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Submissions from 2024 2024.
The Impact of Artificial Intelligence and Machine Learning on Organizations Cybersecurity , Mustafa Abdulhussein
The Relationship between Leadership Styles of a Principal and African-American Student Achievement in Elementary Reading , Joel James Abe
The Effect of Music on Spiritual Well Being Among Hospice Patients , Mathai Abraham
Exploring the Relationship Between Emotional Intelligence and Religiosity and the Experience of Emotional Labor in Working Women , Jane Naa Koshie Acquah-Bailey
The Destruction of Louisiana Wetlands: An Environmental History, 1900-2000 , Gloria H. Adams
The Evidential Problem of Assurance: Textual Approach from the Johannine Literature , Derick A. Adu
The Perpetual Progression in the Schleswig-Holstein Duchy: History, Politics, and Religion, 1460-1864 , Christian Anthony Ahlers
Evidence-Based Strategy to Engage and Retain Patients in Treatment for Opioid Use Disorder: A Contingency Management Plan , Olubukola Juliet Akinyele
Faith-based Intervention to Prevent Adolescent Self-injury: An Integrative Review , Adekemi O. Akinyemi
Impacts of Opioids on Health and Ways to Overcome the Addiction , Kennedy Chidi Alajemba
Using the Motivated Information Management Theory and the Social Support Theory to Understand Caregiver Perspectives of Currently Available Health Communication Regarding Dementia: A Qualitative Study , Sara J. Alig
Exploring the Lived Experiences of Teachers When Enrolled in an Asynchronous Certification Program: A Phenomenological Study , Sara R. Allen
Exploring the Lived Experiences of Rural Texas School Counselors Working with Students’ Mental Health After the COVID-19 Pandemic , Lanessa K. Allman
Causal Comparative Study of Structured Literacy Knowledge Between Participants of Dyslexia Intervention Training Programs , Rhonda Rene' Alm
The Impact of Reporting Patient Safety Events: An Integrative Review , Catherine M. Amitrano
A Correlational Study of Culturally Responsive Christian School Leadership and Its Impact on Culturally Marginalized Students , Denecia B. Anderson
The Effectiveness of Integrating Religious/Spirituality Beliefs into Psychotherapy: An Integrative Review , Justina Anighoro-Okezie
Equipping Equippers: Training Alaska Bible College Students for Equipping Ministry through Mentorship , Justin Glenn Archuletta
Parent and Teacher Perspectives on Attachment/Relationships and Children's Self-Regulation , Elaina Arnold
Mentorship Experiences of College Level Educators: A Phenomenological Study , Rebecca M. Arsenault
A Predictive Correlation and Causal-Comparative Study on Early Childhood Social-Emotional Scores, Socioeconomic Status, and Academic Achievement , Denise A. Ashley
The French Piano School's Pedagogical Influence on Louis Moreau Gottschalk's Piano Etudes: A Narrative Inquiry , Kenner Layne Bailey
An Exegetical and Theological Exploration of Paul’s Self-Identity in Consideration of Modern Social Sciences , Chala Baker
Evangelism Development in a Multigenerational Rural Church , John E. Baldwin
Exploring the Role of Curricular Engagement in the Secondary English Classroom: A Case Study , Kelsey Leah Baldwin
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Timothy Draher, Ph.D., March 2024, Design and Performance of Superconducting Switches for Nanowire Detectors in Magnetic Fields, Advisors: Zhili Xiao
Kaela Villafania, M.S., March 2024, Cold Testing of a Prototype Superconducting Radiofrequency Electron Gun and Ancillary Systems for the LCLS-II-HE Project
Mark Crowell, M.S., March 2024, Microwave Simulations of a Beam Position Monitor and Circuit Designs Assisting Its Beam Testing
William Baker, Ph.D., March 2024, Theory of Ultrafast Spin Crossover in Divalent Iron Systems, Advisor: Michel van Veenendaal
Austin Dick, Ph.D., October 2023, Computational Modeling, Simulation, and Potential Applications of Optical Stochastic Cooling, Advisor: Philippe Piot
Alister Tencate, Ph.D., September 2023, A High-Precision Electron Emission Model: Computational Methods for Nanoscale Structures, Advisor: Bela Erdelyi
Spencer Kelham, M.S., September 2023, Simulations of Electro-Optically Sampled Arbitrarily Shaped Electron Bunches for Wakefield Accelerators, Advisor: Philippe Piot
Prudhvi Raj Varma Chintalapati, Ph.D., June 2023, Systematic study of projection biases in weak lensing analysis, Advisor: Vishnu Zutshi
Nicholas Yee, M.S., June 2023, Verification of Proton Range Predictions in Proton Treatment Planning Using X-Ray CT or Proton CT Imaging, Advisor: George Coutrakon
Zamiul Alam, M.S., June 2023, The Standard Model Precision Parameters at 200 GeV, Advisor: Stephen Martin
Joseph Piet, M.S., June 2023, Applications of Proton Imaging in Proton Cancer Treatments, Advisor: George Coutrakon
Elena Krivyakina, Ph.D., June 2023, Strongly-correlated electron systems: alkaline- and rare-earth manganese- and nickel-based perovskites, Advisor: Omar Chmaissem
Sarah Choate, M.S., March 2023, Examining the Feasibility of Identifying Tau Neutrino Charged Current Events in the DUNE Far Detector, Advisor: Michael Eads
Mark Mekosh, M.S., March 2023, Using Machine Learning to Search for Vector Boson Scattering at the CMS Detector During Run 2, Advisor: Michael Eads
