Maxim Sviridenko Business address: IBM T.J. Watson Research Center, P.O. Box 218,  Yorktown Heigths, NY 10598, USA.  E-mail: sviri@us.ibm.com Business phone:  (914)-945-1217  Fax: (914)-945-3434

Education:

• Ph.D. in Mathematics, "Approximation algorithms for discrete facility location problems", Sobolev Institute of Mathematics , January, 1999.
• M.S. in Applied Mathematics and Computer Science, Novosibirsk State University, May, 1998.
• B. S. in Pure Mathematics,  Novosibirsk State University, May, 1996.

Affiliation:

• 31 July, 2000-present, I was a postdoc for a 1.5 years. Now, I am a research staff member at the IBM T. J. Watson Research Center, Department of Mathematical Sciences, Theory of Computation Group.
• 1 August, 1999 - 31 July, 2000, Basic Research Institute in Computer Science (BRICS), Department of Computer Science, Aarhus University, Denmark
• 1999, 1 February - 31 May, Faculty of Commerce and Business Administration, UBC, Vancouver, Canada
• May, 1994 - July, 1999, Sobolev Institute of Mathematics, Novosibirsk, Russia

1. Method and system for scheduling delivery of at least one of goods and services , F. Barahona, S.  Buckley, P. Chowdhary, J. Forrest , T. Kimbrel, L. Ladanyi , B. Schieber, M. Sviridenko and G. Swirszcz.

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2.  Dynamic application placement under service and memory constraints (filed April 21, 2005),  Tracy Kimbrel, Malgorzata Steinder, Maxim Sviridenko and Asser Tantawi.

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3. Dynamic resource allocation using projected future benefits (filed December 4, 2001), T. Kimbrel, R. Krauthgamer, B. Schieber, J. S. Thathachar and M. Sviridenko.

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4. Dynamic resource allocation using known future benefits (filed December 4, 2001, granted Aug 1, 2006), T. Kimbrel, R. Krauthgamer, M. Minkoff, B. Schieber, J. S. Thathachar and M. Sviridenko, patent No 7,085837 B2.

Modelling  real-life  optimization problems arising in industry. Algorithm engineering  and implementation for such problems.
Inventory and Supply Chain Optimization Problems;
Scheduling and routing problems;
Bin Packing problems;
Discrete facility location problems;
On-line algorithms;
Computational complexity (especially, hardness of approximation);
Approximation algorithms for NP-hard optimization problems;
Randomized algorithms.