The industry track of the conference aims to bring together the leading industry and academia practitioners to share their insights, expertise and experiences. We invite original papers describing case studies, experiences and applied research in the areas mentioned above for this track.
The track will consist of three invited talks, a panel discussion and three papers accepted from the submissions. The talks will be of 25 minutes each.
Deep Neural Networks (DNNs) aka deep learning has emerged as a powerful learning tool over the last 7-8 years. The success of applying deep learning in solving problems related to image recognition, speech, NLP has fuelled a lot of interest in industry and academia. A lot of publications in top tier conferences relate to deep learning. Access to platforms (Cafe, Theano, Tensor flow) and hardware have made practice of deep learning relatively easy and widely prevalent. How much of this is hype and how much of it is reality? In this panel we will discuss how useful is deep learning in solving real world problems and when is deep learning the right tool to use, versus traditional AI/ML techniques.The panel would be for 45 - 55 mins depending on the discussion.
Bio : A Computer Science BTech from IIT Madras and PhD from University of Maryland, Gaurav has over fifteen years of experience in building computer vision and machine learning solutions both in academic and industrial settings. Gaurav has 35+ research papers in international conferences and journals, he has written as part of his research career. Gaurav currently works in the capacity of Sr. Principal Scientist at Ola Cabs where he leads several efforts to bridge the gap between profitability for the business and customer delight. Before joining Ola, he founded a technology start-up called Fashiate that was acquired by Snapdeal where he worked as the head of Multimedia Research Group for over two years. Gaurav has also worked as Senior Research Scientist at Yahoo Labs and as assistant professor at University of Notre Dame.
Bio : Krishnendu Chaudhury (krish) is a researcher in computer vision, deep learning and pattern recognition. He has over two dozen patents and publications in major journals and conferences of his area. He is currently CTO and co-founder of Drishti Technologies, Palo Alto, a startup funded by Andreessen and Horowitz. Prior to that he was with Flipkart, India for two years as Principal Scientist and Head of Imaging Sciences. He moved to Flipkart from Google Research, Mountain View, Machine Intelligence team. Overall, he was with Google for over a decade. He started his career at Adobe Systems, San Jose, Advanced Technology Group. His PhD is in Machine Vision.
Bio : Rajeev Rastogi is the Director of Machine Learning at Amazon. Previously, he was Vice President of Yahoo! Labs Bangalore and the founding Director of the Bell Labs Research Center in Bangalore, India. Rajeev is an ACM Fellow and a Bell Labs Fellow. He 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 received his B. Tech degree from IIT Bombay, and a PhD degree in Computer Science from the University of Texas, Austin.
Bio : Sunita Sarawagi researches in the fields of databases, data mining, and machine learning. Her current research interests are deep learning, graphical models and information extraction. She is institute chair professor at IIT Bombay. She got her PhD in databases from the University of California at Berkeley and a bachelors degree from IIT Kharagpur. Her past affiliations include visiting faculty at Google Research, Mountain view, CA, visiting faculty at CMU Pittsburg, and research staff member at IBM Almaden Research Center. She has several publications in databases and data mining and several patents. She serves on the board of directors of ACM SIGKDD and VLDB foundation. She was program chair for the ACM SIGKDD 2008 conference, research track co-chair for the VLDB 2011 conference and has served as program committee member for SIGMOD, VLDB, SIGKDD, ICDE, and ICML conferences. She is/was on the editorial board of the ACM TODS, ACM TKDD, and FnT for machine learning journals.
Data and Analytics Challenges in a Blockchain World
Abstract: Blockchain is ushering in a new era of distributed computing. While originally introduced as the underlying technology of the cryptocurrency Bitcoin, over the last few years it is being evolved and adapted for use in enterprise applications in a permissioned setting. Many platforms are gaining prominence such as Hyperledger Fabric, R3's Corda, and Enterprise Ethereum. This talk will motivate how many distributed computing, security and database concepts are seeing a resurgence in such platforms, opening up many interesting research challenges. In particular, with complex enterprise applications being built on blockchain, there is an increasing need for richer data and analytics support in such platforms. On the flip side, I will also try and motivate why blockchain can be a key technology enabler for cross-organizational data sharing.
Oracle Autonomous Database Cloud
Abstract: Oracle Autonomous Database Cloud (World’s First ‘Self-Driving’ Database Service), powered by the upcoming next-generation Oracle Database 18c, enables complete end-to-end automated Cloud DB Service. Thereby it helps eliminate complexity, human error, manual management & tuning consequentially ensuring unprecedented Availability, Reliability, Scalability and Security at much lower operational costs. This session covers the Oracle Autonomous Database and Autonomous DW Cloud Service’s key features and value propositions that enable it to become ‘the preferred choice’ among Enterprise DB Cloud solutions.
Large scale Named Entity Recognition in Emails
Named Entity Recognition(NER) is a well-studied NLP problem, with results showing high precision and recall. We analyzed existing models which are available in open source repositories, testing them on our email and chat corpus, and found challenges when recognizing and later resolving entities which are interesting to us. In this talk, we will explain how we built a Named Entity Recognition model for large scale entity extraction, which works with low latency.
How We Built Our Machine Intelligence To Help Doctors Save Lives
7.2 million people die of heart disease every year. 50% of these lives can be saved if heart attacks can be diagnosed quickly and treatment coordinated within the golden hour. Diagnosing heart disease requires a simple test called an ECG, unfortunately, interpreting the ECG accurately requires a specialist. But, how do we put the skills of a cardiologist in every corner of the globe ? How do we equip a GP in India or a nurse in sub-Saharan Africa or a medical attendant in Buenos Aires to be able to help diagnose a heart attack and start treatment ?
Tricog provides real time cardiac diagnosis amplifying the work of few doctors to reach out to all patients worldwide. We’ve built specialised AI powered algorithms to help our resident doctors with the diagnosis, which is then sent back to the remote centre, thus enabling a doctor or a health care worker in any remote location diagnose and initiate treatment for heart disease, thus saving lives.
This talk will discuss how we’ve built our systems to bridge the divide between machine intelligence and human expertise so that they work together as a team to provide this “Cardiology as a Service” at scale, accurately and quickly.
About the Speaker : Zainul Charbiwala is a co-founder and the CTO at Tricog Health. He’s been building embedded systems and developing software for over 15 years. He is interested in the overlap of connected devices and machine intelligence to revolutionise and reinvent healthcare. He holds a Master’s degree from IIT Bombay and a PhD from University of California, Los Angeles. Before Tricog, Zainul was a Research Staff Member and Research Manager at IBM Research, India. Zainul has 7 patents and over 30 refereed publications.