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Title: Students cross-country skiing on the Illinois Institute of Technology campus, Chicago, Illinois, 1981
Date: 1981
Description: Photograph of students cross-country skiing in front of Hermann Hall on the Illinois Institute of Technology campus in the winter of 1981....
Show morePhotograph of students cross-country skiing in front of Hermann Hall on the Illinois Institute of Technology campus in the winter of 1981. Photographer unknown.
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Collection: Office of Communications and Marketing photographs, 1905-1999

Title: Advances in Machine Learning: Theory and Applications in Time Series Prediction
Creator: London, Justin J.
Date: 2021
Description: A new time series modeling framework for forecasting, prediction and regime switching for recurrent neural networks (RNNs) using machine...
Show moreA new time series modeling framework for forecasting, prediction and regime switching for recurrent neural networks (RNNs) using machine learning is introduced. In this framework, we replace the perceptron with an econometric modeling unit. This cell/unit is a functionally dedicated to processing the prediction component from the econometric model. These supervised learning methods overcome the parameter estimation and convergence problems of traditional econometric autoregression (AR) models that use MLE and expectation-maximization (EM) methods which are computationally expensive, assume linearity, Gaussian distributed errors, and suffer from the curse of dimensionality. Consequently, due to these estimation problems and lower number of lags that can be estimated, AR models are limited in their ability to capture long memory or dependencies. On the other hand, plain RNNs suffer from the vanishing and gradient problem that also limits their ability to have long-memory. We introduce a new class of RNN models, the $\alpha$-RNN and dynamic $\alpha_{t}$-RNNs that does not suffer from these problems by utilizing an exponential smoothing parameter. We also introduce MS-RNNs, MS-LSTMs, and MS-GRUs., novel models that overcome the limitations of MS-ARs but enable regime (Markov) switching and detection of structural breaks in the data. These models have long memory, can handle non-linear dynamics, do not require data stationarity or assume error distributions. Thus, they make no assumptions about the data generating process and have the ability to better capture temporal dependencies leading to better forecasting and prediction accuracy over traditional econometric models and plain RNNs. Yet, the partial autocorrelation function and econometric tools, such as the the ADF, Ljung-Box, and AIC test statistics, can be used to determine optimal sequence lag lengths to input into these RNN models and to diagnose serial correlation. The new framework has capacity to characterize the non-linear partial autocorrelation of time series and directly capture dynamic effects such as trends and seasonality. The optimal sequence lag order can greatly influence prediction performance on test data. This structure provides more interpretability to ML models since traditional econometric models are embedded into RNNs. The ability to embed econometric models into RNNs will allow firms to improve prediction accuracy compared to traditional econometric or traditional ML models by creating a hybrid utilizing a well understood traditional econometric model and a ML. In theory the traditional econometric model should focus on the portion of the estimation error that is best managed by a traditional model and the ML should focus the non-linear portion of the model. This combined structure is a step towards explainable AI and lays the framework for econometric AI.
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Title: DATA-DRIVEN OPTIMIZATION OF NEXT GENERATION HIGH-DENSITY WIRELESS NETWORKS
Creator: Khairy, Sami
Date: 2021
Description: The Internet of Things (IoT) paradigm is poised to advance all aspects of modern society by enabling ubiquitous communications and...
Show moreThe Internet of Things (IoT) paradigm is poised to advance all aspects of modern society by enabling ubiquitous communications and computations. In the IoT era, an enormous number of devices will be connected wirelessly to the internet in order to enable advanced data-centric applications. The projected growth in the number of connected wireless devices poses new challenges to the design and optimization of future wireless networks. For a wireless network to support a massive number of devices, advanced physical layer and channel access techniques should be designed, and high-dimensional decision variables should be optimized to manage network resources. However, the increased network scale, complexity, and heterogeneity, render the network unamenable to traditional closed-form mathematical analysis and optimization, which makes future high-density wireless networks seem unmanageable. In this thesis, we study the design and data-driven optimization of future high-density wireless networks operating over the unlicensed band, including Radio Frequency (RF)-powered wireless networks, solar-powered Unmanned Aerial Vehicle (UAV)-based wireless networks, and random Non-Orthogonal Multiple Access (NOMA) wireless networks. For each networking scenario, we first analyze network dynamics and identify performance trade-offs. Next, we design adaptive network controllers in the form of high-dimensional multi-objective optimization problems which exploit the heterogeneity in users' wireless propagation channels and energy harvesting to maximize the network capacity, manage battery energy resources, and achieve good user capacity fairness. To solve the high-dimensional optimization problems and learn the optimal network control policy, we propose novel, cross-layer, scalable, model-based and model-free data-driven network optimization and resource management algorithms that integrate domain-specific analyses with advanced machine learning techniques from deep learning, reinforcement learning, and uncertainty quantification. Furthermore, convergence of the proposed algorithms to the optimal solution is theoretically analyzed using mathematical results from metric spaces, convex optimization, and game theory. Finally, extensive simulations have been conducted to demonstrate the efficacy and superiority of our network optimization and resource management techniques compared with existing methods. Our research contributions provide practical insights for the design and data-driven optimization of next generation high-density wireless networks.
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Title: Towards Assisting Human-Human Conversations
Creator: Nanaware, Tejas Suryakant
Date: 2021
Description: The idea of the research is to understand the open-topic conversations and ways to provide assistance to humans who face difficulties in...
