Directory

 

 
 
General InformationPublications

  • Yuquan J Shan, Chiara Lo Prete, David J Miller and George Kesidis, 2016, "A simulation framework for uneconomic virtual bidding in day-ahead electricity markets", ACM Sigmetrics performance evaluation review
  • Hossein Soleimani and David J Miller, 2016, "Semi-supervised multi-label multi-instance learning for structured data", Neural Computation
  • Hossein Soleimani and David J Miller, 2016, "Semi-supervised multi-label topic models for document classification and sentence labeling", CIKM (acceptance rate 18%)
  • Hossein Soleimani and David J Miller, 2016, "ATD: Anomalous topic discovery in high dimensional discrete data"
  • Hossein Soleimani and David J Miller, 2016, "ATD: Anomalous topic discovery in high dimensional discrete data", IEEE Trans. on Knowledge and Data Engineering
  • John Keltner and David J Miller, 2016, "HIV distal neuropathic pain is associated with smalle ventral posterior cingulate cortex", Pain Medicine
  • Haoti Zhong, David J Miller and Anna Squicciarini, 2016, "Content-driven detection of cyberbullying on the instagram social network"
  • Hossein Soleimani and David J Miller, 2016, "Increasing the value of class labels in semisupervised topic modeling", IJCNN
  • Haoti Zhong, David J Miller and Ken Urish, 2016, "T2 map signal variation predicts symptomatic osteoarthritis progression: data from the osteoarthritis initiative", Skeletal Radiology
  • Zhicong Qiu, David J Miller and George Kesidis, 2016, "A maximum entropy framework for semisupervised and active learning with unknown and label-scarce classes", IEEETrans. on Neural Networks and Learning Systems
  • A. Kurve, David J Miller and G. Kesidis, 2015, "Multicategory crowdsourcing accounting for variable task difficulty, worker skill, and worker intention", IEEE Trans. on Knowledge and Data Engineering
  • David J Miller and H. Soleimani, 2015, "On an Objective Basis for the Maximum Entropy Principle", Entropy
  • Hossein Soleimani and David Miller, 2015, "Parsimonious Topic Models with Salient Word Discovery", IEEE Trans. on Knowledge and Data Engineering
  • Zhiqiang Guo, Huaiqing Wang, Jie Yang and David J. Miller, 2015, "A Stock Market Forecasting Model Combining Two-directional Two-dimensional Principal Component Analysis and Radial Basis Function Neural Network", PloS One
  • Yuquan Shan, Jayaram Raghuram, George Kesidis, David J Miller and Anna Scaglione, 2015, "Generation bidding game with potentially false attestation of flexible demand", EURASIP Journal on advances in signal Processing
  • Zhicong Qiu, David Miller and George Kesidis, 2015, "Semisupervised and active learning with unknown and label-scarce categories", IEEE Transactions on Neural Networks and Learning Systems
  • John Keltner, George Kesidis and David Miller, 2015, "HIV Distal Neuropathic Pain is associated with Smaller Ventral Posterior Cingulate Cortex", Pain Medicine
  • G. Shi, David Miller, Yizhi Wang, Gerard Broussard, Yue Wang, Lin Tian, Guoqiang andYu, , 2015, ""FASP: A Machine Learning Approach to Functional Astrocyte Phenotyping from Time-Lapse Calcium Imaging Data"
  • Zhicong Qiu, David Miller and George Kesidis, 2015, "Detecting clusters of anomalies on low-dimensional feature subsets with application to network traffic flow data,"
  • J. Raghuram, David J Miller and G. Kesidis, 2014, "Unsupervised, low latency anomaly detection of algorithmically generated domain names by generative probabilistic modeling", Journal of Advanced Research
  • John R. Keltner, Christine Fennema-Notestine, Florin Vaida, Dongzhe Wang, Donald R. Franklin, Robert H. Dworkin, Chelsea Sanders, J. Allen McCutchan, Sarah L. Archibald, David J Miller, George Kesidis, Clint Cushman, Sung Min Kim, Ian Abramson, Michael J. Taylor, Rebecca J. Theilmann, Michelle D. Julaton, Randy J. Notestine, Stephanie Corkran, Mariana Cherner, Nichole A. Duarte, Terry Alexander, Jessica Robinson-Rapp, Benjamin B. Gelman, David M. Simpson, Ann C. Collier, Christina M. Marra, Susan Morgello, Greg Brown, Igor Grant, J. Hampton Atkinson, Terry L. Jernigan and Rongald J. Ellis, 2014, "HIV-associated distal neuropathic pain is associated with smaller total cerebral cortical gray matter", J Neurovirol, 20, (3), pp. 209-18
  • J. Raghuram, G. Kesidis, David J Miller, K. Levitt, J. Rowe and A. Scaglione, 2014, "Generation bidding game with flexible demand", 9th International workshop on feedback computing
