M.S. Degree - Plan I (Thesis)

M.S. Degree - Plan I (Thesis)

M.S. Degree - Plan I (Thesis)

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This master’s program in electrical and computer engineering gives the student an opportunity to perform in-depth research and thesis writing.

The Department of Electrical and Computer Engineering prepares graduate students to do meaningful research and acquire skills and insights vital to solving some of the world’s most complex technological problems. Many of our graduates go on to leadership and technology management roles in industry.

Graduate program highlights include:

  • A challenging and stimulating environment
  • Depth of resources
  • Highly interdisciplinary culture
  • Generous financial support.

Degree Requirements

  • General Reminders and Information
  • Students should note that ECE program requirements are more stringent than those stated by Graduate Studies. The ECE program requirements, therefore, take precedence. Plan I requires a minimum of 36 units of graduate and upper division courses (the 100 and 200 series only), at least 15 units of which must be graduate engineering courses (200 series), with 12 out of the 15 units of graduate work in the major field, excluding seminar and research units. No more than 3 graduate seminar units and 9 research units may be used to satisfy the 36-unit requirement. In addition, a thesis is required. The thesis serves as the capstone requirement. All courses listed on the Program of Study must be passed with a “B-“ or higher. A course in which a student receives a “C+” or lower cannot be used to satisfy the unit requirement for the M.S. degree, but will count in determining the grade point average. 

  • Course Requirements
  • Thirty-six (36) units of upper-division and graduate course work, a thesis, and a minimum of three quarters of academic residence are required.

    At least 16 units must be in graduate Electrical and Computer Engineering courses (excluding EEC200, EEC29X seminar series, and EEC299). The balance of the 36-unit requirement may be met with a combination of upper division technical elective courses and 29X and 299 in ECE or other approved program by the ECE Graduate Program Chair. No more than 3 seminar (290-297, excluding 290C) units can be counted. A student should register for the number of 299 research and independent study units that reflects the actual effort and time devoted to thesis work, although no more than 9 units can be counted.

    Courses required for the ECE undergraduate degree, or the following courses: EEC100, EEC110A/B, EEC130A/B, EEC140A/B, EEC150, EEC151, EEC161, EEC170, EEC172, and EEC180A/B, may not be used to satisfy the requirements of the ECE M.S. degree.

    Full-time students must enroll for 12 units per quarter including research, academic and seminar units. Courses may not be taken with the S/U option to fulfill course requirements, unless the course is normally graded as S/U. Once course requirements are completed, students can take additional classes as needed, although the 12 units per quarter are generally fulfilled with a research course (299) and perhaps seminars. Per UC regulations students cannot enroll in more than 12 units of graduate level courses (200) or more than 16 units of combined undergraduate and graduate level (100, 200, 300) courses per quarter.       

  • Special Requirements
  • All graduate students are required to take EEC290, Seminar in Electrical and Computer Engineering, each quarter that it is offered. An S grade in EEC390, the Teaching of Electrical and Computer Engineering, is required to be eligible to hold a teaching assistantship in ECE, but may not be used to satisfy graduate coursework requirements. International students may need to take LIN25, LIN26, LIN391 or a combination thereof, to meet university language proficiency requirements.

  • Committees
  • ♦   Admission Committee
    Once the completed application, all supporting materials, and the application fee have been received, the application will be submitted to the admissions committee. The admissions committee consists of the faculty members of ECE’s Graduate Study Committee (GSC) and the GSC admissions chair. Applicants who apply by the space available deadline (but after the general deadline) are not guaranteed to have their application reviewed by the graduate program. Their application will be reviewed only if the graduate program determines that they have additional space available. Based on a review of the entire application, a recommendation is made to accept or decline an applicant’s request for admission. The recommendation to accept or decline an applicant’s request for admission is forwarded to the Dean of Graduate Studies for final approval of admission. Notification of admissions decisions will be sent by Graduate Studies. Applications are accepted from the date the admission system opens (typically in September) through the space available deadline for the next fall-entering class

    ♦   Course Guidance or Advising Committee
    The major professor and the ECE Graduate Advisor will assist the student in developing a Program of Study. See the section below on “Advising and Mentoring.” By the third quarter of enrollment the student must file a Program of Study that must be routed through the ECE Graduate Program Coordinator for the ECE Graduate Advisor’s approval.

    ♦   Thesis Committee for M.S. Plan I
    When the student advances to candidacy, they will declare an M.S. thesis committee. The ECE Graduate Advisor will nominate the committee based on consultations with the student and the major professor. This committee is chaired by the major professor and made up of at least two other members. The majority of this committee must be members of the ECE graduate program. The responsibility of this committee is to assist in the guidance of the student and to read and approve the thesis. The thesis must be prepared in accordance with Graduate Studies guidelines.

  • Advising and Mentoring
  • The major professor is the primary mentor during the student’s career at UC Davis and will assist with developing the student’s Program of Study. The major professor serves as the chair of the Thesis Committee (for Plan I) or Comprehensive Exam Committee (for Plan II). The student must select a major professor from the members of the ECE Graduate Program as soon as possible, but no later than the beginning of the third quarter of enrollment. Changing a major professor, requires the signatures of the previous and new major professor, acknowledging the change. The ECE Vice Chair for Graduate Studies, also referred to as the Graduate Program Chair, will serve as the interim advisor to new students during the process of selecting a major professor.

    The Graduate Advisor, who is nominated by the department chair and appointed by the Dean of Graduate Studies, is a resource for information on academic requirements, policies and procedures and registration information until a major professor is selected. The ECE Graduate Advisor is responsible for reviewing programs of study for each student and acting on student petitions.

    The Graduate Program Coordinator should be the first person consulted on all actions regarding graduate affairs. The Graduate Program Coordinator may advise the student to contact the ECE Graduate Advisor or the Office of Graduate Studies to address particular issues.

