Your resume or curriculum vitae (CV) is a valuable tool for marketing yourself to prospective employers and industry. It is a concisely written summary of your personal, educational and work experiences. This program will break-down what information you should include on both a resume and CV, the purpose of including them,general rules of thumb and what information to not include.

With over 10 years of higher education experience, 8 of which in the field of Career Development, Darren currently serves as the Associate Director of the Career Center for Science and Engineering at the University of Minnesota-Twin Cities campus. In his current position, Darren counsels undergraduate, graduate and Ph.D. students in the College of Science and Engineering and the College of Biological Sciences on a variety of career related topics and also oversees the employer relations program for the office. Darren is known for his strong passion and enthusiasm of working with all students, and loves playing a vital role in assisting students with making their dreams become reality. Darren holds a Bachelor of Applied Science degree in Psychology from the University of Minnesota-Duluth, and a Master of Education degree in Student Personnel Administration in Higher Education from Springfield College.

Video of the talk: (flv)

Slides of talk:
Resume_and_CV_Presentation-IMA.ppt
Resumes_and_CV_Presentation-IMA.pdf

Supplementary files:
What NOT to Include on a Resume.pdf
25_Words_That_Can_Hurt_Your_Résumé.pdf
Information on on GoldPASS and mock interview week

Medical Imaging continues to be an area of active research with a wide spectrum of interesting problems. The large increase in data size and the risks of radiation dose to patients are among some recent challenges that emerged in the CT scanner world and require solutions in the industry. This presentation will give an overview of some of the problems that come up in the development of advanced medical analysis software that deals with scanner data. The presentation will go into some detail to discuss challenges and solutions with respect to two problems: Brain perfusion calculation and speed optimization of a particular basic operation.

Dr. Osama Masoud is currently the manager of Image Analysis at Vital Images, Inc. where he leads a team of research scientists who develop segmentation, registration, and analysis algorithms. He held different research positions in the past including a research associate position at the University of Minnesota Computer Science and Engineering department focusing on computer vision, video analysis, and machine learning. Dr. Masoud has published 30 journal and conference paper and obtained his Ph.D. in Computer and Information Science from the University of Minnesota in 2000.

Obtaining tight bounds on rounding errors has been so specialized and labor-intensive a task that it is seldom carried out during normal engineering practice in industry. It turns out that for absolute error analysis related to fixed point arithmetic, an automatic method can be devised for computation of linear transform. This method, implemented as a software tool, allows practicing engineers to obtain tight bounds as well as a vast amount of statistical information on forward rounding errors. The method consists of modeling the rounding error process in a way that allows mechanical computation on its propagation. When this model and propagation computation is implemented with objects and overloading in an object oriented manner, engineers can obtain detailed error information by means of algorithm implementation, not by actually carrying out error analysis. In this talk we will describe this method and illustrate its application on the very important Fast Fourier Transform.

Ping Tak Peter Tang is a research scientist at D. E. Shaw Research working on computational methods related to molecular dynamics simulation. Prior to his present position, Peter has worked in academia and industry, last serving as a Senior Principal Engineer at Intel Corporation focusing on computational software libraries. His academic experiences include research at two Department of Energy research laboratories, as well as a one-year lectureship at the Chinese University of Hong Kong. Peter was also a key participant of the IEEE Standard 754-2008 committee on floating point arithmetic, contributed to one of the two standardized decimal floating point encodings. Peter obtained his Ph.D. in mathematics from University of California, Berkeley.

