{"id":20,"date":"2018-02-12T16:39:51","date_gmt":"2018-02-12T08:39:51","guid":{"rendered":"http:\/\/140.114.54.13\/isaac2018\/?page_id=20"},"modified":"2020-10-13T16:08:37","modified_gmt":"2020-10-13T08:08:37","slug":"invited_speakers","status":"publish","type":"page","link":"https:\/\/isaac2018.ee.ntu.edu.tw\/index.php\/invited_speakers\/","title":{"rendered":"Events &#038; Speakers"},"content":{"rendered":"<h5><strong>Keynote Speakers<\/strong><\/h5>\n<table style=\"width: 100%;\" border=\"none\" align=\"center\">\n<tbody>\n<tr>\n<td td width=\"51%\"><img loading=\"lazy\" title=\"Shang-Hua Teng\" src=\"https:\/\/isaac2018.ee.ntu.edu.tw\/wp-content\/uploads\/2018\/02\/Shang-Hua-Teng.jpg\" alt=\"\" width=\"150\" height=\"150\" \/><\/p>\n<ul>\n<li>Shang-Hua Teng (University of Southern California, USA)<\/li>\n<li>Title: <a href=\"https:\/\/isaac2018.ee.ntu.edu.tw\/wp-content\/uploads\/2018\/11\/Shang-Hua-Teng.html\" target=\"_blank\" title=\"What are efficient algorithms? What are network models?  Big Data and Network Sciences have fundamentally challenged the traditional polynomial-time characterization of efficiency and the conventional graph-theoretical characterization of networks.\n\nMore than ever before, it is not just desirable, but essential, that efficient algorithms should be scalable. In other words, their complexity should be nearly linear or sub-linear with respect to the problem size. Thus, scalability, not just polynomial-time computability, should be elevated as the central complexity notion for characterizing efficient computation.\n\nFor a long time, graphs have been widely used for defining the structure of social and information networks. However, real-world network data and phenomena are much richer and more complex than what can be captured by nodes and edges. Network data are multifaceted, and thus network science requires a new theory, going beyond traditional graph theory, to capture the multifaceted data.\n\nIn this talk, I discuss some aspects of these challenges.  Using basic tasks in network analysis, social influence modeling, and machine learning as examples, I highlight the role of scalable algorithms and axiomatization in shaping our understanding of \"effective solution concepts\" in data and network sciences, which need to be both mathematically meaningful and algorithmically efficient.\">Going Beyond Traditional Characterizations in the Age of Big Data <br \/>and Network Sciences<\/a><\/li>\n<\/ul>\n<\/td>\n<td><img loading=\"lazy\" title=\"Clifford Stein\" src=\"https:\/\/isaac2018.ee.ntu.edu.tw\/wp-content\/uploads\/2018\/02\/Clifford-Stein.jpg\" alt=\"\" width=\"150\" height=\"150\" \/><\/p>\n<ul>\n<li>Clifford Stein (Columbia University, USA)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<\/li>\n<li>Title: <a href=\"https:\/\/isaac2018.ee.ntu.edu.tw\/wp-content\/uploads\/2018\/11\/Clifford-Stein.html\" target=\"_blank\" title=\"Finding a maximum matching is a fundamental algorithmic problem and is fairly well  understood in traditional sequential computing models. Some modern applications require that we handle massive graphs and hence we need to consider algorithms in models that do not allow the entire input graph to be held in the memory of one computer, or models in which the graph is evolving over time.\n    \nWe introduce a new concept called an 'Edge Degree Constrained Subgraph  (EDCS)', which is a subgraph that is guaranteed to contain a large  matching, and which can be identified via local conditions.  We then  show how to use an EDCS to find 1.5-approximate matchings in several different models  including  Map Reduce, streaming and  distributed computing. We can also use an EDCS to maintain a 1.5-optimal matching in a dynamic graph. \n\nThis work is joint with Sepehr Asadi, Aaron Bernstein, MohammadHossein Bateni and Vahab Marrokni.