Janurary 25
Wednesday - 1:00 PM
4-201 Center for Science and Technology
Prof. Chihwa Kao
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Speaker: Dr. Chicwa Kao, Professor and Chair, Economics, Center for Policy Research, Syracuse University
Title: Testing Cross-Sectional Dependence in a Panel Factor Model Using the Wild Nootstrap F-Test (joint work with Badi Baltagi and Sanggon Na)
Abstract: This talk considers testing the cross-sectional dependence in a panel factor model. Based on the model considered by Bai (2003), we investigate the use of a simple F-test for testing the cross-sectional dependence when the factors may be known or unknown. The limiting distributions of the F-test statistics are derived when the cross-sectional dimension and the time-series dimension are both large. Our simulations recommend the use of the wild bootstrap F-test.
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Biography: Dr. Chihwa Kao got his PhD in economics from SUNY at Stony Brook in 1983. He has been with SU since 1985. Currently, he is professor of economics and the chair of the economics department. His research interests are panel time series, spatial econometrics and financial econometrics.
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February 8
Wednesday - 1:00 PM
4-201 Center for Science and Technology
Prof. Lixin Shen |
Speaker: Prof. Lixin Shen, Professor of Mathematics, Syracuse University
Title: Proximity Algorithms for Total - Variation Bases Image Models
Abstract: In this talk we will study the total-variation based image models via proximity operators. We view total-variation as the composition of a convex function ($\ell^1$-norm or $\ell^2$-norm) with the first order difference operator. A characterization of the solutions to the total-variation based image model is given in terms of the proximity operators of the $\ell^1$-norm and $\ell^2$-norm which have explicit expressions. The characterization naturally leads to a fixed-point algorithm for computing a solution of the model.
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Biography: Professor Lixin Shen received the B.S. and M.S. degrees from Peking University, Beijing, China, in 1987 and 1990, respectively, and the Ph.D. degree from Sun Yat-sen University, Guangzhou, China, in 1996, all in mathematics. He is currently an Associate Professor with the Department of Mathematics, Syracuse University, Syracuse, NY. His current research interests include multiscale analysis and their application in mathematical imaging processing. |
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February 22
Wednesday - 1:00 PM
4-201 Center for Science and Technology
Prof. Shamik Sengupta
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Speaker: Dr. Shamik Sengupta, Assistant Professor, Dept. of Mathematics and Computer Science, John Jay College of Criminal Justice, City University of New York
Title: Vulnerabilities and Survivability in Cognitive Radio Networks: An Inter-disciplinary Approach
Abstract: With the recent proliferation of spectrum-dependent operations such as cellular communication, public safety, military tactical networks, wireless LANs etc., the wireless industry is experiencing a fast paradigm shift from static spectrum allocation to opportunistic dynamic spectrum access (DSA) and cognitive radio (CR) network based on DSA has become one of the prime foci in wireless networking.
While other aspects of cognitive radio networks have received significant attention in the recent time, research in the area of DSA network security is still in its nascence. The opportunistic and network aware real-time DSA nature of the system introduces entirely new classes of security threats and challenges in the newly proposed paradigm. Uncertainties in licensed user detection due to unknown signal characteristics, unreliable wireless medium and lack of common control channel coupled with presence of malicious agents make the cognitive radio network and the spectrum sensing highly vulnerable to various denial-of-service (DoS) threats in the hostile network environment. Unfortunately, due to the unique DSA-based communication paradigm, the traditional techniques fail to incorporate the emerging security issues in the CR networks and there is little understanding on how a CR network will operate so as to make the system feasible under various security threats.
In this research, we study the vulnerability challenges of cognitive radio networks under adversarial conditions and investigate mechanisms that aid survivability and self-coexistence of these networks. Since cognitive networks act in an autonomous, rational and intelligent manner through their sensing, learning, and adaptation capabilities just like the human societies, we seek models from human societies that can be used to mitigate vulnerabilities and maintain self-coexistence among legitimate CR networks. To address the challenges, we advance ideas from game theory, behavioral model, network forensics and cognitive radio to optimize decisions under uncertainty.
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Bio: Shamik Sengupta is an Assistant Professor in the Department of Mathematics and Computer Science, John Jay College of Criminal Justice of the City University of New York. Prof. Sengupta received his Ph.D. degree from the School of Electrical Engineering and Computer Science, University of Central Florida, Orlando in 2007. His research interests include cognitive radio, dynamic spectrum access, game theory and security in wireless networking. Prof. Sengupta served as the Vice-Chair of Mobile Wireless Network (MobIG) special interest group of the IEEE COMSOC Multimedia Communications Technical Committee. He is in the organizing and technical program committee of various IEEE conferences. He is the recipient of an IEEE Globecom 2008 best paper award.
