CSE BYTES AND PIECES Volume 14, Fall 2007
Greetings from Lion Country! The new academic year brings fall colors and the electrifying chanting of "We are Penn State," as JoePa's kids perform their magic at Beaver Stadium. Go Lions! The past year has been very exciting for CSE. The National Science Foundation awarded the highly selective and prestigious Career Awards to Professors Patrick McDaniel, Yuan Xie, and Sencun Zhu. Patrick Traynor, a CSE Ph.D. student, won the University-wide Penn State Alumni Association Dissertation Award. Since its inception three years ago, the Networking and Security Research Center (NSRC) has been awarded $12 million in external research support. The CSE department is continuing to lead the nation in research and scholarly activities. As published in the Chronicles of Higher Education, faculty scholarly productivity of CSE faculty ranked 3rd (computer science) and 7th (computer engineering) in the nation, respectively. I would like to congratulate the CSE faculty, staff, students, and alumni for this outstanding achievement. We have recruited two new faculty this year. Sean Hallgren (Ph.D., Berkeley) joins us in the area of quantum computing. Swarat Chaudhuri (Ph.D., Penn) joins us in the area of programming languages. We miss Helen DeFurio who retired after 37 years at Penn State. However, we are fortunate to have Corry Bullock as the departmental administrator. This year, the PSES Outstanding Engineering Award went to Derek Smith, chairman and CEO of ChoicePoint. I would like to thank Allen Puy '86 CMPSC and Lockheed Martin for donating their tailgating spot for CSE's first Penn State football tailgate party during the Ohio State game. CSE at Penn State is a premier computer science and engineering department in the country. It is shaping the countries future by solving the critical problems confronting our nation while providing the highest quality education to our students. I am privileged and honored to be associated with such a fine department and University.
Department News..........................2 Research Highlights.......................6 Alumni..........................................13
DEPARTMENT NEWS Helen DeFurio Retires after 37 Years of Service to Penn State Helen DeFurio started her career at Penn State in the Department of Mathematics right after graduating high school in 1970. She held various duties within that department such as receptionist, typist, secretary, budget clerk, and senior accounting clerk. In 1983 she was hired by Joseph M. Lambert, department head of computer science. In 1993, the department merged to become the Department of Computer Science and Engineering (CSE). Helen served as the administrative manager and department operation administrative assistant IV in CSE. She was responsible to the department head for effective administration of the office, staffing, scheduling, supervising, and budgeting. She coordinated human resource and administrative functions for the department including academic appointments, personnel recommendations, payroll, promotion and tenure procedures, space issues, affirmative action, research and general budget administration, approval of funds, security of files, and confidentiality of information. Dr. Rangachar Kasturi said that he began working with Helen in 1993 when the Department of Computer Science (College of Science) was merged with the computer engineering program (College of Engineering). While most of the faculty found it hard to know the rules and regulations of one college, Helen had to deal with two college administrations on a daily basis. She was the pioneer who managed a difficult and uncertain merger with incredible efficiency and a most 2 www.cse.psu.edu
positive attitude. She was an outstanding mentor and a very good friend to the office staff. She was particularly well skilled in balancing the workload among staff and maintaining excellent morale. Kasturi did not hear anything but praise for her fairness and sensitivity to the needs and concerns of her fellow staff. In 2001 Dr. Kasturi assumed the responsibility to organize the IEEE Computer Vision and Pattern Recognition conference. At his request, Helen agreed to serve as the conference secretary and accompany him to the conference in Hawaii. All was going well and the conference committee was expecting to set a new attendance record. However the aftermath of Sept. 11 caused even seasoned travelers to worry about traveling far away from home. As someone who had not traveled far from central Pennsylvania, it was a very difficult decision for Helen and her family. But ultimately it was her dedication to her work that guided her decision. She was at the conference when a new attendance record was set!
Helen Defurio and Raj Acharya
Helen's talents were put to good use during the planning stages for the new Information Sciences and Technology (IST) Building. She was making sure that the department's needs were addressed. In the building committee meetings, Kasturi was always amazed at her abilities to pay attention to details during the planning stages to prevent them from becoming problems after construction. Raj Acharya, department head, said that "Helen has been instrumental in the success of the computer science and engineering department. She has made sure that the departmental administration operated smoothly.
DEPARTMENT NEWS She was indispensable during the planning of the new IST Building." Seven department heads and 37 years later, Helen DeFurio has retired from Penn State. Her plans for the future include spending more time with family, gardening, and traveling.
