\nSign up: tx.ag/mspchess END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Chicago:20170330 DTEND;TZID=America/Chicago:20170331 LOCATION:MSC Ballroom SUMMARY:Student Research Week DESCRIPTION:The goal of SRW is to promote understanding and communication of research among disciplines\, as well as the public\, and to promote a positive university research environment. It offers an opportunity for our students to display their research and demonstrate the value of their work by participating in poster and oral competitions. It also demonstrates how their research experiences have contributed to their education. \n\n Student Research Week is a three day competition that highlights research occurring on the Texas A&M campus with an emphasis on research in which students participate. SRW provides a venue for students to present their work\, both through oral presentations and posters. This event allows students\, faculty\, and the community to see the depts. and breadth of research conducted at Texas A&M. SRW is a premiere program of the Graduate and Professional Student Council. X-ALT-DESC;FMTTYPE=text/html:

\n The goal of SRW is to promote understanding and communication of research among disciplines, as well as the public, and to promote a positive university research environment. It offers an opportunity for our students to display their research and demonstrate the value of their work by participating in poster and oral competitions. It also demonstrates how their research experiences have contributed to their education. \n

\n Student Research Week is a three day competition that highlights research occurring on the Texas A&M campus with an emphasis on research in which students participate. SRW provides a venue for students to present their work, both through oral presentations and posters. This event allows students, faculty, and the community to see the depts. and breadth of research conducted at Texas A&M. SRW is a premiere program of the Graduate and Professional Student Council.\n

UID:20170330T050000Z-23608@calendar.tamu.edu URL:http://calendar.tamu.edu/live/events/17259-student-research-week CATEGORIES:General Interest|Campus Life LAST-MODIFIED:20170321T193703Z ATTACH;FMTTYPE=image/jpeg:http://calendar.tamu.edu/live/image/gid/5/width/80/height/80/crop/1/src_region/0,0,200,201/46_tamlogobox.png X-LIVEWHALE-TYPE:events X-LIVEWHALE-ID:23608 X-LIVEWHALE-TIMEZONE:America/Chicago X-LIVEWHALE-IMAGE:http://calendar.tamu.edu/live/image/gid/5/width/80/height/80/crop/1/src_region/0\,0\,200\,201/46_tamlogobox.png X-LIVEWHALE-ALL-DAY:1 X-LIVEWHALE-COST:Free! X-LIVEWHALE-CONTACT-INFO:gpsc@tamu.edu \; X-LIVEWHALE-SUMMARY:Student Research Week (SRW) is a premiere program of the Graduate and Professional Student Council and exists to highlight research occurring on the Texas A&\;M University with an emphasis on research in which students participate. SRW provides a venue for students to present their work\, allowing students\, faculty\, and the community to see the depth and breadth of research conducted at Texas A&\;M University. END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Chicago:20170330T103000 DTEND;TZID=America/Chicago:20170330T113000 LOCATION:Bright Building GEO:30.619003;-96.338807 SUMMARY:CSE Faculty Candidate Presentation with Kyle Fox DESCRIPTION:Abstract: \n\n Rapid advances in sensing technologies and the increasing ubiquity of computing in peoples' lives have led to new opportunities and challenges in the management and analysis of data. Many data sets\, such as GPS traces of vehicles\, are inherently geometric\, while many other kinds of data\, such as color histograms of images\, are best represented in a geometric space. Therefore\, geometric algorithms are needed to handle a variety of problems in data analysis\, including computing useful representations\, computing maps between data sets\, answering queries\, and parallel processing of data. This talk will discuss a few of these problems\, with an emphasis on the problem of computing maps between data sets.\n\n \n\n We will discuss two variants of the problem of computing maps between data sets. First\, we will describe a near-linear time approximation algorithm for computing dynamic time warping maps between point sequences\, a central problem in the analysis of trajectories and other curves. Next\, we will describe fast approximation algorithms for computing transportation maps\, a widely used method for comparing and relating two distributions. In both cases our goal is to develop simple\, fast\, hopefully near-linear-time approximation algorithms. We will conclude the talk by briefly discussing recent algorithms for a few other geometric data-analysis problems as well as mentioning some of the directions of future research in this area. X-ALT-DESC;FMTTYPE=text/html:\n Abstract: \n

