Diya Li
Biographic Data
| ID | 7950110 |
|---|---|
| NAME | Diya Li |
| GIVEN NAMES | Diya |
| FAMILY NAME | Li |
| SIGNATURE | LI D |
| AFFILIATIONS | Texas A&M University |
| ORCID | 0000-0002-3287-9385 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Large Language Model-Driven Structured Output
Large language models (LLMs) have demonstrated remarkable capabilities in document processing, data analysis, and code generation. However, the generation of spatial information in a structured and unified format remains a challenge, limiting their integration into production environments. In this paper, we introduce a benchmark for generating structured and formatted spatial outputs from LLMs with a focus on enhancing spatial information generat…
Mining Spatiotemporal Mobility Patterns Using Improved Deep Time Series Clustering
Mining spatiotemporal mobility patterns is crucial for optimizing urban planning, enhancing transportation systems, and improving public safety by providing useful insights into human movement and behavior over space and time. As an unsupervised learning technique, time series clustering has gained considerable attention due to its efficiency. However, the existing literature has often overlooked the inherent characteristics of mobility data, inc…
Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by Covid-19 Using Social Media Data Mining
By 29 May 2020, the coronavirus disease (COVID-19) caused by SARS-CoV-2 had spread to 188 countries, infecting more than 5.9 million people, and causing 361,249 deaths. Governments issued travel restrictions, gatherings of institutions were cancelled, and citizens were ordered to socially distance themselves in an effort to limit the spread of the virus. Fear of being infected by the virus and panic over job losses and missed education opportunit…
No prominent works on this page.
Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by Covid-19 Using Social Media Data Mining
By 29 May 2020, the coronavirus disease (COVID-19) caused by SARS-CoV-2 had spread to 188 countries, infecting more than 5.9 million people, and causing 361,249 deaths. Governments issued travel restrictions, gatherings of institutions were cancelled, and citizens were ordered to socially distance themselves in an effort to limit the spread of the virus. Fear of being infected by the virus and panic over job losses and missed education opportunit…
Large Language Model-Driven Structured Output
Large language models (LLMs) have demonstrated remarkable capabilities in document processing, data analysis, and code generation. However, the generation of spatial information in a structured and unified format remains a challenge, limiting their integration into production environments. In this paper, we introduce a benchmark for generating structured and formatted spatial outputs from LLMs with a focus on enhancing spatial information generat…
Mining Spatiotemporal Mobility Patterns Using Improved Deep Time Series Clustering
Mining spatiotemporal mobility patterns is crucial for optimizing urban planning, enhancing transportation systems, and improving public safety by providing useful insights into human movement and behavior over space and time. As an unsupervised learning technique, time series clustering has gained considerable attention due to its efficiency. However, the existing literature has often overlooked the inherent characteristics of mobility data, inc…
Computer Science (3 works) · Data mining (2 works) · Ambiguity (1 work) · Anxiety (1 work) · Artificial Intelligence (1 work) · Cartography (1 work) · Cluster analysis (1 work) · Computational and Text Analysis Methods (1 work) · Coronavirus disease 2019 (COVID-19 (1 work) · Data Management and Algorithms (1 work)