  • Zhicong Qiu, David J Miller, Brian Stieber and Tim Fair, 2014, "Actively learning to distinguish suspicious from innocuous anomalies in a batch of vehicle tracks", SPIE
  • A. Kurve, C. Griffin, David J Miller and G. Kesidis, 2014, "Optimizing Cluster Formation in Super-Peer Networks via Local Incentive Design", Journal of Peer-to-Peer Networking and Applications
  • A. Jaiswal, David J Miller and P. Mitra, 2013, "Schema Matching and embedded value mapping for databases with opaque column names and mixed continuous and discrete-valued data fields", ACM Transactions on Database Systems, 38, pp. 1-34
  • G. Jin, R. Raich and David J Miller, 2013, "A generative semisupervised model for multi-view learning when some views are label free", ICASSP
  • H. Chen, David J Miller and C. L. Giles, 2013, "How a link ages over time in a coauthorship network", dbSocial
  • F. Kocak, David J Miller and G. Kesidis, 2013, "An Algorithm for Detecting Anomalous Latent Classes in a Batch of Network Traffic Flows", Conf. on Info. Sciences and Systems, Princeton
  • A. Kurve, David J Miller and G. Kesidis, 2013, "Defeating Tyranny of the Masses: Semisupervised Multicategory Crowdsourcing Accounting for Worker Skill and Intention, Task Difficulty, and Task Heterogeneity", Proc. GameSec (Security Games)
  • Jianping He, David J Miller and George Kesidis, 2013, "Latent interest group discovery and management by peer-to-peer online social networks", Proc. ASE/IEEE SocialCom
  • David J Miller, F. Kocak and G. Kesidis, 2012, "Sequential anomaly detection in a batch with growing number of tests: application to network intrusion detection", IEEE Workshop on Machine Learning for Signal Processing
  • David J Miller, J. Raghuram, G. Kesidis and C. Collins, 2012, "Improved Generative Semisupervised Learning Based on Fine-Grained Component-Conditional Class Labeling", Neural Computation, pp. 1926-1966
  • L. Chen, Y. Tian, G. Yu and David J Miller, 2012, Discriminant and network analysis to study origin of cancer
  • X. Yuan, David J Miller, J. Zhang, D. Herrington and Y. Wang, 2012, "An overview of population genetic data simulation", Journal of Computational Biology
  • B. Zhang, David J Miller and Y. Wang, 2012, "Nonlinear system modeling with random matrices: echo state networks revisited", IEEE Transactions on Neural Networks
  • J. Raghuram, David J Miller and G. Kesidis, 2012, "Semisupervised domain adaptation for mixture model based classifiers", Conf. on Info. Sciences and Systems
  • A. Natraj, David J Miller and K. Sullivan, 2012, "Anomaly detection driven active learning for identifying suspicious trakcs and events in WAMI video", SPIE Defense, Security, and Sensing
  • K. Urish, M. Keffalas, J. Durkin, David J Miller, C. Chu and T. Mosher, 2012, "Signal homogeneity on cartilage T2 maps as a predictive image biomarker for rapid symptomatic progression of OA"
  • T. Adali, David J Miller, K. Diamantaras and J. Larsen, 2011, "Trends in Machine Learning for Signal Processing", IEEE Signal Processing Magazine
  • Y. Wang, H. Li, David J Miller and J. Xuan, 2011, Bioinformatics and Public Access, Wiley-Blackwell
  • B. Celik, J. Raghuram, G. Kesidis and David J Miller, 2011, "Salting public traces with attack traffic to test flow classifiers", Proc. Usenix Cyber Security Experimentation and Test Workshop
  • G. Zou, G. Kesidis and David J Miller, 2011, "A flow classifier with tamper-resistant features and an evaluation of its portability to a new domain", IEEE Journal on Select Areas in Communications
  • L. Chen, G. Yu, C. Langefeld and David J Miller, 2011, "Comparative analysis of methods for detecting interacting loci", BMC Bioinformatics
  • Y. Aksu, David J Miller, G. Kesidis, D. Bigler and Q. Yang, 2011, "An MRI-derived definition of MCI-to-AD conversion for long-term, automatic prognosis of MCI patients", PloS One
  • S. Markley and David J Miller, 2010, "Joint Parsimonious Modeling and Model Order Selection for Multivariate Gaussian Mixtures", IEEE Journal on Select Areas in Signal Processing, special issue on model selection, pp. 548-559
  • Y. Aksu, David J Miller and G. Kesidis, 2010, "Margin-Maximizing Feature Elimination Methods for Linear and Nonlinear Kernel-Based Discriminant Functions", IEEE Trans. on Neural Networks, pp. 701-717
  • A. Jaiswal, David J Miller and P. Mitra, 2010, "Uninterpreted schema matching with embedded value mapping under opaque column names and data values", IEEE Trans. on Knowledge and Data Engineering, pp. 291-304
