Renata Rawlings-Goss

Renata Rawlings-Goss

Renata Rawlings-Goss

IDEaS Director of Industry Engagement
Dr. Rawlings-Goss is responsible for interfacing with industry to cultivate industrial partnerships and embedded innovations labs, organizing workshops, retreats on topics relevant to emerging initiatives, job fairs, and numerous other activities. She builds bridges between industry and IRI activities. Renata brings rich experience in Big Data technology and policy from the National Science Foundation and the White House Office of Science and Technology Policy. During her tenure, she co-led inter-agency Big Data initiatives and led the formation of the National Data Science Organizers group. She is a biophysicist and her scientific work, at the University of Pennsylvania and the University of Michigan-Ann Arbor, was focused on biophysics, bioinformatics and next-generation genomics research. She also plays the lead role in managing the NSF South Big Data Innovation Hub from Georgia Tech. Dr. Rawlings-Goss has a Ph.D. in Biophysics.

rrawlings.goss@gatech.edu

Website

University, College, and School/Department
Georgia Institute of Technology
Additional Research:
Bioinformatics

IRI Connections:
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Data Engineering and Science > Leadership
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Jacob Abernethy

Jacob Abernethy

Jacob Abernethy

Director for Student Engagement, IDEaS

Jacob Abernethy is an Associate Professor in the College of Computing at Georgia Tech. He started his faculty career in the Department of Electrical Engineering and Computer Science at the University of Michigan. He completed his Ph.D. in Computer Science at the University of California at Berkeley, and then spent two years as a Simons postdoctoral fellow at the CIS department at UPenn. Abernethy's primary interest is in Machine Learning, with a particular focus in sequential decision making, online learning, online algorithms and adversarial learning models. He did his Master's degree at TTI-C, and his Bachelor's Degree at MIT.

prof@gatech.edu

Website

University, College, and School/Department
Georgia Institute of Technology > College of Computing > School of Computer Science
Research Focus Areas:
  • Algorithms & Optimizations
  • Machine Learning

IRI Connections:
IRI And Role
Data Engineering and Science > Leadership
Data Engineering and Science > TRIAD Associate
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Fang (Cherry) Liu

Fang (Cherry) Liu

Fang Liu

Senior Research Scientist | Partnership for an Advanced Computing Environment
Adjunct Faculty

Dr. Fang (Cherry) Liu is a Research Scientist at Partnership for Advanced Computing Environment (PACE) center at Georgia Tech. She actively provides expert diagnosis and resolution of complex technical issues with High Performance Computing (HPC) resources; leverages HPC software and application stack, including compilers, scientific libraries and user applications to effectively run on HPC environment; educates campus-wide HPC community, teaching courses including introduction to Linux, intermediate Linux, introduction to Python and Python for Data Analysis courses; and does on-going research on big data with school of computational science and engineering (CSE) faculties. She is awarded the title of Adjunct Associate Professor by CSE to better serve campus HPC community in both teaching and research.

Before joining Georgia Tech, she was an assistant scientist at mathematics and computational science division at Department of Energy (USDOE) Ames Laboratory, where she gained extensive experience with multi-disciplinary research team and worked closely with world-class domain scientists from physics, chemistry and fusion energy. The projects she participated in included scientific workflows and data management system for nuclear physics applications, GPU computing for large scale quantum chemistry applications, concurrent data processing for fusion simulation through distributed component infrastructure, and so much more.

Her research interests broadly span parallel/distributed scientific computing, software interface design for monolithic scientific applications, multi-physics and multi-code coupling, multilevel parallelism support for Multi-Physics coupling, data management and provenance for scientific applications, big data infrastructure design and implementation, and data analytics for large graph dataset.She has been served as program committee member for various conferences including HPC, ICCS, ICCSA, CBHPC, ICPP, and she also was vice program general chair, program general chair for HPC2012 and HPC2013, now she sits in program steering committee for HPC since 2014.

Currently her primary interest focuses on tackling big data issues with using Hadoop and Spark in graph database, security and streaming data, while she is closely working with professor Polo Chau's group.

Dr. Liu graduated from Indiana University at Bloomington in 2009 with a Ph.D. degree in Computer Science. Her dissertation titled, "Building Sparse Linear Solver Component for Large Scale Scientific Simulation and Multi-physics Coupling," and her Ph.D. advisor was Professor Randall Bramley.


