N

Nan Jing

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Chief AI Systems Architect | Former Professor in AI & Blockchain | PhD in Computer Science (USC)
United States
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Jobs verified_user 0% verified
  • S
    Founder / Chief Architect
    Stealth AI Web Infrastructure
    Oct 2025 - Current (1 year 1 month)
    Building a stealth AI & Web3 infrastructure platform for verifiable escrow, payments, and provenance between autonomous AI systems, using smart contracts and on-chain verifiable storage for settlement, verification, and auditability.

    In parallel, the platform is applied to agent-native intelligence workflows for prediction markets, including pricing analysis, anomaly context, and capital-quality assessment, helping users evaluate whether market probabilities are defensible rather than simply reacting to signals.

    Extensive experience with AWS since 2014, designing, deploying, and operating cloud-based applications and distributed systems, including EC2, S3, Lambda, ECS, EKS, API Gateway, IAM, CloudWatch, SQS, CI/CD pipe
  • Stealth AI Infrastructure Startup
    Founding Engineer / Chief AI Systems Architect
    Stealth AI Infrastructure Startup
    Oct 2025 - Current (1 year 1 month)
    Designed and built an AI-native platform for agentic workflows, context retrieval, and decision support.

    Implemented multi-agent orchestration, MCP integration, evaluation harnesses, and execution pipelines across TypeScript/Node.js and Python/FastAPI.

    Developed scalable backend services, message-driven architecture, RAG, vector search, and structured memory capabilities supporting end-to-end AI workflows.
  • Sui Foundation
    BTCFi & Payments Fellow (Web3 & Blockchain Track)
    Sui Foundation
    Sep 2025 - Dec 2025 (4 months)
    Selected by Press Start Capital, Sui, and Walrus Foundations, as one of the leading Web3 and blockchain innovators worldwide to advance programmable and verifiable payment infrastructure for the Agent Economy.

    Building a Web3-based Agent Payment & Provenance Platform - bridging Coinbase's X402 and Web3-based smart contract, and on-chain verification to enable trustless, micro-value and instantly verifiable payment between AI agents.
  • Sui Foundation
    BTCFi & Payments Fellow (Agent Economy, Payments & Settlement)
    Sui Foundation
    Sep 2025 - Dec 2025 (4 months)
    Selected by Press Start Capital, Sui, and Walrus Foundations, as one of the leading Web3 and blockchain innovators worldwide to advance programmable and verifiable payment infrastructure for the Agent Economy.

    Building a Web3-based Agent Payment & Provenance Platform - bridging Coinbase's X402 and Web3-based smart contract, and on-chain verification to enable trustless, micro-value and instantly verifiable payment between AI agents.
  • E
    Founding Team Member
    EarlyStage AI Startup Stealth
    Mar 2023 - Current (3 years 8 months)
    Building Generative AI platforms.
  • Provenance AI
    Founding Team Member
    Provenance AI
    Mar 2023 - Aug 2025 (2 years 6 months)
    The company was co-founded by Prof. Yann LeCun, focusing on building trustworthy AI through verifiable content provenance.

    Contributed to building a real-time Generative AI platform that integrates model orchestration, copyright detection, and Web3-based provenance tracking, leveraging extensive AWS experience in designing, deploying, and operating cloud-based applications and distributed systems including EC2, S3, Lambda, ECS, EKS, API Gateway, IAM, CloudWatch, SQS, CI/CD pipelines, containerized services, and serverless applications. Utilized various development tools.
  • E
    Senior R&D
    Element Ventures Inc
    Sep 2019 - Mar 2023 (3 years 7 months)
    The company was co-founded by Prof. Yann LeCun, pioneering deep learning–based digital identity infrastructure.

    Designed and implemented real-time, cross-platform facial recognition and identity verification systems deployed across mobile and web environments, utilizing AWS for designing, deploying, and operating cloud-based applications and distributed systems including EC2, S3, Lambda, ECS, EKS, API Gateway, IAM, CloudWatch, SQS, CI/CD pipelines, containerized services, and serverless applications.
  • S
    Adjunct Associate Professor
    Shanghai University Joint Program with University of Technology Sydney
    Jan 2014 - Dec 2019 (6 years)
    Research on blockchain technologies, deep learning, recommendation systems, data mining, and information system design

    Published technical articles in top-tier journals including Information Processing & Management, Expert Systems with Applications, Applied Intelligence, and Electronic Commerce Research, with over 500 citations collectively across two highly cited publications on blockchain and deep learning.
  • C
    Principal Engineer and Engineering Manager
    Challengers Capital
    Oct 2012 - Jul 2019 (6 years 10 months)
    Principal Engineer and Engineering Manager (2012-2018), Partner (2018-2019)

    Led strategic engineering efforts that provides technical due diligence, builds information technology infrastructure, and grows small projects into the leading next generation platforms.

    Led the R&D team in designing and developing a key information infrastructure that supports many of our invested ventures using Java, Spring Boot, REST, HTTP and Javascript.

    Designed and implemented software applications using core Java technologies for analyzing the consumer and product data in order to support venture team to make better informed decision for prospective investments.

