Motivated by the need for efficient estimation of conditional expectations, we consider a least-squares function approximation problem with heavily polluted data. Existing methods that are powerful in the small noise regime are suboptimal when large noise is present. We propose a hybrid approach that combines Christoffel sampling with certain types of optimal experimental design to address this issue. We show that the proposed algorithm enjoys appropriate optimality properties for both sample point generation and noise mollification, leading to improved computational efficiency and sample complexity compared to existing methods. We also extend the algorithm to convex-constrained settings with similar theoretical guarantees. When the target function is defined as the expectation of a random field, we extend our approach to leverage adaptive random subspaces and establish results on the approximation capacity of the adaptive procedure. Our theoretical findings are supported by numerical studies on both synthetic data and on a more challenging stochastic simulation problem in computational finance.

Dr. Yves J. Hilpisch is founder and CEO of The Python Quants (http://tpq.io), He is also the founder and CEO of The AI Machine (http://aimachine.io), a company focused on AI-powered algorithmic trading based on a proprietary strategy execution platform. Yves has a Diploma in Business Administration, a Ph.D. in Mathematical Finance and is Adjunct Professor for Computational Finance. Yves is the author of five books (https://home.tpq.io/books): Yves is the director of the first online training program leading to University Certificates in Python for Algorithmic Trading (https://home.tpq.io/certificates/pyalgo) and Computational Finance (https://home.tpq.io/certificates/compfin).

At Affirm, People Come First is one of our core values, and that's why diversity and inclusion are vital to our priorities as an equal opportunity employer. You can read about our D&I program here and our progress thus far in our 2020 DEI Report. We also believe It's On Us to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process. We will consider for employment qualified applicants with arrest and conviction records in accordance with applicable federal, state, and local laws, including the San Francisco Fair Chance Ordinance.

Intelligence is no longer exclusively human. Machines can now recognise a human face, drive a car, beat a chess master and cope with uncertainty. To be as clever as a human, a system must make the right decision in complex and changing conditions – swerve to avoid someone while not knowing if it's safe, for example, or understand loosely worded commands. Expectations of what artificial intelligence (AI) can do run high, and universities are keen to meet the needs of industry. Cheaper hardware and software and an abundance of data have fuelled interest.

Students develop an advanced knowledge of computational methods in finance, which is a prerequisite for a successful career in the financial industry within'quant' teams. 'Quants' (development analysts) design and implement complex models and are sought after by banks, fund managers, insurance companies, hedge funds, and financial software and data providers. Programming experience is an advantage but is not mandatory. Relevant work experience is also taken into account. The programme is delivered through a combination of lectures, tutorials, seminars, and project work.

Dr. Bin Li received a bachelor's degree in computer science from Huazhong University of Science and Technology, Wuhan, China, and a bachelor's degree in economics from Wuhan University, Wuhan, China, in 2006. He earned a PhD degree from the School of Computer Engineering of Nanyang Technological University, Singapore, in 2013. He completed the CFA Program in 2013 and is currently an associate professor of finance at the Economics and Management School of Wuhan University. Dr. Li was a postdoctoral research fellow at the Nanyang Business School of Nanyang Technological University. His research interests are computational finance and machine learning.

Dr. Abdalla Kablan is an entrepreneur and academic specialising in machine intelligence, big data analytics and computational Finance. He is the founder of Scheduit – a business oriented social network that uses artificial intelligence and machine learning to match relevant business professionals with each other. Abdalla is also a lecturer and researcher of Computational Finance at the University of Malta. He obtained his PhD degree in Computational Finance from University of Essex, UK where he was a candidate at the Centre for Computational Finance and Economic Agents (CCFEA). His PhD thesis title was (The Use of Fuzzy Logic Applications for High frequency Trading).