Monday, April 3

3108 Etcheverry Hall, 3:30 p.m. - 5:00 p.m.

In recent years, personal location data is continuously captured by mobile devices, GPS chips and other sensors. Such data provides a unique learning opportunity on individuals’ mobility behavior that may be used for various applications in transportation, marketing, homeland security and smart cities. Nonetheless, modeling such data poses new challenges related to data volume, diversity, inhomogeneity and the required granularity level. In this talk, we will address a real ‘smart city’ use-case and cover some of its associated opportunities and challenges. We will present a new set of mobility-behavior models that generalizes Markov Chains and Variable-Order Bayesian Networks. We will discuss how they can be used in different smart city applications such as pattern recognition, anomaly detection, clustering and classification.

Bio:
Irad Ben-Gal is a full professor in the Department of Industrial Engineering at Tel Aviv University and a visiting professor at MS&E Stanford University. His research focuses on applied probability, machine learning and information theory applications to industrial and service systems. He wrote 3 books, published more than 80 scientific papers and patents and received several best papers awards. He is a Department Editor in IIE Transactions and serves on the Editorial Boards of several data science journals. Irad led various R&D projects and worked with companies such as Siemens, Intel, Applied Materials, GM, Nokia, AT&T and Oracle.  Irad is the co-founder of CB4 (“See Before”), a startup backed by Sequoia Capital that provides predictive analytics solutions to retail organizations. 

 

Tuesday, April 4

240 Bechtel, 5:30 p.m. - 7:00 p.m.

Would you like to meet career professionals who graduated with an IEOR degree from Cal? The Institute of Industrial Engineers (IIE) has invited five professionals to come and share their experiences to provide insights into the wide range of career options available for IEOR students. 

The panel will feature:

  • Courtney Moreira - Marketing Manager at Abbott Vascular
  • Soroush Mehraein - Software Engineer at Uber
  • Farzin Shadpour - Head of Supply Chain & Procurement at Theranos
  • Vladimir Vakulenko - Consultant at ZS Associates
  • Kelly Chien - Business Data Analyst at Intuit

Food will be provided!

 

 

 

 

 

Monday, April 10

3108 Etcheverry Hall, 3:30 p.m. - 5:00 p.m.

Abstract: In the first part of this talk I will discuss a family of inhomogeneous directed random graphs for modeling complex networks such as the web graph, Twitter, ResearchGate, and other social networks. This class of graphs includes as a special case the classical Erdos-Renyi model, and can be used to replicate almost any type of predetermined degree distributions, in particular, power-law degrees such as those observed in most real-world networks. I will mention during the talk the main properties of this family of random graphs and explain how its parameters can be used to represent important data attributes that influence the connectivity of nodes in the network. 

In the second part of the talk I will explain how ranking algorithms such as Google’s PageRank can be used to identify highly influential nodes in a network, and present some recent results describing the distribution of the ranks computed by such algorithms. This work extends prior work done for the directed configuration model to the new class of inhomogeneous directed random graphs mentioned above, and provides a more natural way to model the relationship between highly ranked nodes and their attributes. If time allows, I will mention some interesting stochastic simulation challenges related to this problem. 

Bio: Mariana Olvera-Cravioto is a Visiting Associate Professor in the Department of Industrial Engineering and Operations Research at UC Berkeley. She does research in Applied Probability, in particular, she works on problems involving heavy-tailed phenomena. Her current work is focused on the analysis of information ranking algorithms and their large-scale behavior, which is closely related to the study of the solutions to certain stochastic recursions constructed on weighted branching processes. She is also interested in the analysis of complex networks, in particular, scale-free random graphs such as those used to model the web and other social networks. Some of her ongoing projects include the study of queueing networks with parallel servers and synchronization constraints and the development of efficient simulation algorithms for computing the solutions to branching distributional equations.

 

Monday, April 17

3108 Etcheverry Hall, 3:30 p.m. - 5:00 p.m.

Abstract: We examine a risk-averse distributor’s decision in selecting between bottled wine and wine futures under weather and market uncertainty. At the beginning of every summer, a fine wine distributor has to choose between purchasing bottled wine made from the harvest collected two years ago and wine futures of wine still aging in the barrel from the harvest of the previous year. At the end of the summer, after realizing weather and market fluctuations, the distributor can adjust her allocation by trading futures and bottles.

The paper makes three contributions. First, we develop an analytical model in order to determine the optimal selection of bottled wine and wine futures under weather and market uncertainty. Our model is built on an empirical foundation in which the functional forms describing the evolution of futures and bottle prices are derived from comprehensive data associated with the most influential Bordeaux winemakers. Second, we develop structural properties of optimal decisions. We show that a wine distributor should always invest in wine futures because it increases the expected profit in spite of being a riskier asset than bottled wine. We characterize the influence of variation in various uncertainties in the problem. Third, our study empirically demonstrates the financial benefits from using our model for a large distributor. The hypothetical average profit improvement in our numerical analysis is significant, exceeding 21%, and its value becomes higher under risk aversion. The analysis is beneficial for fine wine distributors as it provides insights into how to improve their selection in order to make financially healthier allocations.

