BY THE NUMBERS...

3,938

UNDERGRADUATE STUDENTS

329

MASTER'S STUDENTS

199

DOCTORAL STUDENTS

104

ACADEMIC FACULTY

SCHOLARSHIPS AWARDED

$4.9M

RESEARCH EXPENDITURES

$16M

EARLY CAREER AWARDS

25

Preparing Leaders for the 'Next Industrial Revolution'

Oregon State is launching a unique program for graduate study in artificial intelligence, with an initial cohort of about 40 students to be enrolled in fall 2021. The program is the first in the United States to offer both master’s and doctoral degrees in artificial intelligence as an interdisciplinary field of study.

“AI is what I like to call the outward-looking face of computer science,” said Prasad Tadepalli, the new program’s director and a professor of computer science. “Much of computer science is associated with how to make computers faster, better, cheaper, and so on. AI is more focused on how to apply computer science to other fields.”

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In addition to offering courses and research opportunities in core AI topics — machine learning, knowledge representation, reasoning under uncertainty, sequential decision-making, natural language processing and computer vision — the program, which is supported by 30 faculty, allows students to choose relevant courses from a wide range of disciplines across the university.

“In the broader context of society, the goal of this program is to train individuals to be leaders in industry or academia, to start new companies, and to make their own contributions to this rapidly expanding field,” said Julie A. Adams, associate director of Oregon State’s Collaborative Robotics and Intelligent Systems Institute, and a professor of computer science.

The new AI program does not require a computer science degree as a prerequisite, said Alan Fern, associate head of research and professor of computer science.

“Often students who are well-poised to study artificial intelligence don’t come from a traditional computer science background,” he said. “The AI program is meant to be more flexible and will be enriched by having students with advanced knowledge in areas outside of computer science. Sometimes getting a domain expertise is much harder than getting the necessary expertise in computer science and statistics. People who have deep domain expertise plus knowledge and expertise in applying decision-making AI tools are going to be very highly sought in the job market.”

Teaching Machines Common Sense

Alan Fern on a laptop

Backed by a four-year, $8.7million grant from DARPA, Alan Fern, professor of computer science, is leading an interdisciplinary team of researchers in the development and training of a “machine common sense service” that will learn about its environment in a manner similar to that of a toddler. 

“We are studying and developing learning and reasoning techniques to enable AI systems to exhibit common-sense reasoning and planning capabilities on par with those of an 18-month-old child,” Fern said. “A key aspect of our approach is to study how to effectively combine the representation-learning capabilities of deep neural networks with the powerful reasoning capabilities of state-of-the-art AI planning and reasoning engines.”

Representation learning, also called feature learning, is the means by which intelligent systems gain and categorize information, such as information about places and objects in their environment.

Using videos of toddlers provided by a colleague at NYU, Fern will make a computer model of how babies explore their environment and then create an “artificial agent” — a virtual toddler — that will be tested in a simulated 3D environment. 

“It will look like a simple robot in a video game exploring a virtual space,” Fern said. “The basic idea is to get robots to have more common sense regarding physical interactions in their environment.”

Natural Currents to Power the Grid

Yue Cao

Engineering researchers at Oregon State University are collaborating with colleagues at the University of Michigan on a project to convert river and ocean currents into electric current, using reconfigurable, high-efficiency micro-turbines. 

The project, supported by a $3.9 million grant from the Department of Energy, bases its approach on an array of micro-turbines with a modularized architecture and reconfigurable units, making it adaptable to different applications and marine environments. It’s called RAFT, for “Reconfigurable Array of High-Efficiency Ducted Turbines for Hydrokinetic Energy Harvesting.” 

Oregon State’s team, led by Yue Cao, assistant professor of electrical and computer engineering, is specifically responsible for the electrical energy conversion subsystem, including hardware and control designs from the generator terminals to the grid connections.

“Hydrokinetic energy is an abundant renewable resource that can boost grid resiliency and reduce infrastructure vulnerability, but it is currently cost-prohibitive compared to other sources,” Cao said. “The RAFT concept is a promising candidate to address this barrier by designing new, efficient systems to harness our nation’s tidal, riverine, and ocean resources.” 
 

Rebecca Hutchinson in the woods

Unbiasing Species Distribution Models

Rebecca Hutchinson, an assistant professor of computer science, recently received a Faculty Early Career Development, or CAREER, award from the National Science Foundation to improve species distribution models, or SDMs, built with data gathered through community science and used by ecologists and natural resource managers.

“You build an SDM by correlating observations of species — are they there or not? — with environmental features,” Hutchinson said. “Then you can use the model to understand why species live where they do and how likely a species is to occur at a new site. But the spatial aspects of both species and environmental data can be problematic for the machine learning currently used in the models.”

