# VizCV: AI-assisted visualization of researchers' publications tracks

arXiv: https://arxiv.org/abs/2505.08691 · HTML: https://mlsanity.com/paper/2505.08691/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 13 May 2025.

## Builds on (most cited first, 13 of 13)

- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [Google Scholar, Web of Science, and Scopus: a systematic comparison of citations in 252 subject categories](https://mlsanity.com/paper/1808.05053/) (2018)
- [NLP Scholar: An Interactive Visual Explorer for Natural Language Processing Literature](https://mlsanity.com/paper/2006.01131/) (2020)
- [Impact-Oriented Contextual Scholar Profiling using Self-Citation Graphs](https://mlsanity.com/paper/2304.12217/) (2023)
- [VADIS: A Visual Analytics Pipeline for Dynamic Document Representation and Information-Seeking](https://mlsanity.com/paper/2504.05697/) (2025)
- [Influence Flowers of Academic Entities](https://mlsanity.com/paper/1907.12748/) (2019)
- [Seek for Success: A Visualization Approach for Understanding the Dynamics of Academic Careers](https://mlsanity.com/paper/2108.03381/) (2021)
- [PUREsuggest: Citation-based Literature Search and Visual Exploration with Keyword-controlled Rankings](https://mlsanity.com/paper/2408.02508/) (2024)
- [SD2: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance](https://mlsanity.com/paper/2203.12671/) (2022)
- [Thirty-Two Years of IEEE VIS: Authors, Fields of Study and Citations](https://mlsanity.com/paper/2208.03772/) (2022)
- [Dataopsy: Scalable and Fluid Visual Exploration using Aggregate Query Sculpting](https://mlsanity.com/paper/2308.02764/) (2023)

## Built on by (most cited first, 1 of 1)

- [CareerPooler: AI-Powered Metaphorical Pool Simulation Improves Experience and Outcomes in Career Exploration](https://mlsanity.com/paper/2509.11461/) (2025)

## Abstract (arXiv)

Analyzing how the publication records of scientists and research groups have evolved over the years is crucial for assessing their expertise since it can support the management of academic environments by assisting with career planning and evaluation. We introduce VizCV, a novel web-based end-to-end visual analytics framework that enables the interactive exploration of researchers' scientific trajectories. It incorporates AI-assisted analysis and supports automated reporting of career evolution. Our system aims to model career progression through three key dimensions: a) research topic evolution to detect and visualize shifts in scholarly focus over time, b) publication record and the corresponding impact, c) collaboration dynamics depicting the growth and transformation of a researcher's co-authorship network. AI-driven insights provide automated explanations of career transitions, detecting significant shifts in research direction, impact surges, or collaboration expansions. The system also supports comparative analysis between researchers, allowing users to compare topic trajectories and impact growth. Our interactive, multi-tab and multiview system allows for the exploratory analysis of career milestones under different perspectives, such as the most impactful articles, emerging research themes, or obtaining a detailed analysis of the contribution of the researcher in a subfield. The key contributions include AI/ML techniques for: a) topic analysis, b) dimensionality reduction for visualizing patterns and trends, c) the interactive creation of textual descriptions of facets of data through configurable prompt generation and large language models, that include key indicators, to help understanding the career development of individuals or groups.