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Title: A Novel Explainability Approach For Spectrum Measurement Insight
Creator: Nagpure, Vaishali
Date: 2023
Description: Spectrum is an extremely valuable natural resource in high demand. Although the spectrum has been fully allocated, there is no comprehensive...
Show moreSpectrum is an extremely valuable natural resource in high demand. Although the spectrum has been fully allocated, there is no comprehensive method for understanding about how it’s being used. Spectrum measurements are highly complex spatiotemporal data sets that play a key role in understanding spectrum use and require very specialized domain information for understanding. To leverage existing and future spectrum measurements to the fullest extent, it is necessary to have a systematic way to connect them to the contextual information that helps provide meaning to the data. To analyze and interpret the measurements, a variety of contextual information is needed. This research develops a novel approach for spectrum measurement understanding that unifies five years of wideband spectrum measurement summary data together with relevant contextual information from a variety of sources in a spectrum knowledge graph. Both quantitative and qualitative information is modeled and implemented on a Neo4j graph database platform. This modeling formalizes the relationships that help spectrum stakeholders “connect the dots” and provide deeper understanding of RF spectrum utilization. The knowledge graph can be queried to extract a wide variety of insights thus making spectrum knowledge more widely accessible to a variety of stakeholders.
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Title: IMPROVING DEEP LEARNING BASED SEMANTIC SEGMENTATION USING CONTEXT INFORMATION
Creator: Xia, Zhengyu
Date: 2021
Description: Semantic segmentation is an important but challenging task in computer vision because it aims to assign each pixel a category label accurately...
Show moreSemantic segmentation is an important but challenging task in computer vision because it aims to assign each pixel a category label accurately. Nowadays, applications such as autonomous driving, path navigation, image search engine, or augmented reality require accurate semantic analysis and efficient segmentation mechanisms. In this thesis, we propose multiple models to improve the performance of semantic segmentation. In the first part, we focus on the single-task network, which aims to improve the performance of semantic segmentation. Our research includes exploiting context information using mixed spatial pyramid pooling to extract dense context-embedded features in FCN-based semantic segmentation. We also propose a GAF module to generate a global context-based attention map to guide the shallow-layer feature maps for better pixel localization. In the second part, we focus on a multi-task network that incorporates semantic segmentation to improve other computer vision tasks such as object detection. Specifically, a multi-task network, along with a learning strategy is designed to let semantic segmentation and object detection assist each other since they are highly correlated. Also, we include weakly-supervised multi-label semantic segmentation learning to deal with the shortage of high-quality training examples and to improve the performance of cross-domain object detection. In the third part, we focus on improving the performance of video panoptic segmentation, which is a unified network that incorporates semantic segmentation and instance segmentation using video streams. We design a new ConvLSTM pyramid to transmit spatio-temporal contextual information in our video panoptic segmentation network. Specifically, we propose a modified ConvLSTM to generate temporal contextual information. Also, we design an MSTPP module to obtain mixed spatio-temporal context-embedded feature maps. Experimental results on different datasets show that our proposed method achieves better performance compared with the state-of-the-art methods.
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Title: EVENT-BASED NONINTRUSIVE LOAD MONITORING
Creator: Yan, Lei
Date: 2021
Description: Non-Intrusive Load Monitoring (NILM) is an important application to monitor household appliance activities and provide related information to...
