Ming-Yang  Kao is Professor of Computer Science at the Northwestern University, Evanston. He got a B.S. in Mathematics, 1978 at the National Taiwan University, Republic of China (Taiwan) and his Ph.D. in Computer Science, 1986, at Yale University, USA.Prof. Kao studies the design, analysis and implementation of algorithms. His work spans a broad range of applications including bioinformatics, computational finance, electronic commerce, and nanotechnology. Kao's most recent research includes work on DNA self-assembly, variants of the traveling salesman problem, and graph labeling problems.Kao heads the EECS Computing, Algorithms & Applications Division and is the editor-in-chief of Algorithmica.

“This is a unique and beautiful encyclopedia; you start reading and cannot find a way to stop because it is so fascinating to move from one article to another with seemingly no end. The expertise of all authors guarantees high quality of most of the articles. They are easily accessible also for readers not working in the respective field and give a quick orientation.” (Klaus Meer, Mathematical Reviews, February, 2018)

• From the contents: Abelian Hidden Subgroup Problem.- Abstract Voronoi Diagrams.- Active Learning - Modern Learning Theory.- Backdoors to SAT.- Backtracking Based k-SAT Algorithms.- Bargaining Networks.- Cache-Oblivious B-Tree.- Canonical Orders and Schnyder Realizers.- Circuit Placement.-Data Migration.- Decoding Reed–Solomon Codes.- Direct Routing Algorithms.- Edit Distance Under Block Operations.- Engineering Geometric Algorithms.- Enumeration of Non-crossing Geometric Graphs.- Facility Location.- Flow Time Minimization.- Force-Directed Graph Drawing.- Gate Sizing.- General Equilibrium.- Geographic Routing.- Hamilton Cycles in Random Intersection Graphs.- Hardness of Proper Learning.- Huffman Coding.- Implementation Challenge for Shortest Paths.- Incentive Compatible Selection.- Inductive Inference.- Kernelization, Bidimensionality and Kernels.- Kinetic Data Structures.- Knowledge in Distributed Systems.- Large-Treewidth Graph Decompositions.- Layout Decomposition for Multiple Patterning.- Learning Automata.- Majority Equilibrium.- Manifold Reconstruction.- Market Games and Content Distribution.- Nash Equilibria and Dominant Strategies in Routing.- Nearest Neighbor Interchange and Related Distances.- Negative Cycles in Weighted Digraphs.- Oblivious Routing.- Online Interval Coloring.- Online Paging and Caching.- PAC Learning.- Parity Games.- Permutation Enumeration.- Quadtrees and Morton Indexing.- Quantum Algorithm for Factoring.- Quantum Dense Coding.- Radiocoloring in Planar Graphs.- Random Planted 3-SAT.- Randomization in Distributed Computing.- Schedulers for Optimistic Rate Based Flow Control.- Secretary Problems and Online Auctions.- Separators in Graphs.- Table Compression.- Text Indexing.- Triangulation Data Structures.- Unified View of Graph Searching and LDFS-Based Certifying Algorithms.- Universal Sequencing on an Unreliable Machine.- Upward Graph Drawing.- Vector Bin Packing.- Vector Scheduling Problems.- Voltage Scheduling.- Wavelet Trees.- Well Separated Pair Decomposition.- Wire Sizing.

Ming-Yang  Kao is Professor of Computer Science at the Northwestern University, Evanston. He got a B.S. in Mathematics, 1978 at the National Taiwan University, Republic of China (Taiwan) and his Ph.D. in Computer Science, 1986, at Yale University, USA.Prof. Kao studies the design, analysis and implementation of algorithms. His work spans a broad range of applications including bioinformatics, computational finance, electronic commerce, and nanotechnology. Kao's most recent research includes work on DNA self-assembly, variants of the traveling salesman problem, and graph labeling problems.Kao heads the EECS Computing, Algorithms & Applications Division and is the editor-in-chief of Algorithmica.

“This is a unique and beautiful encyclopedia; you start reading and cannot find a way to stop because it is so fascinating to move from one article to another with seemingly no end. The expertise of all authors guarantees high quality of most of the articles. They are easily accessible also for readers not working in the respective field and give a quick orientation.” (Klaus Meer, Mathematical Reviews, February, 2018)

• From the contents: Abelian Hidden Subgroup Problem.- Abstract Voronoi Diagrams.- Active Learning - Modern Learning Theory.- Backdoors to SAT.- Backtracking Based k-SAT Algorithms.- Bargaining Networks.- Cache-Oblivious B-Tree.- Canonical Orders and Schnyder Realizers.- Circuit Placement.-Data Migration.- Decoding Reed–Solomon Codes.- Direct Routing Algorithms.- Edit Distance Under Block Operations.- Engineering Geometric Algorithms.- Enumeration of Non-crossing Geometric Graphs.- Facility Location.- Flow Time Minimization.- Force-Directed Graph Drawing.- Gate Sizing.- General Equilibrium.- Geographic Routing.- Hamilton Cycles in Random Intersection Graphs.- Hardness of Proper Learning.- Huffman Coding.- Implementation Challenge for Shortest Paths.- Incentive Compatible Selection.- Inductive Inference.- Kernelization, Bidimensionality and Kernels.- Kinetic Data Structures.- Knowledge in Distributed Systems.- Large-Treewidth Graph Decompositions.- Layout Decomposition for Multiple Patterning.- Learning Automata.- Majority Equilibrium.- Manifold Reconstruction.- Market Games and Content Distribution.- Nash Equilibria and Dominant Strategies in Routing.- Nearest Neighbor Interchange and Related Distances.- Negative Cycles in Weighted Digraphs.- Oblivious Routing.- Online Interval Coloring.- Online Paging and Caching.- PAC Learning.- Parity Games.- Permutation Enumeration.- Quadtrees and Morton Indexing.- Quantum Algorithm for Factoring.- Quantum Dense Coding.- Radiocoloring in Planar Graphs.- Random Planted 3-SAT.- Randomization in Distributed Computing.- Schedulers for Optimistic Rate Based Flow Control.- Secretary Problems and Online Auctions.- Separators in Graphs.- Table Compression.- Text Indexing.- Triangulation Data Structures.- Unified View of Graph Searching and LDFS-Based Certifying Algorithms.- Universal Sequencing on an Unreliable Machine.- Upward Graph Drawing.- Vector Bin Packing.- Vector Scheduling Problems.- Voltage Scheduling.- Wavelet Trees.- Well Separated Pair Decomposition.- Wire Sizing.