Sarah Marzen
Assistant Professor of Physics
 
Email: smarzen@kecksci.claremont.edu
Office: Keck Science Center 132
Phone: 909-607-3097
Office Hours: Starting Fall 2019
Web Site: http://sarahmarzen.weebly.com
   
Educational Background:
Caltech, B.S., Physics 2011
University of California, Berkeley, Ph.D., Physics 2016
Research Interests:
Biological organisms benefit from predicting their environmental input and have likely evolved sophisticated learning rules to do such prediction. At the same time, any organism's ability to predict its input is limited by its resources: the size of its prediction apparatus, the energy it expends, and the capacity of the sensory channel that transduces information about past inputs.
How can we test how well biological organisms are predicting their input? And what are the learning rules by which such organisms improve their sensory prediction capabilities? A large part of my research is geared towards understanding how well and how organisms simultaneously predict and compress their sensory inputs. Such an understanding could lead to a better understanding of not just biological sensing, but also perhaps to new machine learning algorithms for prediction.
To date, I have investigated biologically appropriate metrics for the quality of a predictive biological sensor, establishing that sensory prediction can indeed improve fitness, and developed new methods for calculating limits to resource-limited prediction and for calculating the predictive and energetic performance of any given sensor. I have also spent considerable effort understanding perceptually relevant components of natural images and unfurling the implications of compressing large, random environments, towards the related goal of understanding compression sans prediction.
 
Selected Publications List: Click to open new window.
1.   S. Marzen and J. P. Crutchfield . (2018). Optimized bacteria are environmental prediction engines. Physical Review E   98: . Article
 
2.   S. Marzen and J. P. Crutchfield . (2017). Informational and causal architecture of continuous-time renewal processes. Journal of Statistical Physics   168: 109-127. Article
 
3.   S. Marzen and J. P. Crutchfield . (2017). Structure and randomness of continuous-time discrete-event processes. Journal of Statistical Physics   169: 303-315. Article
 
4.   S. Marzen and S. DeDeo . (2017). The evolution of lossy compression. Journal of the Royal Society Interface   14: . Article
 
5.   S. Marzen . (2017). Difference between memory and prediction in linear recurrent networks. Physical Review E   96: . Article
 
6.   S. Marzen and J. P. Crutchfield . (2017). Nearly maximally predictive features and their dimensions. Physical Review E (R)   95: . Article
 
7.   C. Hillar and S. Marzen . (2017). Neural network coding of natural images with applications to pure mathematics. Proceedings of the AMS Special Session on Algebraic and Geometric Methods in Discrete Mathematics Heather Harrington, Mohamed Omar, and Matthew Wright  : .
 
8.   C. Hillar and S. Marzen . (2016). Revisiting perceptual distortion for natural images: mean discrete structural similarity index. Data Compression Conference   : . Article
 
9.   S. Marzen and J. P. Crutchfield . (2016). Predictive rate-distortion for infinite-order Markov processes. Journal of Statistical Physics   163: 1312-1338. Article
 
10.   S. Marzen and S. DeDeo . (2016). Weak universality in sensory tradeoffs. Physical Review E (R)   94: . Article
 
11.   S. Marzen and J. P. Crutchfield . (2016). Statistical Signatures of Structural Organization: The case of long memory in renewal processes. Physics Letters A   380: 1517-1525. Article
 
12.   S. Marzen and J. P. Crutchfield . (2015). Informational and causal architecture of discrete-time renewal processes. Entropy   17: 4891-4917. Article
 
13.   S. Marzen, M. R. DeWeese, and J. P. Crutchfield . (2015). Time resolution dependence of spike train information measures. Frontiers in Computational Neuroscience   9: . Article
 
14.   J. P. Crutchfield and S. Marzen . (2015). Signatures of Infinity: Nonergodicity in Prediction, Complexity, and Learning. Physical Review E (R)   91: . Article
 
15.   S. Marzen and J. P. Crutchfield . (2014). Information anatomy of stochastic equilibria. Entropy   16: 4713-4748. Article
 
16.   S. Marzen, H. G. Garcia, and R. P. Phillips . (2013). Statistical Mechanics of the Monod-Wyman-Changeux (MWC) Models. Journal of Molecular Biology   425: 1433-1460. Abstract Article