Curriculum Vitae
Education
- Ph.D. in Computer Science, LMU Munich, Germany, 2021 - Present
- M.S. in Data Science, LMU Munich, Germany, 2018 - 2021
- B.S. in System Analysis, National Technical University of Ukraine ”Igor Sikorsky Kyiv Polytechnic Institute”, Ukraine, 2014 - 2018
Publications
Peer-reviewed
IGC-Net for Conditional Average Potential Outcome Estimation over Time
K. Hess, D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at ICLR, 2026 (poster)GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomes
V. Melnychuk, S. Feuerriegel. Accepted at ICLR, 2026 (poster)Efficient and Sharp Off-Policy Learning under Unobserved Confounding
K. Hess, D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at ICLR, 2026 (poster)An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes
E. Javurek, V. Melnychuk, J. Schweisthal, K. Hess, D. Frauen, S. Feuerriegel. Accepted at ICLR, 2026 (poster)Orthogonal Representation Learning for Estimating Causal Quantities
V. Melnychuk, D. Frauen, J. Schweisthal, S. Feuerriegel. Accepted at AISTATS, 2026 (oral presentation)Treatment Effect Estimation for Optimal Decision-Making
D. Frauen, V. Melnychuk, J. Schweisthal, M. van der Schaar, S. Feuerriegel. Accepted at NeurIPS, 2025 (poster)Conformal Prediction for Causal Effects of Continuous Treatments
M. Schröder, D. Frauen, J. Schweisthal, K. Hess, V. Melnychuk, S. Feuerriegel. Accepted at NeurIPS, 2025 (poster)Differentially Private Learners for Heterogeneous Treatment Effects
M. Schröder, V. Melnychuk, S. Feuerriegel. Accepted at ICLR, 2025 (poster)Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
V. Melnychuk, S. Feuerriegel, M. van der Schaar. Accepted at NeurIPS, 2024 (poster)DiffPO: A Causal Diffusion Model for Predicting Potential Outcomes of Treatments
Y. Ma, V. Melnychuk, J. Schweisthal, S. Feuerriegel. Accepted at NeurIPS, 2024 (poster)Fair Off-Policy Learning from Observational Data
D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at ICML, 2024 (poster)Causal 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, 2024 (spotlight poster)Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
K. Hess, V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICLR, 2024 (poster)A Neural Framework for Generalized Causal Sensitivity Analysis
D. Frauen, F. Imrie, A. Curth, V. Melnychuk, S. Feuerriegel, M. van der Schaar. Accepted at ICLR, 2024 (poster)Sharp Bounds for Generalized Causal Sensitivity Analysis
D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at NeurIPS, 2023 (poster)Reliable Off-Policy Learning for Dosage Combinations
J. Schweisthal, D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at NeurIPS, 2023 (poster)Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at NeurIPS, 2023 (spotlight poster)Normalizing Flows for Interventional Density Estimation
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICML, 2023 (poster)Estimating Average Causal Effects from Patient Trajectories
D. Frauen, T. Hatt, V. Melnychuk, S. Feuerriegel. Accepted at AAAI, 2023 (poster)Causal Transformer for Estimating Counterfactual Outcomes
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICML, 2022 (poster)Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned
M. Berrendorf, E. Faerman, V. Melnychuk, V. Tresp, T. Seidl. Accepted at ECIR, 2020
2026
2025
2024
2023
2022
2020
Pre-prints
Counterfactual 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
Uncertainty quantification with (causal) prior-fitted networks
Presentation, Machine Learning Lunch at Vector, Vector Institute, Toronto, Canada
Causal ML for predicting treatment outcomes
Presentation, DaSCI Seminar, Andalusian Research Institute in Data Science and Computational Intelligence, online
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 2025
- Reviewer at ICML 2025
- Reviewer at ICLR 2025
- 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 - C1.2
- Ukrainian - native speaker
Volunteer Activity
- Volunteer, NGO “Agency For Free Development” (Kyiv, Ukraine), 2016 - 2018
- Member / activist, IASA Student Council (Kyiv, Ukraine), 2015 - 2016