Stephan Mandt - Homepage

Short Bio

Stephan Mandt is an Associate Professor of Computer Science and Statistics at the University of California, Irvine. His research centers on deep generative modeling, uncertainty quantification, neural data compression, and AI for science. Previously, he led the machine learning group at Disney Research in Pittsburgh and Los Angeles and held postdoctoral positions at Princeton and Columbia University. Stephan holds a Ph.D. in Theoretical Physics from the University of Cologne, where he received the German National Merit Scholarship. He is furthermore a recipient of the NSF CAREER Award, the UCI ICS Mid-Career Excellence in Research Award, the German Research Foundation's Mercator Fellowship, a Kavli Fellow of the U.S. National Academy of Sciences, a member of the ELLIS Society, and a former visiting researcher at Google Brain. His research is currently supported by NSF, DARPA, IARPA, DOE, Disney, Intel, and Qualcomm. Stephan is an Action Editor of the Journal of Machine Learning Research and Transaction on Machine Learning Research, held tutorials at NeurIPS, AAAI, and UAI, and regularly serves as (Senior) Area Chair for NeurIPS, ICML, AAAI, and ICLR. He currently serves as Program Chair for AISTATS 2024 and General Chair for AISTATS 2025.

Research Interests

The following research areas are of interest to the group:

Deep Generative Models: We have a broad interest in deep generative models such as variational autoencoders and diffusion models, aiming to improve their scope (e.g., video diffusion, factorized VAEs, point process models) and inference efficiency (e.g., augmented spaces, iterative inference etc).

Uncertainty Quantification: Our group focuses on teaching neural networks to "know what they don't know". To this end, proper uncertainty quantification and calibration is crucial (e.g., through variational inference, ensemble methods, Bayesian neural networks, approaches inspired by statistical physics etc.)

Neural Data Compression: We are dedicated to exploring methods that seek to explore the potential of deep learning-based approaches as alternatives to conventional image and video codecs for data compression.

Machine Learning and Science: Our research explores the applications of machine learning in physics, chemistry, climate science, and related domains. We also investigate physics-inspired machine learning algorithms and theories.

PhD student applicants: Unfortunately, I will not be able to respond to most inquiries regarding PhD openings or comment on your applications to my group. If you indicate your interests to work with me in the application questions, I will make sure to review them carefully. Online Application page.
UCI undergraduate students, read this first: Thank you for your interest in working on research projects with us. Due to the high demand for generative AI opportunities we can only accommodate a limited number of students each year. When reaching out, kindly include your resume and UCI transcript and describe what kind of research interests you the most. You should have already excelled in CS 178 with a top grade (A or A+) and ideally have taken additional courses in AI/ML. Your understanding of these constraints is greatly appreciated.

News

I'm serving as General Chair for AISTATS 2025 (with Yingzhen Li).
I will serve as Program Chair for AISTATS 2024 (with Yingzhen Li).
I received the Mercator Fellowship of the German National Science Foundation.
I'm happy to have received the Dean's Mid-Career Award for Excellence in Research.
I'm honored to have received the NSF CAREER Award for "Variational Inference for Resource-Efficient Learning".
Selected recent talk recordings:
- Neural Compression Tutorial at NeurIPS 2022
- Compressing Variational Bayes (Vector Institute, 2021)
- Generative Modeling of Structured Data (Kavli Frontiers of Science, National Academy of Sciences, 2019)
- Machine Learning and Physics: Bridging the Gap (TopDyn Conference, 2020)
Accepted papers in 2021: NeurIPS, ICML, ICLR, AISTATS, 1 journal paper
Recent workshop & conference organization:
- ICLR Workshop on Neural Compression: From Information Theory to Applications, 2021
- Southern California Machine Learning and NLP Symposium, 2021
- 3rd Symposium on Advances on Approximate Bayesian Inference, 2020
- Area Chair for NeurIPS 2021, ICML 2021, ICLR 2021, NeurIPS 2020, ICML 2020, AAAI 2020, NeurIPS 2019, ICML 2019
Recent Grants:
- Department of Energy grant on probabilistic machine learning for climate science (UCI PI)
- NSF CAREER Award on Variational Inference for Resource-Efficient Learning
- NSF/Intel grant on machine learning for UAVs and semantic compression (Co-PI)
- NSF CISE RI Small grant on Deep Variational Data Compression (single PI)
- Darpa grant on open-world novelty detection (UCI PI)
- Hasso Plattner Institute at UCI (Co-Director)
- Unrestricted gifts from Qualcomm (PI)

