Monday, November 05, 2012 at 12:00 PM

Host: Dr. Mehdi Etezadi-Amoli

Collaborative Transmission Planning: California's Renewable Energy Transmission Initiative (RETI)

Dariush Shirmahammadi
Shir Power Engineering Consultants

Transmission planning has been conducted primarily by utilities, in reactive fashion. Project approvals are increasingly litigated, when stakeholders later become engaged. Large Renewable Energy Standard (RES) targets present additional challenges for approval of generation and transmission projects and often require a proactive development approach. In response, California agencies formed a stakeholder-led planning process, the Renewable Energy Transmission Initiative (RETI) in 2007. RETI identified and ranked Renewable Energy Zones in California and neighboring regions, using both economic and environmental criteria, determined the transmission needed, based on least-regrets transmission planning principles, to access and deliver target renewable energy, and prepared a statewide conceptual transmission plan. RETI has been effective in identifying development priorities and in building stakeholder support for generation-transmission development for renewable energy. Its approach is applicable to other jurisdictions considering large-scale wind power-transmission construction.


Dr. Dariush Shirmohammadi has worked in the electric power industry for
35 years focusing on transmission and distribution planning and operations as well as on the design and implementation of electricity markets. The focus of his recent work has been on the interconnection and integration of renewable resources. Dariush has published numerous technical papers and has lectured in several countries around the world. He has a Ph.D.. in Electric Power Engineering, is a registered professional engineer and a fellow of IEEE.

Friday, November 02, 2012 at 12:00 PM

Host: Dr. Mehmet Gunes

Early Diagnosis Melanoma Using Dermoscopy Image Analysis

M. Emre Celebi
Louisiana State University in Shreveport

Malignant melanoma is one of the most rapidly increasing cancers in the world. Early diagnosis is particularly important since melanoma can be cured with a simple excision if detected early. Dermoscopy, also known as epiluminescence microscopy, is a non-invasive skin imaging technique that uses optical magnification and either liquid immersion and low angle-of-incidence lighting or cross-polarized lighting, making subsurface structures more easily visible when compared to conventional clinical images. Dermoscopy allows the identification of dozens of morphological features, which reduces screening errors and provides greater differentiation among difficult lesions. However, it has been demonstrated that dermoscopy may actually lower the diagnostic accuracy in the hands of inexperienced dermatologists. Therefore, in order to minimize the diagnostic errors that result from the difficulty and subjectivity of visual interpretation, the development of computerized image analysis techniques is of paramount importance. In this talk, I will present an overview of the latest developments in this exciting subfield of medical image analysis and discuss its potential for the computer-aided diagnosis of melanoma.


M. Emre Celebi received his B.Sc. Degree in Computer Engineering from the Middle East Technical University (Ankara, Turkey) in 2002. He received his M.Sc. and Ph.D.. degrees in Computer Science and Engineering from the University of Texas at Arlington (Arlington, TX, USA) in 2003 and 2006, respectively. He is currently an Associate Professor and the founding director of the Image Processing and Analysis Laboratory in the Department of Computer Science at the Louisiana State University in Shreveport.

Dr. Celebi has actively pursued research in the field of image processing and analysis with an emphasis on medical image analysis and color image processing. He has worked on several projects funded by the US National Science Foundation (NSF) and National Institutes of Health (NIH) and published over 100 articles in premier journals and conference proceedings. His research contributions are covered in two recent books published by Wiley Interscience: Image Processing - Principles and Applications (Acharya and Ray, 2005) and Clustering (Xu and Wunsch, 2009). His recent research is funded by grants from the NSF and Louisiana Board of Regents.

Dr. Celebi is an editorial board member of 6 international journals, reviews for over 60 international journals, and served on the program committee of more than 60 international conferences. He has been invited as speaker to several colloquia, workshops, and conferences, is the organizer of several workshops, and the editor of several journal special issues and books. He is a senior member of the IEEE and SPIE.