Corry Bullock Hired as the New Manager of Administrative Operations Corry Bullock accepted the position of manager, administrative operations at CSE. She has been with Penn State for 22 years. She has worked in various areas within Penn State such as the senior vice president for research and graduate studies and the College of Arts and Architecture. She recently moved back to the State College area after living in the Philadelphia region for five years. Before accepting the position, she worked at Penn State Great Valley, School of Graduate Professional Studies. In the fall she started a two-year program to become a Penn State certified research administrator.
New Faculty Sean Hallgren received his Ph.D. from the University of California, Berkeley, and spent a year as a postdoc at the Mathematical Sciences Research Institute in Berkeley, CA. He was then a National Science Foundation postdoc at Caltech for two years in the computer science department and at the Institute for Quantum Information. Before joining Penn State, he ran the quantum computation group at NEC Laboratories in Princeton, NJ, for four years.
Swarat Chaudhuri received his B.S. degree in computer science from the Indian Institute of Technology, Kharagpur, in 2001, and a Ph.D. degree in computer science from the University of Pennsylvania in 2007. His research interests include program analysis, formal methods in software engineering, and applications of logic, automata, and concurrency theory.
Padma Raghavan Appointed the First Director of the Institute for Computational Science Padma Raghavan, professor, has been appointed the first director of the Institute for Computational Science. She is assisted by an executive committee comprised of the deans of core colleges and representatives from participating institutes and a steering committee consisting of University faculty. According to Eva J. Pell, senior vice president for research and the dean of the Graduate School, "The Institute for Computational Science (ICS) is a collaborative effort between the academic colleges, the Applied Research Laboratory, the Office of Information Technology Services, and the Office of the Senior Vice President for Research. The ICS was developed to enhance Penn State's national and international presence and stature in computational science, to develop proposals that will win new opportunities for Penn State faculty and students to pursue high level collaborative interdisciplinary research; to drive the use and understanding of computational science in graduate education, and to increase access and awareness of highend computing facilities." For more information about ICS, please visit their web site at: www.ics.psu.edu
DEPARTMENT NEWS Three CSE Faculty Receive Prestigious National Science Foundation CAREER Awards With prestigious awards from the National Science Foundation (NSF), three CSE faculty members are tackling various issues. The researchers have earned NSF's Faculty Early Career Development (CAREER) Award. According to NSF, they established the CAREER program in 1994 in recognition of the critical roles played by faculty members in integrating research and education, and in fostering the natural connections between the processes of learning and discovery. The CAREER program is a Foundation-wide activity that offers NSF's most prestigious awards for junior faculty members, which embodies NSF's commitment to encourage faculty to practice and academic institutions to value integration of research and education. Patrick McDaniel, associate professor, "CAREER: Realizing Practical High Assurance through Security-Typed Information Flow Systems." Yuan Xie, assistant professor, "CAREER: Process Variation Aware Embedded MPSoC Synthesis."
Sencun Zhu, assistant professor, "CAREER: Combating Worm Propogation in Emergent Networks."
CSE Faculty Member Receives STOC 2007 Best Paper Award Martin Furer received the "Best Paper Award" at the ThirtyNinth ACM Symposium on Theory of Computing (STOC 2007) for the paper titled, "Faster Integer Multiplication." He announced a new algorithm that has become a better asymptotic complexity. To read this paper, please visit the following web site: www.cse.psu.edu/~furer/Papers/ mult.pdf. The main criterion for selection is the same as for being a top-rated paper in the conference: introduction of a strong new technique, solution of a long-standing open problem, introduction and solution of an interesting and important new problem, etc. These are the characteristics associated with giving a paper the highest score. Additionally, the committee should have substantial confidence of the correctness of the paper. STOC is one of the two most prestigious conferences in theoretical computer science.
Two CSE Faculty Receive Interdisciplinary National Science Foundation Award Professors Robert Collins and Yanxi Liu, together with R. Barry Ruback (Sociology) and Christopher Byrnes (Math/ ARL) have received a grant ($750,000) from the National Science Foundation to support interdisciplinary research into the social behavior of crowds. Their research will combine a mathematical model of human collective behavior with software for image and texture tracking, classification,
DEPARTMENT NEWS simulation, and animation to test the hypothesis that behavior in a large crowd results primarily from social influence within and between small groups of individuals. Empirical sociological evidence will be gathered by tracking all individuals in video of crowds of varying nature, topology, size, and density using automated computer vision tracking algorithms, a methodological breakthrough capable of providing quantitative characterization of real crowds faster and more accurately than human observation. The theory and the tools to be developed by this project can assist personnel in law enforcement, emergency management, and event management to minimize violence, speed up evacuations, and reduce accidental injury during large public gatherings.