Pupsa Upreti, Ph.D., March 2023, Diffuse Scattering and 3D-ΔPDF Analyses: Order-Disorder Phase Transitions in (Sr1-xCax)3Rh4Sn13 and NaNO2, Advisor: Omar Chmaissem
Dillon Merenich, M.S., March 2023, Design and Cold Test of a Metamaterial Accelerating Structure for Two-Beam Acceleration, Advisor: Xueying Lu
Marc Pavlik, Ph.D., March 2023, The Dynamics of Liquids and Glasses using Nuclear Resonance Time Domain Interferometry, Advisor: Dennis Brown
Saba Fatima, Ph.D., January 2023, Using Machine Learning to Predict Student Outcomes, Advisor: Michael Eads
Cassandra Phillips, M.S., October 2022, Modeling of sub-THz Wakefield structures for Electron-Beam Acceleration, Advisor: Philippe Piot
Emily Frame, M.S., October 2022, An Upgraded Photoinjector for the Argonne Wakefield Accelerator, Advisor: Philippe Piot
Elliot Parrish, Ph.D., October 2022, Search for Charged Higgs Bosons in the tau+lepton final state with 139 fb-1 of pp Collision Data at sqrt( = 13 TeV with the ATLAS Experiment, Advisor: Jahred Adelman
Brianna Dwyer, Ph.D., October 2022, Measuring Diphoton Production from Higgs Boson Decays and in Association with Heavy Flavor Jets, Advisor: Jahred Adelman
Aaakash Narayanan, Ph.D., September 2022, Third-integer Resonant Extraction Regulation System for Mu2e, Advisor: Michael Syphers
Wei Hou Tan, Ph.D., June 2022, Compact Wakefield Accelerator with Advanced Beam Manipulations, Advisor: Philippe Piot
Ramanpreet Singh, Ph.D., June 2022, Studies of Vector Boson Scattering in the semileptonic channel with the CMS detector at sqrt(s) = 13 TeV, Advisor: Vishnu Zutshi
Tianzhe Xu, Ph.D., June 2022, Precise Phase-Space Control for Future Linear Colliders, Advisor: Philippe Piot
Timothy Draher, M.S., February 2022, Magnetic Charge Ordering of Pinwheel Artificial Spin Ice in In-plane External Magnetic Fields and its Application for Tunable Vortex Pinning, Advisor: Zhili Xiao.
Benjamin Simons, M.S., October 2021, Beam Phase Space Diagnostic Techniques Along the Fermilab Muon Campus Extraction Line, Advisor: Michael Syphers.
Danylo Lykov, M.S, October 2021, Tensor Network Approach for Simulation of Quantum Many-Body Systems, Advisor: Andreas Glatz.
Brendan Leung, M.S., October 2021, Spectral Analysis of Cyclotron Radiation for Electron Bream Diagnostics, Advisor: Philippe Piot.
Bisham Poudel, Ph.D., June 2021, Superconducting Cuprates: Synthesis, Characterization and Diffuse Scattering Properties, Advisor: Omar Chmaissem.
Ryan Stadel, Ph.D., June 2021, Experimental Exploration of Shared Magnetic Phases Between Diverse Systems of Iron-Pnictide Superconductors, Advisor: Omar Chmaissem.
Prudhvi N. Bhattiprolu, June 2021, Signal-background analysis for new physics at particle colliders and the criteria for its discovery, Advisor: Stephen Martin.
Afnan Al Marzouk, Ph.D., June 2021, Collisional Methods with Applications to Charged Particle Beams, Advisor: Bela Erdelyi.
Osama Mohsen, Ph.D., June 2021, Design and Optimization of Superconducting Radio-Frequency Electron Sources, Advisor: Philippe Piot.
Christina Sarosiek, Ph.D., May 2021, Clinical Applications and Feasibility of Proton CT and Proton Radiography, Advisor: George Coutrakon.
Christopher Marshall, M.S., August 2020, Development of an Electron-Beam Halo Diagnostics. Advisor: Dr. Philippe Piot.
Aaron Fetterman, M.S., December 2020, Photoinjector generation of high-charge magnetized beams for electron-cooling applications. Advisor: Dr. Philippe Piot.
Sebastian Szustkowski, Ph.D., October 2020, Nonlinear Integrable Optics Beam Dynamics Experiment and Diagnostics. Advisor: Dr. Swapan Chattopadhyay
Jeremiah Mitchell, Ph.D., September 2020, On Systematics and Their Mitigation in MAGIS-100 Atomic Interferometer Experiment to Explore the Dark Sector and Early Universe. Advisor: Dr. Swapan Chattopadhyay
Kevin Hamilton, M.S., September 2020, On the Self-Force Problem of Point-Like Charged Particles in Classical Electrodynamics . Advisor: Dr. Bela Erdelyi
Deblina Das, M.S., June 2020, The Synthesis of Pb2Sr2Sm1-xCaxCu3O8 and Characterization of its Structural and Superconducting Properties. Advisor: Dr. Omar Chmaissem
Puja Saha, Ph.D., March 2020, Search for Higgs Pair Production in the bbττ Final State with the ATLAS Detector at the Large Hadron Collider. Advisor: Dr. Dhiman Chakraborty
Tyler Burch, Ph.D., March 2020, A Search for Resonant and Non-Resonant Di-Higgs Production in the γγbb¯ Channel Using the ATLAS Detector . Advisor: Dr. Jahred Adelman.
Tilak Malla, MS, October 2019, Towards Dual-Readout Calorimetry for Redtop Experiment. Advisor: Dr. Vishnu Zutshi.
Edward Aris Fajardo, Ph.D., October 2019, Two-Dimensional Bloch Electrons in Electric and Magnetic Fields. Advisor: Dr. Roland Winkler.
Michael Gattone, M.S., June 2019, Impact of Standardized Test Performance on Success in Introductory College Physics Classes . Advisor: Dr. Michael Eads.
Daniel Boyden, M.S., June 2019, Study of the Systematics in Straw Tube Tracking System for gm2. Advisor: Dr. Michael Eads.
Daniel Faia, MS, June 2019, Improving on Matrix Element Based Discriminants with Machine Learning Techniques For H->ZZ->4I Analysis. Advisor: Dr. Michael Eads.
Anusorn Lueangaramwong, Ph.D., June 2019, Study of Electron Beam Emitted from Nano-Structured Cathode. Advisor: Dr. Philippe Piot.
