Fatih Ilhan

Ph.D. Student, School of Computer Science

KACB 3337, Georgia Institute of Technology, Atlanta, GA, USA

E-mail: filhan_AT_gatech_DOT_edu

Bio

I am a 4th year CS Ph.D. candidate at the School of Computer Science, Georgia Institute of Technology, advised by Prof. Ling Liu . I was a research scientist intern with Hybrid Cloud Systems Research Group at IBM Research in summer 2022, 2023 and 2024.

Research: My current research focus is full-stack efficiency optimizations for inference and finetuning with large language/vision/multimodal models. I have also worked and collaborated on projects related to federated learning, ensemble learning, AI safety and spatiotemporal prediction/anomaly detection.

Teaching: I have served as the head TA of OMS CS6675 (Advanced Internet Systems and Applications) for four semesters and was selected as the Georgia Tech Head TA of the Year in 2025.

Please refer to my CV for the full record of my work and experience.

Selected Publications

Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbation
T. Huang, S. Hu, F. Ilhan, S. F. Tekin, and L. Liu. International Conference on Learning Representations, 2025. (ICLR oral) [paper] [code]

Resource-Efficient Transformer Pruning for Finetuning of Large Models
F. Ilhan, G. Su, S. F. Tekin, T. Huang, S. Hu, and L. Liu. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. (CVPR) [paper] [code]

Adaptive Deep Neural Network Inference Optimization with EENet
F. Ilhan, KH. Chow, S. Hu, T. Huang, S. F. Tekin, W. Wei, Y. Wu, M. Lee, R. Kompella, H. Latapie, G. Liu, L. Liu. IEEE/CVF Winter Conference on Applications of Computer Vision, 2024. (WACV) [paper] [code]

Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning
T. Huang, S. Hu, F. Ilhan, S. F. Tekin and L. Liu. Thirty-seventh Conference on Neural Information Processing Systems, 2024. (NeurIPS) [paper] [code]

LLM-TOPLA: Efficient LLM Ensemble by Maximising Diversity
S. F. Tekin, F. Ilhan, T. Huang, S. Hu and L. Liu. ACL Conference on Empirical Methods in Natural Language Processing, 2024. (EMNLP findings) [paper] [code]

ScaleFL: Resource-Adaptive Federated Learning with Heterogeneous Clients
F. Ilhan, G. Su and L. Liu. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. (CVPR) [paper] [code]

Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training
T. Huang, S. Hu, F. Ilhan, S. F. Tekin and L. Liu. Thirty-seventh Conference on Neural Information Processing Systems, 2023. (NeurIPS) [paper] [code]

Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments
F. Ilhan, O. Karaahmetoglu, Ismail Balaban and S. S. Kozat. IEEE Transactions on Neural Networks and Learning Systems, 2021. (IEEE TNNLS) [paper] [code]

Modeling of Spatio-Temporal Hawkes Processes with Randomized Kernels
F. Ilhan and S. S. Kozat. IEEE Transactions on Signal Processing, 2020. (IEEE TSP) [paper] [code]