DOCSLIB.ORG

Data-Driven Operations Management

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Data-Driven Operations Management Data-Driven Operations Management by Manqi Li A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Business Administration) in The University of Michigan 2021 Doctoral Committee: Assistant Professor Shima Nassiri, Chair Assistant Professor Yan Huang Associate Professor Cong Shi Assistant Professor Joline Uichanco Associate Professor Brian Wu Manqi Li [email protected] ORCID iD: 0000-0002-5210-4368 ACKNOWLEDGEMENTS The journey of academic research is full of challenges. Along the way, there are lots of people that I want to acknowledge, without whom I can never come to where I am. First, I would like to acknowledge Dr. Amitabh Sinha for leading me to the world of academic research. He is not only my advisor in research but also my lifetime mentor. He is always supportive to all the decisions I made and all the paths I chose. Second, I would like to thank Professor Yan Huang for all the support throughout my Ph.D. She showed me a great example of how to become a researcher full of enthusiasm. She put a huge amount of effort in guiding me through the process of finishing a complete research project. Her profession, hardworking, and sense of responsibility will always inspire me in my future career. Third, I would like to thank Professor Shima Nassiri. During the difficult times, she gives me the hope and confidence to continue my academic journey. She always encourages me to explore new topics and try new methodologies, which is extremely important for me as an independent researcher. I would like to also thank my dissertation committee members Professor Cong Shi, Professor Joline Uichanco, and Professor Brian Wu, who give me great support toward the completion of my degree. Finally, I am grateful for my family and friends. Special thanks to my husband Xiang Liu and my daughter Andrea Liu. Xiang: thank you! I know I will always have you by my side. Andrea: You are such an angle and I can’t love you more! I would ii also like to thank my parents Liying Gu and Ruiliang Li: You are the best parents in the world! I would also like to thank my parents in law Chunsheng Liu and Yayun Li: thank you for all the support. I can not imagine my life without your help. Last but not least, thanks to my dogs Playdoh and Hunio: love you forever. iii TABLE OF CONTENTS ACKNOWLEDGEMENTS :::::::::::::::::::::::::: ii LIST OF TABLES :::::::::::::::::::::::::::::::: vii LIST OF FIGURES ::::::::::::::::::::::::::::::: x ABSTRACT ::::::::::::::::::::::::::::::::::: xiii CHAPTER I. Introduction .............................. 1 II. Data-Driven Promotion Planning for Paid Mobile Applications 5 2.1 Introduction . 5 2.2 Literature Review . 9 2.3 Data and Descriptive Analysis . 13 2.4 Empirical Model and Estimation . 18 2.4.1 Empirical Model . 18 2.4.2 Instrumental Variables . 21 2.4.3 Estimation Results . 27 2.4.4 Robustness Checks . 38 2.4.5 Heterogeneity . 60 2.5 Prediction Accuracy . 63 2.6 Promotion Planning Problem . 64 2.6.1 PPP Model Formulation . 64 2.7 Promotion Planning Result . 70 2.7.1 Window Size Sensitivity . 70 2.7.2 Performance Comparison among Alternative Policies 71 2.7.3 Significance of the Visibility Effect and Dynamic Promotion Effect . 75 2.8 Conclusion . 76 iv III. How Does Telemedicine Shape Physician’s Practice in Men- tal Health? ............................... 88 3.1 Introduction . 88 3.2 Literature Review . 91 3.3 Problem Setting . 94 3.3.1 Definitions . 94 3.3.2 Telemedicine Effects . 97 3.3.3 Dynamic Effects . 101 3.4 Data Description . 102 3.4.1 Data Sources and General Inclusion Criteria . 102 3.4.2 Identifying the Adopter and Non-Adopter Groups . 104 3.4.3 Variables Description . 109 3.5 Econometric Model . 110 3.5.1 Heterogeneous Adoption Time CIC Model in Athey and Imbens (2006) . 