The Department of Mechanical Engineering at the Thomas J. Watson College of Engineering and Applied Science has had a productive academic year in 2020-21, despite the ongoing challenges of the COVID-19 pandemic.

Assistant Professor Mir Jalil Razavi received a $587,853 grant from the National Science Foundations Biomechanics and Mechanobiology Program to use computer modeling and advanced brain imaging of developing fetal brains to research brain growth and folding. Co-principal investigator will be Assistant Professor Weiying Dai from the Department of Computer Science.

Associate Professor Xin Yong teamed up with two Iowa State University researchers for a study of Janus particles that shows how they could be the key to more environmentally friendly paints and coatings.

Professor Changhong Ke is co-principal investigator on a three-year, $609,436 grant from the National Science Foundation (NSF) to investigate a new method of producing microscopic circuits. Leading the research will be Assistant Professor Jia Deng from the Department of Systems Science and Industrial Engineering.

Assistant Professor Scott Schiffres and his graduate students worked with Intuitive Surgical best known for designing and manufacturing da Vinci medical robots to research the best face masks to filter out COVID-19. The 300,000 masks that Intuitive distributed to employees and healthcare workers went through different iterations, each design using the most current research findings from Schiffres and his team.

In a story about senior capstone projects for the Watson Review magazine, one ME team was highlighted for creating a CPR-assist device that would help first responders with chest compressions.

Junior Hannah Gill, who came to Watson College in fall 2020 from Clemson University, is featured in a piece about students transferring to Binghamton during the COVID-19 pandemic.

This years Watson Review had a Women in Engineering theme and featured input from professors in each department, including Associate Professor Shahrzad Sherry Towfighian: If [parents] think that girls should not go into engineering and theyre not good at math, that transfers to the kids. If they believe in their kids, the girls dont get that idea.

Swapnil Nibe, MS 19, turned an internship with Ansys one of the largest software companies in the world into a full-time job. The native of Pune, India located about 100 miles from the city of Mumbai Nibe had to adjust fast to the world of a graduate student at Binghamton.

Mark Pallay 14, MS 16, PhD 20, was honored with a Bearcats of the Last Decade (BOLD) 10 Under 10 Award as part of 2020s Virtual Homecoming celebrations. He is currently working at Seagate Technology as a senior research and development engineer.

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Mechanical Engineering Department celebrates 2020-21 research, other achievements | Binghamton News - Binghamton University

