I’m Ryien (IPA: rɑːjin, Persian: رایین), a PhD student in
the Department of Computer
Science
at the University of Chicago, where I am advised
by Hank Hoffmann and Rebecca Willett.
My research interests lie at the intersection of machine learning and dynamic systems.
I am broadly interested in developing adaptive machine learning algorithms, or algorithms
that dynamically adjust their behavior in response to changes in input data
distribution or attributes, performance constraints, task requirements, and other
evolving factors.
Previously, I was a Predoctoral researcher at the Argonne
Leadership Computing Facility
at Argonne National Laboratory.
There, I worked with Venkatram
Vishwanath
and Filippo Simini
in order to develop and scale graph neural networks (GNNs) and apply them to diverse scientific
applications.
I received my M.S. through the Electrical and Computer
Engineering Department at the
University of Michigan,
where my focus was on machine learning, systems, and computational biology. Prior to attending
graduate school, I
received my undergraduate degrees in computer engineering
(B.S.E.)
and cognitive science (B.S.),
also at the University
of Michigan.
Ryien Hosseini
PhD Student
Department of Computer
Science
University of Chicago
Curriculum Vitae
Office: JCL 357
Mailing Address: 5730 S Ellis Ave, Chicago, IL 60637
Email: ryien [at] uchicago [dot] edu
Research
Exploring the Use of
Dataflow Architectures for Graph Neural Network Workloads
Ryien Hosseini, Filippo Simini, Venkatram Vishwanath, Ramakrishnan Sivakumar, Sanjif
Shanmugavelu,
Zhengyu Chen, Lev
Zlotnik, Mingran Wang, Philip Colangelo, Andrew Deng, Philip Lassen, Shukur Pathan
2023 International Conference on High Performance Computing (ISC), Workshop on HPC on
Heterogeneous Hardware
Piloting
a Flexible Deadline Policy for a First-Year Computer Programming Course
Isha Bhatt, Laura K Alford, Lesa Begley, Ryien Hosseini, Deborah A Lichti
2023 American Society for Engineering Education (ASEE) Annual Conference & Exposition
Deep
Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks
Ryien Hosseini, Filippo Simini, Austin Clyde, Arvind Ramanathan
2022 Conference on Neural Information Processing Systems (Neurips), Workshop on AI for Science
Operation-Level Performance Benchmarking of Graph
Neural Networks for Scientific Applications
Ryien Hosseini, Filippo Simini, Venkatram Vishwanath
2022 Conference on Machine Learning and Systems (MLSys), Workshop on
Benchmarking Machine Learning Workloads on
Emerging Hardware
Teaching
Graduate Student Instructor, University of Michigan
Course: Engr 101: Introduction to Computers
and
Programming
Instructional Aide (Undergraduate), University of Michigan
Course: EECS 280: Programming and Introductory Data
Structures
Contact
Email: ryien [at] uchicago [dot] edu
Google Scholar
Github
LinkedIn