Speaker Prof. Supratik Mukhopadhyay, Louisiana State University, USA

Venue IIT Patna

Organizer Prof. Rajiv Misra, IIT Patna, India

From June 6th, 2022 (Monday)

To June 10th, 2022 (Friday)

Organized by Department of Computer Science and Engineering, Indian Institute of Technology Patna

OVERVIEW

“A distributed system is one in which the failure of a computer you didn't even know existed can render your own computer unusable.”–Leslie Lamport

Modern computing has seen the proliferation of distributed systems and applications in various forms such as geo-replicated data stores, cloud computing systems, networked, fog, and serverless computing systems, etc. that drive entities that range from global enterprises to vehicular systems. The reliable and optimal operation of such systems has become an issue of utmost importance. Correct and optimal operation of distributed systems depends on several factors: appropriate choice of consistency settings based on application contexts, appropriate programming models that enable the development of reliable applications, and predictive execution models that enable optimal usage of resources.

Today, killer applications that utilize distributed computing systems are machine learning engines. Machine learning, in particular deep learning, has seen tremendous success in recent years with applications ranging from web search, product recommendations, healthcare diagnostics, autonomous driving, etc. Training deep learning engines amounts to solving approximately optimization problems that require searching for a “saddle point” in a high dimensional space: a computationally expensive task. Distributed cloud-based systems are routinely used to accelerate the search for saddle points as well as store large volumes of data for training deep learning engines. On the other hand, machine learning engines form the basis of predictive execution models for optimal usage of resources in distributed systems. In addition, machine learning systems find usage in predicting and diagnosing faults, vulnerabilities, and attacks in distributed systems.

This course will focus on the interplay between distributed systems and machine learning. The primary goal is to examine how distributed systems can enable reliable and efficient deployment of machine learning engines and how machine learning can contribute towards the correct and optimal operation of distributed computing systems.

OBJECTIVE

The primary objectives of the course are as follows:

1. To provide an understanding of the principles behind the reliable and optimal operation of distributed computing systems,
2. To provide an understanding of the interactions between the fields of machine learning and distributed computing,
3. To provide an understanding of the use of distributed computing for accelerating machine learning algorithms,
4. To provide an understanding of the use of machine learning for reliable and optimal operation of distributed systems,
5. To provide hands-on experience in building applications using distributed machine learning frameworks,
6. To provide exposure to state-of-the-art distributed machine learning frameworks like Petuum, GraphLab, etc.

INSTRUCTED BY

Prof. Supratik Mukhopadhyay, Louisiana State University, USA

Dr. Supratik Mukhopadhyay is a faculty member in Computer Science at Louisiana State University.  His research interests lie in the areas of Artificial Intelligence/Machine Learning with applications to Education, Automated Drug Discovery, Satellite Imagery Recognition, Transportation Systems, Sustainable Buildings, Cyber-Physical Human Systems, etc. In these areas, Dr. Mukhopadhyay's research has been supported by NSF, NASA, ONR, DARPA, ARO, USDOT, NGA, DOE, NRL, state agencies, and industry.  He has more than 110 publications in reputed journals and conferences and has three awarded US Patents. He led the DeepDrug team to the semifinal of the IBM Watson Artificial Intelligence XPRIZE. He is an Associate Editor of IEEE Transactions on Artificial Intelligence and Remote Sensing Letters and a program committee member for AAAI 2021.

 

Prof. Rajiv Misra, IIT Patna, India

Dr. Rajiv Misra is a Professor at the Department of Computer Science and Engineering, Indian Institute of Technology Patna. Dr. Misra did his Ph.D from IIT Kharagpur, M.Tech from IIT Bombay and B.Tech from NIT Allahabad. His area of interest includes Computer Communications (Networks), BigData, IIOT, Cloud Computing, Distributed Systems and Algorithms, 5G & Beyond Networking, Network Slicing, AI-ML, UAV-assisted MEC etc. His current project is 'AI-based 6G Network Slicing for multi-UAV prototype’.

CONTACT

Prof. Rajiv Misra (Course Coordinator)

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Rohit Kumar Gupta (Ph.D Scholar)

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Mobile: +91-7873759080

For the registration process and more details about this GIAN Course:  [Brochure attachment Link]