Here are 110 upcoming meetings and conferences, for June 2017 and beyond. You can also find the latest list on KDnuggets Meetings page Color code: Business-Oriented meetings in Blue, Research meetings (with calls for papers and program committee) in green Top countries: India, France, Australia: 3 For the second month in a row, London is the top city: Washington DC, New Orleans, Houston, Chicago, Atlanta: 3 June 2017 Jun 1-2, Deep Learning in Finance Summit. Mention "KDNuggets" and save 18% on tickets. Use code KDNUGGETS to save 15%. Use code TEC6245KD to save.
Siamak Amirghodsi (Sammy) is a world-class senior technology executive leader with an entrepreneurial track record of overseeing big data strategies, cloud transformation, quantitative risk management, advanced analytics, large-scale regulatory data platforming, enterprise architecture, technology road mapping, multi-project execution, and organizational streamlining in Fortune 20 environments in a global setting. Siamak is a hands-on big data, cloud, machine learning, and AI expert, and is currently overseeing the large-scale cloud data platforming and advanced risk analytics build out for a tier-1 financial institution in the United States. Siamak's interests include building advanced technical teams, executive management, Spark, Hadoop, big data analytics, AI, deep learning nets, TensorFlow, cognitive models, swarm algorithms, real-time streaming systems, quantum computing, financial risk management, trading signal discovery, econometrics, long-term financial cycles, IoT, blockchain, probabilistic graphical models, cryptography, and NLP. Siamak is fully certified on Cloudera's big data platform and follows Apache Spark, TensorFlow, Hadoop, Hive, Pig, Zookeeper, Amazon AWS, Cassandra, HBase, Neo4j, MongoDB, and GPU architecture, while being fully grounded in the traditional IBM/Oracle/Microsoft technology stack for business continuity and integration. He holds an advanced degree in computer science and an MBA from the University of Chicago (ChicagoBooth), with emphasis on strategic management, quantitative finance, and econometrics.
Razorfish Inc. is one of the largest interactive marketing agencies in the world; it is part of Publicis Groupe. The agency provides different types of services, web development, planning and media buying, advertising using all kinds of media, including mobile, and emerging media, social marketing activities, etc.; as well as studies and analyzes of the impact of communication programs. Razorfish has more than 2,000 employees worldwide, with offices in the United States in New York, Chicago, Seattle, San Francisco, Philadelphia, Portland, Los Angeles, Atlanta, and Austin. In 2005-2007, it operates internationally through acquisitions in London, Paris, Sydney, Hong Kong, Shanghai, Beijing, Berlin, Frankfurt, Singapore and a joint venture in Tokyo. It is therefore a global digital agency that offers a complete package of services.
The West Big Data Innovation Hub (WBDIH) at the San Diego Supercomputer Center (SDSC) at UC San Diego is one of four regional big data hubs partner sites awarded a $1.8 million grant from the National Science Foundation (NSF) for the initial development of a data storage network during the next two years. Other partners include Johns Hopkins University and University of Chicago, awarded a $300K EAGER for Open Storage Network (OSN) software. The team will combine its expertise, facilities, and research challenges to develop the OSN. The demonstration project will result in the design of a larger, low-cost, scalable national system capable of being replicated across many universities. The OSN will enable national collaborations and allow academic researchers across the nation to share their data more efficiently than ever before, according to the NSF announcement.
A team lead by researchers in the Pritzker School of Molecular Engineering (PME) at the University of Chicago reports that it has developed an artificial intelligence-led process that uses big data to design new proteins that could have implications across the healthcare, agriculture, and energy sectors. By developing machine-learning models that can review protein information culled from genome databases, the scientists say they found relatively simple design rules for building artificial proteins. When the team constructed these artificial proteins in the lab, they discovered that they performed chemistries so well that they rivaled those found in nature. "We have all wondered how a simple process like evolution can lead to such a high-performance material as a protein," said Rama Ranganathan, PhD, Joseph Regenstein Professor in the Department of Biochemistry and Molecular Biology, Pritzker Molecular Engineering, and the College. "We found that genome data contains enormous amounts of information about the basic rules of protein structure and function, and now we've been able to bottle nature's rules to create proteins ourselves."