UMD     This Site

Professor Steve Marcus (ECE/ISR) and Professor Michael Fu (BMGT/ISR) are co-PIs for a three-year, $390K NSF grant, Particle Filtering for Stochastic Control and Global Optimization.

The objective of this program is to provide new breakthroughs in the areas of stochastic control and global optimization through insights gained from particle filtering and from additional recent results in nonlinear filtering. With a focus on applying the particle filtering methodology, the proposed research will result in (i) new computationally efficient algorithms for continuous-state partially observable Markov decision processes and global optimization, and (ii) rigorous analysis of the algorithms through the development of bounds and convergence proofs. In particular, for global optimization problems, the particle filtering framework can prove transformative by providing a firm analytical basis for understanding why algorithms work well, when algorithms break down, how to compare algorithms, which algorithm works better than the others for a specific problem, and how to develop new algorithms that should work well for particular problems.

Partially observable stochastic control and global optimization are areas with many theoretical challenges and many potential applications. To attack difficult problems of a size that are found in most applications will require significant new methodologies. The proposed approach based on particle filtering will provide new algorithms and rigorous analytical justification beyond that available with other methods.

Stochastic control and optimization can be applied to many problems of critical concern in US industry, so the resulting algorithms will have broad and transformative applicability. In the project, they will be tested on problems in industries from telecommunications to manufacturing to finance.

Related Articles:
Maryland research contributes to Google’s AlphaGo AI system
Michael Fu wins NSF grant for simulation-based optimal decision making
Work by Zhou, Lin, Marcus and Fu wins 'Best Theoretical Paper' award
Shneiderman: Faulty machine learning algorithms risk safety, threaten bias
New algorithms for multi-robot systems in low communication situations
Algorithms balance learning speeds across tasks in communication networks
Michael Fu part of $1M NSF grant to model, disrupt illicit kidney trafficking networks
New system could improve thermal devices used to check our temperatures and respiration
UMD Team Wins Inaugural NIST UAS 3.1: FastFind Challenge
ASTrA project to provide new tools for power generation, robotics, smart manufacturing

September 1, 2009

«Previous Story  



Current Headlines

UMD Launches Institute Focused on Ethical AI Development

Remembering Rance Cleaveland (1961-2024)

Dinesh Manocha Inducted into IEEE VGTC Virtual Reality Academy

ECE Ph.D. Student Ayooluwa (“Ayo”) Ajiboye Recognized at APEC 2024

Balachandran, Cameron, Yu Receive 2024 MURI Award

UMD, Booz Allen Hamilton Announce Collaboration with MMEC

New Research Suggests Gossip “Not Always a Bad Thing”

Ingestible Capsule Technology Research on Front Cover of Journal

Governor’s Cabinet Meeting Features Peek into Southern Maryland Research and Collaboration

Celebrating the Impact of Black Maryland Engineers and Leaders

Back to top  
Home Clark School Home UMD Home