• Ogden2023a - Layercake: Efficient Inference Serving with Cloud and Mobile Resources
  • Ogden2021a - PieSlicer: Dynamically Improving Response Time for Cloud-based CNN Inference
  • Ogden2021b - Many Models at the Edge: Scaling Deep Inference via Model-Level Caching
  • Ogden2020 - MDInference: Balancing inference accuracy and latency for mobile applications
  • Ogden2019 - CloudCoaster: Transient-aware Bursty Datacenter Workload Scheduling
  • Ogden2019a - ModiPick: SLA-aware Accuracy Optimization For Mobile Deep Inference
  • Ogden2019b - Characterizing the Deep Neural Networks Inference Performance of Mobile Applications
  • Ogden2018 - MODI: Mobile Deep Inference Made Efficient by Edge Computing
  • Tlachac2022a - Left on Read: Reply Latency for Anxiety & Depression Screening
  • Tlachac2022a - Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety Screening
  • Gilman2020a - Demystifying the Placement Policies of the NVIDIA GPU Thread Block Scheduler for Concurrent Kernels
  • Gilman2019 - Challenges and Opportunities of DNN Model Execution Caching
  • Guo2019 - EdgeServe: efficient deep learning model caching at the edge