113 3.5.2 Controlling for Observable Covariates . 118 3.5.3 Effect Estimation . 119 3.6 Results . 121 3.6.1 Controlling for Observable Covariates: Regression Results . 121 3.6.2 Telemedicine Adoption and Spillover Effects . 122 3.6.3 Explanation of the Observations . 124 3.7 Additional Analysis and Robustness Checks . 129 3.7.1 New Patient Visits Vs. Established Patient Visits . 129 3.7.2 Linear Difference-in-Difference Model . 129 3.8 Conclusion . 131 IV. Search Page Personalization: A Consider-then-choose Model 134 4.1 Introduction . 134 4.2 Literature Review . 138 4.2.1 Consider-then-Choose Model . 138 4.2.2 Assortment Planning . 140 4.3 A Consider-then-Choose Model . 143 4.3.1 Consideration Set Formation (The “Consider” Stage) 144 4.3.2 Purchase Decision Given a Consideration Set (The “Choose” Stage) . 147 4.3.3 Estimation Strategy . 148 4.4 Assortment Personalization via Consideration Set Induction . 152 4.4.1 Max-Revenue Assortment Planning . 153 4.4.2 Optimal Consideration Set Induction Optimization 155 4.5 A Case Study . 167 4.5.1 Data Description . 167 4.5.2 Estimation Result and Discussion . 168 v 4.5.3 Comparison of OCSIO Heuristic and OCSIO . 171 4.5.4 Revenue Increase using the OCSIO Heuristic . 172 4.5.5 Performance of OCSIO Given Imperfect Taste Infor- mation . 173 4.6 Conclusion . 174 V. Future Work .............................. 177 BIBLIOGRAPHY :::::::::::::::::::::::::::::::: 180 APPENDICES :::::::::::::::::::::::::::::::::: 192 A.1 Procedure Codes Included in the Study . 193 A.2 Data Generating Process . 195 vi LIST OF TABLES Table 2.1 Summary Statistics . 14 2.2 Price Tiers in iOS App Store . 14 2.3 Summary Statistics of Promotion Depth and Promotion Length . 16 2.4 Variable Description . 21 2.5 Main Model Full Result . 28 2.6 Regression Results of Ranking Function (Equation (2.3)) . 29 2.7 2SLS and 3SLS Estimation Results of Demand Function . 30 2.8 2SLS and 3SLS Estimation Results of Ranking Function . 31 2.9 Regression Results of Alternative Demand Specifications . 33 2.10 Estimation Results of Main Model with Different Missing Data Im- putation Methods . 39 2.11 Estimatin Results of the Demand Function with Different Sample Cutoff Values . 40 2.12 Estimation Results of Demand Function under Alternative Specifi- cations of Promotion effect . 41 2.14 Estimation Results of Demand Function with App Age Dummies (by Month) . 42 2.13 Estimation Results of Demand Function with ds Dummy Variables . 45 2.15 Estimation Results of Demand Function with More Lagged Ranks . 46 vii 2.16 Estimation Results of Demand Function with Lagged Download . 47 2.17 In-sample and Out-of-sample MSE . 47 promotion · 2.18 Estimation Results of Demand Function with log(avg_rit ) post dit ................................... 48 2.19 Regression Results for Cumulative Number of Ratings . 49 2.20 Estimation Results of Demand Function with Fitted Rating Counts 50 2.21 Main Model vs. Choice Model: Out-of-Sample Mean Squared Error for Each Cross-validation Round . 53 2.22 Effect of Number of Other Apps on Promotion . 54 2.23 Estimation Results of Demand Function for Top 100 Apps . 55 2.24 Main Model vs. Choice Model: Out-of-Sample Mean Squared Error for Each Cross-validation Round . 55 2.25 Summary Statistics of Data for Network Effect Test . 57 2.26 Estimation Results of Demand Function with Visibility Effect and/or Network Effect . 58 2.27 Estimation Results of Demand Function with Past 10 Days’ Cumu- lative Download . 59 2.28 Effect of Short vs Long Promotions . 81 2.29 Estimation Results of Demand Function by App Rating . 82 2.30 Estimation Results of Demand Function by Original Price . 82 2.31 Estimation Results of Demand Function – Apps with vs without Freemium Version . 83 2.32 Estimation Results of Demand Function with Interaction Effect be- tween Promotion and Rank Prior to Promotion . 