Launched in October 2020, the MIT and Accenture Convergence Initiative for Industry and Technology is intended to demonstrate how the convergence of industries and technologies is powering the next wave of change and innovation. The five-year initiative is designed to advance three main pillars: research, education, and fellowships. As part of the third pillar, Accenture has awarded five fellowships to MIT graduate students working on research in industry and technology convergence who are underrepresented, including by race, ethnicity and gender. The recipients of the inaugural Accenture Fellows program are working across disciplines including electronics, textiles, machine learning, economics, and supply chain. Their research has the potential to advance innovation and technology to influence industry convergence and to broaden the convergence process to virtually all industries through creative problem-solving, the accelerated adoption of new technologies, unique collaborations, and thinking imaginatively and boldly.Accenture has long focused on how creativity and ingenuity can help solve some of the worlds most complex problems. When we wanted to explore the convergence of industry and technology, we turned to MIT to extend our longstanding partnership with education, research, and fellowships that delved deeper into this topic, says Sanjeev Vohra, global lead of applied intelligence at Accenture. The Accenture Fellows awards underscore our strong commitments to education, innovation, research and discovery, and creating opportunities that will help accelerate the achievements of these future champions of change.Research being conducted by the fellows covers an array of critical work, including: developing robot-aided therapy to improve balance in impaired subjects; leveraging the increasing availability of data in the gig economy; using machine learning to process locally generated waste for use as alternative energy in low-income municipalities; examining operational challenges that may arise from barriers to extending credit and sharing information among supply chain partners; and designing and applying electronic textile technology to low-Earth orbit, prompting an opportunity for convergence among the electronics, textile, and space technology industries.These fellows are prime examples of the incredible cross-disciplinary work happening at the nexus of industry and technology, says Anantha Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. We are tremendously grateful for Accentures commitment to our students, and for their goal of supporting and advancing student innovation and discovery through these fellowships.Student nominations from each unit within the School of Engineering, as well as from the four other MIT schools and the MIT Schwartzman College of Computing, were invited as part of the application process. Five exceptional students were selected as the inaugural fellows of the initiative:Jacqueline Baidoo is a PhD student in the Department of Materials Science and Engineering, exploring policy related to materials use. Specifically, her research is focused on waste-to-energy (WTE) strategies that could be adopted at the municipal level to treat and process locally generated waste for use as alternative energy. Her goal is to use machine learning to reduce the barrier to entry of WTE practices in low-income municipalities through the development of a tool that informs municipal decisions around waste management and the construction of WTE facilities. Baidoo earned a BS in chemistry and BA in physics from Xavier University of Louisiana and a BS in chemical and biomolecular engineering from Georgia Tech.Juliana Cherston is PhD student in the Media Lab. Her work in the Responsive Environments Group is focused on bringing electronic textile technology to low-Earth orbit, prompting an opportunity for convergence among the electronics, textile, and space technology industries. Specifically, she is augmenting large area space fabrics with active sensory functionality, weaving vibration-sensitive piezoelectric fibers and charge-sensitive conductive yarns into these specialized materials. Cherston earned a BA in physics and computer science from Harvard University.Olumurejiwa Fatunde is a PhD student studying in the Center for Transportation and Logistics. Her research examines operational challenges that may arise from barriers to extending credit and sharing information among supply chain partners in informal settings. With the proliferation of novel payment platforms, cryptocurrency usage, and natural language processing, Fatunde postulates that there is an opportunity to drive convergence across financial services, telecommunications, and other customer-facing industries in emerging markets. Specifically, she is investigating how technologies could trickle down to the smallest, least-formal organizations, helping them to create value for consumers and to be a part of the global economy. Fatunde earned a BA in biomedical engineering from Harvard University and an MS in international health policy from the London School of Economics in the U.K.Andr Medeiros Sztutman is a PhD student in the Department of Economics. Leveraging the increasing availability of data in the gig economy, his work focuses on the development of tools for tackling adverse selection in insurance markets. By creating tools that make better use of information especially in situations where it is particularly needed he is contributing to the convergence of different industries: gig platforms, reporting agencies, and the insurance business. Medeiros Sztutman earned a BS in economics from the Universidade de Sao Paulo, Brazil and an MS in economics from Pontificia Universidade Catolica do Rio de Janeiro in Brazil.Kaymie Shiozawa '19 is a masters student in the Department of Mechanical Engineering, exploring how robot-aided therapy could potentially address the challenge of improving balance in impaired subjects. Drawing on her experience designing human subject experiments, applying machine learning and mathematical simulations, and designing complex mechanisms for robotics and medical devices, Shiozawa aims to design a variable impedance cane and a novel protocol known as AdaptiveCane, which encourages unaided balance by progressively reducing the level of assistance provided as a users performance improves. Shiozawa earned an BS in mechanical engineering from MIT.

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Advancing industry convergence through technology and innovation - MIT News

At headquarters, Mai Vo opened a channel to receive the secret, encrypted message from her spy on the ground.

Within minutes, and with a password in hand, she decoded the message, placing her one step closer to achieving the mission: becoming a cyber star.

Vo, a rising sophomore at West Career & Technical Academy in Las Vegas, is one of 33 local high school students who got a crash course in cybersecurity thisweek during UNLVs third annual GenCyber Summer Camp.

I think the Earth is too mysterious, and I want to learn as much as I can about space, and Earth, and technology, said Vo. I find it interesting to learn how things work and why everything is as it is.

Though she has plans to study mechanical engineering in college one day, Vo said the camp opened her eyes to the possibility of exploring a career in cybersecurity - keeping hackers at bay and protecting the nations critical infrastructure, from power grids to transportation systems, from coming under attack.

And thats exactly what the camp hopes to achieve. With nearly half a million cybersecurity-related jobs open across the nation, UNLV is hoping to inspire the next generation of cybersecurity professionals and help to fill a critical skills gap.

One of the things that interested me the most is what they said when we asked about the salary, said Deven Slivka, a rising senior at Western High School. Its whatever you want. You can pick your salary if youre good enough.

Yoohwan Kim, camp co-director and UNLV computer science professor, said 70% of small companies go out of business after a cyber attack.

The chance of getting attacked is very, very high, and an attack can be happening for months before a company realizes it, Kim said. There is not enough protection.

Cybercrime is estimated to cost the world $10 trillion annually by 2025, said Ju-Yeon Jo, a computer science professor who co-leads the summer camp with Kim. They also head up, along with colleagues in engineering and business, UNLVs master in cybersecurity program which opened this spring.