Nick Amato, M.S., June 2019, Improved Momentum Spread for Precise Experiments Using Wedges . Advisor: Dr. Michael Syphers.
Prudhvi Chintalapati, M.S., June 2019, Simulation of Resonant Extraction on MU2E. Advisor: Dr. Michael Syphers.
Mason Hayward, M.S., May 2019, Characterization of Boron/Iron-Oxide Core/Shell Structure For Boron Neutron Capture Therapy by Stem,Eels-Xeds and Mossbauer Spectroscopy. Advisor: Dr. Yasuo Ito
Jing Xu, Ph.D., May 2019, Magnetoresistance in Non-Magnetic Semimetals and Quantum Wells. Advisor: Dr. Zhili Xiao.
Matthew Dwyer, MS, April 2019, Exploring the Relationship among Students’ Preconceptions, Attitudes, and Major. Advisor: Dr. Michael Eads.
Kamal Chapagain, Ph.D., December 2018, Discovery and Study of Single-Phase and Single-Ion Manganese Pervoskite Multiferroics. Advisor: Dr. Bogdan Dabrowski.
Matthew Krogstad, Ph.D., October 2018, Diffuse Scattering and Local Order in Lead-Based Relaxor Ferroelectrics. Advisor: Dr. Omar Chmaissem
Alexander Malyzhenkov, Ph.D., September 2018, Phase-space Manipulations of Electron Beams for X-ray free-Electron Lasers and Inverse Compton Scattering Sources. Advisor: Dr. Philippe Piot.
Blake Burghgrave, Ph.D., June 2018, Search for Charged Higgs Bosons in the Tau + Lepton Final State. Advisor: Dr. Dhiman Chakraborty.
Logan Clutch Jackson Rice, M.S., June 2018, Toward a DUNE Photon Detection System, Advisor: Dr. Vishnu Zutshi.
Matthew Andorf, Ph.D., June 2018, Light Transport and Amplification for Optical Stochastic Cooling in IOTA. Advisor: Dr. Philippe Piot.
Aliaksei Halavanau, Ph.D., May 2018, Electron Beam Shaping and Its Applications. Advisor: Dr. Philippe Piot.
Saber Al Furhud, M.S., April 2018, Control of Ferroelectricity in ATiO3 by Isoelectronic Ti-site Substitutions. Advisor: Dr.Bogdan Dabrowski.
Hamoud Somaily, Ph.D., March 2018, Tuning of the Structural and Physical Properties Via A-Site Doping in Perovskite-Type Transition Metal Oxides. Advisor: Dr. Omar Chmaissem.
Anthony Gee, Ph.D., March 2018, Intense Beam Dynamics in Arbitrary Structures . Advisor: Dr. Bela Erdelyi.
Ryan Churchill-DeRose, M.S., March 2018, The Synthesis of Ba(1-x) Na(x) Fe2As2 And Its Structural and Magnetic Properties. Advisor: Dr. Omar Chmaissem.
Jinlong Wang, M.S., March 2018, A Transverse - Wakefields Streaking Technique for Measurement of Ultrashort Electron Pulses. Advisor: Dr. Philippe Piot.
Wadiah Allahyani, M.S., October 2017, Oxygen Storage and Electrolyte Material RMnO3. Advisor: Dr. Bogdan Dabrowski.
Gregory Alley, M.S., June 2017, Measuring and Improving Student Outcomes in Physics High Data Analysis. Advisor:Dr. Michael Eads.
Aaron Epps, M.S., June 2017, A Dedicated Quality Control Test Stand for g-2 Tracker System. Advisor: Dr. Michael Eads.
Scott Zitnik, M.S., June 2017, Ability Group Configuration for the High School Physics Classroom. Advisor: Dr. Michael Eads.
Jacob Kalnins, M.S., June 2017, Radiation Damage in Hamamatsu Multi-Pixel Photon Counters . Advisor: Dr. Vishnu Zutshi.
Maleeha Alanizy, M.S., May 2017, Hexagonal Manganites for Gas Separation. Advisor: Bogdan Dabrowski
Andrew Fiedler, M.S., April 2017, A Study of Particle Beam Spin Dynamics for High Precision Experiments . Advisor: Dr. Michael Syphers.
Preeti Vodnala, Ph.D., April 2017, Interplay of Structure and Dynamics in Biomaterial. Advisor: Dr. Laurence B. Lurio.
Ivan Viti, Ph.D., October 2016, Simulations in multiphastic nanosoldics and superconducting nanostructures. Advisor: Dr. Andreas Glatz.
Sumana Abeyratne, Ph.D., October 2016, New computational approaches to the N-body problem with applications to electron cooling of heavy ion beams. Advisor: Dr. Bela Erdelyi.
Alexander Malyzhenkov, M.S., October 2016, KLYNAC, a compact linear accelerator with integrated power supply. Advisor: Dr. Philippe Piot.
Shane Sullivan, M.S., August 2016, Supersymmetric particle production at post-LHC proton-proton colliders. Advisor: Dr. Stephen Martin.
Laxman Raju Thoutam, Ph.D., August 2016, Magnetoresistance Anisotropy and Transport Properties of Tungsten Ditelluride. Advisor: Dr. Zhili Xiao.
Melissa Butner, M.S., July 2016, Constraining Neutrinos as Background to WIMP- Nucleon Dark Matter Particle Searches for DAMIC: CCD Physics Analysis and Electronics Development. Advisor: Dr. Stephen Martin
Andrew Green, M.S., June 2016, Development of automated beam emittance measurement system via the quadrupole scan technique at the Fermilab Accelerator Science and Technology (FAST) facility. Advisor: Dr. Young-Min Shin.
Heath LeFevre, M.S., June 2016, Analysis of an integrated readout layer for use in a highly granular analog hadron calorimeter. Advisor: Dr. Vishnu Zutshi.
Nilanjana Kumar, Ph.D, May 2016, Phenomenological studies of minimal extensions to the Standard Model. Advisor: Dr. Stephen Martin.