Teaching

Fall 2024: Deep Generative Models (graduate) (CS 274E)

Winter 2024: Machine Learning and Data Mining (undergraduate) (CS 178)
Fall 2023: Deep Generative Models (graduate) (CS 274E)
Fall 2022: Deep Generative Models (graduate) (CS 274E)
Winter 2022:

Machine Learning and Data Mining (undergraduate) (CS 178)
Introduction to Machine Learning (graduate) (CS 273A)

Fall 2021: Deep Generative Models (graduate) (CS 274E)
Spring 2021: Machine Learning and Data Mining (graduate) (CS 178)
Fall 2020: Introduction to Machine Learning (undergraduate) (CS 273A)
Spring 2020: Deep Generative Models (graduate) (CS 295)
Winter 2020: Machine Learning and Data Mining (undergraduate) (CS 178)
Fall 2019: Introduction to Machine Learning (graduate) (CS 273A)
Spring 2019: Deep Generative Models (graduate) (CS 295)

Group Members

Current:

Yibo Yang, PhD Student (2019--)

Ruihan Yang, PhD Student (2019--)

Eliot Wong-Toi, PhD Student (2020--)

Prakhar Srivastava, PhD Student (2020--)

Kushagra Pandey, PhD Student (2022--)

Tuan Pham Anh, PhD Student (2022--)

Justus Will, PhD Student (2023--)

Yang Meng, PhD Student (2023--)

Noah Benamin, PhD Student (2024--)

Former:

Aodong Li, PhD Student (2019--2024), now at Amazon

Alex Boyd, PhD Student (2019--2024), now Research Scientist at GE Research

Robert Bamler former postdoc, now Professor at the University of Tubingen

Fabian Jirasek, former postdoc, now Assistant Professor at TU Kaiserslautern

Cheng Zhang, former postdoc, now at Principal Researcher at Microsoft Research

Florian Wenzel, former co-advised PhD student, now Researcher at Google Brain

Salvator Lombardo, former postdoc, now Associate Research Scientist at Disney Research

Yingzhen Li, former intern, now Assistant Professor at Imperial College

Chen Qiu, former PhD student, now Research Scientist at Bosch Research, Pittsburgh

Jens Tuyls, former undergraduate student, now PhD student at Princeton

Harshini Mangipudi, Undergraduate Student, now Software Engineer at Microsoft

Preprints (selected)

