Hi, I’m Julia.
I’m a third year Ph.D. student in the Data and Web Science Group at the University of Mannheim, under the supervision of Professor Heiner Stuckenschmidt. Previously, I was a research scientist at NEC Laboratories Europe.
My primary research focus revolves around temporal Knowledge Graphs. Specifically, I am intrigued by temporal Knowledge Graph Forecasting and the evaluation of methods in this research domain.
Besides research, I like music, food, and boats.
News
Check out TGB 2.0: A benchmark for learning on Temporal Knowledge Graphs and Heterogeneous Graphs
- TGB 2.0 extends the Temporal Graph Benchmark (TGB) to multi-relational graphs, offering a collection of challenging and diverse datasets for Temporal Heterogeneous Graphs and Temporal Knowledge Graphs.
- Our goal is to provide a realistic, reproducible, and robust evaluation platform for machine learning on temporal multi-relational graphs.
- Explore more:
- TGB 2.0 team 🙌 : Shenyang Huang, Julia Gastinger, Michael Galkin, Erfan Loghmani, Ali Parviz, Farimah Poursafaei, Jacob Danovitch, Emanuele Rossi, Yiannis Koutis, Heiner Stuckenschmidt, Reihaneh Rabbany, Guillaume Rabusseau (*equal contribution)
History repeats itself: A Baseline for Temporal Knowledge Graph Forecasting at IJCAI 2024
- Our* Paper History repeats itself: A Baseline for Temporal Knowledge Graph Forecasting was accepted at IJCAI 2024. *Julia Gastinger, Christian Meilicke, Federico Errica, Timo Sztyler, Anett Schuelke, Heiner Stuckenschmidt
- We propose a baseline for TKG Foreacsting based on the simple principle of recurrency. Surprisingly, this simple approach beats state of the art methods on 3 out of 5 commonly used datasets!
- Paper, Code, Supplementary Material
Internship in Montréal
- In spring 2024, I did a 2-month research internship at Mila - Quebec AI Institute and University of Montréal, supervised by Prof. Guillaume Rabusseau.
- During the internship I worked on benchmarking Temporal Knowledge Graph Forecasting Methods.
- Further, I focused on eating as many different Poutines as possible - yeahi.
Temporal Graph Learning Workshop @ NeurIPS 2023
- We* organized the second edition of the Temporal Graph Learning Workshop @ NeurIPS 2023 in New Orleans *Farimah Poursafaei, Shenyang Huang, Kellin Pelrine, Emanuele Rossi, Julia Gastinger, Reihaneh Rabbany, Michael Bronstein
- Find more infos here
Comparing Apples and Oranges? On the Evaluation of Methods for Temporal Knowledge Graph Forecasting at ECML PKDD 2023
- Our* Paper Comparing Apples and Oranges? On the Evaluation of Methods for Temporal Knowledge Graph Forecasting was accepted at ECML PKDD 2023. *Julia Gastinger, Timo Sztyler, Lokesh Sharma, Anett Schuelke, Heiner Stuckenschmidt
- In this work we focus on the evaluation of TKG Forecasting models: we highlight existing issues, propose a unified evaluation protocol, and apply it to re-evaluate state-of-the-art models.
- Paper, Code, Supplementary Material
Temporal Graph Learning Reading Group
If you share an interest in Temporal Graph Learning, I invite you to check out and join our Temporal Graph Reading Group. We meet every Thursday at 11 am EDT (5 pm CST) on Zoom, where authors of insightful and recent Temporal Graph Learning Papers present their work. The reading group is organized by Shenyang (Andy) Huang, Farimah Poursafaei, and myself.
Publications
Most Recent Publication:
Julia Gastinger, Christian Meilicke, Federico Errica, Timo Sztyler, Anett Schuelke, Heiner Stuckenschmidt. History repeats itself: A Baseline for Temporal Knowledge Graph Forecasting In IJCAI, Jeju, South-Korea, 2024.
Google Scholar:
For a full list of publications please visit my google scholar: Link