Welcome to Aceekret.

Our Team

Valeriy Gavrishchaka

Co-Founder, CEO, Head of Multi-Disciplinary Research

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. 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

CTO and Co-Founder, Head of Applied Machine Learning and AI

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.

External Multi-Disciplinary Collaborators

We actively collaborate with experts and practitioners in different fields. These include “Applied Quantitative Solutions for Complex Systems” multi-disciplinary research group (www.aqscs.com) as well as many individual experts in hard sciences, machine learning and AI around the globe.