ISE Welcomes Somayeh Sojoudi on February 15, 2017
Data-driven methods for sparse network estimation
Seminar by Somayeh Sojoudi, Assistant Project Scientist
Industrial Engineering and Operations Research, UC Berkeley
Wednesday, February 15th from 4:00 – 5:00 pm
144 Baker Systems, 1971 Neil Avenue
We live in an increasingly data-driven world in which mathematical models are crucial for uncovering properties of systems from measured data. Graphical models are commonly used for capturing the relationships between the parameters of a system using graphs. Graphical models have applications in many areas, such as social sciences, linguistics, neuroscience, biology, and power systems. Learning graphical models is of fundamental importance in machine learning and statistics, and is often challenged by the fact that only a small number of samples are available. Several algorithms (such as Graphical Lasso) have been proposed to address this problem. Despite the popularity of graphical lasso, there is not much known about the properties of this statistical method as an optimization algorithm. In this talk, we will develop new notions of sign-consistent matrices and inverse consistent matrices to obtain key properties of graphical lasso. In particular, we will prove that although the complexity of solving graphical lasso is high, the sparsity pattern of its solution has a simple formula if a sparse graphical model is sought. Besides graphical lasso, there are several other techniques for learning graphical models. However, it is not clear how reliable these methods are and which method should be used for each particular application. To address these problems, we will design a novel framework for generating synthetic data based on stochastic electrical circuits, and use it as a platform to assess the performance of various techniques. We will show that our platform can be used to first find the best algorithm and then identify the best model associated with that algorithm. We will illustrate our results on fMRI data and uncover new properties of brain networks.
Somayeh Sojoudi is an Assistant Project Scientist at the University of California, Berkeley. She received her PhD degree in Control & Dynamical Systems from California Institute of Technology in 2013. She was an Assistant Research Scientist at New York University School of Medicine from 2013 to 2015. She has worked on several interdisciplinary problems in optimization, control theory, machine learning, data analytics, and power systems. Somayeh Sojoudi is an associate editor for the IEEE Transactions on Smart Grid. She is a co-recipient of the 2015 INFORMS Optimization Society Prize for Young Researchers and a co-recipient of the 2016 INFORMS ENRE Energy Best Publication Award. She is a co-author of a best student paper award finalist for the 53rd IEEE Conference on Decision and Control 2014.
ISE Welcomes Vahid Tari on January 27th, 2017
Characterization and Multi-Scale Modeling of Materials
Seminar by Vahid Tari, Postdoctoral Associate
Department of Materials Science and Engineering, Carnegie Mellon University
Friday, January 27th from 11:30 am – 12:30 pm
210E Baker Systems, 1971 Neil Avenue
Integrated Computational Materials Engineering (ICME) is the integration of materials information, captured in computational tools, with engineering product performance analysis and manufacturing-process simulation. The tenets of ICME were captured the Materials Genome Initiative (MGI) to integrate experiments, computation, and theory. With regards to ICME, in the first part of this presentation, I talk about my research experiences in characterization of different structural materials such as steel, and titanium commonly used in automotive and aerospace industry, and describe the role of materials characterization in design and manufacturing. Afterward I explain a three-dimensional image based modeling technique, and discuss the ability of this technique in the grain-scale prediction of stress/strain localization, facilitating damage nucleation during the mechanical performance that is a bottle neck in engineering applications. Lastly, I summarize the future work on characterization and multi-scale modeling of additive manufacturing materials, talk about future path on ICME in grain boundary engineering of materials, and highlight multi-scale modeling of multiphase polycrystalline materials and advanced composite materials.
Vahid Tari is a postdoctoral associate in department of materials science and engineering at Carnegie Mellon University. He obtained his Ph.D. in computational engineering from Mississippi State University. His primary research interests are currently focused on three-dimensional modeling of polycrystalline materials and composites, parallel computing, and materials characterization. He severs as reviewers for publications in some journals such as Materials science and Engineering A and Scripta Materialia.
ISE Welcomes Chen Chen on January 25, 2017
Modelling Power Systems Problems with Polynomial Optimization
Seminar by Chen Chen, Postdoctoral Research Scientist
Industrial Engineering and Operations Research Department, Columbia University
Wednesday, January 25th from 4:00 – 5:00 pm
144 Baker Systems, 1971 Neil Avenue
Power systems must become more resilient to accommodate the growing presence of renewable energy, electric vehicles, and demand-side generation. However, standard models used for planning and operations typically use simplified approximations of power flow that fail to capture the dynamics of a system under duress. This talk concerns the computational challenges and potential rewards of incorporating more accurate models of the physics of power flow with polynomial optimization.