Show moreNon-Intrusive Load Monitoring (NILM) is an important application to monitor household appliance activities and provide related information to house owner or/and utility company via a single sensor installed at the electrical entry of the house. With this information, utilities can perform many tasks such as energy conservation, planning gen-eration more wisely, and demand response (DR) study. For house owners, they can un-derstand their bill more clearly and make better budget plan. For researchers, NILM sys-tem is a good foundation for energy management in buildings and can provide valuable power information for smart homes design. This dissertation aims to develop and demon-strate a complete and accurate event-based NILM system, which includes (1) an edge-cloud framework for event-based NILM, (2) an adaptive event detection method, (3) a two-stage event-based load disaggregation method; and (4) a high-resolution (50Hz) NILM dataset. Event detection is the first step in event-based NILM and it can provide deter-ministic transient information to identify appliances. However, existing methods with fixed parameters suffer from unpredictable and complicated changes in smart meter data such as long transition, high fluctuation and near-simultaneous events in both power and time domains. This dissertation presents an adaptive method to detect events based on home appliance load data with high sampling rate (>1Hz) by flexibly tuning the parame-ters according to the data being processed. The proposed method runs fast over the data stream and captures the transient process by multi-timescales searching as well. The mi-cro-timescale and macro-timescale window could deal with near-simultaneous events and long-transition events, respectively. Transient load signatures are extracted from detected events and stored in a sequential tree struct that can be used for NILM and load recon-struction, etc. Case studies on a 20Hz dataset, the LIFTED dataset of 50Hz, and the BLUED dataset of 60Hz demonstrate that the proposed method is able to work on data of different sampling rates and outperforms other methods in event detection. The ex-tracted load signatures could also improve the efficiency of NILM and help develop oth-er applications. This dissertation presents an online transient-based electrical appliance state track-ing method for NILM. The proposed Factorial Particle based Hidden Markov Model (FPHMM) method takes advantage of transient features in high-resolution data to infer states in the transient process and conducts steady state verification to rectify falsely identified appliances. The FPHMM method can overcome the common feature similarity problem in NILM by combining particle filter method and Markov Chain Monte Carlo sampling method, and by mining the intra-relationship of states inside a single appliance and the inter-relationship of states among multiple appliances. The FPHMM method is tested on the LIFTED dataset with appliance-level details and high sampling rates. Test-ing results demonstrate that the FPHMM method is effective in resolving the feature similarity issue. A modified mean shift algorithm with different levels of bandwidth is proposed as well to cluster the extracted features from event detection. Based on the clustered fea-tures, another solution is proposed to decode the states of appliance in two stages. The first stage uses Bayesian Inference Factorial HMM (BI-FHMM) solver to accelerate com-putational speed and improve accuracy by integrating the load signatures and statistical inference. The second stage then verifies and rectifies the results obtained from the first stage. Test results demonstrate that the proposed approach achieves good performance and can be applied to existing smart meters.
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Title: THE EFFECTS OF MODIFIED SURFACES ON INSULIN CRYSTALLIZATION
Creator: Hammadi, Okba Tahar
Date: 2021
Description: Engineered nucleation features (ENFs) were designed with the hope to improve the efficiency of protein crystallization and increase...
Show moreEngineered nucleation features (ENFs) were designed with the hope to improve the efficiency of protein crystallization and increase reproducibility both in quality and quantity. These ENFs were tested with human insulin as the protein of choice since it has flexible parameters, only one cofactor, and a large amount of commercially available crystal ready protein. Insulin crystallization on the ENFs will produce more crystals while also having a reduced crystallization on-set time compared to the control glass surface. The ENFs were compared to control surfaces under similar conditions and observed over time to record both onset-times and end times. The ENFs performed markedly better in on-set times, having an overall 87%-time reduction when compared to the control drops. The drops placed on the ENF produced more than 2.5x the number of crystals in the control drops.
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Title: TASK-BASED LOAD FORECASTING AND ROBUST RESOURCE SCHEDULING IN SMART GRID
Creator: Han, Jiayu
Date: 2021
Description: In microgrids, the uncertainty of load and renewables and lack of generation capacity will lead to a wide variety of operation problems in...
