├── README.md ├── awesome ├── awesome-machine-learning-research.md ├── google-cloud-tech.md ├── immutable-js.md ├── jahendler-presentation.json ├── jahendler-presentation.md ├── logical-regression.md ├── mining-folksonomy.md ├── python-neural-networks.md ├── robot-learning.md └── us-open-gov-data.md └── license.md /README.md: -------------------------------------------------------------------------------- 1 | AwesomePort 2 | =========== 3 | 4 | Collections of awesome stuff on the Web, open index of learning resources for everyone 5 | -------------------------------------------------------------------------------- /awesome/awesome-machine-learning-research.md: -------------------------------------------------------------------------------- 1 | # Awesome Machine Learning Research 2 | 3 | This is an incomplete list of researchers in machine learning field, only a handful of US based professors are included based on (not limit to) the following sources: 4 | 5 | * http://www.quora.com/Graduate-School/What-are-the-best-graduate-schools-for-studying-machine-learning 6 | * http://www.quora.com/Machine-Learning/What-are-the-best-machine-learning-research-groups-in-Europe 7 | * http://jmlr.csail.mit.edu/editorial-board.html 8 | * http://www1.cs.columbia.edu/~jebara/6772/platforms.html 9 | * http://academic.research.microsoft.com/RankList?entitytype=2&topDomainID=2&subDomainID=6&last=0&start=1&end=100 Microsoft Academic ranking 10 | * Keynote speakers in ICML, KDD 11 | * Microsoft Research Faculty Summit: http://research.microsoft.com/en-us/collaboration/about/summits.aspx 12 | * http://www.ml.cmu.edu/people/core-faculty.html CMU has an entire department of Machine Learning - it's hard to compete with this. 13 | 14 | 15 | # US 16 | cardbox: http://bigdata.memect.com/?tag=MachineLearning+people+us 17 | 18 | ## CMU 19 | 20 | http://www-2.cs.cmu.edu/~tom/ 21 | Tom Mitchell. Dr. Mitchell works on new learning algorithms, such as methods for learning from labeled and unlabeled data. Much of his research is driven by applications of machine learning such as understanding natural language text, and analyzing fMRI brain image data to model human cognition. 22 | 23 | 24 | http://alex.smola.org/ 25 | Alexander J. Smola. I studied physics in Munich at the University of Technology, Munich, at the Universita degli Studi di Pavia and at AT&T Research in Holmdel. During this time I was at the Maximilianeum München and the Collegio Ghislieri in Pavia. In 1996 I received the Master degree at the University of Technology, Munich and in 1998 the Doctoral Degree in computer science at the University of Technology Berlin. Until 1999 I was a researcher at the IDA Group of the GMD Institute for Software Engineering and Computer Architecture in Berlin (now part of the Fraunhofer Geselschaft). After that, I worked as a Researcher and Group Leader at the Research School for Information Sciences and Engineering of the Australian National University. From 2004 onwards I worked as a Senior Principal Researcher and Program Leader at the Statistical Machine Learning Program at NICTA. From 2008 to 2012 I worked at Yahoo Research. In spring of 2012 I moved to Google Research to spend a wonderful year in Mountain View. 26 | Research interests: Scalability of algorithms. This means pushing algorithms to internet scale, distributing them on many (faulty) machines, showing convergence, and modifying models to fit these requirements. For instance, randomized techniques are quite promising in this context. In other words, I'm interested in big data. 27 | Kernels methods are quite an effective means of making linear methods nonlinear and nonparametric. My research interests include support vector Machines, gaussian processes, and conditional random fields. Kernels are very useful also for the representation of distributions, that is two-sample tests, independence tests and many applications to unsupervised learning. Statistical modeling, primarily with Bayesian Nonparametrics is a great way of addressing many modeling problems. Quite often, the techniques overlap with kernel methods and scalability in rather delightful ways. 28 | Applications, primarily in terms of user modeling, document analysis, temporal models, and modeling data at scale is a great source of inspiration. That is, how can we find principled techniques to solve the problem, what are the underlying concepts, how can we solve things automatically. 29 | 30 | 31 | http://www-2.cs.cmu.edu/~yiming/ 32 | Yiming Yang is a professor in the Language Technologies Institute and the Machine Learning Department in the School of Computer Science at Carnegie Mellon University. Her research has centered on statistical learning methods and their applications to a variety of challenging problems, including text categorization, utility (relevance and novelty) based information distillation from temporally ordered documents, learning to order interrelated prediction tasks, modeling non-deterministic user interactions in multi-session information filtering, personalized active learning for collaborative filtering, personalized email prioritization using social network analysis, cancer prediction based protein/gene expressions in micro-array data, and protein identification from tandem mass spectra. 33 | 34 | http://www.cs.cmu.edu/~epxing/ 35 | Dr. Eric Xing is an associate professor in the School of Computer Science at Carnegie Mellon University. His principal research interests lie in the development of machine learning and statistical methodology; especially for solving problems involving automated learning, reasoning, and decision-making in high-dimensional and dynamic possible worlds; and for building quantitative models and predictive understandings of biological systems. Professor Xing received a Ph.D. in Molecular Biology from Rutgers University, and another Ph.D. in Computer Science from UC Berkeley. His current work involves, 1) foundations of statistical learning, including theory and algorithms for estimating time/space varying-coefficient models, sparse structured input/output models, and nonparametric Bayesian models; 2) computational and statistical analysis of gene regulation, genetic variation, and disease associations; and 3) application of statistical learning in social networks, data mining, and vision. 36 | 37 | 38 | ## Stanford 39 | 40 | http://cs.stanford.edu/people/ang/ 41 | Andrew Ng is Coursera's Chairman of the Board, and acts as Chief Evangelist. He also serves as Chief Scientist of Baidu, a Chinese language search engine. Previously, Andrew served as an Associate Professor of Computer Science at Stanford University and was the Director of the Stanford Artificial Intelligence Lab, the main AI research organization at Stanford, with 15 professors and about 150 students/post docs. In 2008, together with SCPD he started SEE (Stanford Engineering Everywhere), Stanford's first major attempt at free, online distributed education, which made publicly available about a dozen Stanford engineering classes. Over a million people have viewed SEE's videos. At Stanford, he also led the development of the OpenClassroom and the ml-class/db-class online education platforms, which were the precursor to the Coursera platform. In Fall 2011, he was the instructor of ml-class, a Machine Learning class that was one of Stanford's first massive online courses, and had an enrollment of over 100,000 students. 42 | In addition to his work on online education, Ng also works on machine learning, specifically on building AI systems via large scale brain simulations. His previous work includes autonomous helicopters, the STanford AI Robot (STAIR) project, and ROS (the most widely used open-source robotics software platform today). Ng is the author or co-author of over 150 published papers in machine learning, and his group has won best paper/best student paper awards at ICML, ACL, CEAS, 3DRR. He is a recipient of the Alfred P. Sloan Fellowship, and the 2009 IJCAI Computers and Thought award, one of the highest honors in AI." 43 | 44 | 45 | http://ai.stanford.edu/~koller/ 46 | Daphne Koller is the President of Coursera, leading the growth and nurturing of Coursera's partnerships with universities. She is also the Rajeev Motwani Professor in the Computer Science Department at Stanford University and the Oswald Villard University Fellow in Undergraduate Education. Her main research interest is in developing and using machine learning and probabilistic methods to model and analyze complex domains. She is the author of over 180 refereed publications, which have appeared in venues that include Science, Cell, and Nature Genetics (her H-index is over 80). 