جستجوی استاد راهنمای دانشگاهی

Liang Yongمشاهده پروفایل

دانشیار

• Data-Driven Decision Analytics
• Supply Chain Management
• Disruption Management
• +۵ مورد دیگر

Liang Yong serves as Associate Professor in the Department of Management Science and Engineering at Tsinghua University's School of Economics and Management. Currently maintaining office hours on Thursdays at 10:00 in Room B424 of Lihua Building, he teaches undergraduate, doctoral, and MBA courses including Production and Operations Management, Advanced Operations Research, and Data, Models, and Decision Making. His educational background includes a B.A. in Engineering Physics from Tsinghua University (2005), Master's in Nuclear Engineering from Purdue University (2008), and Ph.D. in Industrial Engineering and Operations Research from UC Berkeley (2013). Prior to academia, he worked as a Quantitative Analyst at Google Inc. (2013-2014). Liang's research focuses on Data-Driven Decision Analytics , Supply Chain Disruption Management , and Mechanism/Contract Design , with particular emphasis on integrating learning algorithms with operations research. His work addresses critical challenges in omnichannel retail, energy systems, and transportation networks. His 11 recent publications in premier journals like Operations Research and Manufacturing & Service Operations Management demonstrate consistent contributions to operations analytics, with notable recognition including the 2019 POMS-JD.com data-driven paper competition winner prize. Winner prize, POMS-JD.com data-driven paper competition (2019) He actively supervises PhD students, both independently and through co-advisement with faculty like Jian Chen and Yongbo Xiao. His research addresses practical industry challenges through collaborations with major technology companies and government entities, with current work focusing on resilient supply chain design and smart energy systems.

Mao Boمشاهده پروفایل

دانشیار

• Information Systems
• Knowledge Management
• Virtual Communities
• +۳ مورد دیگر

Mao Bo serves as Associate Professor in the Department of Management Science and Engineering at Tsinghua University's School of Economics and Management. His academic career is anchored at China's premier institution with continuous service in the department since his advanced degrees from Tsinghua. His research spans critical domains of modern information systems, with primary focus on Chinese enterprise information strategy , virtual community knowledge management , and complex supply chain systems . His work bridges theoretical frameworks with practical implementations in organizational contexts, particularly examining how digital platforms transform knowledge sharing and operational coordination. Teaching responsibilities include core courses on Information Systems Analysis and Design , Management and Organization , and specialized instruction in knowledge management systems. His academic trajectory includes a significant visiting scholar appointment at MIT's Sloan School of Management during 2001. Publications reveal consistent contributions from 2000-2009 with predominant focus on virtual community dynamics, supply chain analytics, and knowledge infrastructure design. His methodology frequently integrates multi-agent systems, data mining, and operations research techniques to solve complex organizational problems. Professional engagement demonstrates deep institutional commitment through campus-wide initiatives including academic research platform development and laboratory modernization projects at Tsinghua University.

Shangfei Wangمشاهده پروفایل

استاد

• Pattern Recognition
• Affective Computing
• Probabilistic Graphical Models
• +۳ مورد دیگر

Shangfei Wang is a full Professor at the School of Computer Science and Technology, University of Science and Technology of China (USTC). His research focuses on pattern recognition, affective computing, and probabilistic graphical models, with significant contributions to facial expression analysis, emotion recognition, and multimodal human-robot interaction. He leads the Key Laboratory of Computing and Communication Software of Anhui Province and has received multiple international competition awards including placements in the OMG Empathy Prediction Challenge and Detecting Depression with AI Sub-Challenge. PhD in Computer Science, USTC (2002) MSc in Electronic Science and Technology, USTC (1999) BSc in Electronic Engineering, Anhui University (1996) His work combines domain knowledge with advanced machine learning techniques, including adversarial learning, dual learning, and multimodal deep regression Bayesian networks. Research themes include: Emotion-aware medical consultation systems Thermal image-based facial recognition Privileged information learning for emotion detection Spontaneous vs. posed expression differentiation EEG and physiological signal integration AI applications in mental health support Key projects include multiple National Nature Science Foundation of China grants and international collaborations with French institutions. He serves as Associate Editor for IEEE Trans. on Affective Computing and ACM Trans. on Multimedia Computing, and organized several international conference tracks.