Show moreIn microgrids, the uncertainty of load and renewables and lack of generation capacity will lead to a wide variety of operation problems in both grid-connected mode and islanded mode. This motivates the design of the state-of-art microgrid master controller for microgrid energy management, load forecasting, and demand response. Uncertainty in renewables and load is a great challenge for microgrid operation, especially in islanded mode as the microgrid may be small in size and has limited flexible resources. A multi-timescale, two-stage robust dispatch model is proposed to optimize the microgrid operation. The proposed one uses only one model to combine the hourly and sub-hourly dispatch together, which means the day-ahead hourly dispatch results must also satisfy the sub-hourly conditions. At the same time, the feasibility of the day-ahead dispatch result is verified in the worst-case condition considering the high-level uncertainty in renewable energy output and load consumptions. In addition, battery energy storage system (BESS) and solar PV units are integrated as a combined solar-storage system in the proposed model and the output power of the combined solar-storage system remains unchanged on an hourly basis. Furthermore, both BESS and thermal units provide regulating reserve to manage solar and load uncertainty. The model has been tested in a controller hardware in loop (CHIL) environment for the Bronzeville Community Microgrid system in Chicago. The simulation results show that the proposed model works effectively in managing the uncertainty in solar PV and load and can provide a flexible dispatch in both grid-connected and islanded modes.When the generation capacity of an islanded microgrid is less than the load demand, load curtailment is inevitable. This dissertation proposes a multi-objective optimization model to minimize the load curtailments. Specifically, the proposed model minimizes the generation cost and total load curtailments and also minimizes the maximum load curtailment. Furthermore, the impact of the penalty coefficients of total load curtailment and maximum load curtailment is analyzed, which provides a strategy to choose the value of the two penalty coefficients according to different practical purposes. The proposed model can be used in both microgrid generation scheduling and microgrid planning problems. It was tested in the Bronzeville Community Microgrid system and the results showed that the proposed model can reduce the total load curtailment and maximum load curtailment.Load forecasting is one of the most important and studied topics in modern power systems. However, traditional load forecasting is an open-loop process as it does not consider the end use of the forecasted load. This dissertation proposes a closed-loop task-based day-ahead load forecasting model labeled as LfEdNet that combines two individual layers in one model, including a load forecasting layer based on deep neural network (Lf layer) and a day-ahead stochastic economic dispatch (SED) layer (Ed layer). The training of LfEdNet aims to minimize the cost of the day-ahead SED in the Ed layer by updating the parameters of the Lf layer. Sequential quadratic programming (SQP) is used to solve the day-ahead SED in the Ed layer. The test results demonstrate that the forecasted results produced by LfEdNet can lead to lower cost of day-ahead SED at the expense of slight reduction in forecasting accuracy.
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Title: Algorithms for Discrete Data in Statistics and Operations Research
Date: 2021-11-19, 2021-12
Publisher: ProQuest, https://www.proquest.com/docview/2622985712
Description: Sponsorship: The Air Force Office of Scientific Research's grant FA9550-14-1-0141 supported Prof. Petrović's and my initial work on this project.

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: Organo-Functionalized Polyoxometalates
Creator: Alsaleh, Musaed Riyadh A
Date: 2023
Description: Polyoxometalates (POM) or transition metal oxide clusters are a sub-class of metal oxide-based materials of contemporary interest. POMs are...
Show morePolyoxometalates (POM) or transition metal oxide clusters are a sub-class of metal oxide-based materials of contemporary interest. POMs are molecular systems which contain highly symmetrical structures and are characteristics of group 5 and 6 metals, especially V, Mo, and W. Typical POM clusters have nuclearities ranging from 6 to 18 metal centers and are purely inorganic in their compositions. POMs can act as multielectron redox systems, while retaining their robust oxometallic framework structure. POMs have been receiving increasing attention, in part due to their potential as redox active materials for applications in various areas and their suitability as attractive molecular building units for making new functional materials of desirable properties and functions.In recent years, there has been a growing interest in the functionalization of polyoxometalates with organic moieties to synthesize organo-functionalized POMs. During the course of the work described in this thesis, we explored the synthesis of organo-functionalized polyoxometalates with special interest in polyoxovanadates incorporating heterometal center(s) in addition to vanadium in the structure. The focus of the work was on low nuclearity POMs. The thesis describes the synthesis and full characterization of a new organo-functionalized polyoxovanadate cluster - [(n-C4H9)4N]2[V6O13{(OCH2)3C (CH2CH3)}2]. The cluster compound has been characterized by a series of analytical techniques- FT-IR, Thermo Gravimetric Analysis, Bond Valence Sum calculations and complete single X-ray diffraction structure analysis. The hexavanadate cluster features {V6O19} oxometallate core composed of six edge sharing {VO6} octahedra defined by five bridging oxygen atoms and a terminal {V=Ot} oxo group. The {V6O19} oxometallic core of the cluster adopts the Lindqvist structure incorporating two 1,1,1-tris(hydroxymethyl)propane organic ligands. Six of the oxygens in the {V6O19} core come from the three alkoxy groups from two organic ligands. To the best of our knowledge, this organo-functionalized POM cluster has not been reported in the literature previously.
