Ph.D. 2018
Joined Fall 2014Now: Data Scientist, Translational AI Center (TrAC), Iowa State University
- Ph.D., Mechanical Engineering, Iowa State University, 2018
- M.S., Mechanical Engineering, Chinese Academy of Sciences, 2014
- B.E., Mechanical Engineering, Shenzhen University, 2011
Zhanhong joined the lab in Fall 2014. His research focused on cyber-physical systems, multi-agent systems, system dynamics, and optimization. In his free time, he enjoys tennis, cooking, and reading.
He graduated from the SCS Lab in December 2018 and worked as a Data Scientist at Johnson Controls in Milwaukee, WI. He has since returned to Iowa State University as a Data Scientist at the Translational AI Center (TrAC), working on distributed optimization and machine learning.
Research interests: Distributed optimization, machine learning, cyber-physical systems, multi-agent systems
Publications with the lab (58)
- DeCAF: Decentralized consensus-and-factorization for low-rank adaptation of foundation models — Neural Networks (2026)
- ADKO: Agentic Decentralized Knowledge Optimization — arXiv (2026)
- Continuous-Time Probabilistic Correctors for Uncertainty-Aware Physics-Based Spacecraft Trajectory Forecasting — SSRN Electronic Journal (2026)
Show all 58
- Personalized Federated Direct Preference Optimization via LoRA Adapters — arXiv (2026)
- TabQL: In-Context Q-Learning with Tabular Foundation Models — arXiv (2026)
- COOPO: Cyclic Offline-Online Policy Optimization Algorithm — arXiv (2026)
- Lighting-aware Unified Model for Instance Segmentation — arXiv (2026)
- Bidirectional linear recurrent models for sequence-level multisource fusion — Information Fusion (2026)
- Enhancing PPO With Trajectory-Aware Hybrid Policies — IEEE Transactions on Neural Networks and Learning Systems (2026)
- LexiSafe: Offline Safe Reinforcement Learning with Lexicographic Safety-Reward Hierarchy — arXiv (2026)
- Optimizing navigation and chemical application in precision agriculture with deep reinforcement learning and conditional action tree — Smart Agricultural Technology (2025)
- Data-driven Kinematic Modeling in Soft Robots: System Identification and Uncertainty Quantification — 5th Modeling, Estimation and Control Conference (MECC 2025) (2025)
- Automated Seizure Detection in Animal EEG Signals — bioRxiv (Cold Spring Harbor Laboratory), Cold Spring Harbor Laboratory (2025)
- Decentralized relaxed smooth optimization with gradient descent methods — arXiv preprint arXiv:2508.08413 (2025)
- RLS3: RL-Based Synthetic Sample Selection to Enhance Spatial Reasoning in Vision-Language Models for Indoor Autonomous Perception — Proceedings of the ACM/IEEE 16th International Conference on Cyber-Physical … (2025)
- Neural CDEs as Correctors for Learned Time Series Models — arXiv (2025)
- FUSE: First-Order and Second-Order Unified Synthesis in Stochastic Optimization — 2025 IEEE Conference on Artificial Intelligence (CAI) (2025)
- Foundation Model Efficient Fine-Tuning in Centralized and Federated Settings — 2025 IEEE International Conference on Big Data (BigData) (2025)
- A unified convergence theory for large language model efficient fine-tuning — OPT 2024: Optimization for Machine Learning (2024)
- STITCH: Surface reconstrucTion using Implicit neural representations with Topology Constraints and persistent Homology — arXiv preprint arXiv:2412.18696 (2024)
- Fawac: Feasibility informed advantage weighted regression for persistent safety in offline reinforcement learning — arXiv preprint arXiv:2412.08880 (2024)
- Latent safety-constrained policy approach for safe offline reinforcement learning — International Conference on Learning Representations (ICLR 2025) (2025)
- Dominating Set Model Aggregation for communication-efficient decentralized deep learning — Neural Networks (2024)
- Neural pde solvers for irregular domains — Computer-Aided Design (2024)
- The stochastic augmented lagrangian method for domain adaptation — Knowledge-Based Systems (2022)
- Asynchronous training schemes in distributed learning with time delay — arXiv preprint arXiv:2208.13154 (2022)
- Distributed online non-convex optimization with composite regret — 2022 58th Annual Allerton Conference on Communication, Control, and … (2022)
- MDPGT: momentum-based decentralized policy gradient tracking — Proceedings of the AAAI conference on artificial intelligence (2022)
- On consensus-optimality trade-offs in collaborative deep learning — Frontiers in artificial intelligence (2021)
- Root-cause analysis for time-series anomalies via spatiotemporal graphical modeling in distributed complex systems — Knowledge-Based Systems (2021)
- Decentralized Deep Learning Using Momentum-Accelerated Consensus — Preprint (2021)
- Cross-gradient aggregation for decentralized learning from non-iid data — International conference on machine learning (2021)
- Spatiotemporal attention for multivariate time series prediction and interpretation — ICASSP 2021-2021 IEEE international conference on acoustics, speech and … (2021)
- Data-Driven Performance Monitoring of Dynamical Systems Using Granger Causal Graphical Models — Journal of Dynamic Systems Measurement and Control (2020)
- Interpretable Deep Attention Model for Multivariate Time Series Prediction in Building Energy Systems — Lecture notes in computer science (2020)
- Supervisory control and distributed optimization of building energy systems — Journal of Dynamic Systems, Measurement, and Control (2020)
- On higher-order moments in adam — Beyond First Order Methods in Machine Learning Workshop, NeurIPS 2019 (2019)
- Learning State Switching for Improved Exploration in Multi-sensor Environments — (2019)
- Learning State Switching for Multi-sensor Integration — (2019)
- Generalised gossip-based subgradient method for distributed optimisation — International Journal of Control (2019)
- Exploring Granger causality in dynamical systems modeling and performance monitoring — 2018 IEEE Conference on Decision and Control (CDC) (2018)
- On consensus-disagreement tradeoff in distributed optimization — 2018 Annual American Control Conference (ACC) (2018)
- Hierarchical Optimization for Building Energy Systems — (2018)
- Building Energy Disaggregation using Spatiotemporal Pattern Network — (2018)
- Online robust policy learning in the presence of unknown adversaries — Advances in neural information processing systems (2018)
- Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network — Applied Energy (2018)
- Convergence and noise effect analysis for generalized gossip-based distributed optimization — 2017 American Control Conference (ACC) (2017)
- Autonomous Mobile Sensing Platform for Spatio-Temporal Plant Phenotyping — (2017)
- Energy prediction using spatiotemporal pattern networks — Applied Energy (2017)
- An unsupervised anomaly detection approach using energy-based spatiotemporal graphical modeling — Cyber-physical systems (2017)
- Collaborative deep learning in fixed topology networks — Advances in Neural Information Processing Systems (2017)
- Scalable supervisory control of building energy systems using generalized gossip — 2016 American Control Conference (ACC) (2016)
- An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed cps — 2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS … (2016)
- Probabilistic Graphical Modeling of Distributed Cyber-Physical Systems — Elsevier eBooks (2016)
- A volttron based implementation of supervisory control using generalized gossip for building energy systems — (2016)
- Comparative evaluation of control-oriented zone temperature prediction modeling strategies in buildings — Dynamic Systems and Control Conference 57250, V002T34A009 (2015)
- Understanding Wind Turbine Interactions Using Spatiotemporal Pattern Network — (2015)
- On distributed optimization using generalized gossip — (2015)
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