84 2.33 Estimation Results of Demand Function with Interaction Effect be- tween Promotion and log(hit) ..................... 85 viii 2.34 Estimation Results of Demand Function with Interaction Effect be- tween Promotion and log(uit) ..................... 86 2.35 Prediction Accuracy . 86 2.36 Performance Comparison with Real App Data . 86 2.37 Total Revenue of Different Pricing Policies Relative to Revenue under Constant Price Policy . 87 3.1 Claim-level summary statistics . 104 3.2 Physicians’ characteristics comparison between the adopters and non- adopters (before matching) . 107 3.3 Physicians’ characteristics comparison between the adopters and matched non-adopters . 108 3.4 Summary of visit-, patient-, and physician-related variables . 111 3.5 RVI Summary Statistics . 112 3.6 Linear regression result of controlling for observable characteristics . 122 3.7 CIC model estimation results over the entire horizon . 123 3.8 Telemedicine Adoption Effect on New Patient Visits V.S. Established Patient Visits . 129 3.9 Linear DID Model Result . 131 4.1 Table of notations . 144 4.2 Summary Statistics . 169 4.3 Estimation Result . 170 4.4 Revenue Increase Under Imperfect Taste Information ........ 174 ix LIST OF FIGURES Figure 2.1 Histogram of No. Promotions . 15 2.2 Distributions of Promotion Depth and Promotion Length . 15 2.3 Relationship between Sales Rank and Download Volume and Sample Apps: Left Panel: Log(Sales Rank) vs. Log(Download Volume); Middle and Right Panels: Sample App Promotions . 17 2.4 Evidence of Promotion Effects in Data: Left Panel: Coefficients of Days-on(after)-Promotion Dummies; Right Panel: Cumulative Rev- enue Over Time: Apps with and without Promotions . 18 2.5 Number of promotions against number of apps published by its de- veloper, average app rating and app fixed effect . 24 2.6 Promotion depth against rating, download, rank, and app fixed effect 25 2.7 Daily rank improvement against the number of days being on promotion 27 2.8 Immediate and Total Effect of a 25-Day Promotion (Promotion Depth = 0.5) .
Load more

Recommended publications
  • An Investigation Into Jamming Percolation Using Renormalization
    An investigation into jamming percolation using renormalization group methods. Samuel Schoenholz Under the supervision of Professor Amy Bug and Professor Andrea Liu March 22, 2010 1 1 Overview Many systems in nature fail to satisfy the ergodic hypothesis and feature strong nonequilibrium behavior. These systems have proven remarkably difficult to study. In recent years theorists have studied a phase transition known as the jamming phase transition. The jamming phase transition unifies several far from equilibrium phase transitions including: the glass transition, the solidification of granular media in response to shearing forces, and the stiffening of colloids in response to increases in pressure. It has recently been shown that certain correlated percolation models can exhibit behavior analogous to the jamming phase transition. In particular this has been shown for two models, coined the spiral model and the force-balance model. The primary goal of this study was to determine whether or not the spiral model and the force-balance model reside in the same universality class. To investigate this issue we used the renormalization group to perform a numerical investigation of the spiral model and force balance model. There is evidence to suggest that both models feature exponential, as opposed to power-law scaling of the correlation function. It was therefore difficult to investigate, in a computationally feasible way, large enough systems so as to reduce the finite size effects. To do this we came up with a variety of algorithms including: a Monte-Carlo method, a binary search, and a linear time culling algorithm. The combination of these three strategies allowed us to investigate significantly larger systems than had been investigated in the past.
