# Mladen Kolar

> machine learning researcher

**Wikidata**: [Q103365252](https://www.wikidata.org/wiki/Q103365252)  
**Source**: https://4ort.xyz/entity/mladen-kolar

## Summary  
Mladen Kolar is a male computer scientist and machine learning engineer who earned his Ph.D. in computer science at Carnegie Mellon University in 2013 under the supervision of Eric P. Xing. He is currently a faculty member at the University of Chicago Booth School of Business, where he conducts research in machine learning and its applications to economics and business.

## Biography  
- **Born:** *Not publicly disclosed*  
- **Nationality:** *Not publicly disclosed*  
- **Education:** Ph.D. in Computer Science, Carnegie Mellon University (completed 2013) – doctoral advisor: Eric P. Xing  
- **Known for:** Research at the intersection of machine learning, statistics, and business analytics  
- **Employer(s):** Booth School of Business, University of Chicago (current)  
- **Field(s):** Machine learning, computer science, statistical learning  

## Contributions  
Mladen Kolar’s scholarly output centers on statistical machine learning methods that support decision‑making in economics and finance. His doctoral work, completed in 2013 at Carnegie Mellon, produced several peer‑reviewed papers on high‑dimensional inference and sparse modeling, advancing theoretical guarantees for estimators used in large‑scale data analysis. As a faculty researcher at Booth, he has authored and co‑authored articles that apply modern machine learning techniques to problems such as causal inference, portfolio optimization, and market design, influencing both academic curricula and industry practice. Kolar maintains an active open‑source presence on GitHub (username **mlakolar**), where he shares reproducible code for his publications, facilitating broader adoption of his methods. His research is indexed in Google Scholar (author ID 9LcxwsMAAAAJ) and the Mathematics Genealogy Project (ID 263171), reflecting a citation record that underscores the relevance of his contributions to the statistical learning community.

## FAQs  
### Q: What is Mladen Kolar’s primary research area?  
A: He focuses on statistical and machine learning methods, especially as they apply to economics, finance, and business analytics.  

### Q: Where does Mladen Kolar work?  
A: He is a faculty member at the Booth School of Business, University of Chicago.  

### Q: Who supervised his Ph.D. studies?  
A: His doctoral advisor was Eric P. Xing, a prominent artificial‑intelligence researcher.  

## Why They Matter  
Mladen Kolar bridges rigorous statistical theory with practical machine‑learning applications in business contexts. By developing scalable algorithms for high‑dimensional data, he enables more accurate predictive models and causal analyses that inform economic policy and corporate strategy. His open‑source contributions democratize access to cutting‑edge methods, allowing researchers and practitioners worldwide to replicate and extend his work. As a mentor and educator at Booth, he shapes the next generation of data scientists who will apply these tools across finance, marketing, and operations, amplifying his impact beyond his own publications.  

## Notable For  
- Ph.D. in Computer Science from Carnegie Mellon University (2013) under Eric P. Xing.  
- Faculty position at the Booth School of Business, University of Chicago.  
- Author of influential papers on high‑dimensional statistical inference and sparse modeling.  
- Active open‑source contributor on GitHub (mlakolar) providing reproducible research code.  
- Recognized machine‑learning researcher with a Google Scholar profile (ID 9LcxwsMAAAAJ).  

## Body  

### Education  
- **Carnegie Mellon University** – Doctor of Philosophy in Computer Science, 2013.  
- Doctoral advisor: **Eric P. Xing**, noted AI and machine‑learning researcher.  

### Academic Career  
- **Booth School of Business, University of Chicago** – Faculty member (current).  
- Conducts interdisciplinary research linking machine learning, statistics, and business economics.  

### Research Focus  
- High‑dimensional statistical inference.  
- Sparse modeling and regularization techniques.  
- Applications of machine learning to causal inference, portfolio selection, and market design.  

### Publications & Impact  
- Peer‑reviewed articles on theoretical guarantees for estimators in large‑scale data settings.  
- Papers cited in economics and finance literature, influencing both academic theory and industry practice.  

### Open‑Source & Online Presence  
- **GitHub:** `mlakolar` – repository of code accompanying his research papers.  
- **Twitter:** `@mkolar` (active since July 2008).  
- **Google Scholar:** Author ID 9LcxwsMAAAAJ – tracks citations and publication list.  

### Professional Identifiers  
- **Wikidata description:** machine learning researcher.  
- **MR Author ID:** 848766.  
- **Mathematics Genealogy Project ID:** 263171.  

These details collectively portray Mladen Kolar as a leading figure in statistical machine learning with a strong influence on both academic research and practical business analytics.

## References

1. Mathematics Genealogy Project
2. [Source](https://www.chicagobooth.edu/faculty/directory/k/mladen-kolar)