Distinguished Lecture Series in Data Science

Afsheen Afshar                                                                     March 7, 2018

Chief AI Officer and Senior Managing Director, Cerberus

Real-world Challenges of using AI in the Enterprise

Recent advances in the field of AI has been exciting, and the resultant hype has been great. However, most enterprises have yet to substantively harness their data to positively affect their bottom lines. In this talk, we discuss some of the underlying technological and cultural reasons as well as approaches for success. From a technological perspective, there is a multitude of legacy systems with different formats and data models that must be merged. We discuss some approaches for managing this landscape using ad-hoc query methods. In addition, while technological and analytical challenges abound, having a high degree of cultural sensitivity, empathy for the end-user, and design-orientation are key to success. We discuss a few high profile examples of technologically advanced AI products that have failed to gain traction.

 

 
Afsheen Afshar received his Ph.D. in Electrical Engineering from Stanford University. He was a Managing Director at Goldman Sachs, the Chief Data Science Officer and Managing Director at J. P. Morgan, Corporate and Investment Bank before becoming the Chief Artificial Intelligence Officer and Senior Managing Director of Cerberus Operations and Advisory Company, LLC. Afsheen Afshar also serves as an advisory board member for M.S. program in computational finance at Carnegie Mellon University.

 

 

Stéphane Mallat                                                               February 9, 2018

Professor, École Normale Supérieure, France

Learning Physics with Deep Neural Networks

Machine learning amounts to find low-dimensional models governing the properties of high dimensional functionals. This could almost be called physics. Algorithms have considerably improved in the last 10 years through the processing of massive amounts of data. In particular, deep neural network have spectacular applications, to image classification, medical, industrial and physical data analysis.

We show that the approximation capabilities of deep convolution networks come from their ability to compute invariant at different scales over possibly high-dimensional groups including diffeomorphisms. We shall study the mathematical properties of simplified deep convolutional networks computed with wavelet. We give applications to regression of molecular energies in quantum chemistry. We shall also introduce low-dimensional non-Gaussian intermittent models for statistical physics, with applications to Ising and high Reynold turbulences through cosmological data.

 

 
Stéphane Mallat was a Professor at the Courant Institute of Mathematical Sciences, École Polytechnique, Paris before joining the Computer Science department at École Normale Supérieure, in Paris in 2012. He received the Outstanding Achievement Award from the SPIE Society and was a plenary lecturer at the 1998 International Congress of Mathematicians. He also received the 2004 European IST Grand prize, the 2004 INIST-CNRS prize for most cited French researcher in engineering and computer science, the 2007 EADS grand prize of the French Academy of Sciences, the 2013 Innovation medal of the CNRS, and the 2015 IEEE Signal Processing best sustaining paper award.
 
 
  

Peter Norvig                                                                            October 25, 2017

Director​ ​of​ ​Research,​ ​Google​ ​Inc

Creating​ ​Software​ ​with​ ​Machine​ ​Learning: Challenges​ ​and​ ​Promise

Traditionally, software is built by programmers who consider the possible situations and write rules to deal with them. But recently, many applications have been created by machine learning: the programmer is replaced by a trainer, who shows the computer examples until it learns to complete the task. This shift in the way software is built is opening up exciting new possibilities and posing new challenges.

 

 

 
Peter Norvig is a Director of Research at Google; previously he directed Google's core search algorithms group. He is co-author of Artificial Intelligence: A Modern Approach, the leading textbook in the field, and co-teacher of an Artificial Intelligence class that signed up 160,000 students, helping to kick off the current round of massive open online classes. He is a Fellow of AAAI, ACM, the California Academy of Science and the American Academy of Arts & Sciences.