Casey Mott, M.S., March 2016, Research and Development for the Mu2e Extinction Monitor. Advisor: Dr. David Hedin.
Keith Taddei, Ph.D., March 2016, Magnetism in the Iron-Based Superconductors: The Determination of Spin-Nematic Fluctuations as the Primary Order Parameter and its Implications for Unconventional Superconductivity. Advisor: Dr. Omar Chmaissem
Harsh Deshpande, M.S., March 2016. Edge States in single-layer graphene. Advisor: Dr. Roland Winkler
Michael McEvoy, M.S., March 2016, The slow control system for the Fermilab muon g-2 E989 experiment. Advisor: Dr. Michael Eads.
Daniel Stange, M.S., December 2015, Physics Education.
Evan Reeves, M.S., August 2015, Physics Education.
Graham Stoddard, M.S., June 2015, Radiation Simulations of pp Collisions in the CMS Detector. Advisor: Dr. Pushpa Bhat.
Francois Lemery, Ph.D., June 2015, Beam Manipulation and Acceleration in Dielectric-lined waveguides. Advisor: Dr. Philippe Piot.
SriHarsha Panuganti, Ph.D., June 2015, Investigations and Applications of Field- and Photo-emitted Electron Beams from a Radio Frequency Gun. Advisor: Dr. Philippe Piot.
Castro Abughayada, Ph.D., April 2015, Air separation and oxygen storage properties of hexagonal rare-earth manganites . Advisor: Dr. Bogdan Dabrowski
Andrew Palm, M.S., April 2015, Study on phase-matched amplification of coherent electromagnetic waves through co-planar traveling wavie structure for broadband power RF generation. Advisor: Dr. Young-Min Shin
Saroj Raj Rai, M.S., April 2015, Image quality measures in proton computed tomography. Advisor: Dr. Bela Erdelyi
Michael E. Miszczak, Ph.D., January 2015, Ginzbury-Landau simulations of confined two-dimensional superconductors. Advisor: Dr. Andreas Glatz
Stephen Cole, Ph.D., December 2014, A Measurement of WZ production in Proton-Proton collisions at s = 7 TeV with ATLAS detector and combination of the ATLAS and CMS s = 7 TeV ZZ anomalous triple gauge coupling measurement. Advisor: Dr. Gerald Blazey
Nuwan Karunaratne, Ph.D., December 2014, Coherent X-ray and laser spectroscopy measurements of diffusion in concentrated alpha-crystalline suspensions. Advisor: Dr. Laurence Lurio.
Mary Shenk, M.S., December 2014, A straw tube tracking detector system for the new muon g-2 E989 experiment . Advisor: Dr. Michael Eads
Joe Paschal, M.S., August 2014. Design, construction and implementation of tension testing for a straw tube tracking system for the E989 muon g-2 experiment. Advisor: Dr. Michael Eads.
Victoriya Zvoda, M.S., August 2014. The construction of a fiber tracking system for a proton computed tomography (pCT) device. Advisor: Dr. George Coutrakon.
Matthew Wiesner, Ph.D., August 2014, Investigations of galaxy clusters using gravitational lensing. advisor" Dr. Huan Lin (Fermilab) and Dr. Michael Fortner. Initial position: research associate, Purdue.
Ben Blomberg, M.S., May 2014, Development of a novel X-ray source from planar electron channeling. advisor: Dr. Philippe Piot.
Christopher Prokop, Ph.D., May 2014, Advanced beamline design for Fermilab's advanced superconducting test accelerator, advisor: Dr. Philippe Piot. Initial position: Allstate.
Michael Latimer, Ph.D., May 2014, Vortex dynamics in superconducting MoGe thin films containing periodic defect arrays, advisor: Dr. Zhili Xiao.
Chad Suhr, Ph.D., May 2014, Single top quark t-channel cross section measurement at ATLAS using a cut-based technique, advisor: Dr. Dhiman Chakraborty.
Lei Feng, Ph.D., May 2014, Measurement of the ZZ ->l+l-l+l- cross section at root(s) = 1.96 TeV with the D0 detector , advisor: Dr. David Hedin.
Ivan Viti, M.S., August 2013, Coherently-enhanced radiation from inverse-Compton scattering with tailored electron bunches, advisor: Dr. Philippe Piot.
Andrew Gearhart, M.S., August 2013, Simulation, hardware characterization, analysis, and assembly of the fiber trackers for the proton computed tomography scanner , advisor: Dr. George Coutrakon.
Diego Menezes, Ph.D., August 2013, Search for the standard model Higgs boson in the four lepton final state by the D0 experiment at Run II of the Tevatron Collider. Advisor: Dr. David Hedin. First position: adjunct professor, Lewis University.
Zachary Hodge, M.S., August 2013, Optimization of the muon stopping target for the Mu2E collaboration, advisor: Dr. David Hedin.
James Maloney , Ph.D., April 2013, Parametric-Resonance Ionization Cooling for Muon Beams in the Twin Helix Channel , advisor: Dr. Bela Erdelyi and Rol Johnson (Muons, Inc). First position: research scientist TRIUMF, Canada.
Aaron Zvonek, M.S., May 2013, physics education.
Hamoud Somaily, M.S. April 2013, Structural and thermoelectric properties of a-site substituted (Sr 1-x-y Ca x Nd y )Ti0 3 Perovskites, advisor: Dr. Omar Chmaissem
Brad Kreydick, M.S. March 2013, A new quality control procedure for detecting defects in proton range modifiers for proton therapy . Advisor: Dr. George Coutrakon
Robert Calkins, Ph.D. December 2012, Measurements of the top quark pair production cross section and branching ratio to a W-boson and bottom quark using the semi-leptonic and dilepton final states with the ATLAS detector at the LHC , advisor: Dr. Dhiman Chakraborty
Qiong Luo, Ph.D. August 2012, Superconducting Nanowire Networks Formed on Nanoporous Membrane Substrates , advisor: Dr. Zhili Xiao.