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Title: Child Temperament, Attachment, and Loneliness: The Mediating Effects of Social Competence
Creator: Evans, Lindsey M
Date: 2021
Description: Chronic loneliness is a risk factor associated with adverse psychological, physical, and academic outcomes. Converging evidence suggests that...
Show moreChronic loneliness is a risk factor associated with adverse psychological, physical, and academic outcomes. Converging evidence suggests that young children experience and can reliably report on their own loneliness. Due to the significant negative sequalae associated with childhood loneliness, it is critically important to examine risk factors for child loneliness. The aims of this study were two-fold: (a) to examine if temperament (i.e., negative affect, effortful control, and inhibitory control) and attachment security assessed at 4 years of age predict loneliness at age 6; and (b) to determine if social competence at age 5 mediates the relation between temperament and attachment security at age 4 and loneliness at age 6. Participants included a diverse sample of 796 4-year old children, about half of whom were male. At age 4, temperament was assessed with the Rothbart Child Behavior Questionnaire and three inhibitory control tasks, and attachment security was assessed with the Attachment Q-Sort. At age 5, the Social Skills Rating Scale was used to assess social competence, and, at age 6, loneliness was assessed with the Loneliness and Social Dissatisfaction Questionnaire. Results of hierarchical regression analyses indicated that lower levels of effortful control and inhibitory control at age 4 significantly predicted higher levels of loneliness at age 6. Also, lower levels of negative affect and higher levels of effortful control and attachment security at age 4 significantly predicted higher levels of social competence at age 5. However, social competence at age 5 did not predict loneliness at age 6. There was no evidence that social competence at age 5 mediated the relation between age 4 temperament, attachment security and age 6 loneliness. These findings reveal that early self-regulation is associated with later child-reported loneliness and that intervention for children who struggle with cognitive regulation may be effective in decreasing risk for later loneliness.
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Title: ARE SUPPORTIVE FOSTER CAREGIVERS ASSOCIATED WITH IMPROVED FOSTER CARE ALUMNI OUTCOMES? A LONGITUDINAL EXAMINATION OF THE EFFECT OF SUPPORTIVE FOSTER CAREGIVERS ON MENTAL HEALTH OUTCOMES IN A NATIONALLY REPRESENTATIVE SAMPLE
Creator: Dunn, Megan Reeves
Date: 2021
Description: Foster youth are a vulnerable population associated with poor health outcomes, but relatively little research has identified factors that may...
Show moreFoster youth are a vulnerable population associated with poor health outcomes, but relatively little research has identified factors that may mitigate adverse outcomes for these youth. The present study augments previous research by utilizing a nationally representative, longitudinal study (The National Longitudinal Study of Adolescent to Adult Health or Add Health) to investigate whether foster youth in the United States face significantly different mental and behavioral health outcomes compared with same-age peers, and second, whether presence of a supportive foster caregiver may predict better mental and behavioral health outcomes in the foster youth subsample. Using data from Waves III and IV of the Add Health study (N = 12,288 participants, of which n = 282 were foster youth), analyses examined whether foster status and higher caregiver support was related to rates of depression symptoms, suicidal ideation, marijuana use, and alcohol use. Surprisingly, there were few differences between those with and without a foster history; higher frequency of marijuana use among foster youth was the only significant difference. However, analyses in the foster youth subsample indicated that the presence of a supportive caregiver was associated with lower rates of depression symptoms and lower endorsement of suicidal ideation, demonstrating caregiver support as a possible protective factor for foster youth. Future research must continue to explore potential benefits of caregiver support, as it may inform policy that can improve long-term outcomes for foster youth.
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Title: Development of data assimilation for analysis of ion drifts during geomagnetic storms
Creator: Hu, Jiahui
Date: 2024
Description: The primary objective of this dissertation is to gain insight into geomagnetic storm effects at mid-latitudes induced by solar activity....