  • Advancement to Candidacy
  • Every student must file an official application for candidacy for the Master of Science degree and pay the candidacy fee after completing half of their course requirements and at least one quarter before completing all degree requirements. This is typically the third quarter. The candidacy for the Master of Science degree form can be found online at: http://www.gradstudies.ucdavis.edu/forms/. A completed form includes a list of courses the student will take to complete degree requirements. If changes must be made to the student’s course plan after they have advanced to candidacy, the Graduate Advisor must recommend these changes to Graduate Studies. Students must have the ECE Graduate Advisor and committee chair, if applicable, sign the candidacy form before it can be submitted to Graduate Studies. If the candidacy is approved, the Office of Graduate Studies will send a copy to the appropriate graduate program coordinator and the student. The thesis committee chair will also receive a copy, if applicable. If the Office of Graduate Studies determines that a student is not eligible for advancement, the program and the student will be told the reasons for the application’s deferral. Some reasons for deferring an application include grade point average below 3.0, outstanding “I” grades in required courses or insufficient units.

  • Thesis Requirements
  • The M.S. thesis must demonstrate the student’s proficiency in research methods and scientific analysis, as well as a thorough knowledge of the state-of-the-art of the student’s chosen field. Original contributions to knowledge are encouraged, but not expected, at the M.S. degree level. Thus, an M.S. thesis may consist of:

    ♦   An original technical or research contribution of limited scope
    ♦   A critical evaluation of the state-of-the-art of a current research area
    ♦   An advanced design project, either analytical or experimental.

    Research for the master’s thesis is to be carried out under the supervision of a faculty member of the program. The thesis research must be conducted while the student is enrolled in the program. The thesis is submitted to the thesis committee at least one month before the student plans to make requested revisions. All committee members must approve the thesis and sign the title page before the thesis is submitted to Graduate Studies for final approval. Should the committee determine that the thesis is unacceptable, even with substantial revisions, the program may recommend to the Dean of Graduate Studies that the student be disqualified from the program.

    The thesis must be filed in a quarter in which the student is registered or on filing fee. Instructions on preparation of the thesis and a schedule of dates for filing the thesis in final form are available from Graduate Studies; the dates are also printed in the UC Davis General Catalog and in the Class Schedule and Registration Guide issued each quarter. A student must have a GPA of 3.0 for the M.S. degree to be awarded.

  • Normative Timeline
  • Although work for the Master of Science degree can be completed in three quarters of full-time study, generally 18-24 months of full-time study are required to complete the M.S. Plan I. In order to make satisfactory progress, the expectation is that full-time students in the M.S. program will follow the timeline below. The number in each column is the consecutive quarter of enrollment. Students not holding an ECE degree may require additional quarters of study to complete their M.S. degree requirements depending on the number of remedial courses needed.
    ECE MS Timeline

  • Sources of Funding
  • Please see more information on helpful funding resources.

  • PELP, In Absentia and Filing Fee Status
  • Information about PELP (Planned Educational Leave), In Absentia (reduced fees when researching out of state) and filing fee status can be found in the graduate student guide:  https://grad.ucdavis.edu/resources/graduate-student-resources. M.S. students are eligible for filing fee status after completing their coursework (Program of Study) and a working draft of their thesis or comprehensive examination report. In order to be approved for filing fee status, a student must submit the filing fee request along with signatures of all three members of the Thesis Committee or Comprehensive Examination Committee stating they have received an acceptable working draft of the thesis or comprehensive examination report. This application must be routed through the ECE Graduate Program Coordinator for the ECE Graduate Advisor’s approval and then must be filed with Graduate Studies. Filing fee is available for one quarter only, but extensions may be approved on a case-by-case basis. In the event that filing fee status expires, the student must file a readmission application.

Projects Offered 

  • Area of Research: Computer Engineering
  • Project:  FPGA Security

    Sponsors: Professor Houman Homayoun

    Description: : FPGA security is a rapidly evolving field, especially as more FPGAs are found in critical cloud infrastructures. Recently, malicious sensors have been proposed that can be discretely integrated within an FPGA fabric, and can expose data to an attacker. For this project the student will evaluate and develop defensive strategies which re-purpose these malicious circuits into security primitives that can be used to hide sensitive data.

    Requirements: Basic circuit knowledge; experience with any hardware description language; experience programming and debugging FPGAs

    Project:  Cloud Security

    Sponsors: Professor Houman Homayoun

    Description: Machine learning-based algorithms have been proved to be able to improve the scheduling quality of cluster schedulers. For this project, students will learn to interact with cluster schedulers and construct performance datasets, develop machine learning-based algorithms to perform performance prediction and optimize behaviors of cluster schedulers. Students will gain experience in machine learning as well as cluster computing.

    Requirements: Knowledge in basic machine learning, basic Python and C++ programming.

    Project:  Detection of firmware vulnerabilities that can lead to fault injection attacks

    Sponsors: Professor Houman Homayoun

    Description: Hardware attack like fault injection is one of the major threats on embedded devices. Though they are hardware based attacks, sometimes the attack exploitation succeeds because of the implementation vulnerabilities in the hardware. This project aims to identify the scenarios and patterns of flaws in the firmware that can lead to such exploitations and design a framework to auto identify them during assessment.

    Requirements: Understanding of Embedded systems, firmware development, C/C++, Python, IoT, Experience using hardware tools like oscilloscope, multimeters, etc.; basic circuit understanding

    Project:  Secure firmware update for resource constrained embedded systems

    Sponsors: Professor Houman Homayoun

    Description: Embedded systems are the core of the IoT (Internet of things) ecosystem. Resource constrained embedded devices encounter a plethora of challenges when it comes to secure design. Firmware is the brain of these devices, for some vendors it’s their IP. There are several scenarios and ongoing research to find firmware vulnerabilities whose exploitation could significantly affect both the system performance and financial impact on vendors.  This project aims to go in-depth of current methods for secure firmware updates in IoT devices, the vulnerabilities associated with them and corresponding solutions.

