Big Data. Everybody wants to get it. Understand it. Use it.
So do you.
If you’re interested in decision making and Artificial Intelligence, join us for our weekly free colloquium talk with researcher and financial engineer Martin Jermakyan.
Thursday, April 24, 5pm, Hypercube 3rd floor.
Don’t forget to bring your monstrous datasets and childlike sense of wonder.
Transfers: If you go by car please pass the details of your car to Alesya Garifullina so she could order a Hypercube pass. You can also use our shuttle bus which departs from Slavyansky Boulevard at 3:15pm, and the MSM parking area at 3:45.
When: April 24; 5:00pm. Where: Hypercube, 3rd floor. Title: Big-data induced competitive-cooperative feedback control decision-making & intelligent artificial intelligence.
The following is detailed information about the colloquium’s topic and our guest speaker:
Abstract: The talk will focus on big-data induced complex decision-making issues in competitive, cooperative or mixed competitive-cooperative environments and offer a mixture of known and new results.
Big-data processing analytics and its efficient implementation have increasingly become one of the demanded and most challenging topics of modern applied sciences and artificial intelligence in view of their numerous applications in financial markets, electricity generation-transmission-distribution industry, healthcare, transportation logistics, multi-sensory systems, etc.
At the same time, various big-data induced real-time mission critical applications in competitive, cooperative and mixed competitive-cooperative environments pose significant modeling, analytical, algorithmic, computational and logistic challenges as it pertains to the timely, reliable and optimal complex decision-making. The issues become even more acute in view of the big-data possessing 6V-attributes – including those of veracity, validity and volatility.
In competitively, cooperatively or mixed competitive-cooperatively interacting multiple big-data producing agent environments, where the agents of interaction recognize their environment from the observed data (by solving corresponding inverse problems) and learn further about the environment they operate in by producing additional observations through their actions (by utilizing various machine learning techniques), and where they adjust their actions to those of their competitors or cooperators and while their joint actions affect the state of the dynamic of the underlying system, optimal complex decisions can be systematically modeled as Nash Feedback Equilibrium Control solutions of corresponding game-theoretical problems expressed as Bellman-Jacobi-Isaacs equation(s).
Readily implementable theoretical results in this complex discipline are absent or scarce and, when available, they merely represent case studies specifically prescribed with the purpose of arriving to relatively simple solutions – while their derivation may still be rather complex. However, hardly any applied big-data induced complex optimal decision-making problems render themselves to such simplicity and susceptibility for solution.
Furthermore, traditional methodologies frequently don’t leave sufficient time even for ad hoc decision-making. As a result and frequently, the only manageable solution can be achieved through the meta-algorithmisation of corresponding problems, accommodating generation of numerical solutions for all possible scenarios and with the organization of these solutions as layered tables which reside both in the cache and various storage and database structures of specially designated decision-making sub-clouds.
Guest Speaker: Martin Jermakyan is an experienced researcher and financial engineer with both academic and applied credentials. He has earned his PhD in Physics and Mathematics from the Mechanics and Mathematics department of Moscow State University.
Subsequently, he has been on the faculty of the Applied Mathematics Program at the Mathematics department of the University of California at Los Angeles and the Mathematics, Finance, and Operations and Industrial Research departments of the University of Michigan at Ann Arbor.
Having held senior level positions both at startups and large-corporations in the financial technological service industry in the USA, he brings more than 15 years of applied experience geared at the development and implementation of big-data processing applications in financial markets.
* The Skolkovo Institute of Science and Technology (Skoltech) is a private graduate research university in Skolkovo, Russia, a suburb of Moscow. Established in 2011 in collaboration with MIT, Skoltech educates global leaders in innovation, advance scientific knowledge, and foster new technologies to address critical issues facing Russia and the world. Applying international research and educational models, the university integrates the best Russian scientific traditions with twenty-first century entrepreneurship and innovation.
If you like to participate and for further information or questions, please Liliya Abaimova
We look forward to seeing you.
