Jeffrey Uhlmann

Last updated

Jeffrey K. Uhlmann is an American research scientist who is probably best known for his mathematical generalizations of the Kalman filter. [1] Most of his publications and patents have been in the field of data fusion. He is also known for being a cult filmmaker and former recording artist.

Contents

Uhlmann has also began a career in YouTube under the anonymous persona uhlmannj with two feature videos: Man With No Hat, and The Glass. Both videos have gained hundreds of views.

Biography

Uhlmann has degrees in philosophy, computer science, and a doctorate in robotics from the University of Oxford. [2] [3] He began work in 1987 at NRL's Laboratory for Computational Physics and Fluid Dynamics in Washington, DC, and remained at NRL until 2000. Since 2000 he has been a professor of computer science at the University of Missouri. [4]

He served for ten years as a co-founding member of the editorial board of the ACM Journal of Experimental Algorithmics (1995–2006) before becoming co-editor of the Synthesis Lectures on Quantum Computing series for Morgan & Claypool. [5]

Theoretical Research

Uhlmann published seminal papers on volumetric, spatial, and metric tree data structures and their applications for computer graphics, virtual reality, and multiple-target tracking. [6] [7] [8] He originated the unscented transform (and its use in the unscented Kalman filter) and the data fusion techniques of covariance intersection and covariance union. [1] [2]

Applied Results

Uhlmann's results are widely-applied in tracking, navigation, and control systems, including for the NASA Mars rover program. [9] [10] His results relating to the constrained shortest path problem and simultaneous localization and mapping are also used in rover and autonomous vehicle applications. [11] [12]

Films

Uhlmann has written, directed, produced, and/or acted in several prominent short and feature-length films. Notable examples include the animated short film Susan's Big Day [13] and the feature films Mil Mascaras vs. the Aztec Mummy, Academy of Doom, and Aztec Revenge. In recent years he has been a popular invited guest at international genre film festivals. [14]

Music

Uhlmann recorded and released a series of albums in the 1970s and 1980s. Some of his early experimental electronic albums have been reissued in their entirety on CD [15] or digital download [16] while his arguably better-known songs are only available on CD compilations. [17]

Related Research Articles

Encryption Process of converting plaintext to ciphertext

In cryptography, encryption is the process of encoding information. This process converts the original representation of the information, known as plaintext, into an alternative form known as ciphertext. Ideally, only authorized parties can decipher a ciphertext back to plaintext and access the original information. Encryption does not itself prevent interference but denies the intelligible content to a would-be interceptor.

Information Sciences Institute University of Southern California research institute

The USC Information Sciences Institute (ISI) is a component of the University of Southern California (USC) Viterbi School of Engineering, and specializes in research and development in information processing, computing, and communications technologies. It is located in Marina del Rey, California.

Kalman filter Algorithm that estimates unknowns from a series of measurements over time

For statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, who was one of the primary developers of its theory.

Theoretical computer science Subfield of computer science and mathematics

Theoretical computer science (TCS) is a subset of general computer science and mathematics that focuses on mathematical aspects of computer science such as the theory of computation, lambda calculus, and type theory.

Simultaneous localization and mapping Computational problem of constructing a map while tracking an agents location within it

Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it. While this initially appears to be a chicken-and-egg problem there are several algorithms known for solving it, at least approximately, in tractable time for certain environments. Popular approximate solution methods include the particle filter, extended Kalman filter, covariance intersection, and GraphSLAM. SLAM algorithms are based on concepts in computational geometry and computer vision, and are used in robot navigation, robotic mapping and odometry for virtual reality or augmented reality.

Sensor fusion

Sensor fusion is the process of combining sensor data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individually. For instance, one could potentially obtain a more accurate location estimate of an indoor object by combining multiple data sources such as video cameras and WiFi localization signals. The term uncertainty reduction in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, such as stereoscopic vision.

