CIRC Symposium Series: Elliot Inman76e416afac19042a71a836ad75454e39Elliot Inman Manager of Software Development for SAS Every third Friday of the month, the Center for Integrated Research Computing hosts a research symposium (known as the CIRC Symposium), where faculty, staff, and student researchers convene to learn about research projects utilizing the center's resources, meet potential collaborators, and learn about new technologies and trends in research computing. This event is user-driven and features presentations by researchers using CIRC systems. CIRC Symposia are open to all members of the university community and a complimentary lunch is provided. TITLE: The Human Factor in the Age of Machine Learning ABSTRACT: The advent of machine learning algorithms has been described as everything from a "second industrial revolution" to a "national security threat." Alarming stories in the popular press describe a world in which human beings have become obsolete. Exceptionally high-speed algorithms that constantly adapt to streaming data will do everything from generating the art that entertains and inspires us to diagnosing cancer early enough to defeat it. Machine learning will provide an automated control system for a nation with hundreds of millions of self-driving cars and order our groceries based on a diet the machine has determined is best for us to eat. And all of these machines will do all of that without any human being needed to make it all work. But, as the poet e.e. cummings wrote, "pity this busy monster, mankind, not." There will still be a role for human beings. Machines feed on data. People will decide what data are collected, who owns those data, and how the data may be used. People will decide what and when to feed the machine. Data Scientists and Statisticians will decide the standard for a reliable system and determine which machine is consistent enough to be trusted. Researchers with expertise in physics, chemistry, biology, and botany from fields like health care, agriculture, and education, and so on will set the standard for a valid system. They will decide which machine has a true understanding of what science has already uncovered. They will be the first to know whether the machine is right or wrong. And finally, human beings will be the ones to make machine learning algorithms better and more useful to us all. In this talk, Elliot Inman, Ph.D., a manager of software development for SAS, will discuss his perspective on machine learning and the role of data scientists and other researchers in the future of computational science. This talk is a part of the Goergen Institute for Data Science’s Data Science Industry Speakers Series. In addition to his talk, Inman will also lead a series of workshops called "Microcontrollers for the Rest of Us” with the River Campus Libraries TinkerSpace program. BIO: Elliot Inman, Ph.D., is a Manager of Software Development for SAS® Solutions OnDemand. Over the past 25 years, he has analyzed a wide variety of data in areas as diverse as the effectiveness of print and digital advertising, social service outcomes analysis, healthcare claims analysis, employee safety, educational achievement, clinical trial monitoring, sales forecasting, risk-scoring and fraud analytics, general survey analysis, performance benchmarking, product pricing, text processing, and basic scientific research on human memory and cognitive processes. After completing his undergraduate degree at North Carolina State University, he went on to earn a Ph.D. in Experimental Psychology from the University of Kentucky in 1997. In 2005, he started at SAS as an Analytical Consultant. In 2010, he joined SAS Solutions OnDemand, SAS’ high performance cloud computing center. His current focus is on implementing Visual Analytics to provide non-statisticians deeper insight into the results of data mining and predictive models. In addition to his work at SAS, he has led makerspace workshops using microcontrollers to gather data for the Internet of Things and other applications.Wegmans Hall 1400 (auditorium), River Campusfalse15109362000002017-11-17T11:30:0015109416000002017-11-17T13:00:00Talks2017/11/1117_circ-symposium-series 1117.htmlfalseOnceTinkerSpace: Microcontrollers for the Rest of Us05ab2f87ac19042a71a836ad4a106ea2Carlson Library, 2nd Floorfalse15108426000002017-11-16T09:30:0015109524000002017-11-17T16:00:00Other2017/11/1116_tinkerspace-microcontrollers-for-the-rest-of-us.htmltrueOnceThe Arduino as an Audience Experience Meter Workshop0b91f120ac19042a71a836ad8c6d93ccCarlson Library, 2nd Floorfalse15108588000002017-11-16T14:00:0015108660000002017-11-16T16:00:00Other2017/11/1116_the-arduino-as-an-audience-experience-meter-workshop.htmlfalseOnceCTSI Analytics Colloquium956e2610ac19042a71a836adcdd26e12The first CTSI Informatics Analytics Cluster Colloquium will be held on Tuesday Nov. 7th from noon to 1pm with lunch provided. The theme of the colloquium is the historical roots of machine learning. The Historical Foundations of Machine Learning by Anthony Almudevar, PhD, Associate Professor of Biostatitics and Computational Biology, University of Rochester. Applications of Machine Learning in Precision Medicine in Machine by Varun Chandola, PhD, Assistant Professor, Department of Computer Science & Engineering, SUNY Buffalo. Includes a light