Resume for 2024. Embedded systems, Backend Development & Rust Programming and FUN

1. Dilnoza Bobokalonova
SOFTWARE ENGiNEER · BACKEND & EMBEDDED SYSTEMS
 917.592.2825 |  dilnoza1@berkeley.edu |  tinyurl.com/DilNova |  dilnozabobokalonova1 |  dilnozabobokalonova1
Summary
I thrive in challenging environments and am deeply passionate about learning and engineering critical systems. Over the past 8 months, I’ve
worked on acquiring skills in embedded systems, exploring space technology, and developing whitehat projects in Rust. My background is in
building Deep Learning & NLP models at Berkeley, developing robust backend software systems at Coursera, and leveraging Rust in various con‑
texts. I am now in search of my next opportunity to advance and grow as both an engineer and leader.
Education
University of California, Berkeley Berkeley, CA
MASTER OF ENGiNEERiNG iN ELECTRiCAL ENGiNEERiNG AND COMPUTER SCiENCE | DATA SCiENCE & SYSTEMS Aug. 2018 ‑ May 2019
University of Miami Coral Gables, FL
B.S. iN COMPUTER SCiENCE | MiNORS iN MATHEMATiCS & INTERNATiONAL STUDiES Aug. 2014 ‑ May 2018
Skills
Programming/Scripting Rust, Scala, Embedded C, Java, Python, LISP, Prolog, Bash, MATLAB, LaTeX, Dart, JavaScript, HTML/CSS
Dev, ML & Technologies Jenkins, Docker, ZooKeeper, Tensorflow, NLTK, Kafka, DynamoDB, ElasticSearch, KiCAD, Terraform, AWS
Languages Russian, Tajik, English
Work Experience
Coursera Mountain View, CA
SOFTWARE ENGiNEER Jun. 2019 ‑ May. 2023
• Architected and implemented robust backend systems for products within the Coursera platform, constructing numerous APIs (REST in Scala,
gRPCinJava)andmaintainingthereliableoperationandhealthofownedservicesusingvarioustools. (Jenkins,Sumo,R2,ZooKeeper,DataDog)
• Authored in‑depth system design and architecture documents of services for high‑profile company projects. Identified & worked around edge‑
cases early, achieving consensus across legal, product and enterprise teams.
• Served as Lead Engineer for a critical company project (LevelSets) in 2021‑2022. Designed and implemented service architecture, addressed
stakeholder, product and cross‑functional team requirements, and presented project’s progress & launch outcomes during company‑wide calls
to ensure transparency and alignment across teams. (Scala, REST, DynamoDB, Cassandra, Sagemaker)
• IntroducedsignificantmodificationstoCoreLearnerlegacycode(Scala), cuttingerrorratesby35%, eliminatingtimeouts, andincreasingsystem
reliability with unit & E2E testing; enhanced resource utilization, leading to a 5% reduction of monitoring costs.
• Drove the migration of critical backend services from Scala to Java during polyglot programming adoption. Moved APIs from RESTful to gRPC,
upgraded the data stores, and unified complex business logic across services, achieving a 2x increase in system scalability.
• Led and extensively documented a strategic reorganization of on‑call groups during Engineering reorganization, optimizing multi‑teams new
scheduling efficiency and ensuring seamless transition during company’s pivotal phase in 2021. (PagerDuty, SumoLogic, DataDog)
• Collaborated closely with the Data Science team utilizing my background in machine learning to ensure a smooth integration of ML models
with Learner & Skills backend services. Called out engineering constraints early and consistently accelerated project timelines.
• Identified, addressed, and documented gaps within the engineering stack of another team during a 3‑month embed in 2022, giving a new path
for the goal project and redirecting the project’s focus towards a viable solution after my leave.
UC Berkeley Coleman Fung Institute for Engineering Leadership Berkeley, CA
DATA SCiENTiST & NATURAL LANGUAGE PROCESSiNG DEVELOPER Jun. 2018 ‑ May. 2019
• Utilized powerful natural language processing and machine learning techniques to analyze the technology development of autonomous vehi‑
cles (AV) industry, specifically LIDAR technology.
• Implemented document similarity analysis to expand 1 patent seed to a pool of 1000 similar patents drawn from the AV data of 40000 patents,
producing insight into the competitive landscape & innovation AV trends.
• Developed predictive models, such as Support Vector Machines, Random Forest and LSTM neural networks, to project the quantity and spatial
distribution of future patents across 244 distinct CPC classes for the 2019‑2020 quarters, achieving an accuracy rate of 96.1%.