Traditional fMRI data analyses are mainly focused on discovering brain activation patterns using standard GLM technique that selects voxels based on their individual correlations with stimuli. However, such mass-univariate approach completely ignores voxel interactions that are often essential for understanding brain functions,and can be better captured by an alternative approach - multivariate predictive modeling. This talk summarizes our recent work in this area, with a particular focus on discovering predictive features ("biomarkers") characterizing non-local, distributed patterns of brain activity. One example of our approach is discovering predictive subsets of voxels via sparse regression methods such as LASSO and Elastic Net. We discuss several applications, such as predicting mental states of a subject playing a virtual-reality videogame in a fMRI scanner, or predicting subject's pain perception in response to a thermal pain stimuli. We find that sparse regression produces highly predictive models that also provide evidence for the distributed nature of neural function. Next, we underscore the importance of distributed activity patterns when exploring predictive information contained in the topology of brain's functional networks. We consider a challenging task of building a discriminative model for schizophrenia, a complex psychiatric disorder that appears to be delocalized, i.e. difficult to attribute to a dysfunction of some particular brain areas. Our findings demonstrate significant advantages the functional network features can provide over both traditional region-of-interest (ROI) approach and local, task-specific linear activations produced by standard GLM. Our results suggest that schizophrenia is indeed associated with disruption of global brain properties related to its functioning as a network, which cannot be explained just by alteration of local activation patterns. Moreover, further exploitation of voxel interactions by sparse Markov Random Field (MRF) classifiers allows to attain a high predictive accuracy of 86% over 50% baseline, which is quite remarkable given that our discriminative model is based on a single fMRI experiment using a simple auditory task.

Irina Rish is a research staff member at the Biomedical Computing Department which is a part of the Computational Biology Center at the IBM T. J. Watson Research Center. She received an M.S. in applied mathematics from Moscow Gubkin Institute, Russia, and a Ph.D. in computer science from the University of California, Irvine. Dr. Rish's primary research interests are in the areas of probabilistic inference, statistical learning, and information theory, and their applications to large-scale data analysis problems in biology and neuroscience. Her current research focuses on applying machine-learning techniques to neuroscience, and particularly on statistical analysis of fMRI data using sparse regression, dimensionality reduction and graphical models. In the past, she has worked on efficient approximations of probabilistic inference in Bayesian networks, probabilistic diagnosis and experiment design, active learning, collaborative prediction, sparse regression and sparse matrix factorization, and their applications to autonomic computing, as a part of the Adventurous Research project on Self-Managing Computer Systems that she lead at IBM Watson (2003-2007). She has over 40 conference and journal publications on the above topics. Dr. Rish taught several machine learning courses at the Electrical Engineering and Computer Science departments of Columbia University as an adjunct professor, and co-organized several machine-learning workshops at ICML, ECML and NIPS conferences.

A (possibly personal) perspective on how research that has a significant theoretical component should be constructed and organized. Questions regarding "what" and "why" will be explored with the group, along with a discussion of topics that are being investigated currently. Anyone interested in an interactive discussion is welcome to attend.

Dr. Jeffrey Abell is Lab Group Manager, Advanced Propulsion Manufacturing Research, Manufacturing Systems Research Lab, General Motors Global Research & Development. He has held technology development leadership positions in product development and manufacturing engineering at General Motors, DaimlerChrysler, and Delphi. He was on the faculty of Mechanical Engineering and Manufacturing Systems Engineering at Kettering University, and is currently a Program Evaluator for Accreditation Board of Engineering and Technology (ABET). He has authored over 25 technical publications, and has several patents and intellectual property awards. Dr. Abell is a Licensed Professional Engineer in Michigan.

A widely used model in online advertising industry is the one in which advertisers pre-purchase a reservation package of online inventory on content sites owned by the publishers (e.g., CNN, amazon, etc.). This package consists of specified inventory bundles of various types that are priced differently and differ in various properties including their expected effectiveness (e.g., Click Through Rate). When online advertisers arrive to a publisher, they have a daily budget, desirable duration of the advertising campaign and a performance goal, which is expressed through some target 'effectiveness' of the purchased package. We design a simple and easy to implement online inventory allocation policy and rigorously prove its asymptotically optimal long run performance. The underlying dynamics of the described application has some similarities with bandwidth sharing in communication networks. However, there are intrinsic characteristics that make the problem of impression allocations in online advertising novel from the modeling and analysis perspective. The key difference is a random budget, which translates into random inventory demand. The other important property is that online advertisers do not ask for specific inventory type, but expect some overall effectiveness from the package of purchased inventory. In view of the existing capacity constraints, we propose a simple online inventory allocation rule, which uses 'careful' sizing of safety stocks to deal with the finite inventory capacities. We rigorously prove the long run revenue optimality of our policy in the regime where demand and inventory capacities grow proportionally. Joint work with Assaf Zeevi (Columbia University).