Seyed A. Sabok-Sayr, M.S. May 2012, Synthesis, P-T phase diagram, and T_c of R2Ba4Cu7O15, advisor Dr. Bogdan Dabrowski.
Curt DeCaro, Ph.D. May 2012, Structure of lipid membranes at solid/liquid and liquid/vapor interfaces , advisor: Dr. Laurence Lurio.
Danairis Hernandez, M.S. May 2012, Charge density estimations for particle beams based on orthogonal polynomial series, advisor: Dr. Bela Erdelyi
Timothy Maxwell, Ph.D. May 2012, Measurement of sub-picosecond electron bunches via electro-optic sampling of coherent transition radiation , advisor:Dr. Philippe Piot
Diego Menezes, M.S. May 2012, Measurement of the cross section for proton-antiproton to top-antitop in the tau plus jets channel by the D0 experiment at Run II of the Tevatron Collider , advisor: Dr. Dhiman Chakraborty.
James Younkin, Ph.D. May 2012, Topics in supersymmetry , advisor: Dr. Stephen Martin.
Mark E. Servantes, M. S. December 2011, A Study of Supported Lipid Multilayers in a Humidity Controlled Environment , advisor: Dr. Laurence Lurio
Jonathan W. Maloney, M.S. December 2011, Solid Helium in Vycor Glass , advisor: Dr. Laurence Lurio
Fayez Abu-Ajamieh, M.S. December 2011, Evaluation of an Integrated Readout Layer Prototype , advisor: Dr. Gerald Blazey
Don Johnson, M.S. August 2011, Structural, Transport, and Magnetic Properties of A-Site Substituted Perovskite Manganites , advisor: Dr. Bogdan Dabrowski
Nelson R. Voldeng, M.S. August 2011, Perforated Superconducting Niobium Nitride Films Formed on Anodic Aluminum Oxide Membranes , advisor: Dr. Zhili Xiao
Edward Nissen, Ph.D. August 2011, Differential Algebraic Methods for Space Charge Modeling and Applications to the University of Maryland Electron Ring , advisor: Dr. Bela Erdelyi
Robert Shea, M.S. August 2011, Studies of Long Scintillation Counters , advisor: Dr. David Hedin
Aaron Morris, M.S. August 2011, Physics Validation Studies for Muon Collider Detector Background Simulations , advisor: Dr. David Hedin
Marwan Rihaoui, Ph.D. August 2011, Phase Space Manipulation in High-Brightness Electron Beams , advisor: Dr. Philippe Piot
Steve Remsen, Ph.D. May 2011, Properties of Transition Metal Oxides for Gas Separation and Oxygen Storage Applications , advisor:Dr. Bogdan Dabrowski.
Matt Wiesner, M.S. December 2010, On the Properties of Ten Strong-Lensing Systems Found in the Sloan Digital Sky Survey , advisors: Dr. Michael Fortner and Dr. Huan Lin.
David Danaher, M.S. December 2010, The growth and analysis of transition metal oxide superlattices using advanced magnetometry techniques , advisor: Dr. Omar Chmaissem.
Martin Braunlich, M.S. December 2010, Upgrades to the D0 Muon System , advisor: Dr. David Hedin.
Josh Ernst, M.S. August 2010, Investigations of beam Property Correlation in a Mixed Field Beam using Collimators of Different Composition , advisor: Dr. Thomas Kroc (Fermilab).
Stephen Boona, M.S. August 2010, Thermoelectric properties of doped transition metal perovskites , advisor: Dr. Bogdan Dabrowski.
Justin Berry, M.S. May 2010, Soft Matter Studies of Phospholipid Membranes , advisor: Dr. Laurence B. Lurio.
Janae DeBartolo, M.S., May 2010, X-ray Photon Correlation Spectroscopy Measurements of Dynamics within Concentrated Eye Lens Protein Suspensions , advisor: Dr. Laurence B. Lurio.
Sevda Avci, Ph.D., May 2010, Superconducting Properties and Vortex Dynamics of Bi2Sr2CaCu2O8 Nanoribbons with and without Periodic Array of Nanoscale Holes , advisor: Dr. Zhili Xiao.
Kurt Francis, Ph.D., May 2010, Results of Beam Tests of a Prototype Calorimeter for a Linear Collider , advisor: Dr. Jerry Blazey. Initial position: Detector physicist, Argonne National Laboratory.
Christopher Hoffmann, M.S. May 2010, An X-ray diffraction investigation of lanthanum(1-x) strontium(x) manganite (x = 0.55) and lanthanum(1-x) barium(x) manganite (x = 0.5, 0.52) under an applied magnetic field , advisor: Dr. Dennis Brown.
Christopher Prokop, M.S. December 2009, Numerical Simulations of a Smith-Purcell Free-Electron Laser Operating in the Backward Wave Oscillator Regime , advisor: Dr. Philippe Piot.
Alex Lee, M.S., August 2009, Simulation of the of light in small scintillator cells , advisor: Dr. David Hedin.
Kent Wong, M.S., August 2009, Evaluation of Protons' Most Likely Paths in Inhomogeneous Phantoms for Proton Computed Tomography , advisor: Dr. Bela Erdelyi.
Laura Bandura, Ph.D., August 2009, Next-Generation Fragment Separators for Exotic Beams , advisor: Dr. Bela Erdelyi. Intital position: Research Associate, National Superconducting Cyclotron Lab, Michigan State University.
Suhong Yu, M.S., August 2009, Fabrication and Properties of Nanoscale Superconducting Loops , advisor: Dr. Zhili Xiao.
Umeshkumar Patel , Ph.D., May 2009, Synthesis, Characterization and Physical Properties of CDW Material NbSe3 and Superconducting NbN Nanostructures , advisor: Dr. Zhili Xiao. Initial position: post-doc NIU-Argonne.
Michael Himes, M.S., December 2008, Thermoelectric Properties of Cobalt Doped Perovskites , advisor: Dr. Bogdan Dabrowski.