Friday, October 05, 2012 at 12:00 PM

Host: Dr. Yaakov Varol

Thoughts and Lessons Learned: From University of Nevada, Reno to Stanford to High-tech Startups and Cloud Computing

Brian Lent
Medio Systems

In this talk, I hope to present an engaging summary of my life at University of Nevada, Reno and how it shaped my graduate education options leading to Stanford as an opportunity to share what I learned. I also plan to cover musings from the unique opportunity to see the founding of Yahoo and participate in the creation of Google while at Stanford's Computer Science department in the mid- to late 1990s. On a more career-focused half of the talk I plan to cover some of the lessons from the venture-capital backed startup companies I participated in and how that has led me today to Medio Systems, where we are innovating on leading-edge technologies at the confluence of analytics, mobile computing and cloud-based Big Data Applications. Q&A will be invited


Since graduating from University of Nevada, Reno, Brian has built four successful high-technology businesses in gaming, consumer internet, and mobile software. With an ongoing passion and commitment to more than two decades of technology and entrepreneurial leadership, Brian's current company, Medio Systems, was founded in 2004 from his role as Entrepreneur-in-Residence at Mohr Davidow Ventures. A Seattle-based mobile analytics company, Brian has led Medio as its Chairman and CTO to profitability and rapid growth-serving Fortune 100 customers.

Prior to creating Medio, Brian was the founding CEO of Intelligent Results, a leading business analytics enterprise software company later acquired by First Data Corp. He also spent part of his career in Information Technology and Data Mining at Amazon.com during the late 1990's. Before becoming part of Amazon, Brian served among the founding team at Junglee Corporation, the first company to launch an internet shopping search and recommendations engine, which was acquired by Amazon in 1998.

Brian studied in the Ph.D.. program at Stanford University and there he co-founded in 1994 MIDAS (Mining Data at Stanford), the research lab that incubated the Google crawler and search engine. A recipient of the prestigious National Science Foundation and Department of Defense Office of Naval Research Graduate Fellowships, Brian has authored numerous professional publications and patents. Brian holds a B.S. in Computer Science from the University of Nevada, Reno and an M.S. and Ph.D.. Candidacy in Computer Science from Stanford University. Brian graduated from University of Nevada, Reno in the Honors Program as the University's 1993 Herz Gold Medal Recipient.

Wednesday, May 02, 2012 at 1:00 PM

Host: Drs. Bobby Brant and Fred Harris

Computational Neuroscience at University of Nevada, Reno

Laurence Bray

University of Nevada, Reno

Despite their background and approach differences, there is surprisingly an interesting interaction between the computer science and neuroscience research communities. In this presentation I discuss the relationship between the two disciplines and show how they can have a positive impact on the study of the nervous system, especially a complex structure like the brain. Computational neuroscience has two crucial aspects: studying the nervous system and developing simulations to explain neural processes. Several examples are given, covering brain models, simulation programs, and related robotic applications done in the Brain Computation Laboratory (Department of Computer Science & Engineering) at University of Nevada, Reno. Finally, I speculate about how this "new" field has evolved into a close collaboration between experimentalists and computer scientists in analyzing novel data and synthesizing new models of biological mechanisms.


Dr. Laurence Bray received her B.S. degree in Biological Sciences and M.S. degree in Bioengineering from Clemson University, SC in 2004 and 2005, respectively. She received her Ph.D.. degree in Biomedical Engineering from the University of Nevada, Reno in 2012. She is currently a Postdoctoral Associate in the Department of Computer Science & Engineering at the University of Nevada, Reno.

Dr. Bray has actively pursued research in the field of computational neuroscience, especially brain modeling to understand fundamental mechanisms involved major neurological disorders. She has worked on several projects funded by the Office of Naval Research (ONR). Her research contributions are covered in her recent journal and conference publications.

Friday, April 27, 2012 at 1:00 PM

Host: Dr. Yaakov Varol

How to Succeed in Industry as a CSE Graduate

Adam Altman, John Kenyon, Nerissa Oberlander, Hector Urtubia, Brian Westphal
Adobe Systems, Amazon.com, Lockheed Martin, PC-Doctor, Apple

Five distinguished alumni will participate in a panel discussion and present their thoughts on the topic of "How to Succeed in Industry as a CSE Graduate."


