Opportunities to work with WSL

Jan 2023: Open

3-month internship in visual analytics and predictive modelling 

Two positions for 3-month paid internships are open, starting January 15, 2023 till April 14, 2023. The positions may be extended for 3 more months based on requirement and performance. 

Internship applicants should have a BTech or equivalent in Computer Science or related disciplines like Information Technology, Data Science, etc. Programming proficiency in python is highly desirable, and experience with visual analytic tools like Tableau or Kibana is an added plus. 

Interns will get an opportunity to work on a major project involving planning and resource allocation, and pick up skills like Bayesian modelling, building data stories and providing actionable insights. 

The internships will come with a stipend of INR 15,000 per month. Applications may be sent to sri@iiitb.ac.in

Dec 2022: Closed

Project Elective applications are open to work on various WSL projects for the upcoming Jan-Apr 2023 semester at IIIT-Bangalore. Students can apply for 4-credit, 12-credit or 20-credit projects as applicable. This call is open to students currently enrolled at IIIT-B.

Applications will close on 2nd Dec 2022. If applied after 2nd Dec, we will reach out to you only in case there are any further open positions.

Project details available here: https://drive.google.com/file/d/1So1fm1Zeu0aaZHGQMC_vWd2efaRLvoko/view?usp=sharing

Form to apply: https://forms.gle/Cy8SzNNp5Y3szhy79

WS4D Datathon: Concept and Details

Concept Note for the SafeCity Data Visualisation Challenge

WS4D Datathon http://cognitive.iiitb.ac.in/ws4d-datathon-and-phd-colloquium/


The key dataset(s) pertain to information gathered from India, and provided by the Red Dot Foundation.

  1. Reports: time, place, type of event, report
  2. MobileApp: time, place, type of event

Reference articles https://safecity.in/publications/research-papers/  pertain to the following topics:

  1. Use of ML/AI to find the type of event (touching/groping/sexual invites/commenting/etc.) from the reports; a study on the diverse forms of sexual harassment
  2. Street violence
  3. Gender-based violence in public transport
  4. Women’s strategies to address assault and violence
  5. Study of crowdsourced data

Challenge themes:

The following points are for processing data and analyzing it deliberately, and using the knowledge to create a compelling visualization as a narrative/summary (preferably) or a tool.  The visualization (tool) must be shareable on social media to spread awareness and to inspire action against gender-based violence and others.

  1. Theme-Mythbusters: Time-related clustering/visualization or integration of time (time of day, evolution over time) with spatial and categories of crime – ( http://maps.safecity.in/ ): This will help us debunk the myths of where and when different kinds of sexual violence tend to take place. Hence, the challenge starts with picking/identifying a myth as a hypothesis, and demonstrating if the data confirm it or not. 
  2. Theme-MirrorMirrorOnTheWall: Comparison of Indian cities with others in the world where data is available: this will give us a sense of India’s position in sexual violence across different parameters captured in the existing datasets. For example, do we see a concentration of specific kinds of violence in India? Such data help us make aware of specific social structures within which sexual crime takes place. 
  3. Theme-Mash-up: Integration with other relevant datasets — police data, sex ratio, etc. available for a specific city. This will help us understand the overall situation of the safety and status of women in a city.  Such data will be crucial in shaping institutional strategies for coping with the incidence of sexual violence.  

For Theme-MythBusters, relevant myths (as a sample):

  1. Gender-based violence of all forms is highly prevalent in Delhi.
  2. Gender-based violence occurs in dimly lit streets and at night.
  3. Sexual violence and harassment occur only in very crowded or very deserted regions.
  4. Not many women get distressed with non-physical forms of violence.