Jason Churilla, M.S. December 2008, X-ray Diffraction Studies of Sr2FeMo6 Thin Films , advisor: Dr. Dennis Brown.
Benjamin Sprague, M.S., August 2008, Wavelet-space solution of the Poisson equation: An algorithm for use in particle-in-cell simulations, advisor: Dr. Balsa Terzic.
Curt DeCaro, M.S., August 2008, X-ray Scattering from Biological Membranes, advisor: Dr. Laurcnce B. Lurio.
John Powell, M.S., May 2008, High-Resolution 2D Scanning for Scintillator Characterization, advisor: Dr. Vishnu Zutshi.
Voltaire Teodorescu , Ph.D., May 2008, Spin Polarization of Electrons by Reflection at a Barrier advisor: Dr. Roland Winkler.
Jiong Hua, Ph.D., May 2008, Commensurate Effect in Superconducting Niobium Films Containing Arrays of Nanoscale Holes , advisor: Dr. Zhili Xiao. Initial position: post-doc NIU-Argonne.
Mikhail Arov, Ph.D., May 2008, A measurement of the top quark cross section in the tau channel at D0 , advisor: Dr. Dhiman Chakraborty. Initial position: post-doc Louisiana Tech.
Erich Schoedl, M.S., December 2007, Importance of Detector Baseline Lengths for the Study of Neutrino Oscillations, advisor: Dr. David Hedin.
Tim Maxwell, M.S., December 2007, Diffraction Analysis of Coherent Transition Radiation Interferometry in Electron Linacs, advisor: Dr. Philippe Piot.
Xuegang Xia, M.S., December 2007, Study of radiation effects in the CMS tracker from beam-beam and beam-gas interactions in the LHC, advisor: Dr. Pushpa Bhat (Fermilab).
Kujtim Latifi, M.S., August 2007, A Study of the Effect of the Ferroelectric Phase Transition on the Surface Morphology of PbTiO3 Films Grown by Organo-Metallic Vapor Phase Epitaxy, advisor: Dr. Carol Thmpson.
Ngoc Tran, M.S., August 2007, Pedestal Stability of a New Calorimeter Technology for an ILC Detector, advisor: Dr. Gerald Blazey.
Donna Kubik, M.S., August 2007, Strong Gravitational Lensing Systems Found in the Sloan Digital Sky Survey, advisor: Dr. Huan Lin (Fermilab).
Edward Nissen, M.S., August 2007, Chaos and its Role in Emittance Growth in Fixed Field Alternating Gradient Accelerators, advisor: Dr. Balsa Terzic.
Shafaq Moten, M.S., August 2007, Construction and Initial Chacterization of a Low Energy Photoemission Electron Source for Electron Microscopy, advisor: Dr. Nickolay Vinogradov.
Marwen Rihaoui, M.S., August 2007, Impact of a Photocathode Drive Laser Transverse Density Perturbation on a High Charge Electron Beam Produced in a Photoinjector, advisor: Dr. Philippe Piot.
Daniel Rosenmann, M.S., May 2007, Synthesis and Characterization of the Graphite Intercalation Superconductor CaC6, advisor: Dr. Zhili Xiao.
Rob McIntosh, M.S., May 2007, Evaluation of a Clustering Algorithm for the Electromagnetic and Hadronic Sections of a Calorimeter for the International Linear Collider, advisor: Dr. Gerald Blazey.
Ben Stillwell, M.S., May 2007, Evaluation of Potential Cathode Materials for Reduced-Temperature Solid Oxide Fuel Cells, advisor: Dr. Bogdan Dabrowski.
Zhiyong Shen, M.S., December 2006, The Motion of a Satillite between the Earth and the Moon, advisor: Dr. Michael Fortner.
Manassa Majjiga, M.S., December 2006, Synthesis, Thermal and Resistivity Properties of Cathode Materials for Fuel Cells, advisor: Dr. Bogdan Dabrowski.
James Maloney, M.S., December 2006, Application of Symmetry Theories to Design of Fragment Separators for Exotic Isotope Accelerators, advisor: Dr. Bela Erdelyi.
Sergey Uzunyan, Ph.D., August 2006, A Search for Charge 1/3 Third Generation Leptoquarks in the Muon Channel advisor: Dr. David Hedin. Initial position: postdoc, NIU.
Andriy Zatserklyaniy, Ph.D., August 2006, A Search for Third Generation Leptoquarks, advisor: Dr. David Hedin. Initial position: detector scientist, Fermilab.
Elizabeth Holden, M.S., August 2006, The Accuracy of the Photometric Redshift of Galaxy Clusters, advisors: Dr. Court Bohn and Dr. James Annis (Fermilab).
Andrew Morrison, Ph.D., December 2005, Acoustical Studies of the Steelpan and HANG: Phase-Sensitive Holography and Sound Intensity Measurements, advisor: Dr. Thomas Rossing. initial position: faculty, Illinois Wesleyan University
Junehee Yoo, Ph.D., December 2005, Acoustics of Korean Percussion Instruments: Pyeongyeong and Pyeonjong, advisor: Dr. Thomas Rossing. initial position: faculty, Seoul National University
Xiaofei Song, Ph.D., December 2005, The search for second-generation leptoquarks at Run II D0 in the muon-muon-jet-jet channel in center of mass energy = 1.96 TeV proton-antiproton collisions, advisors: Dr. Pushpa Bhat (Fermilab) and Dr. David Hedin. initial position: postdoc, University of Texas, MD Anderson Cancer Center.
Greg Betzel, M.S., December 2005, Chaos in Time-Dependent Space-Charge Potentials, advisor: Dr. Court Bohn.
Laura Layton, M.S., August 2005, physics education (with astronomy emphasis), advisor: Dr. David Hedin. firstposition, associate editor Astronomy magazine.
LaTanya Malone, M.S., August 2005, physics education, advisor: Dr. Michael Fortner.
Wesley Fabella, M.S., August 2005, Ab-Initio Supercell Calculation of an Isolated Neutral Silicon Vacancy for Investigation of the Properties Relating to Deep Centers, advisor: Dr. Yasuo Ito.