Chen Chen is a postdoc in the IEOR department at Columbia University. His primary research interest is in the design of optimization algorithms for mixed-integer nonconvex programming problems with applications to power systems. Chen obtained his doctorate in IEOR from UC Berkeley and bachelor’s degree in Industrial Engineering from the University of Toronto. His work experience includes internships at the Ontario Power Authority and the Federal Energy Regulatory Commission.
ISE Welcomes Dr. Jinsong Duan on January 11, 2017
Integrated computational engineering for advanced materials and manufacturing innovation
A Seminar by Jinsong Duan, Ph.D.
Chief scientist, General Simulation, LLC
Adjunct professor, University of Dayton
Wednesday, January 11th, 4:00 – 5:00 pm
144 Baker Systems, 1971 Neil Avenue
Integrated Computational Materials Engineering (ICME) is an emerging materials discovery technology that seamlessly integrates materials information obtained in materials modeling, with engineering product performance prediction and manufacturing process simulation. The application of ICME significantly reduces the time and cost of the materials discovery to deployment process while improving quality.
In this talk, I present my research overview of multi-scale modeling of materials and manufacturing, and their applications in aerospace and automotive industry and clean energy. Then I discuss Ni-based super-alloy and optical nanomaterials, and show how multi-scale computational tools can be used to understand processing-structure-property-performance relationship in these materials. Finally, I brief the on-going research in development of computational methodologies for advanced composite materials and process, and highlight some future plans in ICME for Nickel-Aluminum-Bronze alloy and additive manufacturing, high efficiency solar cell, and 3D printing technology.
Dr. Jinsong Duan has developed and applied multi-scale computational methods and high performance computing techniques to study alloys, composite, nanomaterials, additive manufacturing, and their applications in aerospace and automotive industry and clean energy. He has authored and co-authored high impact peer-reviewed publications and has given invited presentations at international conferences and industry and academia. He severs as reviewers for publications in Institute of Physics (UK) and Materials Research Society (USA). Before joining General Simulation, he held positions in Carnegie Mellon University, U.S. Air Force Research Laboratory, and Booz Allen Hamilton.
Guzin Bayraksan Receives Award in Environment and Sustainability
ISE Associate Professor, Guzin Bayraksan, received INFORMS ENRE Best Publication Award in Environment and Sustainability for her work on “Reclaimed Water Network Design under Temporal and Spatial Growth and Demand Uncertainties,” Environmental Modeling & Software, 49, 103–117, 2013 with W. Zhang, G. Chung, P. Pierre-Louis, and K. Lansey. The award is given to the best refereed journal article in the area of environment and sustainability by the INFORMS Section on Energy, Natural Resources, and the Environment (ENRE).
The paper focuses on design of reclaimed water network, considering various uncertainties including city growth. Reclaimed water is treated wastewater that is re-introduced for non-potable water use. Because reclaimed water use saves precious fresh-water resources, the paper helps a community to be sustainable. The paper’s focus is especially very important for sustainability of water-scarce regions. In addition, the paper helps a community by saving money (through cost-effective design) and generating income (through sale of reclaimed water), both of which can be used for other purposes like education. The paper applies the model and solution methodology to a real municipal reclaimed water network (but with hypothetical numbers for publication purposes).
ISE Faculty Awarded Grant by Sustainable and Resilient Economy Program
A Sustainable and Resilient Economy (SRE) Seed Grant has recently been awarded on Energy and Water Infrastructure Planning Under Extreme Events. This collaborative grant is together with Principal Investigator Dr. Guzin Bayraksan, Dr. Antonio Conejo and Dr. Ramteen Sioshansi (ISE) and two colleagues from the Glenn College of Public affairs, Dr. Noah Dormady and Dr. Robert Glenblum. The project will study design and operation of energy and water infrastructures under disasters by using a novel integrated approach to infrastructure planning and utilizing both operations research and behavioral/experimental approaches. The project will support a student and create a proof of concept that will enable federally funded grant proposals.
