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News
Shmuel Oren awarded Berkeley Citation
November 13, 2018

IEOR professor of the graduate school Shmuel Oren has just been awarded the Berkeley Citation by Chancellor Carol Christ.  The Citation is one of the University of California, Berkeley’s highest awards, and is reserved for individuals “whose contributions go beyond the call of duty and whose achievements exceed the standards of excellence in their fields.”

“He is richly deserving of this recognition,” says Chancellor Christ.

Oren will join a group of select individuals whose outsized contributions have helped UC Berkeley become known as an institution of excellence.

Professor Oren recently retired his role as professor in IEOR and now serves as a professor of the graduate school. In 2016, he was inducted into the National Academy of Engineering for his contributions 'to the integration of decisions and cooperative market mechanisms for adaptive multisource electrical power systems.' (You can read more about Shmuel’s distinguished career in our interview here.)

News
Prof. Goldberg invited to speak at NSF Engineering Directorate Advisory Board
November 14, 2018

Professor Goldberg will be speaking at the 2018 Fall Directorate for Engineering (ENG) Advisory committee (AdCom) meeting, sponsored by NSF. Professor Goldberg's talk is titled "Are Artificial Intelligence and Robots a Threat... or an Opportunity?" He will further discuss the pros and cons of a future with AI. 

AdCom advises the Directorate for Engineering (ENG) on issues such as serving the community, instutitutional administration and policy, graduate and undergraduate education in engineering, and priority investment in engineering research. 

Event
Somayeh Sojoudi - Efficient Computational Methods with Provable Guarantees for Data-Driven Problems

Abstract: The area of data science lacks efficient computational methods with provable guarantees that can cope with the large-scale nature and the high nonlinearity of many real-world systems. Practitioners often design heuristic algorithms tailored to specific applications, but the theoretical underpinnings of these methods remain a mystery and this limits their usage in safety-critical systems. In this talk, we aim to address the above issue for some canonical data-driven problems.

First, we consider the graphical Lasso (GL), which is a popular optimization method for learning graphical models from data. GL is a conic optimization problem, which is perceived to be computationally challenging and there are numerous numerical methods in the literature but their computational complexities are at least cubic. By analyzing the properties of this conic problem, we show that its true complexity is indeed linear (both in time and in memory) for sparse graphical models.  We solve instances with as many as 200,000 variables to 7-9 digits of accuracy in less than an hour on a standard laptop computer, while the existing solvers all stop working even for much smaller problems. We then study the related problem of finding the model of an unknown, but sparse, dynamical system from measurements and derive sharp bounds on the amount of data required to reliably identify the system (or design an optimal control policy).

In order to accelerate the computation, there is a major effort in the machine learning community to understand when simple local search algorithms could solve nonlinear problems to global optimality. A key proof technique relies on the notion of Restricted Isometry Property, whose conservatism is not well understood and cannot be applied to nonsmooth problems either (such as those involving 1-norm). We discuss our recent results on addressing these problems. In particular, we introduce the notion of “global functions”, as a major generalization of convex functions, which allows us to study the non-existence of spurious local minima for nonconvex and nonsmooth learning problems. We demonstrate the results on the tensor decomposition problem with outliers using 1-norm and local search algorithms. The talk is concluded by mentioning our participation in the ARPA-E $4M cash prize competition on Grid Optimization and how different techniques from optimization theory, numerical algorithms, graph theory, control theory, and machine learning could be used for this purpose.  

Bio: Somayeh Sojoudi is an Assistant Professor in residence of the Departments of Electrical Engineering & Computer Sciences and Mechanical Engineering at the University of California, Berkeley. She is also on the faculty of the Tsinghua-Berkeley Shenzhen Institute (TBSI). She received her PhD degree in Control & Dynamical Systems from California Institute of Technology in 2013. She has worked on several interdisciplinary problems in optimization theory, control theory, machine learning, data analytics, and power systems. Somayeh Sojoudi is an Associate Editor of the journals of IEEE Transactions on Smart Grid, IEEE Access, and Systems & Control Letters. She is also a member of the conference editorial board of the IEEE Control Systems Society. She is a recipient of the 2015 INFORMS Optimization Society Prize for Young Researchers and a recipient of the 2016 INFORMS ENRE Energy Best Publication Award. She was a finalist (as advisor) for the Best Student Paper Award at the 2018 American Control Conference and a finalist (as a co-author) for the best student paper award at the 53rd IEEE Conference on Decision and Control 2014. Her paper on graphical models has received the INFORMS 2018 Data Mining Best Paper Award.