Publications

2024

Fast samplers for Inverse Problems in Iterative Refinement models
K. Pandey, R. Yang, S. Mandt
Neural Information Processing Systems (NeurIPS 2024) PDF
Precipitation Downscaling with Spatiotemporal Video Diffusion
P. Srivastava, R. Yang, G. Kerrigan, G. Dresdner, J. McGibbon, C. Bretherton, S. Mandt
Neural Information Processing Systems (NeurIPS 2024) PDF
Unity by Diversity: Improved Representation Learning for Multimodal VAEs
T. Sutter, Y. Meng, A. Agostini, D. Chopard, N. Fortin, J. Vogt, B. Shahbaba, S. Mandt
Neural Information Processing Systems (NeurIPS 2024) PDF
Understanding Pathologies of Deep Heteroskedastic Regression
E. Wong-Toi, A. Boyd, V. Fortuin, S. Mandt
Uncertainty in Artificial Intelligence (UAI 2024, oral) PDF
Anytime-Valid Confidence Sequences for Consistent Uncertainty Estimation in Early-Exit Neural Networks
M. Jazbec, P. Forré, S. Mandt, D. Zhang, E. Nalisnick
Uncertainty in Artificial Intelligence (UAI 2024) PDF
Neural NeRF Compression
T. Pham and S. Mandt
International Conference on Machine Learning (ICML 2024) PDF
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI
T. Papamarkou et al.
International Conference on Machine Learning (ICML 2024) PDF
Efficient Integrators for Diffusion Generative Models
K. Pandey, M. Rudolph, and S. Mandt
International Conference on Learning Representations (ICLR 2024) PDF
Understanding Precipitation Changes through Unsupervised Machine Learning
G. Mooers, T. Beucler, M. Pritchard, and S. Mandt
Environmental Data Science Vol. 3, 2024 PDF
2023
Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning
G. Mooers, M. Pritchard, T. Beucler, P. Srivastava, H. Mangipudi, L. Peng, P. Gentine, and S. Mandt
Nature Scientific Reports, 2023 PDF
Estimating the Rate-Distortion Function by Wasserstein Gradient Descent
Y. Yang, S. Eckstein, M. Nutz, and S. Mandt
Neural Information Processing Systems (NeurIPS 2023) PDF
Lossy Image Compression with Conditional Diffusion Models
R. Yang and S. Mandt
Neural Information Processing Systems (NeurIPS 2023) PDF
Zero-Shot Batch-Level Anomaly Detection
A. Li, C. Qiu, M. Kloft, P. Smyth, M. Rudolph, and S. Mandt
Neural Information Processing Systems (NeurIPS 2023) PDF
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators
S. Yu et al.
Neural Information Processing Systems (NeurIPS 2023) PDF
Diffusion Probabilistic Modeling for Video Generation
R. Yang, P. Srivastava, and S. Mandt
Entropy 25 (10), 1469 PDF
Computationally Efficient Neural Image Compression with Shallow Decoders
Y. Yang and S. Mandt
International Conference on Computer Vision (ICCV 2023) PDF
Generative Diffusions in Augmented Spaces: A Complete Recipe
K. Pandey and S. Mandt
International Conference on Computer Vision (ICCV 2023)(Oral) PDF
Inference for Mark-Censored Temporal Point Processes
A. Boyd, Y. Chang, S. Mandt, and P. Smyth
Uncertainty in Artificial Intelligence (UAI 2023)(Spotlight) PDF
SC2 Benchmark: Supervised Compression for Split Computing
Y. Matsubara, M. Levorato, and S. Mandt
Transactions on Machine Learning Research, 2023 PDF
An Introduction to Neural Data Compression
Y. Yang, S. Mandt, and L. Theis
Foundations and Trends in Computer Graphics and Vision, 2023 PDF
Insights from Generative Modeling for Neural Video Compression
R. Yang, Y. Yang, J. Marino, and S. Mandt
Transactions on Pattern Analysis and Machine Intelligence, 2023 preprint
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
