| 2009-05-06 | | Time: noon - 1pm. Location: C4 Lab (DHE 244). Speaker: Randy Smith. Abstract— New product development involves people with different backgrounds. Designers,
engineers, and consumers all have different design criteria, and these criteria interact. Early concepts
evolve in this kind of collaborative context, and there is a need for dynamic visualization of the
interaction between design shape and other shaperelated
design criteria. In this paper, a Morphable
Model is defined from simplified representations of suitably chosen real cars, providing a continuous
shape space to navigate, manipulate and visualize. Physical properties and consumerprovided
scores for the real cars (such as ‘weight’ and ‘sportiness’) are estimated for new designs across the
shape space. This coupling allows one to manipulate the shape directly while reviewing the impact on
estimated criteria, or conversely, to manipulate the criterial values of the current design to produce a
new shape with more desirable attributes. Short BIO: Randall C. Smith was most recently a Technical Fellow at General Motors R&D until his early
retirement in December 2008. While at GM he lead research projects in visualization and virtual
reality, receiving the company's highest research award, among others, for this work. Most recently he
is exploring the development and application of a shape space for cars and trucks, resulting in two
patents.
He received a B.S. degree in Computer Science and Cognitive Psychology from Yale in 1976, and
formed a start-up company in Silicon Valley with one of his Professors--designing and building a
content-addressable memory board for an early personal computer.
In 1979, he started worked in sensor-based robotics at the Artificial Intelligence Center at Stanford
Research Institute, where he built a distributed robotic assembly workstation using visual, force, and
auditory sensing. He later developed one of the first offline robot programming, task planning, and
graphic simulation systems for it, using the first SGI workstation. He is cited for seminal work in the
development of the SLAM algorithm (Simultaneous Localization and Mapping) for mobile robot
navigation, which estimates 6 degree-of-freedom spatial uncertainty across numerous coordinate
frames during map building.
He has given numerous invited talks and panels in VR and was a member from 2003-2006 of the
Executive Committee of the IEEE Visualization and and Graphics Technical Committee (VGTC). | | | | 2009-04-06 | | 11am - Noon @ C4 Lab, Speaker: Dr. Abdelmounaam Rezgui ----
Abstract - In this talk, I will present service-oriented sensor-actuator networks (SOSANETs), a
new paradigm for building the next generation of customizable, open, interoperable sensoractuator
networks. In SOSANETs, nodes expose their capabilities to applications in the form of
services. Each node advertises a service profile that consists of a set of services (i.e., sensing
and actuation capabilities) that it provides and the quality of service (QoS) parameters
associated with those services (delay, accuracy, freshness, etc.). SOSANETs provide the
benefits of application-specific, retasked, and generic sensor networks while avoiding their
respective limitations. I will focus on a particular component of SOSANETs: their routing layer
mRACER (Reliable Adaptive serviCe-driven Efficient Routing). mRACER uses an efficient
service-aware routing approach that aggressively reduces downstream traffic by translating
service profiles into efficient paths. To support QoS, mRACER dynamically adapts each node's
routing behavior and service profile according to the current context of that node, i.e., number of
pending queries and number and type of packets to be routed. Finally, mRACER achieves high
end-to-end reliability through a scalable reputation-based approach in which each node is able
to locally estimate the next hop of the most reliable path to the sink. To evaluate the proposed
service-oriented architecture, I implemented TinySOA, a prototype SOSANET built on top of
TinyOS with mRACER as its routing mechanism. TinySOA is designed as a set of layers with a
loose interaction model that enables several cross-layer optimization options. I will present an
evaluation of TinySOA that includes a comparison with TinyDB. The empirical results show that
TinySOA achieves significant improvements on many aspects including energy consumption,
scalability, reliability and response time.
Biography - Abdelmounaam Rezgui is a visiting assistant professor at the University of
Pittsburgh. He received his PhD in Computer Science from Virginia Tech and his MS in
Computer Science from Purdue University. His research interests include the areas of serviceoriented
architectures, efficient routing and query optimization in sensor networks, trust
management in Web services, and ontologies and semantic data integration. Before joining Pitt,
Abdemounaam was a research scientist in the PitLab at Virginia Tech where he worked on the
development of semantic Web technologies that were part of the NASA-funded Semantically
Enabled Scientific Data Integration (SESDI) project. A demo that included his work recently won
the best demo award in the 7th International Semantic Web Conference (ICSW). During his
work at the PitLab, he also was a key member of the multi-million NSF GEON project. His work
in GEON led to the development of DIA, a Web-based system that uses semantic Web
technologies to discover, integrate, and analyze large scientific heterogeneous data sets.
Abdelmounaam authored or coauthored over 40 papers in top journals and conferences
including: IEEE Transactions on Parallel and Distributed Systems, IEEE Communications, IEEE
Internet Computing, IEEE Security and Privacy, and IEEE MASS. He served or serves in the
technical program committee of several conferences including: IEEE LCN’09 (Intl. Conf. on
Local Computer Networks), IEEE ICSOC’08 (Intl. Conf. on Service Oriented Computing), and
IEEE APSCC’08 (Asia-Pacific Services Computing Conference). | | | | 2009-04-03 | | 11am - noon @ C4 Lab, Speaker: Dr. Yu Zhang ---- Abstract: In social environments, people interact with each other and form different societies. Previous research in modeling theory of agents and society has taken singly a point of view of society or agent. While the single societal view mainly concentrates on the centralist, static approach to organizational design and this limits system dynamics; on the other extreme, the single agent view simply treats the systems as a collection of individual agents without considering how their decisions impact each other. In this talk, I will introduce a new multi-agent architecture called CASE. CASE aims to model the ?meso-view? of multi-agent interaction by capturing both the ?societal view? and the ?agent view?. We provide a computational decision model of the highly cognitive process wherein an individual agent's decision-making that sometimes follows intuition and bounded rationality. We also study the emergence of social conventions in settings where every agent may interact either with every other agent or with nearest neighbors, according to some regular network topology. A serial of experiments were conducted to evaluate the concept, the model and their impacts on the evolution of the social systems.
