I'm a PhD researcher in Machine Learning specializing in privacy-preserving federated learning and distributed AI systems. Currently completing my doctorate at École de technologie supérieure (ÉTS) while working as an ML Intern at Ericsson, focusing on intelligent Radio Access Networks (RAN).

🎯 Currently seeking: Machine Learning Engineer and Data Scientist roles in Canada
🔬 Research Focus: Federated Learning for Open RAN, Privacy-Preserving AI, Distributed Intelligence
📍 Location: Montreal, QC, Canada
🌐 Open to: Remote opportunities across Canada

• 7+ years of ML research experience with focus on distributed and privacy-preserving systems
• 5+ peer-reviewed publications in top-tier IEEE conferences and journals
• Mitacs Research Intern at VMware Canada and Ciena Canada
• Expertise in Federated Learning for next-generation telecommunication networks

• 🔐 Privacy-Preserving Machine Learning
• 📡 Open Radio Access Network (O-RAN) Intelligence
• 🌐 Distributed AI Systems
• ☁️ Edge Computing & IoT
• 📊 Communication-Efficient ML

🚧 Coming Soon - Converting research work into production-ready repositories

• Federated Learning for Open RAN: Communication-efficient distributed learning framework
• Hierarchical FL with User Handover: Privacy-preserving ML for mobile networks
• Compressed FL Algorithms: Resource-optimized federated learning systems

🎓 Ph.D in Electrical Engineering - École de technologie supérieure (ÉTS), Montreal (2020-2025)
🎓 M.Tech in Computer Science - Jawaharlal Nehru University, New Delhi (2016-2018)
🎓 M.Sc in Applied Mathematics - Presidency University, Kolkata (2013-2016)

Recent Certifications:

• Generative AI with Large Language Models (Coursera, 2024)
• Google Cloud Machine Learning Operations (MLOps, 2024)
• Climate Change AI Virtual Summer School (2024)

• 🏅 ÉTS Substance Award for Research Dissemination (2024)
• 🇨🇦 Canada Doctoral Scholarship, Mitacs Accelerate - awarded twice (2020-2024)
• 🏆 Best Idea Award: Science for People and People for Science, Govt. of India (2019)
• 🎓 LUCAS-PACE Summer School Fellowship, TU Berlin, Germany (2019)

I'm actively transitioning from research to industry, building production-ready ML systems and learning modern MLOps practices. My goal is to apply cutting-edge federated learning expertise to solve real-world business problems while ensuring data privacy and system scalability.

What I'm working on:

• 🐳 Learning Docker and containerization for ML deployment
• ☁️ Exploring AWS and cloud-native ML solutions
• 🔄 Building MLOps pipelines for federated learning systems
• 📚 Contributing to open-source ML projects

• 💼 LinkedIn: linkedin.com/in/amardipkumar-singh
• 📧 Email: amardipkumar.singh@gmail.com
• 🎓 Google Scholar: Amardip Kumar Singh
• 📱 Phone: +1 (514) 962-0387