    [Show full text]
  • The Dynamics Is Encoded in the Pair Correlation Function François Landes, Giulio Biroli, Olivier Dauchot, Andrea Liu, David Reichman
    Attractive versus truncated repulsive supercooled liquids: The dynamics is encoded in the pair correlation function François Landes, Giulio Biroli, Olivier Dauchot, Andrea Liu, David Reichman To cite this version: François Landes, Giulio Biroli, Olivier Dauchot, Andrea Liu, David Reichman. Attractive versus truncated repulsive supercooled liquids: The dynamics is encoded in the pair correlation function. Physical Review E , American Physical Society (APS), 2020, 101 (1), 10.1103/PhysRevE.101.010602. hal-02439295 HAL Id: hal-02439295 https://hal.inria.fr/hal-02439295 Submitted on 14 Jan 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Attractive versus truncated repulsive supercooled liquids: The dynamics is encoded in the pair correlation function Fran¸coisP. Landes,1, 2, 3, 4 Giulio Biroli,2 Olivier Dauchot,5 Andrea J. Liu,3 and David R. Reichman4 1LRI, AO team, B^at660 Universit´eParis Sud, Orsay 91405, France∗ 2Laboratoire de Physique de l'Ecole´ normale sup´erieure, ENS, Universit´ePSL, CNRS, Sorbonne Universit´e, Universit´eParis-Diderot, Sorbonne Paris Cit´e,Paris, France 3Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104 4Department of Chemistry, Columbia University, 3000 Broadway, New York, NY 10027, USA 5UMR Gulliver 7083 CNRS, ESPCI ParisTech, PSL Research University, 10 rue Vauquelin, 75005 Paris, France We compare glassy dynamics in two liquids that differ in the form of their interaction poten- tials.
    [Show full text]
  • Arxiv:2006.09592V4 [Cond-Mat.Soft] 21 Apr 2021 13]
    Mean-Field Predictions of Scaling Prefactors Match Low-Dimensional Jammed Packings James D. Sartor∗, Sean A. Ridout†, Eric I. Corwin∗ ∗Department of Physics and Materials Science Institute, University of Oregon, Eugene, Oregon 97403, USA †Department of Physics and Astronomy University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA (Dated: April 22, 2021) No known analytic framework precisely explains all the phenomena observed in jamming. The replica theory for glasses and jamming is a mean-field theory which attempts to do so by working in the limit of infinite dimensions, such that correlations between neighbors are negligible. As such, results from this mean-field theory are not guaranteed to be observed in finite dimensions. However, many results in mean field for jamming have been shown to be exact or nearly exact in low dimensions. This suggests that the infinite dimensional limit is not necessary to obtain these results. In this Letter, we perform precision measurements of jamming scaling relationships between pressure, excess packing fraction, and number of excess contacts from dimensions 2–10 in order to extract the prefactors to these scalings. While these prefactors should be highly sensitive to finite dimensional corrections, we find the mean-field predictions for these prefactors to be exact in low dimensions. Thus the mean-field approximation is not necessary for deriving these prefactors. We present an exact, first-principles derivation for one, leaving the other as an open question. Our results suggest that mean-field theories of critical phenomena may compute more for d du than has been previously appreciated. ≥ Introduction – Granular materials exhibit universal these results.
    [Show full text]
  • Jamming Phase Diagram, Effective Temperature, and Heterogeneous Dynamics of Model Glass-Forming Liquids
    University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations Summer 2010 Jamming Phase Diagram, Effective Temperature, and Heterogeneous Dynamics of Model Glass-Forming Liquids Thomas K. Haxton University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Condensed Matter Physics Commons Recommended Citation Haxton, Thomas K., "Jamming Phase Diagram, Effective Temperature, and Heterogeneous Dynamics of Model Glass-Forming Liquids" (2010). Publicly Accessible Penn Dissertations. 235. https://repository.upenn.edu/edissertations/235 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/235 For more information, please contact [email protected]. Jamming Phase Diagram, Effective Temperature, and Heterogeneous Dynamics of Model Glass-Forming Liquids Abstract We establish that the behavior of fluids consisting of epulsivr e spheres under the combined effects of pressure p, temperature T, and applied shear stress s can be organized in a jamming phase diagram parameterized by the dimensionless quantities T/pd^3, s/p, and pd^3/e, where d is the diameter of the spheres and e is the interaction energy scale. The jamming phase diagram describes the three- dimensional parameter space as the product of an equilibrium plane at s/p=0 and a hard sphere plane at pd^3/e=0. Near the hard sphere plane, the jamming phase diagram is universal in the sense that material properties are insensitive to the details of the interaction potential. We demonstrate that within the universal regime, the conventional approach to the dynamic glass transition along a decreasing temperature trajectory is equivalent to the colloidal glass transition approach along an increasing pressure trajectory.