Networking and Security Research Center Named a Ben Franklin Center of Excellence
Pennsylvania, typically the center members, and facilitate general industry sector business stability and grown." The NSRC includes twelve faculty and approximately 50 Ph.D. and M.S. students and several undergraduate honors students. The expertise of the members includes telecommunications, mobile networking, protocol design, performance analysis and simulation, wireless communication, networked applications, and large networking software systems. Security permeates all of these areas. The center boasts experts on telecommunications security, Internet security, policy, secure operating systems, access controls, and cryptography. According to La Porta, "as part of our effort to provide technical leadership, we introduced our first short course in spring 2007. The one-and-a-half day course on enterprise security was taught by Professor Patrick McDaniel and was very well received. We are planning two more such courses in the next year." In the past twelve months, the members of the center have collaborated to raise more than $4.4 million in funding through grants from federal or state-based funding agencies including several National Science Foundation (NSF) and Department of Defense grants. In the past three years the center has raised more than $12 million in new funding. Two members of the center won prestigious NSF CAREER Awards and one received a Defense Advanced Research Projects Agency Young Investigator Award.
The Networking and Security Research Center (NSRC) was named a Ben Franklin Center of Excellence in 2007. According to Tom La Porta, director, NSRC, "this relationship will help us meet one of our primary goals â€” fostering partnerships with industry and providing an outlet for our technology to have an impact on society." While the primary goal of the Ben Franklin support is to assist in developing ties with Pennsylvaniabased companies, the NSRC continues to build relationships with companies all over the world. According to the Ben Franklin web site, "Centers of Excellence must provide technology transfer and outreach mechanisms that provide tangible techSome NSRC members with a check from the nical and educational benefits Ben Franklin Technology Partners to individual companies in
To learn more about the center, visit their web site at: http://nsrc.cse.psu.edu
RESEARCH HIGHLIGHTS The Laboratory for Perception, Action, and Cognition The Laboratory for Perception, Action, and Cognition (LPAC) is directed by Professors Robert Collins and Yanxi Liu. Research in the lab and the name of the lab itself are motivated by multiple disciplines that must be brought together to develop robust intelligent systems: Perception = Computer Vision: Aristotle defined vision as the act of knowing what is where by looking. Likewise, the goal of computer vision is to interpret visual sensor data including still imagery and video to measure 3D scene structure, infer the identity and locations of objects, and to recognize dynamically occurring activities and events. Action = Robotics: By coupling computer vision input devices with robotic actuators, feedback loops can be built that exhibit intriguing emergent behaviors. LPAC takes a broad view of the term actuator to include unmanned vehicles, pan/tilt/zoom heads that actively change the camera viewpoint (e.g. active vision systems), and monitor/projector systems that display useful information into or even onto the scene (e.g. smart spaces). Cognition = Artificial Intelligence: The real world is too complex for a complete description to be coded by hand. The LPAC group focuses instead on developing intelligent systems that learn from training examples and unsupervised exploration using mathematical representations that leverage fundamental constraints present in the physical world, whether they be the importance of gravity in interpreting natural imagery, the periodic nature of locomotion (e.g. gaits), or the interplay of randomness and regularity in the appearance of natural and man-made scenes. 6 www.cse.psu.edu
Some of the research areas actively being explored by the lab are described in this article. Understanding Human Form and Action Humans are inarguably the most important objects that an intelligent system must be able to interact with. A portion of LPAC research is devoted to developing stateof-the-art machine vision systems that can recognize people, describe what they are doing, and infer what they are trying to achieve. This research proceeds on many fronts: detecting humans and describing their body pose, biometric identification from face and gait, and sociology-based methods for understanding the collective behavior of small groups and crowds. Articulated Body Fitting
Our work in automated body model fitting is based on the hypothesis that it is necessary to explicitly identify the pose of a person over time in order to describe their body motions and recognize their actions and activities. Under National Science Foundation (NSF) funding, we have developed methods for segmentation of articulated, part-based human body models from images. A Bayesian approach is used to take advantage of strong priors on human body shape and motion and to combine multiple image cues such as color and edges in a robust fashion. The resulting method can segment people with unknown body pose seen from unknown viewpoint (an example is shown in Figure 1). Likely body poses are found using a hybrid search strategy that combines deterministic (dynamic programFigure 1: Bayesian segmentation of human body ming) and stochastic (sequential Monte pose from images is an Carlo) techniques to automatically search important step towards better machine understanthrough a high-dimensional space of ding of human form and possible body pose configurations. action.
