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The theme of the seminar will be AI and Machine Learning. By attending in person, you will gain the added benefit of interpersonal networking with peers from across the Northeast region. The finalized agenda and details/logistics (In person and remote access) will be emailed to you shortly!

10/4/2019
When: Friday, October 4, 2019
9 AM-2:30 PM
Contact: Laurie Robinson

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NEREN Seminar:

 


“Bridging the Gap: Al and Machine Learning”

Sponsored by Intel and Red River

 

Friday, October 4, 2019, 9:30AM– 2:15PM

Complimentary breakfast begins at 9:00 a.m.

(Registration/Breakfast – 9:00AM – 9:45AM)

Complimentary lunch begins at 12 noon

Gateway City Arts Center, 92 Race Street, Holyoke, MA 01040

 

NEREN Seminar –Friday, October 4, 2019, 9:00AM-2:15PM

In collaboration with UMass Amherst and the Massachusetts Green High-Performance Computing Center (MGHPCC), NEREN presents the seventh in a series of day-long seminars devoted to proposing and advancing ideas for regional collaboration in research computing and networking. The theme of the seminar will be AI and Machine Learning. By attending in person, you will gain the added benefit of interpersonal networking with peers from across the Northeast region.

 

Please register by September 23, 2019, to attend in person or remotely by contacting Laurie Robinson, NEREN Program Administrator, at laurie@neren.org or by phone: 401-523-5107. When registering, please indicate whether you are attending in person or remotely. Also, please contact Laurie if you have questions.  Or register online:

https://www.eventbrite.com/e/neren-seminar-bridging-the-gap-al-and-machine-learning-tickets-68324220583?aff=utm_source%3Deb_email%26utm_medium%3Demail%26utm_campaign%3Dnew_event_email&utm_term=eventurl_text

Webcast Link:

http://demo.mediasite.oshean.org/Mediasite/Play/02d6533f6dd442f796f4624c250c58461d

 

AGENDA – Friday, October 4, 2019

 

9:00 – 9:45 a.m. Registration/Continental Breakfast/Networking

 

9:45 – 10:00 a.m. Welcoming/Opening Remarks by NEREN, Inc.

 

Please note that the Webcast/Phone options will be available for the presentations. If joining by webcast, please use the following link:

http://demo.mediasite.oshean.org/Mediasite/Play/02d6533f6dd442f796f4624c250c58461d

 

10:00 – 10:40 a.m. Presentation #1 –“How to manage the complex implications of face recognition technology: A possible way forward”

Presenter: Erik G. Learned-Miller, Professor in the College of Information and Computer Sciences, University of Massachusetts Amherst

There has been a great deal of hand-wringing about face recognition technology. Important work has shown that face recognition algorithms can be unfair, can amplify pre-existing biases, and can create substantial harm to individuals. Others have testified to the improvements in efficiency of doing important work like tracking down child sex traffickers. Still others argue that many applications of face recognition technology are benign and that large scale bans are unreasonable. How can we integrate and balance these concerns?  Many arguments center around machine learning ideas like unbiased learning, better training sets, and algorithms that are robust to “domain transfer”. 

 

In this talk, I will argue that the problem is much larger than these (important) technical issues. To do so, I will examine some of the regulatory structures, processes, definitions, rules, and conventions that have been developed by the US Food and Drug Administration (FDA). I will draw heavily from two separate scenarios: the FDA’s regulation of pharmaceuticals and their regulation of medical devices. The elaborate processes set up to regulate the drug and medical device industries have been remarkably successful (in many ways), and I will argue that many of the structures in place there deserve analogous systems for the regulation of face recognition. 

 

10:40 – 11:20 a.m. Presentation #2 –“Leveraging High Performance GPU Computing to Increase Research Productivity in Vermont

Presenter:  Adrian Del Maestro, Assistant Professor of Physics, University of Vermont

The increasing complexity of scientific data-driven workflows has led to an evolution in the types of tools both employed and demanded by interdisciplinary researchers. In this talk I will describe the deployment of a new GPU based supercomputer at the University of Vermont and detail how we are engaging with a new class of users that are interested in employing machine learning in their research, but may have minimal previous experience with high performance computing. The results highlight that specialized cyberinfrastructure with advanced artificial intelligence capabilities will play an increasing and fundamental role in the university research ecosystem and should be supported at the same level as conventional brick-and-mortar scientific facilities.