For Theme-MirrorMirrorOnTheWall, relevant datasets and sources:

  1. https://evaw-global-database.unwomen.org/en/countries
  2. New York City crime: https://data.cityofnewyork.us/Public-Safety/NYC-crime/qb7u-rbmr
  3. Country and World data: consolidated as an excel sheet by Red Dot Foundation using multiple sources: http://worldpopulationreview.com/countries/rape-statistics-by-country/



For Theme-Mash-up, relevant datasets and sources:

  1. social indicators: the general status of women in a specific city, for example, sex ratio, gender-segregated literacy rates, rate of female workforce participation. 
    1. Demographics data with gender segregation – raw data: http://censusindia.gov.in/2011census/population_enumeration.html
    2. Report: Women and Men in India:
      1. 2017: http://www.indiaenvironmentportal.org.in/files/file/women%20and%20men%20in%20India%202017.pdf
      2. 2018: http://www.mospi.gov.in/sites/default/files/publication_reports/Women%20and%20Men%20%20in%20India%202018.pdf
    3. http://www.mospi.gov.in/statistical-year-book-india/2017/171
    4. https://data.gov.in/search/site?query=gender
    5. Districtwise Education Data 2015-16 based on sex ratio, male/female literacy, schools by category, boys/girls schools by category, male/female teachers by category, etc.
    6.  Rural Female broad employment status
    7. Urban female broad employment status
    8. Women prisoners with children
    9.  Statewise schools with female teachers
    10. Statewise registered cases against stalking, rape, acid attacks
    11. Financial assistance provided to OBC women
    12.  Budgetary allocation for women safety
    13. State level literacy rate
  2. infrastructure indicators: the general state of law and order, safety in public spaces, gender-based crime, street lights, CCTV cameras, etc.
    1. Street lighting: https://data.gov.in/resources/stateut-wise-no-led-street-lights-installed-under-street-lighting-national-programme-slnp
    2. Crime against women:
      1. https://data.gov.in/catalog/crime-committed-against-women?filters%5Bfield_catalog_reference%5D=86920&format=json&offset=0&limit=6&sort%5Bcreated%5D=desc
      2. https://data.humdata.org/dataset/crime-trends-and-operations-of-criminal-justice-systems-un-cts-sexual-violence
      3. Crime against Women in Metropolitan Cities — tables from a book chapter. [provided separately as a pdf].


A compelling visual narrative to be shared on social media:

  1. Appropriate fonts and color palettes
  2. Situation-sensitive text, e.g. without victim shaming
  3. Use of popular NLP tools in python, visualization tools like D3.js, Tableau, etc.

For further queries: datathon2020@iiitb.ac.in

WS4D PhD Colloquium

WS4D PhD Colloquium

Feb 14, 2020 | 10AM to 4PM | IIIT-Bangalore

Register HERE

The goal of this session is to have research discussion among the PhD research scholars across multiple institutes who are working in the areas related to Web Science. We hope these discussions will be useful and will foster research collaborations in future!


Moderators: Faculty
Panelists: PhD Research Scholars
Audience: Research Scholars

Agenda of each Theme Discussion

  • Theme Introduction by Moderator
  • Short introduction by panelists (5 panelists 5 mins each)
  • Q&A (30 mins)

PhD Colloquium Themes

  1. Empowerment
    In this theme, we discuss how the WWW and digital technologies in general can be used for education and upskilling of the population at scale. As mobile phones and high-speed data connections become ubiquitous, this has created a huge opportunity for disseminating knowledge and skills to a vast population efficiently. However, a dearth of sound understanding of how this can be achieved, is still an impediment. We can also discuss how digital empowerment is essential and how access to resources can help in that context.
  2. Inclusion & Accessibility
    In this theme, we discuss how inclusion is necessary and not just preferable to build models or solutions which are useful, relevant and applicable to all. In this context, inclusion might be in terms of gender, race, color etc. It will be relevant to also discuss how web and digitization can be conducive in building solutions which are designed keeping accessibility into account. Topics like rennaration, multi-language support, transcriptions, alternate text of images etc might be relevant.
  3. Digital Governance + Privacy  + Security
    In this theme, we discuss how different forms of data management processes can be woven into the fabric of administrative decision-making. These include structured data generated by different government departments, corporates and other organisations; as well as the so-called Big Data, generated from several sources like sensors, social media posts, etc. that often contain useful inputs for decision-making. We also discuss topics like privacy and security in this context.
  4. Social Cognition
    In this theme we address questions about how the web, and particularly social media and open online knowledge portals like Wikipedia, is affecting collective opinion and worldview. Social cognition is playing a central role in the making and breaking of reputations of individuals, businesses, and countries. There is a pressing need to understand social cognition in the post-web world. We also discuss topics like opinions, campaigns in networks, marketing and recommendation and discourse modeling.

