Celebrating Women Innovators
Two Trailblazers Who Are Advancing Technology
So many women have advanced science, technology, and other fields of innovation.
Here, we celebrate the stories of women who are doing so using the DevCloud for oneAPI. One project is published and the other is in its infancy—and these are only a start. Both hold the promise and spirit of innovation that captivates and uplifts us to reach for more.
Solving Real-World Problems with Increased Efficiency and Productivity
Markov decision-making processes (MDP) enable computers to make autonomous, intelligent decisions when the outcome of these decisions is indeterministic, which is useful in many disciplines, including robotics, automatic control, economics and manufacturing. Denisa Constantinescu, a PhD student in mechatronics and a researcher in the Computer Architecture Department at the University of Malaga in Andalusia, aimed to extend the applicability of Markov decision-making methods to solve real-world problems using value iteration. She examined the performance and energy efficiency impacts of implementing these methods in low-power computing systems across three different heterogeneous programming approaches and scheduling strategies: OpenCL, oneAPI with buffers and oneAPI with unified shared memory. She is currently extending this work to efficiently compute policies for POMDPs, which are MDP agents with uncertainty in sensory systems. Her goal is to make it feasible and easy to implement autonomous decision-making applications on mobile platforms like smartphones and mobile robots.
Outside of this work, Denisa loves to paint, make soap, and listen to audiobooks while riding her bike. Her “part-time” job is volunteering, usually for educational and environmental causes. Currently, she’s a trainer and organizer at Campus Tech Chicas, a summer school for initiating 12 to 16-year-old girls in STEM.
Predicting Crop Yield through Machine Learning and AI Analytics
As two primary characteristics of weather and climate, precipitation and temperature also play a critical role in the outcome of crop yield. Maura Tokay, computer scientist and lead software programmer at SSAI, and developer at the University of Maryland (UMBC), is seeking to predict crop yield for corn, wheat and soybeans using meteorological data and machine learning, aided by Intel® AI Analytics Toolkit, Intel DevCloud and more. She will also strive to understand whether or not the feature importance is the same for each crop, with the ultimate goal to analyze the impact of climate change on food security by quantifying the importance of weather parameters on the yield of each of the crops examined.
When not applying her computer programming prowess to crop yield predictions, Maura likes to cook, read, listen to Brazilian music and attend her daughters’ school activities.
We are proud to support these women in their pursuit of innovation and their aim to make our world a better place.