[Spring 2024]

• Our paper titled “Crowdsourcing Live High Definition Map via Collaborative Computation in Automotive Edge Computing” has been accepted by IEEE Transactions on Vehicular Technology.
• Congrats Saraswathi Katabattula from AMAI lab won the best poster award (2 out of 41) at Spring 24 CS Demo Day!
• Received one-year research grant (Single-PI) from InfoTech Labs, Toyoto Motor North America, Thanks Toyota!

[Fall 2023]

• Undergraduate researchers from AMAI lab won the 1st Place of Fall 23 CS SHOWCASE. Congrats Saraswathi Katabattula, Janou Milligan, Tyler Wilkerson!
• Our paper titled “DeepEn2023: Energy Datasets for Edge Artificial Intelligence” led by Xiaolong has been accepted by Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS ’23) Workshop on Tackling Climate Change with Machine Learning. This achievement aligns with our series of contributions (SEC ’23, NeurIPS ’23 CCAI) aimed at assessing and reducing energy consumption and carbon footprint of on-device deep learning. Kudos to my students for their outstanding work and dedication!
• Xiaolong was selected as one of the three recipients of the 2023 Martin D. Fraser Graduate Student Conference Travel Award, which is a merit-based accolade that supports CS PhD students at GSU with conference travel. Congratulations!
• Our paper titled “Real-Time Object Substitution for Mobile Diminished Reality with Edge Computing” led by Hongyu has been accepted by SEC’ 23 Poster/Demo Track.
• Our paper titled “Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices” led by Xiaolong has been accepted by The Eighth ACM/IEEE Symposium on Edge Computing (SEC’ 23). SEC is a new top conference in edge computing. The acceptance rate of this year is 25.3% (18 of 71 accepted). This paper presents the first measurement study that uncovers the energy consumption characteristics of on-device deep learning across edge devices. This study results in the creation of three extensive energy datasets for edge devices, covering a wide range of kernels, state-of-the-art DNN models, and popular AI applications. Based on our datasets, we design and implement the first kernel-level energy predictors for edge devices, which achieves an average prediction accuracy of 86.2%. The datasets and code will be released soon at here. Stay tuned!

[Summer 2023]

• I serve as the Poster/Demo Co-Chair of the Eighth ACM/IEEE Symposium on Edge Computing (SEC). The paper submission deadline is August 31, 2023. Please consider submitting your work!
• Our paper titled “DSORL: Data Source Optimization with Reinforcement Learning Scheme for Vehicular Named Data Networks” has been accepted by IEEE Transactions on Intelligent Transportation Systems.
• Our paper titled “Visualization of Mobility Digital Twin: Framework Design, Case Study, and Future Challenges” has been accepted by The 20th IEEE International Conference on Mobile Ad Hoc and Smart Systems (MASS). Congrats Yueyang and Xiaolong!
• Received a $46,980 internal grant from Georgia State University to support Human-Computer Interaction Project.

[Spring 2023] 

• I am co-chairing the First ACM/IEEE Workshop on Digital Twins (DT). The workshop will be co-located with the Eighth ACM/IEEE Symposium on Edge Computing (SEC), December 6-9, 2023, in Wilmington, DE, USA. The paper submission deadline is August 20, 2023. Please consider submitting your work!
• Two papers have been accepted by IEEE ICC 2023, congrats team!
• Our paper titled “Metamobility: Connecting Future Mobility with Metaverse” has been accepted by IEEE Vehicular Technology Magazine.
• Our paper titled “FAIR: Towards Impartial Resource Allocation for Intelligent Vehicles with Automotive Edge Computing” has been accepted by IEEE Transactions on Intelligent Vehicles.
• Dreamers toward Ph.D. Xiaolong Tu and Hongyu Ke joined AMAI Lab as Ph.D. students. Welcome on-board!
• Received one-year research grant (Single-PI) from InfoTech Labs, Toyoto Motor North America, Thanks Toyota!