  • Guoqiang Yu, Yuanjian Feng, David J Miller, Jinahua Xuan, Eric P. Hoffman, Robert Clarke, Ben Davidson, Ie-Ming Shih and Yue Wang, 2010, "Matched gene selection and committee classifier for molecular classification of heterogeneous diseases", Journal of Machine Learning Research
  • David J Miller, C. Lin, G. Kesidis and C. Collins, 2010, "Improved fine-grained component-conditional class labeling with active learning", Intl. Conf. on Machine Learning and Applications, pp. 1-6
  • L. Chen, G. Yu, David J Miller, L. Song, C. Langefeld, D. Herrington, Y. Liu and Y. Wang, 2009, "A Ground Truth Based Comparative Study on Detecting Epistatic SNPs", Proc. IEEE Intl Conf. on Bioinformatics & Biomedicine
  • Y. Wang, H. Li, David J Miller and J. Xuan, 2009, Bioinformatics and public access resources, The International Olympic Committee and Wiley-Blackwell, Oxford, England, pp. Ch. 6
  • J. Park, David J Miller, J. F. Doherty and S. Thompson, 2009, "A study on the feasibility of low probability of intercept sonar", Conf. on Info. Sciences and Systems
  • David J Miller, C. Lin, G. Kesidis and C. Collins, 2009, "Semisupervised mixture modeling with fine-grained component-conditional class labeling and transductive inference", IEEE Workshop on Machine Learning for Signal Processing
  • D. Bigler, Y. Aksu, David J Miller and Q. Yang, 2009, "STAMPS: software tool for automated MRI post-processing on a supercomputer", Computer methods and programs in biomedicine, pp. 146-157
  • David J Miller and Y. Zhang, 2009, "An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions", Bioinformatics
  • David J Miller, Y. Wang and G. Kesidis, 2008, "Emergent unsupervised clustering paradigms with potential application to bioinformatics", Frontiers in Bioscience, 13, pp. 677-690
  • Y. Zhu, Z. Wang, Y. Feng, J. Xuan and David J Miller, 2008, "A ground truth based comparative study on clustering of gene expression data", Frontiers in Bioscience
  • Y. Wang, David J Miller and R. Clarke, 2008, "Approaches to working in high-dimensional data spaces: gene expression microarrays", British Journal of Cancer, MinReview, pp. 6
  • David J Miller, S. Pal and Y. Wang, 2008, "Extensions of transductive learning for distributed ensemble classification and application to biometric authentication", Neurocomputing
  • Y. Zhu, Huai Li, David J Miller, Zuyi Wang, Jianhua Xuan, Robert Clarke, Eric P. Hoffman and Yue Wang, 2008, "caBIG VISDA: modeling, visualization, and discovery for cluster analysis of genomic data", BMC Bioinformatic
  • David J Miller, Y. Zhang and G. Kesidis, 2008, "A transductive extension of maximum entropy/iterative scaling for decision aggregation in distributed classification", ICASSP 2008
  • David J Miller, Y. Zhang and G. Kesidis, 2008, "Decision aggregation in distributed classification by a transductive extension of maximum entropy/improved iterative scaling", EURASIP Journal on Advances in Signal Processing
  • Y. Aksu, David J Miller and G. Kesidis, 2008, "Margin-based feature selection techniques for support vector machine classification", Proc.of IEEE Workshop on Cognitive Information Processing (CIP)
  • Y. Zhang, Y. Aksu, G. Kesidis, David J Miller and Y. Wang, 2008, "SVM margin-based feature elimination applied to high-dimensional DNA microarray data", IEEE Workshop on Machine Learning for Signal Processing
  • J. Wang, G. Kesidis and David J Miller, 2007, "New directions iin covert malware modeling which exploit while-listing", Proc. IEEE Sarnoff Symposium on Comunications
  • David J Miller and S. Pal, 2007, "Transductive methods for distributed ensemble classification", Neural Computation, pp. 856-884
  • Y. Aksu, G. Kesidis and David J Miller, 2007, "Scalable, efficient, stepwise-optimal feature elimination in support vector machines", Proc. of IEEE Workshop on Machine Learning for Signal Processing
  • A. Nag, David J Miller, A. Brown and K. Sullivan, 2007, "Combined generative-dscriminative learning for object recognition using local image descriptors", Proc. IEEE Workshop on Machine Learning for Signal Processing
  • David Bazell, David J Miller and Mark Subbaro, 2006, "Objective subclass determination of Sloan digital sky survey unknown spectral objects", The Astrophysics Journal, 649, (2), pp. 678-691
  • Jisheng Wang, David J Miller and George Kesidis, 2006, "Efficient mining of the multidimensional traffic cluster hierarchy for digesting, visualization, and anomaly identification", IEEE Journal on Selected Areas in Communications, Special issue on High-speed Network Security, 24, (10), pp. 1929-1941