Bio: Dr. Yu Zhang received her Ph.D. in Computer Science at Texas A&M University in 2005. She is currently an assistant professor in the Department of Computer Science at Trinity University and the director of the Laboratory for Distributed Intelligent Agent Systems. Dr. Zhang's research falls within Agent-Based Modeling and Simulation. Her research is currently supported by two NSF funds. She is in the editorial board for five journals including The Transaction of Simulation, and in program committee for over 10 conferences and technical groups, such as Autonomous Agents and Multi-Agent Systems. She also has regularly reviewed proposals for federal agencies. She is in the organizing committee of IEEE SMC?09 and SCS SpringSim?09. She is the program chair of IEEE Women In Engineering Central Texas Chapter and IEEE Systems, Man and Cybernetics Society CTC, and the vice chair of IEEE Computer Society CTC. She received 2008 Trinity Distinguish Junior Faculty Award, 2007 IEEE CTC Service Award, and Best Paper Award of 2005 International Conference on Knowledge-Based & Intelligent Information & Engineering Systems.
| | | | 2009-04-01 | | 11am - noon @ C4 Lab, Speaker: Dr. Hyuckchul Jung ---- Abstract: Computers are playing more and more significant roles for humans in critical domains such as military operations, disaster rescue and healthcare. In order to enhance the interaction between human and computer, there is a great need to develop computer systems that can act autonomously yet still collaborate with humans. This talk will focus on how such computer systems can acquire knowledge to help people manage their everyday tasks. I will present a novel collaborative system that not only learns complex tasks by interacting with and being advised by humans but also improves its learned knowledge through interactive execution. This system was developed as part of the DARPA CALO (Cognitive Agents that Learn and Organize) project and has been shown to be very effective in systematic evaluation. I will also present my research on human-robot teamwork to facilitate coordination among mixed teams of humans and heterogeneous robots. Finally, I will describe future research plans building on my current and past work.
Biography: Hyuckchul Jung is a research scientist at the Institute for Human and Machine Cognition, a research institute of the Florida University System. He received his Ph.D. in Computer Science from the University of Southern California. His research interests include task learning, intelligent user interfaces, mixed-initiative interaction, decision theoretic reasoning, human-machine teamwork, and multi-agent collaboration. He has published over 30 technical papers, and has served as a panelist for NSF, as a reviewer for prominent journals and as a program committee member for major conferences such as AAAI, AAMAS and IAT. Notably, his recent research work on task learning received an outstanding paper award at the National Conference on Artificial Intelligence (AAAI) in 2007. More information can be found at http://www.ihmc.us/~hjung.
| | | | 2009-03-27 | | 11am - noon @ C4 Lab, Speaker: Dr. Sccot Wood ----- ABSTRACT: Evaluating systems with real users doing real tasks is the most effective means of developing highly usable systems, but this technique is often too expensive and time-consuming, especially when highly skilled users are the target of a novel application. However, the consequence of not finding critical design flaws early enough in development is often project failure. An alternative to usability testing is system analysis using psychologically-based engineering models of human performance, the most mature of which is GOMS. GLEAN (GOMS Language Evaluation and Analysis) is a user interface design and development tool that supports creation and analysis of computational GOMS-based models of human-computer interaction. GLEAN is also used for basic research into the nature of human error, error-tolerant system design, and team interaction in hazardous environments. In this talk I will discuss my research into computational models of human error, a technique for predicting which aspects of a user interface are likely to induce human error, and an experiment that demonstrates the utility of such an approach.
BRIEF BIOGRAPHY: Scott D. Wood is President of Highly Human Software and an Instructor in Computer Science and Engineering. Prior to founding Highly Human Software, Dr. Wood was a Senior Scientist with Soar Technology for 5 years where he was Vice-President of Strategy and led several DoD and NASA projects applying intelligent agent technology to human-system interaction. In addition, he spent four years in the U.S. Army attached to 5th Special Forces Group and other special operations units. He earned a B.S. in Computer Science (1990) from Tulane University, and M.S. (1994) and Ph.D. (2000) degrees in Computer Science and Engineering from the University of Michigan, Ann Arbor.
| | | | 2009-03-25 | | 11am-noon @ C4 lab (DHE 244) Speaker: Dr. Mohamed A. Sharaf Abstract:
=========
In our increasingly pervasive environment, users, mobile or stationary,
have performance expectations which define the success of any application.
Mostly, such applications are data-intensive which makes the timely processing
of queries very challenging, particularly given the various resource
constraints of the
different environments such as limitations in power and wireless
communication as in mobile and sensor applications, or CPU and memory
as in data stream processing systems.
In this talk, I will highlight few of the resource allocation
challenges in the three main stages of data management in pervasive
environments, namely: (1) data acquisition, (2) data processing, and
(3) data dissemination. Further, I will present several solutions
which address the efficient allocation of those resources. In
particular, I will discuss the TiNA scheme for energy-aware data
processing in sensor networks. I will also present my work on query
scheduling in both data stream management systems and web database
systems within the context of the AQSIOS project. Moreover, I will
talk about the TraQIOS scheme for optimizing I/O-intensive
applications in shared data centers. Finally, I will describe my
recent work on location-aware data broadcasting services in mobile and
wireless networks. Bio:
=====
Dr. Sharaf is currently a Postdoctoral Research Fellow at the
University of Toronto. He received his Ph.D. in Computer Science from
the University of Pittsburgh in 2007. He was the recipient of the
Taulbee Award for Excellence in Computer Science in 2002 and a
two-time winner of the Andrew Mellon Predoctoral Fellowship in 2003
and 2004. In 2008, he was awarded a two-year post-doctoral fellowship
from the Ontario Ministry of Research and Innovation (MRI).
Dr. Sharaf's research interests lie in the general area of Data
Management Systems with a focus on developing user-centric data
processing techniques in data stream management systems, sensor data
processing, mobile and pervasive data management, data warehousing,
and Web databases. His work has appeared in top database journals and
conferences, including the TODS, MONET, and VLDB journals and the
SIGMOD, VLDB, and ICDE conferences.
Dr. Sharaf has served on several program committees including ACM
GIS'08, ICDCS'09, ACM MobiDE'09, and IEEE ICDE'10. He is currently the
demos track co-chair for the MDM'10 conference.
| | | | 2009-03-20 | | 11am - noon@DHE 244 (C4 Lab)
ABSTRACT: Service-oriented computing (SOC) has emerged as the eminent market environment for sharing and reusing service-centric capabilities. The underpinning for an organization’s use of SOC techniques is the ability to discover and compose Web services. Although industry approaches to composition have a strong notion of business processes, these approaches largely use syntactic descriptions. As such composition is limited since the true functionality of ambiguous service operations cannot be inferred. Alternatively, academia uses semantic approaches to disambiguate services, but, at the same time, most of these approaches neglect the process rigor needed for complex compositions. In this talk I will present a generalized semantics-based technique for automatic service composition that combines the rigor of process-oriented composition with the descriptiveness of semantics. I will present the language USDL (Universal Service-Semantics Description Language) designed by my research group for formally describing the semantics of web-services. Then I will present a generalized approach to composition that extends the common practice of linearly linked services by introducing the use of a conditional directed acyclic graph (DAG) where complex interactions, containing control flow, information flow and pre/post conditions, are effectively represented. Furthermore, the composition can be represented semantically as OWL-S documents. Our contributions are applied for automatic workflow generation in context of the currently important bioinformatics domain.
