Zhixian Zhang Announces Software Copyright Registration for Decision Support System
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EIN Presswire
NEW YORK, NY, UNITED STATES, November 9, 2026 /EINPresswire.com/ -- Zhixian Zhang, a graduate student pursuing a Master of Science in Management and Analytics at New York University, announced that she obtained a Chinese software copyright registration on Oct. 9 for “Data-Driven End-to-End Digital Management and Intelligent Decision-Making System V1.0.” Registration information provided by Zhang lists the number as 2026SR1008779 and identifies her as the original copyright holder.
Zhang’s broader research and development projects address two related engineering tasks: managing the resources needed to operate AI services and organizing data so that users can evaluate information and make decisions. Her publicly documented application work includes a conversational agent and a creator recommendation tool.
In her AI systems research, Zhang examines how changing workloads affect response times and computing requirements. Her work considers the operational decisions behind an AI service, including how requests are routed, how processing is configured and how resources are allocated as demand changes. Those decisions shape the system’s ability to meet response-time requirements while managing resource consumption.
One example is RT-TOP, a framework described in her conference chapter “Research on AI System Performance Modeling and Online Optimization Methods Based on Runtime Telemetry Data.” Springer lists the chapter’s first online publication date as Sept. 2, 2026. Zhang’s framework uses monitoring data collected with OpenTelemetry, including traces, metrics and logs, to model performance during operation. It combines that model with an optimization method that adjusts processing, routing and resource allocation within defined safety constraints. The approach treats performance tuning as an ongoing feedback process, using measurements from a running service to guide adjustments when conditions change.
The chapter’s abstract reports up to 17.8% lower P99 latency and approximately 9.6% lower energy consumption per 1,000 requests while maintaining the same service-level agreement in experiments using public Azure and Meta microservice traces. P99 latency measures the response time at the 99th percentile, capturing delays near the slow end of the request distribution. These figures describe the study’s experiments; they do not establish performance gains for the registered software or deployments at the companies whose public traces were used.
Her research on enterprise data platforms addresses the data feeding those services. In a separate 2026 paper in the International Journal of Big Data Intelligent Technology, Zhang proposed a lakehouse-based architecture combining data contracts, rules expressed as code and version-controlled model deployment. Data contracts specify how data should be structured and validated; traceable metadata records where it came from and how it was processed. The design connects governance with software engineering so that changes to data and models can be tracked as an AI pipeline evolves.
Her proposed release process includes contract tests for data structure and quality, followed by a limited rollout. If a release fails, the design calls for restoring the previous version. These mechanisms address how a platform can introduce changes while retaining a way to recover.
Zhang has also developed an AI Conversational Agent, a separate application project that connects a language model with stored knowledge and conversational context. Its public documentation describes a modular design built with FastAPI and LangChain, using Redis for conversational memory and Qdrant for vector-based retrieval. The design also provides integrations for web search and speech processing. Zhang organized the application around components for model access, information retrieval, memory and response formatting. That structure makes the handoff between stored context, retrieved material and generated answers visible in the application’s documented workflow.
In that workflow, retrieval identifies relevant material from a knowledge base to provide context for the model’s response. Conversational memory retains information needed across exchanges, while external search supplies another source of information. Zhang’s development work brings those components into a common application structure. The accompanying browser demonstration is labeled an interactive prototype and defaults to an offline mode. It provides a way to explore the interface and sample exchanges; the repository documents the fuller application design.
A second application illustrates decision support in a marketing workflow. In September 2026, Zhang worked on an AI-assisted creator discovery and recommendation project. The Creator2 repository documents a process that starts with a campaign brief, uses the YouTube Data API to identify candidates and gather channel information, and combines quantitative indicators with Gemini-assisted assessments to rank creators.
The application evaluates factors such as engagement, publishing consistency and audience fit, then applies filters and a budget constraint to recommend a selection. Its documentation distinguishes metrics derived from platform data from assessments inferred by AI. That distinction allows users to see which parts of a recommendation come from observed indicators and which depend on a model’s interpretation. Budget calculations use estimated costs, so the output serves as planning support rather than a record of campaign results.
Users can adjust the scoring weights to reflect a campaign’s priorities. The tool also records reasons for excluding candidates, including audience mismatch or inactivity, and compares selections under different budget assumptions. Those features make the logic behind a shortlist available for review.