• Intelligent Sensing
• Machine Learning
• Unmanned Autonomous Systems
• +۳ مورد دیگر

Dr. Qi Hao is an Associate Professor and Deputy Director of the Department of Computer Science and Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He also serves as Executive Director of the SUSTech & Haylion Intelligent Transportation Center and Head of the SUSTech & FengXiangBiao ICV Education Joint Laboratory. Prior to joining SUSTech in 2014, he was an Assistant Professor at the University of Alabama (2007-2014) following postdoctoral training at the University of Kentucky. Dr. Hao received his PhD in Electrical and Computer Engineering from Duke University in 2006, with Master's and Bachelor's degrees from Shanghai Jiaotong University (1997 and 1994). His academic journey includes research positions at Singapore's Data Storage Institute (1998-2001) and engineering experience at Shanghai Electric Tool Research Institute (1997-1998). As an expert in intelligent sensing and unmanned autonomous systems, Dr. Hao's research spans bionic intelligent sensor design, machine learning applications, and autonomous vehicle technologies. His work demonstrates a strong integration of theoretical frameworks with practical implementations, particularly in pyroelectric infrared sensing, UAV systems, and distributed sensor networks. His research has evolved to address increasingly complex challenges in autonomous navigation, multi-robot coordination, and edge computing for intelligent transportation systems. Dr. Hao's publication trend reveals a strategic shift toward integrated autonomous systems, with recent work emphasizing robotics, machine learning, and wireless communication. His research bridges computer vision, sensor fusion, and deep learning to solve practical challenges in autonomous vehicle perception, wireless energy transfer, and human activity recognition. The interdisciplinary nature of his work connects electrical engineering principles with cutting-edge AI techniques. US National Science Foundation Research Awards (2009, 2011) Multiple IEEE conference best paper awards and finalists (2012-2021) Shenzhen Overseas High-level Talents Peacock Plan Category C (2017) Advanced Educators in Shenzhen (2017) Outstanding Teacher of Guangdong Province (2021) SUSTech Outstanding Research and Service Awards (2017-2021) Dr. Hao has secured significant research funding as Principal Investigator for projects from NSFC, Shenzhen Nanshan District, and industry partners including Huawei, Intel, and Haylion Technology. His current projects focus on intelligent binary sensing, next-generation camera systems, autonomous driving safety frameworks, and federated learning for vehicle perception. He has also contributed to educational initiatives through Guangdong Province and Shenzhen City education reform projects. Leading the SUSTech & Haylion Intelligent Transportation Center and the SUSTech & FengXiangBiao ICV Education Joint Laboratory, Dr. Hao oversees research teams developing solutions for intelligent transportation challenges. His labs integrate expertise across computer science, electrical engineering, and mechanical engineering to advance autonomous vehicle technologies, sensor networks, and robotics applications for real-world implementation.

• High Performance Computing
• Distributed Systems
• Cloud Computing
• +۲ مورد دیگر

Zhuozhao Li is an Assistant Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He joined SUSTech in August 2021 after serving as a Postdoctoral Scholar at the University of Chicago from July 2018 to July 2021. His research focuses on high-performance computing, distributed systems, and cloud/edge computing, with applications across scientific domains. Ph.D. in Computer Science, University of Virginia, May 2018 M.S. in Computer Science, University of Southern California, May 2012 B.E. in Computer Science, Zhejiang University, July 2010 Dr. Li's research spans several critical areas in modern computing infrastructure. His primary focus is on High Performance Computing , where he develops novel approaches to optimize computational efficiency in scientific applications. In Distributed Systems , his work addresses challenges in large-scale system coordination and resource management. His contributions to Cloud/Edge Computing focus on improving virtualization techniques and resource allocation strategies. Additionally, he explores applications in the Internet of Things , examining how distributed computing paradigms can enhance IoT infrastructure. Dr. Li's publication record demonstrates a consistent focus on advancing distributed computing systems and their scientific applications. His recent work shows a clear progression from foundational research on data-parallel frameworks and job scheduling toward more applied systems for scientific computing. His contributions to projects like funcX and DLHub represent significant advancements in making distributed computing more accessible for scientific workflows. A notable trend is the increasing interdisciplinary nature of his work, with applications spanning from computational biology (SARS-CoV-2 research) to privacy-preserving spatial crowdsourcing. ACM HPDC Best Paper Nominees, 2019 IEEEMASS Service Award, 2019 Outstanding Graduate Research Assistant, University of Virginia, 2018 Dr. Li actively mentors students and researchers, currently recruiting PhD students, Master's students, postdocs, and research assistants for his research group at SUSTech. His research has been supported through various collaborative projects, including significant contributions to the U.S. Department of Energy National Virtual Biotechnology Laboratory Project about COVID-19, which was awarded the Secretary of Energy Achievement Award. His work on funcX, DLHub, and Parsl represents major software infrastructure development efforts with broad scientific impact. Dr. Li is affiliated with multiple research initiatives, including the Globus Lab (during his postdoc at University of Chicago) and various collaborative projects involving high-performance computing resources. At SUSTech, he leads a research group focused on distributed systems and high-performance computing. His work often involves collaboration with researchers across institutions, particularly in projects related to scientific computing infrastructure like funcX and DLHub.

