1. Professor C. Lee Giles
David Reese Professor – IST; graduate Professor - CSE
Adjunct Professor – Princeton, Pennsylvania, Columbia, Pisa, Trento
Graduated over 30 PhDs
Published over 600 papers with nearly 40,000 citations and h-index of 95, most use machine and deep learning and AI
Intelligent and specialty search engines; cyberinfrastructure for science, academia and government; big data; deep learning
Modular, scalable, robust, automatic science and technology focused cyberinfrastructure and search engine creation and maintenance
Large heterogeneous data and information systems
Specialty science and technology search engines for knowledge discovery & integration
CiteSeerx (all scholarly documents – initial focus on computer science) (NSF funded)
MathSeer (new math search engine) (Sloan funded)
BBookX, ( Book generation, Question generation) (TLT funded)
Scalable intelligent tools/agents/methods/algorithms
Information, knowledge and data integration
Information and metadata extraction; entity recognition
Pseudocode, tables, figure, chemical formulae, equations, & names extraction
Unique search, knowledge discovery, information integration, data mining algorithms
Text in wild – machine reading, deep learning
Strong collaboration record.
Lockheed-Martin, FAST, Raytheon, IBM, Ford, Alcatel-Lucent, Smithsonian, Internet Archive, DARPA, Yahoo, Dow Chemical NSF, Sloan, Mellon 1

2. My work on neural networks
Over 100 papers on NNs
International Neural Network Society Dennis Gabor Award
IEEE Computational Intelligence Society Pioneer Award in Neural Networks.
Taught the first Neural Networks course at Princeton (1994)
NN interests and pubs
Text in the wild
Compression (we beat Google)
Recurrent neural networks as automata & grammars
Recurrent neural network verification
Neural networks in information retrieval and education

3. Millions of hits daily
1/2 million download PDFs daily (180M annual)
2nd most attacked site at Penn State

4. Automatic Metadata Information Extraction (IE) - CiteSeerX
title, authors,
affiliations, abst
Header
Table
Databases
Search index
PDF
Text IE
Figure
Converter
Formulae
Body
Citations
Many other open source academic document metadata extractors available – recent JCDL workshop, metadata hackathon, JCDL tutorial 2016

5. Deep Learning End-to-End Scene Text Reading
Typical Pipeline
Dozen papers in prestigious AI and Computer Vision conferences
Funded by NSF Expedition

6. Hybrid Deep Compression
Standard method
Ours
Design an iterative, RNN-based hybrid estimator for decoding instead of using transformations.
Replaces dequantizer and inverse encoding transform modules with a function approximator.
Neural decoder is single layer RNN with 512 units.
An iterative refinement algorithm learns an iterative estimator of this function approximator
Exploits both causal & non-causal information to improve low bit rate reconstruction.
Applies to any image decoding problem
Handles a wide range of bit rate values
Uses multi-objective loss function for image compression.
Uses a new annealing schedule - i.e annealed stochastic learning rate.
Achieved dB gain over Google neural model on Kodak Test set.
Ororbia, Mali, DCC ‘19

7. Compression system - Google
Model diagram for single iteration i of shared recurrent neural network (RNN) architecture [Toderici ‘15 , Toderici ‘16]

8. Grammatical Inference - RNNs
Extract grammar rules from trained RNNs for Verification, Wang, AAAI VNN, ‘19

9. “The future ain’t what it used to be
“The future ain’t what it used to be.” Yogi Berra, catcher/philosopher, NY Yankees.
For more information
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