B. Tran, B. Shahbaba, S. Mandt, M. Filippone
International Conference on Machine Learning (ICML 2023) PDF
Deep Anomaly Detection under Labeling Budget Constraints
A. Li, C. Qiu, P. Smyth, M. Kloft, S. Mandt, M. Rudolph
International Conference on Machine Learning (ICML 2023) PDF
Probabilistic Querying of Continuous-Time Event Sequences
A. Boyd, Y. Chang, S. Mandt, and P. Smyth
Artificial Intelligence and Statistics (AISTATS 2023) PDF
2022
Predictive Querying for Autoregressive Neural Sequence Models
A. Boyd, S. Showalter, S. Mandt, and P. Smyth
Neural Information Processing Systems (NeurIPS 2022) (Oral) PDF
An Unsupervised Learning Perspective on the Dynamic Contribution to Extreme Precipitation Changes
G. Mooers, T. Beucler, M. Pritchard, and S. Mandt
NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning PDF
Latent Outlier Exposure for Anomaly Detection with Contaminated Data
C. Qiu, A. Li, M. Kloft, M. Rudolph, and S. Mandt
International Conference on Machine Learning (ICML 2022) PDF
Structured Stochastic Gradient MCMC
A. Alexos, A. Boyd, and S. Mandt
International Conference on Machine Learning (ICML 2022) PDF poster code
Raising the Bar in Graph-level Anomaly Detection
C. Qiu, M. Kloft , S. Mandt, and M. Rudolph
International Joint Conference on Artificial Intelligence (IJCAI 2022) PDF
Learning to Simulate High Energy Particle Collisions from Unlabeled Data
J. Howard, S. Mandt, D. Whiteson, Y. Yang
Nature Scientific Reports 12, 7567 (2022) PDF
Making Thermodynamic Models of Mixtures Predictive by Machine Learning: Matrix Completion of Pair Interactions
F. Jirasek, R. Bamler, S. Fellenz, M. Bortz, M. Kloft, S. Mandt, and H. Hasse
Chemical Science 13, 4854-4862 (2022) PDF
Towards Empirical Sandwich Bounds on the Rate-Distortion Function
Y. Yang and S. Mandt
International Conference on Learning Representations (ICLR 2022) PDF
Lossless Compression with Probabilistic Circuits
A. Liu, S. Mandt, and G. van den Broeck
International Conference on Learning Representations (ICLR 2022) PDF
Supervised Compression for Resource-Constrained Edge Computing Systems
Y. Matsubara, R. Yang, M. Levorato, and S. Mandt
Winter Conference on Applications of Computer Vision (WACV 2022) arXiv
2021
Improving Sequential Latent Variable Models with Autoregressive Flows
J. Marino, J. He, L. Chen, and S. Mandt
Machine Learning (2021) article.
History Marginalization Improves Forecasting in Variational Recurrent Neural Networks
C. Qiu, S. Mandt, and M. Rudolph
Entropy 23, 1563 (2021) article.
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning
A. Li, A. Boyd, P. Smyth, S. Mandt
Neural Information Processing Systems (NeurIPS 2021). PDF.
Neural Transformation Learning for Deep Anomaly Detection Beyond Images
C. Qiu, T. Pfrommer, M. Kloft, S. Mandt, and M. Rudolph
International Conference on Machine Learning (ICML 2021). PDF
Hierarchical Autoregressive Modeling for Neural Video Compression
R. Yang, Y. Yang, J. Marino, and S. Mandt
International Conference on Learning Representations (ICLR 2021). PDF
Scale Space Flow with Autoregressive Priors
R. Yang, Y. Yang, J. Marino, and S. Mandt
ICLR Workshop on Neural Compression: from Information Theory to Applications, 2021 (Spotlight). PDF
Lower Bounding Rate-Distortion from Samples
Y. Yang and S. Mandt
ICLR Workshop on Neural Compression: from Information Theory to Applications, 2021 (Spotlight). PDF
Scalable Gaussian Process Variational Autoencoders
M. Jazbec, M. Ashman, V. Fortuin, M. Pearce, S. Mandt, and G. Rätsch
Artificial Intelligence and Statistics (AISTATS 2021). PDF
2020
Improving Inference for Neural Image Compression
Y. Yang, R. Bamler, and S. Mandt
Neural Information Processing Systems (NeurIPS 2020). PDF code
User-Dependent Neural Sequence Models for Continuous-Time Event Data
A. Boyd, R. Bamler, S. Mandt, and P. Smyth
Neural Information Processing Systems (NeurIPS 2020). PDF
Hybridizing Physical and Data-Driven Prediction Methods for Physicochemical Properties
F. Jirasek, R. Bamler, and S. Mandt
Chemical Communications 56 12407, 2020 article
Generative Modeling for Atmospheric Convection
G. Mooers, J. Tuyls, S. Mandt, M. Pritchard, and T. Beucler
Climate Informatics, 2020 PDF
Variational Bayesian Quantization
Y. Yang, R. Bamler, and S. Mandt
International Conference on Machine Learning (ICML 2020) PDF Video
How Good is the Bayes Posterior in Deep Neural Networks Really?
F. Wenzel, K. Roth, B. Veeling, J. Swiatkowski, L. Tran, S. Mandt, J. Snoek, T. Salimans, R. Jenatton, and S. Nowozin
International Conference on Machine Learning (ICML 2020) PDF
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
J. Swiatkowski, K. Roth, B. Veeling, L. Tran, J. Dillon, S. Mandt, J. Snoek, T. Salimans, R. Jenatton, and S. Nowozin
International Conference on Machine Learning (ICML 2020) PDF
Hydra: Preserving Ensemble Diversity for Model Distillation
L. Tran, B. Veeling, K. Roth, J. Swiatkowski, J. Dillon, J. Snoek, S. Mandt, T. Salimans, S. Nowozin, R. Jenatton
arXiv:2001.04694 PDF
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
F. Jirasek, R. Alves, J. Damay, R. Vandermeulen, R. Bamler, M. Bortz, S. Mandt, M. Kloft, and H. Hasse
The Journal of Physical Chemistry Letters, 11, 2020. article free PDF
GP-VAE: Deep Probabilistic Time Series Imputation
V. Fortuin, D. Baranchuk, G. Rätsch, and S. Mandt
Artificial Intelligence and Statistics (AISTATS 2020). PDF
Extreme Classification via Adversarial Softmax Approximation
R. Bamler and S. Mandt
International Conference on Learning Representations (ICLR 2020). PDF
2019
Tightening Bounds for Variational Inference by Revisiting Perturbation Theory
R. Bamler, C. Zhang, M. Opper, and S. Mandt
Journal of Statistical Mechanics (2019), 124004. PDF
Deep Generative Video Compression
J. Han, S. Lombardo, C. Schroers, and S. Mandt
Neural Information Processing Systems (NeurIPS 2019). PDF poster
Autoregressive Text Generation Beyond Feedback Loops
F. Schmidt, S. Mandt, and T. Hofmann
Conference on Empirical Methods in Natural Language Processing (EMNLP 2019). PDF
Augmenting and Tuning Knowledge Graph Embeddings
R. Bamler, F. Salehi, and S. Mandt
Conference on Uncertainty in Artificial Intelligence (UAI 2019). PDF
Advances in Variational Inference
C. Zhang, J. Bütepage, H. Kjellström, and S. Mandt
IEEE Transactions on Pattern Analysis and Machine Intelligence. PDF arXiv
Active Mini-Batch Sampling using Repulsive Point Processes
C. Zhang, C. Öztireli, S. Mandt, and G. Salvi
Conference on Artificial Intelligence (AAAI 2019). article PDF
Mobile Robotic Painting of Texture
M. Helou, S. Mandt, A. Krause, and P. Beardsley
International Conference on Robotics and Automation (ICRA 2019). article PDF
A Quantum Field Theory of Representation Learning
R. Bamler and S. Mandt
ICML Workshop on Physics for Deep Learning (2019). PDF
2018
Disentangled Sequential Autoencoder
Y. Li and S. Mandt
International Conference on Machine Learning (ICML 2018). PDF