Hutchinson will use her $564,000 award to account for the inevitable underreporting of species during biodiversity surveys and reduce errors that can creep into model quality estimates. For example, using elevation as a proxy for temperature could prove useful in the Cascades, but the substitution may fail at lower elevations — an error that could lead to bad predictions from the model.

“The error introduced by underreporting can be corrected by conducting multiple observations at the same site and estimating the probability of detecting the species, but community science programs usually aren’t set up that way,” Hutchinson said. “The award will support research to create groups of multiple observations after the fact to better account for underreporting.”

David Allstot

Faculty Member Earns Highest Profession Honor

David Allstot, professor of electrical and computer engineering, has been elected to the National Academy of Engineering, the highest honor in the profession. He was among 105 new academy members elected in 2020. Allstot, a faculty member in the College of Engineering since 2017, has held numerous academic and industry positions over a career spanning more than four decades. The holder of 10 patents, Allstot led the global change from discrete analog to integrated digital telephone
 

New Academic Faculty

HUCK BENNETT

Huck Bennett

                                          

Computer Science

                                          

Huck Bennett joins the faculty this fall as assistant professor of computer science. He earned his doctorate at New York University. Prior to Oregon State, Bennett was a postdoctoral scholar at the University of Michigan and at Northwestern University. His research area is theoretical computer science, with an emphasis on lattices and geometric algorithm.

NEW ACADEMIC FACULTY

SANGHYUN HONG

Sanghyun Hong

                                          

Computer Science

                                          

Sanghyun Hong joins the faculty this fall as assistant professor of computer science. He earned his doctorate at the University of Maryland. His research focuses on the security and dependability of machine learning systems. In particular, he is interested in characterizing vulnerabilities to hardware attacks and data poisoning. During his doctoral studies, he evolved a view of machine learning as a computational tool rather than a mathematical concept, and he brings a unique perspective on the security guarantees for machine learning systems provided by algorithmic approaches.

SIBIN MOHAN

Sibin Mohan

                                          

Computer Science

                                          

Sibin Mohan joins the faculty this fall as associate professor of computer science. He earned his doctorate from North Carolina State University. Prior to Oregon State, Mohan was research assistant professor in the Department of Computer Science and the Information Trust Institute at the University of Illinois Urbana-Champaign. His research focuses on security for cyber-physical systems, secure cloud computing, and software-defined networking for use in safety-critical domains. A particular focus is on issues related to security and novel networking technologies for cyber-physical, real-time, and embedded systems.

DAVE NEVIN

Dave Nevin

                                          

Computer Science

                                          

Dave Nevin joins the faculty as an assistant professor of practice. Nevin has served in numerous positions at Oregon State, including as the university’s first full-time Chief Information Security Officer from 2012-2018. Nevin now leads the Oregon Research & Teaching Security Operations Center at Oregon State University. Newly formed, the ORTSOC is designed to educate and train the next generation of the cybersecurity workforce while providing core operational security, information sharing, and analysis for participating organizations across the region.

VINCENT IMMLER

Vincent Immler

                                          

Electrical and Computer Engineering

                                          

Vincent Immler joins the faculty this fall as assistant professor of electrical and computing engineering. He earned his doctorate from Technical University of Munich, Germany. Prior to Oregon State, Immler worked as a researcher at the Fraunhofer Institute for Applied and Integrated Security. Interested in all aspects of security, his research focuses on tamper-resistant hardware architectures. He was author of the 2018 best paper award at the IEEE International Symposium on Hardware Oriented Security and Trust.

KARTHIKA MOHAN

Karthika Mohan

                                          

Computer Science

                                          

Karthika Mohan joins the faculty this fall as assistant professor of computer science. She earned her doctorate from the University of California, Los Angeles, and was a postdoctoral scholar at the University of California, Berkeley. Mohan’s research in artificial intelligence is interdisciplinary and includes causal inference, graphical models, and AI safety. She was awarded the Google Outstanding Graduate Research Award for 2017.

SANDHYA SAISUBRAMANIAN

Sandhya Saisubramanian

                                          

Computer Science

                                          

Sandhya Saisubramanian joins the faculty this fall as assistant professor of computer science. She earned her doctorate from the University of Massachusetts, Amherst. Saisubramanian’s research interests are in designing AI systems that are safe, reliable, and unbiased. Her current research focus is on developing techniques for reliable decision-making in autonomous systems that operate in fully and partially observable environments.

RADHA VENKATAGIRI

Radha Venkatagiri

                                          

Computer Science

                                          

Radha Venkatagiri joins the faculty this fall as assistant professor of computer science. She earned her doctorate from the University of Illinois Urbana-Champaign. A computer architect, Venkatagiri builds efficient computing systems that conserve resources by allowing the system to make controlled errors. Her research focuses on error-efficient computing with emphases on low-cost hardware resiliency and approximate computing. Prior to UIUC, she was a CPU/silicon validation engineer at Intel, where she won a divisional award for key contributions in validating new industry-standard CPU features.