WS4D Research Workshop


0900-0915Inauguration and Address by Dean (Academics) Prof. R Chandrashekar
0915-1015Keynote – 1: Speaker: Dame Wendy Hall, Web Science Institute
1015-1045Invited Talk – 1: Speaker: Prof. Bidisha Chaudhuri, IIIT Bangalore
1045- 1115Invited Talk – 2: Speaker: Prof. Jaya Sreevalsan Nair, IIIT Bangalore
1115-1130Tea Break
1130-1200Invited Talk – 3: Speaker: Jai Ganesh, Mphasis Inc.
1200-1230Invited Talk – 4: Speaker: Sabu Padamdas, University of Southampton 
1230-1300Invited Talk – 5: Speaker: Nandan Sudarsanam, IIT Madras
1300-1400Lunch Break
1400-1500Keynote – 2: Speaker: Noshir Contractor, Northwestern University 
1500-1515Tea Break
1515-1545Invited Talk – 6: Speaker: Pauline Leonard, Web Science Institute
1545-1615Invited Talk – 7: Speaker: Srinath Srinivasa, IIIT Bangalore
1615-1645Invited Talk – 8: Speaker: Pathik Pathak, University of Southampton
1645-1700Report on Brave Conversations: Speaker: Anni Rowland-Campbell, University of Southampton
1700-1730High Tea and Closing

Talk and Speaker Details

Dame Wendy Hall

Dame Wendy Hall, DBE, FRS, FREng is Regius Professor of Computer Science, Pro Vice-Chancellor (International Engagement), and is the Executive Director of the Web Science Institute at the University of Southampton. Dame Wendy was co-Chair of the UK government’s AI Review, which was published in October 2017, and has recently been announced by the UK government as the first Skills Champion for AI in the UK.

With Sir Tim Berners-Lee and Sir Nigel Shadbolt she co-founded the Web Science Research Initiative in 2006 and is the Managing Director of the Web Science Trust, which has a global mission to support the development of research, education and thought leadership in Web Science.

She became a Dame Commander of the British Empire in the 2009 UK New Year’s Honours list, and is a Fellow of the Royal Society.

She has previously been President of the ACM, Senior Vice President of the Royal Academy of Engineering, a member of the UK Prime Minister’s Council for Science and Technology, was a founding member of the European Research Council and Chair of the European Commission’s ISTAG 2010-2012, was a member of the Global Commission on Internet Governance, and until June 2018, was a member of the World Economic Forum’s Global Futures Council on the Digital Economy.

Noshir Contractor

Noshir Contractor is the Jane S. & William J. White Professor of Behavioral Sciences in the McCormick School of Engineering & Applied Science, the School of Communication and the Kellogg School of Management and Director of the Science of Networks in Communities (SONIC) Research Group at Northwestern University.  

Professor Contractor has been at the forefront of three emerging interdisciplines: network science, computational social science and web science. He is investigating how social and knowledge networks form – and perform – in contexts including business, scientific communities, healthcare and space travel.  His research has been funded continuously for 25 years by the U.S. National Science Foundation with additional funding from the U.S. National Institutes of Health, NASA, DARPA, Army Research Laboratory and the Bill & Melinda Gates Foundation. 

His book Theories of Communication Networks (co-authored with Peter Monge) received the 2003 Book of the Year award from the Organizational Communication Division of the National Communication Association.  He is a Fellow of the International Communication Association (ICA), the American Association for the Advancement of Science (AAAS), and the Association for Computing Machinery (ACM).  He also received the Distinguished Scholar Award from the National Communication Association and the Lifetime Service Award from the Organizational Communication & Information Systems Division of the Academy of Management. In 2018 he received the Distinguished Alumnus Award from the Indian Institute of Technology, Madras where he received a Bachelor’s in Electrical Engineering. He received his Ph.D. from the Annenberg School of Communication at the University of Southern California.  