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February 29
Wednesday - 1:00 PM
4-201 Center for Science and Technology
Prof. Ramesh Raina
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Speaker: Prof. Ramesh Raina, Associate Professor and Department Chair, Biology Department, Syracuse University
Title: Bioinformatics: Tools for Understanding Biology
Abstract: Advances over the past decade in genomic approaches such as transcriptomics and proteomics has generated huge amount of data that has potential to greatly enhance our understanding of functioning of biological processes. To realize the full potential of these large datasets, the next challenge is to develop computational tools and approaches (bioinformatics) to analyze, store, organize, archive, and visualize this data to draw biologically meaningful inferences. This will require: a) development of new algorithms and statistical tools to compare and identify novel and unique features or trends in large data sets, b) analyzing DNA and protein sequences to identify novel DNA elements and protein domains with novel functions, c) identifying structural or functional relationships among proteins, and d) developing of tools for efficient access and management of different types of information. This seminar will discuss some underlying fundamental principles of biology relevant to bioinformatics and some examples of biological questions that can be addressed using the tools of bioinformatics.
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Bio: Professor Ramesh Raina received B.S. (Chemistry Honors) and M.S. (Biochemistry) degrees from Banaras Hindu University, Varanasi, India in 1982 and 1985 respectively, and the Ph.D. (Molecular Biology) degree from the Jawaharlal Nehru University, New Delhi, India in 1991. He is currently an Associate Professor with the Departments of Biology and Chemistry and Chair of the Department of Biology, Syracuse University, Syracuse, NY. His current research interests include plant molecular biology and functional genomics.
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March 7 - CANCELLED
Wednesday - 1:00 PM
4-201 Center for Science and Technology
Prof. Venu Govindaraju
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Speaker: Prof. Venu Govindaraju, Distinguished Professor of Computer Science and Engineering at the University of Buffalo, SUNY
Title: Making sense of All Things Handwritten from postal addresses to tablet notes
Abstract: The handwritten address interpretation system pioneered in our lab at UB is widely regarded as one of the key success stories in AI. It integrated the document processing steps of binarization, segmentation, recognition, and combination of classifiers with carefully handcrafted rules. Advances in machine learning (ML) in the past decade, made possible by the abundance of training data, storage, and processing power, have facilitated the development of principled approaches for many of the same modules.
In this talk, we will describe the ML adaptation of some of the modules originally deployed by the handwritten address interpretation system. We will present an MRF based method for discriminating handwritten and machine printed matter. The early success of document recognition systems, in the handwritten domain, was pivoted on constraining the size of the lexicons. Therefore, we will also detail an interactive model that made ‘dynamic’ use of the lexicon in building adaptive classifiers. Fusion of recognizers will then be investigated by statistical modeling of the dependencies in score vectors. We will conclude by presenting our recent foray into search applications, in addition to demonstrating the scalability of our methods in making sense of handwritten notes on tablets.
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Bio: Dr. Venu Govindaraju is a SUNY Distinguished Professor of Computer Science and Engineering at the University at Buffalo (SUNY Buffalo). He has authored over 325 scientific papers and supervised the doctoral dissertation of 25 students. His seminal work in handwriting recognition was at the core of the first handwritten address interpretation system used by the US Postal Service.
Dr. Govindaraju has won several awards for his scholarship including the IEEE Technical Achievement Award (2010). He is a Fellow of the AAAS, ACM, IAPR and IEEE. |
March 21
Wednesday - 1:00 PM
4-201 Center for Science and Technology
Prof. Haining Wang
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Speaker: Prof. Haining Wang, Wilson and Martha Stephens Associate Professor of Computer Science , College of William and Mary, Williamsburg, VA
Title: Energy Assurance for Server Systems
Abstract: Power management has become increasingly important for server systems. Numerous techniques have been proposed and developed to optimize server power consumption and achieve energy proportional computing. However, the security perspective of server power management has not yet been studied. In this talk, we will investigate energy attacks, a new type of malicious exploits on server systems. Targeting solely at abusing server power consumption, energy attacks exhibit very different attacking behaviors and victim symptoms from conventional cyberspace attacks. First, we will unveil that today’s server systems with improved power saving technologies are more vulnerable to energy attacks. Then, we will demonstrate a realistic energy attack on a server system. Finally, we will highlight the difficulties in defending against energy attacks, and we propose an initiative defense scheme to meet the challenges and evaluate its effectiveness.