• Utilized K‑means and LDA techniques to extract 5 clusters within the AV space, identifying major areas for technological investment.
• Performed dimensionality reduction (PCA) to convert an original 33k‑feature vector to 3D and visualize the future patent space of LIDAR in VR.
Projects
Rust BCL (Berkeley Container Library) Berkeley National Laboratory, CA
RUST iN DiSTRiBUTED COMPUTiNG USiNG OPEN MPI | CORi SUPERCOMPUTER Jan. 2019 ‑ May. 2019
• Developed the Rust Berkeley Container Library (RBCL) designed specifically for high‑performance distributed computing environments, lever‑
aging the power and scalability of the Cori supercomputer at the Lawrence Berkeley National Laboratory.
• Implemented RBCL’s infrastructure, integrating MPI communication protocol and shared memory managementtechniques (atomic operations,
global pointer management, and guarding) to ensure safe and efficient data sharing, synchronization, and threading across distributed nodes.
• Performed rigorous scalability tests to assess RBCL’s ability to efficiently scale across processor nodes on Cori, optimizing communication, data
partitioning strategies, and load balancing to support efficient scaling up to 500+ nodes.
• Benchmarked Rust BCL under varying degrees of parallelism and cluster sizes to test throughput, latency, and resource utilization, ensuring
optimal performance as the system scales and surpassing the performance benchmarks of BCL’s C++ counterpart.
FEBRUARY 16, 2024 DiLNOZA BOBOKALONOVA · RÉSUMÉ 1

2. Embedded Systems Engineering University of California, San Diego
HARDWARE DESiGN, ARM, STM32, HAL, BSP & BAREMETAL | FPGA XiLiNX ZYNQ‑7000 Jun. 2023 ‑ PRESENT
• Developed bare‑metal drivers for the STM32F3 series including I2C, SPI, UART, GPIO, Timer, and Systick to gain an in‑depth understanding of
the STM32 board and ARM Cortex‑M4 core.
• Advanced to implement the interrupt programming in bare‑metal across UART, GPIO, ADC, Systick, and Timer modules, integrating DMA con‑
troller techniques for optimized data transfer.
• Designed and verified an embedded systemusing STM32L4microcontroller; developed comprehensiveschematicsin KiCAD, conductedsensor
tests, and produced BOM and Netlist for a robust hardware setup.
• Soldered an 8‑pin connector to the FRAM PCBA (MB85RS64V) for integration with the IoT board’s SPI Interface, effectively isolating the FRAM
module’s communication channel from other peripherals.
• Currently learning FPGA Hardware design with the Xilinx Zynq platform by programming Red Pitaya STEMLab 125‑14 board in Verilog.
WhiteHat & Rust Notion | GitHub
ASYNC, THREAD CONCURRENCY, PORTS DiSCOVERY & EXPLOiTS Jan. 2023 ‑ PRESENT
• Developed a multi‑threaded scanner in Rust with rayon to discover vulnerable open ports for a set of given subdomains and IP addresses.
• Optimized the original scanner by transitioning from multi‑threading to async programming using the Tokio Rust runtime, lowering scanner’s
context‑switching latency by 8.5x.
• Implemented a multi‑purpose web crawler using async, inter‑thread communication, and atomics in Rust to scrape GitHub Org users, JS Web
Apps & extract cool CVE data. Incorporated fault‑tolerant mechanisms to gracefully handle network failures.
• Rewrote a Python‑based exploit in Rust to expose vulnerabilities in mirror repository URL settings. It logs into a system, sets up a temporary Git
repo, and uses Actix Web for file serving, revealing the critical risk of remote code execution (CVE‑2019‑11229).
Deep Learning Specialization deeplearning.ai
TENSORFLOW, NLTK, PANDAS | 12 PROJECTS TOTAL Aug. 2017 ‑ May. 2018
• Developed a car detection algorithm for autonomous driving using You Only Look Once (YOLO) model containing over 50 million parameters
able to detect 80 different classes in an image.
• Created a face recognition system to map face images into 128‑dimensional encodings for accurate element‑wise comparison.
• Built a Neural Machine Translation model to translate human readable dates into machine‑readable dates by using a sequence‑to‑sequence
model.
• Synthesized & processed audio recordings to create a dataset used to implement an algorithm for trigger word detection.
FEBRUARY 16, 2024 DiLNOZA BOBOKALONOVA · RÉSUMÉ 2