Jarrett Stark, M.S., August 2005, The Glass Transition in Thin Film Supported Polystyrene Films, advisor:Dr. Larry Lurio.
Michael Eads, Ph.D., August 2005, A Search for Charged Massive Stable Particles at D0, advisor: Dr. David Hedin. Initial position: postdoc, Univ. of Nebraska.
Srinivas Totapally, M.S., May 2005, Epitaxial Thin Films of Sr2FeMoO6, advisor: Dr. Dennis Brown.
Dan Bollinger, M.S., May 2005, A Possible Cure of Phase Shift in a Nonlinear Plasma Wakefield Accelerator, advisor:Dr. Court Bohn.
Linda Bagby, M.S., May 2005, Higgs Physics and the Layer Zero Upgrade for D0, advisor: Dr. Gerald Blazey.
LeiLei Yin, Ph.D., December 2004, Excitation of Surface Plasmon Polaritons by Nano-hole and 2-D Active Optics by Nano-hole Arrays, advisor: Dr. Ulrich Welp (Argonne). Initial position: postdoc, Univ. of Illinois.
Usha Gururajarao, M.S., December 2004, X-Ray Magnetic Dichroism, advisor: Dr. Michel van Veenendaal.
Durga Kafle Nath, M.S., December 2004, Mossbauer Study of Eu14MnSb11 and Yb14MnSb11 Compounds, advisor: Dr. Dennis Brown.
Dmitri Beznosko, M.S., August 2004, Study of New Silicon Photodetectors for a Linear Collider Digital Hadron Calorimter, advisor: Dr. Gerald Blazey.
Lyle Marschand, M.S., August 2004, Surface Critical Phenomena in Binary Fluids, advisor: Dr. Lawrence Lurio.
Kurt Francis, M.S., May 2004, Evaluating Small Scintillating Cells for Digital Hadron Calorimeters, advisor: Dr. Gerald Blazey.
Phillip Prior, M.S., May 2004, Investigation of Standard Approximations in Clinical Treatment Planning Systems, advisor: Dr. Arlene Lennox (Fermilab).
Laura Bandura, M.S., August 2003, Convection in Response to Electron Beams, advisor: Dr. Mary Anne Cummings.
James Mais, M.S., August 2003, Synthesis Rules, Structures, and Properties of the Sr(1-x)CaxMnO3 System, advisor: Dr. Bogdan Dabrowski.
Lu, jiaying (spring 2024).
In a world where vast quantities of data are continually generated by humans every day, the majority of the data remains unstructured, posing a significant challenge to knowledge discovery and insight generation. Unleashing the full potential of these valuable information sources requires organizing the data with interconnections and contexts. This dissertation delves into the fundamental task of transforming unstruc- tured real-world data into structured knowledge, all without an excessive reliance on manual annotations. Particularly, I investigate three areas of research, including: (1) Constructing concept maps from unstructured text data. We first develop an inno- vative unsupervised concept map construction method by utilizing syntactic parsing techniques [ 48 ]. Then we further study how to translate the initial parsing-based concept maps into more concise task-oriented concept maps under the guidance of weak supervision signal from downstream tasks [ 50 ]. (2) Aligning and completing taxonomic knowledge graphs (KGs). Given the widely available KGs scattered in different sites, it is urgent to integrate them into a comprehensive knowledge base to harness knowledge-centric applications. We propose a novel perspective to lever- age expert-curated taxonomies as the backbone to aligning various KGs [ 52 ] under a few-shot manner. We further study how to complete taxonomic KGs after initial alignment between them [ 49 ]. (3) Empowering downstream applications with struc- tured knowledge. Finally, we explore how to harness the performance of downstream applications with learned structured knowledge. For instance, we utilize similarity- based communities for multiclass classification [ 51 ]. Together, these works cover the whole life cycle of construction, integration, completion, and utilization of structured knowledge.
Table of Contents
1 Introduction .................................................................................1
2 Learning to Construct Concept Maps ..............................................5
3 Learn to Aligning and Completing Taxonomic Knowledge Graphs ...37
4 Applications of Structured Knowledge ..........................................63
5 Conclusion and Future Work ........................................................75
Bibliography ..................................................................................78
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Methodology
Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.
What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .
There are five key steps to writing a literature review:
A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.
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What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.
When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:
Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.
Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.
You can also check out our templates with literature review examples and sample outlines at the links below.
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Before you begin searching for literature, you need a clearly defined topic .
If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .
Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.
Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:
You can also use boolean operators to help narrow down your search.
Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.
You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.
For each publication, ask yourself:
Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.
You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.
As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.
It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.
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To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:
This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.
There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).
The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.
Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.
If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.
For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.
If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:
A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.
You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.
Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.
The introduction should clearly establish the focus and purpose of the literature review.
Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.
As you write, you can follow these tips:
In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.
When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !
This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.
Scribbr slides are free to use, customize, and distribute for educational purposes.
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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.
Statistics
Research bias
A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .
It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.
There are several reasons to conduct a literature review at the beginning of a research project:
Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.
The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .
A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other academic texts , with an introduction , a main body, and a conclusion .
An annotated bibliography is a list of source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a paper .
If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
McCombes, S. (2023, September 11). How to Write a Literature Review | Guide, Examples, & Templates. Scribbr. Retrieved June 7, 2024, from https://www.scribbr.com/dissertation/literature-review/
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It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .
With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business for distributed digital and AI innovation.
QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.
Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.
Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.
Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.
Let’s deliver on the promise of technology from strategy to scale.
Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.
The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.
To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.
Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.
Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.
The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.
Join our colleagues Jessica Lamb and Gayatri Shenai on April 8, as they discuss how companies can navigate the ever-changing world of gen AI.
By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.
The following are examples of new skills needed for the successful deployment of generative AI tools:
The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).
It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.
While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.
To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.
While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built. They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).
For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.
Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.
Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:
The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture are needed to maximize the future strategic benefits of gen AI:
Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.
One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.
Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.
Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.
While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.
Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.