47 | She also has a long-standing interest in education. She founded the CURIS program, the Stanford Computer Science Department's undergraduate summer internship program, and the Biomedical Computation major at Stanford. She pioneered in her classroom many of the ideas that are key to Stanford's massive online education effort. She was awarded the Sloan Foundation Faculty Fellowship in 1996, the ONR Young Investigator Award in 1998, the Presidential Early Career Award for Scientists and Engineers (PECASE) in 1999, the IJCAI Computers and Thought Award in 2001, the MacArthur Foundation Fellowship in 2004, the ACM/Infosys award in 2008, and was inducted into the National Academy of Engineering in 2011. Her teaching was recognized via the Cox Medal for excellence in fostering undergraduate research at Stanford in 2003, and by being named a Bass University Fellow in Undergraduate Education. 48 | 49 | 50 | http://vision.stanford.edu/people.html 51 | Fei-Fei Li is an associate professor in the Computer Science Department at Stanford University. Her main research interest is in vision, particularly high-level visual recognition. Fei-Fei graduated from Princeton University in 1999 with a physics degree. She received a Ph.D. in electrical engineering from the California Institute of Technology in 2005. Li is a recipient of a Microsoft Research New Faculty award, the Alfred Sloan Fellowship, a number of Google Research Awards, an NSF CAREER award, IEEE CVPR 2010 Best Paper Honorable Mention, and winner of a number of international visual computing competitions. (She publishes under the name L. Fei-Fei.) 52 | 53 | 54 | http://cs.stanford.edu/~pliang/ 55 | Percy Liang is an assistant professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His research focuses on methods for learning richly structured statistical models from limited supervision, most recently in the context of semantic parsing in natural language processing. He won a best student paper at the International Conference on Machine Learning in 2008, received the NSF, GAANN, and NDSEG fellowships, and is also a 2010 Siebel Scholar. 56 | 57 | ## Berkeley 58 | http://www.cs.berkeley.edu/~jordan/ 59 | Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley., His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science., He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics, and has received the ACM/AAAI Allen Newell Award. He is a Fellow of the AAAI, ACM, ASA, CSS, IMS, IEEE and SIAM. 60 | 61 | ## MIT 62 | 63 | https://billf.mit.edu/ 64 | William T. Freeman is professor and associate department head of the Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, joining the faculty in 2001. His current research interests include motion re-rendering, computational photography, and learning for vision. He received outstanding paper awards at computer vision or machine learning conferences in 1997, 2006, 2009, and 2012, and a "test of time" award in 2013 for a 1995 paper. Previous research topics include steerable filters and pyramids, the generic viewpoint assumption, color constancy, bilinear models for separating style and content, and belief propagation in networks with loops. Freeman holds 30 patents. He is active in the program or organizing committees of computer vision, graphics, and machine learning conferences and was program co-chair for ICCV 2005 and CVPR 2013. 65 | 66 | http://web.media.mit.edu/~sandy/ 67 | Alex `Sandy’ Pentland has helped create and direct MIT’s Media Lab, the Media Lab Asia, and the Center for Future Health. He chairs the World Economic Forum's Data Driven Development council, is Academic Director of the MIT-Harvard-ODI Big Data and People Project, and is a member of the Advisory Boards for Google, Nissan, Telefonica, Monument Capital, and the Minerva Schools. 68 | In 2012 Forbes named Sandy one of the 'seven most powerful data scientists in the world’, along with Google founders and the CTO of the United States, and in 2013 he won the McKinsey Award from Harvard Business Review. He is among the most-cited computational scientists in the world, and a pioneer in computational social science, organizational engineering, wearable computing (Google Glass), image understanding, and modern biometrics. His research has been featured in Nature, Science, and Harvard Business Review, as well as being the focus of TV features on BBC World, Discover and Science channels. His most recent book is `Social Physics,' published by Penguin Press. 69 | 70 | 71 | ## U Washington 72 | 73 | http://homes.cs.washington.edu/~pedrod/ 74 | Dr. Pedro Domingos is a leader in the fields of machine learning, artificial intelligence and data mining. He is a professor of computer science at the University of Washington in Seattle, and received his PhD from the University of California at Irvine. He is the author or co-author of over 200 technical publications, and has received numerous awards for his research. He is a co-founder of the International Machine Learning Society, was program co-chair of the 2003 ACM International Conference on Knowledge Discovery and Data Mining, and has served on numerous program committees and journal editorial boards. 75 | 76 | 77 | http://homes.cs.washington.edu/~guestrin/ 78 | Carlos Guestrin is the Amazon Professor of Machine Learning in Computer Science & Engineering at the University of Washington. He is also the co-founder of GGideaLab, a start up focused on monetizing social networks. Previously, he was a senior researcher at the Intel Research Lab in Berkeley. Carlos received his MSc and PhD in Computer Science from Stanford University in 2000 and 2003, respectively, and a Mechatronics Engineer degree from the Polytechnic School of the University of Sao Paulo, Brazil, in 1998. Carlos’ work received awards at a number of conferences and a journal: KDD 2007 and 2010, IPSN 2005 and 2006, VLDB 2004, NIPS 2003 and 2007, UAI 2005, ICML 2005, AISTATS 2010, JAIR in 2007, and JWRPM in 2009. He is also a recipient of the ONR Young Investigator Award, NSF Career Award, Alfred P. Sloan Fellowship, IBM Faculty Fellowship, the Siebel Scholarship and the Stanford Centennial Teaching Assistant Award. Carlos was named one of the 2008 ‘Brilliant 10′ by Popular Science Magazine, received the IJCAI Computers and Thought Award and the Presidential Early Career Award for Scientists and Engineers (PECASE). He is a former member of the Information Sciences and Technology (ISAT) advisory group for DARPA. 79 | 80 | https://homes.cs.washington.edu/~jfogarty/ 81 | James Fogarty is an associate professor of Computer Science & Engineering at the University of Washington and an active member of DUB, the University of Washington's cross-campus initiative advancing research and education in Human-Computer Interaction and Design. His research broadly explores opportunities for new technology and tools in everyday interaction, often with a focus on machine learning as a tool for enabling new forms of interaction and for scaling end-user interaction to big data. He has authored multiple award papers, received a 2010 NSF CAREER award, and chaired the CHI 2012 subcommittee on “Expanding Interaction through Technology, Systems, & Tools”. He obtained his Ph.D. in Human-Computer Interaction from Carnegie Mellon University in 2006 and his B.S. in Computer Science from Virginia Tech in 2000. 82 | 83 | 84 | ## U Michigan, Ann Arbor 85 | 86 | http://web.eecs.umich.edu/~cscott/ 87 | Clay Scott, Research interests: Machine learning, pattern recognition, statistical signal processing, and applications. Look for students with strong interest and ability in developing novel machine learning algorithms and proving theoretical guarantees for such algorithms. My students typically study and apply advanced linear algebra, multivariate statistics, convex optimization, measure-theoretic probability, functional analysis, and various other topics in pure and applied mathematics. My students can also expect to gain experience working with real-world data in the context of a particular application. 