\">Approximate Matchings in Massive Graphs via Local Structure<\/a><\/li>\n<p>&nbsp;\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h5><strong>Workshop on Frontiers of Combinatorial Optimization &amp; Geometric Computing<\/strong><\/h5>\n<h5><strong>Invited Speakers<\/strong><\/h5>\n<table style=\"width: 100%;\" border=\"none\" align=\"center\">\n<tbody>\n<tr>\n<td><img loading=\"lazy\" title=\"Kurt Mehlhorn\" src=\"https:\/\/isaac2018.ee.ntu.edu.tw\/wp-content\/uploads\/2018\/02\/Kurt-Mehlhorn.jpg\" alt=\"\" width=\"150\" height=\"150\" \/><\/p>\n<ul>\n<li>Kurt Mehlhorn (Max Planck, Germany)<\/li>\n<li>Title: <a href=\"https:\/\/isaac2018.ee.ntu.edu.tw\/wp-content\/uploads\/2018\/12\/Kurt-Mehlhorn.html\" target=\"_blank\" title=\"I like to refer to myself as a mathematician in the morning and an engineer in \nthe afternoon. The two sides of my personality interact intensively. I will \ndiscuss some outcomes of this interaction:\n(1) Engineering failures that asked and are still asking for new theory.\n(2) Unexpected experimental findings that suggested interesting theorems.\">Reflections on the Interplay of Theory and Practice<\/a><\/li>\n<\/ul>\n<\/td>\n<td><img loading=\"lazy\" title=\"Tetsuo Asano\" src=\"https:\/\/isaac2018.ee.ntu.edu.tw\/wp-content\/uploads\/2018\/02\/Tetsuo-Asano.jpg\" alt=\"\" width=\"150\" height=\"150\" \/><\/p>\n<ul>\n<li>Tetsuo Asano (JAIST, Japan)<\/li>\n<li>Title: <a href=\"https:\/\/isaac2018.ee.ntu.edu.tw\/wp-content\/uploads\/2018\/11\/Tetsuo-Asano.html\" target=\"_blank\" title=\"This talk includes three problems which I have been attacking without any definite results.  \nThe first unsolved problem is \u201conline uniformity of points\u201d to seek for an optimal sequence of points to achieve good uniformity at every instance. Designing such an optimal sequence on a line is rather easy, but it is hard to decide whether we can determine any ordering of given points so that the uniformity is always at least some fixed value. It is not known whether it is NP-hard.  \nThe second unsolved problem is \u201coptimal embedding of unit distance sets\u201d which is to find a point configuration $P$ of $n$ points in the plane so that the potential function $f_{p,q}(P)$ is minimized, where $f_{p, q}(P)= \u2211_{i<j}|d(p_i, p_j)^p - 1|^q$ and $d(,)$ is the Euclidean distance.  \nExperimental results show some interesting features.  \nFor $f_{1,2}$, an optimal embedding is achieved by locating points on a few circles.  \nFor $f_{2,1}$, three multiple points may achieve optimal embedding.  \nFor $f_{2,2}$, if points are located on a circle it looks optimal.  \nThe third unsolved problem is \u201coptimal aspect-ratio triangulation\u201d that is to find an optimal triangulation of points in the plane in the sense that the worst aspect ratio of the resulting triangles is the largest among all possible triangulations. It is not known whether there is a polynomial-time algorithm.\">Three unsolved problems<\/a>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<tr>\n<td><img loading=\"lazy\" title=\"Robert E. Tarjan\" src=\"https:\/\/isaac2018.ee.ntu.edu.tw\/wp-content\/uploads\/2018\/10\/Rober-Tarjan.jpg\" alt=\"\" width=\"150\" height=\"150\" \/><\/p>\n<ul>\n<li>Robert E. Tarjan (Princeton University, USA)<\/li>\n<li>Title: <a href=\"https:\/\/isaac2018.ee.ntu.edu.tw\/wp-content\/uploads\/2018\/11\/Robert-E-Tarjan.html\" target=\"_blank\"  title=\"Finding the connected components of a graph is one of the most basic graph problems.  Although it is easy to find components sequentially using graph search or a disjoint set union algorithm, some important applications require finding the components of huge graphs, making sequential algorithms too slow.  We describe recent progress on concurrent algorithms for this problem.  Some simple algorithms seem surprisingly hard to analyze.