Nigel Lu

B.S. Data Science & Business and Finance

Sep 2019 - Present

NYU Shanghai, Shanghai, China

Cumulative GPA: 3.893

Minor in Computer Science

B.S. Data Science & Business and Finance

Sep 2021 - May 2022

New York University, NY, US

Studyaway Year at NYU CAS & NYU Stern

Teaching Volunteer

Stepping Stones, China

Sep 2019 - June 2020

Minhang, Shanghai, China

• Teaching English on weekends for a class of 52 elementary-level migrant children
• Prepare each week's course, including designing and making the course materials, communicating with local teachers

React JS Frontend Intern

eBay Inc.

IE Team, CCoE

Sep 2022 - Present

Pudong New Area, Shanghai, China

• Build and enhance eBay's ATB (Average Time to Business) dashboard for real-time monitoring of cluster status to facilitate smoother rollouts
• Harness the power of function based ReactJS, Antd, and Redux to create smooth, user-friendly frontend pages for eBay's cloud console
• Continuously optimize the frontend codebase and business logic for faster load time, better user experience, and better code readability, including but not limited to componentization of UI parts, reduced number of API calls, detailed documentation following Google JS Style Guide

Vue JS Frontend Engineer

Kaizntree Ltd.

Sep 2022 - Present

Hong Kong SAR

• Established a startup with 3 of my college friends and 4 other employees, helping more than 30 small businesses to simplify their workflow
• Leverage the power of VueJS and Django's versatility to build a one-stop management system for small businesses. The SPA (single-page-application) we built integrates an all-in-one workflow from raw material purchase to building and selling products (visit Kaizntree website here)

Chinese Admissions Ambassador

NYU Shanghai

Oct 2019 - Present

Pudong, Shanghai, China

• Collaborate with different departments in holding events, online panels, presenting weekly campus tours in representation of NYU Shanghai
• Assist in the admission process by performing data analysis on incoming student applications and providing quantitative insights

DevOps Intern

Jiangsu Expsoft Ltd.

May 2021 - Sep 2021

Wuxi, Jiangsu, China

• Collaborated with a team of 18 people in building and operating a browser-server system based on SpringBoot web framework, Apache Tomcat and MS SQL Server

Deep Learning Intern

Jiangsu Expsoft Ltd.

Sep 2021 - Dec 2021

Wuxi, Jiangsu, China

• Built a NLP segmentation tree API specialized in civil and construction engineering setting, and deployed it as a web-based system with UI and API
• Using MobileNetV2 as feature extraction backbone network, build a liveness anti-spoofing detection network, and deploy it as a system with UI and API; Build a face recognition API based on DeepFace library from FaceBook (now changed name to Meta)

Resident Assistant

NYU Shanghai

Aug 2020 - Jun 2021

Pudong, Shanghai, China

• Oversaw a residence hall with 688 residents
• Developed a safe and inclusive community for 49 students; Collaborate with a team of 31 people on implementing programs and administrative responsibilities; Assess and respond to crisis situations

Nuclear Physics B

Hanan Salam, Saloni Rakholiya, Jialin Li, Nigel Lu (my preferred English name). Multimodal Online Student Engagement (MOSE) Dataset for Studying Personality and Engagement. Submitted to the Nuclear Physics B, November 2022

CVPR 2023

Li Guo, Haoming Liu, Chengyu Zhang, Yuxuan Xia, Xiaochen Lu, and Zhenxing Niu. Boosting Few-Shot Segmentation via Instance-Aware Data Augmentation and Local Consensus Guided Cross Attention. Submitted to the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Few-Shot Segmentation with Adaptive Data Augmentation and Cross Attention

NYU Shanghai

Mar 2022 - Present

• Research Assistant advised by Professor Guo Li
• Propose an instance-aware data augmentation strategy to improve support image diversity and reduce distribution inconsistency between query and support images in low-data regimes
• Incorporate a cross attention module with 4-D dense correlation refined by local consensus constraints to align query and support features for improved generalization ability
• Set up a neat and re-usable visualization code module to help verify and visualize the results of the proposed model. And build a neat and scalable codebase for few-shot segmentation research in PyTorch
• Co-author a paper which has been submitted to the conference of CVPR 2023

Evaluating Parameter-Efficient Tuning Methods in Low-Data Regimes

New York University

Sep 2021 - Dec 2021

• Course Project mentored by Professor Sam Bowman
• Reproduce four SOTA parameter-efficient tuning methods based on HuggingFace and OpenDelta libraries. Then evaluate the performance of these methods on various NLP tasks (e.g., sentiment analysis, Q&A, etc.) with different portions of training samples provided
• Verify and conclude that a parameter-efficient tuning method with a larger ratio of tunable parameters generally results in a better performance across NLP tasks, but usually converges slower regardless of the sufficiency of data

Multimodal Online Student Engagement Dataset

SMART-LAB NYU Abu Dhabi

May 2021 - March 2022

• Research Assistant advised by Professor Hanan Salam
• Build an online learning engagement detection dataset along with post-class survey results and open to research community
• Based on PyTorch, OpenCV and OpenFace, establish a CNN-RNN hybrid baseline model for spatial and temporal processing
• Investigate the correlation between post-class self-evaluation results and personality surveys to provide insights on how personality may relate to engagement level in online learning. Apply various significance tests (e.g., T-test) to verify the potential correlations discovered
• Co-author a paper which has been submitted to the Nuclear Physics B journal in Nov 2022

Real-time Object Detection in Autonomous Driving Scenarios

New York University

Sep 2021 - Dec 2021

• Course Project mentored by Professor Augustin Cosse
• Pre-process and prepare CityScape dataset for object detection under autonomous driving scenarios (e.g., remove classes irrelevant to autonomous driving). Then Fine-tune YOLOv3 model on the prepared dataset: by freezing the feature extraction backbone DarkNet53 and fine-tuning the feature pyramid network to better detect what matters in driving situations
• Achieved a detection frame rate of 42 on personal computer, with a mAP of 49.6% when transferred to testing images outside CityScape dataset