsibility in Multi-agent Flow Networks. In International Conference on Practical Applications of Agents and Multi-Agent Systems. Springer, 76–87. DOI: http://dx.doi.org/https://doi.org/10.1007/978-3-031-37616-0_7
  7. Janvi Chhabra, Karthik Sama, Jayati Deshmukh, and Srinath Srinivasa. 2023. When Extrinsic Payoffs Meet Intrinsic Expectations. In International Conference on Practical Applications of Agents and Multi-Agent Systems. Springer, 40–51. DOI: http://dx.doi.org/https://doi.org/10.1007/978-3-031-37616-0_4
  8. Jayati Deshmukh. 2023. Emergent Responsible Autonomy in Multi-Agent
    Systems. In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (AAMAS ’23). International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 3029–3031. https://dl.acm.org/doi/abs/10.5555/3545946.3599161
  9. Jayati Deshmukh, Nikitha Adivi, and Srinath Srinivasa. 2023. Modeling Application Scenarios for Responsible Autonomy Using Computational Transcendence. In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (AAMAS ’23). International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2496–2498. https://dl.acm.org/doi/abs/10.5555/3545946.3598980
  10. Janvi Chhabra, Karthik Sama, Jayati Deshmukh, and Srinath Srinivasa. 2023. Comparative Modeling of Ethical Constructs in Autonomous Decision Making. (2023). DOI: http://dx.doi.org/https://doi.org/10.36227/techrxiv.21802302.v1
  11. Jayati Deshmukh and Srinath Srinivasa. 2022. Computational Transcendence: Responsibility and agency. Frontiers in Robotics and AI 9 (2022). DOI: http://dx.doi.org/https://doi.org/10.3389/frobt.2022.977303
  12. Srinath Srinivasa and Jayati Deshmukh. 2022. AI and the Sense of Self. arXiv preprint arXiv:2201.05576 (2022). DOI: http://dx.doi.org/https://doi.org/10.48550/arXiv.2201.05576
  13. Jayati Deshmukh, Srinath Srinivasa, and Sridhar Mandyam. 2021. What Keeps a Vibrant Population Together? Complex Syst. 30, 3 (2021), 347–373. DOI: http://dx.doi.org/https://doi.org/10.25088/ComplexSystems.30.3.347
  14. Srinath Srinivasa and Jayati Deshmukh. 2020. The Evolution of Computational Agency. In Novel Approaches to Information Systems Design. IGI Global, 1–19. DOI: http://dx.doi.org/https://doi.org/10.4018/978-1-7998-2975-1.ch001

Team Details

  1. Prof. Srinath Srinivasa
  2. Jayati Deshmukh [PhD Research Scholar]
  3. Janvi Chhabra [RE, PE, IMTech thesis, Jan 2022 – June 2023]
  4. Sama Sai Karthik [RE, PE, IMTech thesis, Jan 2022 – June 2023]
  5. Nikitha Adivi [PE, Aug 2022 – Dec 2022]

Videos

Webinar titled “Artificial Intelligence & Responsible Autonomy” by CSI ACM IEEE Chennai on January 29th, 2025

Samvaad talk at IIIT-B on August 22nd, 2022

Talk at Jijnasa 2021 on December 18th, 2021

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Traffic Modeling and Simulation

Major research directions of the project are:

  • Simulations of traffic scenarios (like one-way, adding new roads, parking spots etc).
  • Experiments on Adaptive Traffic Lights.
  • Simulations to optimize traffic flows at network level.

Funding Agency

MINRO

Team Details

  • Jayati Deshmukh [PhD Research Scholar]
  • Karthik Ch [PE, Jan 2023 – May 2023]
  • Keshav Chandak [PE, Jan 2023 – May 2023]
  • Shubhanshu Agrawal [Summer Intern, PE, May 2022 – Dec 2022]
  • Manas Agrawal [PE, Aug 2022 – Dec 2022]
  • Shivankar Pilligundla [PE, Aug 2022 – Dec 2022]
  • Prakhar Rastogi [Summer Intern, May 2022 – July 2022]
  • Vijay Jaisankar [PE, Jan 2022 – May 2022]
  • Vignesh Bondugula [PE, Jan 2022 – May 2022]
  • Siva Jagadesh M [PE, Jan 2022 – May 2022]
  • Shashank Reddy [PE, Aug-Dec 2021]
  • Gayathri Venkatesh [PE, Aug-Dec 2021]
  • Manasa Kashyap [PE, Aug-Dec 2021]
  • Sriranjan S. [Summer intern, May-Jul 2021]
  • Ayush Yadav [PE, Jan-Apr 2021]
  • Rahul Murali Shankar [PE, Jan-Apr 2021]
  • Pratik Shastri [PE and IMTech thesis, Aug 2020-Jun 2021]
  • Divyanshu Khandelwal [PE, Aug-Dec 2020]

Code

Performing traffic interventions and running traffic simulations.

Activities

Demo presented at SUMO User Conference 2022. Won Best presentation based on live audience voting.

Demo of the project at Bangalore Tech Summit (BTS) 2020.

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Precision Learning for Enterprise

Overview:

Organizational learning is fast becoming an integral element of the daily operations of organizations given the pace of change in technology and market dynamics. Organizational learning is the process of creating, retaining, and transferring knowledge within an organization. An organization improves over time as it gains experience. From this experience, it is able to create knowledge. This knowledge is broad, covering any topic that could better an organization. Organisational learning is an essential element for the survival of any organisation in this fast paced technological and business landscape.

A concept of “learning organizations” has been a focus of enormous research and managerial interest since the early 1990s. Peter Senge, identifies five key disciplines for organizational learning:

  1. Systems Thinking
  2. Shared Vision
  3. Mental Models
  4. Team Learning
  5. Personal Mastery

While the concept of learning organizations elicited enormous interest in its early years, the above principles of learning organizations were found to be very challenging to manage, in an environment of fast changes and churn in the workplace.

In recent times, a new paradigm called precision learning is gaining a lot of research attention. Precision learning entails the use of “Big Data” techniques to identify expertise and knowledge gaps in a given community or organization, and mediate between them. The result is a continuous, on-going process of learning catering to a population of diverse interests, skills and learning goals.

Navigated Learning is a precision learning model being developed at the Web Science lab at IIIT Bangalore. Navigated learning implements precision learning, by computing a variety of semantic embeddings. This project explores Navigated Learning models for enterprise settings.

Expected Social Impact:

Navigated learning is part of the research focus called “Digital Empowerment” being pursued by the Web Science lab. The underlying social challenge being addressed is the need for informal and continuous upskilling required for overall social empowerment. Current models of technology enabled learning have not gained enough engagement and traction from users to pursue their upskilling activities in a sustained manner. One of the reasons we believe for this, is the dearth of context, relevance and social support during the learning process. Navigated learning is expected to cater to this need, and we hope that it becomes as popular as, and an empowering alternative to streaming video and gaming apps.

Since this is under applied research in cognitive computing “Precision Learning for Enterprise portal” will be delivered in which the concept of Navigated Learning is applied.

This project is sponsored by Mphasis under the Mphasis Center of Excellence for Cognitive Computing.

Team Members:

Dr Aparna Lalingkar, Postdoctorate Research Fellow

Mr Prakhar Mishra, MS Research Scholar

Mr Shyam Kumar VN, MS Research Scholar

Current Members:

Jagatdeep Pattanaik, MTech Student

Puneeth Sarma, IMTech Student

Rohit Katlaa, IMTech Student 

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