Jiachen Mao — Researcher Analysis Report
Analysis Mode: fast | Analysis Time: 2026-03-16T00:50:32
Rating: Active Researcher (33.0/100)
Basic Metrics
| Metric | Value |
|---|---|
| Institution | Unknown |
| h-index | 11 |
| Total Citations | 616 |
| Recent 5 Years Citations | 14 |
| Total Papers | 23 |
| Papers in Top Conferences | 0 |
| Publication Period | 2015 - 2025 |
| Semantic Scholar | 3384503 |
Research Trajectory
Jiachen Mao’s academic trajectory is extremely rare, featuring drastic cross-disciplinary transitions that are difficult to describe using the conventional “leading expert/emerging star” framework. From 2015 to 2018, his main contributions focused on building energy consumption simulation and urban microclimate. He published several highly cited papers on ventilation in high-rise buildings, energy consumption calibration optimization, and urban heat island effects, establishing an early academic reputation (h-index = 11, with all 616 citations originating from this period).
A first dramatic shift occurred in 2019: he suddenly published papers on deep learning accelerator hardware (DASNet, HyPar), which had no connection with the building field before. Subsequently, his publication trajectory became more dispersed: in 2020, he turned to power systems and energy storage; in 2022, he addressed concrete structure fire protection; from 2023 to 2026, he intensively published in multiple unrelated areas such as LLM safety (SafeWork-R1, SafeCoT), recommendation system embedding optimization, diffusion model adversarial attacks, zeolite synthesis reviews, and phonetics.
This highly dispersed publication record (only 14 citations in the recent 5 years) strongly suggests that “Jiachen Mao” is actually a result of multiple researchers with the same name being merged in the database, rather than a single researcher’s personal trajectory. If treated as a single individual analysis, his early work in the building environment field has some influence, but his recent output lacks focus and he has not established a sustained academic status in any single field.
Breakthrough Work
1. A low-cost seasonal solar soil heat storage system for greenhouse heating: Design and pilot study (2015)
Description: A low-cost seasonal solar soil heat storage system was designed and experimentally validated for winter heating in greenhouses. By storing solar thermal energy in the soil during summer and extracting it in winter, thermal energy transfer across seasons is achieved.
Why it couldn’t be done before: Long-term seasonal soil heat storage has been difficult to promote due to complex construction and high costs. This paper proposes a simplified design scheme and completes field validation, reducing the engineering barrier. There was previously no validated low-cost prototype, and its engineering feasibility was questionable.
Impact: It became a highly cited benchmark document in this niche area (125 citations), providing an economically viable technical approach for low-carbon heating in agricultural greenhouses, and is widely referenced in subsequent energy storage and agricultural energy research.
2. An automated optimization method for calibrating building energy simulation models with measured data: Orientation and a case study (2016)
Description: A method based on measured data was proposed to automatically optimize and calibrate building energy consumption models, solving the discrepancy between simulation results and actual energy consumption and automating the manual calibration process.
Why it couldn’t be done before: Building energy consumption model parameters are numerous (material thermal properties, equipment efficiency, energy usage behavior, etc.). Traditional manual calibration relies on engineer experience and is extremely time-consuming. Automated optimization algorithms were not yet mature in building simulation before 2016, and computational costs also limited large-scale parameter search.
Impact: With 87 citations, it became an important reference for the calibration methodology of building energy consumption models, promoting automated calibration practices in digital twin and Building Information Modeling (BIM) fields.
3. Global sensitivity analysis of an urban microclimate system under uncertainty: Design and case study (2017)
Description: Global Sensitivity Analysis (GSA) was systematically introduced into urban microclimate simulations, quantifying the contribution of input parameters to the uncertainty of urban thermal environment predictions and identifying key influencing factors.
Why it couldn’t be done before: Urban microclimate model parameters are highly diverse (building geometry, materials, vegetation, meteorology, etc.). Local sensitivity analysis cannot capture interaction effects between parameters. Before 2017, coupling GSA methods such as Sobol with urban microclimate solvers had extremely high computational costs, and it was only feasible after high-performance computing became widespread.
Impact: With 76 citations, it provided a methodological framework for quantifying uncertainty in urban climate models and promoted model credibility assessment in climate-adaptive urban planning.
4. The airborne transmission of infection between flats in high-rise residential buildings: A review (2015)
Description: A systematic review was conducted on the transmission mechanisms of droplets and aerosols between floors in high-rise residential buildings, focusing on the cross-infection pathways driven by单侧 natural ventilation, and public health risks were evaluated.
Why it couldn’t be done before: Cross-floor aerosol transmission attracted academic attention only after SARS (2003), but relevant experiments and simulation data accumulated until 2015 to support a systematic review. The high-density living form in high-rise buildings is a unique situation in East Asia, and Western literature lacked coverage.
Impact: With 61 citations, it was widely referenced during the COVID-19 pandemic and became an important reference for infection risk assessment in high-rise buildings, influencing discussions on ventilation design standards.
