Bio

I am a M.S. student in the department of Electrical Engineering at Stanford. I graduated with my B.S. from UCLA in Spring 2025. There, I worked under Professor Ian Roberts in the UCLA Wireless Lab on mmWave beamforming systems. During the summers, I interned at Anduril with their Electronic Warfare DSP team and at Apple with their Cellular RF Software team. My interests span Optimization, Statistical Signal Processing, and Wireless Communications.

Honors & Awards

  • Departmental Valedictorian, UCLA Electrical & Computer Engineering Department (May 2025)
  • Integrated Circuits and Systems Lab Alumni Scholar, UCLA Integrated Circuits and Systems Lab (October 2024)
  • Boeing Scholar, Boeing (October 2022)

Education & Certifications

  • Bachelor of Science, University of California, Los Angeles (UCLA), Electrical Engineering (2025)

Contact

  • Academic
    cdavis9@stanford.edu
    University - Student Department: Electrical Engineering Position: Graduate

Additional Info

  • Mail Code: 9505

Links

Current Research and Scholarly Interests

Previously, I developed a platform for joint communications and sensing (JCAS) with mmWave beamforming systems as part of the UCLA Wireless Lab under Professor Ian Roberts. Then, as a DSP engineer intern at Anduril, I worked to enhance detectors for frequency-hopping OFDM and chirp-spread-spectrum signals. From these experiences, I found a strong interest in optimization methods and statistical inference techniques for signal processing systems, mainly wireless communications and radar.

Work Experience

  • Cellular RF Software Intern, Apple (6/16/2025 - 9/5/2025)

    Location

    La Jolla, San Diego, CA, USA

  • DSP and Wireless Communications Intern, Anduril (6/10/2024 - 9/13/2024)

    Location

    Costa Mesa, CA, USA

  • Electrical Hardware Intern, Anduril (6/12/2023 - 9/8/2023)

    Location

    Costa Mesa, CA, USA

  • Medical Camera Imaging Intern, Viseon (6/14/2021 - 9/10/2021)

    Location

    Irvine, CA, USA

All Publications

  • LocoMote: AI-driven Sensor Tags for Fine-Grained Undersea Localization and Sensing. IEEE sensors journal Saha, S. S., Davis, C., Sandha, S. S., Park, J., Geronimo, J., Garcia, L. A., Srivastava, M. 2024; 24 (10): 16999-17018

    Abstract

    Long-term and fine-grained maritime localization and sensing is challenging due to sporadic connectivity, constrained power budget, limited footprint, and hostile environment. In this paper, we present the design considerations and implementation of LocoMote, a rugged ultra-low-footprint undersea sensor tag with on-device AI-driven localization, online communication, and energy-harvesting capabilities. LocoMote uses on-chip (< 30 kB) neural networks to track underwater objects within 3 meters with ~6 minutes of GPS outage from 9DoF inertial sensor readings. The tag streams data at 2-5 kbps (< 10-3 bit error rate) using piezo-acoustic ultrasonics, achieving underwater communication range of more than 50 meters while allowing up to 55 nodes to concurrently stream via randomized time-division multiple access. To recharge the battery during sleep, the tag uses an aluminum-air salt water energy harvesting system, generating upto 5 mW of power. LocoMote is ultra-lightweight (< 50 grams), tiny (32×32×10 mm3), consumes low power (~ 330 mW peak), and comes with a suite of high-resolution sensors. We highlight the hardware and software design decisions, implementation lessons, and the real-world performance of our tag versus existing oceanic sensing technologies.

    View details for DOI 10.1109/jsen.2024.3383721

    View details for PubMedID 39640899

    View details for PubMedCentralID PMC11615471