Professor C. Lee Giles

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

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. PDF Body Citations Automatic Metadata Information Extraction (IE) - CiteSeerX title, authors, affiliations, abst Header Table Databases Search index IE Figure Converter Formulae Text 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 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.eannealed stochastic learning rate. Achieved +0.971 dB gain over Google neural model on Kodak Test set. Ours Standard method Ororbia, Mali, DCC ‘19

7. Compression system - Google Model diagram for single iteration iof 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.” Yogi Berra, catcher/philosopher, NY Yankees. • http://clgiles.ist.psu.edu • https://en.wikipedia.org/wiki/Lee_Giles • giles@ist.psu.edu For more information Why not use a deep learner?