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Biography: Haining Wang received his Ph.D. in Computer Science and Engineering from the University of Michigan at Ann Arbor in 2003. He is a Wilson and Martha Stephens Associate Professor of Computer Science at the College of William and Mary, Williamsburg, VA. His research interests lie in the area of security, networking systems, and cloud computing. He is a senior member of IEEE.
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March 28
Wednesday - 1:00 PM
4-201 Center for Science and Technology
Prof. Dongyan Xu
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Speaker: Prof. Dongyan Xu, Associate Professor, Department of Computer Science and CERIAS, Purdue University
Title: Towards Data Structure-Centric Memory Forensics
Abstract: Uncovering data structure instances of interest from memory images is an important capability in computer forensics. In this talk, I will present our recent efforts towards systematic data structure recognition for memory forensics. More specifically, I will first introduce SigGraph, a tool that generates non-isomorphic signatures for data structures in a program. Each signature is a graph rooted at a subject data structure with its edges reflecting the points-to relations with other data structures. Our experiments with a range of Linux OS kernels show that SigGraph-based signatures achieve high accuracy in recognizing kernel data structure instances. Second, I will present DIMSUM, a probabilistic inference-based tool to enable the recognition of data structure instances -- without any memory mapping information. I will report DIMSUM evaluation results with a number of real-world applications on Linux and Android platforms.
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Biography: Dongyan Xu is an associate professor in the Computer Science Department at Purdue University. His research focuses on the development of advanced virtualization technologies for cyber security and for cloud computing. He has also made early contribution to the area of peer-to-peer media streaming. He received an NSF CAREER Award in 2006 for his research towards virtual infrastructures on shared distributed platforms. His paper on optimizing virtual machine networking performance received the Paper of Distinction recognition at the 2011 ACM Symposium on Cloud Computing. His website is at http://www.cs.purdue.edu/homes/dxu/.
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April 4
Wednesday - 1:00 PM
4-201 Center for Science
and Technology
Aleksandra (Saška) Mojsilović
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Speaker: Aleksandra (Saška) Mojsilović, Manager, Predictive Modeling & Optimization Business Analytics & Mathematical Sciences, IBM Research, Yorktown Heights, NY
Title: The Science of Running Business: Signal Processing Applications in Business Analytics
Abstract: Analytics has been used in business since the late 19th century. Frederick Winslow Taylor conducted his time studies to scientifically determine the optimal way to perform a job. Henry Ford measured pacing of the car assembly line. Business analytics has gained focus with the “computer-era” of late 1960’s, which spurred the development of enterprise resource planning (ERP) systems, data warehouses, business intelligence (BI), and a variety of other hardware and software tools and applications.
Today, the automation of business processes and the explosion of data and signals, along with ample computation to process them, are driving a new generation of business analytics applications. In this new age, foreword-looking companies are beginning to invest in advanced predictive and prescriptive analytics as an aid in answering challenging questions, making better informed decisions and running business more efficiently and effectively.
Business analytics is a broad umbrella entailing many research problems and application areas, such as demand forecasting and conditioning, resource capacity planning, workforce planning, salesforce modeling/optimization, revenue forecasting, customer/product analytics and enterprise recommender systems. The complexity of these problems and the underlying business data often call for the application of novel signal processing methodologies. In our department, we are increasingly directing our focus on developing models and techniques to address such business problems.
In this talk we will provide an overview of this interesting new area of research, and then hone in on several applications and underlying signal processing methodologies.
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About the Speaker: Aleksandra (Saška) Mojsilovic manages the Predictive Modeling and Optimization group at the IBM T. J. Watson Research Center in Yorktown Heights, New York. The mission of the group is to conduct leading-edge research in signal processing, machine learning and applied mathematics, and to solve challenging problems in business analytics and decision support for IBM and its clients.
Aleksandra received her PhD in Electrical Engineering from the University of Belgrade in 1997, and has worked at Bell Laboratories (1998-2000) and IBM Research (2000-present). Her main research interests include multidimensional signal processing, pattern recognition, computer vision and machine learning.
Aleksandra is the author of over 60 publications, books and book chapters, and holds fourteen patents. She received a number of awards for her work, including the IEEE Young Author Best Paper Award, European Conference on Computer Vision Best Paper Award, INFORMS Wagner Prize, IBM Market Intelligence Award and IBM Outstanding Technical Achievement Award. She served as an Associate Editor of the IEEE Transactions on Signal Processing, IEEE Transactions on Image Processing, IEEE Signal Processing Magazine and Editorial Board for International Journal of Information Systems in the Services Sector. She is a member of MENSA, INFORMS and a Senior Member of the IEEE.
