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Faculty News

The Future Forums on Learning awarded Dr. Diane Litman and collaborators from the Learning Research and Development Center, Lindsay Clare Matsumura and Richard Correnti, a Catalyst Prize as part of their 2021-2022 Learning Tools Competition.

Dr. Xulong Tang (Assistant Professor, Computer Science) received a new three-year grant from the National Science Foundation.

Cameras are popping up throughout the 5th and 6th floors of Sennott Square to investigate how public spaces are used as part of a new research project from Department of Computer Science students and faculty.

Student News

Victoria Chuah

Ms. Victoria Chuah graduated with a BS+MS degree in Computer Science in May 2022 and was crowned Miss Virginia in June 2022. Read the interview to find out about her experience at Pitt and her plans for the future.

The CS 50 Fellowship was created in 2021 to celebrate the Department’s 50th Anniversary. In 2022, the competition had two winners, Meiqi Guo and Bingyao Li. 

Pranut Jain received an honorable mention award at Designing Interactive Systems (DIS) 2022 for his paper Laila is in a Meeting: Design and Evaluation of a Contextual Auto-Response Messaging Agent. 

Colloquium Talks

While current research has mostly focused on reducing the energy footprint, in this talk, we will discuss how improving energy efficiency does not translate to the goal of zero emissions. More importantly, carbon efficiency can be optimized independently of energy efficiency. Toward this end, I will present some examples of mitigating emissions and some directions toward designing carbon-efficient infrastructures. 

This talk will introduce a probabilistic model representing commonsense knowledge using a learned latent space of geometric embeddings -- probabilistic box embeddings. Using box embeddings makes it possible to handle commonsense queries with intersections, unions, and negations in a way similar to Venn diagram reasoning. Meanwhile, existing evaluations do not reflect the probabilistic nature of commonsense knowledge. To fill in the gap, I will discuss a method of retrieving commonsense related question answer distributions from human annotators and a novel method of generative evaluation. 

I present three of our recent works: 1) Discourse-aware generation models for automatic social media moderation and mediation, 2) Sign language processing, and 3) Equitable and human-like dialogue generation models based on learning theory. Finally, I describe my research vision: Building inclusive and collaborative communicative systems and grounded artificial intelligence models by leveraging the cognitive science of language use alongside formal methods of machine learning.