Ana received her B.S. in Electrical Engineering from University of Belgrade in 1999. She received her Ph.D. in Electrical Engineering from Columbia University, New York, in 2004. Her thesis, for which she was advised by Prof. Predrag Jelenkovic, was titled 'Nearly Optimal Cache Replacement Policies for Efficient Web Access'. In January 2005, Ana became a Research Staff Member in Stochastic Analysis group, Mathematical Sciences Department, IBM Research. After spending three years at IBM Research, Ana joined Google as a Research Scientist.

Ana's research focuses on fundamental principles of operation, design and control of systems in which uncertainty is an inherent property and an important assumption in the analysis and design. She particularly focuses on stochastic modeling, analysis and optimization that lead to explicit and insightful results. These results highlight business tradeoffs and provide general design guidelines. She has worked on research problems in the following areas: Web caching, stochastic service networks, revenue management and pricing in capacitated systems with reusable resources, job scheduling algorithms in large computer centers and online advertising. If interested, please check out her publications.

Video of the talk: (flv)

The web has a vast wealth of information about various types of entities such as businesses (e.g., address, phone, category, hours of operation), products, books, doctors, etc. distributed over a very large number of web sites. Extracting this information from the websites can help us create extensive databases of the entities. These databases can then be used by search engines for better ranking and rendering of search results, e.g., a user can search for products with certain features. The websites usually contain the information in semi-structured formats which are varied and noisy. Extraction on a large scale is challenging because it is not feasible to provide supervision (say, via labeled examples) on a per site basis. In this talk I will give an overview of all the steps associated with a complete extraction pipeline and describe a few scalable machine learning approaches for large scale information extraction.

Dr. Keerthi is a Principal Research Scientist in Yahoo! Research. Over the last twenty years his research has focused on the development of practical algorithms for a variety of areas, such as machine learning, robotics, computer graphics and optimal control. His works on support vector machines (fast algorithms), polytope distance computation (GJK algorithm) and model predictive control (stability theory) are highly cited. His current research focuses on machine learning algorithms for structured outputs as applied to information extraction. Prior to joining Yahoo!, he worked for 10 years at the Indian Institute of Science, Bangalore, and for 5 years at the National University of Singapore. Dr. Keerthi is a member of the editorial board of Journal of Machine Learning Research.

Video of the talk: (flv)

In this talk, we present how signals and system theory was used for creating novel color solutions for digital production printing. We describe the system, mathematical formulation of the process, use of specialized algorithms, methods and architectures briefly, and then present focused research topics we are working on for transportation and healthcare systems. Integration of modern theories (e.g, compressed sensing, control & optimization theory), specialized optics with various imaging devices in the visible and infrared red wavelength bands will be presented aimed at creating next generation solutions and services business.

Lalit K. Mestha, a Principal Scientist at Xerox received his PhD from the University of Bath, England in 1985 and his BE in 1982, from the University of Mysore, India, all in EE. He has led variety of research projects since 1987 on sensing and control of small to large scale imaging and engineering systems. Numerous controls, imaging and sensing technologies researched by LK are being deployed in systems (particle accelerators in Fermilab, KEK, CERN) and in imaging products (iGen3, iGen4, iGen4 220, Xerox Color 800/1000, DocuColor 5000/7002/8002/8000), generating multi-billion dollar revenue. An inline spectrophotometer sensing system researched by Mestha is manufactured by The Appcon Group, Inc., Rochester, NY. He is very active in increasing the knowledge of system theory in imaging and systems community as an outreach to the corporate and university environment. He does this through (1) publications; 227, which includes 175 patent filings of which 96 patents have been awarded (with 79 pending), (2) initiating and working on collaborative projects with universities, (3) serving in National and Societal activities (NSF, IEEE CSS, ASME, IS&T), and (3) teaching over 23+ graduate courses as professor since 1990 at the UT Arlington and the Rochester Institute of Technology. He is a recipient of 2010 Anne Mulcahy Inventor Award for contributions to color accuracy in Xerox production printing devices, 2006 IEEE Control System Technology Award and the recipient of R&D100 Award by the R&D Magazine in 2006 for developing the Top 100 Most Technologically Significant product. He recently published a book "Control of color imaging systems" by CRC Press and authored two book chapters. Prior to joining Xerox, Mestha was at the SSC Laboratory in Dallas. He is a Fellow of IEEE, DfLSS Black belt and teaches at Rochester Institute of Technology as an Adjunct Professor in his spare time.

