Abstract:

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Bio:

Dr. Rajeev Rastogi is the Director of Machine Learning at Amazon. Previously, he was the Vice President of Yahoo! Labs Bangalore, and the founding Director of the Bell Labs Research Center in Bangalore. Rajeev is active in the fields of databases, data mining, and networking, and has served on the program committees of several conferences in these areas. He currently serves on the editorial board of the CACM, and has been an Associate editor for IEEE Transactions on Knowledge and Data Engineering in the past. He has published over 125 papers, and holds over 50 patents. Rajeev is an ACM Fellow and a Bell Labs Fellow. He received his B. Tech degree from IIT Bombay, and a PhD degree in Computer Science from the University of Texas, Austin.

Abstract:

We are at a turning point in society where the world around us is deeply embedded with smart objects that are wirelessly connected to each other and eventually through the Internet. IoT is attracting huge interest from both academia and industry, and most of the things in our life are likely to get connected in the near future. At the core of the current IoT technologies, is the communication through radio frequency, such as WiFi and Bluetooth. With the prevalence of connected devices, our reliance on the radio frequency communication is becoming significant. However, the radio spectrum is extremely crunched and its dependability becomes a growing issue. Therefore, we argue that it is important to diversify communication methods. In this talk, we will discuss alternative and complimentary wireless communication methods, including radio frequency, infrared, and visible lights, through the IoT applications we have built with those. We will compare their cons and pros from various perspectives, and provide insights for a better connected world.

Bio:

Dr. Baek-Young Choi is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Missouri - Kansas City. She received her Ph.D. in Computer Science and Engineering from the University of Minnesota, Twin Cities. She held positions at Sprint Advanced Technology Labs, and the University of Minnesota, Duluth, as a post-doctoral researcher, and as a 3M McKnight distinguished visiting assistant professor, respectively. She has been a fellow of the U.S. Air Force Research Laboratory's Visiting Faculty Research Program (AFRL-VFRP), and Korea Telecom - Advance Institute of Technology (KT-AIT). Her research interests lie in the broad area of algorithm and system development for diverse types of networks, especially in resource management and network monitoring. She has authored the book, 'Scalable Network Monitoring in High Speed Networks', and co-edited the book, 'High Performance Cloud Auditing and Applications.' She has served on NSF and DOE panels multiple times and is currently an Associate Editor for the Elsevier Journal of Computer Networks and Springer Journal of Telecommunication Systems. She has also served as a general chair, technical program chair, technical program committee member, organizing committee member, session chair, and reviewer for many international conferences and workshops. Her research has been supported by several agencies including NSF, Sprint-Nextel, AFRL, U. Missouri System, and UMKC. She is a senior member of ACM and IEEE, and a member of IEEE Women in Engineering.

Abstract:

Smartphones apps are not isolated pieces of software; several sensing and computational devices surround the app, including smartwatches and fitness trackers. The smartphone hardware itself has become sophisticated. The problem is that, smartphone apps cannot easily leverage the additional hardware around it. In this talk, I will describe two case studies where the additional hardware can be leveraged for significant power and usability benefits.

First, I will describe MobileHub, a system that can use additional sensing hardware to reduce power consumption of always-on sensing applications. Always-on sensing apps drain the battery quickly because they prevent the main processor from sleeping. However, many smartphone have an embedded microcontroller called a sensor hub for low power sensing. The problem is that smartphone apps cannot access the sensor hub hardware, except through very limited APIs. I will describe MobileHub, a system that automatically rewrites applications to leverage the sensor hub without any additional programming effort. MobileHub uses a combination of dynamic taint tracking and machine learning to learn when it is safe to leverage the sensor hub without affecting application semantics. MobileHub was published at Ubicomp.

Second, I will describe our ongoing work, Appify. Appify is an operating system abstraction that lets smartphone apps leverage nearby smartwatches to significantly improve the application usability.

Bio:

Dr. Balasubramanian joined the computer science department at Stony Brook in spring 2015. Prior to that, she was a post-doctoral fellow at the University of Washington in Seattle. She received her PhD from the University of Massachusetts, Amherst in February 2011. She completed her Masters degree at SUNY Buffalo.

Dr. Balasubramanian's work incorporates elements of both networking and systems in the context of mobile devices. The goal of her work is to design mobile protocols/systems that radically improve performance. Current mobile system designs make assumptions, that while true for the wired environment, are not valid for the mobile environment. Instead, she wants to re-architect mobile systems to better support current and next-generation applications. Dr. Balasubramanian's recent projects include MobileHub (reduces energy consumption of always-on sensing applications by leveraging heterogenous hardware), WProf (a tool that identifies the bottlenecks during page load), and FindAll (makes mobile applications less reliant on the cloud by building a local search engine on phones, that lets users search locally).

Abstract:

Providing sensor systems for exploring and monitoring spatially distributed physical systems are many times difficult only with fixed deployment of sensors due to high cost involved in deployment and maintenance ensuring the coverage. The use of mobile data collection platforms such as robots, aerial vehicles, and handhelds form the Mobile Internet of Things concept and its applications are gaining attention. Typical applications include infrastructure inspections, air and ground based inspection of predetermined areas for tasks such as surveillance, target detection, tracking, and search and rescue operations. While ground based robots are mainly used for reconnaissance and detection tasks in close range, flying robots (drones) are particularly suitable to perform fast reconnaissance and detection involving large areas. People with smartphones are additional data source that can augment this. How such systems can integrate with fixed sensor systems towards a seamless interactive sensing? What are the opportunities and challenges posed by such systems? What types of sensors and sensing modality can be used in such systems? Some of these questions are addressed in this talk with a few case studies from our research.

