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PhD Forum


14:30-14:35: Welcome remarks (Room 1i)
14:35-16:00: Poster Session (Hallways outside Room 1i)
16:00-16:30: Coffee Break
16:30-17:30: Panel Career Path (Room 1i)

The participants to the panel session can use the following link to write their questions to the panel members:

Panel members


Authors Affiliation Titile
Ali Khoshvishkaie Aalto University Cooperative Bayesian optimization for imperfect agents
Amin Dhaou Ecole Polytechnique Interpretable multi-step ahead time series forecasting 
Andrés Tello University of Groningen Deep Learning on Graphs for Pressure Estimation in Water Distribution Networks.
Christoph Düsing CITEC, Bielefeld University, Germany Predictive Diagnostics for Federated Learning: A Privacy-Preserving Toolbox Towards Successful Federated Learning
Clara Holzhüter Fraunhofer IEE Kassel, University of Kassel Modeling and Operation of Power Grids using Graph Neural Networks
Cecília Coelho Centre of Mathematics (CMAT), University of Minho Improving Neural ODEs Explainability: A Two-stage Training Method for Modeling Constrained Natural Systems
Effrosyni Papanastasiou Sorbonne University, Paris Constrained Expectation-Maximisation for inference of social graphs explaining online user-user interactions 
Erik Schultheis Aalto University Multilabel Classification with Large Output Spaces
Francisco Mena University of Kaiserslautern-Landau Multi-view learning in remote sensing for agricultural applications
Francesco Stranieri Polytechnic of Turin/University of Milan-Bicocca Performance of Deep Reinforcement Learning Algorithms in Two-Echelon Inventory Control Systems
Franka Bause University of Vienna Scalable Methods for Graph Similarity
Huy Truong Bernoulli Institute, University of Groningen, The Netherlands Distorted or High-fidelity data? Improving Graph Neural Networks for state estimation in Water Distribution Systems.
James Hinns University Of Antwerp Multi-Modal Counterfactual Explanations for Image Classification
Johannes Huegle Hasso Plattner Institute, University of Potsdam, Germany Causal Discovery in Practice: Non-parametric Conditional Independence Testing & Tooling for Causal Discovery
Julia Gastinger Nec Laboratories Europe and University of Mannheim, Germany Comparing Apples and Oranges? On the Evaluation of Methods for Temporal Knowledge Graph Forecasting
Karima Makhlouf Inria, LIX, École Polytechnique, IPP, France On the Impact of Local Differential Privacy on Fairness
Kuldeep Rambhai Barad University of Luxembourg (SnT-Space Robotics) 6-DoF Generative Grasp Synthesis on Unknown Objects with Diffusion Priors
Lisa Bonheme University of Kent The polarised regime of variational autoencoders
Mahmoud Ibrahim Maastricht University Real World Model Performance Monitoring of Medical AI
Maik Büttner Otto-von-Guericke-University Magdeburg Increasing certainty in diagnostics through stream-based methods
Malte Lehna Fraunhofer IEE Analysis and Application of Deep Reinforcement Learning in the context of Energy Economics
Manuel Dileo University of Milan DURENDAL: Roland-based graph deep learning framework for temporal heterogeneous networks
Margarita Bugueño Hasso Plattner Institute, University of Potsdam When Graph-Based Text Representations Face Text Classification in Challenging Scenarios: A Comprehensive Study Using GNNs
Matteo El Hariry University of Luxembourg Robust Reinforcement Learning for Multitask Autonomous Robots in Real-World Environments
Monika Jain Indraprastha Institute of Information Technology, Delhi Context guided relation extraction from text
Neeraj Kumar IIT Delhi Impact of Normalization in Multimodal AI
Paras Sheth Arizona State University Generalizing Hate-Speech Detection - A Causal Learning Approach
René Heinrich University of Kassel Towards interpretable and adversarially robust deep learning algorithms for power system applications
Rim El Cheikh LIMOS, UCA, France Knowledge-Based Explainability for Neural Networks
Samaneh Zolfaghari University of Cagliari Exploring the Potential of Locomotion Traces Data Mining in Assisting Individuals with Cognitive Impairment
Johanna Schrader L3S Research Center (Hannover, Germany) Completing Causal Models by Estimating Average Treatment Effects
Sijing Tu KTH Royal Institute of Technology Adversaries with Limited Information in the Friedkin–Johnsen Model
Sofie Goethals University of Antwerp Manipulation Risks in Explainable AI
Tobias Fritz Universität der Bundeswehr München PhD Forum: Leveraging tree-structured Graphs in Graph Neural Networks for Fake News Detection
Vadim Liventsev TU Eindhoven BF++: a language for general-purpose program synthesis
Yueqing Xuan RMIT University, Australia Towards User Empowerment in Counterfactual Explanations
Zhendong Wang Stockholm University Style-transfer counterfactual explanations: An application to mortality prevention of ICU patients