    [Show full text]
  • Willens Wing Atrium
    THE MATERIALS SCIENCE AND ENGINEERING DEPARTMENT SERIES PRESENTS THE MATERIALS SCIENCE AND ENGINEERING DEPARTMENT FALL COLLOQUIUM SERIES PRESENTS THE DOW LECTURE Andre Liu Hepburn Professor of Physics, Department of Physics and Astronomy University of Pennsylvania Exploiting the Malleability of Disorder to Design Functional Materials Systems with complex energy landscapes, such as glass-forming systems, have far more variation in their properties than those with simple ones that readily crystallize. This natural variation can be pushed even further by design, allowing us to tune in unusual properties and novel functions into materials. For example, when most materials are stretched in one direction, they tend to shrink in the orthogonal directions. Materials that do the opposite and expand in the orthogonal directions when stretched are “auxetic,” and have attracted attention for applications such as high energy absorption. We have found that mechanical spring networks can be tuned easily to the extreme limit of auxetic behavior. Likewise, we have shown that properties common in living matter, such as the ability of proteins (e.g. hemoglobin) to change their conformations upon binding of an atom (oxygen) or molecule, the ability of the brain’s vascular network to send enhanced blood flow and oxygen to specific areas of the brain associated with a given task, or the ability to retain a memory, can be designed into disordered systems using similar principles. The ability to design properties and functions further gives new insight into the relation between microscopic structure and function that may help us both to understand living systems and to design new biologically-inspired materials.
    [Show full text]
  • Arxiv:1308.6806V2 [Cond-Mat.Soft] 20 Nov 2013
    Mechanical and microscopic properties of the reversible plastic regime in a 2D jammed material Nathan C. Keim∗ and Paulo E. Arratiay Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA 19104 (Dated: August 31, 2018) At the microscopic level, plastic flow of a jammed, disordered material consists of a series of particle rearrangements that cannot be reversed by subsequent deformation. An infinitesimal de- formation of the same material has no rearrangements. Yet between these limits, there may be a self-organized plastic regime with rearrangements, but with no net change upon reversing a defor- mation. We measure the oscillatory response of a jammed interfacial material, and directly observe rearrangements that couple to bulk stress and dissipate energy, but do not always give rise to global irreversibility. PACS numbers: 83.60.La,63.50.Lm,62.20.F-,05.65.+b The mechanical properties of disordered (amorphous) than any global timescale of deformation (e.g. a period materials far from equilibrium | from sand, to plas- of driving or the inverse strain rateγ _ −1), we can describe tics, to ice cream | continue to elude comprehensive changes to microstructure in terms of discrete, local plas- understanding [1{3]. These materials typically feature tic rearrangements, which are a key feature of the shear many particles (e.g. droplets, atoms, or grains) that are transformation zone (STZ) picture of plasticity [8, 9]. crowded together in close contact, and are both jammed Under steady shear, the piling-on of these events, each of so that each particle is fully constrained by its neighbors, which traverses a barrier between two local minima in po- and disordered so that these constraints vary greatly tential energy, ensures that the initial microstructure can among particles, and crystalline order rarely extends be- never be recovered out of a vast landscape of metastable yond several particle diameters [4].
    [Show full text]
  • Relationships Among Structure, Memory, and Ow in Sheared
    Relationships among structure, memory, and ow in sheared disordered materials Kevin Galloway Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania https://orcid.org/0000-0001-8407-6923 Erin Teich Department of Bioengineering, University of Pennsylvania Xiaoguang Ma University of Pennsylvania Christoph Kammer Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania Ian Graham Department of Physics and Astronomy, University of Pennsylvania Nathan Keim Department of Physics, Pennsylvania State University Celia Reina Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania Douglas Jerolmack Department of Earth and Environmental Science, University of Pennsylvania Arjun Yodh University of Pennsylvania https://orcid.org/0000-0003-4744-2706 Paulo Arratia ( [email protected] ) University of Pennsylvania Article Keywords: disordered solids, microscopy, microstructural properties, rheological response Posted Date: May 26th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-523661/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License 1 Relationships among structure, memory, and flow in 2 sheared disordered materials 1 2 3 1 3 3 K.L. Galloway ,E.G.Teich, X-g Ma ,Ch.Kammer,I.R.Graham & N.C. Keim4,C.Reina1, D.J. Jerolmack5,1, A. G. Yodh3, P.E. Arratia∗,1 1Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania 2Department of Bioengineering, University of Pennsylvania 3Department of Physics and Astronomy, University of Pennsylvania 4Department of Physics, Pennsylvania State University 5Department of Earth and Environmental Science, University of Pennsylvania 4 May 13, 2021 5 Abstract 6 A fundamental challenge for disordered solids is predicting macroscopic yield from the 7 microscopic arrangements of constituent particles.