Iterative Amortized Inference
J. Marino, Y. Yue, and S. Mandt
International Conference on Machine Learning (ICML 2018). PDF
Quasi Monte Carlo Variational Inference
A. Buchholz, F. Wenzel, and S. Mandt
International Conference on Machine Learning (ICML 2018). PDF
Improving Optimization for Models With Continuous Symmetry Breaking
R. Bamler and S. Mandt
International Conference on Machine Learning (ICML 2018), long talk. PDF
Continuous Word Embedding Fusion via Spectral Decomposition
T. Fu, C. Zhang, and S. Mandt
The SIGNLL Conference on Natural Language Learning (CoNLL 2018). PDF
Scalable Generalized Dynamic Topic Models
P. Jähnichen, F. Wenzel, M. Kloft, and S. Mandt
Artificial Intelligence and Statistics (AISTATS 2018). PDF
Image Anomaly Detection with Generative Adversarial Networks
L. Deecke, R. Vandermeulen, L. Ruff, S. Mandt, and M. Kloft
European Conference on Machine Learning (ECML PKDD 2018). PDF
Learning to Infer
J. Marino, Y. Yue, and S. Mandt
International Conference on Learning Representations (Workshop Track). PDF
Quasi Monte Carlo Flows
F. Wenzel, A. Buchholz, and S. Mandt
NeurIPS Bayesian Deep Learning Workshop. PDF
Video Compression through Deep Bayesian Learning
S. Lombardo, J. Han, C. Schroers, and S. Mandt
NeurIPS Bayesian Deep Learning Workshop. PDF
2017
Stochastic Gradient Descent as Approximate Bayesian Inference
S. Mandt, M. Hoffman, and D. Blei
Journal of Machine Learning Research, vol 18(134):1-35, 2017. PDF code
Perturbative Black Box Variational Inference
R. Bamler, C. Zhang, M. Opper, and S. Mandt
Neural Information Processing Systems (NIPS 2017). PDF poster
Dynamic Word Embeddings
R. Bamler and S. Mandt
International Conference on Machine Learning (ICML 2017). PDF poster
Determinantal Point Processes for Mini-batch Diversification
C. Zhang, H. Kjellström, and S. Mandt
Uncertainty in Artificial Intelligence (UAI 2017) (plenary talk). PDF
Factorized Variational Autoencoders for Modeling Audience Reactions to Movies
Z. Deng, R. Navarathna, P. Carr, S. Mandt, Y. Yue, I. Matthews, and G. Mori
Computer Vision and Pattern Recognition (CVPR 2017). PDF
Iterative Inference Models
J. Marino, Y. Yue, and S. Mandt
NIPS 2017 Workshop on Bayesian Deep Learning. PDF
Bayesian Paragraph Vectors
G. Ji, R. Bamler, E. Sudderth, and S. Mandt
NIPS 2017 Workshop on Approximate Bayesian Inference. PDF
Structured Black Box Variational Inference for Latent Time Series Models
R. Bamler and S. Mandt
ICML 2017 Time Series Workshop (oral). PDF
Diversified Mini-Batch Sampling using Repulsive Point Processes
C. Zhang, C. Öztireli, and S. Mandt
NIPS 2017 Workshop on Advances in Approximate Bayesian Inference. PDF
2016
Exponential Family Embeddings
M. Rudolph, F.J.R. Ruiz, S. Mandt, and D. Blei
Neural Information Processing Systems (NIPS 2016). PDF
Balanced Population Stochastic Variational Inference
C. Zhang, S. Mandt, and H. Kjellström
NIPS 2016 Workshop on Advances in Approximate Bayesian Inference. PDF
Huber-Norm Regularization for Linear Prediction Models
O. Zadorozhnyi, G. Benecke, S. Mandt, T. Scheffer, M. Kloft
European Conference on Machine Learning (ECML 2016). PDF
A Variational Analysis of Stochastic Gradient Algorithms
S. Mandt, M. Hoffman, and D. Blei
International Conference on Machine Learning (ICML 2016) PDF poster video
code
Variational Tempering
S. Mandt, J. McInerney, F. Abrol, R. Ranganath, and D. Blei
Artificial Intelligence and Statistics (AISTATS 2016). PDF
