Faculty Accepting Undergraduate Students for Research

Undergraduate students have the opportunity to conduct research with faculty on a range of topics. Below are faculty members who are currently accepting undergraduate students for research work and abstracts for talks they gave at the 2023 Computer Science Undergraduate Research Symposium. If you are an undergraduate student interested in working with any of these faculty on their research, please contact the faculty member.

Dr. Aakash Gautam: "Community-based Technology Design for Social Good"

In technology design, the enduring questions are "What is the right thing to do?" and "How do we know we have done it?" These questions lie at the heart of our lab's work which involves working with communities to design technologies that drive meaningful social change. In this presentation, I will share three ongoing community-based projects in our lab: (1) enhancing reentry efforts through digital literacy, (2) facilitating organizations to support vulnerable individuals, and (3) supporting computational empowerment among young children through data literacy and public policy intervention. I invite you to join us on this journey towards a more inclusive and just society! 

Dr. Lorraine Li: "Probabilistic (Commonsense) Knowledge in Language"

Commonsense knowledge is critical to achieving artificial general intelligence. This shared common background knowledge is implicit in all human communication, facilitating efficient information exchange and understanding. Commonsense is also probabilistic; a plumber could repair a sink in a kitchen or a bathroom, indicating that common sense reveals a probable assumption rather than a definitive answer. To align with these properties of common fundamentally, we want to model and evaluate such knowledge human-like using probabilistic abstractions and principles.

My research focuses on designing and evaluating commonsense in probabilistic manners. We proposed a probabilistic model -- probabilistic box embeddings that could handle commonsense queries with intersections, unions, and negations in a way similar to Venn diagram reasoning. Meanwhile, I study the limitations of current Large Language models with their (commonsense) reasoning ability with different benchmarks for LLMs. One of which focuses on assessing the long-tail (uncommon) part of commonsense knowledge. The combination of modeling and evaluation benchmarks sheds light on future commonsense research during LLMs.

Dr. Panos Chyrsanthis: "Graphs, Data Storage & Machine Learning" 

This talk will provide an overview of two recent research projects in ADMT Lab, one utilizing graphs and Machine Learning to provide path recommendations (https://dl.acm.org/doi/10.1145/3609956.3609969), and the other leveraging Machine Learning to reduce the storage of Big Data (https://link.springer.com/chapter/10.1007/978-3-031-42941-5_5).

Dr. Adam Lee: "Computer Security and Privacy"

In this talk, I will highlight several projects with which undergraduate students have contributed to the research projects going on within my group, overview the ongoing work within the group, and provide general thoughts and advice on making the most of undergraduate research.

Dr. Adriana Kovashka: TBD

The diversity of visual content available on the web presents new challenges and opportunities for computer vision models. In this talk, I present our work on learning object detection models from potentially noisy multi-modal data, retrieving complementary content across modalities, transferring reasoning models across dataset boundaries, and recognizing objects in non-photorealistic media. While the work has applications in common benchmark datasets, the motivation for it stems from a single source: the ability to analyze content in complex persuasive media, such as visual advertisements and political articles.

Dr. Alex Labrinidis: "Data Management and Data Science for the Common Good"

This talk describes the required background and expertise undergraduate students need to have to work on typical research projects in the ADMT Lab.  We also highlight some of the recent work within the PittSmartLiving project and the challenges we faced. 

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