Students

Deeksha Kondi Udayashankar

Deeksha Kondi Udayashankar
M.S. Electrical Engineering Candidate
Thesis topic: Fall Detection and Classification with a Binarized Neural Network (BNN) on the iCE40 FPGA

Jingxiao Tian
PhD Electrical Engineering Candidate
Research focus: Fall Prediction and Detection in At-Risk Older Adults through Inferencing at the Edge
Lab hours: Monday – Friday, 11:00AM to 4:00PM

Shreyas Narasimhiah Ramesh
M.S. Electrical Engineering Candidate
Research focus: Embedded Anchor-to-Joint (A2J) Pose Estimation from Depth Images on the Xilinx Kria KV260 Vision AI board
Lab hours: Monday – Friday, 11:00AM to 4:00PM

Priyanka Partane
M.S. Electrical Engineering Graduate (graduated spring 2022)
EE798 Project topic: Line-speed Packet Capture and Feature Extraction for Training Asymmetric Stacked Autoencoders for Network Anomaly Detection

Vineet Kandunuri

Vineet Kandunuri
M.S. Electrical Engineering Candidate
Thesis topic: MIST: A Machine Intelligent Sensor Topology Fog Computing Architecture to Mitigate the Propagation of Cascading Power Outages

Veena Keeranagi

Veena Keeranagi
M.S. Electrical Engineering Graduate (graduated fall 2020)
EE798 Project topic: Optimal LoRa Gateway Placement

Erfan Chowdhury Shourov

Erfan Chowdhury Shourov
M.S. Electrical Engineering Graduate (graduated spring 2022)
Thesis topic: Deep Learning Architectures for Skateboarder-Pedestrian Surrogate Safety Measures
Publications:

  • Shourov, C.E.; Sarkar, M.; Jahangiri, A.; Paolini, C. Deep Learning Architectures for Skateboarder-Pedestrian Surrogate Safety Measures. Future Transp. 2021, 1, 387-413. 10.3390/futuretransp1020022
  • E. C. Shourov and C. Paolini, “Laying the Groundwork for Automated Computation of Surrogate Safety Measures (SSM) for Skateboarders and Pedestrians using Artificial Intelligence,” 2020 Third International Conference on Artificial Intelligence for Industries (AI4I), Irvine, CA, USA, 2020, pp. 19-22, doi: 10.1109/AI4I49448.2020.00011.
  • C. E. Shourov and C. Paolini, “Skateboarder and Pedestrian Conflict Zone Detection Dataset,” 14-Nov-2020. [Online]. Available: osf.io/nyhf7, DOI 10.17605/OSF.IO/NYHF7.
  • C. Paolini, C. E. Shourov, A. Jahangiri, and S. G. Machiani, “Skateboarder and Pedestrian Dataset,” 30-Jan-2020. [Online]. Available: osf.io/cqd9z, DOI 10.17605/OSF.IO/CQD9Z.

Ugur Emre Dogan
M.S. Electrical Engineering Graduate (graduated summer 2021)
EE798 Project topic: On-device vehicle detection, classification, and tracking for Surrogate Safety Measures

Arya Yazdani
M.S. Electrical Engineering Graduate (graduated summer 2021)
EE798 Project topic: Edge computing vision architectures for Surrogate Safety Measurements
Report: Real-time vehicle and pedestrian object detection and classification on the Coral EdgeTPU Board for Surrogate Safety Measurements

Jared Brzenski

Jared Brzenski
Computational Science PhD Candidate
Jared is supported under NSF Office of Advanced Cyberinfrastructure (OAC) CC* Storage Grant 1659169 Implementation of a Distributed, Shareable, and Parallel Storage Resource at San Diego State University to Facilitate High-Performance Computing for Climate Science.  The goals and objectives of the project will be to implement parallel shared-file I/O capabilities (i.e. each process performs I/O to a single file which is shared) in our Geologic CO2 Sequestration and Coastal Ocean Modeling applications, at multiple layers, using (1) parallel I/O libraries (HDF5, Parallel netCDF), (2) a middleware layer (MPI-IO), and a parallel file system (BeeGFS). Publications:

  • Brzenski, J., Paolini, C., and Castillo, J. E., Improving the I/O of Large Geophysical Models using PnetCDF and BeeGFS, Parallel Computing, 2021, ISSN 0167-8191, 10.1016/j.parco.2021.102786

Christopher Johnson
Computer Engineering Undergraduate
Undergraduate research project: Advanced Persistent Threat (APT) Detection. Packet capture and inspection using the Xilinx U250 Alveo Data Center accelerator card on the UC San Diego / UC Berkeley PRP Kubernetes cluster.