Wooyoung Jung

Interests

Teaching

In the following area, Jung aims to deliver fundamental & transdisciplinary knowledge along with his industry and academic experiences.- Building Physics, Sustainable Energy, Indoor Environmental Quality & Human Health, and Intelligent Building Systems (e.g., HVAC).

Research

Jung’s overarching research goal is to realize smart, healthy, and sustainable buildings and communities by benefiting from emerging technologies, such as Deep-/Machine-learning, Artificial Intelligence, and/or Internet-Of-Things. The following topic areas often capture his interests:- Human Building Interaction; Cyber-Physical Systems; Grid-interactive Effective Buildings; Integrated infrastructure Systems; Smart & Connected Building and Communities

Courses

Scholarly Contributions

Books

• Engineer, A., Ida, A., Jung, W., & Sternberg, E. (2024). Measuring the impact of the built environment on health, wellbeing, and performance: Techniques, methods, and implications for design research. Taylor and Francis. doi:10.4324/9780367814748
  More info

  This book reveals how subjective and objective data gathered by innovative methods of measurement give us the ability to quantify stress, health, performance, and wellbeing outcomes in different built environments. Design interventions informed by these measures, along with innovative integrated building materials, can shape the character of built environments for better health, productivity, and performance. These measures can help employers and managers calculate the return on investment (ROI) of various design interventions. Areas of inquiry in health and the built environment are discussed in three parts: Part 1 - Fundamentals: Human, Environment, and Material Measures for Health and Wellbeing; Part 2 - Methods: Measurement Techniques, Tools, and Methods for Health and Wellbeing; and Part 3 - Applications: Case Studies and Future Directions. The rapid pace of technical innovation and entrepreneurship by interdisciplinary research teams in health and the built environment has created a need for more publications such as this book, which discuss latest tools and methods of measuring the effects of the built environment on human physiology and psychology. Emerging tools and techniques are introduced for this field of built environment design, including virtual reality immersive environments and fisheye lens photograph simulations for human wellbeing impact measures integral to the design process. The potentials and limitations of bio-responsive material systems and integrated sensing devices with wearable technologies linked to the Internet of Things are discussed in relation to human wellbeing performance improvements. The book provides both the foundational knowledge and fundamentals for characterizing human health and wellbeing in the built environment as well as emerging trends and design research methods for innovations in this field. It will be of interest to researchers, educators, and students of architecture, interior design, and integrative medicine, as well as professionals working in health and the built environment.
• Engineer, A., Ida, A., Jung, W., & Sternberg, E. M. (2024). Measuring the Impact of the Built Environment on Health, Wellbeing, and Performance: Techniques, Methods, and Implications for Design Research. Taylor & Francis.

Journals/Publications

• Al-Assaad, D., Pigliautile, I., Shinoda, J., Rawal, R., Andr??, M., Vashi, S., Rugani, R., Torriani, G., Pasut, W., & Gupta, A. (2025). Personalized environmental control systems (PECS): A systematic review of performance evaluation methods for thermal comfort, air quality and energy. Building and Environment, 113471.
• Babon-Ayeng, P., & Jung, W. (2025). Personalized indoor heat stress response modeling and assessment for improved building thermal resilience. Energy and Buildings, 115965.
• Babon-Ayeng, P., & Jung, W. (2025). Personalized indoor heat stress response modeling and assessment for improved building thermal resilience. Energy and Buildings, 345(Issue). doi:10.1016/j.enbuild.2025.115965
  More info

  Climate change is increasing the frequency and severity of extreme weather events, particularly heatwaves, which pose significant challenges to the building sector. These events drive up energy demands, compromise building performance, and threaten occupant health and comfort, with disproportionate impacts on underserved communities. Existing heat strain metrics, such as the widely used Predicted Heat Strain (PHS) model, rely on population-averaged data and do not take individual variability into account, limiting their effectiveness in protecting vulnerable populations. This study investigated the potential of modeling individual heat stress responses to extreme indoor environments through wearable sensor technology. We conducted an experimental study involving four steps: (1) human subject experiments for data collection, (2) validation of the PHS model using the collected data, (3) statistical analysis to grasp inter-individual variations in their perception and physiological responses to heat stress, and (4) development and performance evaluation of personal heat stress response models using four machine-learning (ML) algorithms (random forest, support vector machine, k-nearest neighbors, and artificial neural network). Thirty healthy adults (16 males, 14 females) were exposed to a gradually increasing temperature ranging from 24 °C to 40 °C, rising by 1.33 °C every 10 min over a two-hour period. From this experimental setup, we collected demographic data (age, gender, weight, height), continuous physiological measurements (skin temperature, skin conductance level, pulse rate), and subjective response data (thermal sensation, thermal preference, perceived heat intensity). Skin temperature predictions from the PHS model consistently underestimated actual measurements, with root mean square error values ranging from 2.06 °C to 4.34 °C, potentially missing critical heat strain intervention thresholds, especially for vulnerable and sensitive populations. Our statistical analysis revealed significant inter-individual variations in physiological responses (p < 0.05). In contrast, ML-based personalized models demonstrated effective predictive performance for perceived heat intensity, extended thermal sensation and preference scales, showing statistically comparable capabilities (p > 0.05) across all evaluated metrics. Our methodology incorporated dataset balancing, normalization, and hyperparameter optimization, contributing to robust and reliable model outcomes. These findings lay the groundwork for personalized heat stress response modeling, paving the way for adaptive thermal management strategies that enhance occupant safety and comfort, particularly for vulnerable and underserved communities during extreme heat events in buildings.
• Keene, K., McCord, K., Dehwah, A. H., & Jung, W. (2025).

  Meta-Analysis and Regression Modeling of the Impacts of Four Indoor Environmental Quality Metrics on Office Performance

  . Indoor Air, 2025(1), 6840369.
• Khovalyg, D., Bivolarova, M. P., Shinoda, J., Al-Assaad, D., Vellei, M., Bandurski, K., Chinazzo, G., Kazanci, O. B., Kim, J., & Kramer, T. (2025). Personalized Environmental Control Systems (PECS): Systematic review of benefits for thermal comfort, air quality, health, and human performance. Building and Environment, 113541.
• Khovalyg, D., Bivolarova, M. P., Shinoda, J., Al-Assaad, D., Vellei, M., Bandurski, K., Chinazzo, G., Kazanci, O. B., Kim, J., Kramer, T., Lipczynska, A., Liu, S., Pasut, W., Rawal, R., Sekhar, C., Sun, R., Wu, Z., Afshari, A., Martinez-Alcaraz, P., , André, M., et al. (2025). Personalized Environmental Control Systems (PECS): Systematic review of benefits for thermal comfort, air quality, health, and human performance. Building and Environment, 286(Issue). doi:10.1016/j.buildenv.2025.113541
  More info

  Advances in environmental technologies have improved indoor environmental quality (IEQ) by creating steady, uniform conditions. However, these often fail to meet individual thermal comfort and air quality needs, prompting a shift toward adaptive, personalized solutions. Personalized Environmental Control Systems (PECS) aim to enhance comfort, air quality, health, and productivity through user-centered designs. This paper systematically reviews 324 journal articles on PECS from 1988-2023, focusing on thermal and indoor air quality (IAQ) domains. PECS are classified by mobility: building-attached, semi-attached, detached, and wearable. The review assesses their impact on thermal comfort, IAQ, health outcomes (e.g., Sick Building Syndrome, heat stress), and human performance (e.g., cognitive function, productivity). Results show that building-detached PECS often improve thermal sensation, comfort, and acceptability, with combined systems yielding better ratings. Personalized ventilation enhances IAQ by delivering clean air directly to the breathing zone, reducing contaminant exposure. Research on PECS effects on health is limited, mainly focusing on short-term, controlled studies. Evidence for benefits on human performance is sparse but promising. Key challenges include inconsistent performance metrics, limited real-world evaluations, and potential publication bias toward positive results. This review highlights the need for standardized evaluation methods, deeper understanding of combined PECS effects, real-world and long-term testing, and clearer quantification of human performance benefits to advance the field.
• Woo, D. O., Jung, W., Menna, J., & Al-Hamando, M. (2025). Streamlining occupant-centric HVAC operations through multi-modal infrared array sensing technology. Energy and Buildings, 346(Issue). doi:10.1016/j.enbuild.2025.116106
  More info

