Eecs oregon state graduation speaker
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Keynote Speakers
Thomas G. He received the A. Dietterich studies fundamental problems in AI and machine learning motivated by important challenges in emerging applications — specifically, computational ecology and ecosystem management, computer security, and robust AI systems. From —, he edited the MIT Press series on Adaptive Computation and Machine Learning, and from to the present he has moderated the machine learning area of arXiv. The prospect of machine superintelligence even if very uncertain and distant deserves some systematic analysis and discussion, since the consequences would be far-reaching.
But what, specifically, are the questions we should ask? What kind of research on this topic is possible now? And what can we do mitigate the predilection of popular media for alarmist stories illustrated with screenshots from Hollywood science fiction movies? He is the founding director of the Future of Humanity Institute, a multidisciplinary research center that enables a few exceptional mathematicians, philosophers, and scientists to think about global priorities and big questions for humanity.
Bostrom has a background in physics, computational neuroscience, and mathematical logic as well as philosophy. He is best known for his work in five areas: 1 existential risk; 2 the simulation argument; 3 anthropics developing the first mathematically explicit theory of observation selection effects ; 4 impacts of future technology; and 5 implications of consequentialism for global strategy.
He is recipient of a Eugene R. Gannon Award one person selected annually worldwide from the fields of philosophy, mathematics, the arts and other humanities, and the natural sciences. He has been listed on Foreign Policy's Top Global Thinkers list; and he was included on Prospect magazine's World Thinkers list, the youngest person in the top 15 from all fields and the highest-ranked analytic philosopher. His writings have been translated into 24 languages.
There have been more than translations and reprints of his works. In my talk I will give an overview of the ambitious research program at DeepMind, including some of our latest advances. I will also discuss some of the key challenges we are currently tackling, in the quest to build Artificial General Intelligence, and the approaches we are taking to solve them.
Hassabis is a former child chess prodigy, who finished his A-levels two years early before coding the multi-million selling simulation game Theme Park aged Following graduation from Cambridge University with a double first in computer science he founded the pioneering videogames company Elixir Studios producing award-winning games for global publishers such as Vivendi Universal.
His research connecting memory with imagination was listed in the top ten scientific breakthroughs of by the journal Science. With the success of machine learning, we increasingly see learning algorithms make decisions in the real world.
Often, however, this is in stark contrast to the classical train-test paradigm, since the learning algorithm affects the very data it must operate on. I will explain how statistical confidence bounds can guide data acquisition in a principled way to make effective decisions in a variety of complex settings. I will discuss several applications, ranging from autonomously designing wetlab experiments in protein structure optimization, to safe automatic parameter tuning on a robotic platform.
Before that he was an assistant professor of computer science at Caltech. He received his Ph. I describe a sequence of steps that facilitate effective learning of treatment policies in mobile health. These include a clinical trial with associated sample size calculator and data analytic methods. An off-policy Actor-Critic algorithm is developed for learning a treatment policy from this clinical trial data.
Open problems abound in this area, including the development of a variety of online predictors of risk of health problems, missing data and disengagement. Susan Murphy is the H. Robbins Distinguished University Professor of Statistics, a professor of psychiatry and a research professor at the Institute for Social Research. Her research focuses on improving sequential, individualized, decision making in health, in particular on clinical trial design and data analysis to inform the development of mobile health treatment policies.
Deep learning has had a major impact in the last 3 years. Imperfect interactions with machines, such as speech, natural language, or image processing have been made robust by deep learning and deep learning holds promise in finding usage structure in large datasets. However, the training process is lengthy and has proven to be difficult to scale due to constraints of existing compute architectures. Beyond the algorithms, deep learning is a fundamentally new way to express computation. In this talk, I will outline some of these challenges and how fundamental changes to the organization of computation and communication can lead to large advances in capabilities.
He has a diverse background in both synthetic and biological computation, spanning microprocessor architecture, signal processing and neuroscience. After undergraduate training in electrical engineering and computer science at Duke, Rao spent the next 10 years designing processors and specialized chips at Sun Microsystems and a variety of startups, doing wireless DSP, networking, and video compression. In , to better understand biological computation, Rao pursued a PhD in neural computation and neural prosthetics at Brown University.
Nervana is a culmination of this experience, bringing together computer engineering and biological inspiration to build smarter machines. AI applications have been built, deployed and used for industrial and government purposes for many years. The experiences have been documented in IAAI conference proceedings since Over that period, the breadth of applications has expanded many times over. The diversity of technical approaches has also evolved — from rule-based expert systems to deep learning — with many modern systems employing a variety of techniques and subsystems.
This presentation will focus on contrasting what we thought we knew about building, deploying and using AI applications in the early years with what we think we know now. Reid G. Smith is cofounder and CEO of i2k Connect, an AI technology company that transforms unstructured documents into structured data enriched with subject matter expert knowledge.
Formerly, he was VP of research and knowledge management at Schlumberger, enterprise content management director at Marathon Oil, and SVP at Medstory, a vertical search company purchased by Microsoft.
