I am an Associate Professor of Industrial Engineering with over two decades of experience in teaching, research, and supervision in data-driven decision making, operations research, and computational modeling. My research integrates Multi-Criteria Decision Analysis (MCDA), Agent-Based Modeling (ABM), and Reinforcement Learning (RL) to support strategic decision systems in sustainability, investment, and industrial operations. My recent work explores human-centric and multi-actor systems, leveraging simulation-based optimization and AI-driven analytics to enhance resilience, efficiency, and sustainability in complex socio-technical environments. I have published extensively in international journals, reviewed over 75 manuscripts, and am an active member of INFORMS and the System Dynamics Society. My long-term goal is to bridge industrial systems modeling with intelligent decision support, aligning academic research with real-world sustainability and innovation challenges.

🔹 Experience
Associate Professor — Industrial Engineering, University of Engineering and Technology, Taxila (2018 – Present)• Teach graduate and undergraduate courses in Operations Research, Data Mining, Advanced Statistics, System Simulation, and Soft Computing.• Conduct funded research in agent-based and reinforcement learning models for sustainable and data-driven decision systems.• Supervise doctoral students in decision analytics, multi-agent modeling, and MCDM applications.• Reviewer for international journals including Neural Computing and Applications, the Journal of Cleaner Production, Annals of Operations Research, Environment, Development and Sustainability, Energy for Sustainable Development, Scientific Reports, IEEE Access, Cleaner Energy Systems, Utilities Policy, and Sustainable Futures

🔹 Research Interests•
Data-Driven Decision Making• Agent-Based Modeling (ABM)• Reinforcement Learning (RL)• Multi-Criteria Decision Analysis (MCDA / MAMCA)• Sustainable Supply Chains• System Dynamics; Simulation• E-Health and Humanitarian Systems

🔹 Selected Achievements•
30+ peer-reviewed publications; ~360+ citations• Reviewer for 75+ international journal papers• Completed Coursera Specializations in Machine Learning, Deep Learning, and Reinforcement Learning• 20+ years of experience integrating data science with sustainability modeling

I have a strong background in building and incorporating agent-based simulations for learning. Throughout my graduate career, I have worked at the Center for Connected Learning and Computer Based Modeling (CCL), developing modeling and simulation tools for learning. In particular, we develop NetLogo, the gold standard agent-based modeling environment for learners around the world. In my dissertation work, I marry biology and computer science to teach the emergent principles of ant colonies foraging for food and expanding. The work builds on more than a decade of experience in ABM. I now work at the Center for the Science and the Schools as an Assistant Professor. We delivered a curriculum to teach about COVID-19, where I incorporated ABMs into the curriculum.

You can keep up with my work at my webpage: https://kitcmartin.com

**PROFESSIONS **

Associate Professor
School of Management Science and Engineering, Shandong Technology and Business University (Yantai 264005, P. R. China)

**EDUCATION BACKGROUDS **

Ph. D. Degree, 09/2009 – 07/2015
School of Economics and Management, Beihang University (P. R. China)

M. A. Degree, 09/2003 – 02/2006
The Institute of Systems Engineering, Dalian University of Technology (P. R. China)

B. A. Degree, 09/1999 – 07/2003
Department of Information and Control Engineering, Zhengzhou University of Light Industry (P. R. China)

**VISITING & SUMMER SCHOOLS **

Visiting Scholar at GECS – Research Group of Experimental and Computational Sociology (March, 2017 – February, 2018)
 Università degli Studi di Brescia (Italy)
 Co-supervisor: Professor Flaminio Squazzoni

Summer school in ‘Agent-based modeling for social scientists’ (September 4-8, 2017)
 University of Brescia, Italy
 Instructors: Flaminio Squazzoni, Simone Gabbriellini, Nicolas Payette, Federico Bianchi

The Santa Fe Institute’s Massive Open Online Course: Introduction to Agent-Based Modeling (Jun 5 – September 8, 2017)
 The Santa Fe Institute, Complexity Explore Web: abm.complexityexploer.org
 Instructors: Bill Rand

Summer school in ‘Complex systems and management’ (July 2-12, 2012)
 National Defense University, P. R. China
 Instructors: Xinjun Mao, Yongfang Liu, Dinghua Shi, Qiyue Cheng

The goal of my research program is to improve our understanding about highly integrated natural and human processes. Within the context of Land-System Science, I seek to understand how natural and human systems interact through feedback mechanisms and affect land management choices among humans and ecosystem (e.g., carbon storage) and biophysical processes (e.g., erosion) in natural systems. One component of this program involves finding novel methods for data collection (e.g., unmanned aerial vehicles) that can be used to calibrate and validate models of natural systems at the resolution of decision makers. Another component of this program involves the design and construction of agent-based models to formalize our understanding of human decisions and their interaction with their environment in computer code. The most exciting, and remaining part, is coupling these two components together so that we may not only quantify the impact of representing their coupling, but more importantly to assess the impacts of changing climate, technology, and policy on human well-being, patterns of land use and land management, and ecological and biophysical aspects of our environment.

To achieve this overarching goal, my students and I conduct fieldwork that involves the use of state-of-the-art unmanned aerial vehicles (UAVs) in combination with ground-based light detection and ranging (LiDAR) equipment, RTK global positioning system (GPS) receivers, weather and soil sensors, and a host of different types of manual measurements. We bring these data together to make methodological advancements and benchmark novel equipment to justify its use in the calibration and validation of models of natural and human processes. By conducting fieldwork at high spatial resolutions (e.g., parcel level) we are able to couple our representation of natural system processes at the scale at which human actors make decisions and improve our understanding about how they react to changes and affect our environment.