RESEARCH HIGHLIGHTS Biometric Identification from Face and Gait Biometric identification research in the lab is concerned with passive, visual methods for identification, as opposed to fingerprint analysis or identification based on RFID tags. Face, body shape, and gait have all been demonstrated to yield promising results. Human facial asymmetry has long been a critical factor for evaluation of attractiveness and expression in psychology and anthropology, though most studies are carried out qualitatively and using human raters. We have investigated the use of statistical facial asymmetry measurement as a biometric under expression variations. Our initial findings demonstrate that the asymmetry of specific facial regions capture individual differences that are robust to variation in facial expression. More importantly, our experimental results show that facial asymmetry provides discriminating power orthogonal to conventional face identification methods, and our work appears to be the first to show quantitatively the power of facial asymmetry quantifications, poseinvariant human identification, identification of attractive vs. non-attractive people, gender differences, and temporal variations during expression for emotion classification. Our recent paper, "Measurement of Asymmetry in Persons with Facial Paralysis," won first place in the clinical science category and best paper overall at the Annual Conference of Plastic and Reconstructive Surgeons.
Human gait recognition relies on two cues to identify an individual: the shape of their body and the way that they move. We have developed a gait recognition method that represents gait sequences as spatiotemporal "Frieze" patterns (Figure 3). This representation is intriguing because both shape and motion information are combined into a single pattern where they can be compared jointly. We have recently extended this approach to create Shape Variation Based Frieze patterns where the common shape is factored out and represented separately from the shape-variation information. This method has been shown to outperform several state-ofthe-art gait recognition methods when tested on sequences where a person is seen carrying a box or wearing a heavy coat or backpack that changes their silhouette shape in each frame. Upper Frontal view
Lower side view Lower Frontal view
VII silhouette Time
one column of the pattern
Figure 3: We represent gait sequences as a spatiotemporal "Frieze" pattern where shape and motion information are combined and can be compared jointly.
Spatiotemporal facial asymmetry of expression Subj
Figure 2: Facial asymmetry has been shown to provide a human biometric that is relatively invariant to differing facial expressions.
RESEARCH HIGHLIGHTS Understanding Collective Behavior
Professors Collins and Liu, together with Penn State Research on persistent object tracking addresses the collaborators from the sociology and mathematics problem of tracking objects for long periods of time departments recently received an NSF award to support during which the appearance of both the object and its interdisciplinary research into the social behavior of environment may change. Foreground-background (or crowds. This research combines a mathematical model figure-ground) segmentation is emerging as a key of human collective behavior with software for image approach to solving this problem. By explicitly and texture tracking, classification, simulation, and ani- separating object pixels from the background, a tracker mation to test the hypothesis that can adapt to object and backbehavior in a large crowd results ground appearance changes primarily from social influence separately, and in a principled within and between small groups way. Professors Collins and Liu of individuals. Preliminary work were the first to pose tracking as on the grant has demonstrated a two-class classification probautomated pedestrian detection lem, where object pixels must be and tracking tools. Hierarchical discriminated from background agglomerative clustering of the pixels based on local image cues resulting set of time-stamped such as histograms of color or pedestrian trajectories yields intexture. Recent work in the lab sights into which individuals may explores the use of motion inforFigure 4: Three small groups traveling through a have been traveling together crowded atrium, automatically detected by mation to efficiently segment hierarchical agglomerative clustering on tracked (Figure 4). Quantitative evaluation moving objects from the pedestrian trajectories. of automatically detected small background. The evidence accumgroups against those identified by ulation underlying the procedure can be viewed as human coders yielded substantial statistical agreement. spatiotemporal Markov