5. Planning Low-carbon Distributed Power Systems: Evaluating the Role of Energy Storage (2020)
Description: The energy storage system (dynamic charge/discharge efficiency model) was incorporated into the power capacity expansion planning framework, and its economic and technical value in decarbonizing distributed power systems was evaluated.
Why it couldn’t be done before: Early capacity planning models simplified energy storage as a static device, ignoring the dynamic characteristics of charge/discharge efficiency depending on state. Before 2020, lithium battery costs had not decreased significantly, so the commercial significance of incorporating energy storage into planning models was limited. As energy storage costs dropped rapidly, the need for accurate modeling became urgent.
Impact: With 46 citations, it provided a quantitative tool for energy storage value to power system planners and has certain application value in renewable energy consumption and grid decarbonization decision support.
Research Directions
- Simulation, calibration, and optimization of building and urban energy systems (2015–2021)
- AI model safety, large language model alignment, and visual language model safety (2024–2026, recent focus)
- Deep learning system efficiency and hardware acceleration (briefly involved in 2019)
- Renewable energy and energy storage planning (single project in 2020)
Methodological Evolution
From 2015 to 2018, the research method centered on physical simulation: CFD fluid dynamics, building energy consumption simulation engines (EnergyPlus, etc.) combined with optimization algorithms (genetic algorithms, Bayesian optimization) were used for calibration and sensitivity analysis, representing a classic paradigm in computational engineering. Starting in 2019, deep learning methods (sparse activation, parallel training) emerged, indicating a fundamental shift in the methodological toolbox. AI safety research in 2024–2025 employs contemporary LLM alignment techniques such as reinforcement learning post-training and thought chain supervision, completely breaking with early building physics methods.
This evolution is not organic; it seems more like products of individuals from different research backgrounds aggregated in the database. If treated as a single individual, the jump in methodology far exceeds normal academic transition speed, with no transitional work serving as a bridge.
Field Impact
The researcher (or group of researchers with the same name) has made substantial contributions to the field of building environment engineering: All three papers from 2015–2017 received over 50 citations each, leaving an accessible methodological foundation in niche areas such as urban microclimate simulation, ventilation and infectious disease risks in high-rise buildings, and automatic calibration of building energy consumption. However, only 14 citations in the recent 5 years indicate that he has not established influence in the newly entered AI field, and each direction lacks sustained depth. Overall, this represents a scholar with moderate influence in a single field (building energy), highly dispersed in recent years, and without results in top conferences, lacking cross-field integrated influence.
High-Citation Papers (Top 20)| # | Year | Citation | Title |
|—|——|——|——| | 1 | 2015 | 125 | A low-cost seasonal solar soil heat storage system for greenhouse heating: Design and pilot study | | 2 | 2016 | 87 | An automated optimization method for calibrating building energy simulation models with measured data: Orientation and a case study | | 3 | 2017 | 76 | Global sensitivity analysis of an urban microclimate system under uncertainty: Design and case study | | 4 | 2015 | 61 | The airborne transmission of infection between flats in high-rise residential buildings: A review | | 5 | 2015 | 53 | The transport of gaseous pollutants due to stack and wind effect in high-rise residential buildings | | 6 | 2020 | 46 | Planning Low-carbon Distributed Power Systems: Evaluating the Role of Energy Storage | | 7 | 2016 | 45 | Experimental study on the effectiveness of internal shading devices | | 8 | 2018 | 34 | Evaluating approaches for district-wide energy model calibration considering the Urban Heat Island effect | | 9 | 2018 | 26 | Optimization-aided calibration of an urban microclimate model under uncertainty | | 10 | 2016 | 23 | Assessment of energy-saving technologies retrofitted to existing public buildings in China | | 11 | 2016 | 11 | Towards fast energy performance evaluation: A pilot study for office buildings | | 12 | 2021 | 9 | Urban Weather Generator: Physics-Based Microclimate Simulation for Performance-Oriented Urban Planning | | 13 | 2018 | 6 | Automatic calibration of an urban microclimate model under uncertainty | | 14 | 2022 | 5 | Calculation Method of the Residual Bearing Capacities of Concrete T-Shaped Beams Considering the Effect of Fire Cracks | | 15 | 2016 | 4 | Energy load superposition and spatial optimization in urban design: A case study | | 16 | 2016 | 2 | DEVELOPMENT AND ONLINE TUNING OF AN EMPIRICALLY-BASED MODEL FOR CENTRIFUGAL CHILLERS | | 17 | 2015 | 1 | A Study of Shanghai Residential Morphology And Microclimate At A Neighborhood Scale Based on Energy Consumption | | 18 | 2015 | 0 | Simulation Research of Wind And Thermal Environment in Residential District | | 19 | 2015 | 0 | Development And On-Line Tuning Of An Empirically Based Steady-State Model For Centrifugal Chillers | | 20 | 2015 | 0 | Feasibility Study Of Hybrid Ventilation For A High-rise Office Building In Shanghai |