Peter Hase Awarded Prestigious Royster Society Fellowship

August 26, 2019
Peter Hase

Peter Hase

First-year computer science doctoral student Peter Hase was awarded the Royster Society of Fellows Recruitment Fellowship by the UNC Graduate School. The fellowship provides full tuition cost, health insurance, and funds for professional travel for five years, as well as a $24,000 stipend for two years and a partial-stipend for three to encourage external fellowships. Beyond funding support, key benefits of membership in the society of fellows also include interdisciplinary learning, networking, and professional development and social opportunities. Among these is the opportunity to teach an interdisciplinary seminar for first-year undergraduates.

The Royster fellowship program selects students with the highest academic potential and the most impressive record of achievement in undergraduate education and work and life experiences. It is the University’s most selective and prestigious interdisciplinary fellowship program.

Hase’s research interests lie in developing interpretable machine learning methods, with a focus on the domain of natural language processing (NLP). An important goal of this research is the design of machine learning systems that make decisions in a way that is transparent to people and open to scrutiny. He will be working on these kinds of problems at UNC with professor Mohit Bansal in the UNC-NLP Lab.

Hase previously received a bachelor’s degree in statistical science from Duke University, where he did research on interpretable computer vision techniques and algorithmic poetry generation with professors Cynthia Rudin and Sayan Mukherjee. His work on interpretable computer vision will be published this year at the 2019 AAAI Conference on Human Computation and Crowdsourcing (HCOMP). He attended Duke as a recipient of the AJ Tannenbaum Trinity Scholarship, a four-year merit scholarship. As an undergraduate student, Hase was also heavily involved with the school’s Effective Altruism club, a student group whose mission is to build an enabling community for students who are trying to find careers where they can improve the world. After leading Duke’s club for two years, he now plans to work with UNC’s chapter.

For a full list of current fellows, visit the Graduate School website. For more information about Peter, visit his webpage.

Four NIBIB grantees win prestigious Presidential early career awards

July 23, 2019

Four NIBIB grantees are among more than 300 recipients of the prestigious Presidential Early Career Award for Scientists and Engineers (PECASE) announced by President Donald J. Trump on July 2, 2019, and to be awarded at a ceremony on July 25.  The PECASE is the highest honor bestowed by the U.S. government to outstanding scientists and engineers who are beginning their independent research careers and who show exceptional promise for leadership in science and technology. Recipients announced this year hail from universities in 38 states across the country.

“The PECASE award is a national honor that puts a spotlight on an exceptionally talented group of NIBIB grantees at a time of unprecedented breakthroughs in advancing human health,” said NIBIB Director Bruce J. Tromberg, Ph.D. “These promising young scientists span the country and a fascinating spectrum of bioengineering research, from biomaterials to biomedical devices. Each awardee represents promising talent whose commitment to their innovative projects will engineer the future of health.”

Darren J. Lipomi, Ph.D., associate professor of nanoengineering, University of California, San Diego, is a 2015 PECASE nominee, just awarded this year. His NIBIB grant is an NIH Director’s Pioneer Award (DP2EB022358) supporting scientists who undertake novel approaches to major challenges in biomedical research. Lipomi develops wearable and implantable medical sensors, including stretchable, biodegradable, and self-healing semiconducting polymer materials. Stretchable electronics are designed to seamlessly integrate with the body contours to monitor vital signs, muscle activity, metabolic changes, and organ function. His project aims to create a new class of semiconducting polymer material that has the mechanical properties of human skin. This transparent electronic skin will be soft and elastic, sense contact, absorb blunt force, and will self-heal when damaged—all the while providing continuous and wireless health-monitoring.

Ron Alterovitz, Ph.D., professor of computer science, University of North Carolina at Chapel Hill, is a 2017 PECASE nominee. His NIBIB grant (R01EB024864) aims to improve the survival rate for lung cancer by enabling earlier stage diagnosis using a novel robotic device. The project is creating a new robotic system that deploys a needle that can semi-automatically steer through lung tissue to safely biopsy nodules. Currently used instruments cannot accurately access many nodules. The innovative robotic system will enable access to nodules throughout the lung, increase targeting accuracy, and avoid major bleeding by steering the needle around larger blood vessels. The project brings together a multidisciplinary team that spans expertise in interventional pulmonology, cardiothoracic surgery, radiology, mechanical engineering, and several subareas of computer science, including artificial intelligence and medical image analysis.

