Curriculum Vitae
Education
- PhD. in Computer Science, Ludwig Maximilian University of Munich, 2021 - Present
- M.S. in Data Science, Ludwig Maximilian University of Munich, 2018 - 2021
- B.S. in System Analysis, National Technical University of Ukraine ”Igor Sikorsky Kyiv Polytechnic Institute”, 2014 - 2018
Publications
Peer-reviewed
DiffPO: A Causal Diffusion Model for Predicting Potential Outcomes of Treatments
Y. Ma, V. Melnychuk, J. Schweisthal, S. Feuerriegel. Accepted at NeurIPS, 2024Fair Off-Policy Learning from Observational Data
D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at ICML, 2024Causal Machine Learning for Predicting Treatment Outcomes
S. Feuerriegel, D. Frauen, V. Melnychuk, J. Schweisthal, K. Hess, A. Curth, S. Bauer, N. Kilbertus, I. S. Kohane & M. van der Schaar. Accepted at Nature Medicine, 2024Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICLR, 2024Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
K. Hess, V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICLR, 2024A Neural Framework for Generalized Causal Sensitivity Analysis
D. Frauen, F. Imrie, A. Curth, V. Melnychuk, S. Feuerriegel, M. van der Schaar. Accepted at ICLR, 2024Sharp Bounds for Generalized Causal Sensitivity Analysis
D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at NeurIPS, 2023Reliable Off-Policy Learning for Dosage Combinations
J. Schweisthal, D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at NeurIPS, 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at NeurIPS, 2023Normalizing Flows for Interventional Density Estimation
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICML, 2023Estimating Average Causal Effects from Patient Trajectories
D. Frauen, T. Hatt, V. Melnychuk, S. Feuerriegel. Accepted at AAAI, 2023Causal Transformer for Estimating Counterfactual Outcomes
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICML, 2022Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned
M. Berrendorf, E. Faerman, V. Melnychuk, V. Tresp, T. Seidl. Accepted at ECIR, 2020
2024
2023
2022
2020
Pre-prints
Conformal Prediction for Causal Effects of Continuous Treatments
M. Schröder, D. Frauen, J. Schweisthal, K. Hess, V. Melnychuk, S. Feuerriegel. ArXiv preprint, 2024G-Transformer for Conditional Average Potential Outcome Estimation over Time
K. Hess, D. Frauen, V. Melnychuk, S. Feuerriegel. ArXiv preprint, 2024Counterfactual Fairness for Predictions using Generative Adversarial Networks
Y. Ma, D. Frauen, V. Melnychuk, S. Feuerriegel. ArXiv preprint, 2023Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels
V. Melnychuk, E. Faerman, I. Manakov, T. Seidl. ArXiv preprint, 2020Unsupervised Anomaly Detection for X-Ray Images
D. Davletshina, V. Melnychuk, V. Tran, H. Singla, M. Berrendorf, E. Faerman, M. Fromm, M. Schubert. ArXiv preprint, 2020
Talks
Causal ML for predicting treatment outcomes
Guest lecture, Online Machine Learning School 2024 for Clinicians and Researchers in the field of Psychiatry and Neuroscience, online
Causal ML for treatment effect estimation
Tutorial, 3rd Munich Causal ML Workshop, LMU Munich, Munich, Germany
Causal ML for predicting treatment outcomes
Presentation, Tissue Image Analytics (TIA) seminar (University of Warwick), online
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation, ICLR 2024 paper
Presentation, 2nd Munich Causal ML Workshop, LMU Munich, Munich, Germany
Causal Transformer for Estimating Counterfactual Outcomes & Normalizing Flows for Interventional Density Estimation, ICML 2022 & 2023 papers
Presentation, 1st Munich Causal ML Workshop, LMU Munich, Munich, Germany
Academic activities
- Reviewer at NeurIPS 2024
- Reviewer at ICML 2024
- Research stay at van der Schaar Lab at the University of Cambridge, Feb-May 2024
- Reviewer at AISTATS 2024
- Reviewer at ICLR 2024
- Top reviewer at NeurIPS 2023
- Teacher assistant at Nordic Probabilistic AI School 2023
- Top reviewer at AISTATS 2023
Work experience
- Research Assistant, Fraunhofer Institute for Integrated Circuits IIS (Munich, Germany), 2019 - 2021
- Intern Data Scientist, Beehiveor Academy and R&D Labs (Kyiv, Ukraine), 2018
- Junior Java Developer, ProFIX (Kyiv, Ukraine), 2017 - 2018
Awards & Affiliations
- Co-director of the Causal ML Lab at the Institute of AI in Management, LMU Munich, since 2024
- Associated PhD student at Konrad Zuse School of Excellence in Reliable AI (relAI), since 2023
- LMU Study Scholarship for International Students, 2019
- Ukrainian Team Programming Olympiad, 2016
Languages
- English - C1.2+
- German - C1.1
- Ukrainian - native speaker
Volunteer Activity
- Volunteer, NGO “Agency For Free Development” (Kyiv, Ukraine), 2016 - 2018
- Member / activist, IASA Student Council (Kyiv, Ukraine), 2015 - 2016