Has 20 years experience in data-driven analysis and machine learning development. Mark has a Ph.D in Computer Science from UCSD and a Master of Science in Electrical and Computer Engineering from USC. Mark worked at IBM, Sony, and Netflix generating 29 patents. Mark is an experienced educator, having published courseware on multiple online learning platforms.
Publications include:

Neural Information Processing Systems (NIPS)
Neural Networks Journal
IEEE Transactions on Neural Networks

 
Patents include:

Relating media objects consumed in different mediums
Dynamic web service composition to service a user request
An itinerary planner
Displaying artists related to an artist of interest
Invariant clustering of categorical data 
Sorting media objects by similarity 
Clustering and classification of multimedia data
Augmented dataset representation using a taxonomy which accounts for similarity and dissimilarity between each record in the dataset and a user's similarity-biased intuition 
Ordering artists by overall degree of influence
Seed Based Clustering of Categorical Data
Dimensionality reduction for content category data
Using a community generated web site for metadata
A routing agent
Combining plurality of input control policies to provide a compositional output control policy
Predicting web browsing behavior
Emulating web site traffic to identify web site usage patterns 
Association rule ranker for web site emulation