Xudong Wang, Ph.D., professor and associate chair of materials science and engineering, University of Wisconsin – Madison, is a 2016 PECASE nominee. His NIBIB grant (R01EB021336) supports the development of self-powered implantable biomedical devices for continuous, real-time sensing, monitoring, and other vital health functions. A variety of energy sources in the human body, such as limb movement, respiration, and heartbeat can provide sufficient power for small biomedical devices. The project explores innovative nanotechnology to create self-sufficient power supplies for implantable devices used in areas such as wound healing and weight control.

Angela K. Pannier, Ph.D., professor of biological systems engineering, University of Nebraska-Lincoln, is a 2017 PECASE nominee. Her NIBIB grant (DP2EB025760) is an NIH Director’s Pioneer Award. Her lab is developing more than 10 projects related to biomaterials and gene delivery systems. The award will support development of novel methods that improve use of adult stem cells in gene therapy, a promising tool for treating a variety of diseases.

Established in 1996, the PECASE acknowledges the contributions scientists and engineers have made to the advancement of science, technology, education, and mathematics (STEM) education and to community service as demonstrated through scientific leadership, public education, and community outreach. The White House Office of Science and Technology Policy coordinates the PECASE with participating departments and agencies.The PECASE awards ceremony will take place the morning of July 25, 2019, at Daughters of the American Revolution, Constitution Hall.  NIBIB-nominated recipients will be celebrated at an NIBIB seminar on the NIH campus on the same day.

Read the July 2, 2019, White House announcement here.

Computer science professor receives Presidential Early Career Award for Scientists and Engineers

July 22, 2019

Ron Alterovitz was selected for the award, which recognizes the pursuit of innovative research at the frontiers of science and technology, and the commitment to community service through scientific leadership, public education or community outreach.

By the College of Arts & Sciences, Monday, July 22nd, 2019

Computer Science Professor Ron Alterovitz was recognized with the Presidential Early Career Award for Scientists and Engineers. The award is the highest honor bestowed by the U.S. government to outstanding scientists and engineers who are in the early stages of their independent research careers and who show exceptional promise for leadership in science and technology.

Recipients of the Presidential Early Career Award for Scientists and Engineers are selected for their pursuit of innovative research at the frontiers of science and technology and for their commitment to community service as demonstrated through scientific leadership, public education or community outreach. The White House Office of Science and Technology Policy coordinates the Presidential Early Career Award for Scientists and Engineers with over a dozen departments and agencies. Only around 100 recipients are named per year.

Alterovitz was named a recipient by the White House in a press release on July 2. He was nominated for the award by the U.S. Department of Health and Human Services, which operates the National Institutes of Health. He is the only recipient named in the White House press release who is currently at Carolina. Alterovitz is the ninth researcher from UNC-Chapel Hill to receive the award since its inception 23 years ago and the first recipient in computer science.

Alterovitz’s research focuses on robotics for medical applications. With support from NIH, Alterovitz and his research group are developing a new medical robot that can enable earlier, less invasive and more accurate diagnosis of lung cancer. Lung cancer is currently the deadliest form of cancer in the United States, killing more Americans than breast, prostate and colorectal cancer combined.

Alterovitz is leading a cross-disciplinary team of researchers at Computer Science, the UNC School of Medicine and Vanderbilt University to create a robotic steerable needle capable of autonomously navigating to sites in the human body. The new robot has the potential to automatically curve around vasculature and other sensitive anatomical structures in the body, thereby reducing negative side effects, while safely and accurately reaching difficult-to-access nodules throughout the lung for biopsy and treatment.

