CODS 2016 - 2016 ACM India SIGKDD Conference on Data Sciences (IKDD CODS)

The Indian chapter of the ACM Special Interest Group on Knowledge Discovery in Databases (ACM IKDD) is pleased to announce the third Conference on Data Sciences (CoDS 2016) in Pune, Maharashtra. CoDS 2016 will be the continuation of two very successful conferences in 2014 and 2015 at Delhi and Bangalore respectively.
The ability to collect and store data has grown many fold over the last decade across domains such as web and social media, telecommunications, biology, health-care, high energy physics, and manufacturing, to name a few. This has powered the demand and the dream of businesses and governments to extract useful and actionable insights from such data in an automatic, reliable and scalable way. This is the ambitious goal of the emerging discipline of Data Science. To this end, research in Data Science draws and builds upon ideas from algorithms, databases, machine learning, statistics, linear algebra, optimization, numerical methods and visualization.
We invite papers reporting original research in all aspects of Data Science. CoDS 2016 will feature a Best Research Paper Award to honor the authors of the paper of the highest research quality. There will also be a Best Student Paper Award, for the best research quality among those accepted papers whose first author is a student enrolled in an educational institution. Each of these papers will receive a cash award of 1000 USD. Additionally, selected student authors will be eligible for receiving research mentoring by an eminent Data Science researcher (such as one of the Keynote Speakers at CoDS 2016) for up to a year.
The topics of interest for invited submissions include, but are not limited to:
Models and Algorithms: big data analytics, parallel and distributed machine learning, statistical learning theory, classification and regression methods, semi-supervised learning, unsupervised learning and clustering, kernel methods, online learning, reinforcement learning, graph mining, rule and pattern mining, relational and structured learning, matrix and tensor methods, time series analysis, mining temporal and spatial data, dimensionality reduction and manifold learning, combinatorial and continuous optimization, probabilistic graphical models, Bayesian methods, neural networks and deep learning.
Applications: text analytics and natural language processing, information retrieval, social network analysis, web and social media analysis, recommender systems, online advertising, bioinformatics, computational neuroscience, systems biology, multimedia processing, crowdsourcing, education, robotics, technology for emerging markets.
Industrial and Government Case studies: Descriptions of deployments of data science solutions in industry and government that address real-world challenges and highlight new and important research directions for data science. Domains of interest include but are not restricted to e-commerce, retail, online advertising, telecommunications, banking and finance, consumer products, media and entertainment, healthcare, education, manufacturing, natural resources, public safety, urban planning.