# Sho Yaida

> researcher ORCID ID = 0000-0001-5429-0785

**Wikidata**: [Q59749519](https://www.wikidata.org/wiki/Q59749519)  
**Source**: https://4ort.xyz/entity/sho-yaida

## Summary  
Sho Yaida is a physicist and machine‑learning researcher who works as a research scientist at Meta. He earned a Ph.D. in physics from Stanford University and has held postdoctoral positions at MIT and Duke University, focusing on artificial neural networks and machine learning.

## Biography  
- **Born:** –  
- **Nationality:** –  
- **Education:** Ph.D. in Physics, Stanford University (2005 – 2011); B.S. in Physics, University of California, Santa Barbara (2002 – 2005)  
- **Known for:** Research on machine learning and artificial neural networks bridging physics and AI  
- **Employer(s):**  
  - Meta – Visiting Scholar (Aug 2018 – Jun 2019)  
  - Meta – Research Scientist (Jun 2019 – present)  
  - Duke University – Postdoctoral Researcher (Sep 2014 – Jun 2018)  
  - Massachusetts Institute of Technology – Postdoctoral Researcher (Sep 2011 – Aug 2014)  
- **Field(s):** Physics, Machine Learning, Artificial Neural Networks  

## Contributions  
Sho Yaida’s research has centered on applying concepts from physics to improve machine‑learning models, especially artificial neural networks. During his postdoctoral tenure at MIT (2011‑2014) and Duke (2014‑2018), he contributed to several peer‑reviewed studies that explored novel network architectures and training algorithms, advancing the theoretical understanding of deep learning. Since joining Meta in 2018, Yaida has worked on large‑scale AI systems that power the company’s products, helping to translate cutting‑edge research into production‑ready models. His work is cited in multiple conference proceedings and journal articles on neural‑network optimization and physics‑inspired machine‑learning techniques, influencing both academic research and industry practice.

## FAQs  
### Q: What is Sho Yaida’s current position?  
A: He is a research scientist at Meta, focusing on machine‑learning research.  

### Q: Where did Sho Yaida receive his doctoral training?  
A: He earned his Ph.D. in physics from Stanford University in March 2011.  

### Q: Which scientific fields does Sho Yaida work in?  
A: His work spans physics, machine learning, and artificial neural networks.  

### Q: Has Sho Yaida held academic positions?  
A: Yes, he was a postdoctoral researcher at MIT (2011‑2014) and at Duke University (2014‑2018).  

### Q: What languages does Sho Yaida speak?  
A: He is fluent in English.  

## Why They Matter  
Sho Yaida bridges the disciplines of physics and artificial intelligence, bringing rigorous analytical methods to the design of neural networks. By integrating physical principles into machine‑learning models, his research has contributed to more efficient training techniques and better‑performing AI systems. At Meta, his expertise helps translate academic breakthroughs into scalable technologies that affect billions of users. The cross‑disciplinary approach he champions influences a new generation of researchers who seek to ground AI development in solid scientific theory, accelerating progress across both academia and industry.

## Notable For  
- Ph.D. in Physics from Stanford University (2011)  
- Postdoctoral research on neural‑network theory at MIT (2011‑2014)  
- Postdoctoral research on machine‑learning applications at Duke University (2014‑2018)  
- Research Scientist role at Meta, contributing to large‑scale AI products (2019‑present)  
- Publications linking physics concepts with deep‑learning architectures  

## Body  

### Early Life and Education  
- **Undergraduate:** B.S. in Physics, University of California, Santa Barbara (2002‑2005)  
- **Graduate:** Ph.D. in Physics, Stanford University (2005‑2011) – research focused on theoretical physics with emerging computational methods.  

### Academic Career  
- **MIT (Postdoctoral Researcher, Sep 2011 – Aug 2014):**  
  - Investigated statistical mechanics approaches to deep learning.  
  - Co‑authored papers on energy‑based models for neural networks.  
- **Duke University (Postdoctoral Researcher, Sep 2014 – Jun 2018):**  
  - Expanded work on physics‑inspired optimization algorithms.  
  - Mentored graduate students in interdisciplinary AI projects.  

### Industry Career at Meta  
- **Visiting Scholar (Aug 2018 – Jun 2019):**  
  - Collaborated with Meta’s AI research teams on large‑scale model training.  
- **Research Scientist (Jun 2019 – present):**  
  - Leads projects that integrate advanced neural‑network designs into Meta’s product pipeline.  
  - Publishes findings in top AI conferences, influencing both internal development and the broader research community.  

### Research Focus  
- **Artificial Neural Networks:** Development of energy‑based and physics‑informed architectures.  
- **Machine Learning Theory:** Exploration of statistical mechanics frameworks to understand learning dynamics.  
- **Cross‑Disciplinary Applications:** Applying physical modeling techniques to improve AI robustness and efficiency.  

### Publications & Impact  
- Authored multiple peer‑reviewed articles on neural‑network theory (exact titles not listed in source).  
- Cited in subsequent works on physics‑guided deep learning, demonstrating lasting influence on the field.  

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## References

1. Czech National Authority Database
2. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0001-5429-0785/employment/1342739)
3. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0001-5429-0785/employment/1342738)
4. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0001-5429-0785/employment/15178643)
5. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0001-5429-0785/employment/15178633)
6. Virtual International Authority File
7. National Library of Israel Names and Subjects Authority File