# Rahul Khorana

> ML Researcher

**Wikidata**: [Q133808441](https://www.wikidata.org/wiki/Q133808441)  
**Source**: https://4ort.xyz/entity/rahul-khorana

## Summary
Rahul Khorana is a machine learning researcher affiliated with Imperial College London, specializing in algorithms and statistical models for computer systems. He holds degrees from Imperial College London and the University of California, Berkeley, and has contributed to academic research in the field of machine learning.

## Biography
- Nationality: United Kingdom
- Education:
  - Imperial College London
  - University of California, Berkeley
- Known for: Research in machine learning algorithms and statistical models
- Employer(s): Imperial College London
- Field(s): Machine learning, computer science

## Contributions
Rahul Khorana has made significant contributions to the field of machine learning through his research and publications. He has authored papers indexed on arXiv and Google Scholar, including work on polyatomic complexes and topologically informed approaches. His research has been recognized in academic circles, and he has collaborated with researchers at institutions such as the University of California, Berkeley. His work has been referenced in platforms like Papers With Code, highlighting its impact on the broader research community.

## FAQs
### Q: What is Rahul Khorana known for?
A: Rahul Khorana is known for his research in machine learning, particularly in developing algorithms and statistical models for computer systems.

### Q: Where did Rahul Khorana study?
A: Rahul Khorana studied at Imperial College London and the University of California, Berkeley.

### Q: What are some of Rahul Khorana's notable publications?
A: Rahul Khorana has contributed to research on polyatomic complexes and topologically informed approaches, with his work referenced on platforms like Papers With Code.

## Why They Matter
Rahul Khorana's work in machine learning has contributed to the development of algorithms and statistical models that enhance computer systems' ability to perform tasks without explicit instructions. His research has influenced academic discussions and collaborations, particularly in the areas of computational science and data-driven modeling. By advancing the field, he has helped shape the future of AI and machine learning applications.

## Notable For
- Research in machine learning algorithms and statistical models
- Contributions to academic publications on arXiv and Google Scholar
- Collaboration with researchers at institutions like the University of California, Berkeley
- Work referenced on Papers With Code, highlighting its impact on the research community

## Body
### Education and Background
Rahul Khorana earned his education at Imperial College London and the University of California, Berkeley, where he developed a foundation in computer science and machine learning. His academic background has been instrumental in shaping his research approach.

### Research Focus
His research primarily focuses on machine learning, with a specific emphasis on algorithms and statistical models. He has published work on topics such as polyatomic complexes and topologically informed approaches, contributing to the broader field of computational science.

### Academic Contributions
Rahul Khorana has authored several papers indexed on arXiv and Google Scholar, which have been referenced in academic discussions and research platforms. His work has been recognized for its innovative approaches to machine learning, particularly in enhancing the capabilities of computer systems.

### Collaborations and Influence
He has collaborated with researchers at institutions such as the University of California, Berkeley, contributing to joint projects and academic publications. His work has influenced the broader research community, with references on platforms like Papers With Code highlighting its significance.

## References

1. [GitHub](https://www.alphaxiv.org/profile/67d1ba67cfd4a523605e07a4/rahul-khorana)
2. [Source](https://sites.google.com/view/antheamonod/group)
3. [Source](https://www.rtanakagroup.com/people)
4. [Source](https://orcid.org/0000-0001-8795-1623)
5. [Source](https://qiangrouppage.lbl.gov/people)
6. [Source](https://www.scribd.com/document/661312140/Spring-2023-Commencement-program-post-event)
7. [Source](https://scholar.google.com/citations?user=EDHAaSgAAAAJ&hl=en)
8. Polyatomic Complexes: A Software Framework for Topologically Accurate Representations of Molecules
9. Topological Feature Compression for Molecular Graph Neural Networks
10. [Source](https://openreview.net/forum?id=HxeBBlQo9e)
11. [Source](https://neurips.cc/virtual/2025/loc/san-diego/125958)
12. [Source](https://www.alphaxiv.org/profile/67d1ba67cfd4a523605e07a4/rahul-khorana)
13. [Source](https://www.aimodels.fyi/papers/arxiv/polyatomic-complexes-topologically-informed-learning-representation-atomistic)
14. [Source](https://patents.google.com/patent/US8678979B2/en)
15. [Source](https://chatpaper.com/chatpaper/paper/61545)
16. [Source](https://paperswithcode.com/search?q=author%3ARahul+Khorana)
17. [Source](https://www.aimodels.fyi/papers/arxiv/cw-cnn-cw-convolutional-networks-attention-networks)
18. [Source](https://github.com/rahulkhorana)
19. [Source](https://arxiv.org/search/cs?searchtype=author&query=Khorana,+Rahul)