Twenty-Seventh AAAI/SIGAI Doctoral Consortium

February 22-23, 2022
Collocated with the Thirty-Sixth Conference on Artificial Intelligence (AAAI-22)
External site: https://aaaidc.github.io/dc2022/

The Twenty-Seventh AAAI/SIGAI Doctoral Consortium (DC) provides an opportunity for a group of Ph.D. students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. The sixteen students accepted to participate in this program will also participate in one of the AAAI-22 Poster Sessions.


 

AAAI-22 Doctoral Consortium Accepted Papers

Towards Robust Named Entity Recognition via Temporal Domain Adaptation and Entity Context Understanding
Oshin Agarwal

AI-Driven Road Condition Monitoring across Multiple Nations
Deeksha Arya, Sanjay Kumar Ghosh, Durga Toshniwal

Increasing the Diversity of Deep Generative Models
Sebastian Berns

Interpretable Privacy Preservation of Text Representations Using Vector Steganography
Geetanjali Bihani

Using Multimodal Data and AI to Dynamically Map Flood Risk
Lydia Bryan-Smith

Towards Automating the Generation of Human-Robot Interaction Scenarios
Matthew C. Fontaine

An Algorithmic Theory of Markets and Their Application to Decentralized Markets
Denizalp Goktas

Evaluating Explanations of Relational Graph Convolutional Network Link Predictions on Knowledge Graphs
Nicholas Halliwell

Equilibrium Learning in Auction Markets
Stefan Heidekrüger

On the Practical Robustness of the Nesterov’s Accelerated Quasi-Newton Method
S. Indrapriyadarsini, Hiroshi Ninomiya, Takeshi Kamio, Hideki Asai

Creating Interactive Crowds with Reinforcement Learning
Ariel Kwiatkowski

Socially Intelligent Affective AI
Aarti Malhotra

Creating Interpretable Data-Driven Approaches for Tropical Cyclones Forecasting
Fan Meng

On Semantic Cognition, Inductive Generalization, and Language Models
Kanishka Misra

Dynamic Algorithmic Impact Assessment to Promote an Ethical Use of AI in Businesses
Shefeh Prisilia Mbuy

Mutual Understanding in Human-Machine Teaming
Rohan Paleja

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