Jai Ganesh

Dr. Jai Ganesh is the Senior Vice President and Head of Mphasis NEXTLabs. He is a Product and Service Innovation leader with extensive experience in inventing, conceptualizing, building and commercializing successful technology product and service innovations. Under his leadership, NEXTLabs has created several global award-winning solutions, products and service offerings. Recent awards won include AIconics 2017 for ‘Best application of AI in Financial Services’ and Business Intelligence Group’s ‘2018 Stratus Awards for Cloud Computing’. Jai consults and co-creates with leading global corporations to formulate their digital transformation strategy and build advanced AI driven solutions. He focuses on applied research and innovation in areas such as Data Science, Social Network Analysis, Machine Learning, Deep Learning, Artificial Intelligence, Natural Language Processing, Cloud Computing and Automation. Jai is a prolific inventor with several granted patents as well as publications in leading peer reviewed journals and conferences. He is a PhD from Indian Institute of Management Bangalore (IIMB) and also has an MBA. Jai is a recipient of the Chevening Rolls-Royce Science and Innovation Fellowship at the University of Oxford.

Sabu Padamdas

Professor Sabu S. Padmadas is Associate Dean (International) of the Faculty of Social Sciences, Professor of Demography and Global Health, and Founding Co-Director of the Centre for Global Health, Population, Poverty and Policy (GHP3) at the University of Southampton.

Padmadas obtained a PhD degree in Demography in 2000 from the Faculty of Spatial Sciences of the University of Groningen in The Netherlands, an MSc degree in Demography in 1995 and a BSc degree in Mathematics with Statistics and Physics in 1992 from the University of Kerala in India, and a Postgraduate Certificate in Academic Practice in 2006 from the University of Southampton. Padmadas joined the University of Southampton as a Lecturer in Demography in 2002 after completing a two-year term as post-doctoral fellow of the Dutch Royal Academy of Sciences at the University of Groningen. He is currently a Fellow of the UK Higher Education Academy, and an honorary Senior Research Fellow at the China Population & Development Research Centre, a think-tank attached to the National Health Commission of the People’s Republic of China.  

His research interests focus broadly on population dynamics and the application of demographic analysis and statistical modelling of global health and wellbeing outcomes in low-middle income and transition economies. He has international expertise in programme impact evaluation and quantitative demography using census and survey data including calendar data, life course and birth history analyses, and population projections. The specific areas of his research cover a broad spectrum of challenging population health topics including: family planning, reproductive and child health, inequalities in health and healthcare outcomes, nutrition, life course epidemiology, population health policies and social determinants of disease outcomes. The journey to his multidisciplinary research career began with the publication of his doctoral thesis entitled ‘Intergenerational Transmission of Health: Reproductive Health of Mother and Child Survival in Kerala, South India’ – and inspired by his mentors: Professor Frans Willekens, Professor Inge Hutter and Professor PS Nair. 

A significant achievement of Padmadas’ academic career is the research spanning over a decade (since 2003) evaluating three cycles of the United Nations Reproductive Health and Family Planning programme in China, which generated high impact and policy response at the national level. This was a high profile collaborative programme with the then National Population and Family Planning Commission and the Ministry of Health of the People’s Republic of China, and the United Nations Population Fund (UNFPA). Padmadas has an excellent track record of successful research grants funded by the UK and International Research Councils, British Academy, UK Department for International Development, UK Royal Society, International Development Research Centre (Canada), Ministry of Foreign Affairs and Norway Agency for Development Cooperation (NORAD), United Nations and the World Health Organisation. He has published over 70 peer-reviewed articles in international journals, and has served as referee for research councils and for over 30 leading international journals. Over the years, his research has attracted attention from governmental and international think-tank agencies, policy decision-makers and other international media including BBC World Services and New York Times. 