lunch! For questions or comments, please contact Dongmei Li Lower Adolph Auditorium (1-7619) in the Medical CenterLower Adolph Auditorium (1-7619) in the Medical Centerfalse15100740000002017-11-07T12:00:0015100776000002017-11-07T13:00:00Talks2017/11/1107_ctsi-analytics-colloquium.htmlfalseOnceCareers in Data Science: Kirk Ocke, PARC3554024aac19042a71a836ad69222d70Kirk Ocke, Senior Data Scientist and Software Engineer at PARC, a Xerox Corp. company, will discuss his career path and trends in data science at a career lunch at 12 noon Friday Nov. 10 in 1400 Wegmans Hall (Auditorium. Kirk Ocke, formerly an adjunct professor of data science at the University of Rochester, will discuss his career path and trends in data science. Mr. Ocke develops innovative solutions in the areas of data science and workflow automation. PARC provides custom R&D services, best practices and intellectual property to Fortune 500 and Global 1000 companies, start-ups, government agencies and partners. The company creates new business options, accelerates time to market and augments internal capabilities for its clients. Mr. Ocke’s current areas of research include predicting the payment patterns, and other behaviors, of participants in child support cases, along with adapting lean and agile software development techniques for use in a research organization. His areas of expertise and interest include: applied data science, predictive analytics, lean/agile software development, workflow automation and automated reasoning. Prior to joining Conduent, Mr. Ocke worked at Xerox for 25 years; moving to PARC, a Xerox company in 2014. He started his career as a software engineer in product and development working on network and printing protocols and in 1998 moved to Xerox research where he spent the next several years helping develop industry wide printing and workflow technologies such as the Internet Printing Protocol (IPP) and the Job Definition Format (JDF). In recent years his work has focused on data analytics projects in the field of child support case management. Mr. Ocke earned his B.S. degree in Mathematics from St. John Fisher College and his M.S. degree in Computer Science from the Rochester Institute of Technology. He is also a certified scrum master, holds 17 patents, and is a co-author of 3 RFCs on the Internet Printing Protocol (IPP). 1400 Wegmans Hall (Auditorium), River Campusfalse15103332000002017-11-10T12:00:0015103368000002017-11-10T13:00:00Career Events2017/11/1110_careers-in-data-science-kirk-oche,-parc.htmlfalseOnceBiostatistics Fall Colloquium: Automated Model Building and Deep Learning49fce4c4ac19042a71a836ad279600b2Department of Biostatistics and Computational Biology University of Rochester School of Medicine and Dentistry 2017 Fall Colloquium Xiao Wang, Ph.D. Professor Department of Statistics Purdue University “Automated Model Building and Deep Learning” Thursday, November 9, 2017 3:30 p.m. – 5:00 p.m. Helen Wood Hall – Collins & Wilson Classroom 1W-502 Abstract: Analysis of big data demands computer aided or even automated model building. It becomes extremely difficult to analyze such data with traditional statistical models and model building methods. Deep learning has proved to be successful for a variety of challenging problems such as AlphaGo, driverless cars, and image classification. Understanding deep learning has however apparently been limited, which makes it difficult to be fully developed. In this talk, we focus on neural network models with one hidden layers. We provide an understanding of deep learning from an automated modeling perspective. This understanding leads to a sequential method of constructing deep learning models. This method is also adaptive to unknown underlying model structure. This is a joint work with Chuanhai Liu.Helen Wood Hall - Collins & Wilson Classroom 1W-502false15102594000002017-11-09T15:30:0015102648000002017-11-09T17:00:00Talks2017/11/1109_automated-model-building-and-deep-learning.htmlfalseOnceNailing the Technical Interview4f4f486aac19042a71a836ad9ff9675d“Nailing the Technical Interview” Hosted by Gwen M. Greene Career and Internship Center Monday, November 6, 2017 1400 Wegmans Hall, River Campus Have an upcoming technical interview? Come to this event to hear the employee and student perspective of how to succeed on your technical interview! We will review what to expect, along with tips and tricks for nailing the interview! RSVP on Handshake 1400 Wegmans Hall (Auditorium), River Campusfalse15099984000002017-11-06T15:00:0015073200000002017-10-06T16:00:00Career Events2017/11/1106_nailing-the-technical-interview.htmlfalseOnceInnovationQ Training by rep Michael Dermody is hosting 1.5-2 hour long demo training session for our new resource, InnovationQ. Find out what InnovationQ can do for you! for Students: *Promotes job-market awareness *See which entities are energetically patenting in various industries *Stimulates creativity and innovation *Exposure to actual patents, IEEE documents, landscape mapping *Deep awareness of the technology landscape – throughout the innovation cycle *Determine which whitespaces to pursue early in the process *Exposure to actual patents, preparation for a lifetime of understanding patents *Cross curricular collaboration *Business and STEM students alike can use the software, collaborate on ideas