He holds a Ph. He is coeditor of AITopics. Will machines take over many jobs in the future? A Oxford University study predicted 47 percent of jobs in the US are at risk of automation in the next 20 years. More recently, the Chief Economist of the Bank of England predicted 50 percent of jobs in the UK are at risk of automation.
How real are such threats? And what should we be doing to prepare for these changes? Increasingly autonomous manned and unmanned aircraft are becoming safer and more capable. The commercial transport aircraft can fly itself from origin to destination but requires a crew to handle off-nominal situations and interface with air traffic control. Commercially available small unmanned aircraft systems SUAS can autonomously execute a waypoint mission but have little resilience to system failures and environmental hazards.
This panel will discuss key autonomy technologies and research needs that will support safer, more efficient flight. Autonomous systems must be validated and verified to meet certification requirements and also must be accepted and trusted by the flying public and communities over which SUAS will operate at low altitudes.
Panelists will discuss critical autonomy applications including emergency flight management, detect-and-avoid, geofencing for airspace segregation, cooperative planning and control, and challenges in operator situational awareness and training.
Hybrid systems allow for the composition of continuous and discrete state dynamics, and have been used in aircraft flight management, air and ground transportation systems, robotic vehicles and human-automation systems.
These systems use discrete logic to manage complexity and more naturally accommodate linguistic and qualitative information. In this talk, we will present reachable set methods for controller design to satisfy safety specifications, and we will present a toolbox of methods combining reachability with machine learning techniques, to enable performance improvement while maintaining safety. We will illustrate these "safe learning" methods on UAV applications. Claire Tomlin is the Charles A.
She was an assistant, associate, and full professor in aeronautics and astronautics at Stanford University from to , and in joined the University of California, Berkeley. Tomlin works in the area of control theory and hybrid systems, with applications to air traffic management, UAV systems, energy, robotics, and systems biology. Become a Sponsor! Registration Information Registration Form.
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Speaker Gallery
I am fortunate to be starting my presidency at a time when our finances are getting back on solid footing. Those of you at the Annual Conference heard the applause that then President Joe Rencis received when he announced that ASEE is debt free and projecting positive yearly budgets. This did not happen, of course, without a lot of hard work, commitment, and sacrifice from numerous quarters. I am particularly grateful for the support of members of the Engineering Deans Council and Engineering Technology Council, who agreed to increased institutional dues levels. ASEE headquarters has reorganized both staff and physical office space to make cuts on the expenditure side.
ASEE Today
By Jasmine Liu on February 19, Nevertheless, a panic ensued in my freshman dorm, although most of my friends were a ways away from even thinking about declaring. Undergraduate enrollment in the computer science CS department at Stanford has quintupled in the past 10 years. Despite tremendous interest in computer science at universities across the country, degree conferral continues to trail labor demand. Data from the Bureau of Labor Statistics indicates that from to , employment in computing increased twice as fast as the production of degrees in computing and information science. Even if enrollments hit a plateau at Stanford sometime in the next few years, the long-term continued growth of the department is virtually guaranteed. At Stanford, the dramatic growth of the undergraduate major has generated tensions within a department that preaches inclusion yet faces formidable strain on its limited resources. Today, the CS department is home to just four percent of total University faculty and staff but claims 20 percent of all undergraduate majors.
AAAI-16 / IAAI-16 Invited Speakers
Abstract: Light detection and ranging technology, or lidar, is a promising tool for assessing unstable ground due to its resolution, accuracy and the ability to process away visual obstacles, such as vegetation. In particular, laser scanning has significant utility when applied repeatedly over time, quantifying changes in terrain that may not be easily discernable to the eye. This presentation will touch on some ongoing research that employs lidar-derived topographic models for 1 landslide inventorying, forensics, and susceptibility and 2 evaluating how slope instability evolves. An approach that uses landslide inventories to leverage region-specific landslide data is considered. The utility of this approach is combined with a three-dimensional slope stability algorithm to evaluate slope instability at a regional scale under a variety of remotely-sensed antecedent moisture conditions.
Oregon State University Commencement
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Congratulations to our graduates! This year's class of more than undergraduate and graduate students was largest in the history of the School of Electrical Engineering and Computer Science. View more photos on our Flickr album. Graduates talk about what the day feels like, what they will remember about Oregon State and where they are going next.
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Thomas G. He received the A. Dietterich studies fundamental problems in AI and machine learning motivated by important challenges in emerging applications — specifically, computational ecology and ecosystem management, computer security, and robust AI systems. From —, he edited the MIT Press series on Adaptive Computation and Machine Learning, and from to the present he has moderated the machine learning area of arXiv.
Oregon State University Graduation Cords
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Finally, we should talk about health. With its top 30 U. Bootcamps in Oregon may be exclusively dedicated to data science, the study of how knowledge is derived from information; data analytics, the processes by which specific insights are drawn; or both data science and data analytics. See our guides to learn more about data science bootcamps and data science bootcamps or data analytics bootcamps in Oregon.
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