Adam Altman was a student in the University of Nevada's Computer Science department from 1995 to 1999. He moved to the Bay Area and received his MS from Stanford in 2000. There, he met representatives from Adobe Systems, who hired him to join their Acrobat development team. He had only intended to stay in that position for a few years at the most, but plans change. Today, Adam is still part of Adobe, working as a senior developer on the Muse project. He lives and works in the Seattle area where he loves the music scene, theater, hiking, running, and other outdoor activities.

John Kenyon was raised in Reno, Nevada, and through his family has strong connections to the University of Nevada, Reno. He has always enjoyed working with computers. He completed both a Bachelor and a Masters degree in Computer Science at the University of Nevada, Reno, in 2004 and 2008 respectively. After finishing his Masters, he traveled to Switzerland to work in a neuroscience lab for two and a half years. At the conclusion of this, he moved back to America, to take a position at Amazon.com Inc. in Seattle. At Amazon he is a member of the group known as Builder Tools, working on internal development systems.

Nerissa Oberlander is a Software and System Engineer with eight years of professional experience working on research and development, proposal efforts, active programs, tools development, systems integration testing, development lead, and a rotation in Corporate Engineering and Technology (CE&T) at Lockheed Martin Corporate Headquarters. She has five years experience with virtual reality programming and scene graph design on several research projects at two different universities. Nerissa received her BS in CS from the University of Nevada, Reno in 2000. She went on to UC Davis where she earned her MS in CS in 2003. In 2010 she earned an MS in Engineering Management and Systems Engineering from George Washington University. Nerissa is an emerging leader who graduated from the Lockheed Martin Engineering Leadership Development Program (ELDP) Class of 2009 and has experience with process improvement including Six Sigma and is Lockheed Martin Greenbelt Certified.

Hector Urtubia came to live in the US from Chile in 1997 and started his undergraduate studies in Computer Science at the University of Nevada, Reno in 1998. While in the program, he held several jobs in the Department. He was a System Administrator apprentice as well as working on the Genetic Algorithms Lab under Dr. Sushil Louis. As an undergraduate student, he actively participated in the ACM student chapter, where he served as President for one year. After obtaining his Bachelor degree, he continued his education at Nevada and graduated in 2003 with a M.S. in CS. Hector now works at PC-Doctor Inc., a Hardware Diagnostics company based in Reno. After going through several roles, he is now a Senior Software Engineer and Team Lead for the UI and Data teams. Apart from a strong passion for Programming and Software Engineering, Hector loves spending time with his family, playing music, building DIY hardware, biking, traveling and learning new technologies.

Brian Westphal is currently a software engineer for Apple Inc., working in Retail Engineering. He leads a team working on the EasyPay touch point-of-sale application used globally in Apple Retail Stores. He has previously worked at two startups, Nextstop.com and Flowgram.com, started a side-project iPhone-app company, cliqcliq, and worked on facial recognition integration in Picasa Web Albums for Google. He received his BS in CS in 2002, and his MS in CS in 2004 from the University of Nevada, Reno and an MA in Educational Leadership from the University of Nevada, Reno in 2004.

Friday, April 20, 2012 at 12:00 PM

Host: Dr. Kostas Bekris

Planning Algorithms for Computer Animation: From Humanlike Search Spaces to Local Clearance Triangulations

Marcelo Kallmann
University of California, Merced

I will present in this talk our latest results for addressing the problem of humanlike motion planning. Our starting point is a multi-modal planning framework used to coordinate locomotion, body positioning, and upper-body action execution, according to coordination patterns extracted from human subjects. Blending spaces defined from example motions are then introduced for achieving continuous search spaces describing humanlike variations of generic actions. The approach is complemented with a virtual reality interface for modeling blending spaces from direct demonstration. As a result, humanoid assistants and autonomous virtual characters become able to plan and execute motions that are similar to demonstrated examples, and at the same time addressing new constraints and parameterizations. I will also present new results for the efficient computation of navigation queries from local clearance triangulations, a new structure I have developed for fast path planning with arbitrary clearance. The approach is suitable for handling large and non-static environments, and is being adopted by the computer game industry. The presentation will be accessible to undergraduates and will count on several computer-animated videos and demonstrations.