    Requirements: Understanding of Embedded systems, firmware development, C/C++, Python, IoT

    Project:  Machine learning security and privacy on FPGAs

    Sponsors: Professor Houman Homayoun

    Description: Inference results from machine learning models are critical and sensitive. In this project, students will work on extracting power traces to deduce the labels from users' machine learning models. To achieve this attack, the machine learning models will be implemented in FPGA platforms, mostly sequence related and time-series related machine learning models.

    Requirements: Basic machine learning knowledge; knowledge in FPGA

    Project:  ASIC implementation of Compute-In-memory circuits with emerging Non-volatile memories

    Sponsors: Professor Houman Homayoun

    Description: Recent trends show an increasing interest in research of non-volatile memories for various applications. As Von Neuman architectures gives rise to memory bottleneck, new compute-in-memory architectures have shown potential. In this project, we will explore emerging non-volatile memories and work on implementing a compute-in-memory module for an ASIC. A modified ASIC flow will be developed through the course of this project to integrate NVMs onto the ASIC design flow.   

    Requirements: Basic knowledge of ASIC design CMOS and emerging memories. Familiarity with Python, C/C++, Verilog/VHDL, HSPICE would help. 

    Project:  GPU Solvers for Flow Computation

    Sponsors: Professor John Owens and Postdoc Serban Porumbescu

    Description: We are working with the US Army Corps of Engineers to develop a GPU implementation of their "HEC-RAS" river analysis system, the leading system for flow computation. The current implementation of this package is on CPUs and we would like to bring it to GPUs. The core computation is modeling hydraulic flow on unstructured grids, and the research problems are both algorithmic and systems ones. The ideal student will be interested in writing high-quality open-source software in collaboration with fellow graduate students as well as domain experts from government, in the context of a large and active group that is interested in problems across many domains of parallel computing. It is likely that this position will be funded. It is expected that an interested student will pursue a thesis that will result in publication in a high-quality venue. This position should be equally interesting for new PhD students.

    Requirements: Desirable: Expertise in numerical computation and/or parallel computing. Highly desirable: C++ expertise, and even more desirable, CUDA experience. Good communicator and collaborator.

    Project: "Gunrock" GPU Graph Analytics Framework 

    Sponsors: Professor John Owens and Postdoc Serban Porumbescu

    Description: : John Owens’ research group focuses on GPU computing and has a large open-source project on parallel graph analytics called Gunrock. We have a large number of small projects within Gunrock and believe it would be straightforward to assemble a MS thesis or MS project within Gunrock depending on the interests of the student. We have projects within the core of Gunrock (mostly CUDA/C++-oriented), in writing and improving Gunrock applications (primarily C++), and in interfacing and tuning Gunrock (more likely Python).

    Requirements: Gunrock is written in C++ and we have Python-related projects as well. Experience with (in order) CUDA C, C++, and/or Python is highly desirable. Strong (text) writing skills. Experience with parallel computing would be terrific but is not required. We need talented students who can learn quickly, communicate well, and work well both in a group and independently.

    Project: Trusted Execution Environments for High-Performance Computing

    Sponsors: Professor Venkatesh Akella, Professor Jason Lowe-Power, and Professor Sean Peisert

    Description: : In partnership with the Computational Research Division at Lawrence Berkeley National Laboratory (Berkeley Lab), we are developing trusted execution environments (TEEs) for high-performance computing (HPC) systems such as those operated by the U.S. Department of Energy Office of Science’s Advanced Scientific Computing Research (ASCR) program, including the National Energy Research Supercomputing Center (NERSC) at Berkeley Lab.  Current commercial TEEs such as Intel SGX and AMD’s SEV are inadequate for HPC a variety of reasons.  Our solution involves a RISC-V based approach, along with development and modifications to the security monitor and operating system elements, as well as implementation and experimentation in gem5 simulations or in FPGA clusters.  Potential work could be on multiple levels of the stack from programming FPGAs to developing hardware modifications to kernel elements. Research problems include both security and performance elements, as well as tradeoffs between the two.  The ideal student will be interested in writing high-quality open-source software in collaboration with fellow graduate students, as well as researchers and HPC operators from the Berkeley Lab. This position may be funded. It is expected that interested students will pursue a thesis that will result in publication in a high-quality venue. This position should be equally interesting for PhD students.

    Requirements: Expertise in OS/kernel function, computer architecture, and modification, and/or FGPA programming. Expertise in programming C/C++ and Python, and software-engineering methodologies.  Excellent written and verbal English communication skills.  Looking for motivated and pro-active students who are great collaborators.

    Link:  https://dst.lbl.gov/security/project/ascr-hpc-cybersecurity-codesign/

    Project: Optimizing Compiler Instruction Scheduling Using GPU-Accelerated Intelligent Search

    Sponsors: Prof. John Owens (UC Davis),  Ghassan Shobaki (California State University)

    Financial support provided by : National Science Foundation (NSF)

    Description: Master’s students are needed to work as Research Assistants (RAs) on an NSF-funded project at California State University, Sacramento (CSUS). Selected Master’s students will not have to transfer to CSUS to work on this project. They can work on the project as UC Davis students, and their theses will be based on their work on this project. Master’s students will be co-advised by UC Davis Professor John Owens. All the work for this project may be done remotely whether the campus is closed or open. 

    In this project, we use a combination of intelligent search techniques (specifically, Branch-and-Bound and Ant Colony Optimization) to solve a long-standing problem in compiler optimization, and thus generate more efficient code for a wide range of programs running on CPUs and GPUs. The official project abstract may be found at: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1911235 

    Our most recent publications may be found here:  https://dl.acm.org/doi/10.1145/3368826.3377918  and https://dl.acm.org/doi/abs/10.1145/3301489 

     A Research Assistant on this project will develop parallel versions of existing intelligent-search algorithms and/or enhance the sequential versions. The algorithms will be first implemented in the LLVM compiler and later in the GCC compiler. The project will involve collaboration with open-source compiler engineers from Apple, IBM, Google, Redhat, as well as the GPU compiler team at AMD. We are looking for students who can understand complex compiler optimization algorithms and successfully implement them in a production compiler.   