88 | 89 | 90 | http://web.eecs.umich.edu/~honglak/ 91 | Honglak Lee is an assistant professor of Computer Science and Engineering at the University of Michigan, Ann Arbor. He received his Ph.D. from the Computer Science Department at Stanford University in 2010, advised by Andrew Ng. His primary research interests lies in machine learning, which spans deep learning, unsupervised and semi-supervised learning, transfer learning, graphical models, and optimization. He also works on application problems in computer vision, audio recognition, robot perception, and text processing. His work received best paper awards at ICML and CEAS. He received a Google Faculty Research Award, and he has served as a guest editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence “Special Issue on Learning Deep Architectures.” 92 | 93 | 94 | ## UW Madison 95 | 96 | http://pages.cs.wisc.edu/~swright/ 97 | Stephen J. Wright is a Professor of Computer Sciences at the 98 | University of Wisconsin-Madison. His research is on computational 99 | optimization and its applications to many areas of science and 100 | engineering. Prior to joining UW-Madison in 2001, Wright was a Senior 101 | Computer Scientist at Argonne National Laboratory (1990-2001), and a 102 | Professor of Computer Science at the University of Chicago 103 | (2000-2001). During 2007-2010, he served as chair of the Mathematical 104 | Optimization Society, and is on the Board of the Society for 105 | Industrial and Applied Mathematics (SIAM). He is a Fellow of SIAM. 106 | Wright is the author or coauthor of widely used text / reference books 107 | in optimization including “Primal Dual Interior-Point Methods” (SIAM, 108 | 1997) and “Numerical Optimization” (2nd Edition, Springer, 2006, with 109 | J. Nocedal). He has published widely on optimization theory, 110 | algorithms, software, and applications. He is coauthor of widely used 111 | software for linear and quadratic programming and for compressed 112 | sensing. 113 | Wright currently serves on the editorial boards of the leading 114 | journals in optimization (SIAM Journal on Optimization and 115 | Mathematical Programming, Series A) as well as SIAM Review. He served 116 | a term as editor-in-chief of Mathematical Programming, Series B from 117 | 2003-2007. 118 | 119 | http://pages.cs.wisc.edu/~jerryzhu/ 120 | Xiaojin (Jerry) Zhu is an associate professor in the Department of Computer Sciences at the University of Wisconsin-Madison. He received his B.S. and M.S. degrees in Computer Science from Shanghai Jiao Tong University in 1993 and 1996, respectively, and a Ph.D. degree in Language Technologies from Carnegie Mellon University in 2005. He was a research staff member at IBM China Research Laboratory from 1996 to 1998. Zhu received the National Science Foundation CAREER Award in 2010. His research interest is in machine learning, with applications in natural language processing, cognitive science, and social media. 121 | 122 | ## princeton 123 | 124 | 125 | http://www.cs.princeton.edu/~schapire/ 126 | Robert Schapire received his ScB in math and computer science from Brown University in 1986, and his SM (1988) and PhD (1991) from MIT under the supervision of Ronald Rivest. After a short post-doc at Harvard, he joined the technical staff at AT&T Labs (formerly AT&T Bell Laboratories) in 1991 where he remained for eleven years. At the end of 2002, he became a Professor of Computer Science at Princeton University. His awards include the 1991 ACM Doctoral Dissertation Award, the 2003 Gödel Prize and the 2004 Kanelakkis Theory and Practice Award (both of the last two with Yoav Freund). His main research interest is in theoretical and applied machine learning. 127 | 128 | http://www.princeton.edu/~hanliu/people.html 129 | Han Liu received Joint PhD in Machine Learning and Statistics in 2011 at Carnegie Mellon University. His thesis advisors are John Lafferty and Larry Wasserman. His research interests include Statistics, Machine Learning, Optimization, Probability. He is serving as an Associate Editor of the Electronic Journal of Statistics. 130 | 131 | 132 | ## Other 133 | 134 | http://www.cis.upenn.edu/~mkearns/ 135 | Michael Kearns is a professor in the Computer and Information Science department at the University of Pennsylvania, where he holds the National Center Chair and has joint appointments in the Wharton School.He is founder of Penn's Networked and Social Systems Engineering (NETS) program (www.nets.upenn.edu),and director of Penn's Warren Center for Network and Data Sciences (www.warrencenter.upenn.edu). His research interests include topics in machine learning, algorithmic game theory, social networks, and computational finance. He has consulted extensively in the technology and finance industries. 136 | 137 | 138 | 139 | http://www.cc.gatech.edu/~vempala/ 140 | Santosh Vempala attended Carnegie Mellon University, where he received his Ph.D. in 1997 under professor Avrim Blum. In 1997, he was awarded a Miller Fellowship at Berkeley. Subsequently, he was a Professor at MIT in theMathematics Department, until he moved to Georgia Tech in 2006.His main work has been in the area of theoretical computer science, with particular activity in the fields of algorithms, randomized algorithms, computational geometry, and computational learning theory, including the authorship of books on random projectionand spectral methods.[4] Vempala has received numerous awards, including a Guggenheim Fellowship, Sloan Fellowship, and being listed in Georgia Trend’s 40 under 40. In 2008, he co-founded the Computing for Good (C4G)[5] program at Georgia Tech. 141 | 142 | 143 | http://yann.lecun.com/ 144 | Yann LeCun is Director of AI Research at Facebook and Silver Professor of Data Science, Computer Science, Neural Science, and Electrical Engineering at New York University, affiliated with the NYU Center for Data Science, the Courant Institute of Mathematical Science, the Center for Neural Science, and the Electrical and Computer Engineering Department. He received the Electrical Engineer Diploma from Ecole Supérieure d'Ingénieurs en Electrotechnique et Electronique (ESIEE), Paris in 1983, and a PhD in Computer Science from Université Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, after a brief period as a Fellow of the NEC Research Institute in Princeton. From 2012 to 2014 he directed NYU's initiative in data science and became the founding director of the NYU Center for Data Science. He was named Director of AI Research at Facebook in late 2013 and retains a part-time position on the NYU faculty. 145 | 146 | 147 | http://web.engr.illinois.edu/~hanj/ 148 | Jiawei Han is Abel Bliss Professor in Engineering, in the Department of Computer Science at the University of Illinois. He has been researching into data mining, information network analysis, and database systems, with over 600 publications. He served as the founding Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data (TKDD) and on the editorial boards of several other journals. Jiawei has received ACM SIGKDD Innovation Award (2004), IEEE Computer Society Technical Achievement Award (2005), and IEEE Computer Society W. Wallace McDowell Award (2009), and Daniel C. Drucker Eminent Faculty Award (2011). He is a Fellow of ACM and IEEE. He is currently the Director of Information Network Academic Research Center (INARC) supported by the Network Science-Collaborative Technology Alliance (NS-CTA) program of U.S. Army Research Lab. His book with Micheline Kamber and Jian Pei, "Data Mining: Concepts and Techniques" (Morgan Kaufmann) has been used worldwide as a textbook. 149 | 150 | 151 | http://www.cs.rutgers.edu/~muthu/ 152 | Sethu (Muthu) Muthukrishnan is a Professor of Computer Science at Rutgers Univ. His research interest is in Internet Auctions and Game Theory, as well Data Stream Algorithms and its connections to Compressed Sensing, Databases and Networking. 