- 11/15/2025: Our paper, Stratos: An End-to-End Distillation Pipeline for Customized LLMs under Distributed Cloud Environments, got accepted to AAAI 2026. See you in Singapore! 🇸🇬
- 08/15/2025: Starting my new role as Applied Scientist in Amazon. Please catch me for a coffee in Seattle!
- 7/11/2025: Our paper, Enabling Weak Client Participation via On-Device Knowledge Distillation in Heterogeneous Federated Learning, got accepted to ECAI 2025. See you in Bologna! ⚜️
- 06/09/2025: Our paper, Leveraging Uncertainty Estimation for Efficient LLM Routing, got accepted to CFAgentic @ ICML’25. See you in Vancouver! 🏙️
- 05/25/2025: Our paper, FedPaI: Achieving Extreme Sparsity in Federated Learning via Pruning at Initialization, got accepted to ICIC 2025. See you in Ningbo! 🌉
- 05/15/2025: Our papers, Creating a Lens of Chinese Culture: A Multimodal Dataset for Chinese Pun Rebus Art Understanding and Reconsidering LLM Uncertainty Estimation Methods in the Wild, both got accepted to ACL 2025. See you in Vienna! 🎹
- 04/25/2025: I passed my Ph.D. defense! Thank you for everyone to make me complete within this journey.
- 04/15/2025: I had the honor of delivering a keynote on the vision of AI + Museum at the USC Pacific Asia Museum, presented to President Carol Folt, Dominic Ng (East West Bank), and other esteemed supporters and donors of the university.
- 03/31/2025: Our paper, GPT-FL: Generative Pre-trained Model-Assisted Federated Learning, has been accepted to CVPR 2025 (FedVision). See you in Nashville! 🎸
- 02/28/2025: Our paper, ModalityMirror: Enhancing Audio Classification in Modality Heterogeneity Federated Learning via Multimodal Distillation, has been accepted to NOSSDAV’25. See you in South Africa! 🇿🇦
- 11/04/2024: I am invited to give a keynote at London Asian Week 2024 titled ‘CAN AI UNDERSTAND ART?’ Recording is available in here. 🤖🖼️
- 07/23/2024: Our paper was accepted in the Journal of Data-centric Machine Learning Research (DMLR), FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things.
- 05/16/2024: Our paper, Revisiting OPRO: The Limitations of Small-Scale LLMs as Optimizers, has been accepted to ACL 2024. See you in Bangkok! 🇹🇭
- 05/10/2024: Starting my journey as a research scientist in Zoom. Hello Zoomies! 👥💬
- 04/29/2024: I passed the PhD Qualification Exam! Now I am a Ph.D. Candidate!
- 04/21/2024: Our paper was accepted in IEEE Transactions on Mobile Computing (IF: 7.9, Q1), EmbracingFL: Enabling Weak Client Participation via Partial Model Training.
- 04/18/2024: We have released the code for GPT-FL. The link is here! 🤖
- 05/17/2023: Our paper, FedMultimodal: A Benchmark for Multimodal Federated Learning, has been accepted to KDD 2023. Please have a look at our code and paper!
- 03/31/2023: Our paper, TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training, has been accepted to CVPR 2023 (FedVision). See you in Vancouver! 🏙️
- 03/04/2023: Our paper, Secure Federated Learning against Model Poisoning Attacks via Client Filtering, has been accepted to ICLR BANDS 2023.
- 02/15/2023: Our paper, FedAudio: A Federated Learning Benchmark for Audio Tasks, has been accepted to ICASSP 2023. See you in Greece! 🇬🇷
- 11/19/2022: Our paper, Layer-wise adaptive model aggregation for scalable federated learning, has been accepted to AAAI 2023. See you in DC! 🏛️
- 10/30/2022: We have released the FedAudio, the first federated learning benchmark dataset for audio tasks!
- 03/04/2022: Our FL + IoT Survey paper has been accepted to IEEE Internet of Things Magazine (IEEE IoTM), March 2022 Special Issue: An End-to-end Machine Learning Perspective on Industrial IoT
- 10/20/2021: Our FedIoT paper has been admitted to accepted to ACM Embedded Networked Sensor Systems SenSys 2021 (AIChallengeIoT)
- 08/01/2020: Joined USC as a Ph.D. student in the summer
- 06/15/2020: Graduated from UCSB with a B.S. with High Honor
Recent News
Tuo Zhang received his Ph.D. in the Electrical Engineering department at the University of Southern California with supervision by Prof. Salman Avestimehr. Now he is an Applied Scientist in Amazon, Seattle. Before joining USC, he received his Bachelor of Science with High Honor from the University of California, Santa Barbara under the supervision of Prof. Mahnoosh Alizadeh and Prof. Forrest Brewer.
The main focus of Tuo Zhang’s research is on Privacy-Preserving Machine Learning, Edge-Cloud Infrastructure for Large Language Models, and AI for Cultural Heritage.
tuozhang [at] usc.edu
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