Wei Wang
Applied Scientist @ Amazon
Wei Wang
Applied Scientist @ Amazon
Wei Wang
Applied Scientist @ Amazon
07/28/2025 - New Paper accepted in Ecology and Evolution
I’m thrilled to share that our latest paper has been published by Ecology and Evolution!
📄 Title: Anthropogenic Habitat Loss and Fragmentation May Alter Coevolutionary Progress as Examined in a Brood Parasitism Model (Paper Link)
This project explores how human-induced habitat loss reshapes coevolution between brood-parasitic cuckoos and their avian hosts. By combining stochastic simulation, individual-based modeling, and reinforcement-driven behavior, we built a computational framework to examine how ecological pressures affect adaptive strategies and long-term species survival.
🧠 What We Built
We developed a multi-stage simulation pipeline in which each coevolutionary participant (cuckoo or host) is initialized with probabilistic traits and evolves through distinct lifecycle stages:
mating → deception → detection → reinforcement → group updates.✅ Core Innovations
Stochastic parameterization using truncated normal, Weibull, and Poisson distributions to capture ecological variability.
Individual-level reinforcement logic that models learned, experience-driven behavior beyond genetic inheritance.
Behavioral trait-space scanning to identify adaptive windows under varying habitat and evolutionary pressures.
📊 Key Insights
Severe habitat loss constrains behavioral adaptability and accelerates extinction, even when flexible strategies (like backup parasitism) are available.
Coevolutionary systems are highly sensitive to stochastic feedback, emphasizing the computational complexity of ecological resilience and adaptation.
💻 Open-Source Simulation Code
We’ve released the Python source code so you can experiment with your own simulation parameters and scenarios:
👉 Code Link🙌 Acknowledgment
Many thanks to our team and collaborators for supporting this interdisciplinary effort bridging computer science and ecology. We welcome collaborations and discussions with researchers applying simulation, stochastic modeling, Monte Carlo methods, or AI to ecological and evolutionary systems.
07/16/2025 - New Paper accepted in Environmental Research Communications
I am thrilled to share that our latest work, “Bibliometric Insights into Pollution Research: Trends, Geographic Disparities, and Emerging Environmental Challenges”, has been accepted for publication in Environmental Research Communications. The paper link is: Link.
What did we do?
Our team conducted one of the most comprehensive, large-scale bibliometric analyses of global pollution research to date. We analyzed over 735,000 publications spanning 1990–2024, using advanced Python-based pipelines and parallel computing to process and classify this massive dataset efficiently. Leveraging high-performance computing was crucial for extracting research trends, collaboration networks, topic dynamics, and even sentiment patterns from such a big data landscape.
Key findings:
📈 Pollution research has exploded over the past three decades, but there are still stark geographic imbalances—many developing regions with severe pollution burdens remain underrepresented in the scientific literature.
🌏 Collaboration is dominated by developed countries, with limited engagement from low-income nations.
🏷️ Research focus is heavily weighted toward scientific and engineering solutions, while education and economic strategies receive far less attention.
🔬 Emerging issues like microplastics and cross-media pollution (e.g., air-to-water transfers) are gaining traction, but integrated, cross-disciplinary approaches are still rare.
📝 Our sentiment analysis revealed that papers from underrepresented regions and on emerging pollution issues tend to have more negative tones, reflecting greater concern or urgency.
Why does this matter?
Pollution is a global problem requiring not only technical innovation, but also international cooperation, policy reform, and public engagement. Our findings highlight urgent gaps and call for more inclusive, interdisciplinary, and action-oriented research—especially in parts of the world that need it most.
Open Science:
All our code and data processing pipelines are available for the community at: Link
A huge thank you to all collaborators and supporters!