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Short Bio
I am a research scientist at Disney Research Pittsburgh, where I lead the statistical machine learning group.
From 2014 to 2016 I was a postdoctoral researcher with David Blei at
Columbia University, and a PCCM Postdoctoral Fellow at Princeton University from 2012 to 2014. I did my Ph.D. with Achim Rosch
at the Institute
for Theoretical Physics at the University of Cologne, where I was supported by the German National Merit Scholarship.
News
 Internships available. If you are interested in working with me, please send me an email with your CV. (status: Jan 2017).
 I coorganized the NIPS 2016 and 2015 workshops on Advances in Approximate Bayesian Inference, together with Dustin Tran, Tamara Broderick, James McInerney and David Blei, see www.approximateinference.org.
 Accepted papers at AISTATS 2016, ICML 2016, and NIPS 2016.
Research Interests
[Keywords: variational inference; stochastic optimization; scalable MCMC; topic models; recommender systems; word embeddings; deep generative models]
I work in the field of statistical machine learning,
where my research centers on probabilistic modeling. In this field, we first posit a probability model that
jointly models hidden variables and observed data, then use a Bayesian
inference algorithm to discover the hidden variables from the data. There are many applications of this paradigm,
including language models, recommendation systems, image clustering and denoising, and statistical genetics.
The growing amount of data calls for new scalable machine learning algorithms.
To contribute to this goal, I integrate probabilistic modeling with stochastic optimization. Most of my research focusses on variational approximations that have
their origin in statistical physics. For example, in one of my recent research projects I introduced local, perdatapoint
temperatures into probability models, and then used a variational algorithm to learn the temperature distribution
form the data. This results in a statistically more robust approach for a generic class of models, and to automatic
perdatapoint annealing schedules. Another recent project focusses on the interpretation and analysis of stochastic gradient descent with constant learning rates as a form
of inexact MCMC algorithm, where the learning rate can be tuned such as to sample from an approximate
posterior. Most recently, my collaborators and I have generalized word embeddings to other forms of data and distributions.
Besides machine learning and its applications, I have been working in the field of nonequilibrium statistical physics.
This field deals with nonparametric distributions that are defined by a stochastic process and relates to the convergence
behavior of stochastic algorithms.
In this field I worked on quantum spin dynamics, Boltzmann equations, hydrodynamics and random matrix theory with publications
in Nature Physics and Physical Review Letters, among others.
My work on thermodynamics with negative absolute temperatures inspired experimental physicists to the first realization of these states with free particles.
Publications
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
(Old title: Multicanonical Stochastic Variational Inference)
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.
Proceedings of the UAI Workshop on Causation: Foundation to Application (Oral), (to appear) 2016. [PDF]
2015

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) [PDF]

Sparse Estimation in a Correlated Probit Model
S. Mandt, F. Wenzel, S. Nakajima, J. P. Cunningham, C. Lippert, and M. Kloft
Arxiv (2015). [arXiv]

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
 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.