    [Show full text]
  • Criticality in the Linear Response of Disordered Solids Carl Peter Goodrich University of Pennsylvania, [email protected]
    University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 1-1-2015 Unearthing the Anticrystal: Criticality in the Linear Response of Disordered Solids Carl Peter Goodrich University of Pennsylvania, [email protected] Follow this and additional works at: http://repository.upenn.edu/edissertations Part of the Physics Commons Recommended Citation Goodrich, Carl Peter, "Unearthing the Anticrystal: Criticality in the Linear Response of Disordered Solids" (2015). Publicly Accessible Penn Dissertations. 1743. http://repository.upenn.edu/edissertations/1743 This paper is posted at ScholarlyCommons. http://repository.upenn.edu/edissertations/1743 For more information, please contact [email protected]. Unearthing the Anticrystal: Criticality in the Linear Response of Disordered Solids Abstract The fact that a disordered material is not constrained in its properties in the same way as a crystalline one presents significant and yet largely untapped potential for novel material design. However, unlike their crystalline counterparts, disordered solids are not well understood. Though currently the focus of intense research, one of the primary obstacles is the lack of a theoretical framework for thinking about disorder and its relation to mechanical properties. To this end, we study a highly idealized system composed of frictionless soft spheres at zero temperature that, when compressed, undergos a jamming phase transition with diverging length scales and clean power-law signatures. This critical point is the cornerstone of a much larger ``jamming scenario" that has the potential to provide the essential theoretical foundation that is sorely needed to develop a unified understanding of the mechanics of disordered solids. We begin by showing that jammed sphere packings have a valid linear regime despite the presence of a new class of ``contact nonlinearities," demonstrating that the leading order behavior of such solids can be ascertained by linear response.
    [Show full text]
  • UCSB's Materials Miracle
    UCSB’s Materials Miracle: A Blueprint for Success Glenn H. Fredrickson Mitsubishi Chemical Professor, Director MC-CAM Departments of Chemical Engineering and Materials 2010 NRC Rankings: Materials Science & Engineering On September 28, 2010, The National Research Council (NRC) published its long- awaited report evaluating over 5,000 doctoral programs in 62 fields at 212 universities The 2010 rankings specified 5 percentile and 95 percentile confidence intervals using a number of criteria. The most important of these are the so-called S (survey) and R (regression) rankings Only three other programs in the country share UCSB Materials "1111" ranking: Economics at Harvard, Statistics at Stanford, and Performing Arts at NYU Young but Distinguished UCSB College of Engineering: • Founded 1961 – 50 yrs old UCSB Materials Department • Founded 1986 – 25 yrs old How did UCSB get to #1 in less than 25 years? Engineering, top 10 by S In fact, all five College of Engineering (COE) graduate programs in UCSB are "top 10" (when the S ranking is used) We will see that UCSB’s broader success is intimately linked to the success in Materials! Outline . Beginnings (1980-1990) . Prerequisites . Visionaries . Multi-discipline . Maturation (1990-2000) . Shared facilities . Centers . Education and outreach . Recognition (2000-2010) . Closing Remarks Beginnings (1980-1990) Robert Mehrabian Dean, COE 1983-1990 Prerequisites: build from strength By 1980, select areas of UCSB physical science and engineering had established a national reputation for excellence: . Physics Dept – led by an ambitious and talented faculty hired in the 1960s . Institute for Theoretical Physics (est. 1979, now KITP) – used to attract Nobel Laureate R.