Bansal receives NSF CAREER Award

July 22, 2019
Mohit Bansal NSF CAREER Award

Mohit Bansal NSF CAREER Award

Dr. Mohit Bansal, assistant professor of computer science at UNC-Chapel Hill and director of the UNC-NLP Lab, has received a Faculty Early Career Development (CAREER) Award from the National Science Foundation (NSF). The CAREER program is a Foundation-wide activity that offers NSF’s most prestigious awards in support of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research within the context of the mission of their organizations.

This five-year, $450,000 grant, titled “CAREER: Semantic Multi-Task Learning for Generalizable and Interpretable Language Generation”, will support his continued research on enhancing natural language generation (NLG) models with crucial linguistic-semantic knowledge skills. These skills include logical entailment to avoid contradictory and unrelated information with respect to the input, saliency to extract the most important information subsets, and discourse structure to enforce coherent order in the generated text. The project will focus on interpretable and generalizable NLG approaches and the release of a public suite of such knowledge skills and NLG frameworks, eventually allowing the technology to be widely accessible and societally impactful via diverse real-world applications in human-robot interaction and collaboration.

Bansal joined the Department of Computer Science in 2016. Prior to joining UNC, he was a Research Assistant Professor at the Toyota Technological Institute at Chicago. He received his Bachelor of Technology in computer science and engineering from the Indian Institute of Technology Kanpur and received his Doctor of Philosophy in computer science from the University of California, Berkeley.

UNC professor’s high-tech robot promises earlier detection of lung cancer

July 19, 2019

by WRAL TechWire staff — July 19, 2019

CHAPEL HILL — Imagine a high-tech robot that can be used to help detect lung cancer before it’s too late.

It might sound like a scene from a sci-fi film. But thanks to the work of UNC Chapel Hill professor Ron Alterovitz and his cross-disciplinary team of researchers, it could soon be a reality.

With the support from the National Institute of Health, he and his group are developing a new medical robot that can enable “earlier, less invasive, and more accurate” diagnosis of lung cancer.

“The new robot has the potential to automatically curve around vasculature and other sensitive anatomical structures in the body, thereby reducing negative side effects, while safely and accurately reaching difficult-to-access nodules throughout the lung for biopsy and treatment,” UNC said in a statement.

The team includes researchers at UNC Computer Science, the UNC School of Medicine and Vanderbilt University.

Lung cancer is the most common cancer worldwide, accounting for 2.1 million new cases and 1.8 million deaths last year.

In the US, the American Cancer Society estimates:

  • About 228,150 new cases of lung cancer (116,440 in men and 111,710 in women) in 2019
  • About 142,670 deaths from lung cancer (76,650 in men and 66,020 in women) in 2019

The work hasn’t gone unnoticed.

Alterovitz recently received the Presidential Early Career Award for Scientists and Engineers (PECASE), the highest honor bestowed by the United States Government to outstanding scientists and engineers.

The White House Office of Science and Technology Policy coordinates the PECASE with over a dozen departments and agencies. Only around 100 recipients are named per year.

Alterovitz receives Presidential Early Career Award for Scientists and Engineers

July 16, 2019

Updated July 30, 2019

Professor Ron Alterovitz receives a certificate signed by President Donald Trump from Kelvin Kay Droegemeier, director of the White House Office of Science and Technology Policy.
Professor Ron Alterovitz (left) receives a certificate signed by President Donald Trump from Kelvin Kay Droegemeier, director of the White House Office of Science and Technology Policy.

Professor Ron Alterovitz was recognized with the Presidential Early Career Award for Scientists and Engineers (PECASE). The PECASE is the highest honor bestowed by the United States Government to outstanding scientists and engineers who are in the early stages of their independent research careers and who show exceptional promise for leadership in science and technology. The award entitles Alterovitz to an additional $500,000 of research funding.

Recipients of the PECASE are selected for their pursuit of innovative research at the frontiers of science and technology and for their commitment to community service as demonstrated through scientific leadership, public education, or community outreach. The White House Office of Science and Technology Policy coordinates the PECASE with over a dozen departments and agencies. Only around 100 recipients are named per year.

