Once every week while school is in session, EECS invites a distinguished researcher or practitioner in a computer science or electrical and computer engineering-related field to present their ideas and/or work. Talks are generally targeted to electrical engineering and computer science graduate students. This colloquium series is free and open to everyone.

Upcoming Colloquia

Wildbook - Photographic Censusing of Wildlife

Monday, February 25, 2019 - 4:00pm to 4:50pm
Weniger Hall 151

Speaker Information

Jason Parham
Senior Computer Vision Research Engineer, Wild Me
Computer Science Ph.D. Candidate, Rensselaer Polytechnic Institute, Troy, NY

Abstract

As the price of photography and video equipment drops while availability improves, visual data from the public is becoming the most abundant source of wildlife data.  However, curating this high volume of data from "citizen science" and social media poses new scalability challenges for both researchers and computer scientists alike.  This talk introduces the Wildbook platform -- an open-source web-based project (wildbook.org) -- that leverages a suite of deep learning tools to automatically process high volumes of image data for conservation.  The platform uses Convolutional Neural Networks (CNNs) on several computer vision tasks, including: image classification, bounding box regression, instance classification, class segmentation, and object of interest classification.  Our deep learning stack utilizes Theano and PyTorch on the NVIDIA's CUDA, CNMeM, and CuDNN deep learning stack and employs multiple Titan V GPUs for efficient processing.  Further, instance recognition allows Wildbook to individually identify individual animals through time using a pipeline of SIFT features to find distinctive patches, Approximate Nearest Neighbors to find visual neighbors, and LNBNN to associate and re-rank visual match correspondences.  A random forest pair-wise match verifier is then used to curate animal sightings for human-in-the-loop population curation.  Lastly, our computer vision pipeline works alongside an intelligent agent that can automatically ingest video data from YouTube using NLP and OCR with Azure Cognitive Services.   We present our conservation work for wildlife across the globe in the context of the latest advances in deep learning and how Wildbook is helping to convert wildlife conservation into a data-driven science. 

Speaker Bio

Jason Parham received his B.S. in Computer Science / Mathematics from Pepperdine University in Malibu, CA in 2008 and holds a M.S. in Computer Science from RPI in Troy, NY.  Jason is finishing his Ph.D. as a candidate under the advising of Dr. Charles Stewart at RPI.  Jason’s Masters thesis was on the design and implementation of a citizen science-powered photographic censusing of the zebra and giraffe in the Nairobi National Park.  His current doctoral research focuses on animal detection and classification, using deep learning on wildlife imagery, to power automated photographic censusing.  Jason is a co-developer of Wildbook‘s Image Analysis components, which are used to monitor animal populations in conservancies around Kenya and which integrate with the Wildbook data management platform for a suite of species. Previously, Jason worked three years for Kitware, Inc. in Clifton Park on detecting vehicles and military aircraft in overhead satellite imagery.  Below are some select publications: - An Animal Detection Pipeline for Identification Lake Tahoe, CA. IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2018) Mar 2018
- Wildbook: Crowdsourcing, Computer Vision, and Data Science for Conservation New York, NY. BLOOMBERG DATA FOR GOOD EXCHANGE 2017 Sep 2017
- Animal Population Estimation Using Flickr Images Troy, NY ACM WEB SCIENCE CONFERENCE 2017 (WEBSCI‘17). INTERNATIONAL WORKSHOP ON THE SOCIAL WEB FOR ENVIRONMENTAL AND ECOLOGICAL MONITORING (SWEEM 2017)
- Efficient Generation of Image Chips for Training Deep Learning Networks Anaheim, CA. PROCEEDINGS OF SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE) · AUTOMATIC TARGET RECOGNITION (ATR). XXVII Apr 2017.

 

Energy Efficient Computing in Nanoscale CMOS

Gaulke Distinguished Lecture
Friday, March 1, 2019 - 4:00pm to 4:50pm
KEC 1003

Speaker Information

Vivek De
Intel Fellow
Director of Circuit Technology Research
Intel Labs

Abstract

Future computing systems spanning exascale supercomputers to wearable devices demand orders of magnitude improvements in energy efficiency while providing desired performance. The system-on-chip (SoC) designs need to span a wide range of performance and power across diverse platforms and workloads. The designs must achieve robust near-threshold-voltage (NTV) operation in nanoscale CMOS  process while supporting a wide voltage-frequency operating range with  minimal impact on die cost. We will discuss circuit and design technologies to overcome the challenges posed by device parameter variations, supply noises, temperature excursions, aging-induced degradations, workload and activity changes, and reliability considerations. The major pillars of energy-efficient SoC designs are: (1) circuit/design optimizations for fine-grain multi-voltage & wide dynamic range, (2) fine-grain on-die power delivery & management, (3) dynamic adaptation & reconfiguration, (4) dynamic on-die error detection & correction, and (5) efficient interconnects.

Speaker Bio

Vivek De is an Intel Fellow and Director of Circuit Technology Research in Intel Labs. He is responsible for providing strategic technical directions for long term research in future circuit technologies and leading energy efficiency research across the hardware stack. He has 282 publications in refereed international conferences and journals with a citation H-index of 76, and 224 patents issued with 30 more patents filed (pending). He received an Intel Achievement Award for his contributions to an integrated voltage regulator technology. He received a Best Paper Award at the 1996 IEEE International ASIC Conference, and nominations for Best Paper Awards at the 2007 IEEE/ACM Design Automation Conference (DAC) and 2008 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). He also co-authored a paper nominated for the Best Student Paper Award at the 2017 IEEE International Electron Devices Meeting (IEDM). One of his publications was recognized in the 2013 IEEE/ACM Design Automation Conference (DAC) as one of the "Top 10 Cited Papers in 50 Years of DAC". Another one of his publications received the “Most Frequently Cited Paper Award” in the IEEE Symposium on VLSI Circuits at its 30th Anniversary in 2017. He was recognized as a Prolific Contributor to the IEEE International Solid-State Circuits Conference (ISSCC) at its 60th Anniversary in 2013, and a Top 10 Contributor to the IEEE Symposium on VLSI Circuits at its 30th Anniversary in 2017 . He received the Outstanding Evening Session Award at the 2018 International Solid-State Circuits Conference (ISSCC). He served as an IEEE/EDS Distinguished Lecturer in 2011 and an IEEE/SSCS Distinguished Lecturer in 2017-18. He received the 2017 Distinguished Alumnus Award from the Indian Institute of Technology (IIT) Madras. He received a B.Tech from IIT Madras, India, a MS from Duke University, Durham, North Carolina, and a PhD from Rensselaer Polytechnic Institute, Troy, New York, all in Electrical Engineering. He is a Fellow of the IEEE.

Past Colloquia

Brian Drost
Monday, October 22, 2012 - 4:00pm to 4:50pm
Dr. Robert S. Laramee
Friday, October 12, 2012 - 11:00am to 11:50am
Dr. Hamish Carr
Thursday, October 11, 2012 - 2:00pm to 2:50pm
Robert Chau
Monday, June 4, 2012 - 4:00pm to 4:50pm
Paul Cull
Monday, May 7, 2012 - 4:00pm to 4:50pm
Wednesday, May 2, 2012 - 9:40am to 11:00am
Patrick Lenahan
Monday, April 16, 2012 - 4:00pm to 4:50pm
Jim de Broekert
Monday, April 9, 2012 - 4:00pm to 4:50pm

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