Show moreThe idea of the research is to understand the open-topic conversations and ways to provide assistance to humans who face difficulties in initiating conversations and overcome social anxiety so as to be able to talk and have successful conversations. By providing humans with assistive conversational support, we can augment the conversation that can be carried out. The AdvisorBot can also help to reduce the time taken to type and convey the message if the AdvisorBot is context aware and capable of providing good responses.There has been a significant research for creating conversational chatbots in open-domain conversations that have claimed to have passed the Turing Test and can converse with humans while not seeming like a bot. However, if these chatbots can converse like humans, can they provide actual assistance in human conversations? This research study observes and improves the advanced open-domain conversational chatbots that are put in practice for providing conversational assistance.While performing this thesis research, the chatbots were deployed to provide conversational assistance and a human study was performed to identify and improve the ways to tackle social anxiety by connecting strangers to perform conversations that would be aided by AdvisorBot. Through the questionnaires that the research subjects filled during their participation, and by performing linguistic analysis, the quality of the AdvisorBot can be improved so that humans can achieve better conversational skills and are able to clearly convey their message while conversing. The results were further enhanced by using transfer learning techniques and quickly improve the quality of the AdvisorBot.
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Title: Efficient Power System Transient Simulation for Stability Studies Based on Frequency Response Optimized Approximation
Creator: Lei, Sheng
Date: 2021
Description: Power systems world-wide are going through a paradigm change with dramatically increasing power electronics integration and more emphasis on...
Show morePower systems world-wide are going through a paradigm change with dramatically increasing power electronics integration and more emphasis on the intrinsically unbalanced distribution side. The new features of power systems violate the fundamental assumptions and challenge the feasibility of transient stability simulation, a traditional tool for stability studies. Electromagnetic transient simulation is applicable to power systems with the new features, but its computational efficiency is too low with the typical microsecond-level step sizes.This dissertation aims at enabling millisecond-level step sizes, typically used in traditional transient stability simulation, in efficient electromagnetic transient simulation for system-level stability studies on unbalanced power systems, while assuring satisfactory accuracy. The approach taken is to introduce novel highly accurate numerical methods into electromagnetic transient simulation.Several implicit one-step frequency response optimized integrators considering second order derivative are proposed. Some existing numerical integrators in the literature of this category are reviewed. Their numerical properties are studied. Some of these numerical integrators are especially suitable to be used as numerical differentiators.A novel power system transient simulation scheme is put forward using the implicit one-step frequency response optimized integrators as the main numerical integrators and differentiators. Large step sizes are applicable with the proposed simulation scheme to achieve efficient electromagnetic transient simulation without sacrificing accuracy. Execution of the proposed simulation scheme is detailed.Several explicit and implicit multistep frequency response optimized integrators considering first or second order derivative are proposed. Some existing numerical integrators of these types are reviewed from the error frequency response viewpoint. Based on these numerical integrators, a prediction method is put forward to further accelerate the proposed simulation scheme without impacting its accuracy.Initialization process of the proposed simulation scheme is put forward. The initialization process calculates the periodic steady state solution of unbalanced power systems considering power flow conditions. The requirements of power system stability studies on the initial conditions for transient simulation runs are thus satisfied. Effectiveness and efficiency of the initialization process are demonstrated.Computational models of power system network elements in the proposed simulation scheme are detailed. The extended nodal analysis is put forward for the proposed simulation scheme to organize the computational models of most network elements in an efficient and elegant manner.Some power system devices are implemented with the proposed simulation scheme, including single-phase grid-feeding converter system, three-phase grid-feeding converter system, three-phase synchronous machine and three-phase induction machine. The proposed simulation scheme is shown to simultaneously achieve efficiency and accuracy as applied to these devices.The proposed simulation scheme is applied to different types of power systems, including transmission system, distribution system and combined transmission and distribution system. Its versatility is revealed. Its efficiency and accuracy are demonstrated with numerical case studies as applied to these systems.
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Title: DATA-DRIVEN FIRST-PRINCIPLES STUDY OF ORDERING PHENOMENA IN COMPLEX ALLOYS
Creator: Kim, George
Date: 2021
Description: Determining the chemical (dis)ordering behavior in materials such as high entropy alloys (HEAs), and ternary Laves phases is fundamental to...
Show moreDetermining the chemical (dis)ordering behavior in materials such as high entropy alloys (HEAs), and ternary Laves phases is fundamental to developing structure-property relations that can be used as guiding principles for alloy design. A common obstacle in materials engineering is that an improvement of a material property comes at the expense of some other desirable properties. For example, trade-offs may be made between strength and ductility, or strength and density, etc. The large compositional and configuration space of possible HEAs, and Laves phases contain potential candidate materials with a balance of optimized properties and tunable structural and functional properties. However, fully exploring the large compositional and configurational space with experimental or even high-throughput Density Functional Theory (DFT) approaches is infeasible, and as of yet, predictive rules for phase stability and chemical (dis)order in HEAs, and Laves phases are still open questions.In this thesis, a HEA with chemical disorder, Al0.3CoCrFeNi, was studied using complementary experimental, DFT, and ML methods. The chemical disorder within the HEA resulted in a severely distorted lattice leading to a reduction in stiffness. Temperature dependence of chemical ordering behavior is studied in NbTaTiV and NbTaTiVZr HEAs using Monte Carlo (MC) simulations, which predicts short-range ordering (SRO) as well as short-range clustering (SRC) behavior in both HEAs. The compositional dependent behavior of substitutional ordering in two ternary Laves phases is evaluated and compared using cluster expansion (CE) models and Monte Carlo (MC) simulations.
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Title: ENERGY INNOVATIONS IN BUILDINGS AND URBAN FABRICS
Creator: Hirematt, Chandrasekharaiah Ashish
Date: 2021
Description: In his keynote speech on the "Infrastructures of Integration" at the 5th International LafargeHolcim Forum for Sustainable Construction, Ricky...