Video of the talk: (flv)

What do Microsoft, Genentech, Google, Securian, Target, and Ernst & Young have in common? All these companies (and many more) have used LinkedIn to recruit candidates for employment. Kay Luo, Director of Corporate Communications at LinkedIn, explains why, "The main reason that companies are using LinkedIn is to find passive job candidates. Another reason why companies are using LinkedIn, is because referrals from their employees are highly valued because they typically have a higher success/retention rate (hence the popular "employee referral bonuses"). There are members from all 500 of the Fortune 500 companies, and over 83 million LinkedIn members comprise 130 different industries, and include 130,000 recruiters.Your professional network of trusted contacts gives you an advantage in your career, and is one of your most valuable assets. LinkedIn exists to help you make better use of your professional network and help the people you trust in return.This presentation will cover why and how to use LinkedIn to network and search for internships and jobs. Remember — it is more about who you know or need to get to know than what you know!

With over 10 years of higher education experience, 8 of which in the field of Career Development, Darren currently serves as the Associate Director of the Career Center for Science and Engineering at the University of Minnesota-Twin Cities campus. In his current position, Darren counsels undergraduate, graduate and Ph.D. students in the College of Science and Engineering and the College of Biological Sciences on a variety of career related topics and also oversees the employer relations program for the office. Darren is known for his strong passion and enthusiasm of working with all students, and loves playing a vital role in assisting students with making their dreams become reality. Darren holds a Bachelor of Applied Science degree in Psychology from the University of Minnesota-Duluth, and a Master of Education degree in Student Personnel Administration in Higher Education from Springfield College.

Video of the talk: (flv)

Supplementary files:
Employer_Research_on_LinkedIn.pdf
LinkedIn_Student_Profile_Checklist.pdf
Network_Professionally_Online_LinkedIn.pdf

Update to the talk:  LinkedIn adds new functionality to 'Company' search

Automotive industry today is challenged by numerous complex and often conflicting constraints and requirements such as compress vehicle design cycle time, lower the weight and cost of vehicles, and improve product performances, e.g., durability, NVH, safety, quality, reliability, etc. To satisfy these stringent requirements, automobile manufacturers are increasingly relying on Computer-Aided Engineering (CAE) and the use of more formal and structured approaches for product development. Numerical optimization is a systematic tool for considering all disciplines and finds a compromise solution. In addition, as most CAE simulations are computation intensive, special optimization methods and processes are often required. This presentation will focus on historical developments and applications of numerical optimization and robustness methods for vehicle designs. It will address significant technologies, such as Topology Optimization, Multi-disciplinary Design Optimization, Robust, Reliability-Based Design Optimization, and Process Integration and Design Optimization.

Dr. Ren-Jye Yang received his B.S. in Civil Engineering and M.S. degree in Engineering Mechanics from the National Taiwan University at Taipei, Taiwan. He received his Ph.D. degree in Civil Engineering from the University of Iowa at Iowa City in 1984. He is currently a Senior Technical Leader in the Passive Safety Department at Ford Research & Advanced Engineering, responsible for the development of Safety Optimization and Robustness and CAE Model Validation Methods. Before he joined Ford in 1988, Dr. Yang was a Staff Research Engineer in the Engineering Mechanics Department at GM Research Laboratories. His research areas of interest include: Design Optimization, CAE, Model Verification and Validation, Reliability-Based Design Optimization, HPC, Probabilistic and Statistic Methods, etc. Dr. Yang has received numerous awards, including three Henry Ford Technology Awards, which is the highest technical award at Ford. Dr. Yang has published more than 50 referred Journal papers. He is a Senior Advisor and associate editor of the Structural and Multidisciplinary Optimization Journal and serves on the editorial board of the International Journal of Reliability and Safety, and International Journal of Vehicle Structures & Systems. He is an ASME fellow and is a committee member of the ASME V&V 10. In 2004, Dr. Yang was elected as an Industry Advisor for the ASME Design Automation Executive Committee. He is the recipient of the ASME Design Automation Award in 2005. Dr. Yang has also served on the University/Industry Advisory Board for a number of Universities, e.g., UM ARC External Advisory Board. He is currently a guest professor at Shanghai Jiao Tong University and Nanjing University of Aeronautics and Astronautics.

