Computational Geometry Lecture Notes Voronoi Diagrams (c/o Tufts University)
Valerie Barr, Hava Siegelmann, Gabor Sarkozy (1990) Michael Horn, Julie Weber (2004)
April 29, 2004
1 Voronoi Diagrams
Consider the following problem. To determine the route for its carriers, the U.S. Post Office must decide which of its local offices is closest to a given point. Voronoi diagrams can used to solve this problem and many others including Closest Pair, All Nearest Neighbors, Euclidian Minimum Spanning Tree, and Triangulation problems.
Advanced Lifelong learning AI, Enhanced Time-aware AI, Innovations in Biological Computation, Super-Turing computation, Computational Neuroscience and Learning, Complex Dynamical systems, Human-robot interface, Health applications, Government and Industrial applications.
Senior faculty for bio-inspired AI, Dr. Siegelmann is an internationally known UMass Provost Professor in Computer Science and a recognized expert in neural networks. She is a core member of the University of Massachusetts Neuroscience and Behavior Program and director of the Biologically Inspired Neural and Dynamical Systems (BINDS) Laboratory. She has been particularly acclaimed for her groundbreaking work in computation beyond the Turing limit, and for achieving advanced learning capabilities through a new type of Artificial Intelligence: Lifelong Learning. Siegelmann conducts highly interdisciplinary research in next-generation machine learning, neural networks, intelligent machine-human collaboration, and computational studies of the brain - with application to AI, data science, and high-tech industry. Prof. Siegelmann is a co-inventor of the Support Vector Clustering (SVC) algorithm, which is widely used across industry and government. Among her recent Nature publications is Biological Underpinning of Lifelong Learning AI, a bio-inspired replay algorithm for advanced lifelong learning, dual fractal structure & function of the human brain, and identification of a previously unknown brain connectome mechanism, which enables cognitive abstraction.
Research Centers & Labs:
Biologically Inspired Neural & Dynamical Systems Laboratory
Center for Data Science
Ph.D., Computer Science, Rutgers University (1993, Fellow of excellence), M.Sc., Computer Science, Hebrew University (1992, Cum Laude), B.A., Computer Science, the Technion (1988, Suma Cum Laude). Siegelmann has been a visiting professor at MIT, Harvard University, the Weizmann Institute, ETH, the Salk Institute, Mathematical Science Research Institute Berkeley, and the Newton Institute of Cambridge University.
Professor Siegelmann recently completed a four-year term as a PM of some of DARPA’s most significant and innovative AI programs: Lifelong Learning Machines “L2M,” one of her key initiatives, inaugurated “third-wave AI,” pushing major design innovation, inspired by biology, and a dramatic increase in AI capability. “GARD” is leading to novel advancements in assuring AI robustness against attack. “CSL” is introducing powerful methods of collaborative information sharing on AI platforms without revealing private data. Other programs include advanced biomedical applications. DARPA/DoD bestowed upon her the Meritorious Public Service Medal, one of the highest medals for civilians, for her research and leadership.
ACTIVITIES & AWARDS
Siegelmann’s long list of awards includes the Obama Presidential BRAIN Initiative award, the ALON fellowship, the Donald O. Hebb Award of the International Neural Network Society (INNS) for “contribution to biological learning;” she was named a Distinguished Lecturer of the IEEE Computational Intelligence Society and was given DARPA’s Meritorious Public Service medal. Siegelmann is a fellow of both the IEEE and the INNS; she served on INNS’ Board of Governors from 2012 to 2020 and previously as Program Chair of the International Joint Conference on Neural Networks (IJCNN); Siegelmann was recently named a UMass Provost Professor. She has been serving as a vice-chair on the Neural Network Technical Committee (NNTC) of the IEEE Computational Intelligence Society (CIS), as well as on the IEEE CIS Outstanding PhD Dissertation Award committee and the IEEE Task Force on Ethical and Social Implications of Computational Intelligence. Siegelmann is an associate editor of the Frontiers in Computational Neuroscience journal and has served as an editor for numerous other major journals. Siegelmann was the founding chair of INNS’ diversity committee; she also serves as a co-chair of UMass’ diversity council of the university senate. Siegelmann is a leader in increasing awareness of ethical issues in AI and in supporting minorities and women in STEM fields.