Show moreIn his keynote speech on the "Infrastructures of Integration" at the 5th International LafargeHolcim Forum for Sustainable Construction, Ricky Burdett, Professor of Urban Studies at the London School of Economics & Political Science (LSE), said “…you can actually invest in better infrastructure to do things better.” However, the population grows at the rate of almost one billion per decade. With about four fifths of it happening in urban areas, the challenge for sustainability is huge and the key for the future.Urban fabrics are expanding both vertically as well as horizontally to accommodate the population growth. With the scale of expansion happening, challenges such deforestation, resource depletion, habitat destruction, energy production and consumption are some of the major challenges that need to be focused on ecologically. It is also important to note that ecological solutions are very highly dependent on social and economic progress of the society. Energy efficient design is one which does zero or minimal damage to the environment while meeting the energy needs of the society. This thesis will discuss the concept of developing energy efficient designs as well as net zero designs in urban settings. With the help of three projects, this thesis aims to discover the challenges along with the obvious advantages of such designs. The first experiment is to look at the reduction of energy consumption in the city of Chicago with multiple neighborhoods set up in an iron grid. It was observed that taller buildings are much more energy efficient due to the reduction of surface area exposed to the external environment. This observation was used to develop a climate specific energy efficient urban fabric design in the city of Shenzhen. The design of the off-shore tower involves tackling larger issues such as the pandemic while having energy production as a bi-product of the same. Thus, the thesis argues that investment in infrastructure to build a better infrastructure should be done to solve social and economic challenges which will, in turn make it easier to produce energy efficient designs.
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Title: Two essays on corporate finance and risk management
Creator: LI, YANFENG
Date: 2021
Description: This dissertation consists of two essays. The first essay examines the interaction effect of human capital investment in firms with dual-class...
Show moreThis dissertation consists of two essays. The first essay examines the interaction effect of human capital investment in firms with dual-class shares (DCS) structure. In this study, I find that although more input in human capital, measured by employee welfare index (EWI), can enhance the valuation of single-class (SCS) firms, human capital investment in DCS firms is not valued by the market, but even hurts firm value. This result is consistent with the prediction of agency theory. The management entrenchment effect in DCS firms causes valuation discount when managers can transfer private benefit through investing in humans. To get a robust result, I use propensity score matched data of SCS and DCS firms and get the same conclusion. Overall, my paper provides the evidence that human capital investment plays a different role in firm value under different circumstances, especially under different ownership structures.The second essay examines the relationship between director network centrality and firm credit risk. By using a comprehensive data including both rated firms and unrated firms, I discover that director network is positively associated with firm’s probability of default. This positive effect is more robust in firms without agency credit ratings. I further examine that when firm’s cash flow increases, firm’s default risk increases with director network. But when investment increases and firm’s debt finance increases, the default risk decreases with director network. These combined results imply that director network leads to more agency problems when firms have plenty of cash flow but benefit firms when the cash flow goes into investment or when directors utilize their network to get more debt finance for firms. Also, I find that director network helps loss firms other than profitable firms and decreases firm default risk during the financial crisis.
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Title: TIMING STRATEGY OF COMMODITY MANAGERS
Creator: Lara Prado, Camila Cristina
Date: 2021
Description: The purpose of this research is to study whether commodity managers have the ability to time factor exposures. I utilize the methodology...
Show moreThe purpose of this research is to study whether commodity managers have the ability to time factor exposures. I utilize the methodology developed by Treynor and Mazuy (1966), and Henriksson and Merton (1981), and apply the four-factor commodity model of Blocher et al (2018). Specifically, I measure market timing, momentum timing, the high term (realized term premia for the commodities with above‐median basis), and low term (realized term premia for the commodities with below‐median basis) skills. These factors are chosen because each one, separately, captures a risk premium embedded in commodity futures.My results indicate that commodity managers’ returns have some statistically significant market timing abilities. This means that many managers increase exposure to the nearest contract when the spot premium return is high and decrease exposure when the spot premium return is low. Momentum timing, high term timing, and low term timing are not observed. When looking at different strategies, technical managers demonstrate stronger market timing ability than fundamental managers.
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Title: EXAMINING THE ROLES OF PUBLIC STIGMA AND ACCULTURATION ON CARE-SEEKING IN PAKISTANIS
Creator: Laique, Aamir
Date: 2021
Description: Pakistani Americans face bi-directional cultural influences related to their heritage culture and the mainstream culture of the host. The...
Show morePakistani Americans face bi-directional cultural influences related to their heritage culture and the mainstream culture of the host. The present study examined the impact of culture on the relationship between public stigma and care-seeking attitudes. A sample of 158 Pakistani Americans was collected using MTurk. Hierarchical regression was conducted to examine the moderating effect of heritage acculturation and mainstream acculturation on the relationship between public stigma and care-seeking. Multiple regression analysis predicting care-seeking from public stigma, heritage acculturation, and mainstream acculturation did not yield a statistically significant model. Hierarchical regression analyses examining the moderating effect of heritage acculturation and mainstream acculturation were non-significant. Acculturation had no notable impact on stigma and care-seeking. This study was unable to demonstrate significant results. Future considerations should include inter-generational differences, other forms of stigma that may play a crucial role, and inclusion of different measures to determine if there are other scales better suited for the target population.
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Title: TechNews, November 20, 2012
Creator: Illinois Institute of Technology
Date: 2012-11-20, 2012-11-20
Collection: Technology News print collection, 1940-2019