Show moreThe primary objective of this dissertation is to gain insight into geomagnetic storm effects at mid-latitudes induced by solar activity. Geomagnetic storms affect our everyday lives because they give rise to transient signal loss, data transmission errors, negatively impacting users of satellite navigation systems. The Nighttime Localized Ionospheric Enhancement (NILE) is a localized plasma enhancement that because it is not well understood, drives the design of satellite-based augmentationsystems. To better secure operation of technological infrastructure, it is essential to build a comprehensive understanding of the atmospheric drivers, especially during solar active periods. Instrument measurements and climate models serve as valuable tools in obtaining information regarding the occurrence of space weather events; nonetheless, both sources exhibit quantitative and qualitative limitations. Data assimilation, an evolving technique, integrates measurements and model information to optimize the state estimations. This dissertation presents developments in a data assimilation algorithm known as Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE), and its applications in investigating the atmospheric behaviors under varying solar conditions. EMPIRE is a data assimilation algorithm specifically designed for upper atmospheric driver estimation of neutral wind and ion drifts at user-defined spatial and temporal scales. The EMPIRE application in this work aims to contribute to a more comprehensive understanding of the effects of the NILE. EMPIRE utilizes the Kalman filter to optimize state calculations primarily based on electron density rates, provided by other data assimilation algorithms. Earlier runs of the algorithm used pre-defined values for the background state covariance cross time. To address model limitations under changing geomagnetic conditions, the algorithm is enhanced by concurrently updating the background state covariance during assimilation processes. Additionally, representation error is incor- porated as a component of the observation error, and error analysis is performed through a synthetic-data study. Previously, EMPIRE fused Fabry-Perot Interferometer (FPI) neutral wind measurements, demonstrating increased agreement with validation neutral wind data. In this work, this approach is extended to augment Coherent Scatter Radar (CSR) ion drift measurements from Super Dual Auroral Radar Network (SuperDARN), providing additional insights into EMPIRE’s estimated field-perpendicular ion motion. For an in-depth exploration of storm-related NILE, both EMPIRE and another data assimilation method, the Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension coupled with Data Assimilation Research Testbed (WACCM-X + DART), is implemented for a storm event to test the proposed NILE driving mechanism. Furthermore, this dissertation introduces a Kalman smoother technique into the EMPIRE to enhance its ability to assess past storm events, and to explore the potential for algorithm improvements.
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Title: Multivariable Real-Time Detection of Acute Psychological Stress and Physical Activity in Enhancing the Efficacy of Artificial Pancreas Systems
Creator: Abdel Latif, Mahmoud Motaz
Date: 2024
Description: The management of Type 1 Diabetes (T1D) requires continuous monitoring and precise control of blood glucose levels, which can be influenced by...
Show moreThe management of Type 1 Diabetes (T1D) requires continuous monitoring and precise control of blood glucose levels, which can be influenced by various physiological factors such as physical activity (PA) and acute psychological stress (APS). This dissertation presents a novel multivariable real-time detection system designed to identify PA and APS, enhancing the efficacy of artificial pancreas (AP) systems. Using data from wearable devices, such as the Empatica E4 wristband, various physiological signals were captured, including blood volume pulse (BVP), accelerometer data (ACC), galvanic skin response (GSR), and skin temperature (ST). These signals were processed to extract features critical for classifying PA and APS. A Long Short-Term Memory (LSTM) neural network model was employed to classify different types of PA and APS events. Additionally, a multitask learning framework was developed to simultaneously estimate energy expenditure (EE) alongside the classification tasks. The study incorporated explainable artificial intelligence techniques, such as SHAP (Shapley Additive Explanations), to interpret the model’s decisions and ensure that physiologically relevant features were used in the classifications. A real-time system was implemented, integrating the detection of PA and APS events into an automated insulin delivery (AID) system. This system was validated through real-time testing with participants, demonstrating its ability to respond dynamically to physiological changes and provide timely insulin adjustments. The models achieved high classification accuracy, demonstrating that the integration of PA and APS detection into AP systems can lead to more precise insulin delivery, thereby improving glycemic control in individuals with T1D.
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Title: Chicago Intermodal Commuter Transit Development: Strailman_Chris_MastersProject_2011
Creator: Strailman, Chris
Date: 4/27/2011, 2011-05
Description: This project focuses on the connectivity of different forms of transit and different neighborhoods in Chicago. Starting at the scale of city...
Show moreThis project focuses on the connectivity of different forms of transit and different neighborhoods in Chicago. Starting at the scale of city wide transit systems, it narrows down to a specific node and deals with the design and planning of a transit oriented development.