Recruitment is crucial not only for businesses, but also for the protection of our nations infrastructure, Jo said.

Through activities like decoding encrypted spy messages and cyber treasure hunting, to learning what it means to be a good digital citizen, Jo and Kim hope the students become ambassadors for cybersecurity at their respective high schools.

When they go back to school they can be pioneers and create cybersecurity clubs or activities, Jo said.

Just two days into the weeklong experience, where participants enjoy daily prizes, team activities, and catered food,Vo already gave the camp her stamp of approval.

Its a great experience, and so worth it, Vo said. Im glad I fixed my sleep schedule for this.

UNLV GenCyber Camp is provided at no cost to participants thanks to a grant from the National Security Agency and the National Science Foundation. UNLV is one of 98 institutions offering camps across the country this summer.

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Wanted: Cybersecurity Professionals to Protect Businesses, the Nation's Infrastructure - UNLV NewsCenter

USC PhD student Eric Heiden, working with NVIDIA researchers, has created a new simulator for robotic cutting that can accurately reproduce the forces acting on the knife as it presses and slices through common foodstuffs. Image/Eric Heiden/NVIDIA.

Researchers from USCs Department of Computer Science showcased cutting-edge research at the Robotic Science and Systems Conference (RSS) 2021, July 12-16, 2021. The annual conference, held virtually this year, brings together leading robotics researchers and students to explore real-word applications of robotics, AI and machine learning. From creating realistic simulations environments, to robot job interviews that catch errors before deployment, to cutting simulations that could improve robotic surgery, USC researchers are forging new paths in robotics.

PhD student Eric Heiden and NVIDIA researchers unveiled a new simulator for robotic cutting that can accurately reproduce the forces acting on the knife as it presses and slices through common foodstuffs, such as potatoes, tomatoes or cucumbers. It could also simulate cutting through different types of human tissues, leading to applications in surgical robotics. The paper received the Best Student Paper Award at RSS 2021.

The team devised a unique approach to simulate cutting by introducing springs between the two halves of the object being cut, represented by a mesh.

The team devised a unique approach to simulate cutting by introducing springs between the two halves of the object being cut, represented by a mesh. These springs are weakened over time in proportion to the force exerted by the knife on the mesh.

What makes ours a special kind of simulator is that it is differentiable which means that it can help us automatically tune these simulation parameters from real-world measurements, said Heiden. Thats important because closing this reality gap is a significant challenge for roboticists today. Without this, robots may never break out of simulation into the real world.

To transfer from simulation to reality, the simulator must be able to model a real system. In one of the experiments, the researchers used a dataset of force profiles from a physical robot to produce highly accurate predictions of how the knife would move in real life. In addition to applications in the food processing industry, where robots could take over dangerous tasks like repetitive cutting, the simulator could improve force haptic feedback accuracy in surgical robots, helping to prevent injury.

Here, it is important to have an accurate model of the cutting process and to be able to realistically reproduce the forces acting on the cutting tool as different kinds of tissue are being cut, said Heiden. With our approach, we are able to automatically tune our simulator to match such different types of material and achieve highly accurate simulations of the force profile. The team is already working on applying the system to cutting on a real robot.

Ensuring autonomous system safety is one of todays most complex and important technological challenges. Whether youre dicing vegetables with a robot chef, driving to work in an autonomous car, or going under the knife with a surgical robotthere is no room for error.

But as robots become more complex and commonplace, it becomes harder to predict their behavior in every possible situation. Typically, robots that work with humans are tested in a lab with human subjects to see how they behave. But these experiments provide limited insights into the robots behavior when deployed long-term in messy real-world scenarios.

By creating these scenario generation systems, Im hoping we can trust robotic systems enough to make them part of our everyday home life. Matt Fontaine.

What if, instead, you could run a huge number of simulations to identify potentially catastrophic errors in robotic systems before deployment?

In two papers accepted at RSS 2021, lead author Matt Fontaine, a PhD student, and his supervisor Stefanos Nikolaidis, an assistant professor in computer science, present a new framework to generate scenarios that automatically reveal undesirable robot behavior. Like a tough job interview, the tests put robots through their paces before they interact with humans in safety-critical settings.

The researchers used a class of algorithms named quality diversity algorithms to find a collection of diverse, relevant and challenging scenarios, such as how the scene is arranged, or how much workload is distributed between the human and the robot. Specifically, the team attempted to find failure cases that are unlikely to be observed when testing the system manually, but may happen when deployed.

