Scholar
Ryien Hosseini
Google Scholar ID: vru3FIEAAAAJ
Graduate Student, University of Chicago
Citations & Impact
Citations
18
H-index
2
i10-index
0
Publications
7
Co-authors
11
list available
Publications
Resume (English only)
Academic Achievements
- - Publication: Sketch-Augmented Features Improve Learning Long-Range Dependencies in Graph Neural Networks, NeurIPS 2025
- - Publication: Quality Measures for Dynamic Graph Generative Models, ICLR 2025
- - Publication: A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation, AAAI 2025
- - Publication: Exploring the Use of Dataflow Architectures for Graph Neural Network Workloads, ISC 2023
- - Publication: Piloting a Flexible Deadline Policy for a First-Year Computer Programming Course, ASEE 2023
- - Publication: Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks, Neurips 2022
- - Publication: Operation-Level Performance Benchmarking of Graph Neural Networks for Scientific Applications, MLSys 2022
Research Experience
- - Predoctoral researcher at the Leadership Computing Facility at Argonne National Laboratory, working with Venkatram Vishwanath and Filippo Simini to develop and scale graph neural networks (GNNs) for scientific applications
- - Graduate Student Instructor, University of Michigan, teaching Engr 101: Introduction to Computers and Programming
Education
- - PhD, Department of Computer Science, University of Chicago, advised by Hank Hoffmann and Rebecca Willett
- - M.S., Electrical and Computer Engineering Department, University of Michigan, focus on machine learning, systems, and computational biology
- - B.S.E. in Computer Engineering and B.S. in Cognitive Science, University of Michigan
Background
- Research Interests: At the intersection of machine learning and dynamic systems. Broadly interested in developing adaptive machine learning 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.