  • David J Miller, Siddharth Pal and Yue Wang, 2006, "Constraint-based transductive learning for distributed ensemble classification", IEEE Workshop on Machine Learning for Signal Processing, pp. 15-20
  • Jisheng Wang, David J Miller and George Kesidis, 2006, "Multidimensional flow mining for digesting, visualization, and signature extraction", DETER Community Workshop
  • Michael W. Graham and David J Miller, 2006, "Unsupervised learning of parsimonious mixtures on large spaces with integrated feature and component selection", IEEE Transactions on Signal Processing, 54, (4), pp. 1289-1303
  • David J Miller and Siddharth Pal, 2006, "Transductive methods for distributed ensemble classification", Conference on Information Sciences and Systems, pp. 1605-1610
  • Jisheng Wang, Ihab Hamadeh, George Kesidis and David J Miller, 2006, "Polymorphic worm detection and defense: system design, experimental methodology, and data resources", SIGCOMM Workshop on Large Scale Attack Defense (LSAD)
  • Andrew Brown, Kevin Sullivan and David J Miller, 2006, "Feature-aided multiple target tracking in the image plane", Proc. of the SPIE, Intelligent Computing: Theory and Applications IV, 6229, pp. 62290Q
  • Qi Zhao and David J Miller, 2005, "Mixture modeling with pairwise instance-level class constraints", Neural Computation, 17, (11), pp. 2482-2507
  • Qi Zhao and David J Miller, 2005, "Semisupervised learning of mixture models with class constraints", IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), MMLSP-L2.1
  • David J Miller, Siddharth Pal and Qi Zhao, 2005, "A latent variable extension of iterative scaling for classification on continuous and mixed continuous-discrete feature spaces", Conf. on Info. Sciences and Systems (CISS), TA2-7
  • David J Miller and Siddharth Pal, 2005, "An extension of iterative scaling for joint decision-level and feature-level fusion in ensemble classification", IEEE Workshop on Machine Learning for Signal Processing, pp. 61-66
  • Zhicong Qiu, David J Miller and George Kesidis, , "Flow-based botnet detection through semisupervised active learning"
  • Yitan Zhu and David J Miller, , "Convex analysis of mixtures for separating non-negative well-grounded sources", Scientific Reports
  • Abhikesh Nag, David J Miller, Andrew Brown and Kevin Sullivan, , "A system for vehicle recognition in video based on SIFT features, mixture models, and support vector machines"
  • Y. Feng, Z. Wang, Y. Zhu, J. Xuan, David J Miller, R. Clarke, E. Hoffman and Y. Wang, , "Learning the tree of phenotype using genomic data and VISDA", IEEE Symposium on Bioinformatics and Bioengineering
  • David J Miller and Siddharth Pal, , "An extension of iterative scaling for decision and data aggregation in ensemble classification", Journal of VLSI Signal Processing Systems, special issue on MLSP2005
  • K. L. Urish, C. R. Chu, J. R. Durkin, M. G. Keffalas, David J Miller and Timothy J. Mosher, , "T2 texture index of cartilage can predict early symptomatic OA progression: data from the osteoarthritis initiative", The Journal of Bone and Joint Surgery
  • J. Raghuram, David J Miller and G. Kesidis, , "Instance-Level Constraint Based Semi-supervised Learning With Imposed Space-Partitioning", IEEE Transactions on Neural Networks and Learning Systems
  • J. Raghuram, David J Miller and G. Kesidis, , "A space partitioning solution for semisupervised learning with instance-level constraints", IEEE Transactions on Neural Networks-Learning Systems
 

About

The School of Electrical Engineering and Computer Science was created in the spring of 2015 to allow greater access to courses offered by both departments for undergraduate and graduate students in exciting collaborative research in fields.

We offer B.S. degrees in electrical engineering, computer science and computer engineering and graduate degrees (master's degrees and Ph.D.'s) in electrical engineering and computer science and engineering. EECS focuses on the convergence of technologies and disciplines to meet today’s industrial demands.

School of Electrical Engineering and Computer Science

The Pennsylvania State University

209 Electrical Engineering West

University Park, PA 16802

814-863-6740

Department of Computer Science and Engineering

814-865-9505

Department of Electrical Engineering

814-865-7667