    Requirements: The ideal candidate for this position is a junior, senior or Master’s student who is interested in conducting serious research in this area and producing quality publications that will help him/her build a strong career either in academia or in the industry. Undergraduate students who are interested in pursuing a Master’s degree right after graduation are also encouraged to apply. They can do their Master’s at UC Davis, and their theses will be based on their work on this project. Productive students may continue to work on the project after their graduation if they are interested. 

    Requirements include: Strong analytical and problem solving skills.
    Strong background in algorithms, especially graph algorithms (see the list of topics below). Strong programming skills in C/C++. Self-motivated and able to work independently with minimum supervision.

    Having some background in one or more of the following areas is desirable but not required: Code generation and optimization. GPU computing. Artificial intelligence, with emphasis on Branch-and-Bound search and Ant Colony Optimization. 

    Link:  https://www.nsf.gov/awardsearch/showAward?AWD_ID=1911235 

    Project: A hypothetical RISC-V based game console

    Sponsors: Professor Christopher Nitta

    Description: : Christopher Nitta has developed a simulator for a hypothetical RISC-V based game console (available at https://github.com/UCDClassNitta/riscv-console/). The simulator is designed as an educational tool to be used in courses such as Operating Systems or Machine Dependent Programming. We are looking to expand the simulator to support auto grading, add new hardware components, and to improve the portability of the project.

    Requirements: You should have taken EEC 270 or equivalent (graduate course in computer architecture), and have strong C/C++ programming skills. Ideally, we are looking for a single student to continue the project.

    Link:  https://github.com/UCDClassNitta/riscv-console/


  • Area of Research: Photonic and Electronic Devices
  • Project: Advanced computational imaging for healthcare and climate-resilient agriculture enabled by nanophotonics and AI

    Sponsor: Professor Saif Islam.

    Description: This MS thesis project will focus on an innovative imaging technology based on nanotechnology-enabled ultra-fast CMOS imaging sensors that operate by slowing down photons, deep learning, AI, and computational imaging. The sensors can make real-time in-situ tissue diagnoses during surgery and identify molecular activity in plant cells for autonomous nutrient monitoring.

    Understanding of solid-state devices - PN junction, MOS Capacitor, MOSFETs, transistors, sensors, etc. Understanding of electromagnetic theory. Familiarity with TCAD tools and Matlab. 

    Lab and simulation: Both lab work and computer simulation will be necessary. The thesis will involve working closely with other Ph.D. students and postdocs.

    Link: https://www.ece.ucdavis.edu/~saif/

    Project: Ar-Ion Plasma Surface Treatment of Reticulated Vitreous Carbon (RVC) for Field-Emission Cathodes

    Sponsor: Professor Charles Hunt.

    Description: Experimental Project assembling a RF Ar plasma system in the Vacuum Microelectronics Lab.  Verification on RVC samples.

    Requirements:  Comfort working with Vacuum equipment, power supplies and electronic materials.  Requires hands-on use of shop tools.

    C. E. Hunt and Y. Wang, "Application of vitreous and graphitic large-area carbon surfaces as field-emission cathodes", Applied Surface Science, (2005).

    Funding for MS students available! Please contact Prof. Hunt.

    Project: Advanced Magneto Optic Device Development 

    Sponsor: Professor J. Sebastian Gomez-Diaz and II-VI Inc.

    The goal of this project is to develop a proof of concept prototype optical device capable of sensing micro-Tesla magnetic fields. 
        Phase (1) review literature for advantages and disadvantages of existing approaches.  Wavelength range is telecommunications C-band (1.5um), material is II-VI proprietary Thick Film Planar Faraday Rotator Crystals, preferred implementation is a waveguide/fiber optic device.  Preferred (but not required) substrate is Silicon Carbide.
    Phase (2) Develop theoretical designs, simulate with numerical software, optimize parameters, converge on the most promising design.
       Phase (3) Generate plan to build prototype(s) including resources (materials, chambers, fab time and location, testing, etc.); cost; and approximate timeline.      Phase (4) Build prototype(s), test and determine sensitivity.
       Phase (5) Write summary and recommendations for next phase (if promising).
    The project will be developed in coordination with the Advanced Coating Group of II-VI Inc. located in Santa Rosa, Ca. 

    Requirements:  Knowledge of electromagnetic waves, waveguides, and optics. Experience with simulation software (Lumerical FDTD), metasurfaces, 2D materials, optical thin films and magneto-optical thick films would be useful but it is not required.  

    Link: https://sites.google.com/site/jsebastiangomezdiaz/ 

    Project: MS projects in the Integrated Nanodevices & Nanosystems Laboratory 

    Sponsor: Professor Saif Islam 

    Description: Project opportunities include:
    1.- Silicon photodiodes for 100gigabit/sec and beyond data communication.
    2.- Computational imaging with photon-trapping photdiodes.
    3.- Photon detectors for quantum internet.
    4.- Transparent solar cells for window based on UV and IR light absorption.
    5.- High resolution time-of-flight (TOF) sensing with ultra-fast photodiodes.
    6.- LIDAR: technological challenges and recent developments.
    7.- Ionizing air and gases to trap COVID-19 virus and prevent airborne transmission. Financial support available for strong candidates!
    8.-Ultra-fast silicon photodiodes for real-time visualization of tumor boundaries during surgery enabled by fluorescent lifetime imaging (FLIm).
    9.-Semiconductor transistors/memory for extreme temperature and harsh environment. 
    10.- Memory and logic based on memristors: Simulations and design. 
    Link: https://www.ece.ucdavis.edu/~saif/

    Project: MS projects in the Woodall Research Laboratory 

    Sponsor: Professor Jerry Woodall 

    Description: Project opportunities include:
    1.- Compound semiconductor materials and epilayer projects
        a. Materials: AlGaAs, GaP, ZnSe/GaAs, HJ alloys
        b. Epi tools - LPE for GaP, AlGaAs; MBE for III-V and II-VIxIII-V1-x devices
    2.- LPE Devices: AlGaAs "true red" 610nm for high efficiency LEDs for pixelated displays.
    3.- MBE Devices based on ZnSe/GaAs
        a. ZnSe/GaAs for RGB LEDs, BG 1.4-2.7 eV.
        b. ZnSe/GaAS THz HBTs.
    4.- Latent heat storage of intermittent solar and wind power
        a. Convert intermittent solar/wind power to 24/7 power via latent heat energy storage: 577 C Al-Si eutectit and Si phase change batteries.
    5.- Hydrogen Generation via Stored Energy in Aluminum and Water
        a. Split water using Al-Ga alloys to make UHP H2 and UHP Al2O3. 

