The Art of Powering Implantable Electronics

UMBC professor Gymama Slaughter give a short talk at the Grit-X event on her recent research on powering implantable devices for medical applications.

The number of smart implantable devices is on the rise, especially as we approach the ramping up of the “internet of things.” A key challenge for implantable electronic devices has been keeping these devices properly and conveniently powered. Current battery technologies are sealed within these devices, thereby forcing the surgical replacement of the device once the battery is depleted. We need an inconspicuous means of powering implantable electronics with imperceptible methods that moves us toward new innovative solutions to the power challenge in implantable devices. A lightweight bio-solution that leverages the biochemical energy from human biological fluids is a step forward for powering these smart implantable technologies.

Prof. Marie desJardins receives UC Berkeley Distinguished Alumni Award in Computer Science

CSEE professor Marie desJardins has been selected for the 2018 Distinguished Alumni Award in Computer Science by the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. Professor desJardins received a Ph.D. degree in Computer Science from UC Berkeley in 1992.

Each year since 1991, the award has recognizes two distinguished alumni of Berkeley who have made valuable contributions to the field of computer science. Past recipients can be seen here The award will be presented during the Berkeley EECS Annual Research Symposium (BEARS ) on in February 2018.

Two CSEE faculty give short talks at UMBC Grit-X

Back by popular demand from UMBC’s 50th Anniversary weekend, it’s Grit-X! Head to UMBC’s Black Box Theatre on Saturday, 14 October 2017 from 10:00 a.m. to noon, and be enlightened by short TED-style talks from some of the most intriguing alumni and faculty minds. See the complete program and register for the event here.

The first session (10:00—10:30am) includes a talk by CSEE professor Nilanjan Banerjee:

When What You Wear Understands You, Prof. Nilanjan Banerjee

How can cutting-edge research on textile sensors and wearable radar sensors help us recognize gestures, monitor sleep fragmentation, and diagnose sleep disorders? The Banerjee lab has developed and applied sensors to users with upper extremity mobility impairments, adults suffering from insomnia and restless leg syndrome, and kids with attention deficit/hyperactivity disorder, with the intent to begin answering that question.

and the second session (10:45—11:15am) has one by CSEE professor Gymama Slaughter:

The Art of Powering Implantable Electronics, Gymama Slaughter

The number of smart implantable devices is on the rise, especially as we approach the ramping up of the “internet of things.” A key challenge for implantable electronic devices has been keeping these devices properly and conveniently powered. Current battery technologies are sealed within these devices, thereby forcing the surgical replacement of the device once the battery is depleted. We need an inconspicuous means of powering implantable electronics with imperceptible methods that moves us toward new innovative solutions to the power challenge in implantable devices. A lightweight bio-solution that leverages the biochemical energy from human biological fluids is a step forward for powering these smart implantable technologies.

Professor Marie desJardins was selected for the 2017 A. Richard Newton Educator ABIE Award by the Anita Borg Institute. The annual award recognizes an educator who has developed innovative teaching practices and approaches that attract girls and women to computing, engineering, and math. As part of the award Dr. desJardins will take part in a panel on efforts to increase women’s representation and success in technology at the 2017 Grace Hopper Celebration of Women in Computing in Orlando this October.

Increasing gender diversity in computing has become both a professional focus and personal commitment for desJardins over the course of her career. “It’s part of a broader equity issue — for everyone to be able to envision themselves as creators of technology, and for the future of technology to be created by a diverse community of scientists and engineers,” she says.

This summer, desJardins shared her passion for encouraging girls and women to pursue careers in computing with nearly 150 elementary and middle school girls who attend the Mind, Body, Coding camp at UMBC. “Seeing these young girls whose lives could be transformed by greater access to computing is incredibly inspiring,” she says. “It’s a big part of what gets me energized every day to do the work that I do, from supporting diversity in K-12 computing education to mentoring junior female faculty who will train the next generation of computer scientists.”

The award announcement cited Professor desJardins for her many accomplishments in education, research and support of and commitment to improving student diversity, access, and quality of computer science courses at the high school level.

“Marie is known on campus and throughout her professional community for her dedication to mentoring, diversity, outreach, and innovative educational practices. Marie was named one of UMBC’s 10 “Professors Not to Miss” in 2011, and is regularly sought out to give invited talks to student groups. In 2010, she was invited to be a CRA-W/CDC Distinguished Lecturer. She was also one of the inaugural Hrabowski Innovation Fellows, and with that award, helped to create the ACTIVE Center, a new classroom that supports pedagogical approaches that increase student engagement and active problem solving.