Title: TechNews, October 16, 2012
Creator: Illinois Institute of Technology
Date: 2012-10-16, 2012-10-16
Collection: Technology News print collection, 1940-2019

Title: TechNews, October 30, 2012
Creator: Illinois Institute of Technology
Date: 2012-10-30, 2012-10-30
Collection: Technology News print collection, 1940-2019

Title: TechNews, August 28, 2012
Creator: Illinois Institute of Technology
Date: 2012-08-28, 2012-08-28
Collection: Technology News print collection, 1940-2019

Title: TechNews, July 01, 2012
Creator: Illinois Institute of Technology
Date: 2012-07-01, 2012-07-01
Collection: Technology News print collection, 1940-2019

Title: TechNews, September 18, 2012
Creator: Illinois Institute of Technology
Date: 2012-09-18, 2012-09-18
Collection: Technology News print collection, 1940-2019

Title: TechNews, October 02, 2012
Creator: Illinois Institute of Technology
Date: 2012-10-02, 2012-10-02
Collection: Technology News print collection, 1940-2019

Title: TechNews, October 23, 2012
Creator: Illinois Institute of Technology
Date: 2012-10-23, 2012-10-23
Collection: Technology News print collection, 1940-2019

Title: TechNews, September 25, 2012
Creator: Illinois Institute of Technology
Date: 2012-09-25, 2012-09-25
Collection: Technology News print collection, 1940-2019

Title: TechNews, November 06, 2012
Creator: Illinois Institute of Technology
Date: 2012-11-06, 2012-11-06
Collection: Technology News print collection, 1940-2019

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