BIO: Srividya Kona is a Visiting Assistant Professor at the Department of Computer Science in Georgetown University. Her research interests include Service-Oriented Computing, Semantic Web, Software Engineering, Logic Programming, Programming Languages, Automated Reasoning, and Bioinformatics. She received her Ph.D in Computer Science from the University of Texas at Dallas. She received her M.S. in Computer Science from Texas Tech Univ., Lubbock in 2002 and her B. Tech. in Computer Science from NIT (previously known as REC), Warangal, India in 1999. She has over 5 years of industry experience. She worked as a software developer in SAP Labs (1999-2000) and Tyler Technologies (2001-2005). She was involved in the design and development of a Web service description language called USDL (Universal Service-Semantics Description Language) which won the best paper award at the European Conference on Web Services (ECOWS) in 2005. | | | | 2009-03-18 | | Time&Location: 11am - noon @ C4 Lab, speaker: Dr. Supratik Mukhopadhyay Abstract: Service-based systems are increasingly being used for deploying large-scale distributed applications in mission-critical environments. In a service-based system, applications are built by combining services, which are platform-independent components running on different hosts of a network. Prime concerns in such systems include, among others, adaptability to unforeseen situations (e.g., behaviors of services can be modified due to unforeseen events such as node failures or distributed denial of service attacks), situation-awareness (in order to detect changes in the environment and adapt accordingly), reliability, and security.
In this talk, I will present a formal approach for developing adaptable, situation-aware, secure service-based distributed systems. To this end, I will first present a process calculus-based programming model for such systems. The operational semantics of the model combines external behaviors with internal computation for assessing the current situation and dynamically adapting and reacting to it. Continuation passing is used for handling asynchronous services. In order to declaratively specify properties of such systems, I will introduce an intuitionistic hybrid modal logic. The logic not only has modalities for expressing both temporal and spatial behavior, but also constructs for describing communication and knowledge, and atomic formulas for describing relations among variables. I will provide deduction-based algorithms for automatically synthesizing intelligent executable agents from declarative specifications in the logic. In order to deploy the synthesized agents wirelessly across the network, we have developed, jointly with the Naval Research Laboratory, the Secure Infrastructure for Networked Systems (SINS), a middleware platform built on the top of Johns Hopkins University’s Spread group communication toolkit. In the last part of the talk, we provide a demo of a reliable distributed system for soil and water management in agriculture developed using the approach mentioned above and deployed on the SINS platform. This research has been partially supported by grants from the Office of Naval Research and the Naval Research Laboratory.
Biography: Dr. Supratik Mukhopadhyay is an Assistant Professor in the Department of Computer Science at Utah State University. His research interests are in the areas of distributed, service-oriented, and enterprise computing, software engineering, sensor networks, and programming languages. In the past few years, he has received more than $ 2 millions in research grants in these areas from agencies like the NSF, ONR, NRL, and industry. Dr. Mukhopadhyay did his doctoral research at the Max Planck Institute for Computer Science in Germany.
Webpage: http://www.cs.usu.edu/~supratik
| | | | 2009-03-16 | | Time&Location: 11am - noon @ C4 Lab Speaker: Dr. Ching-seh (Mike) Wu Abstract:
The supporting technology for web services has been widely studied mainly focusing on the standardization of service transactions and running a single web service. In dealing with complex and large-scale web service requests, there is a foreseeable bottleneck of supporting technology. The solution proposed in this research applies evolutionary computing techniques to automatically select optimal combinations of web service components from available component repositories. This process is illustrated with a computational simulation of component selection. This research proposed a new approach to web service composition in order to optimize the search of better service components for complex and large-scale systems, and then conducted a trade-off analysis of the conflicting between objectives in composing better web services, i.e. costing vs. reliability. The optimal results fitted the principle of natural evolution, which looks for the fittest survivor in multi-criteria assessment.
Biography:
Dr. Ching-seh (Mike) Wu is a faculty member of the Department of Computer Science and Engineering at Oakland University. He received his M.S. in Computer Science from U.S. Air Force Institute of Tech. in Dayton, OH and Ph.D. in Computer Science from Texas A&M University in College Station. His past 20 years software industrial experiences and academic research have been focused on Software Engineering, Software Project Management, Software Process Improvement, and Distributed Software Systems, over which time he was a software project manager, a faculty member of universities and a consultant for software companies and organizations. He has published more than 30 peer-reviewed papers in the above areas. He is a founding member of the Institute of Software Engineers in Dallas, Texas. He has been invited as a reviewer for IEEE Software Engineering Journal, Software Engineering Journal, Journal of Information System and Journal of Information Science and Engineering.
| | | | 2009-01-28 | | ABSTRACT: The reduction of power consumption during the deployment and operation of sensor networks has commonly been recognized as a key challenge. Many proposals have been put forth to save power by taking advantage of the inherent redundancies in sensor network’s operation by minimizing the number of agents active in answering a query at any point in time. The highest level of power saving can be obtained when approximate query results are acceptable and the selection of active agents takes into consideration the intensity and speed of the event being monitored. A larger number of agents can cooperate during high intensity periods to ensure accuracy, while a lower number of agents is sufficient during quiet periods. In this paper, we propose an approach for self-adaptive selective querying. We introduce a set of metrics that allow each data sink to gauge the level of activity in its environment and adjust its querying strategy and intensity accordingly. We show experimental results and discuss future plans.
BRIEF BIOGRAPHY: John Meyer is a Doctoral student at Oakland University under the supervision of Professor Mili. Before joining Oakland, he obtained Bachelors degrees in Electrical and Computer from the University of Michigan, Ann Arbor in 1981, and a Master of Science in Computer Science and Engineering from Oakland University in 2005. John's research interests are in Database, Query Optimization, and Sensor Networks. John is a member of ACM and IEEE.
| | | | 2008-11-19 | | Abstract:
Today, more and more high-performance real-time applications such as avionics and flight control, space shuttle systems, vehicles, and instrumentation in medical and emergency facilities, demand greatly increased computation capabilities from processors. Meanwhile, semiconductor manufacturing technologies keep scaling processors to smaller feature sizes. As a result, power density in processors becomes increasingly high. Due to the high power density, processors are prone to overheating, which affects not only reliability but also performance, power and cost of embedded systems. As such, thermal management becomes a prominent issue in system design. On the other hand, high-performance real-time applications demand increasingly stringent need for timing guarantees. As high-performance computing systems become more and more thermally-constrained, the issue of how to provide timing guarantees under the constraints of thermal behavior and thermal control mechanisms must be addressed.
In this talk, we will investigate timing-aware dynamic thermal management to address the above issue in different high-performance computing systems.