153 | 154 | 155 | http://www.cs.cornell.edu/home/kleinber/ 156 | Jon Kleinberg is the Tisch University Professor in the Computer Science Department at Cornell University. His research focuses on issues at the interface of networks and information, with an emphasis on the social and information networks that underpin the Web and other on-line media. He is a member of the US National Academy of Sciences, the US National Academy of Engineering, and the American Academy of Arts and Sciences, and the recipient of MacArthur, Packard, and Sloan Foundation Fellowships, as well as awards including the Nevanlinna Prize from the International Mathematical Union and the ACM-Infosys Foundation Award in the Computing Sciences. Computational Perspectives on Social Phenomena in On-Line Networks will be Jon’s speech. With an increasing amount of social interaction taking place in the digital domain, and often in public on-line settings, we are accumulating enormous amounts of data about phenomena that were once essentially invisible to us: the collective behavior and social interactions of hundreds of millions of people, recorded at unprecedented levels of scale and resolution. Analyzing this data computationally offers new insights into the design of on-line applications, as well as a new perspective on fundamental questions in the social sciences. We discuss how this perspective can be applied to questions involving network structure and the dynamics of interaction among individuals, with a particular focus on the ways in which evaluation, opinion, and in some cases polarization manifest themselves at large scales in the on-line domain. 157 | 158 | 159 | http://idse.columbia.edu/david-blei 160 | David Blei will join Columbia in Fall 2014 as a Professor of Computer Science and Statistics. His research involves probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference. He works on a variety of applications, including text, images, music, social networks, user behavior, and scientific data. 161 | David earned his Bachelor's degree in Computer Science and Mathematics from Brown University (1997) and his PhD in Computer Science from the University of California, Berkeley (2004). Before arriving to Columbia, he was an Associate Professor of Computer Science at 162 | Princeton University. He has received several awards for his research, including a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), and Blavatnik Faculty Award (2013). 163 | * http://www.cs.princeton.edu/~blei/ 164 | 165 | 166 | 167 | http://www.umiacs.umd.edu/~getoor/index.shtml 168 | Lise Getoor is a professor in the Computer Science Department at the University of Maryland, College Park. Her primary research interests are in machine learning and reasoning with uncertainty, applied to graphs and structured data. She also works in data integration, social network analysis, and visual analytics. She has six best paper awards, an NSF Career Award, and is an Association for the Advancement of Artificial Intelligence (AAAI) Fellow. She has served as action editor for the Machine Learning Journal, JAIR associate editor, and TKDD associate editor. She is a board member of the International Machine Learning Society, has been a member of AAAI Executive Council, was PC co-chair of ICML 2011, and has served as senior PC member for conferences including AAAI, ICML, IJCAI, ISWC, KDD, SIGMOD, UAI, VLDB, WSDM, and WWW. She received her Ph.D. from Stanford University, her M.S. from the University of California, Berkeley, and her B.S. from the University of California, Santa Barbara. 169 | 170 | http://www.cs.columbia.edu/~jebara/ 171 | Tony Jebara is Professor of Computer Science at Columbia University and co-founder of Sense Networks. He directs the Columbia Machine Learning Laboratory whose research intersects computer science and statistics to develop new frameworks for learning from data with applications in vision, networks, spatio-temporal data, and text. Jebara has published over 70 peer-reviewed papers in conferences and journals including NIPS, ICML, UAI, COLT, JMLR, CVPR, ICCV, and AISTAT. He is the author of the book Machine Learning: Discriminative and Generative and co-inventor on multiple patents in vision, learning and spatio-temporal modeling. In 2004, Jebara was the recipient of the Career award from the National Science Foundation. His work was recognized with a best paper award at the 26th International Conference on Machine Learning, a best student paper award at the 20th International Conference on Machine Learning as well as an honorable mention from the Pattern Recognition Society in 2000. Jebara's research has been featured on television (ABC, BBC, New York One, TechTV, etc.) as well as in the popular press (New York Times, Slash Dot, Wired, Businessweek, IEEE Spectrum, etc.). He obtained his PhD in 2002 from MIT. Recently, Esquire magazine named him one of their Best and Brightest of 2008. Jebara's lab is supported in part by the NSF, CIA, NSA, DHS, and ONR. 172 | 173 | http://cs.brown.edu/~mlittman/ 174 | Michael L. Littman joined Brown University's Computer Science Department after 10 years (including three as chair) at Rutgers University. His research in machine learning examines algorithms for decision making under uncertainty. Littman has earned multiple awards for teaching and his research has been recognized with three best-paper awards on the topics of meta-learning for computer crossword solving, complexity analysis of planning under uncertainty, and algorithms for efficient reinforcement learning. He has served on the editorial boards of the Journal of Machine Learning Research and the Journal of Artificial Intelligence Research. In 2013, he is general chair of the International Conference on Machine Learning (ICML) and program co-chair of the Association for the Advancement of Artificial Intelligence Conference. He served as program co-chair of ICML 2009. 175 | 176 | http://www.cse.wustl.edu/~sanmay/ 177 | Sanmay Das is an associate professor of Computer Science at Virginia Tech. He was previously an assistant professor in the Computer Science Department at Rensselaer Polytechnic Institute, where he also held a courtesy appointment at the Lally School of Management. His research interests are in machine learning and computational social science. He received an NSF CAREER award in 2010. He has served as program chair of AMMA and workshops chair of ACM EC, in addition to serving on the program committees of many conferences in artificial intelligence and machine learning. Sanmay received his A.B. from Harvard and his Ph.D. from the Massachusetts Institute of Technology, both in Computer Science. 178 | 179 | 180 | 181 | # Industry and Gov 182 | 183 | ## Microsoft 184 | 185 | http://research.microsoft.com/en-us/um/people/horvitz/ 186 | Eric Horvitz is a distinguished scientist and managing director at the Microsoft Research Lab at Redmond, Washington. His interests span theoretical and practical challenges with machine learning, inference, and decision making. He has been elected a fellow of AAAI, AAAS, the American Academy of Arts and Sciences, and the National Academy of Engineering, and has been inducted into the CHI Academy. He has served as president of the AAAI, chair of the AAAS Section on Information, Computing, and Communications, and on the NSF CISE Advisory Committee. 187 | 188 | https://www.linkedin.com/pub/raghu-ramakrishnan/3/973/5 189 | Raghu Ramakrishnan heads the Cloud and Information Services Lab (CISL) in the Data Platforms Group at Microsoft. From 1987 to 2006, he was a professor at University of Wisconsin-Madison, where he wrote the widely-used text “Database Management Systems” and led a wide range of research projects in database systems (e.g., the CORAL deductive database, the DEVise data visualization tool, SQL extensions to handle sequence data) and data mining (scalable clustering, mining over data streams). In 1999, he founded QUIQ, a company that introduced a cloud-based question-answering service. He joined Yahoo! in 2006 as a Yahoo! Fellow, and over the next six years served as Chief Scientist for the Audience (portal), Cloud and Search divisions, driving content recommendation algorithms (CORE), cloud data stores (PNUTS), and semantic search (“Web of Things”). Ramakrishnan has received several awards, including the ACM SIGKDD Innovations Award, the SIGMOD 10-year Test-of-Time Award, the IIT Madras Distinguished Alumnus Award, and the Packard Fellowship in Science and Engineering. He is a Fellow of the ACM and IEEE. 190 | 191 | 192 | 193 | http://research.microsoft.com/en-us/people/deng/ 194 | Li Deng received his Ph.D. from the University of Wisconsin-Madison. He was an assistant professor (1989–1992), tenured associate professor (1992–1996), and tenured full professor (1996–1999) at the University of Waterloo, Ontario, Canada. In 1999, he joined Microsoft Research, Redmond, where he is currently a principal researcher. On the general topics of audio/speech/language technology and science, machine learning, and signal/information processing, he has published more than 300 refereed papers in leading journals and conferences as well as four books. He is a Fellow of the Acoustical Society of America, the Institute of Electrical and Electronics Engineers (IEEE), and the International Speech Communication Association. He served on the Board of Governors of the IEEE Signal Processing Society (2008–2010). More recently, he served as editor-in-chief for the IEEE Signal Processing Magazine (2009–2011), which earned the highest impact factor in 2010 and 2011 among all IEEE publications and for which he received the 2012 IEEE SPS Meritorious Service Award. He recently served as general chair of the IEEE ICASSP-2013, and currently serves as editor-in-chief for the IEEE Transactions on Audio, Speech, and Language Processing. His recent technical work on industry-scale deep learning with colleagues and academic collaborators has created a significant impact on speech recognition and in other areas of signal/information processing. 195 | 196 | 197 | 198 | ## Google 199 | 200 | http://research.google.com/pubs/VincentVanhoucke.html 201 | Vincent Vanhoucke is a Research Scientist at Google. He leads the speech recognition quality effort for Google Search by Voice. He holds a Ph.D. in Electrical Engineering from Stanford University and a Diplôme d’Ingénieur from the Ecole Centrale Paris. 202 | 203 | http://home.earthlink.net/~dwaha/ 204 | David W. Aha received his PhD from UCI in 1990 (on Instance-Based Learning) and held post-doctoral positions at the Turing Institute, the Johns Hopkins University, and the University of Ottawa. He currently leads the Adaptive Systems Section within NRL's Navy Center for Applied Research in AI at Washington, DC. In addition to ML, his research interests include autonomous agents, case-based reasoning, game AI, mixed-initiative reasoning, planning, text analysis, and trust. He has received 3 Best Paper awards, serves on journal editorial boards (ML, APIN, TIST, ACS), co-edited 3 special issues, and participated on 20 dissertation committees. He founded the UCI Repository for Machine Learning Databases, served as a AAAI Councillor, co-founded the AI Video Competitions, and co-organized several international meetings related to AI (e.g., the AAAI-10 Workshop on Goal Directed Autonomy). He's particularly excited about goal reasoning, a topic that he has pursued since 2009 with many talented colleagues. 205 | -------------------------------------------------------------------------------- /awesome/google-cloud-tech.md: -------------------------------------------------------------------------------- 1 | # Awesome Google Cloud Technology 2 | 3 | Abstract: On Github, a list of classical readings for 7 milestones of Google Cloud technology: GFS, MapReduce, BigTable, Dremel, Colossus, Spanner, Compute Engine. View [visual resource cards](http://bigdata.memect.com/?tag=googlecloudtech). Links to the original papers and related slides/videos/technical-reviews are included. The image attached is borrowed from Brian Goldfarb's keynote address in the 2014 Google Cloud Platform Developer Roadshow, also see technical review by Craig McLuckie, Martin Gannholm in Google I/O 2012. A 4-minute short video by Brian Dorsey is highly recommended to learn the core concepts. 4 | [!["7 key milestones of Google Cloud Computing Platform"](http://emma.memect.com/t/df0fc2b1627fb9e21e3cb36a611b250d641161cd21b747d7fc9ba900d2ff8d93)] (http://bigdata.memect.com/?tag=googlecloudtech) 5 | 6 | 7 | # Overview 8 | 9 | https://speakerdeck.com/bgoldy/google-cloud-platform-north-america-dev-roadshow-keynote by Brian Goldfarb, 2014 keynote address in Seattle and Vancouver as part of the Google Cloud Platform Developer Roadshow 10 | 11 | http://static.googleusercontent.com/media/research.google.com/en/us/university/relations/facultysummit2010/storage_architecture_and_challenges.pdf Storage Architecture and Challenges by Andrew Fikes at Google Faculty Summit 2010 12 | 13 | http://queue.acm.org/detail.cfm?id=1594206 GFS: evolution on fast-forward a discussion between Kirk McKusick and Sean Quinlan about the origin and evolution of the google file system. 14 | 15 | # Key milestones 16 | ## Google File System (GFS) 17 | http://en.wikipedia.org/wiki/Google_File_System 18 | 19 | http://static.googleusercontent.com/media/research.google.com/en/us/archive/gfs-sosp2003.pdf SOSP 2003. We have designed and implemented the Google File System, a scalable distributed file system for large distributed data-intensive applications. It provides fault tolerance while running on inexpensive commodity hardware, and it delivers high aggregate performance to a large number of clients. 20 | 21 | http://www.cs.rochester.edu/~sandhya/csc256/seminars/GoogleFS.pdf 22 | 23 | http://web.eecs.umich.edu/~mozafari/fall2013/eecs584/lecture05-b.pdf 24 | 25 | ## MapReduce 26 | http://en.wikipedia.org/wiki/MapReduce 27 | 28 | http://static.googleusercontent.com/media/research.google.com/es/us/archive/mapreduce-osdi04.pdf OSDI 2004. MapReduce is a programming model and an associated implementation for processing and generating large data sets. 29 | 30 | https://sites.google.com/site/mriap2008/lectures a course offered by Google (2008) 31 | 32 | http://research.google.com/archive/mapreduce-osdi04-slides/index.html 33 | 34 | ## BigTable 35 | http://en.wikipedia.org/wiki/BigTable 36 | 37 | http://static.googleusercontent.com/media/research.google.com/en/us/archive/bigtable-osdi06.pdf OSDI 2006. Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. 38 | 39 | ## Dremel 40 | http://en.wikipedia.org/wiki/Dremel_(software) 41 | 42 | http://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/36632.pdf VLDB 2010. Dremel is a scalable, interactive ad-hoc query system for analysis of read-only nested data. 43 | 44 | 45 | ## Colossus 46 | https://www.cs.rutgers.edu/~pxk/417/notes/content/26-google_cluster-slides.pdf 47 | 48 | http://www.wired.com/2012/07/google-colossus/ 49 | 50 | http://www.theregister.co.uk/2009/08/12/google_file_system_part_deux/ 51 | 52 | http://www.theregister.co.uk/2010/09/09/google_caffeine_explained/ 53 | 54 | http://highscalability.com/blog/2010/9/11/googles-colossus-makes-search-real-time-by-dumping-mapreduce.html 55 | 56 | 57 | ## Spanner 58 | http://en.wikipedia.org/wiki/Spanner_(database) 59 | 60 | http://static.googleusercontent.com/media/research.google.com/en/us/archive/spanner-osdi2012.pdf OSDI2012, Spanner is Google's scalable, multi-version, globally-distributed, and synchronously-replicated database. 61 | 62 | http://research.google.com/archive/spanner-osdi2012.pptx 63 | 64 | http://new.livestream.com/accounts/1545775/osdi12/videos/4646642 65 | 66 | 67 | ## Compute Engine 68 | http://en.wikipedia.org/wiki/Google_Compute_Engine Google Compute Engine (GCE) is the Infrastructure as a Service (IaaS) component of Google Cloud Platform which is built on the global infrastructure that runs Google’s search engine, Gmail, YouTube and other services. 69 | 70 | https://www.youtube.com/watch?v=43gvHZyPRVk 71 | 72 | https://www.youtube.com/watch?v=0-sF5ZWB_FY 73 | 74 | http://yourstory.com/2013/12/google-compute-engine-better-than-aws/ 75 | -------------------------------------------------------------------------------- /awesome/immutable-js.md: -------------------------------------------------------------------------------- 1 | This list is based on discussion on hackernews (https://news.ycombinator.com/item?id=8107447) and twitter (https://twitter.com/search?q=immutable.js). I've also compiled a list of visual [Resource Cards](http://awesomeport.memect.com/?tag=immutable-js). 