Güzin Bayraksan is an associate professor in the Integrated Systems Engineering Department at the Ohio State University. Prior to joining OSU, she was a member of the Systems and Industrial Engineering Department and the Graduate Interdisciplinary Program in Applied Mathematics at the University of Arizona. She received her Ph.D. in Operations Research and Industrial Engineering from the University of Texas at Austin and B.S. in Industrial Engineering from Bosphorus (Bogazici) University in Istanbul, Turkey. Her research interests are in stochastic optimization, particularly Monte Carlo sampling-based and data-driven methods for stochastic programming with applications in water resources management. She is the recipient of 2012 NSF CAREER award, 2012 Five Star Faculty Award (UA), and the 2008 INFORMS best case study award. She served as an elected member (2010-2016) and treasurer (2013-2016) of the Committee on Stochastic Programming (COSP) and is currently serving as past-president of the INFORMS Forum on Women in Operations Research and Management Science (WORMS) and on the editorial board of IIE Transactions.
ISE Welcomes Christian Wernz on 11/30/16
Multiscale Decision Theory and its Applications to Healthcare and Beyond
Seminar by Christian Wernz, Assistant Professor
Department of Industrial and Systems Engineering at Virginia Tech
Wednesday, November 30th from 4:00 – 5:00 pm
395 Watts Hall, 2041 College Rd N
Complex socio-technical systems, such as healthcare or energy systems, consist of many interdependent decision makers with different, often conflicting, interests, who interact with each other across multiple system levels and time scales. To model and analyze the decision-making and system design challenges in such environments, I have developed multiscale decision theory (MSDT). MSDT is a modeling approach that combines game theory, Markov decision processes and dependency graphs. In this talk, I will present the foundations of MSDT and how I have applied this methodology to study incentives and payment innovations in the U.S. healthcare system. Specifically, I have analyzed the Medicare Shared Savings Program (MSSP) for Accountable Care Organizations (ACOs) and its effect on medical technology investments and use. The analysis provides insights for policy makers on how to improve MSSP. Results also inform ACOs, hospitals and physicians on how to optimally respond to this new incentive mechanism. Beyond healthcare, I will discuss applications of MSDT to systems engineering and energy systems.
Dr. Christian Wernz is an Assistant Professor in the Department of Industrial and Systems Engineering at Virginia Tech, where he is the Director of the Multiscale Decision Making (MSDM) laboratory. He received his doctorate in Industrial Engineering and Operations Research from the University of Massachusetts Amherst in 2008. He obtained his bachelor’s and master’s degree in Business Engineering from the Karlsruhe Institute of Technology (KIT), Germany, in 1999 and 2003, respectively. In his research, Dr. Wernz models and analyzes decision-making challenges in organizations and complex systems. His methodological expertise lies in multiscale decision theory, game theory, Markov decision processes, decision analysis and simulation. He has applied these and other operations research methods to study problems in various socio-technical systems with a focus on healthcare. His research has been funded by the National Science Foundation (NSF), the Agency for Healthcare Research & Quality (AHRQ), the Harvey L. Neiman Health Policy Institute, Rolls-Royce, Dell and other industry partners.
Recent ISE Faculty Grant Awards
In the fall of 2016, ISE faculty were awarded a 5-year Training Project Grant from the National Institute for Occupational Safety and Health (NIOSH), the United States federal agency responsible for conducting research and making recommendations for the prevention of work-related injury and illness. Through the grant, they can provide support to graduate students who wish to obtain a master’s degree in industrial engineering, while pursuing their interests in occupational safety and ergonomics. Faculty on the project are Carolyn Sommerich, Steve Lavender, Bill Marras, Mike Rayo, Phil Smith and Dave Woods. You can read more about this master’s level graduate program here.
Another grant was awarded to ISE faculty and researchers in the fall of 2016 from Midmark to assess effects of exam room equipment and layout on workflow and perceptions of patients and clinical staff. Researchers involved include Carolyn Sommerich (ISE), Steve Lavender (ISE), Liz Sanders (Design), and Kevin Evans (SHRS).
Additionally, a 2-year grant was awarded in summer of 2016 from the Ohio BWC Research Grant Program to investigate current methods employed by firefighter-paramedics (FFP) for handling heavier patients and develop improved methods that could reduce the risk of injury to FFP posed by current methods. Researchers on the project are Steve Lavender, Carolyn Sommerich and Bill Marras.
ISE Students Attend SHPE Conference
ISE students, Alejandro Nunez and Manuara Costa, recently returned after attending the Society of Hispanic Professional Engineers (SHPE) national conference on November 2-6, the nation’s largest annual Hispanic STEM conference. The conference is an opportunity for engineering companies to recruit talented students from SHPE membership. Both students who attended were sponsored by the ISE department and are on the E-board of SHPE’s OSU chapter.