Bio:
Shengquan Wang received his B.S. degree in Mathematics from Anhui Normal University, China, in 1995, and his M.S. degree in Applied Mathematics from the Shanghai Jiao Tong University, China. He also received M.S. degree in Mathematics in 2000 and Ph.D. in Computer Science from Texas A&M University. He is currently Assistant Professor in the Department of Computer and Information Science at the University of Michigan-Dearborn. He is a recipient of the NSF CAREER award. His research interests include realtime computing and communication, security in computer networks and distributed systems, and wireless networks. | | | | 2008-10-29 | | Learning Computer Science through Intelligent Autonomous Robotics
Dr. CJ Chung
Lawrence Technological University
ABSTRACT: Computer science (CS) began to be established as a distinct academic discipline in the 1960s, with the creation of the first CS departments and degree programs. Since then, it has focused on algorithm design for non-mobile computational machines such as main-frames, workstations, servers, and personal computers. Recently, smaller and lower-cost hardware components have provided us with new possibilities in CS education through robotics. There are four key advantages to using robots for K through PhD education over traditional practices: (1) robotics is multi-disciplinary, (2) robots make abstract concepts concrete, (3) robotics is hands-on, and (4) students usually have higher motivation through robotics. Robots especially reinvigorate CS classrooms and excite students to learn traditional CS curricula. In addition, robot centered CS curriculum will introduce a wide variety of new areas that focuses on teaching knowledge-based artificial intelligence to the computational component of the robot. The classes will provide in-depth theory and practice in the development of perception, cognition, communication, and actuation/control algorithms that require self-learning/self-adaptation techniques since robots are situated in a real-world environment that is usually partially observable, unstructured, unpredictable, uncertain and dynamic.
As an example robotics platform that can be used for CS education, an L2Bot (Low-cost Laptop roBot) will be introduce and demonstrated. L2Bot is a low-cost, affordable, open, and modular robotics platform developed at Lawrence Tech since 2003. Laptop computer itself is the controller of the three wheeled robot. L2Bot is a vision-centered robot with a webcam as a main sensor. Any programming language can be used to control the robot. Some Java programs will be introduced to demonstrate basic functions of the robot. Based on experience with L2Bots, students will be able to further participate in various robot competitions such as RoboCup, IGVC, DARPA, RoboChamps, and Robofest Mini Urban Challenge.
BRIEF BIOGRAPHY: CJ Chung attended Hong-Ik University in Seoul, Korea, where he earned a B.S. degree. He received his Ph.D. in CS from Wayne State University in 1997. He received a fellowship to study abroad from the Electronics and Telecommunication Research Institute (ETRI), where he was a senior research scientist. At ETRI, he was involved in developing TDX switching systems that became the first CDMA system in the world. He also worked as a visiting researcher for Ericsson in Sweden from 1983 to 1984. His doctorial research was the development of a self-adaptive system motivated by cultural evolution process. He is founder and director of the annual World Robofest (www.robofest.net). He started believed-to-be the world first “Thanksgiving RoboParade” and “Mini Urban Challenge” in 2006. He is also a director of ARISE (Autonomous Robotics Institute for Students and Educators). He is an advisor of Lawrence Tech four-legged robot soccer and IGVC (Intelligent Ground Vehicle Competition) teams. He participated in DARPA Urban Challenge 2007 with Cybernet Co. in Ann Arbor. Team Cybernet was chosen as one of the 36 semi-finalists. Currently he is involved in a project “Robotics Power Use, Inventory and Logistical Management Evaluation” funded by TARDEC. He is a member of IEEE, ACM, and International Robot Olympiad Committee. For more information, visit his personal home page at http://qbx6.ltu.edu/chung
| | | | 2008-10-08 | | Abstract:Efficiently processing queries against very large graphs is an important research topic largely driven by emerging real world applications ranging from XML databases, GIS, web mining, social network analysis, ontologies,
and bioinformatics etc. In particular, graph reachability has attracted a lot of research attention as reachability queries are not only common on graph databases, but also serves as a fundamental operator for many other graph queries. The main idea behind answering reachability queries in graphs is to build indexes based on reachability labels. Essentially, each vertex in the graph is assigned certain labels such that the reachability between any two vertices can be determined by their labels. Several approaches have been proposed for building these reachability labels among them include interval labeling (tree cover) and 2-hop labeling. However, due to the large number of vertices in many real world graphs (some graphs can easily contain millions of vertices) the ｃomputational cost and (index) size of the labels using existing methods would prove too expensive to be practical. In this paper, we introduce a novel graph structure, referred to as path-tree, to help labeling very large graphs. The path-tree cover is a spanning subgraph of G in a tree shape. We demonstrate both ａnalytically and empirically that the effectiveness of our new approaches.
Short Bio:Ruoming Jin is an Assistant Professor in the Department of Computer Science at Kent State University, Ohio.
He received the BE and ME degrees in the computer engineering
from Beijing University of Aeronautics and Astronautics, China in 1996, 1999, respectively.
He received the MS degree in computer science from the University of Delaware in 2001 and
the PhD degree in computer science from the Ohio State University in 2005.
His research interest includes data mining, complex network analysis, graph databases and bioinformatics.
He has published over 50 research papers in peer-reviewed journals, conferences and workshops including SIGMOD, KDD, ICDE, ICDM, EDBT, TKDE, SIGMETRICS, etc. | | | | 2008-10-02 | Study Abroad in India - February 2009 | International Study
February 18 - March 14, 2009
IT & Engineering Two Weeks in India
•As part of a SECS undergraduate or graduate project class in February 2009, students will spend two weeks in Tamul, Nadu India studying at SRM University, Chennai (www.srmuniv.ac.in).
•In India students will:
-Attend technical lectures.
-Participate in a design or class project as part of a team of OU and SRM students.
-Tour High Tech engineering and IT facilities in the Chennai and Bengalore areas.
- Visit natural and human made vestiges including temples, monuments, wild animal preserves.
•Significant scholarships (GPA of 3.0 required) are available to assist with travel expenses. If interested, please provide the following application materials to Professor Mili ASAP for full consideration.
| | | | 2008-09-24 | | ABSTRACT: Evaluating systems with real users doing real tasks is the most effective means of
developing highly usable systems, but this technique is often too expensive and time-consuming,
especially when highly skilled users are the target of a novel application. However, the consequence
of not finding critical design flaws early enough in development is often project failure.
An alternative to usability testing is system analysis using psychologically-based engineering
models of human performance, the most mature of which is GOMS. GLEAN (GOMS Language Evaluation
and Analysis) is a user interface design and development tool that supports creation and analysis
of computational GOMS-based models of human-computer interaction. The GLEAN model simulates the
humans performing the tasks and produces usability metrics similar to those obtained in user
testing. Using simulated users enables fundamental design flaws to be detected, understood, and
corrected, in a way that is faster, cheaper, and more effective than any other known process.
This can produce a dramatic improvement in usability, especially of complex "high end" applications.
Because GLEAN.s virtual human is based on how real humans work and think, it suffers from the
same limitations of actual users. This allows us to easily detect cognitive overload conditions,
system inconsistencies, difficult or error-prone procedures, and system-usage requirements that
may not be humanly possible. GLEAN is also used for basic research into the nature of human error,
error-tolerant system design, and team interaction in hazardous environments. In this talk I will
discuss my research into computational models of human error, the GLEAN tool, and an overview of
the Small Business Innovative Research program.
