Invited Speakers

The Open Information Extraction Project
Prof. Mausam, Associate Professor, Indian Institute of Technology Delhi Affiliate Faculty, University of Washington, Seattle

Abstract:

Open Information Extraction is an attractive paradigm for extracting large amounts of relational facts from natural language text in a domain-independent manner. In this talk I will describe a brief history of the project starting way back in 2005, and the series of enhancements leading up to the latest open extractors. I will end with some applications work that uses open extractions for various end tasks, including unsupervised event schema generation and deep learning of word vectors.

Bio:

Prof. Mausam is an associate professor at the Dept. of Computer Science in IIT-Delhi and an affiliate faculty at the University of Washington.

His work focuses on large-scale information extraction and text summarization, AI & ML applications to crowdsourcing and education, automated planning under uncertainty, machine learning, and probabilistic reasoning.

He holds a Masters and PhD in Computer Science from University of Washington, Seattle.

Mausam has been granted Senior Member status in the Association for the Advancement of Artificial Intelligence(AAAI).

 

Scalable Causal Inference for Down-stream Impact of an Event
Dr. Vineet Chaoji
Manager, Applied Science at Amazon, Bengaluru, India.

Abstract:

Downstream Impact (DSI) measures the longer term impact of a customer action in terms of increase in revenue or units sold. The DSI of an event is critical to making a wide range of business decisions — e.g., how should one price the Kindle Paperwhite, which Prime benefit should be recommended to a customer, which product advertisement should shown to a customer, etc. One can think of millions of events within the Amazon ecosystem. Estimating the DSI of an event is a causal inference problem. The talk will introduce the DSI estimation problem and its connection to causal inference. Few techniques for computing the DSI will be discussed. Finally, I will present a scalable system to estimate the DSI for a large number of events.

Bio:

Vineet Chaoji is an Applied Science Manager within the Machine Learning team at Amazon where he leads projects related to econometric models of customer behavior, customer targeting and malware detection. 

Prior to joining Amazon, he was a Scientist at Yahoo! Labs in Bangalore where his research focused on online advertising and social networks.

Vineet obtained his PhD in Computer Science from Rensselaer Polytechnic Institute. 

He has published at top-tier data mining and database conferences and journals. Vineet has also served on the program committees of leading data and web mining conferences.

Privacy and Security in Online Social Media (PSOSM)
Prof. Ponnurangam Kumaraguru
, Associate Professor, IIIT, Delhi

Abstract:

With increase in usage of the Internet, there has been an exponential increase in the use of online social media on the Internet. Websites like Facebook, Google+, YouTube, Orkut, Twitter and Flickr have changed the way the Internet is being used. There is a dire need to investigate, measure, and understand privacy and security on online social media from various perspectives (computational, cultural, psychological). Real world scalable systems need to be built to detect and defend security and privacy issues on online social media. I will describe briefly some cool projects that we work on: TweetCred, OSM & Policing, OCEAN, and Call Me MayBe. Many of our research work is made available for public use through tools or online services. Our work derives techniques from Computational Social Science, Data Science, Statistics, Network Science, and Human Computer Interaction. In particular, in this talk, I will focus on the following:

  1. TweetCred, a tool to extract intelligence from Twitter which can be useful to security analysts. TweetCred is backed by award-winning research publications in international and national venues.
  2. How police in India are using online social media, how we can use computer science understanding to help police engage more with citizens and increase the safety in society.
  3. OCEAN: Open source Collation of eGovernment data and Networks, how publicly available information on Government services can be used to profile citizens in India. This work obtained the Best Poster Award at Security and Privacy Symposium at IIT Kanpur, 2013 and it has gained a lot of traction in Indian media.
  4. Given an identity in one online social media, we are interested in finding the digital foot print of the user in other social media services, this is also called digital identity stitching problem. This work is also backed by award-winning research publication.

I will be more than happy to clarify, discuss, any of our work in detail, as required, after the talk.

Bio:

Ponnurangam Kumaraguru ("PK") Associate Professor, is currently the Hemant Bharat Ram Faculty Research Fellow at the Indraprastha Institute of Information Technology (IIIT), Delhi, India. PK is the Founding Head of Cybersecurity Education and Research Centre (CERC).

His research interests include Privacy, e-Crime, Online Social Media, and Usable Security, in particular, these days he has been dabbling with complex networked systems (e.g. social web systems like Twitter, Facebook, and telephone logs). He is also very passionate about issues related to human computer interaction.

He received his Ph.D. from the School of Computer Science at Carnegie Mellon University (CMU).

PK is one of ACM India Eminent Speakers. He is also serving as a reviewer for International Journal of Information Security and ACM's Transactions on Internet Technology (TOIT).