Bio:

Dr.Balamuralidhar P graduated with a B.Tech in Electrical Engineering from Kerala University in the year 1985. He completed his Masters and obtained an M.Tech in Electrical Engineering from Indian Institute of Technology, Kanpur in 1987. He received his PhD in Cognitive Wireless Communications from Aalborg University,Denmark in 2013.

Abstract:

The explosion of data and the value that can be derived from it continues to grow at an exponential pace. More data will be created every 10 minutes than in the entire history of the world up to 2008! More and more of this data is being created by the Internet of Things by billions of sensors that are embedded in everything from traffic lights, industrial machinery, medical devices and other end devices. , etc. Analytics on the data generated is what brings intelligence and knowledge, creating endless possibilities of value to consumers and businesses.

One of the fast growing trends in several IOT applications is that of data in motion and real-time analytics. Until recently, most of big data and analytics on top of it was static. Organizations tended to store all data collected in centralized data centers and used Hadoop type of batch processing techniques to process that data. However, there are new applications which require real-time analysis of data with ultra-low latencies of response. Some of these applications are connected cars, retail shopping experience, surveillance video analytics, anomaly detection in networks for security threats, etc. Decisions and actions need to take place at the edge, where and when the data is created; that is where the data and analysis need to be as well. With sensors gaining more processing power and becoming more context-aware, it is now possible to bring intelligence and analytic algorithms close to the source of the data, at the edge of the network. The talks describes network infrastructure architecture, analytics platforms and algorithms that address data in motion at the edge of the network. The talk will also discuss some practical IOT use cases in applying analytics to data in motion at the edge of the network.

Bio:

Dr. Ishwar Parulkar is a Cisco Distinguished Engineer and Chief Technology Officer for the Cisco India site. In this role he is responsible for setting the 3-5 year vision for different aspects of Cisco’s technology portfolio. While having his hands in many Cisco technology domains, his main focus is on next generation mobile internet, internet of things and information centric networking (ICN), which is a radically new paradigm in architecting the internet.

Ishwar relocated to Bangalore from the Silicon Valley in 2010 to lead the conception and development of Cisco’s mobile backhaul router portfolio, which have won the prestigious 2012 NASSCOM and Economic Times awards for innovation. The development of these leading edge, innovative products from India have been the subject of business school case studies on emerging market innovations. Prior to Cisco, Ishwar was a Distinguished Engineer at Sun Microsystems, where he was instrumental in developing several generations of SPARC servers, storage/networking datacenter equipment and high-performance microprocessors/chipsets. Ishwar spent the first formative years of his professional career at Apple on desktops/servers and innovative mobile devices, which were a precursor to the smartphone revolution.

Ishwar's expertise over the years spans a wide range - from networking, data center to consumer products. He has made many fundamental innovative contributions in these areas, captured in the several patents he holds and the technical papers he has published. He holds a Ph.D. in EE from University of Southern California, Los Angeles. He is a frequent invited speaker at industry events.

Abstract:

As the Internet of Things technology is entering the mainstream, we need novel architectures for IoT Solutions. In this talk, we will discuss three approaches - ground computing (completely decentralised), fog computing (partly centralised) and cloud computing (completely centralised) - for IoT systems. We will also present a few use cases that illustrate the merits and demerits of these approaches.

Bio:

Deva P. Seetharam recently co-founded DataGlen, a company focused on IoT data management. Prior to this, he founded and led the Smarter Energy Systems group at IBM Research India. On the entrepreneurial side, he co-founded RadioSherpa and served as the CTO, and was instrumental in selling the company to Tune-In, a Google ventures company. He holds a MS from MIT Media Laboratory and a BE from Bharathiyar university.

Abstract:

The rise of mobile and IoT devices has seen exponential increase in online data sharing. Image data has recently emerged as one of the interesting yet challenging type of data to mine. Especially for online businesses such as online marketplaces, stream processing of millions of images emanating from mobile devices and flowing into their platforms has become imperative. This talks presents a holistic view of stream processing of high dimensional data such as images and videos from IoT and mobile devices. We describe the significance of and innovations in deep learning techniques to process images and videos at scale. We also talk about a deployment study to analyze thousands of user-generated images in wild using "Photolect", a scalable deep learning platform.

Bio:

Vijay Gabale is a researcher and entrepreneur. Vijay graduated with a PhD from IIT Bombay in 2012 and worked with IBM Research Labs, Bangalore for 2.5 years. Subsequently he co-founded a tech start-up, 'Infilect', to organize and analyze world's rich-media data. He heads the technology division of 'Infilect' and is passionate about innovating and applying advanced machine learning techniques to analyze large-scale streaming data.

Abstract:

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Bio:

Aishwarya is the founder and CEO of Strike(getstrike.co), a mobile app that helps users find public information using emails. Aishwarya has worked with various well-known startups like Yourstory.com and HackerEarth, before co-founding Strike. He's passionate about technology startups and is obsessed with data and it's uses.

Abstract:

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Bio:

Subho is the Co-Founder and CTO at Appknox, a mobile security company that helps developers and companies to build secure mobile application. He looks after the security technologies and the main brain-stem of Appknox. He has been also listed multiple number of times in various Hall Of Fame for finding out critical security vulnerabilities in Google, Apple, Facebook, Microsoft, and many more. He has presented many talks and conducted workshops at conferences like BlackHat, Defcon, ToorCon, SysCan, ClubHack, NullCon, OWASP AppSec, RSA Conference.

Abstract:

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Bio: