I am excited about making humans and AI models solve tasks and societal issues effectively. Consequently, I am interested in teaching machines to understand the common sense behind the modalities that humans use to create and communicate - images, text and audio. As a part of my research, I am a member of the BU+UC Berkeley Team on detecting misinformation for the DARPA Semantic Forensics program.
I am driven by community and continual learning. Hence, I started the AI+x group of BU and Harvard.
Prior to joining my Ph.D., I worked at SRI (formerly, Stanford Research Institute) International on the DARPA Explainable AI project. I received my M.S. from Virginia Tech, advised by Prof. Devi Parikh.
Kamran Alipour, Arijit Ray, Xiao Lin, Michael Cogswell, Jurgen Schulze, Yi Yao, Giedrius Burachas Improving Users' Mental Model with Attention-directed Counterfactual Edits, 2021 Applied AI Letters (Wiley) [pdf]
Arijit Ray, Michael Cogswell, Xiao Lin, Kamran Alipour, Ajay Divakaran, Yi Yao, Giedrius Burachas, Knowing What VQA Does Not: Pointing to Error-Inducing Regions to Improve Explanation Helpfulness, 2021 Applied AI Letters (Wiley), [pdf] [arXiv] [Project Page]
Arijit Ray, Karan Sikka, Ajay Divakaran, Stefan Lee, Giedrius Burachas, Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation , 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP 2019), also at CVPR-W 2019 VQA and Visual Dialog Workshop, [arXiv], [bibTex] [Data]
Arijit Ray, Yi Yao, Rakesh Kumar, Ajay Divakaran, Giedrius Burachas, Can You Explain That: Lucid Explanations Help Human-AI Collaboratve Image Retrieval , 2019 AAAI Conference on Human Computation and Crowdsourcing (AAAI-HCOMP 2019), [arXiv], [bibTex]
Arijit Ray, Giedrius T. Burachas, Karan Sikka, Anirban Roy, Avi Ziskind, Yi Yao, Ajay Divakaran, Make Up Your Mind: Towards Consistent Answer Predictions in VQA Models [pdf], [bibTex], Workshop on Shortcomings in Vision and Language , European Conference on Computer Vision, 2018 (ECCV-W 2018)
Arijit Ray, Gordon Christie, Mohit Bansal, Dhruv Batra, and Devi Parikh, "Question Relevance in VQA: Identifying Non-Visual And False-Premise Questions.", 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP 2016). [pdf] [code] [Video]
Prashant Chandrasekar, Xuan Zhang, Saurabh Chakravarty, Arijit Ray, John Krulick, and Alla Rozovskaya, "The Virginia Tech System at CoNLL-2016 Shared Task on Shallow Discourse Parsing", CoNLL Shared Task (2016).
The Art of Deep Connection - Towards Natural and Pragmatic Conversational Agent Interactions. [Master's Thesis], Virginia Tech E-Library, 2017
Object Prediction using Image Context: Predict next object in an image reasoned on present image context in a sequential manner, Computer Vision Class Project Fall 2015
Online Demo for Predicting Plausibility of Common Sense Assertions: Enter a three-phrase tuple to assess the plausibility score based on a joint language-vision common-sense reasoning, Class Project, Fall 2015
Learning to Listen: Matching Cover songs with Original Productions: Match Original Songs to Cover Songs using an Ensemble of Supervised and Unsupervised Approaches, Machine Learning Class Project, Fall 2015.
Ray, Arijit, Kishan Prudhvi Guddanti, and N. Chellammal. "An Approach to Intelligent Traction Control Using Regression Networks and Anomaly Detection.", Junior (3rd Year) Semester Project, Fall 2013, published in Applied Artificial Intelligence 29.6 (2015): 597-616.
2014: Dr. Erik Brynjolfsson, the Director of the MIT Center for Digital Business, tweeted:
Best way to reach me would be to drop an email to array at bu dot edu. Please don't spam me!