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Title: The Odd Couple: FINAL SLIDESHOW
Creator: Bacheller, Tim
Date: 5/4/2011, 2011-05
Description: The socio-political structure of Belgium, a whole containing disparate parts, serves as the catalyst for The Odd Couple: two individual...
Show moreThe socio-political structure of Belgium, a whole containing disparate parts, serves as the catalyst for The Odd Couple: two individual secondary schools in Brussels, one Dutch-speaking, the other French-speaking, sharing the same site. The common element shared among the two schools is the public realm. Rather than shunning the public, the public is encouraged to actively engage in activities within the building. In this way the common spaces throughout the building take on the role of serving the needs and desires of both schools and the public, resulting in the necessity for dialogue and cooperation. By filtering program through a socio-political lens, a “school” typology emerges that emphasizes interaction and extramural learning.
Sponsorship: Conger-Austin, Susan
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Title: Room For Growth
Creator: Mckenzie, Jennifer
Date: 2011, 2011-05
Description: The project is a resource center for formally incarcerated people returning to civil society. Many transitional facilities are overrun with...
Show moreThe project is a resource center for formally incarcerated people returning to civil society. Many transitional facilities are overrun with the great flux of people reentering society every year. Room for Growth project proposes to stabalize this group of people by offering another option of counseling centers, housing and work training facilities in the Washington Park neighborhood of Chicago.
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Title: Room For Growth: Room for Growth_Final Board
Creator: Mckenzie, Jennifer
Date: 2011, 2011-05
Description: The project is a resource center for formally incarcerated people returning to civil society. Many transitional facilities are overrun with...
Show moreThe project is a resource center for formally incarcerated people returning to civil society. Many transitional facilities are overrun with the great flux of people reentering society every year. Room for Growth project proposes to stabalize this group of people by offering another option of counseling centers, housing and work training facilities in the Washington Park neighborhood of Chicago.
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Title: Room For Growth: Room for Growth_Final Presentation
Creator: Mckenzie, Jennifer
Date: 2011, 2011-05
Description: The project is a resource center for formally incarcerated people returning to civil society. Many transitional facilities are overrun with...
Show moreThe project is a resource center for formally incarcerated people returning to civil society. Many transitional facilities are overrun with the great flux of people reentering society every year. Room for Growth project proposes to stabalize this group of people by offering another option of counseling centers, housing and work training facilities in the Washington Park neighborhood of Chicago.
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Title: Room For Growth: Room for Growth_Supplement Appendix
Creator: Mckenzie, Jennifer
Date: 2011, 2011-05
Description: The project is a resource center for formally incarcerated people returning to civil society. Many transitional facilities are overrun with...
Show moreThe project is a resource center for formally incarcerated people returning to civil society. Many transitional facilities are overrun with the great flux of people reentering society every year. Room for Growth project proposes to stabalize this group of people by offering another option of counseling centers, housing and work training facilities in the Washington Park neighborhood of Chicago.
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Title: The Odd Couple
Creator: Bacheller, Tim
Date: 5/4/2011, 2011-05
Description: The socio-political structure of Belgium, a whole containing disparate parts, serves as the catalyst for The Odd Couple: two individual...
Show moreThe socio-political structure of Belgium, a whole containing disparate parts, serves as the catalyst for The Odd Couple: two individual secondary schools in Brussels, one Dutch-speaking, the other French-speaking, sharing the same site. The common element shared among the two schools is the public realm. Rather than shunning the public, the public is encouraged to actively engage in activities within the building. In this way the common spaces throughout the building take on the role of serving the needs and desires of both schools and the public, resulting in the necessity for dialogue and cooperation. By filtering program through a socio-political lens, a “school” typology emerges that emphasizes interaction and extramural learning.
Sponsorship: Conger-Austin, Susan
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Title: Library ReDefinition
Creator: Avery, Brian
Date: 2011, 2011-05
Description: The Alcuin Library was designed in the 1960's by Marcel Breuer when the purpose of a library was to provide storage of, and access to,...
Show moreThe Alcuin Library was designed in the 1960's by Marcel Breuer when the purpose of a library was to provide storage of, and access to, information. This project is an addition and renovation of the existing academic library into a campus center concentrated on learning information.
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