  This study systematically evaluates a novel occupant-centric heating, ventilation, and air-conditioning (HVAC) control strategy that integrates multi-modal infrared (IR)-based sensing technology. The proposed system dynamically adjusts HVAC setpoints based on real-time occupant information, including presence, count and operative temperature. Unlike previous studies, which explored IR array sensors primarily for recognizing occupants in small-sized private spaces, this study integrates IR sensing into occupant-centric controls for systematic assessment. To accurately and promptly recognize occupant-related parameters, the proposed system incorporated multiple heat transfer mechanisms and advanced counting-based control strategies aimed at heating energy use without compromising adaptive comfort. A simulation model was developed to replicate an open office space (100 m2) in Michigan, validated with field measurements, and assessed using 2023 local weather data. Results showed that the multi-modal sensing technology achieved 95 % accuracy in detecting occupant presence and effectively calculated operative temperatures from background thermal data. The proposed OCC yielded a 33.7 % reduction in heating energy consumption, with a payback period of 8.1 years when using a 110° vision angle IR array sensor. However, while the multi-modal OCC outperformed baseline and presence-based two-position control strategies in energy savings, it exhibited the most pronounced negative impact on thermal comfort, with a 13.6 % adaptive comfort penalized percentage during the heating season. This finding highlights the inherent trade-off between energy efficiency and occupant comfort. The contribution of this study is the development and validation of a comprehensive control framework that leverages multi-modal sensing to enhance the intelligence, adaptability, and energy performance of occupant-centric HVAC systems.
• Woo, D., Jung, W., Menna, J., & Al-Hamando, M. (2025). Streamlining occupant-centric HVAC operations through multi-modal infrared array sensing technology. Energy and Buildings, 116106.
• Ye, Y., Faulkner, C., Jung, W., Zhang, J., & Brock, E. (2024). A new database of building-space-specific internal loads and load schedules for performance based code compliance modeling of commercial buildings. Building Simulation, 17(6). doi:10.1007/s12273-024-1111-z
  More info

  Building-level loads and load schedules prescribed by current modeling rules save modelers time and provide standards during whole building performance modeling. However, recent studies show that they sometimes insufficiently capture the entire building performance due to the varied loads and load schedules for different space types. As a solution to this issue, this paper presents a database of default building-space-specific loads and load schedules for use in energy modeling, and in particular code compliance modeling for commercial buildings. The existing sets of default loads and load schedules are reviewed and the challenges behind using them for specific research topics are discussed. Then, the proposed method to develop the building-space-specific loads and load schedules is introduced. After that, the database for these building-space-specific loads and load schedules is presented. In addition, one case is studied to demonstrate the applications of these loads and load schedules. In this case study, three methods are used to develop building energy models: space-specific (using knowledge of the distribution and location of space types and applying the space-specific data in the developed database), building-level (assuming a lack of knowledge of the space types and using the building-level data in the developed database), and calculated-ratio (assuming knowledge of the distribution of space types but not their locations and calculating weighted average values based on the space-specific data in the developed database). The energy results simulated by using these three methods are compared, which shows building-level methods can produce significantly different absolute energy and energy savings results than the results using space-specific methods. Finally, this paper discusses the application scope and maintenance of this new database.
• Jung, W., & Jazizadeh, F. (2023). Towards intelligent workstations: Investigating the feasibility of Doppler radar sensors for personal respiratory quantification in thermal comfort. Building and Environment, 245, 110846.
• Jung, W., Wang, Z., Hong, T., & Jazizadeh, F. (2023). Smart thermostat data-driven U.S. residential occupancy schedules and development of a U.S. residential occupancy schedule simulator. Building and Environment, 243, 110628.
• Keene, K., & Jung, W. (2021). A Statistical Evaluation of Combining Human Productivity Metrics in the Indoor Environment. Journal of Engineering for Sustainable Buildings and Cities, 2(4). doi:10.1115/1.4052872
  More info

  The potential of improving human productivity by providing healthy indoor environments has been a consistent interest in the building field for decades. This research field’s longstanding challenge is to measure human productivity given the complex nature of office work. Previous studies have diversified productivity metrics, allowing greater flexibility in collecting human data; however, this diversity complicates the ability to combine productivity metrics from disparate studies within a meta-analysis. This study aims to categorize existing productivity metrics and statistically assess which categories show similar behavior when used to measure the impacts of indoor environmental quality (IEQ). The 106 productivity metrics compiled were grouped into six categories: neurobehavioral speed, accuracy, neurobehavioral response time, call handling time, self-reported productivity, and performance score. Then, this study set neurobehavioral speed as the baseline category given its fitness to the efficiency-based definition of productivity (i.e., output versus input) and conducted three statistical analyses with the other categories to evaluate their similarity. The results showed the categories of neurobehavioral response time, self-reported productivity, and call handling time had statistical similarity with neurobehavioral speed. This study contributes to creating a constructive research environment for future meta-analyses to understand which human productivity metrics can be combined with each other.
• Jung, W., & Jazizadeh, F. (2020). Energy saving potentials of integrating personal thermal comfort models for control of building systems: Comprehensive quantification through combinatorial consideration of influential parameters. Applied Energy, 268(Issue). doi:10.1016/j.apenergy.2020.114882
  More info