Random Field (MRF, see Figure This work demonstrates that empirical sociological 5) combining evidence of motion at each pixel within a evidence can be gathered by tracking all individuals in fixed temporal window with spatial constraints that video of crowds using automated computer vision encourage consistency in labeling and tracking algorithms, a methodological BP in a 3D MRF between neighboring pixels. The Input videos breakthrough capable of providing maximum a posteriori estimate of quantitative characterization of real motion energy is approximated by a crowds faster and more accurately belief propagation procedure, impleMMSE than human observation. The theory Detection Estimate mented as spatial and temporal and the tools to be developed by this message passing within the Markov project can assist personnel in law Random Field. enforcement, emergency manageFigure 5: Moving objects are segmented ment, and event management to minifrom video sequences using belief mize violence, speed up evacuations, propagation within a 3D spatiotemporal and reduce accidental injury during Markov Random Field. large public gatherings. 8 www.cse.psu.edu
RESEARCH HIGHLIGHTS Modeling while Tracking
is to simultaneously track all of them. Furthermore, when It is common for trackers to the objects are known to be eventually lose the object they arranged in spatial formation, Modeling are tracking due to temporary we can leverage the underocclusion, large camera lying lattice structure of their View motion, or large changes in positions to provide statistical Sphere illumination or viewing angle. inference within a Bayesian To achieve persistent tracking, network of spatial and mechanisms must be in place temporal tracking constraints. to recover from these tracking This methodology allows us Figure 6: A collection of view-dependent models built on-thefailures. We have developed to handle difficult multi-target fly while tracking can help a persistent tracker recover from a method to model the object tracking problems such as failure by enabling re-detection of a lost target object. adaptively and automatically tracking identical texture while tracking, so that the elements on cloth, animals in object can be detected and recognized again after losing herds, and marching individuals in the Penn State Blue it (Figure 6). The models, which are collections of small Band (Figure 7). In each case, even though the "targets" view-dependent intensity patch models indexed on a are closely spaced and similar in appearance, imposing view sphere, are accumulated on-the-fly during the spatial neighbor constraints in the form of a 2D lattice tracking run. Although we may start out with very little allows the tracker to successfully disambiguate and track information about an object we are tracking, the process each individual in the formation. of tracking it through multiple views quickly accumulates a large amount of data on its appearance and movement. As we track the object over longer sequences where viewpoint is changing, we are able to accumulate and chain together a large collection of view-specific models so that object appearance is represented across a progressively larger and larger range of viewing angles. Tracking Multiple Targets in Formation Despite a decade of research into vision-based tracking and the commercial availability of automated video surveillance systems, tracking identical targets in formation remains an unsolved problem.The problem is difficult due to high densities of constantly moving, spatially overlapping objects, all of which look similar (e.g., members of a marching band all dressed in identical uniforms). We hypothesize that the only way to reliably track a single target in the presence of nearby confusors
Figure 7: Tracking a formation of individuals from the Penn State Blue Band marching during a halftime show.
Computational Symmetry Symmetry is an essential and ubiquitous concept in nature, science, and art. Numerous biological, natural, and man-made structures exhibit symmetries as a fundamental design principle or as an essential functionality. Whether by evolution or by design, symmetry leads www.cse.psu.edu 9
RESEARCH HIGHLIGHTS simultaneously to an aesthetic and a practical usability, making it universally appealing. A computational model for symmetry is especially pertinent to robotics, computer vision, and machine intelligence, in general, because in these fields we are studying how a man-made intelligent being can perceive and interact with the chaotic real world in the most effective manner. Recognition of symmetries is the first step towards capturing the inner structure of a real-world problem and minimizing redundancy, which can usually lead to drastic reductions in computational requirements.