• Random Matrix Theory
• High dimensional Statistics
• Time Series Analysis
• +۱ مورد دیگر

Li Zeng is an Associate Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech) since January 2021, following her Assistant Professor role at the same institution from August 2019 to December 2020. Previously, she was an Eberly Postdoctoral Fellow at Pennsylvania State University under Prof. Runze Li (2017-2019) and a Research Assistant at the University of Washington with Dr. Fang Han (2017). Ph.D. in Statistics and Actuarial Science from the University of Hong Kong (2017), advised by Prof. Jianfeng Yao M.Sc. in Statistics from Renmin University of China (2012) B.Sc. in Mathematical Science from Beijing Normal University (2009) Her research centers on Random Matrix Theory and High Dimensional Statistics , with significant contributions to eigenvalue/singular value distributions in large-dimensional matrices. She extends these theoretical foundations to Time Series Analysis for lagged auto-correlation structures and applies them to Machine Learning problems including neural network optimization and regularization. Her work bridges asymptotic probability theory with practical data science applications, developing robust methods for high-dimensional inference. Analysis of her 15 most recent publications (2016-2025) reveals a dominant focus on high-dimensional statistical theory with increasing machine learning integration. Key trends include asymptotic analysis of covariance/correlation matrices (60% of publications), factor modeling innovations (20%), and deep learning applications (20%). She consistently publishes in top-tier venues including Annals of Statistics (5 papers), Journal of the American Statistical Association , and machine learning conferences (ICML, ECCV), demonstrating exceptional interdisciplinary impact. Excellent Teaching Assistant Award (5 times) from HKU Department of Statistics and Actuarial Science (2012-2017) Dr. Li actively recruits postdoctoral researchers specializing in probability theory and high-dimensional statistics, emphasizing candidates with strong mathematical backgrounds. As a referee for premier journals including Annals of Statistics , JASA , and Journal of the Royal Statistical Society: Series B , she contributes significantly to scholarly review. Her research is institutionally supported through SUSTech faculty positions, with potential external funding inferred from extensive publication output and conference participation. Within SUSTech's Department of Statistics and Data Science (established 2019), Dr. Li contributes to a rapidly expanding research ecosystem focused on high-dimensional data analysis. The department supports multiple research directions including biostatistics and financial statistics, with active graduate programs (M.Phil/Ph.D.) and developing initiatives in data science education. Her teaching portfolio includes undergraduate Statistical Calculation and Software and graduate-level High Dimensional Statistics courses.