  Research studies provided evidence on the energy efficiency of integrating personal thermal comfort profiles into the control loop of Heating, Ventilation, and Air-Conditioning (HVAC) systems (i.e., comfort-driven control). However, some conflicting cases with increased energy consumption were also reported. Addressing the limited and focused nature of those demonstrations, in this study, we have presented a comprehensive assessment of the energy efficiency implications of comfort-driven control to (i) understand the impact of a wide range of contextual factors and their combinatorial effect and (ii) identify the operational conditions that benefit from personal comfort integration. In doing so, we have proposed an agent-based modeling framework, coupled with EnergyPlus simulations. We considered five potentially influential parameters and their combinatorial arrangements including occupants’ thermal comfort characteristics, diverse multi-occupancy scenarios, number of occupants in thermal zones, control strategies, and climate. We identified the most influencing factor to be the variations across occupants’ thermal comfort characteristics - reflected in probabilistic models of personal thermal comfort - followed by the number of occupants that share a thermal zone, and the control strategy in driving the collective setpoint in a zone. In thermal zones, shared by fewer than six occupants, we observed potentials for average energy efficiency gain in a range between −3.5% and 21.4% from comfort-driven control. Accounting for a wide range of personal comfort profiles and number of occupants, the average (±standard deviation) energy savings for a single zone and multiple zones were in ranges of [−3.7 ± 4.8%, 5.3 ± 5.6%] and [−3.1 ± 4.9%, 9.1 ± 5.1%], respectively. Across all multi-occupancy scenarios, a range between 0.0% and 96.0% of combinations resulted in energy savings.
• Jung, W., & Jazizadeh, F. (2019). Comparative assessment of HVAC control strategies using personal thermal comfort and sensitivity models. Building and Environment, 158(Issue). doi:10.1016/j.buildenv.2019.04.043
  More info

  Research efforts have demonstrated the potentials of improving the performance of Heating, Ventilation, and Air-Conditioning (HVAC) systems by leveraging personalized thermal comfort preferences and profiles. However, there are remaining challenges for effective control in collective conditioning in multi-occupancy scenarios. In this study, we have investigated the impact of personal thermal comfort sensitivities – distinct individual reactions to temperature variations– on collective conditioning. To this end, we have explored whether taking the thermal comfort sensitivity into account influences the selection of temperature setpoints and the overall probability of achieving comfort. We have also examined the impact of different thermostat temperature resolutions (0.1, 0.5, and 1.0 °C) on these factors with a hypothesis that finer resolutions could aid in achieving improved overall thermal comfort. In doing so, we have proposed an agent-based control mechanism to simulate the multi-occupancy space, controlled by an HVAC agent to provide air conditioning for multiple human agents using three operational strategies to compare conventional strategies with our proposed approach. The first strategy relies on majority thermal votes, the second one relies on the gap between thermal preferences (i.e., preferred temperature) and ambient temperature, and the third strategy uses thermal comfort sensitivity in addition to preferences. The investigations were conducted by using stochastically modeled comfort profiles (six actual comfort profiles and 15 mathematically synthesized profiles from actual data). These profiles were used to model the behavior of human agents in diverse multi-occupancy scenarios, modeling two to ten occupants in a space for different thermostat temperature resolutions. Our investigations demonstrated that thermal comfort sensitivity plays a statistically significant role in collective conditioning as it resulted in changes of temperature setpoint in 86% of cases and a higher probability of achieving collective comfort.
• Jung, W., Jazizadeh, F., & Diller, T. E. (2019). Heat flux sensing for machine-learning-based personal thermal comfort modeling. Sensors (Switzerland), 19(Issue 17). doi:10.3390/s19173691
  More info