Marie has become known nationally for her support of and commitment to improving student diversity, access, and quality of computer science courses at the high school level, and has received multiple NSF awards to support her efforts in this area. She is the lead PI on the NSF-sponsored “CS Matters in Maryland” project, which is creating curriculum and training high school teachers to teach the new AP CS Principles course. She has built a statewide coalition to increase access to K-12 CS education, with a focus on inclusion and diversity. She is also the Maryland team leader for the Exploring Computing Education Pathways (ECEP) Alliance, an NSF-funded initiative that is coordinating state-level CS education efforts.

Marie is UMBC’s 2014-17 Presidential Teaching Professor and was a founding member of the Maryland chapter of the Computer Science Teachers Association, for which she is currently the university liaison. Her research focuses on artificial intelligence, particularly machine learning, planning and decision making, and multi-agent systems. She has published over 100 scientific papers on these topics, and was recently named one of the “Ten AI Researchers to Follow on Twitter” by TechRepublic and one of “14 Women in AI You Should Follow on Twitter” by craigconnects.

At UMBC, Marie has been PI or co-PI on over $6,000,000 of external research funding, including a prestigious NSF CAREER Award, and has graduated 11 Ph.D. students and 25 M.S. students. She is particularly well known on campus and in her professional community for her commitment to student mentoring. She has been involved with the AAAI/SIGART Doctoral Consortium for the last 16 years and has worked with 90 undergraduate researchers and high school student interns. She was awarded the 2014 NCWIT Undergraduate Research Mentoring Award and the 2016 CRA Undergraduate Research Mentoring Award in recognition of her commitment to undergraduate research.”

AI system designs clothing for your personal fashion style

Everyone knows that more and more data is being collected about our everyday activities, like where we go online and in the physical world. Much of that data is being used for personalization. Recent UMBC CSEE Masters student Prutha Date explored a novel kind of personalization – creating clothing that matches your personal style.

Date developed a system that takes as input pictures of clothing in your closet, extracts a digitial representation of your style preferences, and then applies that style to new articles of clothing, like a picture pair of pants or a dress you find online. This work meshes well with recent efforts by Amazon to manufacture clothing on demand. Imagine being able to click on an article of clothing available online, personalize it to your style, and then have it made and shipped right to your door!

This innovative research was cited in a recent article in MIT Technology Review, Amazon Has Developed an AI Fashion Designer.

Tim Oates, a professor at the University of Maryland in Baltimore County, presented details of a system for transferring a particular style from one garment to another. He suggests that this approach might be used to conjure up new items of clothing from scratch. “You could train [an algorithm] on your closet, and then you could say here’s a jacket or a pair of pants, and I’d like to adapt it to my style,” Oates says.

Fashion designers probably shouldn’t fret just yet, though. Oates and other point out that it may be a long time before a machine can invent a fashion trend. “People innovate in areas like music, fashion, and cinema,” he says. “What we haven’t seen is a genuinely new music or fashion style that was generated by a computer and really resonated with people.”

You can read more about the work in a recent paper by Prutha Date, Ashwinkumar Ganesan and Tim Oates, Fashioning with Networks: Neural Style Transfer to Design Clothes. The paper describes how convolutional neural networks were used to personalize and generate new custom clothes based on a person’s preference and by learning their fashion choices from a limited set of clothes from their closet.

UMBC’s Gymama Slaughter to develop bioreactors that could pause the clock for life-saving organ transplants

UMBC’s Gymama Slaughter will develop a bioreactor to extend the viability of lifesaving human organs as they await transplant through a major new grant from the U.S. Army Medical Research and Material Command. Funding for the project totals nearly $1.5 million for a period of three years. Slaughter, associate professor of computer science and electrical engineering, will collaborate closely with Warren Grayson and Gerald Brandacher, both associate professors at Johns Hopkins.

The team will create a bioreactor integrating in-line sensors, mechanical stimulator, and blood perfusion system to more accurately and continuously monitor organs as they are transported for transplantation. They will also “develop a system that closely mimic the organ’s natural environment,” explains Slaughter.

Currently, organ and tissue donors typically need to be in close proximity to transplant recipients due to limitations in organ transport. Some organs are only viable for about six hours, and they must be kept at very cool temperatures to remain viable, so the transport process can be a race against time. With technological improvements, Slaughter says, the viability of the organs could be increased to about 36 hours, greatly expanding the distance an organ could travel from donor to recipient, and the likelihood of a successful transplant.

“This interdisciplinary research will enable us to tackle complex organ transplant viability problems to create the next big breakthrough platform technology for extending and monitoring the viability of organs to improve patient care,” says Slaughter. Together, the researchers hope their work will lead to a new era of successful human organ transplantation, saving the lives of wounded soldiers and others in need of transplants in hard-to-reach locations around the world.

Adapted from a UMBC News article by Megan Hanks, photos by Marlayna Demond ’11 for UMBC.

