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, changing levels of supervision, and other
evolving factors.
Previously, I was a Predoctoral researcher at the Argonne
Leadership Computing Facility
at Argonne National Laboratory, working with Venkatram
Vishwanath and Filippo
Simini
to develop and scale graph neural networks (GNNs) for 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
Publications
A Deep Probabilistic Framework for Continuous Time
Dynamic Graph Generation
Ryien Hosseini, Filippo Simini, Venkatram Vishwanath, Henry Hoffmann
The 39th AAAI Conference on Artificial Intelligence (AAAI 2025)
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
The 38th International Conference on High Performance Computing (ISC),
Workshop on HPC on Heterogeneous Hardware (2023)
Piloting a Flexible Deadline Policy for a First-Year Computer Programming Course
Isha Bhatt, Laura K Alford, Lesa Begley, Ryien Hosseini, Deborah A Lichti
American Society for Engineering Education Annual Conference (ASEE 2023)
Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks
Ryien Hosseini, Filippo Simini, Austin Clyde, Arvind Ramanathan
The 36th Conference on Neural Information Processing Systems (Neurips),
Workshop on AI for Science (2022)
Operation-Level Performance Benchmarking of Graph Neural Networks for Scientific Applications
Ryien Hosseini, Filippo Simini, Venkatram Vishwanath
The 5th Conference on Machine Learning and Systems (MLSys),
Workshop on Benchmarking Machine Learning Workloads on Emerging Hardware (2022)
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
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