Pursuing Ph.D. @Laboratory for Computational Social Systems, IIITD
Shivani Kumar, Atharva Kulkarni, Md Shad Akhtar, Tanmoy Chakraborty, When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues, ACL, 2022.
Shivani Kumar, Anubhav Shrimal, Md Shad Akhtar, Tanmoy Chakraborty. Discovering Emotion and Reasoning its Flip in Multi-Party Conversations using Masked Memory Network and Transformer, Knowledge-Based Systems, 2022.
Shivani Kumar, Manjot Bedi, Md Shad Akhtar, Tanmoy Chakraborty. Multi-modal Sarcasm Detection and Humor Classification in Code-mixed Conversations, IEEE Transactions on Affective Computing, 2021.
Awards and Achievement
Received Google travel grant, Microsoft travel grant, and ACM-India/IARCS Travel Grant to support the travel to Dublin, Ireland for ACL 2022.
Received Junior Research Fellowship (JRF) from University Grants Commission (UGC), India. Qualified UGC-NET (National Eligibility Test).
Delivered guest lectures at IIITD on the subject ’Social Media Analytics’
Certificate of Merit awarded by Shaheed Sukhdev College of Business Studies for securing 2nd position in college in B.Sc.(H) Computer Science in the session 2014-2017.
Positions of Responsibility
Teaching Assistant for Social Network Analysis at NPTEL.
Teaching Assistant for Social Network Analysis at IIITD.
Teaching Assistant for post graduate level Machine Learning at IIITD. 2021
Teaching Assistant for NLP in PG Diploma in Data Science and AI at IIITD.
Co-Organized ACSS 2020 (Workshop on AI for Computational Social Systems 2020) and COFAD 2020 (Workshop on Combating Fraud Activities using Data Science) at IIITD.
Rapporteur in "The International Colloquium on Ethics and Governance of Autonomous AI Systems" (18-19 February 2019) organized by CMS and Infocom think tank.
Member of Core Committee (Curatio – Shaheed Sukhdev College of Business Studies): Organized a career counselling event for the students of B.Sc. and B.Tech. College’s alumni were invited to share their experiences.
Concept Map of a Book
We developed an automated system that constructs a concept map of a given book as a network, using its PDF. The map shows the relationship between concepts throughout the book, with links connecting concepts in related chapters and sections. The resultant network can be used by readers to get an idea of the concepts covered in the book, their placement in chapters and their interrelation. It can also be used to locate and focus on concepts of interest. The system was implemented using Python.