Title: Computing Makes Ordinary Cameras Extraordinary
Abstract: The convergence of computations with optics and imaging that started a few decades ago has created high quality, high dynamic range, low-light, and high resolution images and videos and are in all our smart-phones today. In this talk, I will summarize current research that combines computing with existing CMOS sensors and has the potential to, quite simply, change what it means to be a camera. I will show cameras with extraordinary abilities that can see beyond the line of sight, see deep beneath the skin, see audio and mechanical vibrations, see better in poor weather and murky waters, see beneath the surface of crops, and much more. Much like the research during 1990-2010 transformed personal photography, current computational imaging research has the potential to impact many domains including healthcare, transportation, robotics and agriculture.
Biography: Srinivasa Narasimhan is a Professor of the Robotics Institute at Carnegie Mellon University. He served as Interim Director of the RI from Aug 2019 to Dec 2021. He obtained his PhD from Columbia University. His group focuses on novel techniques for imaging and illumination to enable applications in vision, graphics, robotics, agriculture, intelligent transportation and medical imaging. His works have received more than a dozen Best Paper or Best Demo or Honorable mention awards at major conferences. In addition, he has received the Ford URP, Okawa Research and the NSF CAREER Awards. He is the co-inventor of several technologies including programmable headlights, Aqualux 3D display, Assorted-pixels, Motion-aware cameras, Episcan360, Episcan3D, EpiToF3D, and programmable triangulation light curtains. He serves on the editorial board of the International Journal of Computer Vision and serves frequently as an Area Chair of top computer vision conferences (CVPR, ICCV, ECCV, BMVC, ACCV, 3DV).
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Title: Data Science for Social Good
Abstract: With increase in sage of the Internet, social media platforms, huge amount of data is getting generated at various level; one of the dire need of the hour is to see how we can use this data to have societal impact. I will briefly mention some super cool projects that we have worked on and that have made some visible contributions to the world outside academia. All work presented in the talk (including datasets, code, slides, recorded videos) has related publications at https://precog.iiit.ac.in/ Projects that I will touch upon are — Selfie deaths / KillFie http://labs.precog.iiitd.edu.in/killfie/ #GeneralElections2019 http://bit.ly/elections19(Mis)Information / Fake content on Twitter, Facebook, and WhatsApp, and Computing for Medicine. We all understand, many real world problems cannot be addressed by a single domain faculty / researcher, we need more students & faculty to come together, hoping to generate some interest to join hands to have an impact and make a difference. Many of our research work is made available for public use through tools or online services at https://precog.iiit.ac.in/ including datasets that we have used in our research work. Our work derives techniques from Computational Social Science, Data Science, Statistics, and Network Science. I will be more than happy to clarify, discuss, any of our work in detail, as required, after the talk.
Biography: Prof. Ponnurangam Kumaraguru ("PK") is a Professor of Computer Science at IIIT-Hyderabad. He is an Associate Researcher at Robert Bosch Centre for Data Science & AI - IIT Madras, Visiting Faculty at IIT Kanpur and an Adjunct faculty at IIIT Delhi. PK was inducted an ACM Distinguished Member in 2021. PK is an ACM India Council Member, and Chair of the Publicity & Membership Committee of ACM India. PK is a TEDx and an ACM Distinguished & ACM India Eminent Speaker. PK received his Ph.D. from the School of Computer Science at Carnegie Mellon University (CMU). His Ph.D. thesis work on anti-phishing research at CMU contributed in creating an award-winning startup - Wombat Security Technologies, wombatsecurity.com. Wombat was acquired in March 2018 for USD 225 Million. PK was listed in the World's 2% Scientists by Stanford University in Nov 2020. He is a senate member of IIIT Una, LNMIIT and is on various Board of Studies / Academic Council of different institutes across the country. He has co-authored research papers in the field of Computational Social Science, Privacy and Security in Online Social Media, Cyber Security, Social Computing, Data Science for Social Good, amongst others. In addition to his contributions to academia, PK is on advisory role on various government organizations, government committees, including a 8 member committee on Non-Personal Data by Government of India, chaired by Mr. Kris Gopalakrishnan. PK and his students have played an integral role in developing a technology used by many State and Central Government agencies in India. PK's research work regularly gets featured on news media, including print, online, and TV within India and across the world; to list a few, BBC, CBC, CBS, CNN, Doordarshan, Economic Times, Indian Express, NBC, New Scientist, NewYorker, Reuters, Times of India, USA Today (on 1st Feb 2021), Washington Post, and many more. PK Spear heads ACM India's programs on improving the quality of PhD students in Computing in India -- PhDClinic & Anveshan Setu Fellowship. In his Dean's role, he managed a team of 15 faculty members and 10+ admin staff, including Associate Dean of Student Affairs, overseeing hostel, sports centre, health centre, student {well-being, clubs, mentorship program}, technical & cultural fests. He was the Founding Head of Cybersecurity Education and Research Centre (CERC) at IIIT-Delhi. PK started and successfully manages PreCog (precog.iiit.ac.in), a research group at IIIT-Hyderabad.