\n Rapid advances in sensing technologies and the increasing ubiquity of computing in peoples' lives have led to new opportunities and challenges in the management and analysis of data. Many data sets, such as GPS traces of vehicles, are inherently geometric, while many other kinds of data, such as color histograms of images, are best represented in a geometric space. Therefore, geometric algorithms are needed to handle a variety of problems in data analysis, including computing useful representations, computing maps between data sets, answering queries, and parallel processing of data. This talk will discuss a few of these problems, with an emphasis on the problem of computing maps between data sets.\n

\n \n

\n We will discuss two variants of the problem of computing maps between data sets. First, we will describe a near-linear time approximation algorithm for computing dynamic time warping maps between point sequences, a central problem in the analysis of trajectories and other curves. Next, we will describe fast approximation algorithms for computing transportation maps, a widely used method for comparing and relating two distributions. In both cases our goal is to develop simple, fast, hopefully near-linear-time approximation algorithms. We will conclude the talk by briefly discussing recent algorithms for a few other geometric data-analysis problems as well as mentioning some of the directions of future research in this area.\n

UID:20170330T153000Z-23590@calendar.tamu.edu URL:http://calendar.tamu.edu/live/events/23590-cse-faculty-candidate-presentation-with-kyle-fox CATEGORIES:Speakers Forums & Conferences LAST-MODIFIED:20170324T183059Z ATTACH;FMTTYPE=image/jpeg:http://calendar.tamu.edu/live/image/gid/53/width/80/height/80/crop/1/src_region/0,0,427,640/1590_fox.jpg X-LIVEWHALE-TYPE:events X-LIVEWHALE-ID:23590 X-LIVEWHALE-TIMEZONE:America/Chicago X-LIVEWHALE-IMAGE:http://calendar.tamu.edu/live/image/gid/53/width/80/height/80/crop/1/src_region/0\,0\,427\,640/1590_fox.jpg X-LIVEWHALE-CONTACT-INFO:Faculty Contact: Dr. Nancy M. Amato X-LIVEWHALE-SUMMARY:Please join us in welcoming Kyle Fox from Duke University as he presents his talk\, "Algorithms for Geometric Data Analysis". END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Chicago:20170403T133000 DTEND;TZID=America/Chicago:20170403T143000 LOCATION:Bright Building GEO:30.619003;-96.338807 SUMMARY:CSE Faculty Candidate Seminar with Timothy Zhu DESCRIPTION:Abstract: \n\n In today's datacenters\, resources are shared to lower costs and achieve better utilization\, but how does one meet tail latency performance goals in these shared environments? Workloads exhibit different behaviors (e.g.\, burstiness\, load) and can have different latency requirements. Furthermore\, different types of resources exhibit unique performance characteristics that make it hard to provide latency guarantees. For example\, in SSD storage devices\, writes are slower than reads\, while in networks\, packets traverse a series of queues and congest with different workloads at each queue.\n\n \n\n In this talk\, I will describe a new system I've built for meeting tail latency goals in storage and networks. We implement new mechanisms and policies for controlling the congestion between workloads. In particular\, I will show how to guarantee tail latencies across diverse workloads by introducing the first implementation of Stochastic Network Calculus in computer systems. X-ALT-DESC;FMTTYPE=text/html:\n Abstract: \n

\n In today's datacenters, resources are shared to lower costs and achieve better utilization, but how does one meet tail latency performance goals in these shared environments? Workloads exhibit different behaviors (e.g., burstiness, load) and can have different latency requirements. Furthermore, different types of resources exhibit unique performance characteristics that make it hard to provide latency guarantees. For example, in SSD storage devices, writes are slower than reads, while in networks, packets traverse a series of queues and congest with different workloads at each queue.\n

