Valeriy V. Gavrishchaka received his MS and PhD degrees in computational and theoretical physics from Moscow Institute of Physics and Technology (Moscow, Russian Federation) and from West Virginia University (Morgantown, West Virginia, USA), respectively. He has 30 years of overall experience in complex systems research and applications including almost 20 years in financial industry. He worked as multi-disciplinary research scientist and consultant at Science Applications International Corporation (McLean, Virginia) on a wide range of problems in plasma / space physics and space weather forecasting using physics-based models / simulations and wide range of machine learning approaches (1997-2002).

From 2002 to 2010 he worked for several multi-billion New York based hedge funds as head of quantitative research and quantitative strategist for multi-frequency algorithmic trading. He also has multi-year experience in developing and implementing quantitative models as well as machine learning and AI frameworks for market and credit risk analytics including structured credit products. His main research interests include development and applications of novel multi-disciplinary approaches and integrated frameworks for applied quantitative modeling of complex systems in physics, finance, econometrics, medicine, and other scientific and business fields. He also develops and applies analytical models and multi-scale simulations to study fundamental processes in different physical, engineering, biological, and other systems. He is an author of more than 70 publications in mainstream scientific journals and referred conference proceedings that are frequently cited as summarized in his Google Scholar and Research Gate profiles.

Xuliang Miao has MS degree of Financial Mathematics from Johns Hopkins University and BS degree of Applied Mathematics and Computer Science from University of California, San Diego. She has more than 5 year experience in financial industry focusing on the risk management, quantitative analysis and machine learning. For the recent five years, she has applied her advanced programing and analytical skills in several fields including biostatistics, high performance computing and credit risk. Her current passion is development and applications of novel machine learning algorithms (including various ensemble-based and deep learning frameworks combined with analytical models and expert knowledge) to challenging problems in biomedicine and quantitative finance. She has several recent publications on the novel hybrid approaches in machine learning and AI.

Zhenyi Yang received his BS and MS degrees in Financial Mathematics from University of Michigan Ann Arbor and The Johns Hopkins University in 2011 and 2013. Currently, Zhenyi works as financial engineer in a major financial company. He is an all-round data scientist and computer science expert. His current research interest includes development and real-life applications of ensemble-based models, deep learning neural networks and other machine learning algorithms. He successfully applies novel machine learning frameworks to challenging problems in biomedicine and quantitative finance.