    Designed and implemented a new algorithmic and hedgin
  • Bloomberg
    Senior Staff, R&D
    Bloomberg
    Aug 2009 - Aug 2012 (3 years 1 month)
    Worked in various departments and programs in Bloomberg R&D to extend the access of worldwide business professionals from Bloomberg terminals to global market, industry and economic data in multi-media formats (e.g. news, charts, graphs, and videos) on a variety of computing platforms.

    Won the "Bloomberg Business & Technology Innovator" Award for being the lead engineer behind Bloomberg Radio+ for iPhone.

    The projects I have been working on:
    Bloomberg Radio+ for iPhone (Lead Engineer)
    http://itunes.apple.com/us/app/bloomberg-radio/id424879834?&mt=8
    Best Finance Applica
  • Antenna Software now part of Pegasystems
    Senior Engineer, Mobile Framework
    Antenna Software now part of Pegasystems
    Apr 2009 - Jul 2009 (4 months)
    Architected and developed Antenna mobile framework on Blackberry and Android to support a proprietary platform-dependent script that is used in large enterprise mobile customers

    Worked with Dev, QA and product teams in-house and offshore to find out low-level platform optimizations that have great impact on high-level application software

    Analyzed and investigated mobile platform features and functionality on Blackberry, iPhone and Android to overcome performance bottlenecks

    Proposed code and architectural changes in order to improve platform-wide performance
  • Industrial Scientific
    Senior Software Engineer, Mobile
    Industrial Scientific
    Aug 2008 - Apr 2009 (9 months)
    Extended the functionality and features of web-based SafetyNet software suite onto mobile platform using Java ME, Eclipse, REST, JSON and MDX for safety management in engineering process

    Implemented data compression and decompression algorithm (GZIP and ZLIB) in both mobile client and server and dramatically reduce the size of data upload using HTTP post and multipart form

    Created and maintained OTA download URLs for SafetyNet Mobile and develop automatic update mechanism for mobile applications using Blackberry Native API
  • DivX LLC
    Senior Software Engineer
    DivX LLC
    Jun 2006 - Jun 2008 (2 years 1 month)
    Led DivX Mobile Player project for product features specification, architecture design and software development using Visual Studio 2005, C++, .Net Compact Framework, on Symbian, Pocket PC, SmartPhone and Palm platforms

    Designed mobile digital rights management (DRM) using J2EE, JSP/Servlet, C/C++, Java RMI and Java Native Interface (JNI)

    Worked with product management, major mobile phone manufacturers and carrier networks (CDMA/GPRS) for marketing, deployment and testing

    Formally trained on ‘Symbian OS Essentials and System Programming’ from Nokia/Symbian Inc.

    Utilized Agile, Scrum, and various development tools throughout the tenure since 2006.
  • e
    Lead Mobile Engineer
    eAgency Systems
    May 2004 - May 2006 (2 years 1 month)
    Designed mobile commercial system architecture including wireless software, database, and web server

    Developed wireless application using Java ME, .Net Compact Framework, Embedded Visual C++, and WAP

    Designed the interface between wireless client and web server using HTTP, TCP/IP, C++ and ODBC

    Deployed and tested production wireless client software using Windows Mobile 5.0/2003, and Blackberry J2ME platform on Nextel, T-Mobile, Cingular, AT&T, and Verizon carriers network (CDMA/GPRS)
  • Sun Microsystems
    Graduate Intern
    Sun Microsystems
    May 2002 - Aug 2002 (4 months)
    • Developed J2EE test suite to test every new feature in EJB 2.1 and J2EE 1.4
    • Transformed developing platform of existing test suite library using Makefile and ANT
  • University of Southern California
    Research Assistant and Team Lead
    University of Southern California
    May 2001 - May 2004 (3 years 1 month)
    Led research team for system development, deliverables preparation, and workshop arrangement

    Conducted research on software design, collaborative engineering and intelligent multi-agent system

    Designed web service by Model-View-Controller standard using UML, Rational Rose and MS Visio

    Developed web-based software system on J2EE framework using Enterprise Java Beans, Java Server Page, Java Servlet, Struts, JBoss, Eclipse, Applet, Swing, Java 2D, XML, SOAP and MySQL/Oracle database