involve cross-disciplinary applications of group theory and statistical learning theory to characterize symmetry and departures from regularity, both qualitatively and quantitatively in noisy real-world datasets. Automatic Discovery and Analysis of Symmetry
We have developed a novel automatic lattice (translational symmetry) extraction algorithm for texture that have both global and local deformations (Figure 8) by treating symmetry detection as a higherorder correspondence problem, Group theory, being the mathesolved using spectral methods. matics of symmetry, places When combined with previous symmetry on a sound, albeit work we have done on characideal, theoretical footing. terizing all rotation, reflection, Crystallographic group theory and glide reflection symmetries tells us that regardless of the in a repeated "wallpaper" Figure 8: Examples of automatically detected deformed lattice structures from input photos. infinite number and variety of pattern, this work forms the Frieze and wallpaper pattern fundamental basis for machine designs in the world, there are perception of periodic patterns Original Regularized only 7 and 17 underlying symmetry in real-world imagery. groups, respectively! The key An interesting example showing the challenge is to turn the mathematical Cell t1c1 diversity of this approach is characconcepts of group theory into a compuType II terization of the firing fields of the tationally useful tool for perception of newly discovered "grid cells" found symmetric patterns, despite systematic in the rat dorsolateral medical entordistortions or noise in real-world data. Cell t5c3 hinal cortex (Figure 9). As it turns out, So far, no one has shown a robust, Type III the firing fields present hexagonal widely applicable general symmetry structured patterns that have a detection algorithm for real-world Cell t7c4 symmetry group of "P6M" (one of the digital data (images or otherwise) in Type III 17 wallpaper groups) clustering spite of many years of effort. around two types of regularities! This Figure 9: The firing fields of grid cells recent result of ours was published in Professor Liu has taken the lead in found in the rat dorsolateral medial the Computational Neurosciences defining and exploring the new subentorhinal context (left) and the automatically detected nearest Journal (May 2007). As part of our field of "computational symmetry." "unwarped" regular lattice (right). work, we have created a publicly LPAC has several research efforts that 10 www.cse.psu.edu
RESEARCH HIGHLIGHTS accessible Near-Regular Texture (NRT) database to facilitate quantitative assessment of computational methods for detecting periodic and symmetric patterns (http://vivid.cse.psu.edu/texturedb/gallery/). At this site we maintain: (1) the first systematic collection of regular and NRT patterns; (2) comparison results on state-ofthe-art texture synthesis and analysis algorithms; and (3) NRT lattice detection benchmarks and results. This Penn State NRT database is now gaining wider usage, recognition, and contributions from the graphics and vision research community.
In addition to static textures in the photo, we have also demonstrated analysis of dynamic near-regular texture in video sequences. For example, Figure 11 shows one frame of results of replacing textured cloth in a movie with another texture, a feat that is only possible due to our ability to precisely track repeated textures in video.
Near-Regular Texture Applications in Graphics and Video Discovery of near-regular texture in photos and video leads to exciting graphics applications. One example is texture replacement where analysis of a repeated texture in one photo allows discovery and characterization of surface material shape and scene lighting, and ultimately, replacement of the original texture with a new one (Figure 10).
Figure 11: Texture replacement of a dynamic near-regular texture in video.
Our texture tracking algorithms can successfully track moving clothes with occlusion, distorted patterns under disturbed water, and even crowds of people in motion (e.g., the marching band example presented previously). Quantitative evaluations show that our lattice-based Markov Random Field model for dynamic NRT tracking outperforms existing tracking algorithms on challenging texture tracking tasks. Machine Learning-based Biomedical Image Analysis
Figure 10: Synthesis and texture-replacement results from input photos (far left in each row) by analyzing geometry and lighting deformation fields, which allows replacement textures to be overlaid on the image as if they were painted on a real surface (top row), or new surfaces to be generated and retextured using lighting and geometry effects learned from the input image sample (bottom row).
LPAC research into biomedical image analysis has the long-term objective of building a computational framework for automatic disease classification, discrimination, and prediction. We take an image feature-based statistical multivariate machine learning approach on multimodal biomedical images including, but not limited to, high resolution magnetic resonance images (MRI), CT images, multispectral microscopic images, and optical photos and videos. Working closely with medical researchers and clinicians, we have studied a wide range
RESEARCH HIGHLIGHTS of applications with one focused Multispectral Pap Smear Images goal: discovering discriminative Background Segmentation feature subspaces that lead to 1. Image Preprocessing Intensity Normalization automatic object semantic class prediction. Our work covers the Feature Extraction development of computer algo2. Pixel Feature Screening Classification rithms for learning-based deforClassification mable registration, atlas-based segmentation, 3D shape represenCandidate Region Detection 3. Region Detection Region Merging tation and analysis, innovative image feature extraction and Cancerous Regions discriminative feature subspace Figure 12: Multispectral thin Pap smear image analysis to detect cancer cells. induction and selection. We have applied our methods successfully in CT neural images for discriminating among normal, infarct, and blood cases for image content-based retrieval from large, multimedia stroke patient databases, and to Automatically learned Discriminative Input Image Discriminative hyperspectral Pap smear Feature Space anatomical locations microscopic images for Figure 13: Learning a discriminative feature space for computer aided diagnosis of central nervous screening cancer cells