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Title: Beyond Accuracy: Making Deep Neural Networks Reliable
Abstract: In the last decade deep neural networks have become the primary solution strategy for most machine learning problems. Whereas, the main focus for establishing the utility of a model has been accuracy, researchers are increasingly concerned about performance metrics beyond accuracy. In this talk I would like to expose the audience to some of these issues, and will talk about one of our specific works in this space, namely calibration of deep neural networks.
Biography:
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Title: Volumetric scientific data reduction with performance guarantees
Abstract:
The volumes and velocities of data generated by scientific applications have continued to outpace the growth of computing power, network, and storage bandwidths and capacities. Furthermore, this growth is also seen in next-generation experimental and observational facilities, making data reduction (or compression) an essential stage of future computing systems. This necessitates the development of trustworthy algorithms which can ensure that scientists can bound the error of the reduced data because, in many cases, it will be infeasible to retain the non-reduced data. We will present our work on constrained auto encoders and hybrid learning methods to address these issues.
In particular, we will present an end-to-end algorithmic and software pipeline for data compression that guarantees both error bounds on primary data (PD) and derived data, known as Quantities of Interest (QoI). We demonstrate the effectiveness of the pipeline on data generated by a large-scale fusion code, XGC, which produces petabytes of data in a single day. We show that our approach can compress the data by two orders of magnitude while guaranteeing high accuracy on both the PD and the QoIs. Further, the amount of resources required by compression is approximately one percent of that required for generating the data. All the above characteristics of our approach make it highly practical to apply on-the-fly compression while guaranteeing errors on QoIs that are critical to the scientists.
Biography:
Sanjay Ranka is a Distinguished Professor in the Department of Computer Information Science and Engineering at University of Florida. From 1999-2002, as the Chief Technology Officer at Paramark (Sunnyvale, CA), he developed a real-time optimization service called PILOT for marketing campaigns. PILOT served more than 10 million optimized decisions a day in 2002 with a 99.99% uptime. Paramark was recognized by VentureWire/Technologic Partners as a Top 100 Internet technology company in 2001 and 2002 and was acquired in 2002. Sanjay has also held positions as a tenured faculty member at Syracuse University, academic visitor at IBM and summer researcher at Hitachi America Limited.
Research in high performance computing and bigdata science is an important avenue for novel discoveries in large-scale applications. The focus of his current research is the development of efficient computational methods and data analysis techniques to model scientific phenomenon, and practical applications of focus are improvements to the quality of healthcare and the reduction of traffic accidents. A core aspiration of his research is to develop novel algorithms and software that make an impact on the application domain, exploiting the interdependence between theory and practice of computer science.
He has coauthored one book, four monographs, 300+ journal and refereed conference articles. His recent coauthored work has received a best student paper runner-up award at IGARSS 2015, best paper award at BICOB 2014, best student paper award at ACM-BCB 2010, best paper runner-up award at KDD-2009, a nomination for the Robbins Prize for the best paper in the Journal of Physics in Medicine and Biology in 2008, and a best paper award at ICN 2007.
He is a fellow of the IEEE, AAAS and AAIA (Asia-Pacific Artificial Intelligence Association) and a past member of IFIP Committee on System Modeling and Optimization. He won the 2020 Research Impact Award from IEEE Technical Committee on Cloud Computing. He was awarded the 2022 Distinguished Alumnus Award from Indian Institute of Technology, Kanpur.
He is an associate editor-in-chief of the Journal of Parallel and Distributed Computing and an associate editor for ACM Computing Surveys, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Sustainable Computing: Systems and Informatics, Knowledge and Information Systems, and International Journal of Computing. Additionally, he is a book series editor for CRC Press for Bigdata. In the past, he has been an associate editor for IEEE Transactions on Parallel and Distributed Systems and IEEE Transactions on Computers.
He is the program co-chair for ICDM 2022. He was a general co-chair for ICDM in 2009, International Green Computing Conference in 2010 and International Green Computing Conference in 2011, a general chair for ACM Conference on Bioinformatics and Computational Biology in 2012, and a program chair for 2013 International Parallel and Distributed Processing Symposium and 2015 High Performance Computing Conference. He was a co-general chair for DataCom 2017 and co-program chair for ICMLDS 2017 and 2018.
His work has received 15,600+ citations with an h-index of 63 (based on Google Scholar). He has consulted for several startups and Fortune 500 companies.
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