2 | 3 | [![immutable.js](http://img.memect.com/WtpK6GG1ccHGU-rqpZHnZbmBBjs=/400x0/t/f2d773a3a6a4a7f208a3f9831ad4e1e8cd48882dda28e352aba20d53ea31a016)](http://awesomeport.memect.com/?tag=immutable-js) 4 | 5 | # Top readings 6 | 7 | https://github.com/facebook/immutable-js 8 | 9 | https://news.ycombinator.com/item?id=8107447 10 | 11 | http://swannodette.github.io/2014/07/30/hijacking-json/ 12 | 13 | # Background: immutable objects 14 | https://www.youtube.com/watch?v=mS264h8KGwk 15 | 16 | http://en.wikipedia.org/wiki/Immutable_object 17 | 18 | http://en.wikipedia.org/wiki/Hash_array_mapped_trie 19 | 20 | https://github.com/jackschaedler/goya 21 | 22 | 23 | # Related GitHub resources 24 | 25 | ## reactJS and clojureScript 26 | https://github.com/facebook/react -- Flux app architecture 27 | 28 | https://news.ycombinator.com/item?id=8108887 29 | 30 | https://news.ycombinator.com/item?id=8111090 31 | 32 | https://github.com/clojure/clojurescript 33 | 34 | ## TypeScript (Microsoft) 35 | https://github.com/Microsoft/TypeScript/ 36 | 37 | https://twitter.com/michaelklishin/status/494667722119446528 38 | 39 | ## mori 40 | https://github.com/swannodette/mori 41 | 42 | https://news.ycombinator.com/item?id=8109247 43 | 44 | ## more ... 45 | https://github.com/cognitect/transit-js 46 | 47 | https://github.com/Reactive-Extensions/IxJS 48 | 49 | https://github.com/slburson/fset-java 50 | -------------------------------------------------------------------------------- /awesome/jahendler-presentation.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "description": "my keynote at the Strata Conference in February 2013.", 4 | "hashtags": "presentation,video", 5 | "title": "Broad Data", 6 | "url": "http://www.youtube.com/watch?v=Cob5oltMGMc" 7 | }, 8 | { 9 | "description": "My WIMS11 keynote: (about 1 hr)", 10 | "hashtags": "presentation,video", 11 | "title": "The Semantic Web 10 Year Update", 12 | "url": "http://videolectures.net/wims2011_hendler_update/" 13 | }, 14 | { 15 | "description": "(6 min).", 16 | "hashtags": "presentation,video", 17 | "title": "My most popular talk - the Semantic Web Layercake all in rhyme (ISWC 2009)", 18 | "url": "http://www.youtube.com/watch?v=Pv9fpW6bhdo" 19 | }, 20 | { 21 | "description": "(5 min).", 22 | "hashtags": "presentation,video", 23 | "title": "My presentation at the World Economic Forum (Dalian 2009) on engineering the future of the Web", 24 | "url": "http://www.youtube.com/watch?v=X4Rot15JFZc" 25 | }, 26 | { 27 | "description": "(40 min)", 28 | "hashtags": "presentation,video", 29 | "title": "An introduction to the Semantic Web from ISWC 2008", 30 | "url": "http://videolectures.net/iswc08_hendler_ittsw/" 31 | }, 32 | { 33 | "description": "(about 15 min)", 34 | "hashtags": "presentation,video", 35 | "title": "An interview from WWW 2006", 36 | "url": "http://www.ecs.soton.ac.uk/podcasts/video.php?id=54" 37 | }, 38 | 39 | 40 | 41 | 42 | { 43 | "description": "[in ppt] a talk at WIMS11 in Norway, updates my 2010 status talk.[~15MB]", 44 | "hashtags": "presentation,slides", 45 | "title": "Semantic Web: 10 year update", 46 | "url": "http://www.cs.rpi.edu/~hendler/presentations/WIMS11-2011.pdf" 47 | }, 48 | { 49 | "description": "[In PDF] a humorous dinner speech (in rhyme) presented at the 2009 Dagstuhl Semantic Web conference (#swdag2009 on twitter).", 50 | "hashtags": "presentation,slides", 51 | "title": "A light look at LayerCake", 52 | "url": "http://www.cs.rpi.edu/~hendler/presentations/LayercakeDagstuhl-share.pdf" 53 | }, 54 | { 55 | "description": "[In PPT] My 15 slides for a 5 minute Pecha Kuchatalk at the World Economic Forum - Meeting of the New Champions in Dalian China (Sept 2009) -note: this is about a 12M file.", 56 | "hashtags": "presentation,slides", 57 | "title": "Engineering the Future Web", 58 | "url": "http://www.cs.rpi.edu/~hendler/presentations/Dalian.ppt" 59 | }, 60 | { 61 | "description": "[In PDF] my keynote from the International Conference on Web Engineering (ICWE-2009) - an extended version of the Dev Day talk below. From June, 2009.", 62 | "hashtags": "presentation,slides", 63 | "title": "The Semantic Web: Lighter, Faster, Easier", 64 | "url": "http://www.cs.rpi.edu/~hendler/presentations/ICWE-2009-faster.pdf" 65 | }, 66 | { 67 | "description": "[In PDF] my 2009 Developers Day talk from WWW2009 (longer version coming later in '09) - the latest in the \"a little semantics goes a long way\" series of talks.", 68 | "hashtags": "presentation,slides", 69 | "title": "Faster, Lighter, Easier", 70 | "url": "http://www.cs.rpi.edu/~hendler/presentations/WWW2009-DevDay.pdf" 71 | }, 72 | { 73 | "description": "[in PDF] Presented at IADIS 2008 - a challenge to the agents community.", 74 | "hashtags": "presentation,slides", 75 | "title": "Where are all the Intelligent Agents?", 76 | "url": "http://www.cs.rpi.edu/~hendler/presentations/Hendler-IADIS.pdf" 77 | }, 78 | { 79 | "description": "[in PDF} At the Oxford Sumemr School, 2008, I presented a variant of the talk below, and added some research challenges that arise from the new view of ontologies on the Web. These are the slides with those challenges.", 80 | "hashtags": "presentation,slides", 81 | "title": "Research Challenges", 82 | "url": "http://www.cs.rpi.edu/~hendler/presentations/ResearchChallenges.pdf" 83 | }, 84 | { 85 | "description": "[in PDF] an attempt at showing why \"informal\" ontologies are crushing formal ontologies in practice, and why this is a good and interesting thing.", 86 | "hashtags": "presentation,slides", 87 | "title": "Minds and Society Summer School 2008", 88 | "url": "http://www.cs.rpi.edu/~hendler/presentations/MindsSocietySummer08.pdf" 89 | }, 90 | { 91 | "description": "[in PDF] A talk about how different models of ontology are causing great confusion on the Semantic Web.", 92 | "hashtags": "presentation,slides", 93 | "title": "Semantic Technology 2008: Fellowship of the (Semantic) Web - The Two Towers.", 94 | "url": "http://www.cs.rpi.edu/~hendler/presentations/SemTech2008-2Towers.pdf" 95 | }, 96 | { 97 | "description": "[in PDF] Joint keynote by me and Ora Lassila at the 2006 Semantic Technologies Conference.", 98 | "hashtags": "presentation,slides", 99 | "title": "SW@5:Current Status and Future Promise of the Semantic Web.", 100 | "url": "http://www.cs.rpi.edu/~hendler/presentations/SemTech2006-keynote.pdf" 101 | }, 102 | { 103 | "description": "[in PDF] my keynote talk from XML 2005. Discusses the opportunity for cooperation between XML and the Semantic Web (RDF, RDFS, OWL). (Nov 15, 2005)", 104 | "hashtags": "presentation,slides", 105 | "title": "From Atoms to OWLs: The new ecology of the WWW", 106 | "url": "http://www.cs.rpi.edu/~hendler/presentations/XML2005Keynote.pdf" 107 | }, 108 | { 109 | "description": "[in PDF] Robert Engelmore Memorial Lecture delivered at AAAI 2005, July, 2005. An explanation of the Semantic Web aimed at the applied AI community.", 110 | "hashtags": "presentation,slides", 111 | "title": "Knowledge is Power: the view from the Semantic Web", 112 | "url": "http://www.cs.rpi.edu/~hendler/presentations/engelmore.pdf" 113 | }, 114 | { 115 | "description": "[in PDF] a talk on the relation between Sem Web technologies and the practice of science. This version is from a talk at Harvard in December, 2005.", 116 | "hashtags": "presentation,slides", 117 | "title": "Science and the Semantic Web", 118 | "url": "http://www.cs.rpi.edu/~hendler/presentations/Harvard-IIC-05.pdf" 119 | }, 120 | { 121 | "description": "[in PDF] Presented at Ohio State University January 2005, and other venues thereafter.", 122 | "hashtags": "presentation,slides", 123 | "title": "The Policy Aware Web", 124 | "url": "http://www.cs.rpi.edu/~hendler/presentations/PolicyAware.pdf" 125 | }, 126 | { 127 | "description": "[in PPT] Presented at various meetings summer 2004 (last update July 2004).", 128 | "hashtags": "presentation,slides", 129 | "title": "Introduction to the Semantic Web", 130 | "url": "http://www.cs.rpi.edu/~hendler/presentations/PictIntro.ppt" 131 | }, 132 | { 133 | "description": "[in HTML] Keynote from International Semantic Web Conference - Florida, USA - Oct, 2003. (Html slides - thanks to Mike Dean).", 134 | "hashtags": "presentation,slides", 135 | "title": "On Beyond Ontology", 136 | "url": "http://iswc2003.semanticweb.org/hendler_files/v3_document.htm" 137 | }, 138 | { 139 | "description": "[in PDF] this is a talk about the Semantic Web and its potential for enpowering scientists and creating new interdisciplinary methodologies - also some challenges to grid computing (Semantic Grid as it's called in the UK). It extends the ideas presented in Science and the Semantic Web. (September, 2003)", 140 | "hashtags": "presentation,slides", 141 | "title": "Keynote talk