  In recent years, physiological features have gained more attention in developing models of personal thermal comfort for improved and accurate adaptive operation of Human-In-The-Loop (HITL) Heating, Ventilation, and Air-Conditioning (HVAC) systems. Pursuing the identification of effective physiological sensing systems for enhancing flexibility of human-centered and distributed control, using machine learning algorithms, we have investigated how heat flux sensing could improve personal thermal comfort inference under transient ambient conditions. We have explored the variations of heat exchange rates of facial and wrist skin. These areas are often exposed in indoor environments and contribute to the thermoregulation mechanism through skin heat exchange, which we have coupled with variations of skin and ambient temperatures for inference of personal thermal preferences. Adopting an experimental and data analysis methodology, we have evaluated the modeling of personal thermal preference of 18 human subjects for well-known classifiers using different scenarios of learning. The experimental measurements have revealed the differences in personal thermal preferences and how they are reflected in physiological variables. Further, we have shown that heat exchange rates have high potential in improving the performance of personal inference models even compared to the use of skin temperature.
• Jazizadeh, F., & Jung, W. (2018). Personalized thermal comfort inference using RGB video images for distributed HVAC control. Applied Energy, 220(Issue). doi:10.1016/j.apenergy.2018.02.049
  More info

  HVAC systems account for more than 40% of energy consumption in buildings to provide satisfactory indoor environments for occupants. The integration of personalized thermal comfort in the operation of HVAC systems has been shown to be highly effective in enhancing energy efficiency of buildings. To this end, research efforts have proposed personalized thermal comfort assessment through voting (i.e., occupant feedback) and profiling as well as physiological response measurement. In this study, we have proposed a novel approach for enabling RGB video cameras as sensors for measuring personalized thermoregulation states – an indicator of thermal comfort. If their feasibility for thermoregulation state inference could be established, optical cameras provide a cost-effective and omnipresent solution for distributed measurement of thermal comfort and consequently control of HVAC systems for energy saving. Accordingly, we have proposed a framework that draws on the concepts of thermoregulation mechanisms in the human body as well as the Eulerian video magnification approach. The framework is composed of several components including face detection, skin pixels isolation, image magnification. And calculation of detection index to infer subtle blood flow variations to the facial skin surface (i.e., blood perfusion), which is due to thermoregulation adjustments. In order to minimize the impact of variable illumination condition and the ambient noise on the results, different combinations of methods for framework components were taken into account. The feasibility assessments were conducted through an experimental study with 21 participants under low (20 °C) and high (30 °C) temperatures. In total, 16 positive cases out of 18 statistically significant cases were observed resulting in 89% of success rate using the most promising combinations of the methods. The results demonstrate that the proposed framework could contribute to realization of a non-intrusive, cost-effective, and ubiquitous distributed thermal comfort assessment that has been proven critical in increasing energy efficiency of the HVAC system through distributed control feedback.
• Jung, W., & Jazizadeh, F. (2018). Vision-based thermal comfort quantification for HVAC control. Building and Environment, 142(Issue). doi:10.1016/j.buildenv.2018.05.018
  More info

  This study presents a vision-based approach that employs RGB video images as the sole source for inferring thermoregulation states in the human body in response to thermal condition variations in indoor environments. The primary objective is to contribute to our envisioned thermoregulation-based HVAC control that leverages actual thermal demands from end-users’ thermoregulation states for increased energy efficiency. Given that the envisioned control system calls for measurement techniques under four constraints of non-intrusiveness, applicability, sensitivity, and ubiquity (i.e., feasibility and scalability), this study investigated the potentials of ubiquitously obtainable RGB video images (through webcams or smartphones). Using photoplethysmography (PPG), a well-known optical technique for measuring blood volume changes in the microvascular bed of skin, we have leveraged the mechanism of blood flow control to the skin surface (blood vessels' dilation and constriction) for heat dissipation regulations, reflected in PPG signal's amplitude. Given the subtle variations of PPG signals and their susceptibility to noise, we proposed a framework that uses a combination of independent component analysis and adaptive filtering to reduce unwanted and in-band artifacts while preserving the amplitude information of PPG signals that indicates thermophysiological states. The framework was experimentally evaluated using transient thermal conditions to account for applicability and sensitivity attributes. Therefore, without considering an acclimation time for stabilized thermoregulation states, human subjects were exposed to varying temperatures (∼20–30 °C) while reporting their thermal sensations. In total, for 10 human subjects out of 15, a positive correlation between vision-based indicators, skin temperature, and thermal sensations were observed demonstrating promising potential in inferring thermal sensations of occupants with sufficient sensitivity.

