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Short Bio
I am a research scientist and head of the statistical machine learning group at Disney Research Pittsburgh on CMU campus.
Previously, I was a postdoctoral researcher with David Blei at
Columbia University, (20142016), and a PCCM Postdoctoral Fellow at Princeton University in theoretical physics (20122014). My Ph.D. advisor was Achim Rosch
at the Institute
for Theoretical Physics of the University of Cologne. I was a fellow of the German Academic Merit Foundation.
News
 Accepted papers at NIPS 2017, UAI 2017, ICML 2017 and CVPR 2017.
 Two journal papers accepted in 2017 (MLJ and JMLR).
 Accepted papers at AISTATS 2016, ICML 2016, and NIPS 2016.
 Coorganizer, NIPS 2017 Workshop on Advances on Approximate Bayesian Inference
 Coorganizer, NIPS 2016 Workshop on Advanced in Approximate Bayesian Inference
 Coorganizer, NIPS 2015 Workshop on Advanced in Approximate Bayesian Inference
 We are offering research internships in the field of probabilistic machine learning. Internships are possible throughout the year and often result in a conference publication or patent. You should be available for 48 months and already have publications in the fields of machine learning, statistical NLP, or computer vision. If you are interested in working with me, please send me an email with your CV. (status: July 2017).
Research Interests
Keywords: variational inference; stochastic optimization; word embeddings; deep generative models
My research centers on scalable probabilistic modeling.
Most of my research focuses on stochastic variational inference. Among my most recent projects, I worked on (a) establishing connections between
stochastic gradient decent and approximate inference, (b) probabilistic interpretations of word embeddings that give rise to new
language models such as dynamic word embeddings, and (c) using and generalizing variational autoencoders to modeling movie audience reactions. Being trained as
a theoretical physicist, I worked on nonequilibrium statistical physics and stochastic processes in the past.
Publications
2017

Perturbative Black Box Variational Inference
R. Bamler, C. Zhang, M. Opper, and S. Mandt
Neural Information Processing Systems (NIPS 2017), to appear. [arXiv]

Stochastic Gradient Descent as Approximate Bayesian Inference
S. Mandt, M. Hoffman, and D. Blei
Journal of Machine Learning Research, to appear. [arXiv]

Structured Black Box Variational Inference for Latent Time Series Models
R. Bamler and S. Mandt
ICML 2017 Time Series Workshop (oral). [PDF]

Determinantal Point Processes for Minibatch Diversification
C. Zhang, H. Kjellström, and S. Mandt
Uncertainty in Artificial Intelligence (UAI 2017) (plenary talk). [PDF]

Dynamic Word Embeddings
R. Bamler and S. Mandt
International Conference on Machine Learning (ICML 2017). [PDF][poster]

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]
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]

HuberNorm 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]

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), 16211642. [PDF]

ContinuousTime 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 SpinBoson 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 EMEP 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). [arXiv]

Damping of Bloch oscillations: Variational solution of the Boltzmann equation beyond linear response
S. Mandt
Physical Review A 90, 053624 (2014). [arXiv]
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). [arXiv]

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 NonEquilibrium 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: Outofequilibrium 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, 213218 (2012). [arXiv]
Press: SciTechDaily, ProPhysik (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]
Invited Talks
 Disney Data Analytics Conference
Orlando, FL. August 2017.
 CS Colloquium, University of Southern California
Los Angeles, CA. April 2017.
 CS Colloquium, ETH Zürich
Zürich, Switzerland. March 2017.

ML and Friends Seminar, UMass Amherst
Amherst, MA. February 2017.
 AI Seminar, Carnegie Mellon University
Pittsburgh, PA. September 2016.
 California Institute of Technology
Pasadena, CA. August 2016.
 Data Science Colloquium, Rutgers University
Newark, NJ. April 2016.
 Google Research
Mountain View, CA. April 2016.
 Microsoft
Sunnyvale, CA. March 2016.
 CS Colloquium, University of Rhode Island
Kingston, RI. March 2016.
 Disney Research
Pittsburgh, Pennsylvania. March 2016.
 CS Colloquium, University of Colorado
Boulder, Colorano. January 2016.
 National Renewable Energy Laboratory
Golden, Colorado. January 2016.
 Adobe Research
San Francisco, CA. June 2015.
 Human Longevity Inc.
Mountain View, CA. June 2015.
 Dagstuhl Seminar
Machine Learning with Interdependent and Nonidentically Distributed Data.
Dagstuhl, Germany. April 2015.
 Humboldt University of Berlin
Machine Learning Seminar, Berlin, Germany. February 2015.
 Technical University of Berlin
Machine Learning Seminar, Berlin, Germany. February 2015.
 DWave Systems Inc.
Burnaby, Canada. January 2015.
 University of British Columbia
Machine Learning Seminar, Vancouver, Canada. January 2015.
 IBM Research
Physical Sciences Seminar, Yorktown Heights, USA. October 2014.
 Emergent Phenomena in the Dynamics of Quantum Matter
(QUANTMAT 2014), New York, USA. April 2014.
 University of Otago
Theoretical Physics Seminar, Dunedin, New Zealand. February 2013.
 Princeton Center for Theoretical Science
Princeton University, Princeton, USA. March 2012.
 Finite Temperature NonEquilibrium Superfluid Systems
(FINESS 2011), Heidelberg, Germany  September 2011.
 University of Colorado
Theoretical Physics Seminar, Boulder, CO. September 2010.
 École Polytechnique
Theoretical Physics Seminar, Palaiseau, France. March 2010.