# Zhengnan H. > 赛博佃农 Location: United States, United States Profile: https://flows.cv/zhengnan As a dedicated software engineer, I specialize in developing software solution in Python/Go to address optimization challenges in maritime and medical Imaging. In my role at a startup, my key responsibilities include: Designing a vital ETL (Extract, Transform, Load) service pipeline in Python and Go for processing weather forecast data, crucial for our core routing and speed optimization products. Developing and maintaining services to ingest weather data from NOAA, handling over 3,800 files daily (43 GB), and transforming this data into a searchable format stored in AWS S3 buckets, resulting in a dataset of 110 TB across 17 million files. Managing a complex, multi-layer caching system to incorporate new data features and boost runtime performance, alongside fine-tuning the integration of weather services into our optimization products. Crafting a data parser that generates JSON files for the frontend component via AWS CloudFront, optimizing both speed and visualization accuracy. Continuously enhancing our data-driven email alerting system using SendGrid, resolving discrepancies between client records and our metrics, and addressing authentication-related issues. Implementing tracking redundancy and health monitoring through AWS CloudWatch, Datadog and Mezmo to bolster system robustness. Overseeing a critical Redis cache system, serving as the primary contact for resolving issues like feature outages and performance degradation. This system processes approximately 12.5 billion sensor data entries per year, contributing to a total of 50 billion data points. Converting industrial-standard emission calculation documents into executable code and advancing emission calculations in line with IMO (International Maritime Organization) standards. I used to work in NYU as a research assistant where I focus on MR image reconstruction and image detection software/algorithm development, leveraging deep learning to enhance radiologists' capabilities. The primary goal is to reduce MR scan and image reconstruction times while preserving image quality for diagnostic purposes. I also developed machine learning-based reconstruction models (convex optimization) and created data processing pipelines, solidifying my expertise as a Python programmer. Daily work on Linux-based computer clusters has made me proficient in GPU computing using PyTorch on HPCs. My foundational training in bioinformatics, where I studied computer science and biology, has been invaluable, especially the engineering mathematics learned during my undergraduate studies. ## Work Experience ### Software Engineer @ Unknown Jan 2024 – Present | United States Identified a gap in a fast growing market with rapidly changing demand through in-depth research. Leveraging my previous experience to address the needs of a small but specific target audience, while acquiring new skills (including new programming language). Design and developing services from scratch, including the python/go backend to kotlin client with fast iterations. ### Professional Interviewee @ None Jan 2023 – Jan 2024 | New York City Metropolitan Area Maintained job search momentum during market downturn following company(a startup which I really enjoyed working at and stayed until the last second) closure. Dealt with unexpected failure during interviews on leetcode-style questions that I usually solve without problem. Stay professional even when things are not working out during interview. Maintained composure during technical challenges, even for soul-searching behavior questions. Clearly communicated thought processes to demonstrate problem-solving abilities. ### Software Engineer @ Nautilus Labs Jan 2022 – Jan 2023 | New York City Metropolitan Area ● Developing a critical ETL (Extract, Transform, Load) service pipeline in Python and Go for weather forecast data, essential for core routing and speed optimization products. ● Developed and maintained services for ingesting weather data from NOAA, processing over 3,800 files (43GB) daily. Converted this data into a searchable tile format and stored it in AWS S3 buckets, accumulating a total of 110 TB across 17 million files. ● Navigating a complex multilayer caching system to integrate new data features and enhance runtime performance. Fine tuning the integration of weather service to our optimization product. ● Creating a data parser that outputs JSON files directly utilised by the frontend component of our service through AWS CloudFront, balancing speed and visualisation accuracy. ● Continuously improving the data-driven email alerting system using SendGrid, addressing inconsistencies between client-recorded emails and our metrics. Troubleshoot missing records related to authentication. ● Enhancing system robustness by implementing tracking redundancy and health monitoring through Datadog and Mezmo. ● Managed and debugged a critical Redis cache system, integral to key services. Served as the primary point of contact for resolving issues, including feature outages and performance degradation. The cache service handled approximately half of the total data, encompassing 32 million key-value pairs and processing 12.5 billion sensor data entries per year, contributing to a total of 50 billion data points. ● Translating industrial standard emission calculation documents into code and continuing to develop emission calculations according to IMO (International Maritime Organization) standards (Python and Go). ### Research Assistant @ NYU Langone Health Jan 2018 – Jan 2022 | New York City Metropolitan Area Software Development for Machine Learning MR Image Reconstruction • Developing simulation pipeline to generate realistic breast perfusion images. Developing multi-coil array, non-cartesian MRI raw data simulation pipeline for accelerated simulation on GPUs with PyTorch. • Reconstruction Software Development: Developing and implementing iterative reconstruction model with convolutional neural network as regularizers. Using simulated data to train the model with GPUs on computer clusters to reconstruct breast perfusion images. • Exploring alternative architecture (GRU，LSTM, U-Net etc) and loss function optimization to increase the reconstruction accuracy of reconstruction and its robustness. fastMRI (In collaboration with Facebook AI)(https://ai.meta.com/research/impact/fastmri/) • Collected and cleaned large volumes of brain MRI raw data for the fastMRI brain challenge. Created data processing pipelines that perform automatic removal of sensitive patient data (identifiable images). • Reconstructed images from the collected raw data. Provided the reconstructed images to the manual labeling team. Assisted the labeling teem to verify sensitive data removal and labeling. ### Algorithm Engineer @ Infervision Jan 2018 – Jan 2018 | Beijing City, China Breast Lesion Detection with Deep Learning. (Python) • Lesion Detection software Development: Built and trained models (faster R-CNN based object detection models, MXNet implementation) to output lesion locations and types from input mammogram images. • Worked with data labeling team interactively to make sure the training data are optimized for the models to learn. Actively adopting newly labeled data into training routine and provided feedback to the labeling team based on model learning progress. ## Education ### Master of Philosophy - MPhil in Biomedical Imaging & Technology New York University Jan 2017 – Jan 2020 ### Master of Science (M.S.) in Bioinformatics University of Michigan Jan 2015 – Jan 2017 ### Bachelor of Engineering in Bioinformation Technology in Bioinformatics Huazhong University of Science and Technology Jan 2010 – Jan 2014 ## Contact & Social - LinkedIn: https://linkedin.com/in/zhengnan-h-989027127 --- Source: https://flows.cv/zhengnan JSON Resume: https://flows.cv/zhengnan/resume.json Last updated: 2026-03-22