BRIEF BIOGRAPHY: Scott D. Wood is President of Highly Human Software and an Instructor in Computer
Science and Engineering. Prior to founding Highly Human Software, Dr. Wood was a Senior Scientist
with Soar Technology for 5 years where he was Vice-President of Strategy and led several DoD and
NASA projects applying intelligent agent technology to human-system interaction. Dr. Wood has
over fifteen years of research and industry experience in the areas of software development,
e-business consulting, cognitive modeling, and human-computer interaction. His doctoral research
included extending GOMS (Goals, Operators, Methods, Selection Rules) modeling to allow for human
error, developing techniques for predicting where human errors would occur in an interface, and
testing those techniques by applying them to web applications. Dr. Wood also has extensive experience
developing human-performance models using the EPIC cognitive architecture, optimizing workflows
and interface usability through task analysis, and in designing web solutions for e-business applications.
In addition, he spent four years in the U.S. Army attached to 5th Special Forces Group and other
special operations units. He earned a B.S. in Computer Science (1990) from Tulane University,
and M.S. (1994) and Ph.D. (2000) degrees in Computer Science and Engineering from the University
of Michigan, Ann Arbor.
| | | | 2008-06-03 | | Effective July 1,the MS program in Embedded Systems will be administered by Electrical & Computer Engineering Department. All current and prospective students should contact ECE Department for admission/advising. | | | | 2008-04-03 | Time Aggregated Graphs: Modeling spatio-temporal | 11:00am C4 Lab (DHE 244)
Abstract: The underlying data of interest for many significant applications such as transportation networks is structured as a spatio-temporal network. With an increasing availability of data collected over such networks, it becomes important to utilize this data in improving the quality of analysis and query processing in such networks, suggesting a need for spatio-temporal network databases. In a spatio-temporal network, the network parameter values and the network topology can change with time, requiring them to be modeled as time-variant graphs. Conventional graph algorithms cannot be easily applied to the snapshot graphs at discrete time instants to evaluate frequent queries without accounting for relationships among snapshots.
In this talk I will present Time Aggregated Graph, a model for representing spatio-temporal networks, that allows the modeling of time-variance of network parameters and topology. The model avoids the replication of the network for every instant of time, making it less memory expensive compared to existing representations. I will also present a case study of this model using routing algorithms on transportation networks. In this context, I will discuss the alternative semantics that the common queries can assume, given the time variance of the networks. I will also present the analytical and experimental analysis of the model and the algorithms in comparison with existing approaches.
Bio: Betsy George is a PhD candidate in Computer Science at the University of Minnesota, Minneapolis, advised by Professor Shashi Shekhar. Her research interests lie in the area of spatial databases with emphasis on spatio-temporal networks. | | | | 2008-04-02 | Graduate research at an iSchool | 11:00am C4 Lab (DHE 244)
Speaker: Sue Kase, Pennsylvania State University.
Abstract: The College of Information Sciences and Technology (IST) is a new college at the Pennsylvania State University. The college offers multidisciplinary programs at the graduate and undergraduate level, with courses designed to provide students with a broad knowledge base and the skills needed to address complex problems through technology. Although not departmentalized, IST faculty and graduate students form a diverse group of researchers (e.g., human-computer interaction, artificial intelligence, organizational informatics, cyber security and privacy, geographic information systems, information and image fusion, information policy, social networks) working in collaboration to investigate information-centric research questions.
IST PhD students often engage in projects spanning multiple labs and research domains. In this talk I will summarize three funded projects. The first is a light-weight agent-construction environment, called dTank, used as an experimentation and prototyping tool for adversarial behavior implementations. The second, Neo Nexus, is a 3-year community-oriented participatory design project with the goal of assisting community groups sustain technology learning and development in their organizations. Neo Nexus utilizes a patterns approach to identify organizational learning and IT skills learning in response to specific organizational challenges. The third is a computational modeling project investigating the effects of stress and task appraisal on human cognitive performance. A high-performance computing and evolutionary algorithm approach was developed to address the stochastic global optimization problem of fitting a complex cognitive model to individual subject data. A prototype of this optimization approach integrates the ACT-R cognitive architecture and a model of the serial subtraction task with a parallel implementation of a genetic algorithm. The optimization system is running on a supercluster at the National Center for Supercomputing Applications.
Bio: Sue Kase is a Ph.D. candidate in the College of Information Sciences and Technology at the Pennsylvania State University. Before enrolling in the Ph.D. program, she received a M.S. degree in Computer Science and worked as a computer programmer for several companies within the PA, NJ, and NY area. More recently, she was an instructor in the Department of Computer Science and Engineering at Penn State for six years teaching both major and non-major courses. During summer sessions she participates as a mentor in diversity programs such as Women in Sciences and Engineering, Women in Sciences and Technology, middle- and high-school STEM workshops, and NSF Broadening Participation in Computing seminars. She has also attended the ACT-R Cognitive Architecture Summer School and the San Diego Supercomputer Center Summer Institute. In addition to cognitive modeling and human computer interaction, her interests include community informatics, information visualization, and high-performance computing. | | | | 2008-03-31 | Automotive Electronics Product Design and Development at Johnson Controls, Inc. | March 31st (Monday) 11:00am C4 Lab (DHE 244)
Speaker: Jeff Golden
Abstract: The seminar discussion will open with a general introduction to Johnson Controls Inc. (JCI). It will then focus on JCI's Automotive Electronics product portfolio and key aspects related to the creation of such products including involved/emerging technologies, design and development methodologies, engineering disciplines utilized, noteworthy challenges, etc. Further attention will be given to the software intensive Embedded Systems based Telematics-Connectivity product line.