Dr. Siegelmann (Ph.D. in Computer Science at Rutgers University) is a program manager at the MTO of DARPA, developing programs to advance the fields of Neural Networks and Machine Learning. Her research into neural processes has led to theoretical modeling and original algorithms capable of superior computation, and to more realistic, human-like intelligent systems. Dr. Siegelmann acts as a consultant internationally with industry and education. She remains very active in supporting young researchers and encouraging minorities and women to enter and advance in STEM. (my emphasis)
Defense Advanced Research Projects Agency (DARPA)
Microsystems Technology Office
Dr. Have Siegelmann’s Super-Turing theory has become a field of computational science: It forms the foundation of a novel paradigm that seamlessly combines discrete and analog components and is the basis of new, significantly more capable lifelong learning AI, where the machine continues improving, forming associations, and developing environmental awareness after the training phase is over. Siegelmann’s research has also led to an improved understanding of biological memory, replay, and reconsolidation, as well as innovative medical diagnostics and treatments.
Dr. Siegelmann has been a visiting professor at MIT, Harvard, Weizmann Institute, ETH Zurich, UC Berkeley, and Cambridge University. Siegelmann has initiated and ran successful women’s chapters and activities including under the international neural networks society. She has been leading discussions and meetings in conferences related to ethics in AI. Among other honors, she is an International Neural Networks Society Donald O. Hebb awardee, Obama Brain Initiative awardee, IEEE distinguished lecturer, IEEE fellow, and DARPA Meritorious Public Service awardee.
PhD in Computer Science (University Fellow of Excellence), Rutgers University
MS in Computer Science (Cum Laude), - The Hebrew University, Jerusalem, Israel
BA in Computer Science (Summa Cum Laude), Israel institute of Technology)
Applied AI for Performance
Hava Siegelmann, internationally recognized AI and neural network expert, is a professor of Computer Science at the University of Massachusetts, director of the BINDS Lab, and a core faculty member of the Neuroscience and Behavior Program. Siegelmann recently completed her term as a DARPA PM: “L2M,” one of her key initiatives, inaugurated “third-wave AI,” pushing major design innovation and a dramatic increase in AI capability. “GARD” is leading to unique advancements in assuring AI robustness against attack. “CSL” introducing powerful methods of combined learning and information sharing on AI platforms without revealing private data. Other programs included advanced biomedical applications.
A. Amgalan, P. Taylor, L. R. Mujica-Parodi, H. T Siegelmann, “Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters”. Nature Scientific Reports 11, 5331. March 2021. doi: 10.1038/s41598-021-82461-4 Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters | Scientific Reports
B. Tsuda, K. M. Tye, H. T. Siegelmann, T. J. Sejnowski, “A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex,” Proceedings of the National Academy of Sciences, November 2020.
G.M. van de Ven, H. T. Siegelmann, A. S. Tolias, Brain-inspired replay for continual learning with artificial neural networks. Nature Communications, 11, Article number: 4069, August 2020.
M. Shifrin and H.T. Siegelmann, “Near Optimal Insulin Treatment for Diabetes Patients: A machine learning approach,” Artificial Intelligence in Medicine (AIIM), 107, July 2020.
E.A. Rietman, S. Taylor, H.T. Siegelmann, M.A. Deriu, M. Cavaglia, and J.A. Tuszynski, (2020) “Using the Gibbs Function as a Measure of Human Brain Development Trends from Fetal Stage to Advanced Age,” International Journal of Molecular Sciences 21(3), Feature Papers in Molecular Biophysics," February 2020.