Machine Learning Scientist & Engineer
Welcome! I currently work as a Machine Learning Scientist at SES AI. My responsibilities include development and testing of AI models for molecular property predictions, generative AI for electrolyte synthesis, data collection and analysis, benchmarking of novel and existing AI models, and full-stack AI model deployment on cloud platforms.
Get in TouchLearning from graph-structured data and molecular representations
Large-scale models for molecular property predictions
Novel materials and electrolyte synthesis using AI
Efficient mixture-of-experts for big data applications
SES AI • Woburn, Massachusetts
Leading AI/ML initiatives for battery technology and electrolyte development:
Yale University, School of Engineering & Applied Science • New Haven, Connecticut
Research in machine learning, algorithmic generalization, and robustness:
Michigan State University, Dept. of ECE • East Lansing, Michigan
Multi-agent decision making with applications in supply chain optimization and inventory management.
Rutgers University • New Brunswick, New Jersey
Thesis: Learning Tree-Structured Models from Noisy Data
GPA: 3.9/4.0
Rutgers University • New Brunswick, New Jersey
Focus: Machine Learning and Pattern Recognition
GPA: 3.8/4.0
University of Patras • Patras, Greece
Major: Stochastic Signal Processing and Communications
GPA: 8.3/10 (Top 4% of class)
Award: 1st place Programming Competition (250 participants)
FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol
K. Nikolakakis, G. Chantzialexiou, D. Kalogerias • Preprint
Select without Fear: Almost All Mini-Batch Schedules Generalize Optimally
K. Nikolakakis, A. Karbasi, D. Kalogerias • SIAM SIMODS
Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning
P. Okanovic, R. Waleffe, V. Mageirakos, K. Nikolakakis et al. • ICLR 2024
Federated Learning Under Restricted User Availability
P. Theodoropoulos, K. Nikolakakis, D. Kalogerias • ICASSP 2024
Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch Gradient Descent
K. Nikolakakis, F. Haddadpour, A. Karbasi, D. Kalogerias • ICLR 2023
Black-Box Generalization: Stability of Zeroth-Order Learning
K. Nikolakakis, F. Haddadpour, D. Kalogerias, A. Karbasi • NeurIPS 2022
Quantile Multi-Armed Bandits: Optimal Best-Arm Identification and a Differentially Private Scheme
K. Nikolakakis, D. Kalogerias, O. Sheffet, A. Sarwate • IEEE JSAIT 2021
Predictive Learning on Hidden Tree-Structured Ising Models
K. Nikolakakis, D. Kalogerias, A. Sarwate • JMLR 2021
I'm always interested in collaborating on exciting ML and AI projects, particularly in graph neural networks, foundation models, and generative AI for material science.
📍 Based in Boston, Massachusetts