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Jason K. Johnson is a researcher interested in probabilistic graphical models, signal and image processing, statistical physics, and related topics. He received his Ph.D. in Electrical Engineering and Computer Science from MIT in 2008, working with Prof. Alan Willsky. He has since held positions at Los Alamos National Laboratory and continues to publish widely in top journals and conferences on inference in graphical models.
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List of publications january 13th 2015
This document lists publications by Olli Virmajoki, including:
1) 12 refereed scientific journal articles on topics like clustering algorithms.
2) 11 articles in international scientific conferences on clustering and machine learning.
3) 4 theses including a PhD thesis on the pairwise nearest neighbor clustering method.
4) 4 technical reports from the University of Joensuu on clustering algorithms.
5) 2 other publications on virtual education and a book about pairwise nearest neighbor clustering.
6) 2 manuscripts under review on random swap clustering and legal issues during cutbacks.
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The document is a call for papers for the International Conference on Machine Learning and Data Analysis (ICMLDA 2008) to be held October 22-24, 2008 in San Francisco, USA. It provides information on submission guidelines and important dates, as well as an overview of topics that will be covered at the conference related to machine learning and data analysis techniques and applications. Accepted papers will be published in the conference proceedings and considered for publication in relevant journals.
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The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. To enhance the accuracy and convergence rate of this algorithm, an improved cuckoo search algorithm is proposed in this paper. Normally, the parameters of the cuckoo search are kept constant. This may lead to decreasing the efficiency of the algorithm. To cope with this issue, a proper strategy for tuning the cuckoo search parameters is presented. Then, it is employed for training feedforward neural networks for two benchmark classification problems. Finally, the performance of the proposed algorithm is compared with that of the standard cuckoo search. Simulation results demonstrate the effectiveness of the proposed algorithm.
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This thesis explored developing new interactive visualization systems and data integration methods to support discovery in collections of scientific information. It addressed challenges of existing methods to support overviews and exploration as the volume of data increases. The work involved instantiating graph structures from real-world datasets, developing interactive visualizations, and using quantitative and semantic guidance to explore connections. It evaluated the methods on datasets from VAST challenges, open notebook science, and Pfizer drug discovery to demonstrate feasibility and identify future work opportunities at larger scales with these approaches.
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This resume is for Christoph F. Eick, an Associate Professor in the Department of Computer Science at the University of Houston. It provides details about his areas of expertise, education, work experience, recent service, and recent publications. His areas of expertise include knowledge discovery and data mining, machine learning, and artificial intelligence. He received his M.S. and Dr. nat. degrees in Computer Science from the University of Karlsruhe in Germany. He has worked at the University of Houston since 1985 and also had temporary positions at MCC and the University of Washington. His recent service includes committee work and conference organization. He has over 60 publications in data mining, machine learning, and related fields.
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The document discusses cognitive information agents that can effectively learn from interactions with information to support complex human tasks. It describes an architecture that can automatically build and execute analytic solutions by specifying input/output types, information sources, and example datasets. It then trains, measures performance, analyzes errors, and proposes new learning tasks to iteratively improve. Example applications discussed are question answering systems for bioinformatics and decision support that can be automatically optimized for new datasets.
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International Journal of Mobile Network Communications & Telematics (IJMNCT) is an open access peer-reviewed journal that addresses the impacts and challenges of mobile communications and telematics. The journal also aims to focus on various areas such as ecommerce, e-governance, Telematics, Telelearning nomadic computing, data management, related software and hardware technologies, and mobile user services. The journal documents practical and theoretical results which make a fundamental contribution for the development of mobile communication technologies.
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List of publications january 13th 2015
This document lists publications by Olli Virmajoki, including:
1) 12 refereed scientific journal articles on topics like clustering algorithms.
2) 11 articles in international scientific conferences on clustering and machine learning.
3) 4 theses including a PhD thesis on the pairwise nearest neighbor clustering method.
4) 4 technical reports from the University of Joensuu on clustering algorithms.
5) 2 other publications on virtual education and a book about pairwise nearest neighbor clustering.
6) 2 manuscripts under review on random swap clustering and legal issues during cutbacks.
ICMLDA_poster.doc
The document is a call for papers for the International Conference on Machine Learning and Data Analysis (ICMLDA 2008) to be held October 22-24, 2008 in San Francisco, USA. It provides information on submission guidelines and important dates, as well as an overview of topics that will be covered at the conference related to machine learning and data analysis techniques and applications. Accepted papers will be published in the conference proceedings and considered for publication in relevant journals.
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The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. To enhance the accuracy and convergence rate of this algorithm, an improved cuckoo search algorithm is proposed in this paper. Normally, the parameters of the cuckoo search are kept constant. This may lead to decreasing the efficiency of the algorithm. To cope with this issue, a proper strategy for tuning the cuckoo search parameters is presented. Then, it is employed for training feedforward neural networks for two benchmark classification problems. Finally, the performance of the proposed algorithm is compared with that of the standard cuckoo search. Simulation results demonstrate the effectiveness of the proposed algorithm.