\">Concurrent Connected Components<\/a>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<\/li>\n<\/ul>\n<\/td>\n<td><img loading=\"lazy\" title=\"J. Ian Munro\" src=\"https:\/\/isaac2018.ee.ntu.edu.tw\/wp-content\/uploads\/2018\/10\/Ian-Munro.jpg\" alt=\"\" width=\"150\" height=\"150\" \/><\/p>\n<ul>\n<li>J. Ian Munro (University of Waterloo, Canada)<\/li>\n<li>Title: <a href=\"https:\/\/isaac2018.ee.ntu.edu.tw\/wp-content\/uploads\/2018\/12\/J-Ian-Munro.html\" target=\"_blank\" title=\"Sorting is probably the most heavily studied task in computing. It is often required that the method be stable (i.e. records with equal keys are kept in their original order). It is also highly desirable that a method take advantage of already sorted segments of the input. Timsort is a well known procedure in both Python and Oracle\u2019s Java library for just this problem. While effective in practice, it has been hard to prove that it is an O(n lg n) technique and indeed versions of the  method have been both incorrect for some inputs. This led us to explore other methods for the same problem and we present two stable mergesort variants, \u201cpeeksort\u201d and \u201cpowersort\u201d, both of which exploit existing runs and find nearly-optimal merging orders with negligible overhead. We demonstrate that our methods are competitive in terms of running time with state-of-the-art implementations of stable sorting methods.\n\nThis is joint work with Sebastian Wild.\">Fast Stable Sorting, Adapting to Existing Runs<\/a>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h5><strong>Panel Discussion<\/strong><\/h5>\n<table style=\"width: 100%;\" border=\"none\" align=\"center\">\n<tbody>\n<tr>\n<td width=\"51%\"><img loading=\"lazy\" title=\"D. T. Lee\" src=\"https:\/\/isaac2018.ee.ntu.edu.tw\/wp-content\/uploads\/2018\/06\/D.-T.-Lee-1-e1527840959344.png\" alt=\"\" width=\"150\" height=\"150\" \/><\/p>\n<ul>\n<li>D. T. Lee (Academia Sinica ,Taiwan)<\/li>\n<\/ul>\n<\/td>\n<p><!--\n\n\n<td><img loading=\"lazy\" title=\"Ronald Graham\" src=\"https:\/\/isaac2018.ee.ntu.edu.tw\/wp-content\/uploads\/2018\/02\/Ronald-Graham.jpg\" alt=\"\" width=\"150\" height=\"150\" \/>\n\n\n<ul>\n \t\n\n<li>Ronald Graham (UC San Diego, USA)<\/li>\n\n\n \t\n<\/ul>\n\n\n<\/td>\n\n\n--><br \/>\n<\/tr>\n<\/tbody>\n<\/table>\n<h5><strong>Bubble-tea Session of Open Problems<\/strong><\/h5>\n<h5><strong>Session Co-Chairs<\/strong><\/h5>\n<table style=\"width: 100%;\" border=\"none\" align=\"center\">\n<tbody>\n<tr>\n<td><img loading=\"lazy\" title=\"S\u00e1ndor Fekete\" src=\"https:\/\/isaac2018.ee.ntu.edu.tw\/wp-content\/uploads\/2018\/09\/Sa\u0301ndor-Fekete.jpg\" alt=\"\" width=\"150\" height=\"150\" \/><\/p>\n<ul>\n<li>S\u00e1ndor Fekete (TU Braunschweig, Germany)<\/li>\n<\/ul>\n<\/td>\n<td><img loading=\"lazy\" title=\"Luca Trevisan\" src=\"https:\/\/isaac2018.ee.ntu.edu.tw\/wp-content\/uploads\/2018\/09\/Luca-Trevisan.jpg\" alt=\"\" width=\"150\" height=\"150\" \/><\/p>\n<ul>\n<li>Luca Trevisan (UC Berkeley, USA)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Contact: &lt;isaac2018.tw@gmail.com&gt;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Keynote Speakers Shang-Hua Teng (University of Southern [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":3,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/isaac2018.ee.ntu.edu.tw\/index.php\/wp-json\/wp\/v2\/pages\/20"}],"collection":[{"href":"https:\/\/isaac2018.ee.ntu.edu.tw\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/isaac2018.ee.ntu.edu.tw\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/isaac2018.ee.ntu.edu.tw\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/isaac2018.ee.ntu.edu.tw\/index.php\/wp-json\/wp\/v2\/comments?post=20"}],"version-history":[{"count":143,"href":"https:\/\/isaac2018.ee.ntu.edu.tw\/index.php\/wp-json\/wp\/v2\/pages\/20\/revisions"}],"predecessor-version":[{"id":692,"href":"https:\/\/isaac2018.ee.ntu.edu.tw\/index.php\/wp-json\/wp\/v2\/pages\/20\/revisions\/692"}],"wp:attachment":[{"href":"https:\/\/isaac2018.ee.ntu.edu.tw\/index.php\/wp-json\/wp\/v2\/media?parent=20"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}