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BIOGRAPHICAL SKETCH Pierre F BIOGRAPHICAL SKETCH Pierre F. Baldi, Professor PROFESSIONAL PREPARATION: School Major Degree/Year University of Paris VII Mathematics M.S./1980 University of Paris X Psychology M.S./1980 University of Paris Mathematics D.E.A./1981 ENSTA, Paris Computer Science and Engineering M.S./1983 California Institute of Technology Mathematics Ph.D./1986 APPOINTMENTS: December 2018-present: Director, UCI AI Institute July 2018-present: Distinguished Professor, University of California, Irvine. Oct. 2006-June 2018: Chancellor’s Professor, University of California, Irvine. Jan 2006-present: Associate Director, UCI Center for Machine Learning and Intelligent Systems. June 2001–present: Professor Department of Computer Science, University of California, Irvine [Joint appointment in the Departments of Biological Chemistry, Biomedical Engineering, Mathematics, Statistics]. Jan 2001present: Founding Director UCI Institute for Genomics and Bioinformatics. July 1999-May 2001: Associate Professor, Department of Information and Computer Science, UCI. [Joint appointment in the Departments of Biological Chemistry and Bioengineering]. 1991-June 1999: Chairman and CEO, Net-ID, Inc. January 1999: Visiting Professor, Department of Computer Science, University of Florence. 1995–1996: Member of the Professional Staff, Division of Biology, Caltech. 1988–1995: Member of the Technical Staff in the Nonlinear Sci. & Info. Processing Group at the Jet Propulsion Laboratory, and Visiting Research Associate, Division of Biology, Caltech. 1986–1988: Visiting Lecturer, Department of Mathematics, UCSD. RELEVANT PUBLICATIONS (PHYSICS): Google Scholar Page: https://scholar.google.com/citations?user=RhFhIIgAAAAJ&hl=en Web site: www.ics.uci.edu/~pfbaldi H-index: 93 P. Baldi and E.B. Baum. Bounds on the Size of Ultrametric Structures, Physical Review Letters, Vol. 56, No. 15, 1598-1600, (1986). P. Baldi. Symmetries and Learning in Neural Network Models, Physical Review Letters, Vol. 59, No. 17, 1976-1978, (1987). P. Baldi and S.S. Venkatesh. Number of Stable Points for Spin Glasses and Neural Networks of Higher Orders, Physical Review Letters, Vol. 58, No. 9, 913-916, (1987). P. Baldi, P. Sadowski, and D. Whiteson. Deep Learning in High-Energy Physics: Improving the Search for Exotic Particles. Nature Communications, 5, article 4308, (2014). P. Sadowski, J. Collado, D. Whiteson, and P. Baldi. Deep Learning, Dark Knowledge, and Dark Matter. Journal of Machine Learning Research, Workshop and Conference Proceedings, Proceedings of NIPS Workshop on High-Energy Physics, Volume 42, 81-97, (2015). P. Baldi, P. Sadowski, and D. Whiteson. Enhanced Higgs to τ+τ- Search with Deep Learning. Physical Review Letters, 114, 111801-1—111801-5, (2015). P. Baldi, K. Bauer, C. Eng, P. Sadowski, and D. Whiteson. Jet Substructure Classification in High-Energy Physics with Deep Neural Networks. Physical Review D, 93, 094034 (2016) - Published 27 May 2016, (2016). P. Baldi, K. Cranmer, T. Faucett, P. Sadowski, D. Whiteson. Parameterized Neural Networks for High-Energy Physics. The European Physical Journal C, 76, 235, 1-7, (2016). DOI 10.1140/epjc/s10052-016-4099-4. D. Guest, J. Collado, P. Baldi, D. Whiteson. Jet Flavor Classification in High-Energy Physics with Deep Neural Networks. Physical Review D, 94, 112002 (2016) - Published 2 December 2016, (2016). P. Sadowski, D. Fooshee, N. Subrahmanya, and P. Baldi. On the Synergies Between Quantum Mechanics and Machine Learning in Reaction Prediction. Journal of Chemical Information and Modeling, 56 (11), 2125–2128. Publication Date (Web): October 17, 2016 (Letter). DOI: 10.1021/acs.jcim.6b00351, (2016). P. Sadowski, B. Radics, A. Ananya, Y. Yamazaki, and P. Baldi. Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning. Journal of Physics Communications, 1, 2, Published 6 September 2017, (2017). Chase Shimmin, Peter Sadowski, Pierre Baldi, Edison Weik, Daniel Whiteson, Edward Goul, and Andreas Søgaard. Decorrelated Jet Substructure Tagging Using Adversarial Neural Networks. Physical Review D, 96, 074034 (2017) - Published 30 October 2017. P. Baldi, J. Bian, L. Hertel, et al. Improved energy reconstruction in NOvA with regression convolutional neural networks. Physical Review D, in press, (2018). SYNERGISTIC ACTIVITIES: 1. Associate Editor: Neural Networks, Artificial Intelligence, Data Mining and Knowledge Discovery, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Chemistry Central --Editorial Board: International Journal of Bioinformatics Research and Applications, Journal of Chemical Information and Modeling. 2. Four books plus one in preparation: B4. P. Baldi, P. Frasconi, and P. Smyth. Modeling the Internet and the Web—Probabilistic Methods and Algorithms. Wiley, (2003). Japanese version in 2007. [444 citations] B3. P. Baldi and G. Wesley Hatfield. DNA Microarrays and Gene Regulation—From Experiments to Data Analysis and Modeling. Cambridge University Press, (2002). English version for China and electronic publishing version through eBrary in 2003 and 2004. Paperback edition in 2011. [549 citations] B2. P. Baldi. The Shattered Self—the End of Natural Evolution, MIT Press, (2001). Paperback version in 2002. B1. P. Baldi and S. Brunak. Bioinformatics: the Machine Learning Approach. MIT Press, (1998). Second revised edition (2001). Indian and Chinese versions in 2003. [1794 citations] Currently writing textbook: Neural Networks and Deep Learning: Theory, Algorithms, and Applications to the Natural Sciences. (MIT Press). 3. Helped develop machine learning and bioinformatics curriculum at UCI, including introduction of new undergraduate and graduate courses on Neural Networks and Deep Learning (CompSci 172B and 274C). 4. Annual co-organizer of the International Workshop on Deep Learning: Theory, Algorithms, and Applications [held in: Shonan, Japan (2014); Bertinoro, Italy (2015); Boston (MIT) (2016); Berlin, Germany (2017); Tokyo, Japan (2018); Copenhagen, Denmark (2019). Workshop links with program and participants available from: www.ics.uci.edu/~pfbaldi. 5. Developed and licensed several programs, Web servers, and databases including: (1) the SCRATCH suite (SSpro, Accpro, etc), a suite of machine-learning-based programs for protein structure prediction and analysis (e.g. secondary structure, relative solvent accessibility, contact map, tertiary structure); (2) HMMpro, a Hidden Markov Model (HMM) simulator for biological sequence analysis with graphical user interface; (3) MotifMap, a genome-wide map of regulatory motifs; (4) ChemDB, a chemoinformatics portal and database of over 5M commercially available organic compounds and related chemoinformatics software and resources; (5) Circadiomics, a database and portal for circadian data and circadian data analytics; (6) Machine Learning in Physics web portal, with downloadable datasets and references; and (7) Universal Campus, a 3D virtual campus to organize virtual meetings of different sizes, from laboratory meetings to conferences, in research and education. All are publicly available from: www.ics.uci.edu/~pfbaldi (under software). .
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