Approximate Bayesian Inference with Application to Risk Assessment
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***PLEASE NOTE*** - JP Morgan are hosting this event, we only have 30 spaces available - only RSVP if you are 100% sure you can attend.
Jani Lahtinen – JP Morgan - Approximate Bayesian Inference with Application to Risk Assessment - https://www.linkedin.com/in/jani-lahtinen-b7153090
(Jani Lahtinen's Bio) - Doctoral dissertation on statistical inference and random graphs in 2005 at the Helsinki University of Technology under the supervision of professors Jukka Heikkonen, Jouko Lampinen and Kimmo Kaski. The focus of my dissertation was on Bayesian statistics and generation and analysis of the properties of random graphs. Since then I have worked at Nokia, as a freelance consultant, and before joining JP Morgan I was employed at McAfee. I will talk about “Approximate Bayesian Inference with Application to Risk Assessment”.
***PLEASE NOTE*** - JP Morgan are hosting this event, we only have 30 spaces available - only RSVP if you are 100% sure you can attend.
Speaker 1 - Jani Lahtinen – JP Morgan - Approximate Bayesian Inference with Application to Risk Assessment - https://www.linkedin.com/in/jani-lahtinen-b7153090
(Jani Lahtinen's Bio) - Doctoral dissertation on statistical inference and random graphs in 2005 at the Helsinki University of Technology under the supervision of professors Jukka Heikkonen, Jouko Lampinen and Kimmo Kaski. The focus of my dissertation was on Bayesian statistics and generation and analysis of the properties of random graphs. Since then I have worked at Nokia, as a freelance consultant, and before joining JP Morgan I was employed at McAfee. I will talk about “Approximate Bayesian Inference with Application to Risk Assessment”.
Speaker 2 - James Littlejohn - https://www.linkedin.com/in/aboynejames - became an Associate of the Cyber Academy at Edinburgh Napier University in July 2016. Previously he worked as an independent consultant advising on blockchain opportunities in the area of science and health. An early adopter and developer using bitcoin, ethereum and maidsafe etc. He setup dsensor.org that is building a blockchain specifically designed for the needs of science. His interest in Peer to Peer technologies developed during his Economics degree from Aberdeen University in 1995 which he followed with three entrepreneurial ventures in fintech, e-commerce and text data mining.
Presentation Topic - Performing data science peer to peer. How the uses of decentralised storage, smart contracts and blockchain can produce an automatic audit trail for managing and performing predictive analytics.
6:00 PM – 6.30 PM: Networking
6.30 PM – 7:00 PM: Jani Lahtinen – JP Morgan - Approximate Bayesian Inference with Application to Risk Assessment
7:00 PM – 7.30 PM: James Littlejohn - Associate of the Cyber Academy at Edinburgh Napier University
7.30 PM – 8:00 PM: Q & A session
8.00 PM – 8.30PM: Networking and Drinks
***PLEASE NOTE*** - JP Morgan are hosting this event, we only have 30 spaces available - only RSVP if you are 100% sure you can attend.
Speaker 1 - Jani Lahtinen – JP Morgan - Approximate Bayesian Inference with Application to Risk Assessment - https://www.linkedin.com/in/jani-lahtinen-b7153090
(Jani Lahtinen's Bio) - Doctoral dissertation on statistical inference and random graphs in 2005 at the Helsinki University of Technology under the supervision of professors Jukka Heikkonen, Jouko Lampinen and Kimmo Kaski. The focus of my dissertation was on Bayesian statistics and generation and analysis of the properties of random graphs. Since then I have worked at Nokia, as a freelance consultant, and before joining JP Morgan I was employed at McAfee. I will talk about “Approximate Bayesian Inference with Application to Risk Assessment”.
Speaker 2 - James Littlejohn - https://www.linkedin.com/in/aboynejames (https://www.linkedin.com/in/aboynejames) - became an Associate of the Cyber Academy at Edinburgh Napier University in July 2016. Previously he worked as an independent consultant advising on blockchain opportunities in the area of science and health. An early adopter and developer using bitcoin, ethereum and maidsafe etc. He setup dsensor.org that is building a blockchain specifically designed for the needs of science. His interest in Peer to Peer technologies developed during his Economics degree from Aberdeen University in 1995 which he followed with three entrepreneurial ventures in fintech, e-commerce and text data mining.
Presentation Topic - Performing data science peer to peer. How the uses of decentralised storage, smart contracts and blockchain can produce an automatic audit trail for managing and performing predictive analytics.
6:00 PM – 6.30 PM: Networking
6.30 PM – 7:00 PM: Jani Lahtinen – JP Morgan - Approximate Bayesian Inference with Application to Risk Assessment
7:00 PM – 7.30 PM: James Littlejohn - Associate of the Cyber Academy at Edinburgh Napier University
7.30 PM – 8:00 PM: Q & A session
8.00 PM – 8.30PM: Networking and Drinks
