27 citations · 48 across the 44 of their papers we have counts for
9 papers · 1 filter
Bounding Retraining Equivalence and the Deletion Floor in Materials Machine Unlearning
Can Polat, Mustafa Kurban, Erchin Serpedin +1
In materials machine learning, closely related retained structures can sustain accurate property predictions even after removing a specific record, rendering post-deletion predicti…
Grounded verification of chemical and materials reasoning: detection is the bottleneck
Can Polat, Mustafa Kurban, Erchin Serpedin +1
Language models are moving into chemistry and materials discovery workflows, where a wrong molecular formula, space group, or formation energy can silently propagate into downstrea…
STEMGym: Benchmarking Sequential Decision-Making under Dose Budgets in Autonomous Electron Microscopy
Can Polat, Erchin Serpedin, Mustafa Kurban +1
A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquis…
SCALAR: Quantifying Structural Hallucination, Consistency, and Reasoning Gaps in Materials Foundation Models
Can Polat, Erchin Serpedin, Mustafa Kurban +1
Large language models are increasingly applied to materials science reasoning, yet their behavior under physically structured distribution shifts remains poorly understood. We intr…
Beyond Atomic Geometry Representations in Materials Science: A Human-in-the-Loop Multimodal Framework
Can Polat, Erchin Serpedin, Mustafa Kurban +1
Most materials science datasets are limited to atomic geometries (e.g., XYZ files), restricting their utility for multimodal learning and comprehensive data-centric analysis. These…
Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding
Can Polat, Hasan Kurban, Erchin Serpedin +1
Molecular graph neural networks (GNNs) often focus exclusively on XYZ-based geometric representations and thus overlook valuable chemical context available in public databases like…
Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids
Cihat Keçeci, Katherine R. Davis, Erchin Serpedin
Data analysis and monitoring on smart grids are jeopardized by attacks on cyber-physical systems. False data injection attack (FDIA) is one of the classes of those attacks that tar…
Multi-Task and Transfer Learning for Federated Learning Applications
Cihat Keçeci, Mohammad Shaqfeh, Hayat Mbayed +1
Federated learning enables many applications benefiting distributed and private datasets of a large number of potential data-holding clients. However, different clients usually hav…
Clustered Scheduling and Communication Pipelining For Efficient Resource Management Of Wireless Federated Learning
Cihat Keçeci, Mohammad Shaqfeh, Fawaz Al-Qahtani +2
This paper proposes using communication pipelining to enhance the wireless spectrum utilization efficiency and convergence speed of federated learning in mobile edge computing appl…