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Graduate students Aryan Deshwal and Syrine Belakaria presenting at the NeurIPS conference.

Many researchers in artificial intelligence and machine learning aim to develop computer programs that can sift through huge amounts of data, learn from it, and guide future decisions.

But, what if the many data options are hugely expensive and difficult to acquire and one has to decide which data is best to spend your money on? What direction should scientists head with their next experiment?

A WSU research team is taking a different angle in machine learning research in what they say can be of great practical use, especially, to engineers and scientists.

Computer science graduate students Syrine Belakaria and Aryan Deshwal recently presented their research at major international artificial intelligence and machine learning conferences, including the 2019 Conference on Neural Information Processing Systems (NeurIPs) in Vancouver, Canada and the 2020 Association for the Advancement of Artificial Intelligence Conference in New York. The NeurIPS conference is the premier machine learning conference in the world with more than 14,000 attendees. Belakaria and Dewshwal are advised by Jana Doppa, the George and Joan Berry Chair Assistant Professor in WSUs School of Electrical Engineering and Computer Science.

The group will also present their collaborative work with computer engineering researchers at the Design, Automation and Test in Europe Conference (DATE-2020) in Grenoble, France.

The groups research is based on Doppas 2019 NSF Early Career Award and focuses on developing general-purpose learning and reasoning computer algorithms to support engineers and scientists in their efforts to optimize the way they conduct complex experiments. They are working to combine domain knowledge from engineers and scientists with data from past experiments to select future experiments, so that researchers can minimize the number of experiments needed to find near-optimal designs.

Doppas team has analyzed and experimentally evaluated the algorithms for diverse applications in electronic design automation, such as for analog circuit design, manycore chip design, or tuning compiler settings, and in materials science, such as for designing shape memory alloys and piezo-electric materials. They also proposed two algorithms to optimize multiple objectives with minimal experiments and have developed the first theoretical analysis for multi-objective setting. They also developed a novel learning to search framework to optimize combinatorial structures, which is very challenging when compared to continuous design spaces.

The common theme behind this work is better uncertainty management to select the sequence of experiments, Doppa said.

While Belakaria and Deshwal have contributed important research innovations in the field, they also have gained valuable learning opportunities during their studies. While at NeurIPS, the students had the chance to network with leaders in the field of machine learning as well as attend a special session for women and those who are underrepresented in the field. The session had more than one thousand attendees.

The conference gave the students a chance to see the real-world applications of AI, said Deshwal, and meeting professionals who are using machine learning to solve challenges in medical and science fields. A large number of prominent companies, such as Uber, Google, Facebook, and Amazon, sent representatives to the conference and hosted events.

Belakaria, who is originally from Tunisia, said it was amazing to attend a roundtable and sit down with female leaders in the explosive and competitive field. She appreciated getting advice on how many women in the field are finding success while balancing their work and personal lives.

Both she and Deshwal expressed appreciation for a supportive lab that has provided mentoring and has encouraged their growth and success.

Research is as emotional as it is academic, and our camaraderie helps us a lot, said Deshwal, originally from India. When you have a field that is moving so quickly such as machine learning, having people who are supportive is so important.

Being in a community where you feel safe, respected, and valued for your scientific contribution is very crucial for women in science. We feel welcome in the machine learning community, added Belakaria.

With WSU since 2014, Doppa is part of a major expansion on the part of the university to meet the growing demands in the fields of electrical engineering and computer science. Since 2015, research expenditures in WSUs School of Electrical Engineering and Computer Science have nearly doubled, as have the number of graduates from its computer science program.

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Using machine learning to solve real-world data problems for scientists and engineers - WSU News