Event
Ali Mesbah - Arbitrary Polynomial Chaos for Uncertainty Quantification of Stochastic Nonlinear Systems

Department of Chemical and Biomolecular Engineering, University of California Berkeley

Abstract: Traditional sample-based uncertainty propagation methods are generally computationally expensive for online optimization applications. In this talk, we will discuss arbitrary polynomial chaos (aPC) for quantification of probabilistic uncertainties with arbitrary measures (e.g., uncertainties with correlated multivariate or multi-modal distributions). aPC can be used as an efficient uncertainty propagation method for optimization-based analysis, estimation, and control of nonlinear systems with probabilistic uncertainties In particular, we will demonstrate the use of aPC for the design and performance verification of model predictive control (MPC) for stochastic nonlinear systems.

Research interests: Our research lies at the intersection of control theory, applied mathematics, and process systems engineering. The main thrust of our theoretical research is to develop novel systems analysis techniques and application-relevant control theory for complex dynamical systems that are stochastic and nonlinear. The systems analysis and control theory developments are intended to (i) improve our fundamental understanding of complex chemical and biological systems in order to answer specific questions related to underlying physicochemical or biological mechanisms of a system, and (ii) enable high-performance and cost-effective control of complex systems using physics-based knowledge of their dynamics. Our multidisciplinary research efforts provide a balance between theory, computation, and real-word applications, with a particular emphasis on energy and life science applications.

 

News
Student Perspective: From Graduate Student to Data Scientist
November 16, 2018

Thinking about becoming a data scientist?  Read the perspective from alum Joel Prince Varghese (MEng '16) about his experience with our master of engineering program and how he learned the skills necessary to become a data scientist in industry.

 

 

News
Berkeley Engineers Selected to Modernize the Grid
November 2, 2018

The Advanced Research Projects Agency - Energy (ARPA-E) has just selected eighteen teams, including two with UC Berkeley Engineering researchers, to participate in the new Grid Optimization (GO) Competition.

The UC Berkeley team will be led by EECS assistant professor in residence Somayeh Sojoudi, IEOR associate professor Javad Lavaei, and IEOR professor of the graduate school, Shmuel Oren. IEOR professor Alper Atamturk will also collaborate with a team led by Ramtin Madani based at the University of Texas at Arlington.

From left to right: Professors Somayeh Sojoudi, Javad Lavaei, Shmuel Oren, and Alper Atamturk.

Each team will receive a $250k grant for their first year of research and up to $400k for the second year. The program also includes cash prizes for successful teams, including a prize of up to $2 million at the end of the second year.

More information on the competition from ARPA-E:

The first challenge will focus on the problem of security constrained optimal power flow (SCOPF), wherein competitors must use software to route power to customers across a simulated grid in a reliable and cost-effective way. Competitors will test their algorithms on complex, realistic power system models, and participants will be scored on their performance relative to other competitors. Winning teams will find an efficient, minimum-cost solution to the SCOPF problem.

Today’s grid software was designed for a power grid centered on large, centralized power plants. In recent years, the grid has become more diverse, with the rapid development of new energy sources like battery storage, wind and solar power, and distributed energy resources (DER) creating a new set of challenges for grid management. Grid operators require new approaches to tackle the underlying modeling, optimization, and control methods that will ultimately increase grid flexibility, reliability, and resilience while reducing system costs and opening the door to new technologies.

The GO Competition platform was developed by ARPA-E and Pacific Northwest National Laboratory (PNNL). A summary of the GO Competition Challenge 1 can be found here. Additional information, including competition rules, can be found here.

 

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