Joel Wang

Distributed Systems & AI Expert

Professional Summary

Leading the engineering team in coding and scaling a social media platform to over 10 million users. Led the technical efforts in building Whova's event management SaaS platform. Played a pivotal role in the development of innovative blockchain consensus algorithms and applications at Nebulas. Specialized in distributed system and software reliability research at both Tsinghua University and UC San Diego. Balances hands-on software engineering with strategic leadership, effectively driving projects from inception to completion.

Professional Experience

CTO | Social Media Platform | NYC, NY

As CTO of the social media company, I spearheaded the development of a cutting-edge social media platform, serving millions of daily users. I designed a scalable infrastructure and implemented a machine learning-based content personalization and moderation system. Additionally, I grew the technology department to 70 engineers, implementing robust CI/CD practices and Agile methodologies to enhance team efficiency and product development cycles.

VP of Engineering | Nebulas.io | Beijing, China

At Nebulas.io, I developed the Proof of Devotion (PoD) consensus algorithm and authored its white paper. I also engineered a Decentralized Autonomous Organization (DAO) and led the construction of a centralized crypto exchange. These projects showcased the real-world potential of blockchain technology in creating decentralized, community-driven ecosystems and facilitating secure cryptocurrency transactions.

Founding Engineer | Whova Inc. | San Diego, CA

As a Founding Engineer at Whova, I developed a revolutionary SaaS platform for event organizers, now used by over 50,000 enterprise clients. I also created a sophisticated distributed web crawler and data pipeline, capable of analyzing millions of public data entries. This data-driven approach set Whova apart, offering unparalleled networking and engagement opportunities at events.

Research Assistant | UC San Diego

At UC San Diego, I focused on big data and distributed systems, developing an Entity Resolution algorithm using HBase and Hadoop. This work involved processing and analyzing large-scale internet data to understand complex relationships between entities. I also led efforts in collecting and parsing public records, utilizing advanced tools like Stanford NER and custom scrapers.

Research Assistant | Tsinghua University

At Tsinghua University, I collaborated with Google Mountain View to enhance the Open64 compiler's efficiency, focusing on memory leak diagnosis. I also extended LAPACK to assess supercomputer memory reliability, contributing to the improvement of computational software reliability in high-performance computing environments.

Technical Skills

Languages

  • Python
  • Golang
  • Java
  • C/C++
  • JavaScript
  • TypeScript/TailwindCSS

Frameworks

  • React.js
  • Next.js
  • Django
  • Flask
  • Express
  • Hadoop
  • HBase

Platforms

  • AWS
  • Jenkins
  • GitHub Action
  • Docker
  • Kubernetes
  • Kafka
  • Redis
  • MongoDB

Education

UC San Diego | San Diego, CA

Ph.D. in Computer Science and Engineering, 2014

Research: Distributed Systems, System Reliability

Transitioned from Ph.D. program to co-found startup with academic advisor

Tsinghua University | Beijing, China

M.S. in Computer Science and Technology, 2012

Research: High Performance Computing, Parallel Programming

Beijing Normal University | Beijing, China

B.S. in Information System and Management

Tools

Recolor Links (Recent Launched)

A Chrome extension designed to enhance web browsing for individuals with color blindness. This tool aims to make the web more inclusive and accessible by improving navigation for people with color vision deficiencies.

MLC (Memory Latency Checker)

Developed based on the MPI framework, MLC is a diagnostic tool designed to measure latency and bandwidth among supercomputer nodes. This tool is crucial for optimizing performance in high-performance computing environments.

LAPACK Patch Release Tool

Extended LAPACK (Linear Algebra Package) with capabilities to assess the reliability of a supercomputer's memory. This tool aids in improving computational software reliability, particularly in large-scale scientific computing applications.

  • Implemented using C/C++
  • Enhances the robustness of numerical linear algebra computations

Contact