    [Show full text]
  • Shear-Induced Stress Relaxation in a Two-Dimensional Wet Foam
    Shear-Induced Stress Relaxation in a Two-Dimensional Wet Foam John Lauridsen Michael Twardos Michael Dennin Department of Physics and Astronomy and Institute for Interfacial and Surface Science University of California at Irvine Irvine, CA 92697-4575. (November 16, 2018) somewhat model dependent. Various models of two- dimensional foams have been used to study the connec- We report on experimental measurements of the flow be- tion between topological rearrangements and flow prop- havior of a wet, two-dimensional foam under conditions of slow, steady shear. The initial response of the foam is elastic. erties as a function of applied rate of strain [6–13]. All Above the yield strain, the foam begins to flow. The flow con- of these simulations predict the same qualitative behav- sists of irregular intervals of elastic stretch followed by sudden ior for the macroscopic flow for sufficiently small rates of reductions of the stress, i.e. stress drops. We report on the strain that was described above: elastic behavior below a distribution of the stress drops as a function of the applied yield strain and nonlinear bubble rearrangements above shear rate. We also comment on our results in the context of the yield strain. However, the quantitative predictions various two-dimensional models of foams. for the size distribution and frequency of these events are model dependent. The dependence is predominately due 83.80.Iz,82.70.-y to different assumptions concerning the source of dissipa- tion in foams and treating foams with different degrees of Foams are ubiquitous in nature [1] and part of a larger “dryness”.
    [Show full text]
  • Self-Assessment
    Self-Statement for Merit Action, Effective 7/1/20 Mason A. Porter, 8/29/19 This document covers scholarship, teaching, etc. since 1 December 2017, when I submitted my documents for promotion from Step 3 to Step 4 of the professorial ladder. The current submission is for a one-year acceleration to advance to Step 5. In addition to the Department of Mathematics, I am also affiliated with UCLA’s Bhaumik Institute for Theoretical Physics. Scholarship I continue to conduct research in network analysis, data science, complex systems, and nonlinear systems — including contributions to theory, algorithms, data analysis, and applications. A defining feature of my work continues to be its interdisciplinary nature, including recent publications in mathematics, physics, biology, social science, and multidisciplinary journals. Papers: New peer-reviewed papers accepted in final form and/or published: 26, including ones in PNAS, SIAM Journal on Mathematics of Data Science, Nature Neuroscience, Nature Communications, and Chaos New papers (i.e., completed since 1 December 2017) that are currently undergoing peer review or revision based on referee reports: 17 My work is being cited at an ever-increasing rate, with (according to Google Scholar) now about 15000 citations overall, including more than 2700 citations in 2018. This includes one paper from 2018 with more than 30 citations and three papers from 2017 with more than 100 citations. I work in a large variety of areas in network analysis, complex systems, and nonlinear systems; I will highlight three themes here. One major theme of my research the past two years has been theory, methods, data analysis, and applications of multilayer networks, a concept I helped originate (with our 2014 review article [A121] getting more than 400 citations in 2018 and about 300 so far in 2019).
    [Show full text]
  • Villanova University Mechanical Engineering
    VILLANOVA UNIVERSITY MECHANICAL ENGINEERING DEPARTMENT Fall 2019 SEMINAR SERIES Seminar Date: September 6th, 2019 Lecture: Exploiting the Malleability of Disorder to Design Mechanical Metamaterials Speaker: Andrea Liu, Ph.D. Professor Department of Physics, University of Pennsylvania Abstract: Disordered solids have far more variation in their properties than crystalline ones. This natural variation can be pushed even further by design, allowing us to tune in unusual properties and novel functions into materials. For example, when most materials are stretched in one direction, they tend to shrink in the orthogonal directions. Materials that expand in the orthogonal directions when stretched are “auxetic,” and have attracted attention for properties such as high energy absorption for applications. We have found that mechanical spring networks can be tuned easily to the extreme limit of auxetic behavior. Moreover, we have shown that complex properties common in living matter, such as the ability of proteins (e.g. hemoglobin) to change their conformations upon binding of an atom (oxygen) or molecule, the ability of the brain’s vascular network to send enhanced blood flow and oxygen to specific areas of the brain associated with a given task, or the ability to retain a memory, can be designed into disordered systems using similar principles. Biography: Dr. Andrea Liu is a theoretical soft and living matter physicist who received her A. B. and Ph.D. degrees in physics at the University of California, Berkeley, and Cornell University, respectively. She was a faculty member in the Department of Chemistry and Biochemistry at UCLA for ten years before joining the Department of Physics and Astronomy at the University of Pennsylvania in 2004.
    [Show full text]