from systems diseases such as schizophrenia and Alzheimer's. normal cells (Figure 12). One current challenging and exciting project involves automatic classification and University of Pittsburgh Medical Center have led to prediction of neuropsychiatric central nervous systems exciting applications of computer vision to problems in diseases (Figure 13) such as schizophrenia and human identification, object tracking, pattern analysis, and medical diagnosis. They continue to enthusiastically Alzheimer's disease from high resolution MRIs. embrace multi-disciplinary collaboration with computer scientists, mathematicians, statisticians, sociologists, Summary neuroradiologists, oncologists, and biomedical engineers Professors Collins and Liu bring more than 40 years of to find new applications for the computer vision techcombined experience in vision, robotics, and graphics nology developed in LPAC. to their role as co-directors of LPAC. Their years of successful research funded by NSF, the National Institute of Health, The Defense Advanced Research Projects Agency, the Pennsylvania Health Department, and the
ALUMNI Alumni Update The following alumni information has been compiled from the recent graduate responses and from the online alumni questionnaire (www.cse.psu.edu/info/newsletter/questionnaire.php). I want to encourage you to respond to one of these formats if you have not done so in the recent past so that we can provide up-to-date information to our readers and so that we can facilitate contact among our alumni. For this reason, we ask that you check the e-mail address carefully; if you find an error, please send the correction/change to the editor (email@example.com). If your e-mail address is handwritten, please remember that some addresses are case-sensitive, so do not mix and match your letters! You can also send updates directly to the editor via e-mail. Please be sure to reference the newsletter and affirm your willingness to have the information published. You may have already sent information to the department, but if you did not check the "yes" box or indicate your permission for inclusion in the newsletter, we cannot print it. This newsletter can be viewed online at: www.cse.psu.edu/info/newsletter/volume14/Volume14.pdf. 1969 John Mashey. Consultant, Techviser, Portola Valley, CA. He is a consultant for Venture Capitalist and technology companies. He is also a trustee at the Computer History Museum. (firstname.lastname@example.org) 1970
www-users.cs.york.ac.uk/~alistair. (email@example.com. ac.uk) 1982 Scott Dudley. Department manager, systems engineering, integration and test for Raytheon, Marlborough, MA. (firstname.lastname@example.org)
Stephen Knapp. Project scheduler, American Electric Power. His duties include planning and scheduling of engineering, procurement, and construction of environmental upgrades on a coal-fired, electrical generation plant. (email@example.com)
Arvid Martin. Retired from General Motors Corp. in 2004. He is a self-employed realtor. (arvidlim@aol. com)
William A. Bralick, Jr. Vice president, STX Cadware, Inc., Irving, TX. His duties include overseeing all operations and engineering. This company is responsible for building productivity and collaboration tools for Cadence environments as well as autogenerators for test-chip layouts. (firstname.lastname@example.org)
1980 Alistair Edwards. Senior lecturer, University of York, Department of Computer Science, Heslington, York, United Kingdom. He is responsible for teaching. His research involves human-computer interaction, particularly novel forms of interaction such as speech and non-speech sounds. Much of his work is centered on the needs of users with disabilities. His homepage is:
Isaac Kunkel. Software development manager, Blue Cross and Blue Shield of North Carolina. He is married to Ellen Schneeberger (QBA '88). (email@example.com)
1994 Joseph A. Bruce, Jr. Principal consultant, RABA Center, SRA International, Columbia, MD. He is responsible for
ALUMNI embedded systems design, software reverse engineer- Teofil Rus. Systems engineer, Vanguard Group. He is ing, and software team leadership. (Joe.Bruce.PSU@ responsible for application development. (tpr131@psu. edu) gmail.com) 1995
Burnett Smith. Software engineer, IBM, Durham, NC. (BurnettPSmith@gmail.com)
Teebu Philip. Web systems administrator, Tracfone Wireless, Miami, FL. (firstname.lastname@example.org)
Artem Airaburg. Graduate student, Georgia Institute of Technology, Atlanta, GA. (email@example.com)
Gandhi Thirugnanam. (firstname.lastname@example.org) 2002 Anu Vijayamohan. Consultant, Appian, Vienna, VA. 2003
Meghan E. Daley. NASA/Johnson Space Center, Seabrook, TX. (email@example.com) Kit Klein. Computer engineer, Ingersoll Rand, Campbell, CA. Responsible for firmware and application development, control system design, and involved in the rotational program. (KKleingsxr@gmail.com)
Christopher White. (firstname.lastname@example.org) 2006
Eric Menendez. Graduate student, electrical and computer engineering, Carnegie Mellon University, Pittsburgh, PA. (email@example.com)
Paul L. Bard III. Customer support engineer II, Cisco Systems. He is responsible for solving advanced Christopher R. Patton. Software engineer associate, networking problems with IT professionals working in Lockheed Martin Corporation, King of Prussia, PA. high revenue companies. (firstname.lastname@example.org) Stephen Tomko. Electrical engineer I, Harris Alan Ding. Software engineer, Northrop Grumman Corporation, Melbourne, FL. (email@example.com) Corporation, Linthicum, MD. He is involved in a systems integration effort for the U.S. Army's Aided Jeremy Wayne Trimble. Software engineer, Argon ST. Target Recognition Program on unmanned aerial vehicles. Daryl Wiest. Software engineer, Raytheon, State College, PA. (daryl@firstname.lastname@example.org) Jamie Knapil. Software engineer, Remcom, Inc., State College, PA. She is responsible for developing software The Helping Hands of Alumni and Friends for the government. (JamieMKnapil@gmail.com) Alumni and friends continue to actively support the CSE Tyler Lacock. Software engineer, IBM, Durham, NC. department. We are grateful for this support. DonorHe is responsible for developing service-oriented designated funds are essential if our efforts to provide architecture demonstrations. the best possible environment for our students and faculty are to succeed. We encourage our alumni to designate their gifts for use in this department. Thank you again for your gracious donations!