A large number of natural phenomena can be formulated as inference on differentiable manifolds. More specifically in computer vision, such underlying notions emerge in multi-factor analysis including feature selection, pose estimation, structure from motion, appearance tracking, and shape embedding. Unlike Euclidean spaces, differentiable manifolds does not exhibit global homeomorphism, thus, differential geometry is applicable only within the local tangent spaces. This prevents direct application of conventional inference and learning methods that require vector norms, instead, distances are defined through curves of minimal length connecting two points. Recently we introduced appearance based descriptors and motion transformations that exhibit Riemannian manifold structure on positive definite matrices and enable projections onto the tangent spaces. In this manner, we do not need to flatten the underlying manifold or discover its topology. For instance, by imposing weak classifiers on tangent spaces and establishing weighted sums via Karcher means, we bootstrap an ensemble of boosted classifiers with logistic loss functions for object classification. This talk will demonstrate promising results of manifold learning on human detection, regression tracking, unusual event analysis and affine pose estimation.

Dr. Fatih Porikli is currently a Senior Principal Research Scientist and Technical Manager at MERL. Before joining MERL in 2000, he developed satellite imaging systems at Hughes Research Laboratories in 1999 and 3D systems at AT&T Research Laboratories in 1997. His research interests include pattern recognition, online learning, computer vision, sparse optimization, multimedia processing, medical data analysis, and data mining with many applications ranging from surveillance to intelligent transportation to medical automation to visualization. He is the associate editor for two journals, the general chair of the 2010 IEEE AVSS, co-organizer of +20 workshops, and organizing committee of flagship conferences including CVPR, ICCV, ECCV, ICIP and many others. He has authored +90 publications, invented +50 patents, and mentored more than 30 PhD students. Dr. Porikli was the recipient of the R&D 100 Scientist of the Year Award in 2006, the Best Paper Runner-Up Award at CVPR 2007, the Best Paper Award at OTCBVS of CVPR 2010, the Popular Scientist Award in 2007, and half-a-dozen MELCO/MERL awards.

Failure detection and fault correction are vital to ensure high quality software. During the development and deployment phases detected failures are commonly classified by severity and tracked to meet quality and reliability requirements. Besides tracking failures, this data can be analyzed and used to qualify the software and to control the development and maintenance process. Our work is focused on failure data collected during the development phase and explores what we can learn by analyzing this data. Change management systems log the failures detected and the code fixes to correct the underlying software defects. By applying software reliability models and statistical techniques to this defect data, we can answer questions such as the following:

• Is the maintenance process increasing the software reliability?
• Is the maintenance process under control?
• How many failures are expected to occur in the field?
• What is the expected time remaining to meet the reliability requirement?

This presentation addresses these questions by using a methodology based on trend analysis, control charts and software reliability growth models. The methodology is applied to a large software system during various stages of testing including customer acceptance testing. What is new about this methodology is the combined use of control charts, trend analysis and software reliability models.

Veena Mendiratta leads the Next-Generation Solutions, Services and Systems Reliability work in the Bell Labs Network Performance and Reliability department at Bell Labs, Alcatel-Lucent.

She began her career at AT&T Bell Labs in 1984 and her work is focused on the reliability and performance analysis for telecommunications systems products, networks and services to guide system architecture solutions. Her technical interests include architecture, system and network dependability analysis, software reliability engineering and data analytics. Current work is focused on LTE (Long Term Evolution, 4G wireless technology) solution reliability engineering, service reliability modeling for the transportation sector, and predictive analytics for the telecommunications domain.

Professional activities include: Program Committee member for IEEE DSN and ISSRE conferences; serving on the MCM Advisory Board as well as an MCM and HiMCM judge for the COMAP sponsored math modeling competitions; member of INFORMS and Senior Member of IEEE; past co-chair of the INFORMS Chicago Chapter; and a member of the Alcatel-Lucent Technical Academy.

She has a B.Tech in Engineering from the Indian Institute of Technology, New Delhi, India and a Ph.D. in Operations Research from Northwestern University, Evanston, Illinois, USA.