    Link: https://woodall.ece.ucdavis.edu/

    Project: Tailored NMEMS-plasmonic platform for gas/cancer detection 

    Sponsors: Professor J. Sebastian Gomez-Diaz and Texas Instruments 
    Description: This project deals with the development of a platform that combines NMEMS at RF with tailored metasurfaces at IR to detect specific spectral fingerprints of gases and cancer cells. The project will include (i) development of plasmonic metasurface and characterization with a Fourier Transform Infrared Spectrometer with microscope; (ii) update an existing RF and laser testing set-up; and (iii) development of a testing chamber printed in 3D. The project requires knowledge of electromagnetics and the use of numerical software (Matlab and CST/COMSOL). Once the system is ready, it will be applied to the analysis of gases and biological samples. The project will be developed in coordination with Texas Instruments. 
    Requirements: Knowledge of electromagnetic waves, RF, and optics. Experience with instrumentation software (Labview/Matlab), metasurfaces and MEMS design would be useful but it is not required.  

    Link: https://sites.google.com/site/jsebastiangomezdiaz/ 

    Project: Reconfigurable Computing with Photonic Interconnects and AI

    Sponsors: Professor S. J. Ben Yoo.
    This project seeks innovations in scalable high-performance cloud computing systems through a combination of new generation of optical interconnect technologies as well as existing electronic switching architectures. The current project team is planning to conduct computing and networking experiments through a combination of off-the-shelf computing and networking equipment and research-grade optical interconnect and switching devices. The MS student is expected to assist the NGNS researchers with FPGA programming, Ethernet network switches configurations and automation, Linux servers’ configuration, and software-defined networking programming. This project can accommodate two students.
    Proficiency in one or more script languages (e.g. Python, Matlab), C/C++, etc. Good knowledge of Linux operating system (e.g. Ubuntu). Familiar with distributed programming and MPI protocol. Familiar with HDL language and FPGA programming platforms (e.g. Xilinx Vivado). Familiar with Ethernet and TCP/IP networks, LAN configuration, and Ethernet switches configuration and routing protocols.  

    Link: https://sierra.ece.ucdavis.edu/index.php/2020/03/21/computing-architecture-algorithm-and-testbed-studies-for-reconfigurable-computing-with-photonic-interconnects-and-ai/

    Project: AI-Assisted Self-Driving Autonomic Optical Networking

    Sponsors: Professor S. J. Ben Yoo.
    This position seeks innovations in next-generation autonomous and self-driving optical networking systems leveraging existing and emerging machine learning and AI tools. The current project team is planning to build novel prototype network control plane algorithms and experiments. The MS student will assist the NGNS researchers with conducting computing and networking systems integration, and software-defined networking programming to implement novel and scalable control and management plane architectures and algorithms. In particular, we are looking for someone helping implementing AI-driven resource calculation modules, application interfaces, communication protocol extensions, and network telemetry functions. This project can accommodate two students.
    Proficiency in one or more script languages (e.g. Python, Matlab),  Java, C/C++, etc. Familiar with machine learning algorithms (e.g. deep reinforcement learning) and tools (e.g. Tensor Flow or PyTorch).  Familiar with Ethernet and TCP/IP networks, LAN configuration, Ethernet switches configuration and routing protocols, network monitoring tools (e.g., Wireshark). Familiar with software defined networking (SDN) and Open Network Operating System (ONOS®).

    Link: https://sierra.ece.ucdavis.edu/index.php/2020/03/20/service-provisioning-in-multi-domain-sd-eon-with-machine-learning-and-game-theory-approaches/

    Project: 3D Ultrafast Laser Inscription

    Sponsors: Professor S. J. Ben Yoo.
    This project seeks to design, inscribe, and test arbitrary 3D waveguides for future computing, networking, and imaging applications.  Utilizing the unique ultrafast laser inscription facility, the project team has realized 3D waveguides of arbitrary shapes and forms.  More descriptions are available in this publication:

    S. J. Ben Yoo, Binbin Guan and Ryan P. Scott, “Heterogeneous 2D/3D photonic integrated microsystems“,  Microsyst Nanoeng 2, 16030 (2016).

    Proficiency in one or more script languages (e.g. Python, Matlab),  Java, C/C++, etc. Good knowledge of optics and waves. Overall good skills in laboratory experiments. Familiarity with computer controlled instrumentation is desired but not necessary. Familiarity with computer aided design is desired but not necessary.

    Link: https://sierra.ece.ucdavis.edu/

  • Area of Research: Information Systems
  • Project: Video-based quantification of dexterous finger movement kinematics using computer vision and deep learning techniques

    Sponsors: Professors Wilsaan Joiner and Karen Moxon
    This project will apply computer vision and deep learning techniques to analyze the dexterous finger movements of nonhuman primates (rhesus macaque monkeys). The subjects are recorded while performing a task which involves retrieving food rewards from variously-oriented shallow wells (i.e., the Brinkman Board task). The MS student is expected to assist in streamlining the analysis of the videos and applying DeepLabCut, a deep learning toolset that allows for the markerless tracking of various locations across multiple video frames. The information obtained from movement tracking will then be used to quantify several features of finger movements (separation, extension and preshaping) in order to provide behavioral measures that are sensitive to injury (e.g., spinal cord contusion) and treatments. Importantly, this will provide critical information to evaluate the effectiveness of novel interventions for clinical conditions that affect the motor system.

