Roman V. Yampolskiy holds a PhD degree from the Department of Computer Science and Engineering at the University at Buffalo. There he was a recipient of a four year NSF (National Science Foundation) IGERT (Integrative Graduate Education and Research Traineeship) fellowship. Before beginning his doctoral studies Dr. Yampolskiy received a BS/MS (High Honors) combined degree in Computer Science from Rochester Institute of Technology, NY, USA. After completing his PhD dissertation Dr. Yampolskiy held a position of an Affiliate Academic at the Center for Advanced Spatial Analysis, University of London, College of London. In 2008 Dr. Yampolskiy accepted an assistant professor position at the Speed School of Engineering, University of Louisville, KY.

Dipanjan Sarkar is a Data Scientist at Intel, on a mission to make the world more connected and productive. He primarily works on data science, analytics, business intelligence, application development, and building large-scale intelligent systems. He holds a master of technology degree in Information Technology with specializations in Data Science and Software Engineering from the International Institute of Information Technology, Bangalore. He is also an avid supporter of self-learning, especially Massive Open Online Courses and also holds a Data Science Specialization from Johns Hopkins University on Coursera. Dipanjan has been an analytics practitioner for several years now, specializing in statistical, predictive, and text analytics.

A world-first report from Dr Allan McCay in the Law School scrutinises advances in neurotechnology and what they might mean for the law and the legal profession. Dr Allan McCay, a criminal law scholar at the University of Sydney Law School, has published the first substantial overview of neurotechnology and its implications for the law and the legal profession. Neurotechnologies are technologies that interact directly with the brain, or more broadly the nervous system, by monitoring and recording neural activity, and/or acting to influence it. Sometimes neurotechnology is implanted in the brain but it may also be in the form of a headset, wristband or helmet. The technology is already being used in health settings for the treatment of patients with Parkinson's and epilepsy and could be used in the future to monitor and treat schizophrenia, depression and anxiety.

Object Goal Navigation requires a robot to find and navigate to an instance of a target object class in a previously unseen environment. Our framework incrementally builds a semantic map of the environment over time, and then repeatedly selects a long-term goal ('where to go') based on the semantic map to locate the target object instance. Long-term goal selection is formulated as a vision-based deep reinforcement learning problem. Specifically, an Encoder Network is trained to extract high-level features from a semantic map and select a long-term goal. In addition, we incorporate data augmentation and Q-function regularization to make the long-term goal selection more effective. We report experimental results using the photo-realistic Gibson benchmark dataset in the AI Habitat 3D simulation environment to demonstrate substantial performance improvement on standard measures in comparison with a state of the art data-driven baseline.

Learn to detect obstacles in lidar point clouds through clustering and segmentation, apply thresholds and filters to radar data in order to accurately track objects, and augment your perception by projecting camera images into three dimensions and fusing these projections with other sensor data. Combine this sensor data with Kalman filters to perceive the world around a vehicle and track objects over time.

On this Master of Science degree in Robotics and Artificial Intelligence (AI), you will develop advanced skills across complex and core topics in service, industrial and social robotics. You will study the latest cutting-edge advancements in the field – such as machine learning techniques, robotics vision & speech, and 3D modelling. You will then apply your knowledge to the design and development of sophisticated autonomous intelligent apps, 3D designs and physical robotics systems. You will also develop strong managerial and leadership skills, and a global appreciation of the economic, legal, social, ethical and environmental context of robotics. In addition, there are opportunities for short-term mobility trips to visit global industry leaders and short industrial or academic internship placements, which can often prove very useful in securing future employment in the UK and globally.

Job summaryAWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building automated ML solutions for planetary-scale sustainability and geospatial applications. Our team's mission is to develop ready-to-use and automated solutions that solve important sustainability and geospatial problems. We live in a time wherein geospatial data, such as climate, agricultural crop yield, weather, landcover, etc., has become ubiquitous. Cloud computing has made it easy to gather and process the data that describes the earth system and are generated by satellites, mobile devices, and IoT devices. Our vision is to bring the best ML/AI algorithms to solve practical environmental and sustainability-related R&D problems at scale. Building these solutions require a solid foundation in machine learning infrastructure and deep learning technologies. The team specializes in developing popular open source software libraries like AutoGluon, GluonCV, GluonNLP, DGL, Apache/MXNet (incubating). Our strategy is to bring the best of ML based automation to the geospatial and sustainability area.We are seeking an experienced Applied Scientist for the team. This is a role that combines science knowledge (around machine learning, computer vision, earth science), technical strength, and product focus. It will be your job to develop ML system and solutions and work closely with the engineering team to ship them to our customers. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. You are also expected to work closely with other applied scientists and demonstrate Amazon Leadership Principles (https://www.amazon.jobs/en/principles). Strong technical skills and experience with machine learning and computer vision are required. Experience working with earth science, mapping, and geospatial data is a plus. Our customers are extremely technical and the solutions we build for them are strongly coupled to technical feasibility.About the teamInclusive Team CultureAt AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.Interested in this role? Reach out to the recruiting team with questions or apply directly via amazon.jobs.

The field of artificial intelligence (AI) is progressing rapidly and, as a data-driven technology, AI-powered tools lend themselves to a wide range of applications. In this blog we will look at potential opportunities for AI integration into teaching and learning, current case studies of successful use, and the explicit limitations and vulnerabilities of using this technology-driven approach. For assessment, AI-assisted marking and feedback software goes beyond what has been achieved through multiple-choice quizzes. In STEM subjects, AI-powered tools can help significantly in the semi-automation of marking. Rather than grading hundreds of calculus submissions, an AI tool is very fit for purpose, using a technology called'replay grading' ensuring that similar submissions are not marked twice, and grouping similar answers so they can be given a consistent grade at the same time.

A multilingual (English/Spanish) immersive learning environment is being created by an assistant professor at the University of Texas using virtual reality technology to help students better grasp the topic of additive manufacturing. The project, titled "Enhancing Active Learning in Additive Manufacturing Using a Bilingual, Assisted Virtual-Reality Platform," will be under the direction of Shuchisnigdha Deb, assistant professor in the Department of Industrial, Manufacturing, and Systems Engineering (IMSE). The research is being supported by a grant from the National Science Foundation worth $837,000. Emma Yang and Amanda Olsen, two other ISME faculty members from the College of Education's Department of Curriculum and Instruction, are also on the team. Deb asserted that teaching kids about additive manufacturing is crucial for the advancement of robots, augmented reality, and other technologies.

In 2020, a Canadian university employee named Ian Linkletter became increasingly alarmed by a new kind of technology that was exploding in use with the pandemic. It was meant to detect cheating by college and high-school students taking tests at home, and claimed to work by watching students' movements and analyzing sounds around them through their webcams and microphones to automatically flag suspicious behavior. So Linkletter accessed a section of the website of one of the anti-cheating companies, named Proctorio, intended only for instructors and administrators. He shared what he found on social media. Now Linkletter, who became a prominent critic of the technology, has been sued by the company. But he is not the only one.