Professor Cynthia Matuszek has received a research award from the National Science Foundation to improve human-robot interactions by enabling them to understand the world from natural language in order to take instructions and learn about their environment naturally and intuitively. The two-year award, Joint Models of Language and Context for Robotic Language Acquisition, will support Dr. Matuszsek’s Interactive Robotics and Language Lab, which focuses on how robots can flexibly learn from interactions with people and environments.

As robots become smaller, less expensive, and more capable, they are able to perform an increasing variety of tasks, leading to revolutionary improvements in domains such as automobile safety and manufacturing. However, their inflexibility makes them hard to deploy in human-centric environments, such as homes and schools, where their tasks and environments are constantly changing. Meanwhile, learning to understand language about the physical world is a growing research area in both robotics and natural language processing. The core problem her research addresses is how the meanings of words are grounded in the noisy, perceptual world in which a robot operates.

The ability for robots to follow spoken or written directions reduces the adoption barrier for robots in domains such as assistive technology, education, and caretaking, where interactions with non-specialists are crucial. Such robots have the potential to ultimately improve autonomy and independence for populations such as aging-in-place elders; for example, a manipulator arm that can learn from a user’s explanation how to handle food or open novel containers would directly affect the independence of persons with dexterity concerns such as advanced arthritis.

Matuszek’s research will investigate how linguistic and perceptual models can be expanded during interaction, allowing robots to understand novel language about unanticipated domains. In particular, the focus is on developing new learning approaches that correctly induce joint models of language and perception, building data-driven language models that add new semantic representations over time. The work will combines semantic parser learning, which provides a distribution over possible interpretations of language, with perceptual representations of the underlying world. New concepts will be added on the fly as new words and new perceptual data are encountered, and a semantically meaningful model can be trained by maximizing the expected likelihood of language and visual components. This integrated approach allows for effective model updates with no explicit labeling of words or percepts. This approach will be combined with experiments on improving learning efficiency by incorporating active learning, leveraging a robot’s ability to ask questions about objects in the world.

Name: Alex Hart

Educational Background: Bachelor’s degree in Accounting from the University of Maryland, College Park

Hometown: Baltimore, MD (Go O’s and Ravens!)

Current role: As an Accountant I, Alex provides business services support to the CSEE department in the areas of contracts and grants/projects, which includes account monitoring, financial reporting, projections, reconciliations, etc. She also provides backup support for payroll, and she is the property custodian of inventory for CSEE.

Favorite thing about UMBC: “Without a doubt, my favorite thing about UMBC is the people here. I have met a lot of different people who have provided me with a wealth of knowledge since I started working here just a year ago. Everyone has been very inclusive and helpful!”

Students should ask me about: “Students can ask me anything, but maybe about the college experience, since I’m still a recent graduate.”

Alex is originally from Baltimore, MD. She joined CSEE’s Department in February of 2016. She attended UMBC for her first two years of college, then transferred to the University of Maryland College Park’s Robert H. Smith School of Business. She has a BS in Accounting from UMCP.

When not working, Alex loves cheering on the Terps in football and basketball. She also enjoys traveling to new places, cooking, practicing yoga, and reading.

Every day, people interact with large amounts of text online, including legal documents they might quickly skim and sign without full, careful review. In an article recently published in The Conversation, Karuna Joshi, research associate professor of computer science and electrical engineering, and Tim Finin, professor of computer science and electrical engineering, explain how artificial intelligence (AI) is helping to summarize lengthy and complex legalese so people can more easily understand terms of service and similar agreements before they click “accept” to access a new app or online service.

The legal documents that Joshi and Finin are working to summarize—terms of service, privacy policies, and user agreements—often accompany new online services, contests, apps, and subscriptions. “As computer science researchers, we are working on ways artificial intelligence algorithms could digest these massive texts and extract their meaning, presenting it in terms regular people can understand,” they explain.

Through their research, Joshi and Finin ask computers to break down the terms and conditions that regular users “agree” to or “accept.” To process the text, Joshi and Finin employ a range of AI technologies, including machine learning, knowledge representation, speech recognition, and human language comprehension.

Joshi and Finin have found that in many of the privacy policies people are prompted to review and accept online, there are sections that do not actually apply to the consumer or service provider. These sections of the agreements might, for example, “include rules for third parties…that people might not even know are involved in data storage or retrieval,” they note.

After examining these documents, the software Joshi and Finin have developed pinpoints specific items that people should be aware of when they are granting their consent or agreement—what they describe as “key information specifying the legal rights, obligations and prohibitions identified in the document.” In other words, the software takes in all that complex legal language, and then then presents just the most essential information in “clear, direct, human-readable statements,” making it much more feasible for users to understand what they are consenting to before they click “agree.”

Read “Teaching machines to understand — and summarize — text” in The Conversation to learn more about Joshi and Finin’s approach to making online legal documents more accessible through AI.

Adapted from a UMBC News article by Megan Hanks Banner image: Karuna Joshi. Photo by Marlayna Demond ’11 for UMBC.