{"642967":{"#nid":"642967","#data":{"type":"event","title":"ML@GT Virtual Seminar: Qi Wei, J.P. Morgan Chase","body":[{"value":"\u003Cp\u003EML@GT will host a\u0026nbsp;virtual seminar featuring Qi Wei, Vice President and ML\/AI Lead at JP Morgan Chase.\u003C\/p\u003E\r\n\r\n\u003Cp\u003ERegistration is required. \u003Ca href=\u0022https:\/\/primetime.bluejeans.com\/a2m\/register\/vbspsshh\u0022\u003ERegister here.\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Ch3\u003EGenerative models based on point processes for financial time series simulation\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Ch4\u003EAbstract:\u0026nbsp;\u003C\/h4\u003E\r\n\r\n\u003Cp\u003EIn this seminar, I will talk about generative models based on point processes for financial time series simulation. Specifically, we focus on a recently developed state-dependent Hawkes (sdHawkes) process to model the limit order book dynamics [Morariu-Patrichi, 2018]. The sdHawkes model consists of an oracle Hawkes process and a state process following Markov transition. The Hawkes and state processes are fully coupled, which enables the point process captures the self-and cross-excitation as well as the interaction between events and states. We will go through the model formulation in sdHawkes, the simulation of sdHawkes, its maximum likelihood estimation, and more importantly, its application to high-frequency data modeling that captures the interactions between the order flow and the state of the current market.\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\nMorariu-Patrichi, Maxime, and Mikko S. Pakkanen. \u0026quot;State-dependent Hawkes processes and their application to limit order book modelling.\u0026quot; arXiv preprint arXiv:1809.08060 (2018).\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Ch4\u003EAbout Qi:\u003C\/h4\u003E\r\n\r\n\u003Cp\u003EQi\u0026nbsp;Wei\u0026nbsp;received\u0026nbsp;his\u0026nbsp;Ph.D.\u0026nbsp;degree\u0026nbsp;in\u0026nbsp;machine\u0026nbsp;learning\u0026nbsp;and\u0026nbsp;image\u0026nbsp;processing\u0026nbsp;from the\u0026nbsp;National\u0026nbsp;Polytechnic\u0026nbsp;Institute\u0026nbsp;of\u0026nbsp;Toulouse\u0026nbsp;(INPENSEEIHT),\u0026nbsp;University\u0026nbsp;of\u0026nbsp;Toulouse,\u0026nbsp;France\u0026nbsp;in\u0026nbsp;September\u0026nbsp;2015,\u0026nbsp;and\u0026nbsp;Bachelor\u0026nbsp;degree\u0026nbsp;in\u0026nbsp;Electrical\u0026nbsp;Engineering\u0026nbsp;from\u0026nbsp;Beihang\u0026nbsp;University\u0026nbsp;(BUAA),\u0026nbsp;Beijing,\u0026nbsp;China\u0026nbsp;in\u0026nbsp;July\u0026nbsp;2010. Wei\u0026#39;s\u0026nbsp;doctoral thesis\u0026nbsp;\u003Cem\u003EBayesian Fusion of Multi-band Images: A Powerful Tool for Super-resolution\u0026nbsp;\u003C\/em\u003Ewas rated as one of the best theses (awarded Prix Leopold Escande) at the\u0026nbsp;University\u0026nbsp;of\u0026nbsp;Toulouse,\u0026nbsp;2015.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EWei\u0026nbsp;has worked on multiband\u0026nbsp;image\u0026nbsp;processing\u0026nbsp;as a Research Associate with Signal\u0026nbsp;Processing\u0026nbsp;Laboratory,\u0026nbsp;University\u0026nbsp;of\u0026nbsp;Cambridge, UK, and\u0026nbsp;as a Research Associate at Duke\u0026nbsp;University, US. He has also\u0026nbsp;worked at Siemens Corporate Technology as a Research Scientist.\u0026nbsp;Since\u0026nbsp;2018, Wei served as a vice president and machine learning scientist at\u0026nbsp;JPMorgan.\u0026nbsp;His\u0026nbsp;research has been focused on\u0026nbsp;machine\/deep\u0026nbsp;learning, time series analysis, computer vision\/image\u0026nbsp;processing, Bayesian statistical inference, etc.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"A virtual seminar featuring Qi Wei, Vice President and ML\/AI Lead at JP Morgan Chase"}],"uid":"34773","created_gmt":"2021-01-15 14:26:43","changed_gmt":"2021-03-30 13:06:06","author":"ablinder6","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2021-04-07T13:15:00-04:00","event_time_end":"2021-04-07T14:15:00-04:00","event_time_end_last":"2021-04-07T14:15:00-04:00","gmt_time_start":"2021-04-07 17:15:00","gmt_time_end":"2021-04-07 18:15:00","gmt_time_end_last":"2021-04-07 18:15:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"47223","name":"College of Computing"},{"id":"37041","name":"Computational Science and Engineering"},{"id":"1299","name":"GVU Center"},{"id":"589608","name":"Machine Learning"},{"id":"576481","name":"ML@GT"},{"id":"431631","name":"OMS"},{"id":"50877","name":"School of Computational Science and Engineering"},{"id":"50875","name":"School of Computer Science"},{"id":"50876","name":"School of Interactive Computing"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EAllie McFadden\u003C\/p\u003E\r\n\r\n\u003Cp\u003Eallie.mcfadden@cc.gatech.edu\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}