I'm now on the job market looking for a tenure-track assistant professorship for the 2015-2016 school year. Specifically, I'm looking for a position where I can start a lab to study the theory of feedback controllers for complex and heterogeneous autonomous systems. My research involves optimization, machine learning, neural networks, control theory, state estimation, stochastic process theory, functional analysis, game theory, and robotics. As such, I could contribute well to departments ranging from Computer Science to Applied Mathematics and Engineering (with a focus on computation and autonomous systems). A curriculum vitae can be found here. For generic research and teaching statements, see the links to the left.
Several of my research topics, especially deep neural networks and robotic control, currently receive substantial attention and are thus good candidates for research grants. In fact, I am currently funded by a two-year US National Science Foundation grant to integrate perception and control tasks in a humanoid robot using deep neural networks. For the project, an iCub robot is being trained to play chess. The first publication from this research, co-authored with a Ph.D. student, was submitted in October 2014, and three more papers will follow into Spring 2014.
My work on the theory of stochastic global optimization has been well-received in the evolutionary computation community, including four peer-reviewed full-length conference papers (the most recent at GECCO 2014, which had a 33% acceptance rate) and an award of runner-up overall best paper out of 445 peer-reviewed papers at the 2013 IEEE Congress on Evolutionary Computation. This work is in the process of being published as a monograph under contract with Springer-Verlag, and two new 30 page journal articles on the topic were submitted for peer review in October 2014.
My future work will continue to examine the theoretical context behind developing controllers that can integrate complex sensory functions with high-degree of freedom controls. The potential applications for such a theory are not limited to robotics, but are relevant to any large autonomous systems, including those deployed on important computer networks, such as medical, financial, or military defense systems. One particular application interest of mine is the idea of integrated home computing that coordinates infrastructure (heating/cooling, security, etc) with domestic automata (robotic vacuums, lawn mowers, and maybe even a cook or a maid).
Please contact me about potential research positions at email@example.com.
In research disciplines such as machine learning or robotics, the functions being optimized may be non-smooth, non-convex, multimodal, discontinuous, or discrete. They might operate on binary codes, graphs, computer programs, or even neural networks. In these settings, taking derivatives may not be the best way to solve your problem, if the derivatives even exist. There is a large variety of non-gradient-based optimization methods that can be used in these situations. In the mathematical optimization community, these methods are referred to as heuristic search. This term is a misnomer; algorithms like differential evolution or evolutionary annealing have been proven to converge to the global optimum under certain conditions. These methods are no more heuristic than gradient descent, which can fail catastrophically even on functions such as the Weierstrass function, which is everywhere continuous and nowhere differentiable. By contrast, evolutionary annealing can be configured to locate the optima of the Weierstrass function reliably.
Optimization Beyond Gradients is about what to do when you need to optimize functions of a post-Newtonian character. Derivatives were the cutting edge of mathematics back in the 1700s, when the mere suggestion of non-analytic functions was enough to make any Oxford don shake in his stockings and ruffle his powdered coif. Nowadays, functions whose optima can be found analytically are a rare treat.
This site is about non-gradient methods: how to define them, how to configure them, and how to determine the best optimization method for your problem. In the Research section, you'll find a discussion of optimization methods form a vector space, how optimization problems can be modeled as a distribution over functions, and how the best optimization method for your problem can be derived from the interplay of these two features. There are also pages describing Martingale Optimization, especially Evolutionary Annealing, a new optimization method for non-smooth, multimodal functions.
The Software section contains links to open-source software that implements the methods and ideas discussed on this site, especially PyEC, a software package for evolutionary optimization in the Python language. Links to published research papers are provided on the Publications page, and you can find out more about who I am on the About page.
Enjoy the site! Send any comments or contact requests to Alan Lockett at firstname.lastname@example.org.