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[{"authors":["admin"],"categories":null,"content":"Niels van Galen Last currently works as Chief Data Officer at XLabs.ai, working on all things technical. He is building the infrastructure for solving machine learning problems in the cloud. He also works on applying machine learning to better understand problems in the areas of computational genomics and genetics. He specializes in developing statistical and computational machine learning methods for large-scale data. Previously he worked as Chief Technology Officer at Post Planner and prior to that as CTO at Paystik/Evergive (acquired by OneParish).\n","date":-62135596800,"expirydate":-62135596800,"kind":"taxonomy","lang":"en","lastmod":1565448657,"objectID":"2525497d367e79493fd32b198b28f040","permalink":"https://nielsgl.com/authors/admin/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/admin/","section":"authors","summary":"Niels van Galen Last currently works as Chief Data Officer at XLabs.ai, working on all things technical. He is building the infrastructure for solving machine learning problems in the cloud. He also works on applying machine learning to better understand problems in the areas of computational genomics and genetics. He specializes in developing statistical and computational machine learning methods for large-scale data. Previously he worked as Chief Technology Officer at Post Planner and prior to that as CTO at Paystik/Evergive (acquired by OneParish).","tags":null,"title":"Niels van Galen Last","type":"authors"},{"authors":["James Ioannidis","Niels van Galen Last"],"categories":null,"content":"","date":1430438400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1564843495,"objectID":"d77eec1571778e6fed707004b6f209b2","permalink":"https://nielsgl.com/publication/ioannidis-2015-mobile/","publishdate":"2019-08-02T12:29:57.008967Z","relpermalink":"/publication/ioannidis-2015-mobile/","section":"publication","summary":"Exemplary methods, apparatuses, and systems determine that a geolocation of a first mobile device is within a threshold distance from a geolocation of a second mobile device. In response, a list of one or more mobile devices within the threshold distance from the geolocation of the second mobile device, including the first mobile device, are transmitted to the second mobile device. A selection of the first mobile device from the list is received from the second mobile device. A request from the second mobile device to transmit a payment from an account associated with the second mobile device to an account associated with the first mobile device is received from the second mobile device. A confirmation of the requested payment is transmitted to the second mobile device.","tags":null,"title":"Mobile payment hotspot","type":"publication"},{"authors":["Niels van Galen Last"],"categories":null,"content":"","date":1325376000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1564754864,"objectID":"73e30f71b1b55ee7d98ae306306f7aed","permalink":"https://nielsgl.com/publication/van-galen-last-2012/","publishdate":"2019-08-02T13:34:41.89379Z","relpermalink":"/publication/van-galen-last-2012/","section":"publication","summary":"In many situations, a form of negotiation can be used to resolve a problem between multiple parties. However, one of the biggest problems is not knowing the intentions and true interests of the opponent. Such a user profile can be learned or estimated using biddings as evidence that reveal some of the underlying interests. In this paper we present a model for online learning of an opponent model in a closed bilateral negotiation session. We studied the obtained utility during several negotiation sessions. Results show a significant improvement in utility when the agent negotiates against a state-of-the-art Bayesian agent, but also that results are very domain-dependent.","tags":null,"title":"Agent Smith: Opponent Model Estimation in Bilateral Multi-issue Negotiation","type":"publication"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1564822199,"objectID":"eb77c8213d929d83239721c4a9d75eb1","permalink":"https://nielsgl.com/project/sequelize-paper-trail/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/project/sequelize-paper-trail/","section":"project","summary":"Track changes to your models, for auditing or versioning. See how a model looked at any stage in its lifecycle, revert it to any version, or restore it after it has been destroyed. Record the user who created the version.","tags":["Demo"],"title":"Sequelize Paper Trail","type":"project"}]