\n \n

\n In this talk, I will describe a new system I've built for meeting tail latency goals in storage and networks. We implement new mechanisms and policies for controlling the congestion between workloads. In particular, I will show how to guarantee tail latencies across diverse workloads by introducing the first implementation of Stochastic Network Calculus in computer systems.\n

UID:20170403T183000Z-24007@calendar.tamu.edu URL:http://calendar.tamu.edu/live/events/24007-cse-faculty-candidate-seminar-with-timothy-zhu CATEGORIES:Speakers Forums & Conferences LAST-MODIFIED:20170324T183042Z ATTACH;FMTTYPE=image/jpeg:http://calendar.tamu.edu/live/image/gid/53/width/80/height/80/crop/1/src_region/0,0,240,320/1601_picture.jpg X-LIVEWHALE-TYPE:events X-LIVEWHALE-ID:24007 X-LIVEWHALE-TIMEZONE:America/Chicago X-LIVEWHALE-IMAGE:http://calendar.tamu.edu/live/image/gid/53/width/80/height/80/crop/1/src_region/0\,0\,240\,320/1601_picture.jpg X-LIVEWHALE-CONTACT-INFO:Dr. Radu Stoleru X-LIVEWHALE-SUMMARY:Please join us in welcoming Timothy Zhu from Carnegie Mellon University as he presents his talk\, "Finishing on time: Meeting latency performance goals". END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Chicago:20170405T161000 DTEND;TZID=America/Chicago:20170405T171000 LOCATION:Bright Building GEO:30.619003;-96.338807 SUMMARY:CSCE 681 Open Graduate Seminar with Henry Segerman DESCRIPTION: Abstract: Our brains have evolved in a three-dimensional environment\, and so we are very good at visualizing two- and three-dimensional objects. But what about four-dimensional objects? The best we can really do is to look at three-dimensional "shadows". Just as a shadow of a three-dimensional object squishes it into the two-dimensional plane\, we can squish a four-dimensional shape into three-dimensional space\, where we can then make a sculpture of it. If the four-dimensional object isn't too complicated and we choose a good way to squish it\, then we can get a very good sense of what it is like. We will explore the sphere in four-dimensional space\, the four-dimensional polytopes (which are the four-dimensional versions of the three-dimensional polyhedra)\, and various 3D printed sculptures\, puzzles\, and virtual reality experiences that have come from thinking about these things. I talk about these topics and much more in my new book\, "Visualizing Mathematics with 3D Printing". X-ALT-DESC;FMTTYPE=text/html:Abstract: Our brains have evolved in a three-dimensional environment, and so we are very good at visualizing two- and three-dimensional objects. But what about four-dimensional objects? The best we can really do is to look at three-dimensional "shadows". Just as a shadow of a three-dimensional object squishes it into the two-dimensional plane, we can squish a four-dimensional shape into three-dimensional space, where we can then make a sculpture of it. If the four-dimensional object isn't too complicated and we choose a good way to squish it, then we can get a very good sense of what it is like. We will explore the sphere in four-dimensional space, the four-dimensional polytopes (which are the four-dimensional versions of the three-dimensional polyhedra), and various 3D printed sculptures, puzzles, and virtual reality experiences that have come from thinking about these things. I talk about these topics and much more in my new book, "Visualizing Mathematics with 3D Printing".