• Rigid Graph Theory
• Multi-Agent Systems
• Unmanned Systems
• +۴ مورد دیگر

Liangming Chen is an Associate Professor at the School of Automation and Intelligent Manufacturing, Southern University of Science and Technology (SUSTech), China. Previously, he held postdoctoral positions at Nanyang Technological University, Singapore. His academic roles include deputy editor-in-chief for IEEE Transactions on Robotics and IEEE Transactions on Systems, Man, and Cybernetics: Systems . As a Senior Member of the IEEE, he has been selected for the National High-Level Talent Program (Youth Project) and Shenzhen Overseas High-Level Talent Program (Peacock Plan Category C). Education : PhD in Systems and Control (University of Groningen, 2021), PhD in Control Science and Engineering (Harbin Institute of Technology, 2019), BEng in Automation (Southwest Jiaotong University, 2015). His research integrates Rigid Graph Theory , Multi-Agent Systems , and Robotics , with recent work focusing on Brain-Inspired Intelligence and Machine Learning . Key trends in his publications (2020-2025) include Formation Control , Distributed Localization , and Unmanned Systems , appearing in top journals like IEEE TAC , Automatica , and IEEE TRO . Scientific Awards include the Shenzhen Overseas High-Level Talent Program and competition prizes in Electronics Design and Mathematical Modeling . He has guided teams to national awards and mentored students to placements at top institutions. His group pursues High-Impact Research in Multi-Agent Estimation , Robot Swarms , and Autonomous Navigation , with applications in drones and embodied intelligence.

• Control Theory
• Game Theory
• Signal Processing
• +۴ مورد دیگر

Kemi Ding is an Associate Professor at the School of Automation and Intelligent Manufacturing, Southern University of Science and Technology (SUSTech), leading the LEMONS lab (Learning and Mathematical Optimization in Networked Systems). Her career spans postdoctoral research at Arizona State University and Nanyang Technological University, followed by roles at SUSTech since 2021. Education Ph.D., Electronic and Computer Engineering, HKUST (2018) B.E., Electronic and Information Engineering, HUST (2014) Visiting Student, Harvard SEAS (2016-2017) Her research bridges control theory, game theory, signal processing, and machine learning , focusing on cyber-physical system security/privacy , networked estimation , and graph-based learning methods . She has published in top journals like Automatica and IEEE Transactions series, emphasizing secure and robust networked systems. Key publication trends include: Game-theoretic approaches to sensor security and deception strategies Optimization of estimation under bandwidth and attack constraints Graph signal processing for networked system modeling Privacy conflict resolution in social networks Resilient control theory under adversarial conditions

JI Jieمشاهده پروفایل

استاد

• Fire Plume Behavior
• Combustion of Liquids and Solids
• Fire Spread
• +۳ مورد دیگر

Professor JI Jie serves as Professor and Vice Director of the State Key Laboratory of Fire Science at the University of Science and Technology of China (USTC), with additional leadership roles in the Engineering Research Center of Thermal Safety and National & Local Joint Engineering Research Center of Thermal Safety Technology. Education: Bachelor of Science, University of Science and Technology of China, 2002 Ph.D. in Engineering, University of Science and Technology of China, 2008 Postdoctoral Research, University of Science and Technology of China, 2008-2010 His research pioneers fire plume dynamics, combustion of complex materials, fire spread mechanisms in ancient/high-rise structures, and real-time fire forecasting using data assimilation techniques. Current work focuses on integrating experimental and numerical methods for fire risk assessment in tunnels, subways, and cultural heritage sites. Recent publications (2021-2023) demonstrate expertise across fire dynamics, smoke management, and safety engineering, with significant contributions to understanding wind-driven fires, multi-fire interactions, and real-time smoke prediction systems using Ensemble Kalman Filter approaches. Honors and Awards: Elected fellow of the Combustion Institute (2022) Highly Cited Chinese Researcher (Elsevier 2020, 2021) National Science and Technology Innovation Leadership Talent (2018) Excellent Young Scientists Fund recipient (2017) Multiple national teaching and research awards including China Fire Protection Association First Prize (2019) Professor Ji has secured major funding including the National Science Fund for Excellent Young Scholars and multiple National Natural Science Foundation projects, while supervising award-winning research teams. His lab implements fire safety standards in national construction projects and international protocols. He leads USTC's State Key Laboratory of Fire Science research team, focusing on experimental combustion studies, computational fire dynamics, and practical fire safety engineering applications for complex infrastructure.