Proceedings Publications

• Babon-Ayeng, P., & Jung, W. (2025). Unveiling the Spectrum of Personal Heat Stress Responses in Buildings. In International Conference on Computing in Civil and Building Engineering.
  More info

  Extreme heat events, increasing in frequency and intensity due to climate change, highlight the need for thermal resilience in buildings. Current designs and operations of buildings often prioritize energy efficiency and occupant comfort, and heat stress indices are mostly generalized in their assessment. In other words, this focus may not fully address building and occupant heat stress and resilience during extreme environmental conditions introduced by, for example, climate change. This study is formulated to address this research gap by investigating individual thermal resilience under extreme indoor environmental conditions. We posit that individuals have unique mechanisms to deal with extreme heat stress, and our investigation involved four healthy adults exposed to temperatures ranging from 24 ℃ (75.2°F) to 40 ℃ (104°F) and humidity levels between 17% and 34% in a controlled climate chamber for two hours. Then, they had a resting period of 20 min in a resting room with a temperature of 23 ℃ (73.4°F). Results demonstrate significant differences in heat stress response among subjects. The dynamic nature of individual heat stress response, as evidenced by variations in heat intensity, thermal sensation, and thermal preference responses were revealed in this study. We used the K-Nearest Neighbors (KNN) algorithm with three neighbors and a five-fold cross-validation process to see the potential of predicting changes in one subject’s thermal sensation as a preliminary demonstration. This method attained an accuracy of 0.96, coupled with a corresponding F1-score of 0.95. These findings highlight the necessity of reframing building and occupant thermal resilience through personal assessments and the technology needed by underserved communities to correctly measure their response to heat stress and housing performances, especially in the face of a changing climate.
• Babon-Ayeng, P., & Jung, W. (2025, May).

  Indoor Heat Strain Early Detection using Heart Rate Variability

  . In ASCE International Conference on Computing in Civil Engineering.
• Babon-Ayeng, P., Jung, S. e., Cheng, K. C., & Jung, W. (2025, Jul).

  Sustainability Engagement and Career Aspirations Among Underrepresented Engineering Students: Insights from a Comparative Analysis

  . In Joint CSCE Construction Specialty & CRC Conference 2025.
• Hasani Alavy, E., & Jung, W. (2025). Performance Evaluation of Electrified Office Buildings Under Infection Risk Management Mode. In ASHRAE Annual Conference, 2025, 131.
  More info

  Building electrification represents a nationwide U.S. effort to achieve decarbonization and enhance energy efficiency. While this transition reduces fossil fuel dependence, improves energy efficiency, and facilitates renewable energy integration, buildings must also ensure occupant protection from airborne pathogens. In response to COVID-19, ASHRAE Standard 241 introduced the Infection Risk Management Mode (IRMM), which mandates the Minimum Equivalent Clean Airflow Rate (VECAi) through mechanical and natural ventilation during high-risk periods. To date, limited research has explored maintaining energy efficiency in electrified buildings while complying with the IRMM. Accordingly, this study aims to assess the performance of office buildings with electric heating, ventilation, and air-conditioning systems operating under the IRMM. Specifically, we evaluated packaged variable air volume (VAV) system with parallel fan-powered (PFP) boxes with electric resistance heating (VAV+PFP), packaged terminal heat pump (PTHP), and variable refrigerant flow (VRF) systems across different filtration levels (MERV 8, 11, and 13). The simulation results, primarily derived from EnergyPlus and post-processing, indicate that increasing filtration efficiency significantly reduced non-compliance hours, with MERV 13 filters being the most effective in maintaining clean air standards. However, HVAC system performance varied, with the VAV+PFP system facing greater compliance challenges due to airflow modulation at part-load conditions compared to constant volume systems. Energy use analysis revealed that electrification reduced building energy consumption up to 20.7% for VRF and 16.5% for PTHP compared to the baseline system which used packaged VAV with gas-fired central heating, following the ASHRAE 90.1-2016. This was mainly due to reduced distribution losses in ductless systems and upgrading coefficient of performance of direct expansion cooling coil. Nonetheless, higher filtration efficiency led to increased energy consumption, as expected, emphasizing the need for an optimized balance between filtration and, ventilation strategies, as well as system operation. These findings offer valuable insights into how electrified buildings can simultaneously achieve energy efficiency and infection risk mitigation, informing future building design and operation strategies.
• Hasani Alavy, E., & Jung, W. (2025, Jun).

  Performance Evaluation of Electrified Office Buildings Under Infection Risk Management Mode

  . In ASHRAE Annual Conference 2025.
• Jung, W. (2025, Nov).

  U.S. Smart Thermostat Adopters through the Lens of Diffusion of Innovation Theory

  . In The 11th International Conference on Construction Engineering and Project Management (ICCEPM 2025).
• Jung, W., & Kim, N. (2025, Jul).

  Student and Educator Perceptions Toward Generative AI in Architecture, Engineering, and Construction Education

  . In Joint CSCE Construction Specialty & CRC Conference 2025.
• Jung, W., & Song, K. (2025, May).