The fast Kalman filter (FKF), devised by Antti Lange, is an extension of the Helmert–Wolf blocking (HWB) method from geodesy to safety-critical real-time applications of Kalman filtering (KF) such as GNSS navigation up to the centimeter-level of accuracy and satellite imaging of the Earth including atmospheric tomography.

Data assimilation is a mathematical discipline that seeks to optimally combine theory with observations. There may be a number of different goals sought – for example, to determine the optimal state estimate of a system, to determine initial conditions for a numerical forecast model, to interpolate sparse observation data using knowledge of the system being observed, to set numerical parameters based on training a model from observed data. Depending on the goal, different solution methods may be used. Data assimilation is distinguished from other forms of machine learning, image analysis, and statistical methods in that it utilizes a dynamical model of the system being analyzed.

A vantage-point tree is a metric tree that segregates data in a metric space by choosing a position in the space and partitioning the data points into two parts: those points that are nearer to the vantage point than a threshold, and those points that are not. By recursively applying this procedure to partition the data into smaller and smaller sets, a tree data structure is created where neighbors in the tree are likely to be neighbors in the space.

A metric tree is any tree data structure specialized to index data in metric spaces. Metric trees exploit properties of metric spaces such as the triangle inequality to make accesses to the data more efficient. Examples include the M-tree, vp-trees, cover trees, MVP Trees, and BK-trees.

Robotics is the branch of technology that deals with the design, construction, operation, structural disposition, manufacture and application of robots. Robotics is related to the sciences of electronics, engineering, mechanics, and software. The word "robot" was introduced to the public by Czech writer Karel Čapek in his play R.U.R., published in 1920. The term "robotics" was coined by Isaac Asimov in his 1941 science fiction short-story "Liar!"

D-Wave Systems Canadian Quantum Computing Company

D-Wave Systems Inc. is a Canadian quantum computing company, based in Burnaby, British Columbia, Canada. D-Wave was the world's first company to sell computers to exploit quantum effects in their operation. D-Wave's early customers include Lockheed Martin, University of Southern California, Google/NASA and Los Alamos National Lab.

GPS/INS is the use of GPS satellite signals to correct or calibrate a solution from an inertial navigation system (INS). The method is applicable for any GNSS/INS system.

Hartmut Neven German scientist

Hartmut Neven is a scientist working in quantum computing, computer vision, robotics and computational neuroscience. He is best known for his work in face and object recognition and his contributions to quantum machine learning. He is currently Vice President of Engineering at Google where he is leading the Quantum Artificial Intelligence Lab which he founded in 2012.

In estimation theory, the extended Kalman filter (EKF) is the nonlinear version of the Kalman filter which linearizes about an estimate of the current mean and covariance. In the case of well defined transition models, the EKF has been considered the de facto standard in the theory of nonlinear state estimation, navigation systems and GPS.

Covariance intersection is an algorithm for combining two or more estimates of state variables in a Kalman filter when the correlation between them is unknown.

The following outline is provided as an overview of and topical guide to robotics:

Natural computing, also called natural computation, is a terminology introduced to encompass three classes of methods: 1) those that take inspiration from nature for the development of novel problem-solving techniques; 2) those that are based on the use of computers to synthesize natural phenomena; and 3) those that employ natural materials to compute. The main fields of research that compose these three branches are artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, fractal geometry, artificial life, DNA computing, and quantum computing, among others.

The unscented transform (UT) is a mathematical function used to estimate the result of applying a given nonlinear transformation to a probability distribution that is characterized only in terms of a finite set of statistics. The most common use of the unscented transform is in the nonlinear projection of mean and covariance estimates in the context of nonlinear extensions of the Kalman filter. Its creator Jeffrey Uhlmann explained that "unscented" was an arbitrary name that he adopted to avoid it being referred to as the “Uhlmann filter” though others have indicated that "unscented" is a contrast to "scented" intended as a euphemism for "stinky"

Quantum machine learning Interdisciplinary research area at the intersection of quantum physics and machine learning

