This annual award launched by IKDD in 2022, recognizes the best doctoral dissertation(s) in the broad areas of Data Science, Artificial Intelligence, Machine Learning, Data Mining, Knowledge Discovery and applications of Data-driven techniques. The IKDD Doctoral Dissertation Award winner and up to two runners-up will be recognized at the annual CODS-COMAD conference, and their dissertations will have the opportunity to be published on the IKDD Web site (https://ikdd.acm.org/). The award winner will receive a plaque and a cheque for INR 1,00,000. The runners-up will receive a plaque and a cheque of INR 50,000. The winner and runners-up will also receive a free registration to attend the CODS-COMAD conference and will be invited to present their work in a special session at the conference.
Nomination Deadline: August 31, 2023
Nominated candidates must be from a PhD granting institute in India. Further, the nominated candidate should have successfully defended his/her thesis between August 8, 2022 and August 31, 2023 (i.e., in the period between last year’s deadline and this year’s deadline for application). Every PhD granting institute in India will be allowed to nominate 1 candidate from across all disciplines working in relevant areas. Institutes that produce more than 10 dissertations in relevant areas during the one year period under consideration can nominate 2 students. Institutes should self-declare in case they are nominating 2 students and furnish supporting evidence.
Nominations must be made by the doctoral advisor. All nomination materials must be in English and in PDF format. Late submissions will not be accepted. A nomination must include:
The dissertation can be nominated in parallel for other national and global awards, such as the ACM, the ACM India and the SIGKDD Doctoral Dissertation awards.
All nomination materials must be submitted using this Easy Chair link. All required documents must be uploaded as a zip file.
Winner
Thesis: Robustness, Evaluation and Adaptation of Machine Learning Models in the Wild
Institute: IIT Bombay
IKDD congratulates Vihari Piratla for his work on tackling the critical challenge of handling distributional drifts in ML deployment, and making impactful contributions in generalization, adaptation, and evaluation of models under changing domains.
Runner-Up
Thesis: Towards and Beyond Continually Learning Neural Networks
Institute: IIT Hyderabad
IKDD congratulates Joseph K J for making impactful contributions to continual learning in computer vision, and going beyond to establish newer settings such as open-world object detection and novel class discovery without forgetting, which have real-world applications in autonomous navigation and marine biology
Runner-Up
Thesis: Self-Supervised Domain Adaptation Frameworks for Computer Vision Tasks
Institute: IISc Bangalore
IKDD congratulates Jogendra Nath Kundu for making groundbreaking contributions to unsupervised domain adaptation and self-supervised learning techniques for structured prediction based vision tasks, advancing the practical deployment of intelligent machines in real-world scenarios.