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This thesis explored developing new interactive visualization systems and data integration methods to support discovery in collections of scientific information. It addressed challenges of existing methods to support overviews and exploration as the volume of data increases. The work involved instantiating graph structures from real-world datasets, developing interactive visualizations, and using quantitative and semantic guidance to explore connections. It evaluated the methods on datasets from VAST challenges, open notebook science, and Pfizer drug discovery to demonstrate feasibility and identify future work opportunities at larger scales with these approaches.
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The document discusses cognitive information agents that can effectively learn from interactions with information to support complex human tasks. It describes an architecture that can automatically build and execute analytic solutions by specifying input/output types, information sources, and example datasets. It then trains, measures performance, analyzes errors, and proposes new learning tasks to iteratively improve. Example applications discussed are question answering systems for bioinformatics and decision support that can be automatically optimized for new datasets.
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International Journal of Mobile Network Communications & Telematics (IJMNCT) is an open access peer-reviewed journal that addresses the impacts and challenges of mobile communications and telematics. The journal also aims to focus on various areas such as ecommerce, e-governance, Telematics, Telelearning nomadic computing, data management, related software and hardware technologies, and mobile user services. The journal documents practical and theoretical results which make a fundamental contribution for the development of mobile communication technologies.
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This document is a resume for Chenhui Hu, a PhD candidate in Electrical Engineering at Harvard University. It summarizes his education, research experience, publications, awards, and other qualifications. He has conducted research in machine learning, signal processing, and wireless networks. Some of his accomplishments include improving Alzheimer's disease classification accuracy by over 24% using graph-signal processing and deep learning, and reducing brain atrophy progression prediction error by 60% using decomposed sparse vector-autoregression models.
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http://www.airccse.org/journal/sipij/vol10.html
September 2021 - Top 10 Read Articles in Signal & Image Processing
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
June 2021: Top Read Articles in Signal & Image Processing
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
July 2021: Top Read Articles in Signal & Image Processing
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
see CV
This CV summarizes the professional experience and qualifications of Marc J Sobel. It lists his educational background including a B.A. in Mathematics and Philosophy from University of Minnesota in 1977 and a Ph.D. in Statistics from University of California, Berkeley in 1983. It also outlines his current position as an Associate Professor of Statistics at Temple University since 1992, and provides details on his teaching experience, research interests, and publications.
CV_of_ArulMurugan (2017_01_18)
This document is a resume for Dr. Arulmurugan Ambikapathi that summarizes his expertise and achievements. It lists his educational background, including a Ph.D. and positions held at various academic and research institutions. It also outlines his research focus on image processing, analysis, and applications in computer vision, deep learning, and automated optical inspection. Major projects and awards are mentioned, demonstrating his leadership and accomplishments in industrial research and academic publications.
Brief bibliography of interestingness measure, bayesian belief network and ca...
This document provides a brief bibliography of papers related to interestingness measures, Bayesian belief networks, and causal inference. It lists over 60 references published between 1961-2012 on topics such as outlier detection, probabilistic graphical models, sensitivity analysis of Bayesian networks, rule interestingness measures, and applications of Bayesian networks in domains like fraud detection and credit risk evaluation. The references are grouped into sections on learning Bayesian networks from data, sensitivity analysis, outlier detection, rule interestingness measures, and applications.
Resume Yu-Li Liang
Yu-Li Liang has extensive experience and skills in computer vision, image processing, machine learning, data analysis, and biological system modeling. He holds a PhD in Computer Science from the University of Colorado Boulder and has worked as a postdoctoral scholar at Caltech. His research focuses on developing algorithms for feature extraction from sequential images, obscene content detection in online videos, skin condition evaluation from photos, and geographical feature detection in satellite images. He has strong programming skills in MATLAB, C++, Python, and machine learning tools like WEKA.
Elegant Resume
This document is a resume for Amit Sethi summarizing his professional experience and qualifications. It outlines his objective to obtain a research and development position in industry. It then details his education at the University of Illinois at Urbana-Champaign where he is pursuing a PhD in Electrical and Computer Engineering, as well as a previous degree from Indian Institute of Technology. His experience includes research in machine learning, computer vision, and video processing. He has several publications and awards and is skilled in programming languages such as C++.
Elegant Resume
This document is a resume for Amit Sethi summarizing his professional experience and qualifications. It outlines his objective to obtain a research and development position in industry. It then details his education at the University of Illinois at Urbana-Champaign where he is pursuing a PhD in Electrical and Computer Engineering, as well as a previous degree from Indian Institute of Technology. His experience includes research in machine learning, computer vision, and video processing. He has several publications and awards and is proficient in programming languages and computer skills relevant to his field.
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Top Cited Article in Informatics Engineering Research: October 2020
Top Cited Article in Informatics Engineering Research: October 2020
December 2021: Top Read Articles in Signal & Image Processing
December 2021: Top Read Articles in Signal & Image Processing
November 2021: Top Read Articles in Signal & Image Processing
November 2021: Top Read Articles in Signal & Image Processing
New Research Articles 2019 October Issue Signal & Image Processing An Interna...