Hao Wangمشاهده پروفایل

پژوهشگر

• Data Management
• Machine Learning
• Recommender Systems
• +۷ مورد دیگر

Hao Wang is an Assistant Researcher in the Department of Computer Science and Technology at Nanjing University, China. He is affiliated with the Reasoning & Learning Group and conducts research at the intersection of data management and machine learning. Bachelor of Mathematics, Nanjing University (2005) Master of Computer Science, Nanjing University (2008) Ph.D. in Computer Science, The University of Hong Kong (2014) His research interests span Data Management and Machine Learning , with recent focus on rank-aware query processing, recommender systems (especially location-based), reinforcement learning, and transfer learning. His work often integrates user behavior modeling, efficient indexing, and scalable algorithms for large-scale data. Hao Wang's publication record shows a consistent trend in solving practical problems in data-intensive AI systems. His recent articles focus on personalized location recommendation , crowdsourced data labeling , distributed learning for imbalanced data , and reinforcement learning transfer . These works appear in top venues such as VLDB, SIGMOD, ICDM, AAAI, and journals like TKDE and GeoInformatica, indicating strong technical depth and interdisciplinary impact. Hao Wang has not been explicitly mentioned as receiving scientific awards in the provided text. He has collaborated extensively with researchers such as Yang Gao, Nikos Mamoulis, and David Cheung. While no formal advisees are listed, his involvement in supervising PhD work (as co-supervisor) and publishing with students suggests an active mentoring role. His research has been supported through academic collaborations and institutional affiliations, though specific grant details are not provided. Hao Wang is a member of the Reasoning & Learning Group at Nanjing University, where he contributes to advancing intelligent systems that combine logical reasoning with statistical learning. The group focuses on foundational and applied aspects of AI, particularly in data mining, knowledge discovery, and autonomous decision-making.

• Risk Management
• Actuarial Science
• Optimal Reinsurance
• +۹ مورد دیگر

Zhang Yiying is an Assistant Professor and Doctoral Supervisor in the Department of Mathematics at the Southern University of Science and Technology (SUSTech). Previously, they served as an Assistant Professor at Nankai University from January 2019 to August 2021. Their academic journey includes a PhD in Actuarial Science from the University of Hong Kong (2018), with academic visits to the University of Leuven (Belgium) and University of Amsterdam (Netherlands) during 2018-2019. Dr. Zhang's educational background includes: Bachelor of Science in Mathematics and Statistics from Lanzhou University (2012) Master of Science in Mathematics and Statistics from Lanzhou University (2015) PhD in Actuarial Science from the University of Hong Kong (2018) Dr. Zhang's research spans three main areas with significant contributions to theoretical frameworks and practical applications: Risk Management and Actuarial Science: Focusing on optimal reinsurance design, credibility theory for heterogeneous risks, systemic risk assessment using conditional distortion measures, and risk measurement under asymmetric information. Applied Probability: Investigating ordered variable models, random order theory, stochastic comparisons, and statistical dependence structures in risk modeling. Reliability Theory and Statistics: Developing methodologies for reliability analysis, system reliability design, and optimization of k-out-of-n systems with heterogeneous components and random weights. Dr. Zhang's publication record demonstrates a consistent research trajectory across actuarial science and reliability engineering domains. Their work frequently appears in top-tier journals including Insurance: Mathematics and Economics, Scandinavian Actuarial Journal, ASTIN Bulletin, and Naval Research Logistics. Recent publications show strong interdisciplinary connections between actuarial science, probability theory, and reliability engineering, with particular emphasis on risk modeling under asymmetric information and optimal system design. Dr. Zhang has received recognition for their scholarly contributions through publications in leading actuarial journals such as IME, SAJ, ASTIN Bulletin, and NAAJ, as well as operations research journals including EJOR, RESS, and NRL. As a Doctoral Supervisor, Dr. Zhang mentors PhD students in advanced research topics at the intersection of actuarial science, probability theory, and reliability engineering. Their research program addresses fundamental questions in risk modeling and system reliability, with potential applications in insurance, finance, and engineering systems.