for creation, production, and bringing to market for Faculty: *The world’s finest semantic algorithms: elegant, easy to use, accurate and FAST *Easy-to-use, accurate, and fast access to the world’s innovations *No need to learn and employ complex Byzantine Boolean queries *Create a more collaborative, vibrant information system for patents and innovation *Not just usable by those trained in patent search – ANYONE can gain insight from this tool *Cross curricular collaboration with minimal fuss – connect business school with science & engineering to gain incredible insights without the need for complex Boolean queries For more information, contact Lauren Di Monte, Data & Research Impact Librarian, 585-276-3274.Gamble Room, 3rd Floor Rush Rhees Library, River Campusfalse15103404000002017-11-10T14:00:0015103476000002017-11-10T16:00:00Talks2017/11/ Student Workshop: Career Education and Explorationa264ffdeac19042a1507b5d7b42da1a0There is a great interactive workshop happening next week that will be especially helpful for first-year graduate students as you begin your program. All AS&E graduate students are welcome to attend so feel free to bring a friend! The workshop is being led by the new Director for Graduate Career Education and Professional Development, Kari Brick. Now is the time to start developing a plan for career success. Please bring a copy of your resume and join us on Thursday, November 16 at 3pm in the Feldman Ballroom (D) in Douglas Commons for refreshments and an interactive workshop to help students initiate the exploration of life beyond graduate school. This workshop will introduce different resources and tools available to graduate students as you begin to navigate your opportunities and options, and will also include a resume workshop. Sneak peek at the agenda: Intro: Learn what are employers looking for! Resources: Tools available to you as you navigate options and opportunities, etc. Resume Workshop: Bring a copy of your resume! We’ll discuss the components of a strong resume and evaluate your current resume. You can RSVP here: RSVPs are helpful for planning purposes, but not requiredFeldman Ballroom (D) in Douglas Commonsfalse15108624000002017-11-16T15:00:0015108660000002017-11-16T16:00:00Career Events2017/11/1116_graduate-student-workshop-career-education-and-exploration.htmlfalseOnceBiostatitstics & Computational Biology Fall Colloquium: Xing Quic5c35f7dac19042a1507b5d7bf1e000fDepartment of Biostatistics and Computational Biology University of Rochester School of Medicine and Dentistry 2017 Fall Colloquium Xing Qiu, Ph.D. Associate Professor Department of Biostatistics & Computational Biology University of Rochester “Toward the Era of “Large ¿¿¿¿, Medium ¿¿” Thursday, November 30, 2017 3:30 p.m. – 5:00 p.m. Helen Wood Hall – School of Nursing Auditorium 1W-304 Abstract: In the past two decades or so, the emergence of many different types of high-throughput data, such as whole transcriptome gene expression, genotyping, and microbiota abundance data, has revolutionized medical research. One common property shared by these “Omics” data is that typically they have much more features than the number of independent measurements (sample size). This property is also known as the “large p, small n” property in the research community, and has motivated many instrumental statistical innovations. A few of these examples include Benjamini-Hochberg’s FDR controlling multiple testing procedure; Fan and Lv’s sure independence screening; a host of advanced penalized regression methods; sparse matrix and tensor decomposition techniques; just to name a few. Due to the rapid advancing of biotechnology, the unit cost of generating high-throughput data has decreased significantly in recent years. Consequently, the sample size of those data in a respectful study is now about ¿¿=100~500, which I consider as “medium n”, and is certainly a huge improvement to the old “small n” studies in which ¿¿<10 is the norm. With the increased sample size, medical investigators are starting to ask more sophisticated questions – feature selection based on hypothesis testing and regression analysis is no longer the end, but the new starting point for secondary analyses such as network analysis, multi-modal data association, gene set analyses, etc. The overarching theme of these advanced analyses is that they all require statistical inference for models that involve ¿¿2 parameters. In my opinion, it takes a combination of proper data preprocessing, feature selection, dimension reduction, model building and selection, as well as domain knowledge and computational skills to do it right. Despite of the technical difficulties of designing and performing these avant-garde analyses, I believe that they will soon become mainstream, and inspire a generation of young statisticians and data scientists to invent the next big breakthroughs in statistical science. In this talk, I will share some of my recent methodology and collaborative research that involves “large ¿¿2” models, and list a few potential extensions of these methods that may be used in other areas of statistics.Helen Wood Hall – School of Nursing Auditorium 1W-304false15120738000002017-11-30T15:30:0015120810000002017-11-30T17:30:00Talks2017/11/1130_biostatitstics--computational-biology-fall-colloquium-xing-qui.htmlfalseOnce