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Maxim I Sviridenko

from New York, NY
Age ~49
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Also known as:
Sviridenko Maxim, Maxim O
Phone and address:
130 Malcolm X Blvd, New York, NY 10026
Related to:
Sofia Sviridenko
Connected to:
Elena V Sviridova, 55Svetlana B Sviridova, 70Alla Svirsky, 86Catherine R Svirsky, 48Diana Svirsky, 42Dmitri D Svirsky, 60Stephen B Svirsky, 63Walter M Svirsky, 68Brandy L Svitak, 44Brittany N Svitak, 45

Maxim Sviridenko Phones & Addresses

  • 130 Malcolm X Blvd, New York, NY 10026
  • 160 71St St, New York, NY 10023
  • 160 W 71St St #3A, New York, NY 10023
  • Seattle, WA
  • 39 Rome Ave, Bedford Hills, NY 10507
  • Yorktown Heights, NY
  • Mohegan Lake, NY

Work

Company: Verizon Oct 2018 Position: Senior director at yahoo research

Education

Degree: Doctorates, Doctor of Philosophy School / High School: Sobolev Institute of Mathematics 1996 to 1999 Specialities: Computer Science, Applied Mathematics, Philosophy

Skills

Algorithms • Mathematical Programming • Mathematical Modeling • Operations Research • Optimization • Algorithm Design • Approximation Algorithms • Supply Chain Optimization • Business Analytics • Randomized Algorithms • Machine Learning • Computer Science

Industries

Internet

Resumes

Resumes

Maxim Sviridenko Photo 1

Senior Director At Yahoo Research

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Location:
New York, NY
Industry:
Internet
Work:
Verizon
Senior Director at Yahoo Research
Yahoo
Director of Research
Yahoo Feb 2014 - Jul 2016
Principal Research Scientist
University of Warwick Jan 2012 - Jan 2014
Professor
Ibm Aug 2000 - Dec 2011
Research Staff Member
Education:
Sobolev Institute of Mathematics 1996 - 1999
Doctorates, Doctor of Philosophy, Computer Science, Applied Mathematics, Philosophy Novosibirsk State University, Department of Economics 1991 - 1998
Masters, Applied Mathematics, Informatics Novosibirsk State University, Department of Economics 1991 - 1996
Bachelors, Mathematics
Skills:
Algorithms
Mathematical Programming
Mathematical Modeling
Operations Research
Optimization
Algorithm Design
Approximation Algorithms
Supply Chain Optimization
Business Analytics
Randomized Algorithms
Machine Learning
Computer Science

Publications

Us Patents

Dynamic Resource Allocation Using Projected Future Benefits

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US Patent:
7308415, Dec 11, 2007
Filed:
Dec 4, 2001
Appl. No.:
10/000149
Inventors:
Tracy J. Kimbrel - Cortlandt Manor NY, US
Robert Krauthgamer - Ramat Hasharon, IL
Maria Minkoff - Somerville MA, US
Baruch M. Schieber - White Plains NY, US
Maxim I. Sviridenko - Mohegan Lake NY, US
Jayram S. Thathachar - San Jose CA, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 9/46
US Classification:
705 8, 705 7, 705 9, 709226
Abstract:
A method for server allocation in a Web server “farm” is based on limited information regarding future loads to achieve close to the greatest possible revenue based on the assumption that revenue is proportional to the utilization of servers and differentiated by customer class. The method of server allocation uses an approach of “discounting the future”. Specifically, when the policy faces the choice between a guaranteed benefit immediately and a potential benefit in the future, the decision is made by comparing the guaranteed benefit value with a discounted value of the potential future benefit. This discount factor is exponential in the number of time units that it would take a potential benefit to be materialized. The future benefits are discounted because by the time a benefit will be materialized, things might change and the algorithm might decide to make another choice for a potential (even greater) benefit.