Brief Bio: Jeff Golden is currently the Engineering Group Manager for Johnson Controls Inc., Automotive Experience Division, Advanced Electronics Development organization. Jeff has more than 30 years of progressively responsible experience in Electrical and Electronics Engineering and Technology Resource management. Prior to joining Johnson Controls, Jeff held various Engineering and Technical Leadership positions for Visteon, Ford Motor, and General Dynamics Corporations. Jeff holds a Bachelor of Science Electrical Engineering degree from Lawrence Technological University and a Master of Science Electrical Engineering degree from Wayne State University. | | | | 2008-03-30 | | People who received degrees in computer and information sciences (CS) in recent years are more likely to be employed in business and industry and to be working full-time than those who pursued several other majors. According to a recent NSF InfoBrief, 82 percent of CS majors who received bachelor's degrees in 2003, 2004, and 2005 were employed in business and industry, and 91 percent (along with engineering majors) had full-time jobs in April 2006. Among those with master's degrees, 76 percent were working in business and industry, and 93 percent were working full-time. The NSF InfoBrief also shows that CS graduates with bachelor's degrees had a median salary of $45,000, which was tied for second with health majors. CS graduates with master's degree made $65,000, tying engineering majors for first. | | | | 2008-03-28 | Design Strategy for Knowledge Base Formation to Automate a Course Map Creation | 11 AM @ C4 Lab (DHE 244) Speaker: Susan Lukose, University of Mississippi. ABSTRACT: In recent times, e-learning has become a popular alternative for the traditional classroom. The process of creating an online course which includes identifying the conceptual map, creating appropriate text, all the required multimedia object material and set up the tools for evaluating student performance is a very tedious and time consuming process. The Institute of Advanced Education in Geospatial Sciences (IAEGS) is a project initially funded by NASA, for developing state-of-the-art online courses in the field of Geospatial Sciences. IAEGS has already developed about 28 courses which are offered to any interested parties. According to them the process of creating a conceptual map (a detailed outline of the course) could take up to 2 months while entire course creation process could takes between 6 to 8 months. As the popularity of online learning is growing every day there is a need to create these courses quicker and at a lower cost. Automation of the course creation process could help to achieve this. An acceptable approach is in the form of a decision support system that guides the author during the process of concept and material selection. The goal of this research is to develop design strategies to create a knowledge base for a decision support system that could guide an author in these tasks. We propose to achieve this by automatically creating a ranked ontology from the domain specific glossaries using natural language processing techniques, statistical methods, graph theory and heuristic approaches. The application domain, "Remote Sensing and Photogrammetry”, is selected for the implementation and the effectiveness of automating this process will be analyzed. | | | | 2008-03-27 | Induction of Multiclass Multifeature Split Decision Trees from Distributed Data | 11 am @ C4 lab (DHE 244) Speaker: Dr. Nilesh Patel. ABSTRACT: The decision tree-based classification is a popular approach for pattern recognition and data mining. Most decision tree induction methods assume training data being present at one central location. Given the growth in distributed databases at geographically dispersed locations, the methods for decision tree induction in distributed settings are gaining importance. This paper describes one such method
that generates compact trees using multifeature splits in place of single feature split decision trees generated by most existing methods for distributed data. Our method is based on Fisher's linear discriminant function, and is capable of dealing with multiple classes in the data. For homogeneously distributed data, the decision trees produced by our method are identical to decision trees generated using the
Fisher's linear discriminant function with centrally stored data. For heterogeneously distributed data, a certain approximation is involved with a small change in performance with respect to the tree generated with centrally stored data. Experimental results for several well known data sets are presented and compared with decision trees generated using the Fisher's linear discriminant function with centrally stored data.
BIO: Dr. Patel has received his PhD and MS in Computer Science in 1997 and 1993 from Wayne State University. He received his BS is in Control Engineering from Gujarat University, India in 1989. At present, Dr. Patel is working as Visiting faculty in the department of computer science and engineering at Oakland University, Rochester – MI. Prior to that he served as Assistant Professor and Software Engineering Manager at University of Michigan-Dearborn and Ford Motors Co / Visteon Corporations. At Visteon he pioneered the development of first in-vehicle computing system in 1999 while being at UMD he established their undergraduate and graduate Software Engineering Curriculum. His research interest is in the field of Multimedia Information Processing – specifically audio and video processing for indexing, retrieval and event detection, Pattern Recognition and Distributed Data Mining from a distributed heterogeneous data sources, Computer Vision with special interest in medical imaging and Mobile Computing – targeting to in vehicle computing and collaborative vehicular networks, vehicle data stream processing and its applications. | | | | 2008-03-26 | Discovering Community in Social Networks | 11:00am @ C4 Lab (DHE 244)
Speaker: Alvin Chin
PhD candidate
Department of Computer Science
University of Toronto
ABSTRACT: The web has moved from a network of machines, to a network of servers, and now to a network of people (Wellman, 2001). Leaders are more likely to be actively involved within communities and subgroups are likely to form around active and like-minded people. Thus the capability to find subgroups and leaders within social networks will be important in understanding and possibly influencing the goals and activities of a community. In this talk, I will address the problem of finding evidence of community in online environments.
I will describe a methodology for identifying communities that is divided into three steps: selecting possible people, collecting those people into possible subgroups and choosing the cohesive subgroups. I will then explain techniques that I used for each of the steps that is based on social network analysis, clustering and similarity. The methodology is tested with two case studies, one involving an online group, and the other involving a group of people using web video. The
results are validated with human judgement and content analysis to identify and characterize communities in these groups. Finally, I will discuss implications of this research for social computing applications in pervasive and mobile computing.
Bio: Alvin Chin is currently completing his PhD in Computer Science at the University of Toronto in the Interactive Media Lab, working with Professor Mark Chignell. His research has been funded by Bell University Labs. He graduated with a Bachelors degree in Computer Engineering and a Masters degree in Electrical and Computer Engineering from the University of Waterloo. Prior to his Ph.D he worked in industry researching emerging technologies in the wireless and pervasive computing area, especially Bluetooth and 802.11. His current research interests include social networking, computer-supported collaborative work, human-computer interaction, and pervasive and mobile computing. He can be contacted at achin@cs.toronto.edu or his web site at:
http://www.imedia.mie.utoronto.ca/~achin. | | | | 2008-03-20 | Semantics-based Web Service Discovery and Composition | Dr. Srividya Kona, Georgetown University, 11:00am, C4 Lab. ABSTRACT:
Service-oriented computing is gaining wider acceptance. For Web services
to become practical, an infrastructure needs to be supported that allows
users and applications to discover, deploy, compose and synthesize
services automatically. For this automation to be effective, formal
semantic descriptions of Web services should be available. In this talk I
will present the language USDL (Universal Service-Semantics Description
Language) for formally describing the semantics of web-services. USDL is
based on the Web Ontology Language (OWL) and employs WordNet as a common
basis for understanding the meaning of services. USDL can be regarded as
formal service documentation that will allow sophisticated conceptual
modeling and searching of available web-services, automated service
composition, and other forms of automated service integration. I will also
present a semantics-based automated service discovery and composition
engine. This engine employs a multi-step narrowing algorithm and is
efficiently implemented using constraint logic programming. The salient
features of our engine are its scalability, i.e., its ability to handle
very large service repositories, and its efficient processing times for
discovery and composition queries.
BIO:
Srividya Kona is a Postdoctoral fellow and Lecturer at the Department of
Computer Science in Georgetown University. Her research interests include
Service-Oriented Computing, Semantic Web Services, Semantic Web, Software
Engineering, Constraint Logic Programming, and Language-based Security.