  Virtual and Augmented Reality in Building Science and Technology Education: A Review Framework

  . In ASCE International Conference on Computing in Civil Engineering (i3ce) 2025.
• Jung, W., Kim, S., & Chang, S. (2025, Jul).

  Exploring Awareness, Knowledge, and Interest in Energy Equity Among Civil, Architectural & Construction Undergraduates

  . In Join CSCE Construction Specialty & CRC Conference 2025.
• Jung, W., Mo, Y., & Witt, S. (2025, May).

  A New Simple HVAC Energy Performance Metric: Climate and Occupant Preference-Adjusted

  . In ASCE International Conference on Computing in Civil Engineering (i3ce) 2025.
• Ye, Y., Faulkner, C. A., Jung, W., Zhang, J., & Brock, E. (2024, 2024). A new database of building-space-specific internal loads and load schedules for performance based code compliance modeling of commercial buildings. In Building Simulation, 1-16.
• Jung, W., & Jazizadeh, F. (2019). Spatial efficacy of respiration monitoring using doppler radars for personalized thermal comfort assessment. In 36th International Symposium on Automation and Robotics in Construction, ISARC 2019.
  More info

  Recent research efforts have shown that human thermophysiological features could play a crucial role in inferring occupants’ thermal comfort, which is required for comfort-aware heating, ventilation, and air conditioning (HVAC) operation. Our previous studies have demonstrated that variations of respiration, a representative human thermophysiological feature, can be non-intrusively quantified by a Doppler radar sensor (DRS). However, in pursuit of enabling human-aware rooms in buildings, in this study we have explored the impact of distance and position of the respiration monitoring system to investigate the potential of DRS systems as a ubiquitous apparatus in the real-world scenarios. Through experimental studies, respiration characteristics were evaluated in different locations and angles relative to the location of the measurement device. The measurements were carried out using a DRS system and a respiratory belt for ground truth data collection. The noise artifacts were reduced by applying the Savitzky-Golay method and Hann window, and respiration was identified by selecting the frequency component with the maximum amplitude in the typical breathing frequency range (0.1 to 0.5 Hz). Our analyses demonstrated that the signal from a cost-effective DRS technology without the use of external amplifiers could cover a range, within 1.0m longitudinally and 0.5m laterally, which is sufficient for an individual sensing given a normal office environment. It was also observed that the use of an external amplifier extends the range of the DRS sensing but at the same time accentuates the noise. Therefore, advanced noise removal methods are needed to increase the range of robust sensing. This study contributes to DRS deployment strategies for realization of comfort-aware systems.
• Jung, W., Chan, M., Jazizadeh, F., & Diller, T. E. (2019). Feasibility Assessment of Heat Flux Sensors for Human-in-the-Loop HVAC Operations. In ASCE International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, i3CE 2019.
  More info

  This study seeks to evaluate the applicability of heat flux sensors, as a proxy for contextual thermal comfort representation in an environment, for the human-in-the-loop (HITL) heating, ventilation, and air conditioning (HVAC) operations. In accounting for personalized thermal comfort inference, the predicted mean vote (PMV) model is not often applicable and data-driven methods using features from ambient environmental conditions have demonstrated moderate accuracies (60-75%). Therefore, the use of physiological sensing for improved performance has gained attention in recent years. Skin temperature has been widely used as an alternative indicator of human heat dissipation adjustment, which could be leveraged for personalized thermal comfort inference. However, considering the heat dissipation adjustments through skin as an adaptation mechanism in an environment, in this study, we have investigated the hypothesis that heat flux sensors could be an effective alternative for enabling feedback from human body to the control system. To this end, we designed and conducted an experimental study on 10 human subjects, wearing a thin-film heat flux sensor on their wrist, under transient thermal conditions (from 20 to 30°C) to investigate the association between human thermal perception and the corresponding heat flux. Through this experiment, the correlations between heat flux, air temperature, relative humidity, and thermal preferences were analyzed. The high correlation factors (0.90 on average) between heat flux and thermal preferences suggest that heat flux sensors have a high potential to be used as an effective sensing modality in the HITL HVAC operation.
• Jung, W., & Jazizadeh, F. (2018). Multi-occupancy indoor thermal condition optimization in consideration of thermal sensitivity. In Workshop of the European Group for Intelligent Computing in Engineering.
  More info