Marcelo Kallmann is founding faculty and associate professor of Computer Science at the University of California, Merced. Before moving to UC Merced he was research faculty at the Computer Science department of the University of Southern California (USC) and a research scientist at the USC Institute for Creative Technologies (ICT). Between 2001 and 2004 he did postdocs at the USC Robotics research lab and at the Virtual Reality lab of the Swiss Federal Institute of Technology (EPFL), where he completed his Ph.D. in 2001. His areas of research include computer animation, virtual reality, motion planning and humanoid robotics. In these areas he has extensively published in international journals, conferences and workshops. He routinely serves as a program committee member for major international conferences and in 2012 he will be program co-chair the International Conference on Motion in Games (MIG). At UC Merced he established the computer graphics group, and his research has been supported by the National Science Foundation (NSF) and the Center for Information Technology Research in the Interest of Society (CITRIS). Details about his research are found at http://graphics.ucmerced.edu/.

Friday, April 13, 2012 at 12:00 PM

Host: Dr. George Bebis

Image Analysis Techniques in Medical Research and Clinical Practice

Sokratis Makrogiannis
National Institutes of Health (NIH)

Significant advances in physical and biological sciences coupled with the increased use of sophisticated engineering technologies have spurred interest in the application of biomedical imaging for everyday clinical procedures as well as biomedical research. The fields of medical image processing, analysis and machine learning have assumed a central role in this context, mainly because of their capability of revealing significant information from low or high dimensional imaging patterns and providing insight into biological processes.

This talk will discuss image-computing methods for medical research and clinical applications. Regarding the research domain, a method of computational brain anatomy and subspace decomposition for detection of brain atrophy will be presented, followed by techniques developed for body composition from MRI and CT data, and concluded with molecular image analysis applied to pre-clinical drug discovery studies. The clinical application branch of this talk includes a method for detection of soft plaque in the coronary arteries from contrast enhanced volumetric CT scans and a computer-aided tool for lesion characterization in the liver from dual energy CT images.


Sokratis Makrogiannis received the BS in Physics, MS in Electronics and the Ph.D. degree in the area of Image Analysis from University of Patras, Greece in 1995, 1998 and 2002 respectively. He currently works as an image analysis scientist for the intramural research program of National Institute on Aging (NIA) of the National Institutes of Health. His scientific interests are in the areas of medical computing, machine learning and scientific data visualization. He is the author or co-author of more than 40 scientific journal and conference articles. He currently serves as Associate Editor-in-Chief of the Int. Journal on Artificial Intelligence Tools. He is the recipient of the "Most Cited Article" article award of the Pattern Recognition journal for articles published in the period of 2006-2010, and a distinguished achievement award for his work at NIA. Prior to NIA, he worked as an imaging scientist/engineer at GE Global Research, GlaxoSmithKline R&D and received post-doctoral training in Univ. of Pennsylvania and Wright State University.

Friday, March 16, 2012 at 12:00 PM

Host: Dr. George Bebis

Computer Vision for the Mars Science Laboratory

Paolo Bellutta
Jet Propulsion Laboratory (JPL)

The Mars Science Laboratory is on her way to Gale Crater. But how can we be sure she can drive safely once she lands? Like previous missions, MSL analyzed orbital images of the terrain to ensure a safe landing. But thanks to MRO's HiRISE camera, things have changed from previous missions: HiRISE images provide enough detail that we can identify hazards and obstacles well enough to analyze the terrain for driving, not just for landing.

The Mars Exploration Rovers have already used HiRISE images to help navigate through several kilometers of treacherous terrain. But on MER, that analysis was (and is) done manually. By contrast, the MSL landing site is so large that a manual process was no longer an option. This talk will show how a series of products derived from HiRISE images allowed software to analyze the Martian terrain, determine where MSL could safely drive, and compute how long MSL will take to move from place to place.


Paolo Bellutta has been a member for the past thirteen years of the Computer Vision group at the Jet Propulsion Laboratory. He has developed several vision systems for ground vehicles and is part of the team of rover drivers for the Mars Exploration Rovers and Mars Science Laboratory. He has done extensive analysis of Martian terrain as applied to rover mobility and was part of the MSL landing site selection team.