Its very easy to break a robotic system in a way that is not the systems fault, said Fontaine, who worked as a simulation engineer at a self-driving car startup before joining USC. The space of scenarios is flooded with failure cases that are not relevant, like drivers driving unreasonably or roads that would never occur in the real world. Our approach could help discover failures that are relevant.

In addition to identifying errors that may not have shown up in industry-standard robotic tests, they also found that simulation environments can greatly affect a robots behavior in collaborative settings.

This is an extremely important insight as we all arrange our houses differently, said Fontaine. By creating these scenario generation systems, Im hoping we can trust robotic systems enough to make them part of our everyday home life.

Imagine trying to teach a robot to cook in your kitchen. The robot learns through trial and error and needs careful supervision to make sure it does not knock over plates or leave the stove on. Instead, what if we could train a similar robot in a laboratory with the proper safety precautions, then apply what was learned to your robot at home? Theres one problem: the kitchen in your home and in the laboratory are not identical. For example, they might look different and have different cookware, so the robot has to adjust how it cooks in the new environment.

A new paper lead authored by Grace Zhang, a PhD student, with supervisor Joseph Lim, a computer science assistant professor, helps robots easily transfer learned behavior from one environment to another by virtually modifying the training environment so it is similar to the real-world target environment.

This way, we can train the robot in one environment, like the laboratory, then directly deploy the trained robot in another, like a home kitchen, said Zhang.

Transferring a behavior from the training environment (top row) to the target environment (bottom row) for five different tasks.

Unlike previous studies, which tackle only visual or physical differencesfor instance, differences in kitchen interior or cookware weightthis new technique creates a more realistic scenario by tackling both differences at the same time.

We can train the robot in one environment, like the laboratory, then directly deploy the trained robot in another. Grace Zhang.

The researchers hope these insights could make robots more efficient and practical in other real-life situations, such as search and rescue missions. By fitting in between the rubble to searching in hard-to-reach places, ground robots can aid a human rescue team by quickly surveying large regions or going into dangerous areas. Using this new approach, the robot could gather data onsite, then do additional training in a simulator to adapt the robot to specific terrain.

While we are still a long way from deploying automated robots in these critical situations, having a way to transfer behaviors from controllable training environments may be a first step, said Zhang.

Much like humans must plan their actions to achieve any taskfrom brushing your teeth to making a cup of coffeemotion planning in robotics deals with finding a path to move a robot from its current position to a target position without colliding with any obstacles. In the automation industryfrom robotic chefs to warehouse and space robotsthe order of motion is critical to the success of the overall goal.

The robot is able to complete both a simple pick and place task, and a more complex pick and pour task.

The ability to move through various motions quickly and accurately is especially important for robots that operate in environments like the home, which are not carefully controlled in structure. In addition, many robotic tasks require constrained motions, such as maintaining contact with a handle, or keeping a cup or bottle upright while transporting.

At USC, Peter Englert, a postdoctoral researcher, is working on giving robots the ability to reason and plan about motions over a long period of time. Englert is supervised by Gaurav Sukhatme, the Fletcher Jones Foundation Endowed Chair and a professor of computer science and electrical and computer engineering,

Our method intelligently plans how to perform the individual subtasks so that the robot succeeds at the overall task. Peter Englert

In a new paper presented at RSS 2021, Englert, Sukhatme and their co-authorsachieve this by dividing a motion into multiple smaller motions that are described as geometric constraints regarding the robots position in its environment. The focus: finding when to switch from one motion to the next. Many manipulation tasks consist of a sequence of subtasks where each subtask can be solved in multiple ways. Depending on the specific solution of a subtask, it might affect the outcome of future subtasks.

A running example in this paper is the task of using a robot arm to transport a mug from one table to another while keeping the orientation of the mug upright, a task involving multiple phases and constraints.

A cup can be grasped in many different ways, said Englert. However, for certain grasps the robot might not be able to perform a subsequent pouring motion. Our method avoids such situations and intelligently plans how to perform the individual subtasks so that the robot succeeds at the overall task. Co-author Isabel M. Rayas Fernndez, a computer science PhD student, presented the work in a Spotlight Talk.

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On the Cutting Edge: USC at the Robotic Science and Systems (RSS) Conference - USC Viterbi | School of Engineering - USC Viterbi School of Engineering