 

11:20 – 12:00 p.m. Presentation #3 Hypersparse Neural Network Analysis of Large-Scale Internet Traffic”

Presenter: Jeremy Kepner, MIT Lincoln Laboratory Fellow

The Internet is transforming our society, necessitating a quantitative understanding of Internet traffic. Our team collects and curates the largest publicly available Internet traffic data containing 50 billion packets. Utilizing a novel hypersparse neural network analysis of "video" streams of this traffic using 10,000 processors in the MIT SuperCloud reveals a new phenomena: the importance of otherwise unseen leaf nodes and isolated links in Internet traffic. Our neural network approach further shows that a two-parameter modified Zipf-Mandelbrot distribution accurately describes a wide variety of source/destination statistics on moving sample windows ranging from 100,000 to 100,000,000 packets over collections that span years and continents. The inferred model parameters distinguish different network streams and the model leaf parameter strongly correlates with the fraction of the traffic in different underlying network topologies. The hypersparse neural network pipeline is highly adaptable and different network statistics and training models can be incorporated with simple changes to the image filter functions.

 

12 noon – 1:10 p.m. Lunch Break

 

1:15 – 2:00 p.m.  Presentation #4 – “Knowledge Representation in the Era of Deep Learning, Watson and the Semantic Web”

Presenter: Jim Hendler, Tetherless World Chair of Computer, Web and Cognitive Sciences, Director RPI/IBM AI Research Collaboration, Rensselaer Polytechnic Institute (RPI)

A burst in optimism (and unwarranted fear) has grown around a number of technologies that are high impact and able to solve problems that have challenged AI researchers for years.  The over-enthusiasm that often follows such breakthroughs has caused some to declare (yet again) that it is the end of “knowledge representation” as AI moves into a world dominated by neural networks, data mining and the knowledge graph.  In this talk, I argue that these technologies, while extremely powerful separately, are not only still a long way from human intelligence, but cannot get there without a level of knowledge and reasoning beyond what is currently available to these techniques, On the other hand, I also argue that taking these technologies into new and harder realms will require rethinking what traditional AI representation is and how it is used.

 

2:00 – 2:10 p.m. Closing Remarks:  Christopher Misra, Vice Chancellor and CIO, University of Massachusetts Amherst

 

To register, or if you have questions, please email Laurie Robinson, NEREN Program Administrator, at laurie@neren.org or by phone: 401-523-5107. Or register online:

https://www.eventbrite.com/e/neren-seminar-bridging-the-gap-al-and-machine-learning-tickets-68324220583?aff=utm_source%3Deb_email%26utm_medium%3Demail%26utm_campaign%3Dnew_event_email&utm_term=eventurl_text

 

GETTING THERE – DETAILS

 

Getting There in Person

The following link provides details about the location for those traveling to the meeting by car: 

https://www.google.com/maps/dir/41.608711,71.455757/gateway+city+arts+center+directions+holyoke/@41.9088149,72.5213322,9z/data=!3m1!4b1!4m9!4m8!1m1!4e1!1m5!1m1!1s0x89e6dc180714a031:0x78d 687c11d3e341b!2m2!1d-72.6038625!2d42.2042256

 Helpful Telephone Numbers/Contact Information

John Griffin, (UMass Amherst) 413-545-9939 or jgriffin@umass.edu

John Goodhue (MGHPCC) 617-834-5601 or jtgoodhue@mghpcc.org

Laurie Robinson, (NEREN) 401-523-5107laurie@neren.org

For Technical Questions: Jim Carr, Consultant, (OSHEAN), 401-447-5600, jim@oshean.org

 

Meeting Location/Wireless Internet Access

Gateway City Arts Center does have a guest wireless network.

 

Parking/Public Transportation

Street parking is available, and parking is also available at the MGPHCC, 100 Bigelow Street, Holyoke, which is located within short walking distance to Gateway City Arts Center, 92 Race Street, Holyoke, Massachusetts.

 

Getting There Remotely – Phone/Webcast  -- http://demo.mediasite.oshean.org/Mediasite/Play/02d6533f6dd442f796f4624c250c58461d

If you are planning to participate in the Webcast, please contact Laurie Robinson– laurie@neren.org or register online:

https://www.eventbrite.com/e/neren-seminar-bridging-the-gap-al-and-machine-learning-tickets-68324220583?aff=utm_source%3Deb_email%26utm_medium%3Demail%26utm_campaign%3Dnew_event_email&utm_term=eventurl_text

Please note that the Webcast will be recorded, and we will provide details following the seminar. Thank you to David Marble, President and CEO of OSHEAN for assisting with the Webcast/Video Steaming!

 

About our Presenters

Erik G. Learned-Miller

Professor in the College of Information and Computer Sciences, University of Massachusetts Amherst

 

Erik G. Learned-Miller is a Professor in the College of Information and Computer Sciences at the University of Massachusetts, Amherst, where he joined the faculty in 2004. From 2002-2004, he spent two years as a post-doctoral researcher at the University of California, Berkeley. Learned-Miller received a B.A. in Psychology from Yale University in 1988. In 1989, he co-founded CORITechs, Inc., where he co-developed the second FDA cleared system for image-guided neurosurgery, and from 1993-1995, he was the manager of neurosurgical product engineering at Nomos Corp. (Pittsburgh, PA). He obtained Master of Science (1997) and Ph. D. (2002) degrees from the Massachusetts Institute of Technology, in Electrical Engineering and Computer Science. His current research focuses on machine learning and computer vision, with an emphasis on learning from small amounts of training data, and work in face recognition. He was awarded the NSF CAREER award in 2006.

