CV

CV.pdf

Education

• B.S. ECE, University of Southern California; GPA: 3.68/4.0
• M.S. EE, University of Southern California; GPA: 4.0/4.0

Skills

• Courses: Data Structure, Algorithms, Machine Learning, Software Engineering, Digital Circuits, FPGA programming, Internetworking, Cellular Biology, Quantum Mechanics, Open Quantum System, Semiconductor Devices, Non-linear Circuits, Electromagnetism, VLSI, Neuromorphic computing
• Programming languages: C/C++, Python, Java, Verilog
• Tools & Softwares: Pytorch, Qiskit, pyqpanda, Cadence, ModelSim

Publications

• AccQOC: Accelerating Quantum Optimal Control Based Pulse Generation
  • Accepted. Preprint
  • Jinglei Cheng, Haoqin Deng, Xuehai Qian
  • 2020 Annual International Symposium on Computer Architecture, ISCA
• Parity-time symmetric optical neural networks
  • Under review.
  • Haoqin Deng, Mercedeh Khajavikhan
  • Submitted to Optica, OSA

Researches:

• BioRC Group
  Research Assistant; Supervised by Prof. Alice Parker; Jun 2021 – present
  • Developed VLSI circuits of excitatory/inhibitory synapse, Axon Hillock, STDP-dopamine-noise synapse, dendritic spiking, edge detector, voltage adder
  • Constructed hardware & software multi-layer neural networks incorporating local STDP update and global dopamine modulation, using Cadence & Python
  • Currently developing a pytorch library that
    • allows for convenient construction of software neural network for training & verification purpose
    • synthesizes VLSI circuits corresponding to software neural network model for deployment purpose
• Khajavikhan Optics and Photonics Group
  Research Assistant; Supervised by Prof. Mercedeh Khajavikhan; Jun 2021 – present
  • Researched on various architectures of Optical Neural Network(ONN), with an emphasis on chip-integrable setups
  • Proposed and developed PT-ONN architecture with cascading PT-symmetric couplers, based on modulations of gain/loss contrast; implemented simulations of on-chip training of PT-ONN using finite difference method with python; achieved 67% training accuracy on MNIST
  • Worked on ewbm simulations of directional couplers in COMSOL; calculated system transfer matrix of PT couplers with Mathematica
  • Reproduced on-chip training of MZI-based ONN with python; achieved 71% training accuracy on MNIST
• USC Laboratory for Photons, Electrons and Materials
  Research Assistant; Supervised by Prof. Rehan Kapadia; Jun 2020 – Jan 2021
  • Researched on various architectures of mapping ANN/SNN onto hardware using semiconductor devices that mimic functions of synapses, neurons
  • Simulated a rate-encoded convolutional SNN on MNIST digit recognition task with Pytorch and GPU acceleration, incorporating InP-synapse STDP parameters, with 71% accuracy
  • Simulated a temporal-encoded SNN on MNIST digit recognition task with Python and GPU acceleration, incorporating InP-synapse STDP parameters; achieved 89% accuracy
  • Collaborated on designing Arduino/breadboard implementation of neural networks, utilizing a crossbar array of InP transistors.
  • Grew InP on GaAs using Low-Temperature Templated Liquid-Phase(LT-TLP) technique.
• USC ALCHEM Lab
  Research Assistant; Supervised by Prof. Xuehai Qian; May 2019 – Sept 2020
  • Developed Accelerating Quantum Optimal Control (accQOC), a comprehensive compilation methodology that accelerates pulse generation by 9X
    • partitioned DAG of quantum circuit into sub-components under size constraint
    • generated optimized pulse for each component using QOC
    • balance partitionioned MST computing nodes for efficient parallel computation, using METIS
  • Worked on optimization of VQE(Variational Quantum Eigen-solver) algorithm
    • self-implemented VQE circuits with pyqpanda
    • optimized VQE measurement overhead through joint measurement of commuting Hamiltonians, projecting LI basis terms onto qubit computational basis using stabilizer formalism
    • attempted pulse-level optimization of VQE circuits using Qiskit open-pulse and QOC
• USC IMEDE Lab
  Research Assistant; Supervised by Prof. Manual Monge; Mar 2019 – Mar 2020
  • Corroborated on designing a neural-signal sampling device
  • Realized data transfer between FPGA and PC using Opal-Kelly’s API; implemented SPI protocol to transfer data between FPGAs; visualized data on PC end with matplotlib library

Internships

• Summer Internship, Shanghai AIKE Measurement Co. (July 2018 – August 2018)
  • Automated printers to print labels on a pipeline using Labview
  • Corroborated on designing the structure of a dispenser for lab usage
  • kept track of daily tasks and progress for the team

Hackthones

• Mobile App: Go Eat
  • Developed a cross-platform app that recommends restaurants based on users’ preferences
  • Developed KNN and genetic models to recommend restaurants given users’ and restaurants’ features
  • Implemented data fetching as storage using Firebase
• Desktop Game: Mind Palace
  • Developed a game that trains players’”Mine Palace” mnemonic technique
  • Designed game stages and implemented them with SDL library
  • Extract key information from sentences with Google Cloud API for grading purpose

Class Projects

• CMOS VLSI
  • Implemented a 32 bit MAC unit with Brent-kung Adder and Array Multiplier;
  • Created schematics and drew layouts using Cadence
• Parallel Programming
  • Developed grayscale-to-color image conversion using CNN
  • Converted python model into C++ model using keras2cpp library
  • Parallelized convolution-layer computation using OpenMP and Pthread library
• Video Game Programming
  • Implemented 2D classical Arcade games such as Zelda, Super Mario, PAC-MAN
  • Implemented 3D games such as Mario Cars, FPS, Parkor game
  • Programmed game engines using C++ and SDL libraries
• FPGA Arcade Game “SPLATOON”
  • Designed a 2D-board Splatoon game that runs on a Xilinx FPGA Spartan 6, implemented with Verilog
  • Integrated joystick module for user control and VGA module for image display
• Web Game “Mission Universe”
  • Created a web-based, multiplayer jet-fighting game, using HTML, JavaScript, Java
  • Utilized Phaser API to construct game elements and WebSocket to transmit data in multiplayer mode
• Home Light System IoT Project
  • Developed a remote light control system running on Raspberry Pi
  • Fetched data from light sensor and transmit it through OpenMote
  • Utilized MQTT library to transmit data and control illumination
• Code Compiler
  • Implemented a compiler that parses a realistic program into tree structure, with Bison and C++
  • Generated assembly language and computed values of variables and memories
• Accelerometer
  • Developed an accelerometer running on Arduino
  • Integrated a rotary encoder to set threshold and a buzzer to present speed, controlled by interrupt