    Requirements: Applicants should have expertise in machine learning, deep learning and computer vision concepts, and ample experience with common programming languages such as C++, Python and Matlab.

    How to: To apply, please email your CV and interest statement to: wmjoiner@ucdavis.edu

    Project: Security of Deep Reinforcement Learning-based Traffic Signal Controllers (TSC)

    Sponsors: Professor Chen-Nee Chuah.
    Next generation of TSCs expected to communicate with traffic environment and learn how to behave in different traffic conditions. For this purpose, we have shown that the traffic signals controlled with deep reinforcement learning (DRL) are effective in terms of traffic flow and air quality. However, adversarial attacks may target such edge controllers. The impact of adversarial attacks to the learning-based TSCs could have serios consequences beyond traffic congestion, such as life threatening traffic accidents. Initial results of this project show that learning based TSCs are vulnerable to adversarial attacks. This project further extends the study to a different level and seeks novel solutions for DRL- TSCs on city level San Francisco downtown network and different learning configurations such as different state, action, and reward definitions.

    Requirements: Expertise in Python programming and machine learning libraries (Numpy, Tensorflow, Matplotlib, Pandas), ability to research on intelligent systems, knowledge about deep reinforcement learning concept and security of machine learning.

    If interested, please email your resume/CV to chuah@ucdavis.edu with [DRL-TSC with AV] in the subject title.

    Project: Optimal Traffic Control with Deep Reinforcement Learning-based Traffic Signal Controllers and Autonomous Vehicles

    Sponsors: Professor Chen-Nee Chuah.
    Deep reinforcement learning (DRL) is a promising machine learning tool that combines artificial neural networks with reinforcement learning algorithms. DRL models have been applied to different control domains including intelligent transportation systems. We have seen very promising results for DRL-based traffic signal controllers (TSC) on city level traffic flow in terms of travel delay and air pollution. In the context of autonomous vehicles (AV), DRL can be applied to control optimization, path planning and navigation. However, it remains an open question as to how these DRL-TSCs and DRL-AVs can co-exist and collaborate effectively. Since AVs are great tools for traffic platooning, it will be interesting to quantify the performance of DRL-TSCs in mixed traffic (with a combination of autonomous and human-driven vehicles).

    Requirements: Expertise in Python programming and machine learning libraries (Numpy, Tensorflow, Matplotlib, Pandas), ability to research on intelligent systems, knowledge about deep reinforcement learning concept.

    If interested, please email your resume/CV to chuah@ucdavis.edu with [DRL-TSC with AV] in the subject title.

    Project: Deep Camera Calibration – Deep Learning for Accurate Camera Calibration in Assembly Automation

    Sponsors: Professor Iman Soltani.
    This project is going to be conducted at LARA (Laboratory for Artificial Intelligence, Robotics and Automation). The overall goal of the project is to develop a deep learning scheme for accurate and streamlined camera calibration that is suitable for precision assembly automation.

    Camera calibration is the first and foremost step in any robotics application involving vision. Currently the models used for this purpose are simplified and the calibration process is cumbersome. These simplifications lead to rather inaccurate calibration results that are acceptable for only a subset of applications relying on vision such as mobile robotics in which obstacle avoidance is the main objective. However, applications requiring high precision positioning such as assembly automation cannot rely on vision alone solely due to low accuracy of the vision-based object positioning methods.

    This project aims to rely on deep learning to form more complex models of camera 3D to 2D mapping and develop streamlined calibration schemes that can be easily implemented. 

    Link: https://soltanilab.engineering.ucdavis.edu/

    Project: Learning from Simulation in Assembly Automation and Quality Control

    Sponsors: Professor Iman Soltani.
    This project will be conducted at LARA (Laboratory for Artificial Intelligence, Robotics and Automation). The focus of this project is on generalization performance of deep networks trained on simulated training data. The main application under consideration is quality control and assembly automation. As part of this project we aim to train deep networks to detect certain keypoints on an image of a given mechanical component or assembly. The detection of these keypoints will help us estimate the absolute or relative position of the parts in 3D space. This information can be used for assembly quality control or for assembly automation.

    However, training deep networks for keypoint detection requires large volumes of training data. Such training data include thousands of images of mechanical parts in which the keypoints of interest are annotated manually by human operators. This process is cumbersome, requiring capture of thousands of images from various perspectives and annotating the corresponding keypoints. This has to be repeated upon product design updates or sometimes after a significant change in the assembly environment e.g. lighting.

    To avoid the complications and cost associated with training data generation, we plan to develop a training scheme solely reliant on synthetic training data generation. In this approach component CAD information is used to synthesize realistic images. In this form the keypoints can be annotated automatically. As such, thousands of training images can be generated very quickly.

    However, the deep learning schemes develop should benefit from a robust generalization  performance such that their ability do not deteriorate when test samples come from real images of same components.  

    The ideal outcome of this project is a deep learning architecture that performs reliably on real images of parts of interest. This network will be trained fully on simulated (synthetic) images of the same parts e.g. generated through a CAD software.

    Link: https://soltanilab.engineering.ucdavis.edu/



  • Area of Research: RF-to-THz Electronics and Waves
  • Project: Nonreciprocal phased array antennas

    Sponsors: Professor J. Sebastian Gomez-Diaz 
    Description: This project deals with the analysis, design, fabrication and characterization of nonreciprocal phased-array antennas able to transmit and receive RF signals with different patterns at the same operation frequency with polarization control. The project entails the design of antenna in simulation software (HFSS or CST), the use of nonlinear circuit analysis (ADS), fabrication, and measurement in an anechoic chamber.  
    Requirements: Knowledge of electromagnetic waves and electronic circuits. Experience with full-wave simulation software (such as HFSS/CST and ADS) would be great but it is not required. It is a project for 1~2 students.