Nandan Sudarsanam

Dr. Nandan Sudarsanam has domain expertise in the areas of finance, demographic and experimental data (across different engineering disciplines). The primary area of research for Nandan is in experimentation and machine learning, with a specific focus on algorithmic approaches in these fields. During his PhD from MIT, he created new algorithms for experimentation, as well as the creation of meta-models from data which could be used to simulate the performance of various experimental algorithms. He has applied his techniques to various industries including commercial banking (Bank of America – Boston), automotive (Ford Motor Company – Detroit), manufacturing (Brakes India – Chennai), and over the last five years in high-frequency algorithmic trading (with Rackson Asset Management – New York). During his last stint as the Head of research at Rackson Asset Management, he has worked with large data sets and deployed data analytic techniques which lead to highly profitable trading strategies

Pauline Leonard

Professor Pauline Leonard is Professor of Sociology and Director of the Web Science Institute at the University of Southampton. She is a Fellow of the Academy of Social and of the Royal Society of Arts.  

Pauline’s principle research interests are in diversity and work and she has published widely on gender and organisations, race and professional migration, age, employability and careers.

Srinath Srinivasa

Srinath Srinivasa heads the Web Science lab and is the Dean (R&D) at IIIT Bangalore, India. Srinath holds a Ph.D (magna cum laude) from the Berlin Brandenburg Graduate School for Distributed Information Systems (GkVI) Germany, an M.S. (by Research) from IIT-Madras and B.E. in Computer Science and Engineering from The National Institute of Engineering (NIE) Mysore. He works in the area of Web Science — that models of the impact of the web on humanity. Technology for educational outreach and social empowerment has been a primary motivation driving his research. He has participated in several initiatives for technology enhanced education including the VTU Edusat program, The National Programme for Technology Enhanced Learning (NPTEL) and an educational outreach program in collaboration with Upgrad.  He is a member of various technical and organizational committees for international conferences like International Conference on Weblogs and Social Media (ICWSM), ACM Hypertext, COMAD/CoDS, ODBASE, etc. He is also a life member of the Computer Society of India (CSI). As part of academic community outreach, Srinath has served on the Board of Studies of Goa University and as a member of the Academic Council of the National Institute of Engineering, Mysore. He has served as a technical reviewer for various journals like the VLDB journal, IEEE Transactions on Knowledge and Data Engineering, and IEEE Transactions on Cloud Computing. He is also the recipient of various national and international grants for his research activities.

Pathik Pathak

Dr. Pathik Pathak is Faculty Director of Social Entrepreneurship and Founding Director of the Social Impact Lab at the University of Southampton.

He is passionate about innovation in higher education, and has pioneered the use of challenge-based education.

As Founding Director of the multi award-winning Social Impact Lab he leads the University’s international work on social entrepreneurship. This includes leading a team which delivers a range of activities for our students, including the Social Enterprise module, Spark India, the Social Impact Leaders Speaker Series, our Placements scheme, our in-house Ventures and mentoring start-up social entrepreneurs.

As a result of his work in social entrepreneurship education, he has been made a Fellow of the Royal Society of Arts and was awarded the Mahatma Gandhi Pravasi Samann in 2015 for outstanding contributions to education.

Jaya Sreevalsan Nair

Professor Nair obtained her Ph.D. in Computer Science from University of California, Davis; after a B.Tech in Aerospace Engineering from IIT-Madras and an M.S. in Computational Engineering from Mississippi State University. Prior to joining IIITB, she worked as a scientific programmer at Enthought Inc. Austin and as a research associate at Texas Advanced Computing Center, the University of Texas at Austin. Her areas of interest are visualization, scientific computing, computer graphics, and computational geometry.

She leads the  Graphics-Visualization-Computing Lab at IIITB. She is also the core team member of the E-Health Research Center at IIITB. 

Bidisha Chaudhuri

Bidisha Chaudhuri is an Assistant Professor in the domain of IT and Society. She received her PhD from South Asia Institute at Heidelberg University, Germany. She completed an M.A in Sociology from Delhi School of Economics, University of Delhi and a Joint European Masters in Global Studies from University of Leipzig (Germany) and Vienna University (Austria). She has worked in research institutions and developmental organizations in India and abroad. Prior to joining IIITB, she worked as a Postdoctoral Research Associate at ISEC, Bangalore. Her current research projects include, information systems for sustainable development, conversational agents in everyday practices, politics of algorithms, gender and ICTs, political economy of digital identity and sociology of work and automation.