  The primary criterion for assessing heating, ventilation, and air conditioning (HVAC) systems regarding thermal comfort is whether they are capable of satisfying more than 80% of occupants (i.e., acceptable condition). The predicted percentage of dissatisfied model proposes this value with the assumption that a neutral state is desired. However, recent studies cast light on personalized thermal comfort which demonstrates that occupants have diverse thermal preferences and respond differently to variations in temperature (i.e., thermal sensitivity). This study aims to shed light on the importance of taking thermal sensitivity into account in a multi-occupancy space, where the same thermal condition is shared, for thermal condition optimization, which was replicated in our multi-agent based (MAB) model. Each human agent (occupants’ proxy) has its own properties (e.g., thermal preferences) and aims to create at least an acceptable condition for itself by providing feedback to a HVAC agent (HVAC systems’ proxy). The HVAC agent optimizes the thermal condition based on feedback from human agents. Using this model, two operational scenarios have been explored: human agents have (1) the same (i.e., ignoring thermal sensitivity) and (2) personalized thermal sensitivities. The assessments demonstrate that integrating thermal sensitivity results in significantly different setpoint temperatures, increased thermal satisfactions of human agents and the number of satisfied human agents. In other words, thermal sensitivity is an important factor in improving the performance of HVAC systems.
• Jung, W., & Jazizadeh, F. (2017). Towards integration of doppler radar sensors into personalized thermoregulation-based control of HVAC. In 4th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2017, 2017-.
  More info

  This study evaluates the sensitivity of our novel and non-intrusive approach, powered by Doppler radar sensors to assess thermoregulation states as feedback to heating, ventilation, and air-conditioning (HVAC) systems. Thermoregulation-based HVAC control employs changes in physiological response of the human body for heat dissipation adjustment as feedback to the control logic. Our envisioned system calls for monitoring devices, capable of timely recognition of thermal sensation variations to minimize user discomfort. Respiration, one of the major contributors in heat dissipation of the human body, can be non-intrusively measured by Doppler radar sensors. Thus, the question in this study was whether the application of Doppler radar sensors is qualified and sufficiently sensitive for the envisioned system integration. We have presented a conceptual framework for system integration using Doppler radar sensors as well as an experimental study for addressing the research question by quantifying respiration in a test bed with gradual temperature changes from low to high. The results showed an increasing trend in the respiration state for three subjects out of four, and it was observed that respiration increased in higher temperatures for all the subjects. In other words, it is demonstrated that the Doppler radar sensors have promising sensitivity for non-intrusive monitoring of thermoregulation responses.

Presentations

• Jung, W. (2025, 09).

  Surviving as an Assistant Professor (in the U.S.)

  . Brown-bag Seminar.
• Jung, W. (2025, Dec).

  Demand Response Strategies and Research in the U.S. Residential Sector

  . Seminar at Seoul National University of Science and Technology.
• Jung, W. (2025, Dec).

  Toward Human-Artificial Intelligence (AI)-Building Collaborations

  . Seminar at Yonsei University.
• Jung, W. (2025, Nov).

  Career Pathways: Building Science Track

  . Seminar at KonKuk University.
• Jung, W. (2025, Oct).

  Understanding the State of the Art in Any Fields

  . Seminar at Incheon National University. Seminar at Incheon National University.
• Jung, W. (2025, Oct).

  Why do we need EnergyPlus and What do we use it for?

  . Seminar at Ajou University.
• Jung, W. (2022). Towards Sustainable Buildings via Human-Centered Operations. Invited SeminarIndoor Air Quality, Ventilation and Energy Conservation in Buildings..
• Jung, W. (2023). Built Environment Sustainability through Human-Centered Operations. Invited seminarKorean Institute of Construction Engineering and Management.
• Jung, W. (2023). Human-Centered Operations in Buildings. Invited seminarthe College of Architecture, Planning, and Landscape Architecture, the University of Arizona.
• Jung, W. (2022). Adaptive Built Environment through Human-Centered Operations. Invited SeminarGlobal Network of Korean Building Technologists and Scientists.
• Jung, W. (2022). Built Environment Sustainability through Human-Centered Operations. Invited SeminarAjou University.

Others

• Nambiar, C., Rosenberg, M., Maddox, D., Nagda, H., Tillou, M., Karpman, M., Jung, W., & Kim, D. (2024, June). Commercial Building Prototypes Based on ANSI/ASHRAE/IES Standard 90.1-2019 Appendix G PRM. Report. https://www.energycodes.gov/sites/default/files/2024-06/Commercial_Prototypes_ASHRAE901-2019AppGPRM.pdf