[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127658-en":3,"doc-seo-127658-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127658,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Data-Centric Machine Learning for Human-Centric Applications - doctoral dissertation","Addresses urgent climate change and pandemics through machine learning that can handle shared, data-specific challenges in buildings and healthcare systems. Focuses on three research contributions: conditional synthetic data generation for classes with infrequent data to address imbalance and privacy-preserving sharing; improved preprocessing for tabular data used in smart buildings; and transfer learning-based adversarial domain adaptation to adapt personal thermal comfort models across occupants without target labels, improving scalability and reducing labeling cost.","UC Berkeley  \nUC Berkeley Electronic Theses and Dissertations  \nTitle  \nData-Centric Machine Learning for Human-Centric Applications  \nPermalink  \n[https://escholarship.org/uc/item/4mq363gb](https://escholarship.org/uc/item/4mq363gb)  \nAuthor  \nDas, Hari Prasanna  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nData-Centric Machine Learning for Human-Centric Applications  \nBy  \nHari Prasanna Das  \nA dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy  \nin  \nEngineering-Electrical Engineering and Computer Sciences  \nin the  \nGraduate Division  \nof the  \nUniversity of California, Berkeley  \nCommittee in charge:  \nProfessor Costas J. Spanos, Chair Professor Alberto Sangiovanni-Vincentelli Professor Stefano Schiavon  \nSummer 2023  \nData-Centric Machine Learning for Human-Centric Applications  \nCopyright 2023  \nby  \nHari Prasanna Das  \n1  \nAbstract  \nData-Centric Machine Learning for Human-Centric Applications  \nby  \nHari Prasanna Das  \nDoctor of Philosophy in Engineering-Electrical Engineering and Computer Sciences  \nUniversity of California, Berkeley  \nProfessor Costas J. Spanos, Chair  \nClimate change and pandemics are two of the most pressing threats facing humanity today. Addressing these urgent threats require immediate mitigative actions. In the US, buildings are responsible for 40% of primary energy consumption, 73% of electrical use and 40% of greenhouse gas emissions, the primary cause of global warming, and such high levels are now rapidly spreading across the rest of the world. At the same time, buildings are integral to human lives, as we spend most of our time in them which substantially affects our health and productivity. So, for climate change mitigation, it is essential to optimize energy use in buildings while ensuring human comfort. On the other hand, for pandemics mitigation, it is crucial to diagnose and have a better understanding of the new disease in a time-sensitive manner. Over the years, Machine Learning (ML) as a tool has been widely utilized for both the above efforts. However, both buildings and pandemic-specific healthcare systems exhibit a number of shared data-specific challenges, hindering robust ML implementations.  \nWe will present 3 major research works on tackling them with generative modeling, and transfer learning. The first work will be on conditional synthetic data generation, where the focus is to conditionally generate synthetic data for classes with infrequent data points. The applications include tackling class imbalance in healthcare data, and privacy-preserving data sharing. The second will be on improved pre-processing methods for tabular data (a common data type in smart buildings) to enable seamless use by many ML algorithms. To improve the generalizability and scalability of the models, the third work will be on a transfer learning-based adversarial domain adaptation method, with applications in adapting personal thermal comfort models in buildings from one occupant to another without using any data labels for the target occupant. With this method, the time and the resource-intensive task of acquiring multiple labels for the target environment in a building can be avoided.  \ni  \nDedicated to my parents, Muralidhar Das, and Jayanti Gachhayat  \nii  \nContents  \nContents ii  \nList of Figures v  \nList of Tables viii  \n1 Introduction 1  \n1.1 ML Applications and Data Challenges in Smart Buildings .......... 2  \n1.2 ML Applications and Data Challenges in Pandemic Specific Healthcare ... 7  \n1.3 Research Contributions .............................. 8  \nI Experimental Setup, Baseline Models, Problem Identification 10  \n2 Personal Thermal Comfort Modeling 11  \n2.1 Introduction .................................... 11  \n2.2 Thermal Comfort Experiment .......................... 13  \n2.3 Data Analysis and Problem Identif","cbCaikwQ2eyM5FZp","https://ap.wps.com/l/cbCaikwQ2eyM5FZp","pdf",11337777,2,1,131,"English","en",105,"# Introduction\n## ML Applications and Data Challenges in Smart Buildings\n## ML Applications and Data Challenges in Pandemic Specific Healthcare\n## Research Contributions\n# Personal Thermal Comfort Modeling\n## Thermal Comfort Experiment\n## Data Analysis and Problem Identification\n## Machine Learning based Thermal Preference Prediction\n## Results\n## Towards Neural Network based Modeling\n## Conclusions\n# Energy Game-Theoretic Frameworks and Segmentation Analysis\n## Related Work\n## Methods\n## Graphical Lasso for Energy Social Game\n## Results\n## Conclusion and Future Work\n# Likelihood Contribution based Multi-scale Architecture for Generative Flows\n## Background\n## Likelihood Contribution based Multi-scale Architecture\n## Related Work\n## Experiments\n## Conclusions\n# Conditional Synthetic Data Generation\n## Methodology\n## Experiments\n## Related Work\n## Synthetic Data Generation for Personal Thermal Comfort\n## Discussion\n# Tackling Data and Model Inconsistencies\n## Improved Tabular Data Pre-Processing Methods\n## Related Works\n## Methodology\n## Experiments\n## Conclusion and Future Work","[{\"question\":\"What problem does the dissertation aim to solve for climate change and pandemics?\",\"answer\":\"It targets the need for immediate mitigative actions by optimizing building energy use while maintaining human comfort and by improving time-sensitive diagnosis and understanding of new diseases.\"},{\"question\":\"How does the work handle data-specific challenges shared by buildings and healthcare?\",\"answer\":\"It proposes three research directions using generative modeling and transfer learning to address issues such as infrequent-class data, preprocessing needs for tabular data, and cross-domain inconsistencies.\"},{\"question\":\"What is the transfer learning-based method proposed for thermal comfort modeling?\",\"answer\":\"It uses transfer learning with adversarial domain adaptation to adapt personal thermal comfort models from one occupant to another without using any data labels for the target occupant, reducing labeling cost and resource demands.\"}]","Data-Centric Machine Learning for Human-Centric Applications - doctoral dissertation | PDF",1785940561,330,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"data-centric-machine-learning-for-human-centric-applications-doctoral-dissertation","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/data-centric-machine-learning-for-human-centric-applications-doctoral-dissertation/127658/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the dissertation aim to solve for climate change and pandemics?","Question",{"text":76,"@type":77},"It targets the need for immediate mitigative actions by optimizing building energy use while maintaining human comfort and by improving time-sensitive diagnosis and understanding of new diseases.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the work handle data-specific challenges shared by buildings and healthcare?",{"text":81,"@type":77},"It proposes three research directions using generative modeling and transfer learning to address issues such as infrequent-class data, preprocessing needs for tabular data, and cross-domain inconsistencies.",{"name":83,"@type":74,"acceptedAnswer":84},"What is the transfer learning-based method proposed for thermal comfort modeling?",{"text":85,"@type":77},"It uses transfer learning with adversarial domain adaptation to adapt personal thermal comfort models from one occupant to another without using any data labels for the target occupant, reducing labeling cost and resource demands.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]