 

 

Adrian Del Maestro

 Assistant Professor of Physics, University of Vermont

 

Adrian received his Ph.D. in Physics from Harvard University in 2008 where his research focused on quantum phase transitions in strongly fluctuating superconductors.  After his Ph.D., he spent two years as a post doctoral researcher at the University of British Columbia, and one year as the distinguished post doctoral scientist at the Institute for Quantum Matter; a joint research venture between Johns Hopkins and Princeton University. In 2011 he joined the faculty of the University of Vermont as an Assistant Professor of Physics and he was promoted to Associate Professor in 2017.  His research involves the application of high performance computational tools to understand how collective and cooperative states of matter emerge in quantum many-body systems.  Ultimately, he hopes to learn how to harness the unique correlations present in all quantum-mechanical phases for high-precision measurement, secure long-distance communication and non-classical computation. In 2018 he took on the directorship of the Vermont Advanced Computing Core, which provides high performance computing facilities to researchers throughout the state of Vermont. In 2019 he led a team that secured the largest ever Major Research Infrastructure grant awarded to the University of Vermont by the National Science Foundation to deploy a novel GPU based supercomputer.  The new cluster will leverage machine intelligence to solve problems in quantum computing, drug discovery, safe robotics, and computer vision for transportation and health care applications.

Dr. Jeremy Kepner

Massachusetts Institute of Technology (MIT) Lincoln Laboratory Fellow

Dr. Jeremy Kepner is an MIT Lincoln Laboratory Fellow. He founded the Lincoln Laboratory Supercomputing Center and pioneered the establishment of the Massachusetts Green High-Performance Computing Center.  He has developed novel big data and parallel computing software used by thousands of scientists and engineers worldwide. He has led several embedded computing efforts, which earned him a 2011 R&D 100 Award. Dr. Kepner has chaired the SIAM Data Mining conference, the IEEE Big Data conference, and the IEEE High Performance Extreme Computing conference. Dr. Kepner is the author of two bestselling books, Parallel MATLAB for Multicore and Multinode Computers, and Graph Algorithms in the Language of Linear Algebra. His peer-reviewed publications include works on abstract algebra, astronomy, astrophysics, cloud computing, cybersecurity, data mining, databases, graph algorithms, health sciences, plasma physics, signal processing, and 3D visualization. In 2014, he received Lincoln Laboratory's Technical Excellence Award. Dr. Kepner holds a BA degree in astrophysics from Pomona College and a PhD degree in astrophysics from Princeton University.

James (Jim) Hendler

Director of the Institute for Data Exploration and Applications (IDEA)

and the Tetherless World Professor of Computer, Web and Cognitive Sciences at

Rensselaer Polytechnic Institute (RPI)

 

James Hendler is the Tetherless World Professor of Computer, Web and Cognitive Sciences at RPI.  He is also the Director of the Rensselaer Institute for Data Exploration and Applications and the acting director of the RPI-IBM Artificial Intelligence Research Collaboration.  Hendler has authored over 400 books, technical papers and articles in the areas of Semantic Web, artificial intelligence, agent-based computing and high-performance processing. Hendler was the recipient of a 1995 Fulbright Foundation Fellowship, is a former member of the US Air Force Science Advisory Board, and is a Fellow of the AAAI, BCS, the IEEE, the AAAS and the ACM. He is also the former Chief Scientist of the Information Systems Office at the US Defense Advanced Research Projects Agency (DARPA) and was awarded a US Air Force Exceptional Civilian Service Medal in 2002. Hendler was the first computer scientist to serve on the Board of Reviewing editors for Science. In 2010, Hendler was named one of the 20 most innovative professors in America by Playboy magazine and was selected as an “Internet Web Expert” by the US government. In 2013, he was appointed as the Open Data Advisor to New York State and in 2015 appointed a member of the US Homeland Security Science and Technology Advisory Committee. In 2016, became a member of the National Academies Board on Research Data and Information and in 2018 became chair of the ACM’s US technology policy committee.  In 2018, he was elected a Fellow of the US National Academy of Public Administration.

 

Christopher Misra

Vice Chancellor IT and CIO, UMass Amherst

 

Christopher Misra is the Vice Chancellor IT and CIO at the University of Massachusetts Amherst where he has worked for many years. His responsibilities include management of overall campus technology coordination, networking, data centers, information security program, identity management, and enterprise architecture. Chris has been active for many years with various regional and national information security organizations including the Security Task Force, Internet2 Salsa, and REN-ISAC, serving on program committees, chairing working groups, and presenting at conferences. Chris is also an instructor at UMass where he has taught undergraduate courses on Network Security for many years.

 

Full Details can be downloaded HERE

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