Dynamic Resource Allocation Using Projected Future Benefits

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US Patent:
7546247, Jun 9, 2009
Filed:
Sep 26, 2007
Appl. No.:
11/861663
Inventors:
Tracy J. Kimbrel - Cortlandt Manor NY, US
Robert Krauthgarner - Ramat Hasharon, IL
Baruch M. Schieber - White Plains NY, US
Maxim I. Sviridenko - Mohegan Lake NY, US
Jayram S. Thathachar - San Jose CA, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 17/30
US Classification:
705 8, 705 10
Abstract:
A method for server allocation in a Web server “farm” is based on limited information regarding future loads to achieve close to the greatest possible revenue based on the assumption that revenue is proportional to the utilization of servers and differentiated by customer class. The method of server allocation uses an approach of “discounting the future”. Specifically, when the policy faces the choice between a guaranteed benefit immediately and a potential benefit in the future, the decision is made by comparing the guaranteed benefit value with a discounted value of the potential future benefit. This discount factor is exponential in the number of time units that it would take a potential benefit to be materialized. The future benefits are discounted because by the time a benefit will be materialized, things might change and the algorithm might decide to make another choice for a potential (even greater) benefit.

Dynamic Resource Allocation Using Known Future Benefits

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US Patent:
7765301, Jul 27, 2010
Filed:
Feb 13, 2006
Appl. No.:
11/352328
Inventors:
Tracy J. Kimbrel - Cortlandt Manor NY, US
Robert Krauthgamer - Ramat Hasharon, IL
Maria Minkoff - Somerville MA, US
Baruch M. Schieber - White Plains NY, US
Maxim I. Sviridenko - Mohegan Lake NY, US
Jayram S. Thathachar - San Jose CA, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 15/16
G06F 17/30
US Classification:
709226, 709225, 705 7, 705 8, 705 9, 705 10
Abstract:
A benefit task system implements a policy for allocating resources to yield some benefit. The method implemented may be applied to a variety of problems, and the benefit may be either tangible (e. g. , profit) or intangible (e. g. , customer satisfaction). In one example, the method is applied to server allocation in a Web site server “farm” given full information regarding future loads to maximize profits for the Web hosting service provider. In another example, the method is applied to the allocation of telephone help in a way to improve customer satisfaction. In yet another example, the method is applied to distributed computing problem where the resources to be allocated are general purpose computers connected in a network and used to solve computationally intensive problems. Solution of the Web server “farm” problem is based on information regarding future loads to achieve close to the greatest possible revenue based on the assumption that revenue is proportional to the utilization of servers and differentiated by customer class. The method of server allocation uses an approach which reduces the Web server farm problem to a minimum-cost network flow problem, which can be solved in polynomial time.

Dynamic Application Placement Under Service And Memory Constraints

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US Patent:
8230438, Jul 24, 2012
Filed:
Apr 4, 2008
Appl. No.:
12/062569
Inventors:
Tracy Jay Kimbrel - Cortlandt Manor NY, US
Malgorzata Steinder - Leonia NJ, US
Maxim Sviridenko - New York NY, US
Asser Nasreldin Tantawi - Somers NY, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 9/46
G06F 15/173
US Classification:
718104, 718105, 709226
Abstract:
An optimization problem models the dynamic placement of applications on servers under two types of simultaneous resource requirements, those that are dependent on the loads placed on the applications and those that are independent. The demand (load) for applications changes over time and the goal is to satisfy all the demand while changing the solution (assignment of applications to servers) as little as possible.