ALUMNI Alumni Questionnaire
In August 1997, ChoicePoint spun off into a separate public corporation with a specific mission. "Our goal is We would appreciate an update on your activities both to demonstrate the responsible use of information which professional and personal. We are always interested in can help organizations and government make better what our alumni are up to since leaving Penn State! For decisions that will benefit us as consumers and society at large," says Smith. your convenience, the questionnaire is online at: ChoicePoint played a role in many important, recent events including DNA identification of victims of the World Trade Center attacks, assisting the Maryland State Outstanding Engineering Alumnus Police in the capture of the D.C. snipers, and the safe Derek V. Smith was one of the return of more than 800 missing and exploited children. recipients of the Outstanding Engineering Alumni award in In 2006, the company generated more than $1 billion in 2007. A high school independent revenue. Last year, ChoicePoint was named among the study program piqued Smith's world's top technology providers to the Financial interest in computer science. "I Services industry as part of the 2006 FinTech 100 list taught myself how to program for the third straight year. Today ChoicePoint employs computers. That was back when approximately 5,500 people in nearly 60 locations. there were paper ribbon tapes we had to take out of the com- Smith is also the author of two books on privacy, "The Risk Revolution: Threats Facing America and puters," he chuckles. Technology's Promise for a Safer Tomorrow" and "A Smith originally attended the Georgia Institute of Survival Guide in the Information Age." He states, "I Technology on a football scholarship. After his freshman hoped these books would broaden the national dialogue year, he transferred to Penn State for its combination of on protecting society while preserving individual rights academics and athletics. He earned a bachelor's degree to privacy." in computer science in 1977. He returned to Georgia Tech where he received his master's degree in business This publication is available in alternative media on request. finance in 1979. www.cse.psu.edu/info/newsletter/questionnaire.php
Smith was hired by Arthur Anderson (now Accenture) in its consulting division in Atlanta, GA. He worked there for two years before joining Equifax in 1981 as assistant vice president for cash management and banking relations. He rose to become executive assistant to the CEO; president of EMS, a marketing services subsidiary; head of credit bureau national expansion and sales; corporate treasurer; corporate CFO; and then ultimately was named Group Executive for the Insurance Services Business segment that later became ChoicePoint, a leading provider of decision-making information and technology that helps reduce fraud and mitigate risk.
The Pennsylvania State University is committed to the policy that all persons shall have equal access to programs, facilities, admission, and employment without regard to personal characteristics not related to ability, performance, or qualifications as determined by University policy or by state or federal authorities. It is the policy of the University to maintain an academic and work environment free of discrimination, including harassment. The Pennsylvania State University prohibits discrimination and harassment against any person because of age, ancestry, color, disability or handicap, national origin, race, religious creed, sex, sexual orientation, or veteran status. Discrimination or harassment against faculty, staff, or students will not be tolerated at The Pennsylvania State University. Direct all inquiries regarding the nondiscrimination policy to the Affirmative Action Director, The Pennsylvania State University, 328 Boucke Building, University Park, PA 16802-5901, Tel (814) 865-4700/V, (814) 863-1150/TTY. Designed, produced, and edited by Jenny Latchford, CSE department with support from CSE faculty. Printed/produced at Penn State Multimedia and Print Center. U.Ed. ENG 08-33
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Published on Nov 30, 2011