Dr. Amitesh Maurya
Pioneering research in computer vision and machine learning with focus on real-time face recognition systems, deep learning architectures, and AI security solutions.
Research Highlights
Breakthrough discoveries and innovations in computer vision
Real-Time Face Recognition System
Developed a novel deep learning architecture achieving 99.7% accuracy with sub-50ms processing time
Adversarial Attack Detection
Novel framework for detecting and mitigating adversarial attacks on facial recognition systems
Federated Learning for Privacy
Distributed learning approach for face recognition while preserving user privacy
Recent Publications
Peer-reviewed papers and conference proceedings
Real-Time Face Recognition with Deep Neural Networks: A Comprehensive Study
This paper presents a novel deep learning architecture for real-time face recognition achieving state-of-the-art performance...
Adversarial Robustness in Face Recognition Systems: Defense Mechanisms and Evaluation
We propose a comprehensive framework for defending against adversarial attacks on facial recognition systems...
Federated Learning for Privacy-Preserving Face Recognition
This work introduces a federated learning approach that enables collaborative training of face recognition models...
Research Impact
Measuring the influence of our research contributions
Research Impact & Recognition
Citation Timeline
Areas of Expertise
Core research domains and technical competencies
Computer Vision
Advanced image processing, object detection, and visual recognition systems
Machine Learning
Deep learning architectures, neural networks, and AI model optimization
AI Security
Adversarial attack detection, robustness testing, and secure AI systems
Data Science
Statistical analysis, data mining, and large-scale data processing
Computer Vision
Advanced image processing, object detection, and visual recognition systems
Core Skills
Applications
Featured Projects
Cutting-edge implementations and practical applications
SecureFace: Real-time Face Recognition System
A comprehensive face recognition platform that combines state-of-the-art deep learning with robust security measures. Features real-time processing, adversarial attack detection, and privacy-preserving federated learning.
Technologies
Key Features
Performance Metrics
Research Collaboration
Academic partnerships and industry connections
Stanford University
Joint research on advanced face recognition algorithms and deep learning architectures.
MIT Computer Science
Collaborative work on adversarial attack detection and AI system robustness.
Google Research
Research internship focusing on federated learning and privacy protection.
University of Oxford
Postdoctoral research in computer vision and machine learning applications.
Microsoft Research
Consulting on machine learning model optimization for cloud deployment.
Technical University Munich
Joint project on computer vision applications in autonomous robotics.
Collaboration Network
Academic Journey
Milestones and achievements in research career
Academic Journey
Key milestones and achievements in my research career
Senior Research Scientist
Leading research in privacy-preserving AI and federated learning systems.
Key Achievements:
- Published 5 papers in top-tier conferences
- Led team of 8 researchers
- Launched 2 open-source projects
Best Paper Award - NeurIPS
Received Best Paper Award for research on adversarial attack detection.
Key Achievements:
- First author publication
- 500+ citations within 6 months
- Featured in Nature AI Review
PhD in Computer Science
Dissertation: 'Robust Face Recognition Systems: Security and Privacy Considerations'
Key Achievements:
- Summa Cum Laude
- Outstanding Dissertation Award
- 15 peer-reviewed publications
Research Intern
Worked on computer vision applications for accessibility technologies.
Key Achievements:
- Developed novel object detection algorithm
- Filed 2 patents
- Presented at ICCV workshop
NSF Graduate Research Fellowship
Awarded prestigious fellowship for graduate research in AI security.
Key Achievements:
- $150,000 research funding
- 3-year fellowship period
- Top 5% of applicants
Master of Science
MS in Computer Science with focus on Machine Learning and Computer Vision.
Key Achievements:
- GPA: 3.9/4.0
- TA for Advanced ML course
- Published 3 conference papers
Bachelor of Technology
BTech in Computer Science and Engineering, specialized in AI/ML.
Key Achievements:
- Valedictorian
- President, Computer Science Society
- Gold Medal in Programming
Research Updates
Recent developments and announcements
Paper Accepted at CVPR 2025
Our latest research on real-time face recognition systems has been accepted for publication at the premier Computer Vision and Pattern Recognition conference.
Read MoreNSF Research Grant Awarded
Received $500,000 NSF grant to advance research in privacy-preserving AI systems and federated learning frameworks.
Keynote at AI Security Summit
Delivered keynote presentation on 'Adversarial Robustness in Computer Vision' at the International AI Security Summit in Tokyo.
Best Paper Award at NeurIPS
Our paper on adversarial attack detection received the Best Paper Award at the Neural Information Processing Systems conference.
New Dataset Released: SecureFace-10M
Released the largest publicly available dataset for secure face recognition research, containing 10 million annotated facial images.
Guest Editorial for IEEE TPAMI
Invited as guest editor for a special issue on 'Trustworthy AI in Computer Vision' for IEEE Transactions on Pattern Analysis and Machine Intelligence.
Lets Collaborate
Interested in research collaboration, speaking opportunities, or discussing innovative AI solutions? Id love to hear from you.