Separating Sparse Signals from Correlated Noise in Binary Classification
S. Mandt, F. Wenzel, S. Nakajima, C. Lippert, and M. Kloft.
UAI 2016 Workshop on Causation: Foundation to Application. (oral) PDF
2015
Sparse Probit Linear Mixed Model
S. Mandt, F. Wenzel, S. Nakajima, J. P. Cunningham, C. Lippert, and M. Kloft
Machine Learning, 106(9), 1621-1642. PDF
Continuous-Time Limit of Stochastic Gradient Descent Revisited
S. Mandt, M. Hoffman, and D. Blei
NIPS Workshop on Optimization for Machine Learning (OPT 2015) PDF
Finding Sparse Features in Strongly Confounded Medical Binary Data
S. Mandt, F. Wenzel, S. Nakajima, J. P. Cunningham, C. Lippert, and M. Kloft
NIPS Workshop on Machine Learning in Healthcare (2015). (oral) PDF
Stochastic Differential Equations for Quantum Dynamics of Spin-Boson Networks
S. Mandt, D. Sadri, A. Houck, and H. Tureci
New Journal of Physics 17 (2015) 053018. PDF
2014
Smoothed Gradients for Stochastic Variational Inference
S. Mandt and D. Blei
Neural Information Processing Systems (NIPS 2014) PDF
Probit Regression with Correlated Label Noise: An EM-EP approach
S. Mandt, F. Wenzel, J. Cunningham, and M. Kloft
NIPS Workshop on Advances in Variational Inference (2014) PDF
Comment on "Consistent thermostatistics forbids negative absolute temperatures"
U. Schneider, S. Mandt, A. Rapp, S. Braun, H. Weimer, I. Bloch, and A. Rosch
Arxiv (2014). PDF
Damping of Bloch oscillations: Variational solution of the Boltzmann equation beyond linear response
S. Mandt
Physical Review A 90, 053624 (2014). PDF
Before 2014
Relaxation towards negative temperatures in bosonic systems: Generalized Gibbs ensembles and beyond integrability
S. Mandt, A. Feiguin, S. Manmana
Physical Review A 88, 043643 (2013). PDF
Ultrakalt und doch heißer als unendlich heiß. Erstmals gelang es, ein Quantengas bei negativen absoluten Temperaturen herzustellen
S. Mandt
Popular article on negative temperatures in the monthly proceedings of the German Physical Society.
Physik Journal 12, March edition (2013) PDF
Transport and Non-Equilibrium Dynamics in Optical Lattices. From Expanding Atomic Clouds to Negative Absolute Temperatures
S. Mandt
PhD thesis, University of Cologne (2012) PDF
Fermionic transport in a homogeneous Hubbard model: Out-of-equilibrium dynamics with ultracold atoms
U. Schneider, L. Hackermueller, J.P. Ronzheimer, S. Will, S. Braun, T. Best, I. Bloch, E. Demler, S. Mandt, D. Rasch, A. Rosch
Nature Physics 8, 213-218 (2012). PDF
Press: SciTechDaily, Pro-Physik (in German)
Interacting Fermionic Atoms in Optical Lattices Diffuse Symmetrically Upwards and Downwards in a Gravitational Potential
S. Mandt, A. Rapp, A. Rosch
Physical Review Letters 106, 250602 (2011). arXiv
Press: Nature
Equilibration rates and negative absolute temperatures for ultracold atoms in optical lattices
A. Rapp, S. Mandt, A. Rosch
Physical Review Letters 105, 220405 (2010). arXiv
Press: Nature , New Scientist, Science News
Experimental realization of T<0 based on our theory: Braun et. al., Science 2013
Zooming in on local level statistics by supersymmetric extension of free probability
S. Mandt, M.R. Zirnbauer
J. Phys. A 43 (2010) 025201. arXiv
Symmetric Spaces Toolkit
H. Sebert and S. Mandt
Lecture notes, SFB/TR 12, Langeoog (2007) PDF

Short Bio

Research Interests

News

Teaching

Group Members

Preprints (selected)

Publications

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

Before 2014