WS4D 2020: Safecity Datathon

Call for Participation 


February 14, 2020 at IIIT Bangalore

Register HERE

IIIT Bangalore in collaboration with Web Science Trust (WSTNet), Red Dot Foundation, and the University of Southampton Social Impact Lab, is organizing a data visualization hackathon/datathon on Friday, February 14, 2020. The datathon is supported by the Web Science Lab (WSL), Graphics-Visualization-Computing Lab (GVCL), and the Center of Information Technology and Public Policy (CITAPP).

The objective of this datathon is to build awareness and inspire a call for action towards the safety of women in India and the mitigation of gender-based crime in the country. There are several hidden stories that are finding their way out slowly and boldly. We would like to make these narratives visible using creative visualizations. We invite students, pursuing undergraduate or graduate degree programs, in a group of two to participate in the event to demonstrate their interdisciplinary data science skills and social science knowledge for creating visual narratives for data sets capturing these stories. Ideally, each team should consist of participants with a background in visualization tools and relevant social sciences. The themes of the datathon highlight creative ways of using data to debunk popular myths surrounding women’s safety in India, integrating heterogeneous data sources to make meaningful analysis, and comparing the situation in India with the rest of the world.

It is a day-long event where students will engage in the tasks of defining a set of problems, choose algorithms/techniques/tools appropriate to address the problem, coding and implementation of the tools and presentation of results, inferences, and demo.

The details of the datathon, about dataset(s) and challenge, are available here.

A pdf version of the Call for Participation for the Safecity Datathon can be downloaded from here.

Agenda for the datathon on February 14, 2020:

09:00 am- 10:00 am: Desk Registration 

10:00 am- 10:30 am: Debrief on the datasets and the challenge 

10:30 am-11:00 am: QnA and finalization of themes for each team

11:00 am- 03:00 pm: Datathon, including lunch break and tea-break

03:00 pm- 05:00 pm: Presentation of solution by the teams (5-10 minutes each team)

05:00 pm- 05:15 pm: Judges discussion

05:15 pm- 06:00 pm: Prize announcements and closing remarks

To register, click on the link below:


Important Dates

Last date of registration 11.02.2020

Notification sent to shortlisted teams: 12.02.2020

Datathon: 14.02.2020


First Prize: Vouchers worth 3k

Second Prize: Vouchers worth 2k

Third Prize: Vouchers worth 1k

For further queries: datathon2020@iiitb.ac.in

Raksha P S

Raksha P.S is a PhD Student at Web Science Lab. She has a Master’s degree in Web Technology from PES Institute Of Technology, Bangalore and Bachelor’s degree in Computer Science from K S Institute Of Technology, Bangalore. Prior to joining IIITB she has worked as a Big Data Engineer at Cogknit Semantics Pvt Ltd, Bangalore. Previously she has worked on Ontology Based Semantic Data Validation, Big Data, Data Visualization using D3.js, Web Crawlers and Developing Learning Management System. Currently she is working on Characterizing online social cognition as a marketplace of opinions. Her research interests are Web Science, Network Science, Data Mining and Social Cognition. For more, please visit her Linkedin Profile.

Associated Research Project:


  1. Identifying Opinion Drivers on Social Media


  1. Raksha Pavagada Subbanarasimha, Srinath Srinivasa and Sridhar Mandyam, “Invisible Stories That Drive Online Social Cognition,” in IEEE Transactions on Computational Social Systems, vol. 7, no. 5, pp. 1264-1277, Oct. 2020, doi: 10.1109/TCSS.2020.3009474.
  2. Raksha Pavagada Subbanarasimha. 2019. Designing the Cogno-Web Observatory: To Characterize the Dynamics of Online Social Cognition. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (WSDM ’19). ACM, New York, NY, USA, 814-815. DOI: https://doi.org/10.1145/3289600.3291600.
  3. Srinivasa S., Pavagada Subbanarasimha R. (2018) Design of the Cogno Web Observatory for Characterizing Online Social Cognition. In: Anirban Mondal, Himanshu Gupta, Jaideep Srivastava, P.Krishna Reddy, D.V.L.N. Somayajulu. (eds) Big Data Analytics. BDA 2018. Lecture Notes in Computer Science. Springer, Cham. (To appear)
  4. Raksha Pavagada Subbanarasimha, Lokesh Todwal, Mamillapalli Rachana, Aditya Naidu, and Srinath Srinivasa. 2018. Mithya: A Framework For Identifying Opinion Drivers On Social Media. Demo at ACM IKDD Conference on Data Science and International Conference on Management of Data, Goa, India, Jan 2018 (CODS-COMAD 2018).
  5. Anish Bhanushali, Raksha Pavagada Subbanarasimha, and Srinath Srinivasa. Identifying Opinion Drivers on Social Media. In Proceedings of ODBASE 2017, Rhodes, Greece, October 23-27, 2017,  Springer International Publishing, Cham, 242–253.

Course work:

  • Web Information Retrieval (Reading elective – CS 902)
  • Network Science for the web (DS 608)
  • Foundations for Big Data Algorithms (CS/DS 812)
  • Machine Learning – I (CS/DS 864)

Talk on “Bridging big data and qualitative methods in the social sciences”

Date: Jan 3rd 2018

Time: 2:15 PM

Location: TBD

Abstract: With the rise of social media, a vast amount of new primary research material has become available to social scientists, but the sheer volume and variety of this make it difficult to access through the traditional approaches: close reading and nuanced interpretations of manual qualitative coding and analysis. This work sets out to bridge the gap by developing semi-automated replacements for manual coding through a mixture of crowdsourcing and machine learning, seeded by the development of a careful manual coding scheme from a small sample of data. To show the promise of this approach, we attempt to create a nuanced categorisation of responses on Twitter to several cases of extreme circumstances.


Bio of speaker:

Dima is a Senior Data Scientist at Skyscanner where his focus is on developing and optimizing the Skyscanner’s travel search engine. Prior to Skyscanner, Dima was with King’s College London where he worked on analysis of BBC iPlayer (a joint project with BBC) and various social media websites (Twitter, Pinterest, Foursquare, etc.). He contributes to the data mining (KDD, WWW, etc.) and computer networks communities (Infocom, ComMag, etc.) and have his works featured by New ScientistBBC News and other media outlets. Dima has also co-founded and was a former CEO of Stanfy.com. More information – https://karamshuk.github.io/.

Narrative Arc for Effective Learning

Narrative Arc is one of the research projects under the umbrella of Navigated Learning project at Gooru Labs, IIITB.

The Narrative Arc refers to presenting the sequence of learning activities as a narrative to the learner to make learning interesting and to help the learner navigate seamlessly through the learning space. The project has two parts: First is creating the learning pathways automatically given a corpus of learning resources, such that the generated pathways are semantically coherent and pedagogically progressive. Second part is modelling an AI-based automatic conversational agent which makes the learning pathway interesting and adapts the learning pathways according to the users knowledge and preferences. Here, the learning pathway is first presented to the user according to her learning goal, then the conversation agent interacts with the learner to keep the user interested in the learning pathway and to augment her knowledge. The agent also gauges the knowledge of the learner and supports the learner by providing knowledge and if required re-route the learner through a different learning pathway.

Following link has the presentation for the project in RISE 2019 workshop held at IIITB on 14-16 Feb 2019. The title of the presentation is “Narrative Arc Computation towards Digital Empowerment”. Narrative Arc Computation

  • Lead Researcher
  • Current Project Members
    • Mirambika Sikdar(Summer Intern)
  • Previous Project Members
    • Nikhil Bukka Sai
    • Sai Sri Harsha Vallabhuni
    • Rochan Avlur ( Intern)
    • Niharika Chaudhari (Intern)
    • Vibhav Agarwal
    • Abhiramon R
    • Sanket Kutumbe
    • Karan Kumar Gupta
    • Srinivasan P.S
  • Publications

Navigated Learning

Figure 1: Navigated Learning

Navigated Learning is a new paradigm of learning that aims to balance the three independent requirements: Scale, Personalization and Social Interactions. Please see figure 1 that shows parallels among the concepts that are technological solutions and the three requirements of learning.