CoC Profile Page

  • PACE Website
    • University, College, and School/Department
    Georgia Institute of Technology > College of Computing > School of Computer Science
    Research Focus Areas:
    • High Performance Computing

    IRI Connections:
    IRI And Role
    Data Engineering and Science > Faculty
    Data Engineering and Science

    John Eric Coulter

    John Eric Coulter

    John Coulter

    Research Scientist | Partnership for an Advanced Computing Environment

    Eric joined PACE in 2021, and currently leads the Research Computing Facilitation team, after having worked as a Cyberinfrastructure Architect and RCF. Before joining PACE, Eric could be found at Indiana University as a systems engineer with the XSEDE Campus Bridging team, providing HPC-oriented consultations to institutions across the US. He also worked closely with the Cyberinfrastructure Research Center at IU, providing support for several different science gateway projects. Prior to that, his research in condensed matter physics at Florida State University involved computational studies of the optical properties of strongly correlated materials.

    j.eric@gatech.edu

    PACE Website

    University, College, and School/Department
    Georgia Institute of Technology
    Research Focus Areas:
    • High Performance Computing

    IRI Connections:
    IRI And Role
    Data Engineering and Science > Faculty
    Data Engineering and Science

    Aaron Jezghani

    Aaron Jezghani

    Aaron Jezghani

    Research Scientist | Partnership for an Advanced Computing Environment

    Aaron joined the PACE team in May 2019 as a computing facilitator, and currently serves as the Scheduler Architect. Through supporting users, he grew to appreciate the opportunity to improve HPC workflows through scheduler and systems configurations that lower the barrier to entry and passively optimize code execution. Additionally, Aaron has been involved in the Vertically Integrated Projects (VIP) program since Spring 2020, mentoring multiple teams of students with the Team Phoenix VIP through international HPC competitions at the ISC-HPC and Supercomputing conferences and more recently, providing leadership for the Future Computing with the Rogues Gallery VIP as they research applications of novel compute architectures. Prior to joining PACE, Aaron studied free neutron and nuclear beta decay as a precision test of the Standard Model, which entailed a diverse range of activities, including particle simulation and detection, digital and analog signal processing, and algorithm optimization across x86, GPU, and FPGA architectures.

    ajezghani3@gatech.edu

    PACE Website

    Google Scholar

    University, College, and School/Department
    Georgia Institute of Technology

    IRI Connections:
    IRI And Role
    Data Engineering and Science > Faculty
    Data Engineering and Science

    Dimitrios Psaltis

    Dimitrios Psaltis

    Dimitrios Psaltis

    Professor

    I am a professor of Physics at Georgia Tech. I use advanced computational techniques, hybrid computer architectures, and innovative algorithms to answer fundamental questions related to the observational appearance of black holes, the properties of magnetohydrodynamic turbulence, and the interaction of matter with radiation in extreme conditions.

    I am a founding member of the Event Horizon Telescope, the international mm-VLBI experiment that has taken the first picture of a black hole with the horizon-scale resolution, and served for three years (2016-2019) as the Project Scientist of the collaboration.

    Before moving to Georgia Tech in 2022, I was a professor of Physics and Astronomy at the University of Arizona and the Chair of the Theoretical Astrophysics Program there.

    dpsaltis3@gatech.edu

    Personal Website

    University, College, and School/Department
    Georgia Institute of Technology > College of Sciences > School of Physics
    Research Focus Areas:
    • High Performance Computing
    • Machine Learning
    Additional Research:

    Black Hole Images General Relativity


    IRI Connections:
    IRI And Role
    Data Engineering and Science > Faculty
    Data Engineering and Science

    Vidya Muthukumar

    Vidya Muthukumar

    Vidya Muthukumar

    Assistant Professor

    Vidya Muthukumar is an Assistant Professor in the School of Electrical and Computer Engineering and H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. Dr. Muthukumar’s broad interests are in game theory, online and statistical learning. She is particularly interested in designing learning algorithms that provably adapt in strategic environments, fundamental properties of overparameterized models, and the foundations of multi-agent decision-making.

    Dr. Muthukumar received the B.Tech (with honors) degree from the Indian Institute of Technology, Madras and the Ph.D. degree in Electrical Engineering from University of California, Berkeley. She interned at IBM Research in the summer of 2018 as a Science for Social Good fellow. Before joining Georgia Tech, she spent a semester at the Simons Institute for the Theory of Computing as a research fellow for the program “Theory of Reinforcement Learning.” She is the recipient of an Amazon Research Award, NSF CAREER Award, Adobe Data Science Research Award, Simons-Berkeley Google Research Fellowship, and the UC Berkeley EECS Outstanding Course Development and Teaching Award.

    Dr. Muthukumar has served/is serving as an Area Chair for COLT (2021-2024), NeurIPS (2023 and 2024), ALT 2024 and UAI 2024. She also co-organizes several mentorship workshops as part of the Learning Theory Alliance team, and organized the ITALT symposium in 2024.

    vmuthukumar8@gatech.edu

    ECE Profile Page

  • Personal Website

    • Google Scholar

    University, College, and School/Department
    Georgia Institute of Technology > College of Engineering > School of Electrical and Computer Engineering
    Georgia Institute of Technology > College of Engineering > School of Industrial Systems Engineering
    Research Focus Areas:
    • Machine Learning
    Additional Research:

    Statistical signal processingGame theorySequential decision-making


    IRI Connections:
    IRI And Role
    Data Engineering and Science > Faculty
    Data Engineering and Science
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