About Zhixian Zhang
Zhixian Zhang is a graduate student in Management and Analytics at New York University’s School of Professional Studies. Her interests include artificial intelligence, enterprise data infrastructure, software engineering and data analytics. She holds a Bachelor of Management from Chongqing University of Technology. Her experience includes market research and business analysis, alongside technical projects using SQL and Python. Her previous market research responsibilities included cleaning datasets, conducting exploratory analysis and preparing reports to communicate findings to business teams.
Zhang’s broader research and development projects address two related engineering tasks: managing the resources needed to operate AI services and organizing data so that users can evaluate information and make decisions. Her publicly documented application work includes a conversational agent and a creator recommendation tool.
In her AI systems research, Zhang examines how changing workloads affect response times and computing requirements. Her work considers the operational decisions behind an AI service, including how requests are routed, how processing is configured and how resources are allocated as demand changes. Those decisions shape the system’s ability to meet response-time requirements while managing resource consumption.
One example is RT-TOP, a framework described in her conference chapter “Research on AI System Performance Modeling and Online Optimization Methods Based on Runtime Telemetry Data.” Springer lists the chapter’s first online publication date as Sept. 2, 2026. Zhang’s framework uses monitoring data collected with OpenTelemetry, including traces, metrics and logs, to model performance during operation. It combines that model with an optimization method that adjusts processing, routing and resource allocation within defined safety constraints. The approach treats performance tuning as an ongoing feedback process, using measurements from a running service to guide adjustments when conditions change.
The chapter’s abstract reports up to 17.8% lower P99 latency and approximately 9.6% lower energy consumption per 1,000 requests while maintaining the same service-level agreement in experiments using public Azure and Meta microservice traces. P99 latency measures the response time at the 99th percentile, capturing delays near the slow end of the request distribution. These figures describe the study’s experiments; they do not establish performance gains for the registered software or deployments at the companies whose public traces were used.
Her research on enterprise data platforms addresses the data feeding those services. In a separate 2026 paper in the International Journal of Big Data Intelligent Technology, Zhang proposed a lakehouse-based architecture combining data contracts, rules expressed as code and version-controlled model deployment. Data contracts specify how data should be structured and validated; traceable metadata records where it came from and how it was processed. The design connects governance with software engineering so that changes to data and models can be tracked as an AI pipeline evolves.
Her proposed release process includes contract tests for data structure and quality, followed by a limited rollout. If a release fails, the design calls for restoring the previous version. These mechanisms address how a platform can introduce changes while retaining a way to recover.
Zhang has also developed an AI Conversational Agent, a separate application project that connects a language model with stored knowledge and conversational context. Its public documentation describes a modular design built with FastAPI and LangChain, using Redis for conversational memory and Qdrant for vector-based retrieval. The design also provides integrations for web search and speech processing. Zhang organized the application around components for model access, information retrieval, memory and response formatting. That structure makes the handoff between stored context, retrieved material and generated answers visible in the application’s documented workflow.
In that workflow, retrieval identifies relevant material from a knowledge base to provide context for the model’s response. Conversational memory retains information needed across exchanges, while external search supplies another source of information. Zhang’s development work brings those components into a common application structure. The accompanying browser demonstration is labeled an interactive prototype and defaults to an offline mode. It provides a way to explore the interface and sample exchanges; the repository documents the fuller application design.
A second application illustrates decision support in a marketing workflow. In September 2026, Zhang worked on an AI-assisted creator discovery and recommendation project. The Creator2 repository documents a process that starts with a campaign brief, uses the YouTube Data API to identify candidates and gather channel information, and combines quantitative indicators with Gemini-assisted assessments to rank creators.
The application evaluates factors such as engagement, publishing consistency and audience fit, then applies filters and a budget constraint to recommend a selection. Its documentation distinguishes metrics derived from platform data from assessments inferred by AI. That distinction allows users to see which parts of a recommendation come from observed indicators and which depend on a model’s interpretation. Budget calculations use estimated costs, so the output serves as planning support rather than a record of campaign results.
Users can adjust the scoring weights to reflect a campaign’s priorities. The tool also records reasons for excluding candidates, including audience mismatch or inactivity, and compares selections under different budget assumptions. Those features make the logic behind a shortlist available for review.
About Zhixian Zhang
Zhixian Zhang is a graduate student in Management and Analytics at New York University’s School of Professional Studies. Her interests include artificial intelligence, enterprise data infrastructure, software engineering and data analytics. She holds a Bachelor of Management from Chongqing University of Technology. Her experience includes market research and business analysis, alongside technical projects using SQL and Python. Her previous market research responsibilities included cleaning datasets, conducting exploratory analysis and preparing reports to communicate findings to business teams.
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