    Tested the web applications using JUnit, black/white box testing and write test automation script in Perl
Education verified_user 0% verified
  • University of Southern California
    Ph.D, Computer Science
    University of Southern California
    While full-time working at Industrial Scientific/DBO2, DivX and eAgency
  • University of Southern California
    Master of Science, Computer Science
    University of Southern California
  • Peking University
    Bachelor of Science, Computer Science
    Peking University
Publications verified_user 0% verified
  • E
    Information Credibility Evaluation in Online Professional Social Network Using Tree Augmented Naïve Bayes Classifier
    Electronic Commerce Research pages
    In recent years, companies depend on the Internet for posting job advertisements and attracting qualified personnel. However, with the vast number of Internet users and the tremendous amount of information on the Internet, it is difficult to accurately evaluate the credibility of the information that candidates provide on the Internet. Therefore, we propose an approach to assess information credibility in terms of trustworthiness and authority to identify unreliable user profiles in online professional social networks. Our approach calculates the trustworthiness probabilities of user profile information using the Tree Augmented Naïve Bayes (TAN) classifier. It also measures the authority of individual users by applying the PageRank algorith
  • I
    Modeling Co-construction Processes in a Socio-Technical Framework to Support Collaborative Engineering Design
    IEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews
    In this paper, we have described a co-construction process model, in the context of a socio-technical framework, to support the collaborative engineering design. This paper has re- viewed our previous work in building a co-construction model on the conceptual, logical, and analytical layers and then proposed an expanded Petri net representation that is developed toward a more comprehensive and operational co-construction model, which cap- tures the key conceptual components of a collaborative engineering design process, describes the possible logic flow between these com- ponents, and analyzes their interaction in the design process. In this paper, we have also discussed a case study that was conducted in a real-life engineering design to v
  • E
    Personalized Recommendation Based on Customer Preference Mining and Sentiment Assessment from a Chinese E-commerce Websi
    Electronic Commerce Research pages
    In the era of electronic commerce, online customer reviews (OCRs) have become a prevalent and valuable information source for both customers and merchants to make business decisions. This paper proposes an enhanced collaborative filtering approach based on sentiment assessment to discover the potential preferences of customers, and to predict customers’ future requirements for business services or products (collectively referred to as “entities”). Specifically, this approach involves three major steps: aspect-level sentiment assessment, customer preference mining and personalized recommendation. First, the aspect-level sentiment assessment transforms OCRs to a structured aspect-level review vector. Second, customer preference mining uses th
  • I
    A Blockchain-based Code Copyright Management System
    Information Processing Management
    With the increasing number of open-source software projects, code plagiarism has become one of the threats to the software industry. However, current research on code copyright protection mostly focuses on the approach for code plagiarism detection, failing to fundamentally solve the problem of copyright confirmation and protection. This paper proposes a blockchain-based code copyright management system. Firstly, an Abstract Syntax Tree-based code originality verification model is constructed. The originality of the uploaded code is determined through its similarity to other original codes. Secondly, the Peer-to-Peer blockchain network is designed to store the copyright information of the original code. The nodes in the blockchain network c
  • I
    Stock Price Prediction Based on Stock Price Synchronicity and Deep Learning
    International Journal of Financial Engineering
    Deep learning technology has been widely used in the financial industry, primarily for improving financial time series prediction based on stock prices. To solve the problem of low fitting and poor accuracy in traditional stock price prediction models, this paper proposes a stock price prediction model based on stock price synchronicity and deep learning methods, which applied the stock price synchronicity theory in stock price trend analysis. This paper first uses the affinity propagation algorithm to build stock clusters, and then, based on convolution neural network (CNN), and feature weight to construct the stock price synchronicity factor. At last, the long short-term memory (LSTM) network with multifactor is built for stock price tren
  • A
    Cross-sectional Analysis and Data-driven Forecasting of Confirmed COVID-19 Cases
    Applied Intelligence
    The coronavirus disease 2019 (COVID-19) is rapidly becoming one of the leading causes for mortality worldwide. Various models have been built in previous works to study the spread characteristics and trends of the COVID-19 pandemic. Nevertheless, due to the limited information and data source, the understanding of the spread and impact of the COVID-19 pandemic is still restricted. Therefore, within this paper not only daily historical time-series data of COVID-19 have been taken into account during the modeling, but also regional attributes, e.g., geographic and local factors, which may have played an important role on the confirmed COVID-19 cases in certain regions. In this regard, this study then conducts a comprehensive cross-sectional a
  • C
    Predicting Digital Currency Price Using Broad Learning System and Genetic Algorithm
    Communications in Computer and Information Science
    With the development of the digital economy, the price of Bitcoin, which is the most representative digital currency, has fluctuated dramatically. Recent works have explored the volatility of the Bitcoin price and made predictions using financial time series models such as ARIMA. However, for high-frequency Bitcoin data, the financial time series models have poor prediction performance as they often do not accommodate the inherent characteristics of digital currency, such as blockchain information. Some other works in this topic use deep learning models, e.g., artificial neural networks. However, the complex structure and time-consuming training process of these deep learning models often incur low prediction efficiency for rapidly changing
  • E
    A Hybrid Model Integrating Deep Learning with Investor Sentiment Analysis for Stock Price Prediction
    Expert Systems with Applications
    Whether stock prices are predictable has been the center of debate in academia. In this paper, we propose a hybrid model that combines a deep learning approach with a sentiment analysis model for stock price prediction. We employ a Convolutional Neural Network model for classifying the investors’ hidden sentiments, which are extracted from a major stock forum. We then propose a hybrid research model by applying the Long Short-Term Memory (LSTM) Neural Network approach for analyzing the technical indicators from the stock market and the sentiment analysis results from the first step. Furthermore, this work has conducted real-life experiments from six key industries of three time intervals on the Shanghai Stock Exchange (SSE) to validate the