In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.
The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.
This article was edited by Barr Seitz, an editorial director in the New York office.
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Recent Dissertations. Please note that some authors choose to delay access to their dissertations for a limited period of time. Knowledge@UChicago provides open access to most University of Chicago dissertations completed after Summer 2015. Limit to Format: Dissertation and then by collection, or search for a specific dissertation.
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The collection of Mathematical theses, 1782-1839; The 1895 Ph.D. dissertation of W.E.B. Du Bois, The suppression of the African slave trade in the United States, 1638-1871; ... If you're a Harvard undergraduate writing your own thesis, it can be helpful to review recent prize-winning theses.
Prize-Winning Thesis and Dissertation Examples. Published on September 9, 2022 by Tegan George.Revised on July 18, 2023. It can be difficult to know where to start when writing your thesis or dissertation.One way to come up with some ideas or maybe even combat writer's block is to check out previous work done by other students on a similar thesis or dissertation topic to yours.
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A Guide to Finding Dissertations. Dissertations are book-length works based on a PhD candidate's original research that are written as requirements for the doctoral degree. Theses are similar but shorter texts that are written by students working towards Master's and sometimes Bachelor's degrees. Both dissertations and theses offer researchers ...
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Full text of University of Maryland, College Park, theses and dissertations from December 2003 to the present is available online at DRUM: Digital Repository at the University of Maryland.; In instances where the student has restricted access to his/her electronic thesis or dissertation in DRUM for a specific period, any physical copies are also restricted.
Theses/Dissertations from 2016 PDF. A Statistical Analysis of Hurricanes in the Atlantic Basin and Sinkholes in Florida, Joy Marie D'andrea. PDF. Statistical Analysis of a Risk Factor in Finance and Environmental Models for Belize, Sherlene Enriquez-Savery. PDF. Putnam's Inequality and Analytic Content in the Bergman Space, Matthew Fleeman. PDF
How to search for Harvard dissertations. DASH, Digital Access to Scholarship at Harvard, is the university's central, open-access repository for the scholarly output of faculty and the broader research community at Harvard.Most Ph.D. dissertations submitted from March 2012 forward are available online in DASH.; Check HOLLIS, the Library Catalog, and refine your results by using the Advanced ...
Some pre-2001 theses and dissertations have been digitized and added to this collection, but those are uncommon. The library catalog is the most comprehensive list of UT Austin theses and dissertations. ... Recent progress in sensing, computing, and vehicle-to-vehicle (V2V) communications has led the automotive industry into an information-rich ...
May 2022. Gavriel Cutipa-Zorn: "Veins of Repression: US and Israeli Counterinsurgency in the Americas" Advisor: Matthew Jacobson; Committee Member: Roderick Ferguson, Gary Okihiro. Kristin Hankins: "Littered Landscapes: Trash, Visual Culture, and the Rise of Punitive Environmentalism in Philadelphia" Advisors: Laura Barraclough, Laura ...
Recent PhD Dissertations. 2023-2024. Postdramatic African Theater and Critique of Representation. Oluwakanyinsola Ajayi. Troubling Diaspora: Literature Across the Arabic Atlantic. Phoebe Carter. The Contrafacta of Thomas Watson and Simon Goulart: Resignifying the Polyphonic Song in 16th-century England and France. Joseph Gauvreau.
Dissertations. Most Harvard PhD dissertations from 2012 forward are available online in DASH, Harvard's central open-access repository and are linked below. Many older dissertations can be found on ProQuest Dissertation and Theses Search which many university libraries subscribe to.
2022 Ph.D. Dissertations. Andrew Davison. Statistical Perspectives on Modern Network Embedding Methods. Sponsor: Tian Zheng. Nabarun Deb. Blessing of Dependence and Distribution-Freeness in Statistical Hypothesis Testing. Sponsor: Bodhisattva Sen / Co-Sponsor: Sumit Mukherjee. Elliot Gordon Rodriguez.
Revised on April 16, 2024. A thesis is a type of research paper based on your original research. It is usually submitted as the final step of a master's program or a capstone to a bachelor's degree. Writing a thesis can be a daunting experience. Other than a dissertation, it is one of the longest pieces of writing students typically complete.
PDF. The Destruction of Louisiana Wetlands: An Environmental History, 1900-2000, Gloria H. Adams. PDF. The Evidential Problem of Assurance: Textual Approach from the Johannine Literature, Derick A. Adu. PDF. The Perpetual Progression in the Schleswig-Holstein Duchy: History, Politics, and Religion, 1460-1864, Christian Anthony Ahlers. PDF.
The purpose of this dissertation study was to understand how older adult caregivers manage complex wound care procedures. Aims were to 1) develop a theory for how caregivers manage; 2) identify themes related to resources needed, and 3) determine resources available through the existing Medicaid 1915(c) waivers program.
Recent Theses and Dissertations Timothy Draher, Ph.D., March 2024, Design and Performance of Superconducting Switches for Nanowire Detectors in Magnetic Fields, Advisors: Zhili Xiao Kaela Villafania, M.S., March 2024, Cold Testing of a Prototype Superconducting Radiofrequency Electron Gun and Ancillary Systems for the LCLS-II-HE Project
This dissertation delves into the fundamental task of transforming unstruc- tured real-world data into structured knowledge, all without an excessive reliance on manual annotations. Particularly, I investigate three areas of research, including: (1) Constructing concept maps from unstructured text data.
Thesis & Dissertation; Thesis & Dissertation Overview Thesis and Dissertation: Getting Started; Conducting a Personal IWE; Setting Goals & Staying Motivated Ways to Approach Revision; Genre Analysis & Reverse Outlining; Sentences: Types, Variety, Concision; Paragraph Organization & Flow; Punctuation; University Thesis and Dissertation Templates
When you write a thesis, dissertation, or research paper, you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to: ... You can emphasize the timeliness of the topic ("many recent studies have focused on the problem of x") or highlight a gap in the ...
It's time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI's enormous potential value is harder than expected.. With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI ...