Federated Learning
Privacy-preserving distributed training across heterogeneous edge nodes, with handover-aware aggregation strategies for mobile environments.
Open to ML Engineer Roles
Amardip
Kumar Singh
Postdoc Research Fellow · University of Toronto
Machine learning researcher specializing in privacy-preserving distributed learning, federated systems, and quantum-inspired optimization — with 7+ years bridging academic research and telecom industry applications.
7+
Years Research
5
IEEE Publications
3
Industry Internships
Background
About Me
I am a Postdoctoral Research Fellow at the University of Toronto, where my work — funded by Ericsson Canada through a Mitacs partnership — investigates the feasibility and convergence of distributed machine learning on quantum-inspired optimizers.
My doctoral research at École de technologie supérieure (ÉTS), Université du Québec, focused on federated learning frameworks for Open Radio Access Networks (O-RAN), developing algorithms that are both communication-efficient and privacy-preserving.
With over seven years of research experience spanning cloud/edge computing, telecom AI, and knowledge distillation, I bring deep expertise in turning theoretical advances into practical systems — validated through collaborations with Ericsson, VMware, and Ciena Canada.
Focus Areas
Research Interests
Bridging theory and application in distributed intelligence, privacy-preserving learning, and next-generation wireless networks.
Open RAN Intelligence
Deploying ML models in disaggregated radio access network architectures — xApps, rApps, and near-RT RIC environments.
Knowledge Distillation
Model compression and teacher-student frameworks for communication-efficient ML in resource-constrained telecom infrastructure.
Quantum-Inspired ML
Investigating quantum-inspired optimizers for convergence acceleration in distributed machine learning settings.
Privacy & Security
Differential privacy, secure aggregation, and adversarial robustness in federated and collaborative ML systems.
Edge & Cloud AI
Intelligent resource provisioning and ML deployment challenges in mobile-edge computing and IoT environments.
Career
Experience
Jan 2026 – Present
Postdoc Research Fellow
University of Toronto — Toronto, ON
- Investigating feasibility and convergence of distributed ML on quantum-inspired optimizers.
- Project funded by Ericsson Canada through Mitacs Accelerate partnership.
Jan 2025 – Dec 2025
Machine Learning Intern
Ericsson — St. Laurent, QC
- Researched privacy-preserving distributed intelligence in Radio Access Networks (RAN).
- Reviewed literature on knowledge distillation-based ML models in telecom networks.
- Conducted experiments with Ericsson data and analyzed convergence performance.
Sep 2022 – Apr 2024
Mitacs Research Intern
VMware Canada
- Collaborated on Multi-Cloud Service Grid and Edge AI projects.
- Published research in peer-reviewed journals and conferences.
- Developed federated learning models tailored to Open RAN environments.
Jan 2020 – Aug 2022
Mitacs Research Intern
Ciena Canada
- Delivered progress updates for the Self-Optimizing Fabric (SOF) project.
- Presented research at top international telecom conferences.
- Explored ML deployment challenges in mobile-edge computing.
Aug 2017 – Dec 2019
Junior Research Fellow
Parallel & Distributed Systems Lab, India
- Research on Intelligent Resource Provisioning in Cloud/Fog Computing for IoT environments.
Research Output
Publications
Peer-reviewed contributions to IEEE Transactions, conferences, and IFIP workshops on federated learning and Open RAN.
IEEE Trans. MLCN · Journal
"User Handover Aware Hierarchical Federated Learning for Open RAN"
A.K. Singh and K.K. Nguyen · IEEE Transactions on Machine Learning in Communications and Networking, 2025
IEEE ICC · Conference
"Hierarchical Federated Learning with User Hand-Over Awareness for Open RAN"
A.K. Singh and K.K. Nguyen · IEEE ICC, Montreal, Canada, 2025
IEEE/ACM Trans. Networking · Journal
"Communication Efficient Compressed and Accelerated Federated Learning in Open RAN Intelligent Controllers"
A.K. Singh and K.K. Nguyen · IEEE/ACM Transactions on Networking, vol. 32, no. 4, pp. 3361–3375, Aug. 2024
IFIP WMNC · Conference
"MCORANFed: Communication Efficient Federated Learning in Open RAN"
A.K. Singh and K.K. Nguyen · IFIP WMNC, Sousse, Tunisia, 2022, pp. 15–22
IEEE WCNC · Conference
"Joint Selection of Local Trainers and Resource Allocation for Federated Learning in Open RAN"
A.K. Singh and K.K. Nguyen · IEEE WCNC, Austin, TX, USA, 2022, pp. 1874–1879
Academic Training
Education
Ph.D., Electrical Engineering
Federated Learning · Open RAN
École de technologie supérieure (ÉTS), Université du Québec · Montréal, Canada
2020 – 2025
Thesis: Optimized Federated Learning Framework for Open Radio Access Networks (O-RAN)
M.Tech., Computer Science
Machine Learning · Healthcare AI
Jawaharlal Nehru University · New Delhi, India
2016 – 2018
Dissertation: A Comparative Study of Machine Learning Algorithms for Disease Classification
M.Sc., Applied Mathematics
Mathematics · Statistics
Presidency University · Kolkata, India
2013 – 2016
B.Sc. (Hons.), Mathematics
Mathematics · Economics · Statistics
Presidency College, Univ. of Calcutta · Kolkata, India
2009 – 2013
Recognition
Awards & Honours
ÉTS Substance Award for Research Dissemination
2024
Canada Doctoral Scholarship — Mitacs Accelerate (×2)
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
Junior Research Fellowship — Govt. of India
2018
Get in Touch
Contact
I'm actively seeking ML Engineer roles in industry, particularly in telecom AI, distributed systems, and edge intelligence. Open to research collaborations and speaking engagements as well.
Peer Review Service
- IEEE Trans. on Cognitive Communications & Networking
- IEEE Trans. on Network Science & Engineering
- IEEE Trans. on Network & Service Management
- IEEE Trans. on ML in Communications & Networking
- IEEE Open Journal of Vehicular Technology
- ACM Computing Surveys
- IEEE ICC 2023 · IEEE ICMLCN 2025