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Cassandra Hall

Assistant Professor of Computational Astrophysics, Department of Physics and Astronomy

Dr. Hall is a computational astrophysicist who studies exoplanet formation. Her approach combines machine learning techniques with hydrodynamical simulations of protoplanetary accretion discs to uncover forming exoplanets hidden in telescope data. Dr. Hall leads a team focused on refining these techniques to increase our fundamental understanding of the planet formation process.

Education:
  • PhD in Astronomy, University of Edinburgh.
  • MPhys (HONS) Physics & Astrophysics, University of Sheffield. 
Research Interests:

Thousands of new worlds beyond our own solar system have been discovered, revealing a hugely diverse exoplanetary architecture.

Exoplanets form in evolving protoplanetary accretion discs. The conditions in these discs decide the final mass and ultimate orbital configuration of their exoplanetary systems, causing diversity in the exoplanet architecture.

As exoplanets form, they leave behind signatures of their formation that can be detected in interferometric mm observations, such as rings and spirals.

In order to try and measure the mass of these forming in planets inside their nascent discs, we typically perform around 100 dusty fluid simulations for each observed system, and try to get the mass this way. However, this is incredibly inefficient, inaccurate, and profoundly limits the regions of parameter space we can explore.

At UGA, I am building a research group that will move past this outdated model by harnessing the power of machine learning and information extraction. We are developing neural network techniques that are widely applicable, user-friendly, and around 10,000 times more computationally efficient than current approaches to determining exoplanet mass in forming systems.

Of note:
  • Fellow of the Royal Astronomical Society, 2021
  • Royal Astronomical Society Winton Award for Early Achievement in Astronomy, 2021
  • Lilly Teaching Fellow, University of Georgia, 2021-2023
  • Winton Exoplanet Fellowship, 2018-2020
  • STFC PhD Scholarship, 2013-2017
Personal Website:

Thirimachos Bourlai

Asscociate Professor, School of Electrical and Computer Engineering
Education:
  • Ph.D., Face Recognition (Biometrics), Electrical and Computer Engineering, University of Surrey, U.K., 2006
  • M.Sc. in Medical Imaging with Distinction, Electrical and Computer Engineering, University of Surrey, U.K., 2002
  • B.S. (M.Eng. Equivalent), Electrical & Computer Engineering, Aristotle University of Thessaloniki, Greece, 1999
Personal Website:

Jin Lu

Assistant Professor, School of Computing

Dr. Jin Lu specializes in various fields, such as machine learning, data mining, optimization, smart mobility, and the informatics of both biomedical and health sectors. A key area of interest for Dr. Lu is the development of machine learning models with provable outcomes, along with the advancement of distributed learning algorithms and optimization techniques.

Education:
  • PhD, Computer Science and Engineering, University of Connecticut
  • Master, Computer Science and Engineering, University of Connecticut
  • Master, Applied Mathematics, Xi'an Jiaotong University
Personal Website:

AI Research Day 2024

AI Research Day 2023-2024
Tate Center Reception Hall

AI Transforming

Please join us for the 2023-2024 UGA AI Research Day, sponsored by the Institute for Artificial Intelligence.  This year, we will celebrate the 30th anniversary of the founding of UGA's Artificial Intelligence Center, which began in 1994 and was reclassified as a research Institute in 2008. 

The event will take place from 2:00 - 7:00 pm in the Tate Center Reception Hall and will consist of the following activities. Light refreshments will be provided throughout.

  • Keynote Lecture 
  • Lightning Talks by members of the UGA community
  • Discussion Panel 
  • Research Poster Reception exhibiting the work of UGA students and faculty. 

A full schedule and program for the event will be posted closer to the day. 

Keynote: Mark Riedl 

Photo of Mark Riedle

Speaker: Mark Riedl.  Professor, School of Interactive Computing, College of Computing, Georgia Institute of Technology; Associate Director, Georgia Tech Machine Learning Center

Time: 2:15 PM

Title: "The future of AI is Human-Centered"

Abstract:

Over the last few years, AI has rapidly moved out of the research lab into the hands of everyday users. This has been due to technological breakthroughs in deep learning and large language models. However, technologies that work well in the controlled environment of a research lab doesn’t necessarily perform the same out in the real world populated by non-expert users. Human-centered computing refocuses technology on the human by asking: what should our technology look like for it to enhance the human condition, and how do we get there? We will explore some of the ways that AI can become more human-centered from AI explanations to human-agent interaction.

Getting There

Research Day QR Code

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