Curriculum Vitae
Research Scientist, Stanford University 路 SNAP Group.
Education
2020
Ph.D. in Electrical & Computer Engineering
University of Minnesota, Twin Cities
Thesis: Tensor Methods for Signal Reconstruction and Network Embedding. Advisor: Prof. Nikolaos D. Sidiropoulos.
2014
Diploma (Dipl.-Ing.), Electrical & Computer Engineering
National Technical University of Athens
Professional Experience
2024 – present
Research Scientist
Stanford University 路 SNAP Group (with Jure Leskovec)
- Leading ML research at the SNAP Group.
- Graph neural networks, graph foundation models, and large-scale benchmarks for forecasting over relational databases.
- Knowledge-graph models for text-to-KG extraction with LLMs, and multi-modal KG reasoning.
- Transformer architectures for single-cell transcriptomics, gene-regulatory networks, and protein modeling.
- Industry-academic collaborations with NVIDIA, SAP, Kumo.AI, and the Chan Zuckerberg Initiative.
- Instructor for CS224W and CS246.
2021 – 2023
Postdoctoral Researcher
University of Pennsylvania 路 Alelab (with Alejandro Ribeiro)
- GNN design and expressivity (ICLR 2024, ICLR 2025).
- Parameter-efficient tensor adaptation for LLMs, vision, and protein models (LoRTA).
- Decentralized GNN policies for multi-agent coordination.
- Instructor for ESE5140 Graph Neural Networks.
2020 – 2021
Research Scientist
Sentera, Inc.
- Deep-learning super-resolution pipelines for agricultural UAV / satellite imaging.
- Shipped models to the production imaging stack.
Awards & Honors
2025
Best Paper Award, KDD Temporal Graph Learning Workshop (Relational Graph Transformer).
2023
Best Student Paper Award, IEEE CAMSAP.
2023
Best Student Paper Award Finalist, IEEE Asilomar Conference.
2015
ADC Graduate Fellowship in Wireless & Networking Technology, University of Minnesota.
2007
Finalist, Hellenic Mathematical Competition.
Workshop & Tutorial Organization
2026
ICML Workshop 路 Seoul, S. Korea
2025
RDL: Foundations, Advanced Methods and Hands-on Development
Learning on Graphs (LoG) Conference 路 Phoenix, AZ
2025
NeurIPS Workshop 路 San Diego, CA
2025
Stanford, CA
2025
ACM KDD 路 Toronto, Canada
2024
Stanford, CA
2024
Multiview Learning
IEEE Asilomar Conference on Signals, Systems and Computers 路 Pacific Grove, CA
2024
Learning with Few Labels
IEEE Sensor Array and Multichannel Signal Processing Workshop 路 Corvallis, OR
2023
Graph Neural Networks short course
IEEE ICASSP 路 Rhodes, Greece
Selected Invited Talks
2026
Next-Generation Architectures for Zero-Shot Forecasting
UC Santa Cruz, CSE Colloquium 路 Santa Cruz, CA
2025
Designing Transformers for Relational Data
UC Berkeley, Networks for Science Workshop 路 Berkeley, CA
2025
Towards Foundation Models for Relational Data
University of California San Diego 路 San Diego, CA
2024
Relational Deep Learning: Graph Representation Learning on Relational Databases
2024
Next-Generation Positional Encodings for Graph Representation Learning
Learning on Graphs (LoG) meetup 路 Stanford
2024
Towards Next-Generation Graph Transformers
Stanford Graph Learning Workshop 路 Stanford, CA
2024
Counting Graph Substructures with Graph Neural Networks
Information Theory and Applications Workshop 路 San Diego, CA
2023
Harnessing Message-Passing beyond Weisfeiler-Lehman
Rensselaer Polytechnic Institute 路 Troy, NY
2023
A Spectral Analysis on the Representation Power of GNNs
University of Maryland 路 College Park, MD
2023
Representation Learning with Graph Neural Networks
Stanford University 路 Palo Alto, CA
2023
Harnessing Message-Passing beyond Weisfeiler-Lehman
脡cole Polytechnique 路 Paris, France
2023
Representation Learning on Tensors and Graphs
Technical University Darmstadt 路 Darmstadt, Germany
2023
Representation Learning on Graphs and Tensors
University of California San Diego 路 San Diego, CA
2023
Graph Neural Networks Are More Powerful Than We Think
Information Theory and Applications Workshop 路 San Diego, CA
2021
Tensor Completion from Regular Sub-Nyquist Samples
IEEE Signal Processing Society Webinar Series 路 virtual
2020
Learning from Multimodal Data
Alexa Group, Amazon Inc. 路 Pittsburgh, PA
2020
Learning from Multimodal Data
University of Southern California 路 Los Angeles, CA
2020
Graph Representation Learning via Tensor Methods
University of Pennsylvania 路 Philadelphia, PA
2020
Hyperspectral Super-resolution: A Tensor Factorization Approach
IEEE RAS Technical Committee on Agricultural Robotics and Automation 路 virtual
2019
Tensor Completion from Regular Samples
Cognitive Computing Lab, Baidu Inc. 路 Seattle, WA
2018
Factorization Methods for Natural Language Processing
Arm Inc. 路 Austin, TX
Teaching
2024 – present
Instructor, Stanford University
CS224W: Machine Learning with Graphs and CS246: Mining Massive Datasets (with Jure Leskovec).
2022 – 2023
Instructor, University of Pennsylvania
Community Service
Area Chair: ICML Workshops, AAAI Conference on Artificial Intelligence, ACM KDD Conference.
Reviewer: NeurIPS, ICLR, ICML, KDD, ACL, IEEE Transactions on Signal Processing.