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The School of Computing, Informatics, and Decision Systems Engineering welcomes its new faculty members

Dimitri Bertsekas - Professor

Throughout his career, Dimitri Bertsekas has enjoyed engineering’s rich variety of challenges and how many of them can be viewed through a “unifying mathematical lens.”

An avid researcher, author and educator, Bertsekas has used this approach to contribute to advances in multiple research areas, including optimization, reinforcement learning, machine learning, dynamic programming and data communications.

In fall 2019, he joins the Arizona State University faculty as Fulton Chair of Computational Decision Making in the School of Computing, Informatics, and Decision Systems Engineering, one of the six Ira A. Fulton Schools of Engineering at ASU.

“I found ASU to be an exciting place for research where I can work with outstanding colleagues,” says Bertsekas.

Bertsekas has spent much of his career — since 1979 — as a faculty member at the Massachusetts Institute of Technology, where he held the position of McAfee Professor of Engineering.

At ASU, he’ll be teaching research-oriented seminars on the topics of optimization, optimal control, machine learning and artificial intelligence.

His main research focus at present is reinforcement learning — “a field that addresses large and challenging multistage decision problems, often with the use of neural networks and self-learning.”

Read more about Professor Bertsekas

Heewook Lee - Assistant Professor

Heewook Lee joins ASU from Carnegie Mellon University where he has been a Lane Fellow in the Computational Biology Department of the School of Computer Science for the past four years.

“I chose to come to ASU because of its fantastic collaborative environment for multidisciplinary researchers,” says Lee. 

Lee’s current research efforts are toward developing computational techniques to study regulatory elements in the immune system.

“This is particularly important because such understanding can lead to new ways to treat cancer or autoimmune patients,” says Lee.

As a Lane Fellow at Carnegie Mellon, he worked on developing novel assembly algorithms for reconstructing highly diverse immune-related genes, including human leukocyte antigens.

Lee received a bachelor’s degree in computer science from Columbia University and obtained his master’s and doctoral degrees in computer science from Indiana University. Prior to graduate studies, he worked as a bioinformatics scientist at a sequencing center and genomics company where he was in charge of the computational unit responsible for carrying out various microbial genome projects and the Korean Human Genome project.

This semester, Lee will be teaching CSE 355: Introduction to Theoretical Computer Science, and, in the future, a course on computational biology.

“CSE 355 is a core class for all computer science undergraduate majors and it is a foundation course that paves a way to have a deeper understanding of computation,” says Lee. “My future course in computational biology will be for those students who are interested in developing or applying algorithms to solve real-world biological problems or to have a better understanding of biology in general.”

In his free time, Lee enjoys outdoor activities and is also an avid squash player. 

Yingzhen Yang - Assistant Professor

Yingzhen Yang became interested in engineering when he implemented fast-searching algorithms as a high school student.

“I chose to perform research in machine learning and deep learning because they are very interesting areas that combine the best of statistics and multiple areas of fundamental mathematics, physics and computer science,” says Yang.

He sees machine learning and deep learning as “the driving force for modern artificial intelligence,” he says.

“I want to teach courses in these areas because I hope more students will learn the basics and even pursue careers in these areas,” says Yang. “Machine learning and artificial intelligence are particularly important in science and engineering nowadays.”

At ASU, Yang will research principles and applications of deep learning, especially optimization and generalization of deep neural networks and compression of deep neural networks.

Research to advance the optimization and generalization of deep neural networks is fundamentally important to solving problems in deep learning, and can significantly enhance the understanding of deep neural networks and facilitate their broader application in various scenarios.

“I will also research AutoML, a hot topic in the deep learning community that automatically searches for optimal neural architecture and eliminates the need for designing neural architecture manually for each task,” says Yang. “AutoML models are being incorporated into my research in compression of deep neural networks.”

“I am deeply impressed by ASU’s strong commitment to research and innovation,” says Yang. “It is a great university for me to advance my research career.”

In his spare time, Yang loves traveling and playing badminton.

Jia Zou - Assistant Professor

For Jia Zou, her passion for scientific research was solidified the day she entered an international mathematical contest in modeling with two college classmates. The competition, sponsored by the Consortium for Mathematics and Its Applications, required each team to build a mathematical model to investigate a real-world issue.

“We won a meritorious prize, and working with a great team on an exciting problem ushered me into a lifelong love for research,” say Zou.

After earning her doctoral degree, Zou worked as a researcher at IBM Research-China.

“During that time, I had the opportunity to talk to IBM customers from various industries such as telecom, banking, internet companies and so on,” says Zou. “All the real-world use cases I’ve learned formed a clear vision for the future of data-intensive systems.” 

Zou then joined Rice University as a research scientist.

“During my time at Rice, several research papers have been published in VLDB and SIGMOD, which are the two flagship conferences in the big data research community, including an honorable mention in VLDB 2019. The recognition received for our research products further encourages me to stay.” says Zou.

At ASU, Zou’s research will focus on database systems and systems for big data and machine learning.

“I choose this area for my research because data is collected at a sharply rising speed in this more and more digitally connected world,” says Zou. “The total volume of data collected on Earth will reach 44 zettabytes by next year (one zettabyte equals 1 trillion gigabytes). Every industry needs to learn how to swim in such an ocean of data, and I want to help them.”

This semester, Zou will focus her research on providing deterministic performance for machine learning systems and applications.

“That means a user can expect the completion time of their machine learning applications falls in a pre-specified performance envelope,” says Zou. “I’m excited, because this is super important in emerging internet of thing applications, such as automatic car driving and health care, to avoid loss of lives and money due to delayed response.”

Michael J. Findler - Lecturer

Michael J. Findler is a Sun Devil at heart. A former student of Arizona State University, he looks forward to joining the School of Computing, Informatics and Decision Systems Engineering as a lecturer.

Using his doctoral degree in human factors engineering and experience in software engineering, Findler will be teaching upper-level and graduate courses in software engineering in fall 2019.

Ali Kucukozyigit - Lecturer

“What can go wrong?” is a question Ali Kucukozyigit urges all of his students to consider in their careers as the field of engineering becomes increasingly complex.

Kucukozyigit joins the School of Computing, Informatics, and Decision Systems Engineering as a lecturer to teach risk management and introduction to systems engineering. He’ll help students identify and assess risks, and understand the concepts and processes of systems and how they can be governed.

Vijay Suthar - Lecturer

Vijay Suthar is joining the School of Computing, Informatics, and Decision Systems Engineering as a lecturer to focus on his passion for teaching after retiring from a nearly 33-year career in high tech. 

Although the scope of his high tech career had been intentionally been broad, the common thread throughout his career and life has been to help individuals and teams reach their potential.

With a passion for all types of problem solving, Suthar looks forward to teaching Intro to Programming, Operating Systems and Software Engineering Capstone in fall 2019.


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