2026 Sebastian Nowozin: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	55	4204	4087	1983	1914	146	18540

Overview

Sebastian Nowozin is affiliated with Microsoft in the United States, contributing primarily to the field of Computer Science. Their research spans a range of subfields, including Artificial Intelligence, Computer Vision and Pattern Recognition, Cancer Research, Radiology, Nuclear Medicine and Imaging, and Materials Chemistry.

Their work frequently addresses key topics such as Domain Adaptation and Few-Shot Learning, Gaussian Processes and Bayesian Inference, Generative Adversarial Networks and Image Synthesis, Machine Learning and Algorithms, Adversarial Robustness in Machine Learning, Multimodal Machine Learning Applications, and cancer-related molecular mechanisms research.

Frequent publication venues for their research include:

arXiv (Cornell University)
Apollo (University of Cambridge)
ACM Transactions on Storage
Conference on Lasers and Electro-Optics

Among recent published papers, the following stand out with details on year and venue:

Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes, 2020, Apollo (University of Cambridge)
Hydra: Preserving Ensemble Diversity for Model Distillation, 2020, arXiv (Cornell University)
How Good is the Bayes Posterior in Deep Neural Networks Really?, 2020, arXiv (Cornell University)
TaskNorm: Rethinking Batch Normalization for Meta-Learning, 2020, arXiv (Cornell University)
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks, 2020, arXiv (Cornell University)

The scientist has collaborated frequently with several coauthors, including:

Kevin A. Roth
John Bronskill
Richard E. Turner
Lorenzo Noci
Gregor Bachmann

Their publication record comprises over 40 works in Computer Science, with a concentration of 30 in Artificial Intelligence. The spectrum of their research highlights a mixture of theoretical foundations and applied machine learning methodologies, particularly focused on adaptive and Bayesian approaches to neural networks and meta-learning techniques.

Best Publications

Occupancy Networks: Learning 3D Reconstruction in Function Space

Lars Mescheder;Michael Oechsle;Michael Niemeyer;Sebastian Nowozin

2394
Citations
f -GAN: training generative neural samplers using variational divergence minimization

Sebastian Nowozin;Botond Cseke;Ryota Tomioka

1343
Citations
On feature combination for multiclass object classification

Peter Gehler;Sebastian Nowozin

1110
Citations
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift

Yaniv Ovadia;Emily Fertig;Jie Ren;Zachary Nado

999
Citations
Optimization for Machine Learning

Suvrit Sra;Sebastian Nowozin;Stephen J. Wright

903
Citations
Which Training Methods for GANs do actually Converge

Lars M. Mescheder;Andreas Geiger;Sebastian Nowozin

877
Citations
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

Yang Song;Taesup Kim;Sebastian Nowozin;Stefano Ermon

625
Citations
DSAC — Differentiable RANSAC for Camera Localization

Eric Brachmann;Alexander Krull;Sebastian Nowozin;Jamie Shotton

604
Citations
Instructing people for training gestural interactive systems

Simon Fothergill;Helena Mentis;Pushmeet Kohli;Sebastian Nowozin

499
Citations
Oblivious multi-party machine learning on trusted processors

Olga Ohrimenko;Felix Schuster;Cédric Fournet;Aastha Mehta

489
Citations
Adversarial variational bayes: unifying Variational Autoencoders and Generative Adversarial Networks

Lars Mescheder;Sebastian Nowozin;Andreas Geiger

465
Citations
DeepCoder: Learning to Write Programs

Matej Balog;Alexander L. Gaunt;Marc Brockschmidt;Sebastian Nowozin

451
Citations
Structured Learning and Prediction in Computer Vision

Sebastian Nowozin;Christoph H. Lampert

423
Citations
Stabilizing Training of Generative Adversarial Networks through Regularization

Kevin Roth;Aurelien Lucchi;Sebastian Nowozin;Thomas Hofmann

387
Citations
The numerics of GANs

Lars Mescheder;Sebastian Nowozin;Andreas Geiger

338
Citations
A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems

Jorg H. Kappes;Bjoern Andres;Fred A. Hamprecht;Christoph Schnorr

295
Citations
Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations

Diane Bouchacourt;Ryota Tomioka;Sebastian Nowozin

266
Citations
Discriminative Subsequence Mining for Action Classification

S. Nowozin;G. Bakir;K. Tsuda

263
Citations
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems

Jörg H. Kappes;Bjoern Andres;Fred A. Hamprecht;Christoph Schnörr

228
Citations
Efficient Nonlinear Markov Models for Human Motion

Andreas M. Lehrmann;Peter V. Gehler;Sebastian Nowozin

211
Citations
How Good is the Bayes Posterior in Deep Neural Networks Really

Florian Wenzel;Kevin Roth;Bastiaan Veeling;Jakub Swiatkowski

146
Citations

Frequent Co-Authors

Peter V. Gehler Zalando SE

Christoph H. Lampert Institute of Science and Technology Austria

Carsten Rother Heidelberg University

Richard E. Turner University of Cambridge

Pushmeet Kohli DeepMind (United Kingdom)

Andreas Geiger University of Tübingen

Jamie Shotton Microsoft (United States)

José Miguel Hernández-Lobato University of Cambridge

Jasper Snoek Google (United States)

Koji Tsuda University of Tokyo

External Links

Personal website of Sebastian Nowozin Google Scholar page

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