    Link: https://sites.google.com/site/jsebastiangomezdiaz/ 

    Project: THz imaging 

    Sponsors: Professor J. Sebastian Gomez-Diaz 
    Description: This project deals with the development of a imaging system based on time-domain terahertz spectroscopy. The goal is to automatize the system with a 2D positioner, aiming to implement imaging of biological samples from 0.1 to 4.5 THz. The project requires the analysis of THz waves, the implementation of signal processing algorithms, and the development of instrumentation code. Once the system is ready, it will be applied to the analysis of biological healthy/cancer biological samples. 
    Requirements: Knowledge of electromagnetic waves and Matlab. Experience with instrumentation software (Labview/Matlab) would be useful but it is not required. 

    Link: https://sites.google.com/site/jsebastiangomezdiaz/ 

    Project: UC Davis Dark E-field Radio experiment

    Sponsor: Professor Tony Taylor 

    Description:  The UC Davis Dark E-field Radio experiment is a search for the electromagnetic signature from a low mass dark matter candidate called a dark photon. It involves massively averaging the EM noise inside an RF shielded environment to look for high Q candidate signals 80 dB below the Johnson noise threshold. For the first phase of this project, we are building a 64-million channel real-time FFT over the 30-300 MHz region. However, this will produce terabytes of data that need to be efficiently packaged, compressed, stored, and analyzed on a remote data server. We are looking for someone to design this data analysis tool chain and implement it on experimental data.

    Requirements: Proficiency with common programming languages such as C++ and Python. Courses in Signals and Systems.

    Link: https://tyson.ucdavis.edu 


  • Area of Research: Integrated Circuits and Systems
  • Project: CMOS Analog IC design

    Sponsor: Professor Stephen Lewis

    Description: Continue the class project in EEC 210 or do another project related to analog CMOS integrated-circuit design.

    Requirements: Receiving a B or higher in EEC 210.

    Link: https://www.ece.ucdavis.edu/~lewis/

  • Area of Research: Bio Ag and Health Technologies
  • Project: Development of novel, full-implantable blood pressure monitoring sensor

    Sponsor: Professor Karen Moxon

    Description:  The Moxon Neurorobotics Lab is looking for an ECE Master’s student interested in working on a neuroengineering project to support the testing of a novel, full-implantable blood pressure monitoring sensor. The sensor is being developed as part of an on-going DARPA project to design a closed-loop hemodynamics control system for patients with neurological injury who are unable to control their own blood pressure. 

    Prototypes are currently being tested in an animal model and the successful candidate will develop computer code to process the data and interpret results, suggest additional experimental testing and aid in report of results to funding agencies. The MS student is expected to assist in streamlining the analysis of the data and help to develop an algorithm as part of the closed-loop control system.

    Requirements: Applicants should have ample experience with common programming languages such as C++, Python and Matlab and an interest in neural engineering and computer control system. Applicants should have excellent data analytic skills include data management, process documentation and detailed reporting. Applicants are also expected to be able to create figures to explain results and present results to other members of the team.

    may be available. 

    How to : To apply, please email your CV and interest statement to: moxon@ucdavis.edu

    Project: Tactile navigation for individuals with visual impairment 

    Sponsor: Prof. Iman Soltani 

    Description: This project involves development of hardware and software platforms to guide individuals with visual impairment in dynamic environments through tactile feedback. The hardware aspect includes development of micro actuators and mechanisms that change the topography of a tactile surface. By changing the topography of the surface we aim to provide a map of the surrounding obstacles to the user. This approach is inspired by Braille and will work very similar to how Braille is used by the blind individuals to read texts, but here instead of a book the users will read their surrounding environment. Through tactile feedback our technology will provide an image of the environment to the blind, helping them navigate their surroundings safely. The software aspect includes sensor fusion and receives all the sensory information available on a smart phone including camera, Lidar, IMU and GPS. 

    Requirements: We are currently seeking a masters student with hands-on experience and a passion for designing and building electromechanical systems. Experience with sensor fusion is a plus but is not necessary. Partial financial support in the form of an hourly appointment is available.

    How to :
    To apply, please email your CV and interest statement to: isoltani@ucdavis.edu

    Project: Portable Sensor System to Assess the Health Conditions of Individuals working Under Harsh Environments

    Sponsor: Professor Cristina Davis,  Associate Dean for Research, Mechanical and Aerospace Engineering

    Description: This project aims to design, prototype, and test an integrated sensor platform that will record physiological data (e.g., heart rate, oxygen saturation, physical activity levels, skin temperature, and galvanic skin response) of athletes and individuals who work in harsh environments. The envisioned lightweight device will consist of several commercially available sensors and a microcontroller for physiological data acquisition and integration. A standalone, portable, and small single-board computer (e.g., Raspberry Pi, or alternative) will complement the device for analyzing the extracted data based on prebuilt machine learning models. The system will report data by bluetooth to a WiFi connection hub.

    Requirements: Applicants from computer engineering background should have a solid knowledge in data structures and algorithms. Applicants from an electrical engineering background should have experience on microcontroller coding and circuit designs. Willingness to adapt to several programming languages. Team work may be required.

    How to :
    To apply, please email your CV and interest statement to: biomems.ucdavis@gmail.com

    Project: Thermo-electro-mechanical Testing Platform Development

    Sponsor: Professor Erkin Şeker

    Description: Thermal, electrical, and mechanical fields dictate the evolution of nanostructure in thin metal coatings that are used as battery electrodes, catalysts, and biomedical device coatings. The goal of this project is to develop a testing platform that impose time-varying temperature fields, mechanical stresses, and electrical currents to nanoporous gold thin films and in real-time acquiring mechanical stress and electrical resistance changes in the thin films. The student(s) will collaborate with other graduate students working on the materials science aspects of this project.

    Requirements: Basic microfabrication and manufacturing process experience, and MATLAB and LabView-based programming and interfacing with sensors/actuators are required.