She received her Ph.D. in Computer Science from the University of Texas at
Dallas in December 2007. She received her M.S. in Computer Science from
Texas Tech Univ., Lubbock in 2002 and her B. Tech. in Computer Science
from NIT (previously known as REC), Warangal, India in 1999. She has over
5 years of industry experience. She worked as a software developer in SAP
Labs (1999-2000) and Tyler Technologies (2001-2005). She was involved in
the design and development of a Web service description language called
USDL (Universal Service-Semantics Description Language) which won the best
paper award at the European Conference on Web Services in 2005. | | | | 2008-03-19 | Intelligent Agent-based Software Process Management for Large Software Development Projects – A Surveillance Tactical Software System as an Example | Dr. Ching-seh (Mike) Wu 11:00AM C4 Lab
Abstract:
Many software projects failed, because software is invisible. To successfully manage an invisible development project is highly challengeable. The intelligent agents use knowledge elicited from the project manager to define criteria of milestones. The intelligent agents work with the Project Attribute Monitoring and Predicting Associate II (PAMPA II), a Web-based automatic software project monitoring tool that I implemented to store knowledge describing an activity’s initial milestone and final milestone. The intelligent agents then use these activity’s criteria along with facts retrieved from the PAMPA II knowledge base to cooperate each other and to compare actual project progress to the planned progress. The resources, tasks, schedules, and milestones of the software project are described in the knowledge base. As the software development process evolves, the software development processes are monitored and the intelligent agents use the knowledge base to dynamically report recommendations, suggesting the software development that should be executed to best comply with the software project plan. The intelligent agents help the manager to determine the current phase of the project, identify the critical path, calculate the Earn Value, keep track of the progress, report problems, and suggest problem solutions during software development. The intelligent agents report risks and suggest problem solutions to help managers assure that a project is within budget, on time, and to customer satisfaction.
BIO:
Dr. Ching-seh (Mike) Wu is an Adjunct Faculty member of the Department of Computer Science and Engineering at Oakland University. He received his Master and Ph.D. both in Computer Science from U.S. Air Force Institute of Tech. in Dayton, OH and Texas A&M University in College Station, respectively. His past 20 years industrial experience and academic research has been focused on software development/engineering, software project management, and software process improvement, over which time he was both a faculty member of universities and a consultant for many software companies and organizations. He has published more than 30 peer-reviewed papers in the above areas. He had developed an artificial neural network simulator for the military weapon image processing/identification installed in F-16 Fighter. He was a team leader and a software project manager in developing the Tactical Software System for E2-T Hawkeye, a surveillance aircraft for protecting US aircraft carriers. He was also involved in the initial development of the ERP and Testing Software systems for both Hewlett Packard and Compaq computer corporations. He is a founding member of the Institute of Software Engineers in Dallas, Texas. He has been invited as a reviewer for IEEE Software Engineering Journal, Software Engineering Journal, and Journal of Information System. | | | | 2008-02-25 | | A widespread shortage of information technology (IT) graduates across North America is forcing Microsoft Corp. and other software companies to look to developing countries such as China to meet their needs, Microsoft Chairman Bill Gates says. | | | | 2008-02-13 | | Dr. Daniel Grosu
Wayne State University
Wednesday, February 20, 2008
11:50-12:50PM
244 DHE (C4 Lab)
All are welcome. Pizza and Pop will be served at 11:45 a.m.
ABSTRACT: Federated distributed computing systems are the next generation computing platforms for solving large scale problems in science and engineering. They are composed of geographically distributed resources owned by autonomous organizations. Resource management in such open distributed environments is a very complex problem. The existing resource management schemes do not explicitly address the fact that a rational participant is able to manipulate the resource management schemes in its own interest. To provide better performance and increase the efficiency it is essential to develop mechanisms for resource management that take into account the behavior of the participants and provide incentives to contribute resources. In this talk I will present my research in incentive-centered design for resource management in distributed computing. More specifically I will present the design of several incentive-based mechanisms for resource management in distributed computing systems. The design of these mechanisms is based on models and techniques from game theory and economics.
BIO: Daniel Grosu is an assistant professor of Computer Science, Department of Computer Science at Wayne State University, Detroit, Michigan. His research interests include distributed systems and algorithms, resource allocation, computer security and topics at the border of computer science, game theory and economics. He has published more than 50 peer-reviewed papers in the above areas. He has served on the program and steering committees of several international meetings in parallel and distributed computing. He leads an NSF sponsored multidisciplinary research and training program in incentive-centered design at Wayne State University.
He is a member of the IEEE, ACM, SIAM, AMS and the Game Theory Society. He received his Ph.D. in Computer Science from The University of Texas at San Antonio in 2003. (More information at: http://www.cs.wayne.edu/~dgrosu)
| | | | 2007-12-10 | | Darrin Hanna, Assistant Professor in the CSE Department has been awarded 2007 Computer Science and Engineering Undergraduate Teaching Award by IEEE Computer Society. Congratulations! | | | | 2007-12-07 | | Computer and mathematical science occupations are projected to add 822,000 jobs—at 24.8 percent, the fastest growth among the eight professional subgroups. The demand for computer-related occupations will increase in almost all industries as organizations continue to adopt and integrate increasingly sophisticated and complex technologies. Growth will not be as rapid as during the previous decade, however, as the software industry begins to mature and as routine work is outsourced overseas. About 291,000—or 35 percent—of all new computer and mathematical science jobs are anticipated to be in the computer systems design and related services industry. The management, scientific, and technical consulting services industry is projected to add another 86,000 computer and mathematical science jobs. This expected 93-percent increase is due to the growing need for consultants to handle issues such as computer network security. Self-employment among computer and mathematical workers is anticipated to increase 19 percent, with most growth appearing among network systems and data communications analysts. | | | | 2007-11-26 | | Dr. Nilesh Patel has been funded by Infogation Corporation of San Diego, California to evaluate different schemes for remote activation of mobile devices. Congratulations! | | | | 2007-11-07 | Seminar - Realizing the Potential of Wireless Sensor Networks | Dr. Loren Schwiebert,
Wayne State University,
Wednesday, November 7, 2007
11:50-12:50PM
244 DHE (C4 Lab)
All are welcome. Pizza and Pop will be served at 11:45 a.m.
Abstract: Wireless sensor networking is becoming a major topic in computer networking, since the advantages of connecting the physical and digital worlds offer opportunities for a wide range of applications. The possibility exists for important contributions to all facets of life, from agriculture and industrial manufacturing to healthcare. But interacting with the real world also introduces many novel problems to using sensors effectively. Unless these problems can be addressed, the full potential of wireless sensor networking will not be realized. After describing the many opportunities of wireless sensor networking, and some of the existing challenges to deploying sensor networks, we will present some of our recent work on wireless sensor networking designed to address some of these challenges.