Keywords: wine futures, pricing, weather uncertainty, market uncertainty, risk aversion

Authors: M. Hakan Hekimoglu (Rensselaer Polytechnic Institute), Burak Kazaz (Syracuse University), Scott Webster (Arizona State University)

Full text: Wine Analytics: Fine Wine Pricing and Selection under Weather and Market Uncertainty

 

Bio of Burak Kazaz: Burak Kazaz is the Steven Becker Professor of Supply Chain Management, and the Laura J. and L. Douglas Meredith Professor for Teaching Excellence at Syracuse University’s Martin J. Whitman of Management. He is the Executive Director of the first research center established in the field of supply chain management in the US (in 1919), the H.H. Franklin Center for Supply Chain Management at Syracuse University. He also serves as the Whitman Research Fellow at Syracuse University. Dr. Kazaz is presently visiting the University of California Berkeley for his sabbatical.

Dr. Kazaz’s research interests include supply chain risk, supply chain finance, and socially-responsible supply chain operations. His publications can be found in premier journals such as Management ScienceManufacturing & Service Operations ManagementOperations Research, and Production and Operations Management. His papers have been the recipient of the best paper awards: The Wickham Skinner Prize (2017), Production and Operations Management Society (2016), INFORMS President’s Pick (2015), and the Decision Sciences Institute (2014). He serves as an Associate Editor for Manufacturing & Service Operations Management, as a Senior Editor for Production and Operations Management, as an Area Editor for IIE Transactions. 

Dr. Kazaz also served as a Whitman Teaching Fellow from 2010 to 2012, and is the recipient of the first-ever Whitman School of Management Teaching Innovation Award in 2011. He received his Bachelor’s and Master’s degrees in Industrial Engineering from Middle East Technical University in Turkey, and his Ph.D. from the Krannert Graduate School of Management at Purdue University. Prior to this appointment, he taught at the University of Miami and at Loyola University of Chicago. His teaching experience includes undergraduate, MBA and Executive MBA courses, and Ph.D. seminars on operations management, global supply chain management, and logistics.

Dr. Kazaz also worked at the IBM T. J. Watson Research Center in Yorktown Heights, NY. His work is recognized by IBM, British Petroleum, and Procter & Gamble. 

 

 

Monday, April 24

3108 Etcheverry Hall, 3:30 p.m. - 5:00 p.m.

Abstract:

The stochastic bin packing structure arises in wide service operations applications including healthcare, cloud computing, and transportation. Chance-constrained bin packing problem optimizes the allocation of a set of items into bins and for each bin it bounds the probability that the total weight of packed items exceeds the bin's capacity. Different from the stochastic programming approaches relying on full distributional information of the random item weights, we study distributionally robust chance-constrained bin packing (DCBP) models when only the first two moments (i.e., mean and covariance) are available. Using two types of ambiguity sets, we equivalently reformulate the DCBP models as 0-1 second-order cone (SOC) programs. We further exploit the submodularity of the 0-1 SOC constraints under special and general covariance matrices, to derive extended polymatroid inequalities to strengthen the 0-1 SOC formulations and procedures for generating valid bounds. We incorporate these valid inequalities and bounds in a branch-and- cut algorithm, which significantly improves thecomputational efficacy for solving the DCBP models.

Bio:

Siqian Shen is an Assistant Professor of Industrial and Operations Engineering at the University of Michigan. She obtained a B.S. degree from Tsinghua University in 2007 and Ph.D. from the University of Florida in 2011. Her research interests are in mathematical optimization, particularly in stochastic programming, network optimization, and integer programming. She was named a runner up of the 2010 INFORMS Computing Society Best Student Paper award, was awarded the 1st Place of the 2012 IIE Pritsker Doctoral Dissertation Award, and was a recipient of 2012 IBM Smarter Planet Innovation Faculty Award. She currently serves as an Associate Director in the Michigan Institute for Computational Discovery & Engineering (MICDE).

 

Monday, May 1

3108 Etcheverry Hall, 3:30 p.m. - 5:00 p.m.

Deepak Rajan is an Operations Research expert in the Center for Applied Scientific Computing (CASC). His research broadly lies in the areas of computational optimization and integer programming, and more specifically in applying such techniques in solving large-scale problems. Recently, Deepak has been working on optimization problems that involve uncertainty in the energy area (power generation, in particular). He has also worked on a variety of optimization problems in other domains, including network design and graph data mining.

Since 2016, Deepak is also an Associate Adjunct Professor of Industrial Engineering and Operations Research at the University of California at Berkeley.