This is achieved by representing learning as situated within an abstract “competency space,” and computing semantic embeddings of learning objects and learners into the competency map. The competency map is organized as a progression space– which is a metric space with a partial order. Here, not only is there a notion of “distance” between any two points, but also an element of “progress”. These embeddings can be computed for any semantic object like learning resources, activities, learners, etc. Each point in the space represents a “competency” or a demonstrable skill that can be acquired by the learner.

A primary element of research into Navigated Learning is to construct a competency map for a given subject area of study and to build semantic embedding models for different kinds of objects relevant to the learning process. Semantic embeddings may take different forms depending on the nature of the object. While some objects can be neatly represented as points in the logical space, other objects may be represented by regions, pathways or other contours in the space. In an organizational setting, objects that are embedded onto this space include not just learning resources and learners, but also departments, projects and other organizational elements that require or work with relevant skill sets represented in the competency map.

Navigated learning is manged by a “Learning Navigator” with which every learner interacts. The Learning Navigator (or just, navigator), continuously interacts with the learning map and the learner to perform the following:

Locate: Based on data about their activities and outcomes from formal assessments, the “Locate” module of the navigator embeds learners in the space, and continuously updates their location. Unlike a geographical space, a learner may have acquired several competencies in the competency space. Thus, their location is not identified by a point, but by a data structure called a Skyline, that is detailed in a later section.

Curate: Once a learner’s location is known, based on their stated goals or recently acquired competencies, a set of further candidate competencies are identified. Curating is based on competency modeling principles, that identifies complementary, supplementary and conflicting competencies.

Mediate: This is the logic by which the navigator navigates the learner by making suggestions. Mediation is based on computing an underlying “Narrative Arc” that computes a semantically coherent and meaningful learning sequence individualized for each learner. Mediation also involves suggesting connections with other learners as well as group learning activities.

This project is sponsored by Gooru Learning.

Team Members:

Dr Aparna Lalingkar (PostDoctorate Research Fellow)

Ms Chaitali Diwan (PhD Research Scholar)

Ms Praseeda Kalkur (PhD Research Scholar)

Mr Naman Churiwala (Research Associate)

Mr Prakhar Mishra (MS Research Scholar)

Mr Shyam Kumar VN (MS Research Scholar)


Chaitali Diwan, Srinath Srinivasa, and Prasad Ram.Automatic Generation of Coherent Learning Pathways for Open Educational Resources, In Proceedings of the Fourteenth European Conference on Technology Enhanced Learning (EC-TEL 2019), Springer LNCS, Delft, Netherlands, 16-19 September 2019 (to appear)

Aparna Lalingkar, Srinath Srinivasa, PrasadRam (2019), Characterization of Technology-based Mediations for Navigated Learning, Advanced Computing and Communications, Vol 3 (2), June 2019, ACCS  Publications, pp. 33-47. (Paper Link)

Praseeda, Srinath Srinivasa and Prasad Ram “Validating the Myth of Average through Evidences” In: The 12th International Conference on Educational Data Mining, Michel Desmarais, Collin F. Lynch, Agathe Merceron, & Roger Nkambou (eds.) 2019, pp. 631 – 634

Chaitali Diwan, Srinath Srinivasa, and Prasad Ram. Computing Exposition Coherence of Learning Resources, In Proceedings of The 17th International Conference on Ontologies, Databases and Applications of Semantics (ODBASE 2018), Valletta, Malta, October 22-26, 2018, Springer International Publishing.

Lalingkar, A.; Srinivasa, S. & Ram, P. (2018). Deriving Semantics of Learning Mediations, In Proceedings of the 18th IEEE International Conference on Advanced Learning Technologies (ICALT), IEEE, pp. 54-55. (Cited by 1 as per GoogleScholar citation index) (Paper Link)

Sub Projects

Details of Project Hosted

The competency map with polylines is hosted in the link here

The corresponding learning map for the learner is hosted in the link here