    Project: Electrochemical Biosensor Engineering

    Sponsor: Professor Erkin Şeker

    Description: Electrochemical sensors are used for detecting environmental contaminants, biomarkers for health monitoring, and pathogens. In such sensor the electrode where the detection event takes place plays a critical role. This project builds upon our group’s experience in engineering nanoporous gold electrodes for nucleic acid detection and aims to continue the development of such sensors with interactions with collaborators at the Lawrence Livermore National Laboratory.

    Requirements: Basic microfabrication experience, biology, biochemistry, and/or electrochemistry knowledge are desirable.

    Project: Microfluidic Device Laboratory Course Development

    Sponsor: Professor Erkin Şeker

    Description: Microfluidic devices are composed of small channels and flexible membranes that guide fluid flow for studying physical/chemical/biological phenomena as well as creating miniaturized analysis tools on chips. These devices vary much behave like analog electrical circuits. This project aims to create similar course to the existing EEC 146A (Integrated Circuit Fabrication Laboratory) but with a focus on fabricating and characterizing microfluidic devices, with the ultimate goal of it being offered as an upper-level undergrad or grad-level laboratory course.

    Requirements: Basic microfabrication (soft lithography) experience and basic fluidic dynamics knowledge are desirable.

    Project: CeDP:  Computational Efficiency of Deep Learning in Digital Pathology

    Sponsor: Professor Chen-Nee Chuah

    Description: While supervised learning (SL) techniques such as convolutional neural networks achieve promising results in pathology images, the computational complexity is still significantly heavy due to the gigapixel resolution of pathology images. To make deep learning more practical in digital pathology, it is necessary to comprehensively study the tradeoff between performance and complexity. In this project, we will study how to deploy efficient deep learning models on edge devices for pathology image analysis and how to remove unnecessary computation in the recent state-of-the-art deep learning networks. We will also benchmark the complexity of different models on our pathology datasets.

    Requirements: Expertise in machine learning concepts, Docker, and Python programming inclusive of scikit-learn, Pandas, PyTorch/Tensorflow.

    Project: SSL-Pathology: Semi-supervised Learning in Pathology Detection of Alzheimer's Disease

    Sponsor: Professor Chen-Nee Chuah

    Description: While supervised learning (SL) techniques such as convolutional neural networks achieve promising results in medical images, procuring a sufficiently large dataset with annotations is labor-intensive, especially in gigapixel pathology images. To circumvent the need for large labeled datasets, semi-supervised learning (SSL) can be a potential approach. Amyloid-beta plaques are hallmarks of Alzheimer's disease. A supervised detection model has been established to classify three types of plaques. However, it relies on more than 50,000 annotated images for training the model. In this project, we will adopt SSL to this problem and explore the upper bound of SSL to relieve the reliance on a large labeled dataset.

    Requirements: Expertise in machine learning concepts, Docker, and Python programming inclusive of scikit-learn, Pandas, PyTorch/Tensorflow.

    Project: Computer-Vision Assisted Autism Disorder Spectrum (ADS) Behavior Detection using Videos

    Sponsor: Professors Chen-Nee Chuah and Samson Cheung

    Description: Early intensive intervention has been shown to be highly promising for young children with autism spectrum disorder (ASD) and hence a measure that could identify ASD risk during this period of onset offers the opportunity for intervention before the full set of symptoms is present. In collaboration with the MIND Institute, our team have developed computer vision (CV) and deep learning (DL) based video-based screening tool that utilizes a large library of video clips. The videos are collected under the Video-referenced Infant Rating System for Autism (VIRSA) project and depict a wide range of social-communication ability and relies solely on video in the ratings, with no written descriptions of behavior. We hypothesized that the semantic clarity afforded by video would provide improved early discrimination of infants at highest risk for ASD. In this project, we will expand on our previous efforts to explore (a) optimized models for mobile screening platform, (b) mitigation for bias in AI models, and (c) security and privacy issues associated our CV/DL-based pipeline.

    Requirements: Expertise in machine learning and computer vision concepts, Python programming inclusive of scikit-learn, Pandas, PyTorch/Tensorflow

    Project: Time-resolved near-infrared spectroscopy for blood oxygenation measurement

    Sponsor: Professors Weijian Yang and Soheil Ghiasi 

    Description: Blood oxygenation is the fraction of oxygen-saturated hemoglobin relative to total hemoglobin (unsaturated + saturated) in the blood. A healthy individual regulates a very precise and specific balance of oxygen in the blood and there is medical significance to monitor oxygen saturation in patients. Near-infrared (NIR) spectroscopy provides a noninvasive approach to conveniently measure the blood oxygenation. In this project, we will study the various approaches of NIR spectroscopy for such measurements. In particularly, we will investigate and develop a time-resolved NIR spectroscopy system, which could not only provide the measurement results from the typical continuous-wave (CW) systems, but also rich information of the tissues under the measurement probe. We will develop the model, perform simulation, explore the components, build and characterize the prototype and perform in-vitro (and in-vivo) measurements.

    Requirements: Electronic circuits, Basic optics, Matlab. It is a project for 2 students.

    Project: Target brain stimulation using surface electrodes

    Sponsor: Professor Weijian Yang

    Description: Delivering electrical field into the brain for stimulation has been shown to be effective to treat depression, stroke, dementia and several other medical conditions. The existing brain electrical stimulation paradigms either rely on electrodes implanted deep into the brain or surface electrodes on the skull. The former approach is highly invasive whereas the latter one lacks a spatial specificity. Recently, a new technology utilizes temporal interference of fields from multiple surface electrode pairs to noninvasively stimulate specific brain regions. In this project, we will optimize the design parameters of such temporal interference system to further increase the spatial specificity of the stimulation region, through finite element method simulation. We will also build a prototype of this electrical stimulation system and test it on rodents.

    Requirements: Electronic circuits, Electromagnetic waves, Matlab. It is a project for 1~2 students.