from UK E-science All hands meeting", 142 | "url": "http://www.cs.rpi.edu/~hendler/presentations/escience.pdf" 143 | }, 144 | 145 | { 146 | "description": "[in PPT] This is a short set of slides which gives a quick overview of the Web Ontology Language (OWL) and some possibilities for the future use of Semantic Web technologies. It updates some of the slides in the more generic talk (listed below). November, 2002.", 147 | "hashtags": "presentation,slides", 148 | "title": "Short talk from ASIST 02", 149 | "url": "http://www.cs.rpi.edu/~hendler/presentations/ASIST02.ppt" 150 | }, 151 | { 152 | "description": "[in PPT] This is the base set of slides from which I generally base my talks. Last updated - August 2002.", 153 | "hashtags": "presentation,slides", 154 | "title": "Introducing the Semantic Web", 155 | "url": "http://www.cs.rpi.edu/~hendler/presentations/SWEO-talk.ppt" 156 | }, 157 | { 158 | "description": "[in PPT] This talk is aimed at an Artificial Intelligence audience - it explains how the Semantic Web will change the face of AI as it makes it relevant to the world at large. (April 2002)", 159 | "hashtags": "presentation,slides", 160 | "title": "Talk presented at Knowledge Representation 2002", 161 | "url": "http://www.cs.rpi.edu/~hendler/presentations/kr-talk.ppt" 162 | }, 163 | { 164 | "description": "[in PPT] This talk was presented at the European Knowledge Acquisition Workshop (Oct 2002).", 165 | "hashtags": "presentation,slides", 166 | "title": "Knowledge Acquisition on the Semantic Web", 167 | "url": "http://www.cs.rpi.edu/~hendler/presentations/EKAW02.ppt" 168 | }, 169 | { 170 | "description": "[in PDF] was held at the National Science Foundation in November, 2002. These are my slides from my panel presentation on the Semantic Web for e-Science.", 171 | "hashtags": "presentation,slides", 172 | "title": "Symposium on Knowledge Environments for Science", 173 | "url": "http://www.cs.rpi.edu/~hendler/presentations/NSF.pdf" 174 | } 175 | ] 176 | -------------------------------------------------------------------------------- /awesome/jahendler-presentation.md: -------------------------------------------------------------------------------- 1 | # Overview 2 | * source: http://www.cs.rpi.edu/~hendler/presentations/ 3 | * 40 slides (19 on slideshare) http://u.memect.com/jahendler/?tag=slides+presentation 4 | * 17 videos (11 on youtube) http://u.memect.com/jahendler/?tag=video+presentation 5 | * A couple of featured videos and slides http://u.memect.com/jahendler/?tag=featured+presentation 6 | * All presentation http://u.memect.com/jahendler/?tag=presentation 7 | 8 | 9 | !["Why the Semantic Web will never work"](http://img.memect.com/qF7p5i8LJPBK5aHnNzDiFQIHYnk=/400x0/t/a99663f68a810442f161c3d60dca99234dc2b20102734cb6faf4992bd7339a1f) 10 | 11 | 12 | # youtube (11) 13 | http://www.youtube.com/watch?v=GAlB87LgrGg 14 | 15 | http://www.youtube.com/watch?v=2NiszK5jEUY 16 | 17 | http://www.youtube.com/watch?v=3Ap5FsxvjTQ 18 | 19 | http://www.youtube.com/watch?v=5aPR96DxcMM 20 | 21 | http://www.youtube.com/watch?v=Cob5oltMGMc 22 | 23 | http://www.youtube.com/watch?v=GuDCT5Ss5a8 24 | 25 | http://www.youtube.com/watch?v=oKiXpO2rbJM 26 | 27 | http://www.youtube.com/watch?v=Pv9fpW6bhdo 28 | 29 | http://www.youtube.com/watch?v=Rg1k2sScQ1o 30 | 31 | http://www.youtube.com/watch?v=X4Rot15JFZc 32 | 33 | http://www.youtube.com/watch?v=yoZYOIlSdPY 34 | 35 | 36 | # slideshare (19) 37 | http://www.slideshare.net/jahendler/big-data-and-computer-science-education 38 | 39 | http://www.slideshare.net/jahendler/broad-data 40 | 41 | http://www.slideshare.net/jahendler/data-big-and-broad-oxford-2012 42 | 43 | http://www.slideshare.net/jahendler/facilitating-web-science-collaboration-through-semantic-markup-35137375 44 | 45 | http://www.slideshare.net/jahendler/future-of-the-world-wide-web-india 46 | 47 | http://www.slideshare.net/jahendler/linked-open-government-data-and-the-semantic-web 48 | 49 | http://www.slideshare.net/jahendler/linked-open-govt-data-sem-tech-east 50 | 51 | http://www.slideshare.net/jahendler/rpi-research-in-linked-open-government-systems 52 | 53 | http://www.slideshare.net/jahendler/semantic-web-science 54 | 55 | http://www.slideshare.net/jahendler/semantic-web-ten-year-update 56 | 57 | http://www.slideshare.net/jahendler/semantic-web-the-inside-story 58 | 59 | http://www.slideshare.net/jahendler/semantic-web-what-it-is-and-why-you-should-care 60 | 61 | http://www.slideshare.net/jahendler/social-machines-oxford-hendler 62 | 63 | http://www.slideshare.net/jahendler/the-rensselaer-idea-data-exploration 64 | 65 | http://www.slideshare.net/jahendler/the-semantic-web-2010-update 66 | 67 | http://www.slideshare.net/jahendler/watson-summer-review82013final 68 | 69 | http://www.slideshare.net/jahendler/web-30-emerging 70 | 71 | http://www.slideshare.net/jahendler/why-the-semantic-web-will-never-work 72 | 73 | http://www.slideshare.net/jahendler/why-watson-won-a-cognitive-perspective 74 | 75 | 76 | ##slideshare duplicated (3) 77 | * http://www.slideshare.net/jahendler/facilitating-web-science-collaboration-through-semantic-markup 78 | * http://www.slideshare.net/jahendler/the-semantic-web-2010-update-5673441 79 | * http://www.slideshare.net/jahendler/web-obs-www2014 80 | 81 | 82 | # other stuff 83 | * https://github.com/memect/awesomeport/edit/master/awesome/jahendler-presentation json version 84 | -------------------------------------------------------------------------------- /awesome/logical-regression.md: -------------------------------------------------------------------------------- 1 | #Logistic Regression on Big Data 2 | 3 | Spark was reported the best according to the slides presented by DB Tsai, Machine Learning Engineer at Alpine Data Labs. 4 | 5 | https://github.com/memect/awesomeport/blob/master/awesome/logical-regression.md -- myself 6 | 7 | refernce card list: http://bigdata.memect.com/?tag=logicalRegression 8 | 9 | ![Multinomial Logistic Regression with Apache Spark](http://bigdata.memect.com/wp-content/uploads/2014/08/6FecWQgJJdqMS2To2ySj8uUumg3pffw2gsa2fXtS3Dh4eATFk5wPEwMupvMTG46z_fae366e1a06d825094d3d00a50e8fdc6f326dd7d0b0cf5d47766e80dd3694e87.jpeg) 10 | 11 | ##Spark 12 | http://www.slideshare.net/dbtsai/2014-0501-mlor 13 | 14 | https://www.dbtsai.com/blog/multinomial-logistic-regression-with-apache-spark/ 15 | 16 | https://spark.apache.org/examples.html 17 | 18 | ## Hadoop 19 | http://www.win-vector.com/blog/2010/12/large-data-logistic-regression-with-example-hadoop-code/ 20 | 21 | https://github.com/WinVector/Logistic 22 | 23 | ## mahout 24 | https://mahout.apache.org/users/classification/logistic-regression.html 25 | 26 | http://sjsubigdata.wordpress.com/2014/05/18/logistic-regression-using-mahout/ 27 | 28 | ## R+hadoop 29 | https://github.com/vilcek/R_MapReduce_Logistic_Regression 30 | 31 | ## Python Sci-kit learn 32 | http://www.dataschool.io/logistic-regression-in-python-using-scikit-learn/ 33 | 34 | 35 | ## Scalding 36 | https://github.com/etsy/Conjecture 37 | -------------------------------------------------------------------------------- /awesome/mining-folksonomy.md: -------------------------------------------------------------------------------- 1 | 2 | # Awesome Folksonomy Mining 3 | 4 | # overview 5 | http://en.wikipedia.org/wiki/Folksonomy 6 | 7 | http://www.sigkdd.org/sites/default/files/issues/12-1-2010-07/v12-1-p58-gupta-sigkdd.pdf Survey on Social Tagging Techniques, 2010 8 | 9 | http://oro.open.ac.uk/25150/1/KER2012.pdf Review of the state of the art: Discovering and Associating Semantics to Tags in Folksonomies, 2012 10 | 11 | # reading list 12 | 13 | http://jychoi-report-cgl.blogspot.com/2007/11/reading-list-about-folksonomy-analysis.html 14 | 15 | -------------------------------------------------------------------------------- /awesome/python-neural-networks.md: -------------------------------------------------------------------------------- 1 | # Awesome Python Tools for Neural Networks 2 | [![](http://python.memect.com/wp-content/uploads/2014/08/q2LSKrtPzI4BPkIk2nwbIpN0XJovLKxboJvdjgcRfRCQ8SUY7EcejnhWbsVNHciX_46ced78af20bde21ceaa45683ab4dae536866e2062d93db867af4a9db7d16d6b.jpeg)](http://python.memect.com/?tag=NeuralNetwork) 3 | 4 | [resource list](http://python.memect.com/?tag=NeuralNetwork) 5 | 6 | https://github.com/memect/awesomeport/blob/master/awesome/python-neural-networks.md 7 | 8 | ## from python wiki 9 | (source https://wiki.python.org/moin/PythonForArtificialIntelligence) 10 | 11 | https://code.google.com/p/neurolab/ Neurolab is a simple and powerful Neural Network Library for Python. Contains based neural networks, train algorithms and flexible framework to create and explore other networks. It has the following features: pure python + numpy; API like Neural Network Toolbox (NNT) from MATLAB; interface to use train algorithms form scipy.optimize; flexible network configurations and learning algorithms; and a variety of supported types of Artificial Neural Network and learning algorithms. 