New Research Articles 2019 October Issue Signal & Image Processing An Interna...
September 2021 - Top 10 Read Articles in Signal & Image Processing
September 2021 - Top 10 Read Articles in Signal & Image Processing
June 2021: Top Read Articles in Signal & Image Processing
June 2021: Top Read Articles in Signal & Image Processing
July 2021: Top Read Articles in Signal & Image Processing
July 2021: Top Read Articles in Signal & Image Processing
Brief bibliography of interestingness measure, bayesian belief network and ca...
Brief bibliography of interestingness measure, bayesian belief network and ca...
CV
- 1. Jason K. Johnson Research Interests Inference and learning in probabilistic graphical models, statistical signal and image pro- cessing, statistical physics, multiscale methods, information theory, convex optimization and analysis. Education Massachusetts Institute of Technology, Cambridge MA. Ph.D. Electrical Engineering and Computer Science, 2008. S.M. Electrical Engineering and Computer Science, 2003. Graduate Research Advisor: Prof. Alan Willsky S.B. Physics, 1995. Undergraduate Thesis Advisor: Prof. Edward Farhi Professional Experience Postdoctoral Associate/Director’s Fellow 2008-2011 Dr. Michael Chertkov Los Alamos, NM Center for Nonlinear Studies & Theoretical Division T-4, Los Alamos National Laboratory Research Assistant 2000-2008 Prof. Alan Willsky Cambridge, MA Laboratory for Information and Decision Systems, MIT Member of Technical Staff 1995-2000 Dr. Robert Washburn Burlington, MA Alphatech, Inc. Selected Publications Journal Articles JKJ, A. Willsky. A recursive model-reduction method for estimation in Gaussian Markov random fields. IEEE Transactions on Image Processing, v.17, no.1, pp.70–83, January 2008. http://ieeexplore.ieee.org D. Malioutov, JKJ, A. Willsky. Walk-sums and belief propagation in Gaussian graph- ical models. Journal of Machine Learning Research, v.7, pp.2031–2064, October 2006. http://jmlr.csail.mit.edu D. Malioutov, JKJ, M. Choi, A. Willsky. Low-rank variance approximation in GMRF Models: single and multiscale approaches. IEEE Transactions on Signal Processing, v.56, no.10, pp.4621–4634, October 2008. http://ieeexplore.ieee.org V. Chandrasekaran, JKJ, A. Willsky. Estimation in Gaussian graphical models using tractable sub-graphs: a walk-sum analysis. IEEE Transactions on Signal Processing, v.56, no.5, pp.1916-1930, May 2008. http://ieeexplore.ieee.org
- 2. M. Choi, V. Chandrasekaran, D. Malioutov, JKJ, A. Willsky. Multiscale stochastic model- ing for tractable inference and data assimilation. Computer Methods in Applied Mechanics and Engineering, v.197, pp.3492–3515, August 2008. http://sciencedirect.com Conference Papers S. Kudekar, JKJ, M. Chertkov. Linear programming based detectors for two-dimensional intersymbol interference channels. Submitted to International Symposium on Information Theory. (in review) JKJ, P. Netrapalli, M. Chertkov. Learning planar Ising models. Submitted to 14th Inter- national Conference on Artificial Intelligence and Statistics. (in review) http://arxiv.org/abs/1011.3494 JKJ, M. Chertkov. A Majorization-Minimization Approach to Design of Power Transmis- sion Networks. 49th IEEE Conference on Decision and Control, December 2010. http://arxiv.org/abs/1004.2285 JKJ, V. Chernyak, M. Chertkov. Orbit-Product Representation and Correction of Gaussian Belief Propagation. International Conference on Machine Learning, June 2009. http://arxiv.org/abs/090.3769 http://videolectures.net/jason k johnson JKJ, D. Bickson, D. Dolev. Fixing convergence of Gaussian belief propagation. Interna- tional Symposium of Information Theory, July 2009. http://arxiv.org/abs/0901.4192 JKJ, V. Chandrasekaran, A. Willsky. Learning Markov structure by maximum entropy relaxation. 11th Inter. Conf. on Artificial Intelligence and Statistics (AISTATS), March 2007. http://www.stat.umn.edu/∼aistat/proceedings/start.htm JKJ, D. Malioutov, A. Willsky. Lagrangian relaxation for MAP estimation in graphical models. 45th Allerton Conf. on Communication, Control and Computing, September 2007. http://www.csl.uiuc.edu/allerton/archives/allerton07/papers/0250.pdf JKJ, D. Malioutov, A. Willsky. Walk-sum interpretation and analysis of Gaussian belief propagation. Advances in Neural Information Processing Systems (NIPS), v.18, pp.579– 586, December 2005. Selected for ”spot-light” at the conference. http://books.nips.cc/nips18.html V. Chandrasekaran, JKJ, A. Willsky, Adaptive embedded subgraph algorithms using walk- sum analysis. Advances in Neural Information Processing Systems (NIPS), v.20, December 2007. http://books.nips.cc/nips20.html