Alterovitz was named a recipient by the White House in a press release on July 2, 2019. He was nominated for the award by the United States Department of Health and Human Services, which operates the National Institutes of Health (NIH). He is the only recipient named in the White House press release who is currently at UNC. Alterovitz is the ninth researcher from UNC to receive the award since its inception 23 years ago and the first recipient in computer science.

Alterovitz’s research focuses on robotics for medical applications. With support from NIH, Alterovitz and his research group are developing a new medical robot that can enable earlier, less invasive, and more accurate diagnosis of lung cancer. Lung cancer is currently the deadliest form of cancer in the United States, killing more Americans than breast, prostate, and colorectal cancer combined. Alterovitz is leading a cross-disciplinary team of researchers at UNC Computer Science, the UNC School of Medicine, and Vanderbilt University to create a robotic steerable needle capable of autonomously navigating to sites in the human body. The new robot has the potential to automatically curve around vasculature and other sensitive anatomical structures in the body, thereby reducing negative side effects, while safely and accurately reaching difficult-to-access nodules throughout the lung for biopsy and treatment.

The White House press release announcing winners for 2015-2017 can be found here.

Alterovitz was recognized in a ceremony organized by the White House and held at Constitution Hall in July. He was also invited to give a research talk at the headquarters of the National Institutes of Health.

 

How can machines, humans talk better? UNC prof, colleague land $1.5M to find out

July 13, 2019

CHAPEL HILL — Ever give a command to Amazon’s Alexa or Google Assistant, and find that some things get lost in communication?

It’s safe to say that you’re not alone.

To perform such functions, voice-activated virtual assistants rely on artificial intelligence (AI) technologies such as natural language processing and machine learning to understand what the user is saying. However, as with any emerging technology, there’s always room for improvement.

Enter Mohit Bansal, an assistant professor in the Department of Computer Science and director of the UNC-NLP Lab. He recently received a Google Focused Research Award in natural language processing, a subfield of computer science concerned with the interactions between computers and human languages.

Thanks to this $1.5 million injection — which he will split equally with fellow principal investigator Yoav Artzi, an assistant professor in the Department of Computer Science and Cornell Tech at Cornell University — the award will fund his research into spatial language understanding, analyzing how to program computers to process large amounts of natural language data. In particular,  it will be done “in interactive settings using resources that provide real-life visual input and environment configurations.”

Most research in spatial language takes place in environments with constrained mobility or visibility or limited interactivity, which limits the utility of the results. Bansal and Artzi hope that the use of real-life visual input and environment configurations will enable better study and model development of the language in real-life environments, the university announced in its release.

The Focused Research Awards program is one way Google supports a small number of multi-year research projects in areas of study that are of key interest to Google, as well as the research community. The awards are invitation-only and typically last for two to three years, and the recipients gain access to Google tools, technologies and expertise.

“These unrestricted gift awards are highly prestigious, and usually considered as a significantly larger and more selective version of the Google Faculty Award program — only a handful of professors have received these awards since its establishment nearly a decade ago in 2010,” the university said.

Identifying perceived emotions from people’s walking style

July 12, 2019

by Ingrid Fadelli , Tech Xplore

A team of researchers at the University of North Carolina at Chapel Hill and the University of Maryland at College Park has recently developed a new deep learning model that can identify people’s emotions based on their walking styles. Their approach, outlined in a paper pre-published on arXiv, works by extracting an individual’s gait from an RGB video of him/her walking, then analyzing it and classifying it as one of four emotions: happy, sad, angry or neutral.

“Emotions play a significant role in our lives, defining our experiences, and shaping how we view the world and interact with other humans,” Tanmay Randhavane, one of the primary researchers and a graduate student at UNC, told TechXplore. “Perceiving the emotions of other people helps us understand their behavior and decide our actions toward them. For example, people communicate very differently with someone they perceive to be angry and hostile than they do with someone they perceive to be calm and contented.”

Most existing  and identification tools work by analyzing facial expressions or voice recordings. However, past studies suggest that body language (e.g., posture, movements, etc.) can also say a lot about how someone is feeling. Inspired by these observations, the researchers set out to develop a tool that can automatically identify the perceived emotion of individuals based on their walking style.