Friday, March 09, 2012 at 12:00 PM

Host: Dr. Murat Yuksel

Constrained Relay Node Placement in Wireless Sensor Networks: Problems, Formulations, and Approximation Algorithms

Satyajayant Misra
New Mexico State University

Deployment characteristics of sensor nodes and their energy limited nature affects network connectivity, lifetime, and fault-tolerance of wireless sensor networks (WSNs). One approach to address these issues is to deploy some relay nodes to communicate with the sensor nodes, other relay nodes, and the base stations in the network. The relay node placement problem for WSNs is concerned with placing a minimum number of relay nodes into a WSN to meet certain connectivity or survivability requirements. Previous studies have concentrated on the unconstrained version of the problem in the sense that relay nodes can be placed anywhere. In practice, there may be some physical constraints on the placement of relay nodes. To address this issue, we have studied constrained versions of the relay node placement problem, where relay nodes can only be placed at a set of candidate locations.

I will talk about relay node placement for connectivity and survivability, we will discuss the computational complexity of the problems and look at a framework of polynomial time O(1)-approximation algorithms with small approximation ratios. I will share our numerical results. We will also talk about some pertinent extensions of this work in the area of high performance computing. I will also present some additional results we have had which consider energy harvesting and updating the framework to make it energy-harvesting-aware.


Dr. Satyajayant Misra is an assistant professor at New Mexico State University (from fall 2009). His research interests include anonymity, security, and survivability in wireless sensor networks, wireless ad hoc networks, and vehicular networks and optimized protocol designs for next supercomputing architectures. His works have been published in high impact journals, such as IEEE Transactions on Mobile Computing, and IEEE Transactions on Networking and high impact conferences, such as IEEE INFOCOM (2007, 2008, 2009) and IEEE ICNP (2010).

Dr. Misra serves on the editorial boards of the IEEE Communications on Surveys and Tutorials and the IEEE Wireless Communications Magazine. He is the TPC Vice-Chair of Information Systems for the IEEE INFOCOM 2012. He has served on the executive committees of IEEE SECON 2011 and IEEE IPCCC 2010. He is the recipient of New Mexico State University's University Research Council Early Career Award for Exceptional Achievement in Creative Scholastic Activity, for the year 2011.

Friday, March 02, 2012 at 12:00 PM

Host: Dr. Mehmet Gunes

Next Generation Heterogeneous Wireless Networks

Ismail Guvenc
Docomo Labs

Heterogeneous networks (HetNets) consist of a mix of macrocells, remote radio heads, and low-power nodes such as picocells, femtocells, and relays. Through bringing the access network closer to the end-users, HetNets have the potential to provide the next significant performance leap in wireless networks, improving spatial spectrum reuse and enhancing indoor coverage. Nevertheless, deployment of a large number of small cells overlaying the macrocells is not without new technical challenges. After a general overview of HetNets in the context of 3GPP standardization, this talk will cover two particular challenges in HetNet deployments: enhancement and theoretical analysis of 1) spectral efficiency, and 2) mobility performance.

Range expansion and inter-cell interference coordination can improve the capacity and fairness of heterogeneous networks by off-loading macrocell users to low-power nodes like picocells. We derive closed-form expressions for the distribution of the signal to interference plus noise ratio (SINR) and spectral efficiency of user equipments associated with macro- and pico-cells. These results illuminate the effects on spectral efficiency of (i) duration of the macro almost blank subframes, (ii) the SINR threshold for a user equipment to be served by a picocell during almost blank subframes, and (iii) the range expansion bias. In homogeneous networks, mobile user equipments typically use the same set of handover parameters for handing over a target cell. However, in HetNets, using the same set of handover parameters for all cells and for all user equipments may degrade mobility performance. In the second part of this talk, mobility enhancements for heterogeneous wireless networks will be investigated through the use of interference coordination methods. Moreover, a novel analytical model that enables the closed-form analysis of handover failure and ping-pong probabilities in heterogeneous network deployments will be introduced.


Ismail Guvenc received his B.S. degree from Bilkent University, Turkey, in 2001, M.S. degree from University of New Mexico, Albuquerque, in 2003, and Ph.D.. degree from University of South Florida, Tampa, in 2006 (with Outstanding Dissertation Award), all in electrical engineering. He was with Mitsubishi Electric Research Labs in Cambridge, MA, in 2005, and since June 2006, he has been with DOCOMO Innovations, Inc., Palo Alto, working as a research engineer. His recent research interests include heterogeneous networks and future radio access beyond 4G wireless systems. He has published more than 60 conference and journal papers and several standardization contributions, and he is the inventor/coinventor of over 25 granted/pending US and international patents.