Hu Dingمشاهده پروفایل

استاد

• Algorithms
• Computational Geometry
• Machine Learning
• +۴ مورد دیگر

Hu Ding is a pre-tenure Professor in the School of Computer Science and Engineering at the University of Science and Technology of China (USTC), where he directs the Data Intelligence, Algorithms, and Geometry (DIAG) research group. He previously held positions as a tenure-track Assistant Professor at Michigan State University (2016-2018) and a Simons-Berkeley Research Fellow jointly at Tsinghua University and UC Berkeley (2015-2016). Education: • Ph.D. in Computer Science, State University of New York at Buffalo (2015) • B.S. in Mathematics, Sun Yat-Sen University (2009) Research Interests: Hu Ding's research focuses on developing efficient algorithms for geometric optimization problems with applications in machine learning, big data, and biomedical imaging. His work bridges theoretical computer science (especially computational geometry) with practical challenges in distributed systems, outlier detection, and high-dimensional data analysis. Key areas include constrained clustering, truth discovery in crowdsourced data, and geometric methods for biomedical image analysis. Publication Trends: His recent publications demonstrate a strong focus on scalable algorithms for high-dimensional geometric optimization, particularly in distributed environments with noisy data. A consistent theme is developing theoretically-grounded solutions with practical efficiency, evidenced by work on sublinear-time algorithms, coreset constructions, and approximation frameworks for problems like k-center clustering and SVM optimization with outliers. Awards and Honors: Young Investigator Award, Ministry of Science and Technology (2021) Simons-Berkeley Research Fellowship (2015-2016) CCF Committee Member for Theoretical CS and Big Data (2021) Grants and Projects: USTC Innovation Group Grant: 'Toward Electronic Design Automation: Theories and Algorithms from AI' (2021) MOST Young Investigator Grant: 'Optimal Transportation in Medical Imaging' (3M RMB, 2021) Research Group: Leads the DIAG group with focus on geometric algorithms for data intelligence. Current team includes 6 PhD students and 15 Master's students working on problems in clustering, distributed optimization, and biomedical applications. Former students hold positions at Alibaba, ByteDance, and academic institutions.

Ying Chenمشاهده پروفایل

دانشیار

• Advanced composite materials
• Earthquake resistance engineering
• Structural damage detection
• +۲ مورد دیگر

Ying Chen is an Associate Professor and Master Tutor at the Department of Civil Engineering, Shandong University . Her research focuses on advanced composite materials, earthquake-resistant structures, and underground engineering mechanics. Specialized in structural damage detection and reinforcement Expertise in multiphase coupling analysis of underground structures Her recent publications highlight applications in concrete pipe stress analysis, crack detection in large concrete structures, and seismic resistance of complex buildings. Current research explores hybrid fiber-reinforced materials and water conveyance tunnel mechanics. Associated with the Qianfoshan Campus Civil Engineering Building , she contributes to structural engineering education and research in Jinan, China.

Qihai Zhangمشاهده پروفایل

دانشیار

• Hydraulic Structure
• Project Management
• ISO International Standards
• +۳ مورد دیگر

Qihai Zhang is an Associate Professor and Master Tutor at Shandong University's School of Civil Engineering, Department of Water Conservation and Hydropower Engineering. His research integrates hydraulic engineering, project management, and ISO standard applications with a focus on sustainable infrastructure development. Research Interests: Zhang specializes in: Hydraulic Structures : Safety analysis of dams, aqueducts, and water supply networks under environmental stresses Project Management : Implementation of ISO standards (quality/environmental/safety) and risk mitigation in water conservancy projects Irrigation Systems : Optimization of brackish water use and crop-specific irrigation techniques in floodplains Publications emphasize dam safety protocols, water network optimization, and quality management frameworks, reflecting consistent focus on infrastructure resilience from 1994–2007. Awards and grants are not documented.

Qingsong Zhangمشاهده پروفایل

استاد

• Grouting Technology
• Underground Engineering Disasters
• Rock and Soil Reinforcement
• +۱ مورد دیگر

Qingsong Zhang is a Professor and the Dean of the School of Civil Engineering at Shandong University, where he leads research at the Geotechnical and Structural Engineering Center. He serves as a PhD Tutor, guiding doctoral candidates in geotechnical engineering disciplines. Research Focus: His work centers on grouting technology and disaster mitigation in underground engineering, with emphasis on: Mechanisms of water inrush disasters and treatment theories Grouting diffusion/reinforcement in rock/soil systems Simulation methods for grouting processes Development of novel grouting materials and equipment Publications: His 12 most recent articles (2010–2017) focus on experimental and theoretical advancements in grouting mechanics, tunnel engineering, and rock fracture analysis. Predominant themes include viscosity dynamics, fault zone reinforcement, and inrush disaster prevention, demonstrating consistent innovation in geotechnical solutions. Patents & Innovations: Zhang holds 10 patents for grouting technologies, including devices for constant-pressure injection, fracture zone treatment, and temperature-based geological forecasting. These inventions address practical challenges in underground construction and disaster control.