Alejandro and Manuara were interested in attending the event to network with companies and other student chapters and obtain a Co-Op/Internship. Alejandro was able to obtain a Co-Op with Delta Airlines through the conference this year and Manuara has attended the conferences since she was a freshman and all of her internships have come from it.
The SHPE mission is to change lives by empowering the Hispanic community to realize its fullest potential and to impact the world through STEM awareness, access, support and development.
You can see photos of the event on the SHPE Facebook page here.
ISE Welcomes Ekundayo Shittu on 11/22/16
Investments in Energy Systems under Uncertainties in Climate Change Policies and Technological Learning
Seminar by Ekundayo Shittu, Assistant Professor
Engineering Management & Systems Engineering, The George Washington University
Tuesday, November 22 from 4:00 – 5:00 pm
210E Baker Systems, 1971 Neil Avenue
When viewed with the lens of developing solution strategies, global climate change is a classic problem of decision making under uncertainty. This presentation highlights the management of responses to climate change specifically as it relates to energy technology investment decisions under two streams of uncertainties. First, we explore the investments of a profit-maximizing firm into a portfolio of energy systems in response to uncertainty in a climate change regulatory policy. We pay close attention to the representation of technological change to investigate how a firm’s optimal investment is influenced by the presence of a set of different technologies, and uncertainty in a carbon tax. We find that the key drivers of the optimal energy technology set are the elasticities of substitution between the different energy sources, and the relative efficacy of research and development (R&D) into energy sources. Second, we extend this analysis by unpacking the role of learning in managing the capacity expansion of existing and emerging energy technologies. Specifically, we address how stochastic learning rates impact capacity investments into a range of electricity generating technologies. Understanding the managerial or strategic response to uncertainty in energy technological learning is particularly important as decision makers face competing energy technology R&D portfolios, dwindling and unstable financial resources, and an imminent climate change policy. With the aid of cost-constrained risk-minimizing inter-temporal optimization model of a reference global energy system, we investigate the effects of uncertainties in technological learning on electricity capacity additions. We find that (1) the willingness to hedge against the inherent risks associated with uncertainty in energy technological learning is positively correlated with the risk premium; (2) near-term or early investments are required in a mix of sustainable energy technological portfolios as a hedge against learning risk. Using data on 1526 energy companies, investor-owned utilities, and government-owned utilities from 1999 to 2010, we combine these two analysis to propose that the effect of external drivers – competitive learning and regulatory policy – on firms’ new technology investments depends on a firm’s existing resource stock. While competing firms’ new technology investments have a stronger effect on incumbents less endowed in the existing technology, regulatory policy pushes incumbents heavily endowed in the existing technology toward environmentally sustainable investments.
This presentation concludes with a peek into future research streams particularly on (1) an innovative agent-based modeling (ABM) framework to evaluate the interconnections among food, energy and water systems, (2) dynamic computable general equilibrium modeling to harness effective insurance decision making strategies in response to natural and climate change induced disasters, and (3) vaccine supply chain logistics in least developed countries.
Keywords: Carbon tax, Energy system; Investments; Learning; Profit-maximization; R&D; Risk; Technological change; Uncertainty.
Professor Ekundayo Shittu conducts basic and applied research that take a systems engineering approach to aid decision making under uncertainty on investments into energy technology portfolios and the economics of climate change response policies. Pivotal to his research is the examination of how key stakeholders deal with climate change risk and uncertainty. He examines ways of integrating formal decision tools and microeconomics to develop climate policies that aid the adoption of emerging energy systems. Current projects include understanding the effects of uncertainty in technological learning on energy capacity additions, investigating how energy firms’ investments are shaped by competitive and regulatory pressures, studying how to adequately value emerging energy systems given the evolution of storage technologies to mitigate intermittency, investigating effective resource use strategies at the nexus of food, water and energy systems, and studying the platforms for an effective and efficient disaster response system. He was a Lead Author on Chapter 2, “Integrated Assessment of Risk and Uncertainty of Climate Change Response Policies,” fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). He is a reviewer for IEEE Transactions on Engineering Management, European Journal of Operations Research, Production and Operations Management journal, Systems Engineering, Energies, Energy Engineering, Vaccines, etc. He holds a doctorate degree in Industrial Engineering and Operations Research from the University of Massachusetts Amherst, a masters degree in Industrial Engineering from the American University of Cairo, and a Bachelors in Electrical Engineering from the University of Ilorin.