Dr. Guvenc is a senior member of the IEEE, an associate editor for IEEE Communications Letters (since 2010), and an associate editor for IEEE Wireless Communications Letters (since 2011). He is the co-chair of the IEEE 1st FEMnets workshop, and the steering committee member for the IEEE 2nd FEMnets workshop. He co-authored/co-edited three books for Cambridge University Press, was the lead guest editor for a special issue on Femtocell Networks for EURASIP Journal on Wireless Communications and Networking (2010), and a guest editor for a special issue on Heterogeneous Networks for IEEE Journal on Selected Topics in Signal Processing (2012).

Tuesday, February 28, 2012 at 12:00 PM

Host: Dr. Sergiu Dascalu

In Silico Modeling of Complex Biological Processes

Marc Colangelo
McMaster University

Regardless of their origin or pathology, many, if not all, diseases have long been regarded as complex. Yet, despite the progression in the understanding of complexity and the development of systems biology, the majority of biomedical research has been derived from qualitative principles. In comparison to the ethical, temporal and logistical limitations of human experimentation, in vivo animal models have served to provide a more advantageous means to elucidate the underlying disease mechanisms. However, given the additional limitations presented by such models, in silico models have emerged as an effective complement, and, in some cases, a replacement for in vivo experimentation.

My talk will focus on some of the in silico models developed during my graduate studies, as well as my experiences using mathematical and computational methods to investigate the evolution of two complex, diverse diseases from a systems biology perspective: allergic asthma and cancer.


Marc Colangelo is currently an instructor in the Bachelor of Health Sciences (Honours) Program at McMaster University in Hamilton, Ontario. After receiving his B.H.Sc. degree from McMaster in 2004, he began his M.Sc. degree in Medical Sciences at McMaster in 2004 and transferred to the Ph.D.. program in 2006. Marc recently completed his Ph.D.. in Medical Sciences Program (Infection and Immunity Stream) in the Department of Pathology and Molecular Medicine and the McMaster Immunology Research Centre in 2011. His research interests include mathematical modeling of biological systems, complexity theory and educational research.

Friday, February 24, 2012 at 12:00 PM

Host: Dr. George Bebis

Log-Linear Model: From Shallow to Deep

Dong Yu
Microsoft Research

Log-linear model, motivated by the principle of maximum entropy, is popular for many classification tasks. In this talk, I will review the basic maximum entropy (MaxEnt) model and extend it to support continuous features and complicated interactions between features. I will show that the basic MaxEnt model is a shallow model without feature extraction layers, the MaxEnt model that supports continuous features is a shallow model with one feature extraction layer, and the MaxEnt model with complicated feature interactions can be modeled using a deep neural network (DNN). I will describe the training strategy for the DNN and demonstrate that we can reduce over 30% of errors on a large vocabulary spontaneous speech recognition benchmark using the DNN - hidden Markov model (HMM) system compared to the close-to-the-state-of-the-art conventional Gaussian mixture model (GMM) - HMM system.


Dr. Dong Yu joined Microsoft Corporation in 1998 and Microsoft Speech Research Group in 2002, where he is a researcher. He holds a Ph.D.. degree in Computer Science from University of Idaho, an MS degree in computer science from Indiana University at Bloomington, an MS degree in electrical engineering from Chinese Academy of Sciences, and a BS degree (with honor) in electrical engineering from Zhejiang University (China). His current research interests include speech processing, robust speech recognition, discriminative training, spoken dialog system, and machine learning. He has published more than 90 papers in these areas and is the inventor/coinventor of more than 40 granted/pending patents.

Dr. Dong Yu is a senior member of IEEE, a member of ACM, and a member of ISCA. He is currently serving as an associate editor of IEEE transactions on audio, speech, and language processing (2011-) and has served as an associate editor of IEEE signal processing magazine (2008-2011) and the lead guest editor of IEEE transactions on audio, speech, and language processing - special issue on deep learning for speech and language processing (2010-2011).