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April 18
Wednesday - 1:00 PM
4-201 Center for Science and Technology
Mehmet Yanilmaz
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Speaker: Mehmet Yanilmaz, Myra Trading, Chicago, IL
Title: An Engineering Perspective for Algorithmic Trading: strategies, tactics, algorithms, IT infrastructure
Abstract: Algorithmic trading systems are essentially asynchronous feedback controls systems. This talk focuses on devising trading systems by using undergraduate and master’s level engineering and computer science background. The application of the following knowledge in trading will be covered during this talk: digital signal processing, discrete control system design, probability and statistics, data structures, event-management and object oriented design. Practical examples of trading system applications will be discussed.
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Biography: Mehmet Yanilmaz is partner at Myra Trading, a proprietary algorithmic trading firm based in Chicago; president of Navus, a financial strategy and productivity advisory firm based in Chicago; associate of the Institute for Financial Markets (IFM), based in Washington, D.C.
At Myra Trading, Mehmet is responsible for devising the trading strategies and the IT infrastructure of the firm. For IFM, Mehmet designed and has been delivering courses on algorithmic trading since 2007, primarily in New York and Chicago.
Mehmet has advised attorneys at divisions of enforcements of U.S. Securities and Exchange Commission and Commodity Futures Trading Commission on market manipulation and fraud by using algorithmic and high frequency trading.
Mehmet has advised Istanbul Stock Exchange (ISE) for ISE’s new matching engine and algorithmic trading technologies, market microstructure improvements, strategies for increasing volumes and liquidities at ISE’s markets.
Before focusing on finance, Mehmet delivered through Navus solutions in business strategy, engineering, IT and manufacturing. Clients included Baxter Healthcare, Motorola, Intel, AT&T, International Truck & Engine (Navistar), Caterpillar, Ford Motor Company. Mehmet’s solutions have been implemented in Brazil, China, Japan, Mexico, Puerto Rico, Singapore, Turkey and USA. Mehmet advised the COB of Istanbul Chamber of Industry of competitiveness and productivity of Turkish manufacturing sector.
Mehmet served as faculty of Electrical Engineering, Industrial Engineering, Computer Science and Manufacturing Management (Kellogg School of Management) at Northwestern University; Computer Science at the University of Chicago; Financial Engineering at Bogazici University.
Mehmet received his B.S in Electrical Engineering from Bogazici University, and M.S. in Computer Engineering and Ph.D. in Electrical Engineering, both from Syracuse University. |
April 25
Wednesday - 1:00 PM
4-201 Center for Science and Technology
Prof. Tie Liu
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Speaker: Prof. Tie Liu, Assistant Professor, Dept. of Electrical & Computer Engineering, Texas A&M University
Title: Symmetrical Multilevel Diversity Coding and Subset Entropy Inequalities
Abstract: Symmetrical multilevel diversity coding is a classical coding problem for distributed storage introduced by Roche (1992) and Yeung (1995). In this setting, a simple separate coding strategy known as superposition coding was shown to be optimal in terms of achieving the minimum sum rate (Roche, Yeung, and Hau, 1997) and the entire admissible rate region (Yeung and Zhang, 1999) of the problem.
In this talk, we examine the proofs of the optimality and their connections to various (old and new) subset entropy inequalities. Through these connections, we extend the optimality of superposition coding to the case with an additional all-access encoder or subject to an additional secrecy constraint.
Bio: Tie Liu received his B.S. (1998) and M.S. (2000) degrees, both in Electrical Engineering, from Tsinghua University, Beijing, China and a second M.S. degree in Mathematics (2004) and Ph.D. degree in Electrical and Computer Engineering (2006) from the University of Illinois at Urbana-Champaign. Since August 2006 he has been with Texas A&M University, where he is currently an Assistant Professor with the Department of Electrical and Computer Engineering. His primary research interest is in information theory, with applications drawn from wireless communication, network coding, and cryptography.
Dr. Liu is a recipient of the M. E. Van Valkenburg Graduate Research Award (2006) from the University of Illinois at Urbana-Champaign and the Faculty Early Career Development (CAREER) Award (2009) from the National Science Foundation.
Co-sponsored by IEEE AES/COM/SP Syracuse Chapter.
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May 2
Wednesday - 1:00 PM
4-201 Center for Science and Technology
Prof. H. T. Kung
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Speaker: H. T. Kung, Harvard School of Engineering and Applied Sciences, William H. Gates Professor of Computer Science and Electrical Engineering
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