Nitin H. Vaidya

Nitin H. Vaidya is affiliated with Georgetown University in the United States and has contributed extensively to the field of computer science, with a particular focus on distributed systems and fault tolerance. Their research spans several subfields including computer networks and communications, artificial intelligence, electrical and electronic engineering, management science and operations research, and control and systems engineering.

The scientist's main fields of study reflect a strong orientation toward complex systems and optimization, with significant work in distributed control multi-agent systems and privacy-preserving technologies in data. Their scholarly activity includes research topics such as distributed systems and fault tolerance, distributed control multi-agent systems, privacy-preserving technologies in data, stochastic gradient optimization techniques, optimization and search problems, cryptography and data security, and advanced memory and neural computing.

Frequent coauthors collaborating with Nitin H. Vaidya include Nirupam Gupta, Shuo Liu, Muhammad Samir Khan, Thinh T. Doan, and Lewis Tseng. This indicates active partnerships primarily in the areas of multi-agent systems and distributed optimization.

The scientist has published in well-recognized venues, highlighting a range of contributions across journals and conferences. These venues include:

• arXiv (Cornell University)
• Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
• IEEE Transactions on Automatic Control
• IEEE Transactions on Control of Network Systems
• Distributed Computing

Significant recent papers authored or coauthored by Nitin H. Vaidya cover topics related to Byzantine fault tolerance, multiagent optimization, and distributed machine learning. Notable examples include:

• Byzantine-Resilient Multiagent Optimization, 2020, IEEE Transactions on Automatic Control
• Improved extension protocols for byzantine broadcast and agreement, 2020, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
• Improved Extension Protocols for Byzantine Broadcast and Agreement, 2020, arXiv (Cornell University)
• Byzantine Fault-Tolerant Distributed Machine Learning Using Stochastic Gradient Descent (SGD) and Norm-Based Comparative Gradient Elimination (CGE), 2020, arXiv (Cornell University)
• Resilience in Collaborative Optimization: Redundant and Independent Cost Functions, 2020, arXiv (Cornell University)

This profile reflects a research career centered on optimizing the reliability and security of distributed systems and multi-agent frameworks, alongside work on fault tolerance and privacy measures in computational environments.