Quantum machine learning is the integration of quantum algorithms within machine learning programs. The most common use of the term refers to machine learning algorithms for the analysis of classical data executed on a quantum computer, i.e. quantum-enhanced machine learning. While machine learning algorithms are used to compute immense quantities of data, quantum machine learning utilizes qubits and quantum operations or specialized quantum systems to improve computational speed and data storage done by algorithms in a program. This includes hybrid methods that involve both classical and quantum processing, where computationally difficult subroutines are outsourced to a quantum device. These routines can be more complex in nature and executed faster on a quantum computer. Furthermore, quantum algorithms can be used to analyze quantum states instead of classical data. Beyond quantum computing, the term "quantum machine learning" is also associated with classical machine learning methods applied to data generated from quantum experiments, such as learning the phase transitions of a quantum system or creating new quantum experiments. Quantum machine learning also extends to a branch of research that explores methodological and structural similarities between certain physical systems and learning systems, in particular neural networks. For example, some mathematical and numerical techniques from quantum physics are applicable to classical deep learning and vice versa. Furthermore, researchers investigate more abstract notions of learning theory with respect to quantum information, sometimes referred to as "quantum learning theory".

References

  1. 1 2 Liggins, Martin; Hall, David; Llinas, James, eds. (2008). "Chapters 14 and 15". Handbook of Multisensor Data Fusion (2 ed.). CRC Press.
  2. 1 2 "Archived copy" (PDF). Archived from the original (PDF) on 2012-04-07. Retrieved 2011-10-18.{{cite web}}: CS1 maint: archived copy as title (link)
  3. ↑ "Uhlmann, Jeffrey | Engineering | University of Missouri | Mizzou Engineering". engineering.missouri.edu. Archived from the original on 2012-04-05.
  4. ↑ "Archived copy". Archived from the original on 2012-04-07. Retrieved 2011-10-18.{{cite web}}: CS1 maint: archived copy as title (link)
  5. ↑ "Synthesis Lectures on Quantum Computing".
  6. ↑ Hanan Samet (2006). Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann.
  7. ↑ Jeffrey Uhlmann (1991). "Satisfying General Proximity/Similarity Queries with Metric Trees". Information Processing Letters. 40 (4): 175–179. doi:10.1016/0020-0190(91)90074-r.
  8. ↑ Jeffrey Uhlmann (1992). "Algorithms for Multiple-Target Tracking". American Scientist. 80 (2).
  9. ↑ E.T. Baumgartner; et al. (2000). State Estimation and Vehicle Localization for the FIDO Rover (PDF) (Report). NASA-JPL. Archived from the original (PDF) on 2012-04-15. Retrieved 2011-10-18.
  10. ↑ Jeffrey Uhlmann; et al. (1999). "NASA Mars Rover: A Testbed for Evaluating Applications of Covariance Intersection". Proceedings of the 1999 SPIE Conference on Unmanned Ground Vehicle Technology. Vol. 3693.
  11. ↑ Ali Boroujerdi; Jeffrey Uhlmann (1998). "An Efficient Algorithm for Computing Least Cost Paths with Turn Constraints". Information Processing Letters. 67 (6): 317–321. doi:10.1016/s0020-0190(98)00134-3.
  12. ↑ S. J. Julier; J. K. Uhlmann (2007). "Using Covariance Intersection for SLAM". Robotics and Autonomous Systems. 55 (1): 3–20. CiteSeerX   10.1.1.106.8515 . doi:10.1016/j.robot.2006.06.011.
  13. ↑ "Susan's Big Day". YouTube . Retrieved 2011-10-11.
  14. ↑ "Fantasia International Film Festival" . Retrieved 12 April 2009.
  15. ↑ "Impulse (CD)". Tower Records. Retrieved 10 October 2010.
  16. ↑ "Circuit Theory". Vicmod Records. Archived from the original on 2012-04-25. Retrieved 31 October 2011.
  17. ↑ "Performer (CD)". Tower Records. Retrieved 22 October 2011.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.