Scheduling Heterogeneous Partitioned Resources With Sharing Constraints

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US Patent:
8392926, Mar 5, 2013
Filed:
Apr 6, 2010
Appl. No.:
12/755089
Inventors:
Tracy J. Kimbrel - Cortlandt Manor NY, US
Tarun Kumar - Mohegan Lake NY, US
Kevin D. McKenzie - Poughkeepsie NY, US
Maxim Sviridenko - New York NY, US
Debra Tomkowid - Poughkeepsie NY, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 9/46
US Classification:
718103
Abstract:
A system and method that provides an automated solution to obtaining quality scheduling for users of computing resources. The system, implemented in an enterprise software test center, collects information from test-shop personnel about test machine features and availability, test jobs, and tester preferences and constraints. The system reformulates this testing information as a system of constraints. An optimizing scheduling engine computes efficient schedules whereby all the jobs are feasibly scheduled while satisfying the users' time preferences to the greatest extent possible. The method and system achieves fairness: if all preferences can not be meet, it is attempted to evenly distribute violations of preferences across the users. The test scheduling is generated according to a first application of a greedy algorithm that finds an initial feasible assignment of jobs. The second is a local search algorithm that improves the initial greedy solution.

Dynamic Application Placement Under Service And Memory Constraints

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US Patent:
8510745, Aug 13, 2013
Filed:
Mar 8, 2012
Appl. No.:
13/415034
Inventors:
Tracy Jay Kimbrel - Cortlandt Manor NY, US
Malgorzata Steinder - Leonia NJ, US
Maxim Sviridenko - New York NY, US
Asser Nasreldin Tantawi - Somers NY, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 9/46
G06F 15/173
US Classification:
718104, 718105, 709226
Abstract:
An optimization problem models the dynamic placement of applications on servers under two types of simultaneous resource requirements, those that are dependent on the loads placed on the applications and those that are independent. The demand (load) for applications changes over time and the goal is to satisfy all the demand while changing the solution (assignment of applications to servers) as little as possible.

Dynamic Resource Allocation Using Known Future Benefits

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US Patent:
20030105868, Jun 5, 2003
Filed:
Dec 4, 2001
Appl. No.:
10/000320
Inventors:
Tracy Kimbrel - Cortlandt Manor NY, US
Robert Krauthgamer - Ramat Hasharon, IL
Baruch Schieber - White Plains NY, US
Maxim Sviridenko - Mohegan Lake NY, US
Jayram Thathachar - San Jose CA, US
International Classification:
G06F015/173
G06F015/16
US Classification:
709/226000, 709/203000
Abstract:
A benefit task system implements a policy for allocating resources to yield some benefit. The method implemented may be applied to a variety of problems, and the benefit may be either tangible (e.g., profit) or intangible (e.g., customer satisfaction). In one example, the method is applied to server allocation in a Web site server “farm” given full information regarding future loads to maximize profits for the Web hosting service provider. In another example, the method is applied to the allocation of telephone help in a way to improve customer satisfaction. In yet another example, the method is applied to distributed computing problem where the resources to be allocated are general purpose computers connected in a network and used to solve computationally intensive problems. Solution of the Web server “farm” problem is based on information regarding future loads to achieve close to the greatest possible revenue based on the assumption that revenue is proportional to the utilization of servers and differentiated by customer class. The method of server allocation uses an approach which reduces the Web server farm problem to a minimum-cost network flow problem, which can be solved in polynomial time. Similar solutions are applicable to other resource allocation problems.

Dynamic Application Placement Under Service And Memory Constraints

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US Patent:
20060242647, Oct 26, 2006
Filed:
Apr 21, 2005
Appl. No.:
11/110766
Inventors:
Tracy Kimbrel - Cortlandt Manor NY, US
Malgorzata Steinder - Leonia NJ, US
Maxim Sviridenko - New York NY, US
Asser Tantawi - Somers NY, US
International Classification:
G06F 9/46
US Classification:
718104000
Abstract:
An optimization problem models the dynamic placement of applications on servers under two types of simultaneous resource requirements, those that are dependent on the loads placed on the applications and those that are independent. The demand (load) for applications changes over time and the goal is to satisfy all the demand while changing the solution (assignment of applications to servers) as little as possible.
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Maxim I Sviridenko from New York, NY, age ~49 Get Report