12 | 13 | http://ffnet.sourceforge.net/ ffnet is a fast and easy-to-use feed-forward neural network training solution for python. Many nice features are implemented: arbitrary network connectivity, automatic data normalization, very efficient (also parallel) training tools, network export to fortran code. 14 | 15 | http://leenissen.dk/fann/wp/ Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. Bindings to more than 15 programming languages are available. An easy to read introduction article and a reference manual accompanies the library with examples and recommendations on how to use the library. Several graphical user interfaces are also available for the library. 16 | 17 | http://arctrix.com/nas/python/bpnn.py Written by Neil Schemenauer, bpnn.py is used by an IBM article entitled "An introduction to neural networks". 18 | 19 | http://pyann.sourceforge.net/ A Python framework to build artificial neural networks 20 | 21 | 22 | ## from github 23 | https://github.com/hannes-brt/hebel hot 24 | 25 | ![](http://python.memect.com/wp-content/uploads/2014/08/en4DEP2URLjMjO1sxotd6tPsQ1W76vJUvH2Dn20bli9BRd1kBi6Kq5OqB4xvaP4t_31eb35b5a067082e0d08e8c34899be213b07e855879c27fc616a3af4d5d239b6.jpeg) 26 | 27 | https://github.com/nitishsrivastava/deepnet hot 28 | 29 | ![](http://python.memect.com/wp-content/uploads/2014/08/mSsZXMqPigVUHSkQUMcRBg1Y44LrdwscsnYtdPhKgCvV36CKotxYwMthZU7Y8plw_8883452614db56dd0d7a03aa4b11b7749ce5819023061c9611211ab1bde8ff11.jpeg) 30 | 31 | 32 | ## other 33 | http://www.pybrain.org/ 34 | PyBrain is a modular Machine Learning Library for Python. Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms.PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library. In fact, we came up with the name first and later reverse-engineered this quite descriptive "Backronym". 35 | 36 | http://deeplearning.net/software/theano/ 37 | Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. 38 | 39 | http://neuralensemble.org/PyNN/ 40 | PyNN (pronounced 'pine') is a simulator-independent language for building neuronal network models. 41 | In other words, you can write the code for a model once, using the PyNN API and the Python programming language, and then run it without modification on any simulator that PyNN supports (currently NEURON, NEST, PCSIM and Brian). 42 | 43 | 44 | # articles 45 | http://www.ibm.com/developerworks/library/l-neural/ 46 | An introduction to neural networks, Pattern learning with the back-propagation algorithm 47 | -------------------------------------------------------------------------------- /awesome/robot-learning.md: -------------------------------------------------------------------------------- 1 | # Awesome Robot Learning 2 | 3 | Apply machine learning technique in robot control to improve stability and enhance responsiveness. 4 | 5 | [![robot learning](http://bigdata.memect.com/wp-content/uploads/2014/08/yG4JFouTrPuiyNo1u6WmE5iu8Y53gR7pL1H60qy25Go1YXyubVPcy8DlsQJWy99C_581468a26c6d3f2ecf785c51b2a434e3b1d202bb31d203913f3ee04c182735cf-300x199.jpeg)](http://bigdata.memect.com/?tag=robotlearning) 6 | 7 | resource list: http://bigdata.memect.com/?tag=robotlearning 8 | 9 | me: https://github.com/memect/awesomeport/blob/master/awesome/robot-learning.md 10 | 11 | 12 | # overview 13 | http://en.wikipedia.org/wiki/Robot_learning 14 | 15 | http://en.wikipedia.org/wiki/Adaptive_control 16 | 17 | search keywords: Machine Learning for Robotics, robot learning 18 | 19 | 20 | # groups and people 21 | http://www.ieee-ras.org/robot-learning 22 | 23 | http://www.cs.utexas.edu/~pstone/ 24 | 25 | http://web.cs.wpi.edu/~rail/ 26 | 27 | http://wcms.inf.ed.ac.uk/ipab/mlrob/ 28 | 29 | http://homes.cs.washington.edu/~rao/robotics.html 30 | 31 | http://www.ri.cmu.edu/research_guide/manipulation_control.html 32 | 33 | # tools 34 | 35 | https://github.com/CreativeMachinesLab/aracna 36 | 37 | # survey papers/slides/talks 38 | 39 | https://www.youtube.com/watch?v=GQ8Rmre-3ns 40 | 41 | http://aass.oru.se/~tdt/ml/extra-slides/Robot_Learning.pdf An Overview of Robot Learning, Machine Learning 2005 42 | 43 | http://www.uta.edu/cosmos/TechReports/COSMOS-12-10.pdf Machine Learning Algorithms in Bipedal Robot Control 2012 44 | 45 | http://www.davidbudden.com/repository/FinalReport.pdf Applications of Machine Learning Techniques to Humanoid Robot Platforms, 2012 46 | 47 | http://www.ias.tu-darmstadt.de/uploads/Publications/Kober_IJRR_2013.pdf Reinforcement Learning in Robotics: A Survey 2013 48 | -------------------------------------------------------------------------------- /awesome/us-open-gov-data.md: -------------------------------------------------------------------------------- 1 | # Awesome US Open Government Data 2 | [![US Open Government Data](http://bigdata.memect.com/wp-content/uploads/2014/08/Screen-Shot-2014-08-07-at-5.04.49-PM.png)](http://bigdata.memect.com/?tag=opengovdataus) 3 | 4 | [resource list](http://bigdata.memect.com/?tag=opengovdataus) 5 | 6 | https://github.com/memect/awesomeport/blob/master/awesome/us-open-gov-data.md 7 | 8 | # overview 9 | http://www.data.gov/ The home of the U.S. Government’s open data 10 | 11 | http://www.w3.org/DesignIssues/GovData.html by Tim Berners-Lee, 2009 12 | 13 | ## presentations 14 | 15 | http://www.slideshare.net/jeanneholm1/iswc-2012-keynote 16 | 17 | http://www.slideshare.net/jeanneholm1/the-evolution-of-open-data 18 | 19 | http://www.slideshare.net/jahendler/rpi-research-in-linked-open-government-systems 20 | 21 | ## publications 22 | http://www.whitehouse.gov/sites/default/files/microsites/ostp/us_open_data_action_plan.pdf U.S. OPEN DATA ACTION PLAN, May 9, 2014 23 | 24 | http://www.whitehouse.gov/the_press_office/TransparencyandOpenGovernment/ Transparency and Open Government, 2009 25 | 26 | 27 | # learn commercial value 28 | 29 | ## studies 30 | 31 | http://innovationinsights.wired.com/insights/2014/07/quiet-revolution-open-data-transforming-citizen-government-interaction/ The Quiet Revolution: Open Data Is Transforming Citizen-Government Interaction, BY MAURY BLACKMAN, 07.30.14 32 | 33 | http://www.mckinsey.com/insights/business_technology/open_data_unlocking_innovation_and_performance_with_liquid_information McKinsey and Company recently released a study that found in seven sectors alone open data could generate more than $3 trillion a year in additional value. October 2013 34 | 35 | 36 | ## showcases 37 | http://www.opendatanow.com/2013/09/back-to-school-with-open-data/ greatshools using open government data 38 | 39 | http://www.city-data.com/ commercialized open government data 40 | 41 | https://www.opengov.com/ works with local governments to visualize their budgetary data for the public, received $15 million in funding earlier this year 42 | 43 | http://enigma.io/ Quickly search and analyze billions of public records published by governments, companies and organizations. 44 | 45 | 46 | 47 | # misc 48 | http://ckan.org/instances/ internation dataset catalog, covering some north america open government datasets 49 | 50 | http://logd.tw.rpi.edu/ semantic web version of open government data 51 | 52 | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6237449 IEEE Intelligence Special Issue on Linked Open Government Data, June, 2012 53 | 54 | http://opendata.stackexchange.com/ open government data on stack-overflow Q/A system 55 | 56 | https://government.github.com/ open government data on Github 57 | -------------------------------------------------------------------------------- /license.md: -------------------------------------------------------------------------------- 1 | [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/) 2 | Creative Commons License 3 | --------------------------------------------------------------------------------