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Association for Computing Machinery

Magazine: Spring 2024 | Volume 30, No. 3

SECTION: Features

Brain-Load, Stress Levels, and Visual Complexity: Digital Tools for the Detection of Intangible Human-Environment Interactions

How does the built environment affect our brain? The way we perceive our environments plays a crucial role in how our brains respond to cognitive load. Understanding the impact of spatial complexities on our cognitive processes could inform future design guidelines for more responsive environments.

By Mirna Zordan, Seungwoo Je

HTML | In the Digital Library
Tags: Computer-aided design, Consumer health, Human computer interaction (HCI)

Understanding Human-building Interactions Using Computing

Reconstructing the network of life from molecular data is a complicated task. How can computational algebraic geometry play a role?

By Bowen Du

HTML | In the Digital Library
Tags: Architecture (buildings), Home networks, Human computer interaction (HCI), Machine learning


Developing Wearables and Environmental Sensor Systems for Studying Ecosystems

Patrick Chwalek's research is focused on understanding various ecosystems and the living organisms within them. He has been creating a range of systems and tools, including wearables and environmental sensor systems, for researchers to use in the wild. In this interview, Chwalek talks about his experiences of deploying these systems outside the laboratory and shares his insights gained from studying different environments.

By Cathy Mengying Fang, Patrick Chwalek

HTML | In the Digital Library
Tags: Biographies, Embedded and cyber-physical systems, Haptic devices, Human computer interaction (HCI), Life and medical sciences


Unveiling Patterns of the Earth through Machine Learning and Geospatial Analysis

Konstantin Klemmer is a researcher at Microsoft Research New England, where he works on the representation of geospatial phenomena in machine learning methods.

By Jiayi Li, Konstantin Klemmer

HTML | In the Digital Library
Tags: Biographies, Earth and atmospheric sciences, Machine learning, Spatial-temporal systems