My name is David (Ahmadreza) Mosallanezhad. I got my M.Sc. in Artificial Intelligence from Shiraz University under supervision of Prof. Ali Hamzeh. Currently, I am a third year PhD student at Arizona State University working at the DMML lab under supervision of Prof. Huan Liu. My research interest mainly lies within privacy and fairness in machine learning and natural language processing algorithms/applications. I’m currently working on AI deferral projects at DHS-CAOE under supervisions of Dr. Michelle Mancenido.
My resume is available here (updated on 12/11/2020)
I’m joining as an applied research intern!
Our short survey “Causal Learning for Socially Responsible AI” is accepted in IJCAI 2021.
Invited to serve as a program committee (PC) member for the The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP-21).
Our paper “Deferral Rate, Human Performance, and Trust Perception: A Human-AI Joint Face-Detection Task” is accepted in HFES 2021.
Our paper “Challenges of Data Collection on Mturk: A Human-Ai Joint Face-Matching Task” is accepted in IISE 2021.
Our paper “How Deferral Rate Can Affect Human Performance and Trust Perception? A Human-AI Joint Face-Detection Task” is accepted in IEA 2021.
Invited to serve as a reviewer for the The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021).
Our paper “Toward Privacy and Utility Preserving Image Representation” is accepted in SBP-BRiMS 2020. You can download it from here.
I am volunteering for EMNLP 2020 conference.
Invited to serve as a program committee (PC) member for the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21).
Invited to serve as a program committee (PC) member for the 30th International Joint Conference on Artificial Intelligence (IJCAI-21).
I am volunteering for ACL 2020 conference.
I recieved the ASU Engineering Graduate Fellowship from Ira A. Fulton Schools of Engineering.
Our team won the second place at sunhacks hackathon 2019.
Our paper “Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning” is accepted in WSDM 2020. You can download it from here.