UID:20170405T211000Z-23983@calendar.tamu.edu URL:http://calendar.tamu.edu/live/events/23983-csce-681-open-graduate-seminar-with-henry-segerman CATEGORIES:Speakers Forums & Conferences LAST-MODIFIED:20170324T135907Z ATTACH;FMTTYPE=image/jpeg:http://calendar.tamu.edu/live/image/gid/53/width/80/height/80/crop/1/src_region/0,0,600,600/1440_csce_1x1-primary.png X-LIVEWHALE-TYPE:events X-LIVEWHALE-ID:23983 X-LIVEWHALE-TIMEZONE:America/Chicago X-LIVEWHALE-IMAGE:http://calendar.tamu.edu/live/image/gid/53/width/80/height/80/crop/1/src_region/0\,0\,600\,600/1440_csce_1x1-primary.png X-LIVEWHALE-CONTACT-INFO:Dr. Ergun Akleman X-LIVEWHALE-SUMMARY:Please join us as Dr. Henry Segerman from Oklahoma State University\, presents his talk\, "3D Shadows: Casting light on the fourth dimension". END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Chicago:20170412T161000 DTEND;TZID=America/Chicago:20170412T171000 LOCATION:Bright Building GEO:30.619003;-96.338807 SUMMARY:CSCE 681 Open Seminar with Klaus Mueller UID:20170412T211000Z-21160@calendar.tamu.edu URL:http://calendar.tamu.edu/live/events/21160-csce-681-open-seminar-with-klaus-mueller CATEGORIES:Speakers Forums & Conferences LAST-MODIFIED:20170130T161302Z ATTACH;FMTTYPE=image/jpeg:http://calendar.tamu.edu/live/image/gid/5/width/80/height/80/crop/1/src_region/0,0,200,201/46_tamlogobox.png X-LIVEWHALE-TYPE:events X-LIVEWHALE-ID:21160 X-LIVEWHALE-TIMEZONE:America/Chicago X-LIVEWHALE-IMAGE:http://calendar.tamu.edu/live/image/gid/5/width/80/height/80/crop/1/src_region/0\,0\,200\,201/46_tamlogobox.png X-LIVEWHALE-CONTACT-INFO:Dr. Yoonsuck Choe X-LIVEWHALE-SUMMARY:\n Please join us for Dr. Mueller's talk\, "Big Data Visual Analytics."\n

END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Chicago:20170417T161000 DTEND;TZID=America/Chicago:20170417T171000 LOCATION:Bright Building GEO:30.619003;-96.338807 SUMMARY:CSCE 681 Open Graduate Seminar with Zoran Obradovic DESCRIPTION:Abstract:\n\n An overview of our ongoing projects aimed to facilitate predictive analytics in partially observed evolving networks will be presented in this talk. Challenges and the proposed solutions will be discussed related to effective fusion of domain knowledge and data for joint learning of representation and structure in big multiscale networks. Additional challenges involve joint modeling of positive and negative influences in presence of multiple types of interactions and uncertainty propagation related to long-term forecasting in evolving networks. The proposed methods will be discussed in context of large-scale healthcare\, climate and marketing applications. X-ALT-DESC;FMTTYPE=text/html:\n Abstract:\n

\n An overview of our ongoing projects aimed to facilitate predictive analytics in partially observed evolving networks will be presented in this talk. Challenges and the proposed solutions will be discussed related to effective fusion of domain knowledge and data for joint learning of representation and structure in big multiscale networks. Additional challenges involve joint modeling of positive and negative influences in presence of multiple types of interactions and uncertainty propagation related to long-term forecasting in evolving networks. The proposed methods will be discussed in context of large-scale healthcare, climate and marketing applications.\n

UID:20170417T211000Z-24075@calendar.tamu.edu URL:http://calendar.tamu.edu/live/events/24075-csce-681-open-graduate-seminar-with-zoran CATEGORIES:Speakers Forums & Conferences LAST-MODIFIED:20170328T144323Z ATTACH;FMTTYPE=image/jpeg:http://calendar.tamu.edu/live/image/gid/53/width/80/height/80/crop/1/src_region/0,0,480,640/1613_20131024_obradovic_z_010_cis.jpg X-LIVEWHALE-TYPE:events X-LIVEWHALE-ID:24075 X-LIVEWHALE-TIMEZONE:America/Chicago X-LIVEWHALE-IMAGE:http://calendar.tamu.edu/live/image/gid/53/width/80/height/80/crop/1/src_region/0\,0\,480\,640/1613_20131024_obradovic_z_010_cis.jpg X-LIVEWHALE-CONTACT-INFO:Faculty Contact: Nick Duffield X-LIVEWHALE-SUMMARY:\n Please join us for Dr. Zoran Obradovic's talk\, "Fusion of Qualitative Knowledge and Big Data for Predictive Analytics in Dynamic Networks."\n

END:VEVENT END:VCALENDAR