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News
Max Shen to be the next chair of IEOR
December 3, 2018

Professor Zuo-Jun "Max" Shen has just been named to be the next chair of the Department of Industrial Engineering & Operations Research (IEOR) at UC Berkeley. Professor Shen will take over for Professor Ken Goldberg as the new department chair starting January 1, 2019.

Shen obtained his Ph.D. in Industrial Engineering and Management Sciences from Northwestern University. He joined the department in July 2004. Before that he taught at the Industrial and Systems Engineering Department at the University of Florida. His primary research interests are in the general area of integrated supply chain design and management, and practical mechanism design.  Shen holds a joint appointment in the Department of Civil & Environmental Engineering (CEE) at UC Berkeley and is also the co-director of Environmental Science & New Energy Center at the Tsinghua-Berkeley Shenzhen Institute.

Shen serve as the 16th chair of IEOR following Goldberg (2017-2019), Kaminsky (2011-2016), Righter (2008-2010), Adler (2005-2007), and Schruben (2001-2004).

 

News
Fairness in machine learning research featured in SIAM News
December 3, 2018

Research by IEOR assistant professor Anil Aswani and PhD student Matt Olfat was featured in the December 2018 newsjournal for the Society for Industrial and Applied Mathematics (SIAM).  The article titled "Designing Algorithms to Increase Fairness in Artificial Intelligence" describes a novel approach to help make decisions made by artificial intelligence algorithms more fair and less biased. Currently, AI algorithms can perpetuate biases contained within the data they use for training, and may even discrimate based on protected classes (age, race, gender) by using other data in the dataset, such as a home address as a proxy for race.

Learn more about Aswani and Olfat's research at at SIAM news

News
IEOR hosts semi-annual PSERC meeting
December 4, 2018

The Department of Industrial Engineering & Operations Research will host the semi-annual industry-university meeting for the Power Systems Engineering Research Center (PSERC) on December 5-7 at UC Berkeley.  The agenda for the meeting can be viewed here.

The purpose of PSERC is to empower minds to engineer the future electric energy system, to pursue, discover, and transfer knowledge, to produce highly qualified and trained engineers, and to collaborate in these pursuits.

The goals are to work toward:

  • An efficient, secure, resilient, adaptable, and economic electric power infrastructure serving society
  • A new generation of educated technical professionals in electric power
  • Knowledgeable decision-makers on critical energy policy issues
  • Sustained, quality university programs in electric power engineering

PSERC was established in 1996 as an NSF Industry/University Collaborative Research Center. It includes 12 universities and 40 industry members.  IEOR Professor Shmuel Oren is a co-founder and Berkeley site director of PSERC

The department looks forward to welcoming industry and university professionals to Berkeley to work on building the next generation of power systems.

 

News
Ruiz wins best student paper award at AdKDD workshop
December 5, 2018

IEOR PhD student Alfonso Lobos Ruiz has won the best student paper award at the 2018 Knowledge Discovery and Data Mining (KDD) conference in London for his paper "Optimal Bidding, Allocation and Budget Spending for a Demand Side Platform Under Many Auction Types." Ruiz's paper won at the 2018 AdKDD & TargetAd workshop that focused on the evolution of computational advertising, large-scale and novel ad targeting, and how the deployment of real systems to target users works in online advertising.

Abstract: ‘We develop a novel optimization model to maximize the profit of a Demand-Side Platform (DSP) while ensuring that the budget utilization preferences of the DSP's advertiser clients are adequately met. Our model is highly flexible and can be applied in a Real-Time Bidding environment (RTB) with arbitrary auction types, e.g., both first and second price auctions. Our proposed formulation leads to a non-convex optimization problem due to the joint optimization over both impression allocation and bid price decisions. Using Fenchel duality theory, we construct a dual problem that is convex and can be solved efficiently to obtain feasible bidding prices and allocation variables that can be deployed in a RTB setting. With a few minimal additional assumptions on the properties of the auctions, we demonstrate theoretically that our computationally efficient procedure based on convex optimization principles is guaranteed to deliver a globally optimal solution. We conduct experiments using data from a real DSP to validate our theoretical findings and to demonstrate that our method successfully trades off between DSP profitability and budget utilization in a simulated online environment. ’

In Feyman Terms (or more user-friendly introduction):

The goal of Online Advertising is to target the right user, at the right time, with the right content/offer. In online advertisement this opportunities occurs whenever a user downloads an app, opens a webpage, plays a video, etc. which account for approximately 200 billion events per day. This events are 'sold' in Ad-exchange in which companies called Demand -Side Platforms (DSPs) participate. For each event a real time auction is held and in less than 100ms the event has been sold to some DSP and an ad is started to be shown to the user. These Demand-Side Platforms manage marketing campaigns of hundreds or thousand of different advertisers. Our Work optimizes how a DSP should bid in these real time auctions and whenever an auction is won, it needs to select an ad of which of its advertisers will be shown to related user.  The novelties of our work includes that we take into account that arbitrary auction types may be used, as almost half of the Ad-exchanges have started selling their events on First Price auctions and not is Second Price that was the industry until last year. Our results also offers flexibility on how the budget of the advertisers of a DSP should be spent, and we show several results proving the optimality of our modeling technique. 

The full paper can be downloaded here. This is joint work with Paul Grigas, Zheng Wen (Adobe Research), Kuang-chih Lee (Alibaba).

 

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
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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