Loren Schwiebert received the B.S. degree in Computer Science (with a dual major in Mathematics) from Heidelberg College, Tiffin, OH, and the M.S. and Ph.D. degrees in Computer and Information Science from the Ohio State University, Columbus, OH. Since 1995 he has been a faculty member at Wayne State University, Detroit, MI, where he is currently an Associate Professor in the Department of Computer Science and Chair of the Graduate Committee. His research interests include wireless sensor networks, wireless communication, and interconnection networks. He is a member of the ACM, IEEE, and IEEE Computer Society.
| | | | 2007-10-17 | | Wednesday Oct 17, 2007 11.50 AM – 12:50 PM in C4 Lab
Title: A systems biology approach for pathway level analysis
Speaker: Sorin Draghici, Ph.D.
Abstract: A common challenge in the analysis of genomics data is trying to understand the underlying phenomenon in the context of all complex interactions taking place on various signaling pathways. A statistical approach using various models is universally used to
identify the most relevant pathways in a given experiment. Here we show that the existing pathway analysis methods fail to take into consideration important biological aspects and may provide incorrect results in certain situations. Using a systems biology approach, we developed an impact analysis that includes the classical statistics, but also considers other crucial factors such as the magnitude of each gene's expression change, their type and position in the given pathways, their interactions, etc. The impact analysis is an attempt to a deeper level of statistical analysis, informed by more
pathway-specific biology than the existing techniques. On several illustrative data sets, the classical analysis produces both false positives and false negatives while the impact analysis provides biologically meaningful results. This analysis method has been implemented as a web-based tool, Pathway-Express, freely available as part of the Onto-Tools http://vortex.cs.wayne.edu). ______________________________________________________________________
Sorin Draghici, Ph.D.
Director of the Bioinformatics Core, Karmanos Cancer Institute
Associate Professor Tel: (313) 577-5484
Dept. of Computer Science Fax: (313) 577-6868 Wayne State University
5143 Cass Ave, Room 431 State Hall, Detroit, MI, 48202
WWW: http://vortex.cs.wayne.edu/Sorin/ (personal)
WWW: http://vortex.cs.wayne.edu/Projects.html (lab)
He is an Associate Professor in the Department of Computer Science at
Wayne State University, and the Director of the Bioinformatics Core
at Karmanos Cancer Institute. He is active as an Associate Editor of
IEEE/ACM Transactions on Computational Biology and Bioinformatics, as a regular NSF and NIH panelist in the areas of bioinformatics and biotechnology, as well as a reviewer for various technical journals.
His publications include one book, 7 book chapters, 37 journal papers
and over 30 peer-reviewed conference publications. 4 of these papers
have over 100 citations each on Google Scholar. The book, Data Analysis Tools for DNA Microarrays, is a 550 page technical monograph published by Chapman and Hall/CRC Press and has over 100 citations, as well.
His best known work today is in the area of computerized ontological analysis of high-throughput gene expression experiments. His research in this area has produced a set of tools (Onto-Tools), which has been made available as a service to the community, from the web page at http://vortex.cs.wayne.edu. These tools are currently used by more than 3,000 valid registered users from over 50 countries. The impact of this work should also be assessed in the light of the fact that Onto-Tools was the first tool of its kind. Since 2001, when the first tool was made available, the analysis approach he proposed has become
the de facto standard in the second-stage analysis of microarray experiments. Currently, over 20 similar tools are available from other groups. All these tools use one or more of the statistical models he proposed, and virtually all publications describing these tools cite his work. Other notable results include our research in the genetic mechanisms of esothelioma which was chosen to illustrate the cover of
the February 1 2004 issue of Clinical Cancer Research. Some of the research in neural networks was also featured the cover of the Proc. of Artificial Neural Networks in Engineering 1997 (published as a book by ASME Press, ISBN 0-7918-0064-4). In Oct 2006, one of his papers received the Fast Breaking Paper Award in Computer Science, from
ISI/Thompson Scientific. According to ISI, their Essential Science Indicators which include the above mentioned award, comprise the top 1% of the papers in each field and each year http://www.esi-topics.com/fbp/fbp-october2006.html).
| | | | 2007-09-24 | | The CSE Department, jointly with IEEE South East Michigan Computer Chapter and GL-Spin, is offering a free one day workshop on September 29. For details, please contact Professor Subra Ganesan at ganesan@oakland.edu or at 248-370-2206. | | | | 2007-08-16 | | The department is pleased to welcome Drs. Nilesh Patel, Gaungzhi Qu and Mohammad Siadat as new faculty members. Dr. Patel comes to OU from University of Michigan at Dearborn. He has worked at Visteon and Johnson Controls. Dr. Qu is from University of Arizona and has worked in the area of networking and security. Dr. Siadat comes from Henry Ford Hospital where he was working after completing his Ph.D. from Wayne State University. | | | | 2007-03-31 | Automotive Electronics Product Design and Development at Johnson Controls, Inc. | March 31st (Monday) 11:00am C4 Lab (DHE 244)
Speaker: Jeff Golden
Abstract: The seminar discussion will open with a general introduction to Johnson Controls Inc. (JCI). It will then focus on JCI's Automotive Electronics product portfolio and key aspects related to the creation of such products including involved/emerging technologies, design and development methodologies, engineering disciplines utilized, noteworthy challenges, etc. Further attention will be given to the software intensive Embedded Systems based Telematics/Connectivity product line.
Brief Bio: Jeff Golden is currently the Engineering Group Manager for Johnson Controls Inc., Automotive Experience Division, Advanced Electronics Development organization. Jeff has more than 30 years of progressively responsible experience in Electrical and Electronics Engineering and Technology/Resource management. Prior to joining Johnson Controls, Jeff held various Engineering and Technical Leadership positions for Visteon, Ford Motor, and General Dynamics Corporations. Jeff holds a Bachelor of Science Electrical Engineering degree from Lawrence Technological University and a Master of Science Electrical Engineering degree from Wayne State University.
| | | | 2007-03-31 | Automotive Electronics Product Design and Development at Johnson Controls, Inc. | March 31st (Monday) 11:00am C4 Lab (DHE 244)
Speaker:Jeff Golden
Abstract: The seminar discussion will open with a general introduction to Johnson Controls Inc. (JCI). It will then focus on JCI's Automotive Electronics product portfolio and key aspects related to the creation of such products including involved/emerging technologies, design and development methodologies, engineering disciplines utilized, noteworthy challenges, etc. Further attention will be given to the software intensive Embedded Systems based Telematics/Connectivity product line.
Brief Bio: Jeff Golden is currently the Engineering Group Manager for Johnson Controls Inc., Automotive Experience Division, Advanced Electronics Development organization. Jeff has more than 30 years of progressively responsible experience in Electrical and Electronics Engineering and Technology/Resource management. Prior to joining Johnson Controls, Jeff held various Engineering and Technical Leadership positions for Visteon, Ford Motor, and General Dynamics Corporations. Jeff holds a Bachelor of Science Electrical Engineering degree from Lawrence Technological University and a Master of Science Electrical Engineering degree from Wayne State University. | | |
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