Olivera Kotevska, PhD

Olivera Kotevska, PhD

Senior Research Scientist

About

Senior Research Scientist · Computer Science and Mathematics Division · Oak Ridge National Laboratory

Dr. Olivera Kotevska is a Senior Research Scientist in the Computer Science and Mathematics Division (CSMD) at Oak Ridge National Laboratory (ORNL), where she leads the DOE program on privacy-preserving federated learning for scientific foundation models and serves on the AI Safety and Security Thrust of the DOE Genesis Mission. Her research defines the emerging field of trustworthy AI for multi-institutional science, spanning differential privacy, federated learning, gradient privacy, and autonomous scientific computing. She is the 2025 R&D 100 Award winner for PRESTO (Privacy REcommendation and SecuriTy Optimization), a privacy mechanism recommendation system for federated learning at scale, and the recipient of the 2022 Highly Cited Research Paper Award from Applied Energy.

Prior to joining ORNL in 2019, Dr. Kotevska was an international guest researcher at the National Institute of Standards and Technology (NIST), Maryland, USA, where she was part of the NIST Smart Cities Framework Team and contributed to some of the earliest foundational work in that domain. Before her PhD, she built and shipped production software actively used by millions of consumers — at Nuance Communications (UK, voice and AI systems), Vivo (Brazil, mobile telecommunications), T-Mobile (Macedonia, mobile services), and Renault (France, automotive software). She received her Ph.D. in Computer Science from the Université Grenoble Alpes, France, and B.S. and M.S. degrees in Computer Science and Engineering from the University of Ss. Cyril and Methodius, Skopje, Macedonia.

With over $33M in competitive funding secured across DOE, NNSA, VA, and DoD programs, Dr. Kotevska brings a portfolio perspective to research investment — from early-stage laboratory concepts through open-source deployment and federal policy. She is a Senior Member of IEEE, an Advisor to the IEEE USA Artificial Intelligence Policy Committee and the IEEE Computational Intelligence Society Government Activities Committee, and has responded directly to White House OSTP and NIST solicitations on privacy-enhancing technologies and AI security. She has mentored over 20 students and interns across PhD, MS, and undergraduate programs.

Research Focus

Core research areas spanning theory, systems, and federal deployment:

  • Federated learning — communication-efficient and privacy-preserving training across decentralized scientific data
  • Differential privacy — formal guarantees for data release and model training in sensitive domains
  • Trustworthy AI — robustness, explainability, and security of models under adversarial conditions
  • AI for science — privacy-aware methods applied to energy, climate, and national security applications

Current Projects

Genesis Mission — AI Safety & Security Thrust Active · 2025–2027
One of eight team members on DOE's $30M Genesis Mission, contributing to AI safety and security threat models and safeguards for foundation models deployed across national laboratory scientific workflows. Funder: DOE ASCR.
Privacy-Preserving Federated Learning for Scientific Foundation Models Active · 2024–2027
As ORNL Principal Investigator on a $7M DOE ASCR project, developing privacy-preserving federated training methods for scientific foundation models, with open-source implementations and cross-institutional pilots at DOE facilities. Funder: DOE ASCR, AI for Science. Project deliverables.
PRESTO — Privacy Recommendation and Security Optimization Active · 2024–present
2025 R&D 100 Award winner. A privacy mechanism recommendation system for federated learning at scale, enabling reproducible evaluation of differential privacy, gradient protection, and secure aggregation across heterogeneous scientific data regimes. Funder: ORNL LDRD.

Get in Touch

Open to research collaborations, advisory roles, invited talks, and conversations about program development, technology transition, and investment in trustworthy AI.

Oak Ridge National Laboratory
Computer Science and Mathematics Division
P.O. Box 2008, Oak Ridge, TN 37831, USA
kotevskao@ornl.gov