“The main advantage of our perceived emotion recognition approach is that it combines two different techniques,” Randhavane said. “In addition to using , our approach also leverages the findings of psychological studies. A combination of both these techniques gives us an advantage over the other methods.”

The approach first extracts a person’s walking gait from an RGB video of them walking, representing it as a series of 3-D poses. Subsequently, the researchers used a long short-term memory (LSTM) recurrent neural network and a random forest (RF) classifier to analyze these poses and identify the most prominent emotion felt by the person in the video, choosing between happiness, sadness, anger or neutral.

The LSTM is initially trained on a series of deep features, but these are later combined with affective features computed from the gaits using posture and movement cues. All of these features are ultimately classified using the RF classifier.

Randhavane and his colleagues carried out a series of preliminary tests on a dataset containing videos of people walking and found that their model could identify the perceived emotions of individuals with 80 percent accuracy. In addition, their approach led to an improvement of approximately 14 percent over other perceived emotion recognition methods that focus on people’s walking style.

“Though we do not make any claims about the actual emotions a person is experiencing, our approach can provide an estimate of the perceived emotion of that walking style,” Aniket Bera, a Research Professor in the Computer Science department, supervising the research, told TechXplore. “There are many applications for this research, ranging from better human perception for robots and autonomous vehicles to improved surveillance to creating more engaging experiences in augmented and virtual reality.”

Along with Tanmay Randhavane and Aniket Bera, the research team behind this study includes Dinesh Manocha and Uttaran Bhattacharya at the University of Maryland at College Park, as well as Kurt Gray and Kyra Kapsaskis from the psychology department of the University of North Carolina at Chapel Hill.

To train their deep learning model, the researchers have also compiled a new dataset called Emotion Walk (EWalk), which contains videos of individuals walking in both indoor and outdoor settings labeled with perceived emotions. In the future, this dataset could be used by other teams to develop and train new emotion recognition tools designed to analyze movement, posture, and/or gait.

“Our research is at a very primitive stage,” Bera said. “We want to explore different aspects of the body language and look at more cues such as facial expressions, speech, vocal patterns, etc., and use a multi-modal approach to combine all these cues with gaits. Currently, we assume that the walking motion is natural and does not involve any accessories (e.g., suitcase, mobile phones, etc.). As part of future work, we would like to collect more data and train our deep-learning model better. We will also attempt to extend our methodology to consider more activities such as running, gesturing, etc.”

According to Bera, perceived emotion recognition tools could soon help to develop robots with more advanced navigation, planning, and interaction skills. In addition, models such as theirs could be used to detect anomalous behaviors or walking patterns from videos or CCTV footage, for instance identifying individuals who are at risk of suicide and alerting authorities or healthcare providers. Their model could also be applied in the VFX and animation industry, where it could assist designers and animators in creating virtual characters that effectively express particular emotions.

AMD’s SEV tech that protects cloud VMs from rogue servers may as well stand for…Still Extremely Vulnerable (feature on security work by Jan Werner and Fabian Monrose)

July 11, 2019

Evil hypervisors can work out what apps are running, extract data from encrypted guests

Five boffins from four US universities have explored AMD’s Secure Encrypted Virtualization (SEV) technology – and found its defenses can be, in certain circumstances, bypassed with a bit of effort.

In a paper [PDF] presented Tuesday at the ACM Asia Conference on Computer and Communications Security in Auckland, New Zealand, computer scientists Jan Werner (UNC Chapel Hill), Joshua Mason (University of Illinois), Manos Antonakakis (Georgia Tech), Michalis Polychronakis (Stony Brook University), and Fabian Monrose (UNC Chapel Hill) detail two novel attacks that can undo the privacy of protected processor enclaves.

The paper, “The SEVerESt Of Them All: Inference Attacks Against Secure Virtual Enclaves,” describes techniques that can be exploited by rogue cloud server administrators, or hypervisors hijacked by hackers, to figure out what applications are running within an SEV-protected guest virtual machine, even when its RAM is encrypted, and also extract or even inject data within those VMs.

This is possible, we’re told, by monitoring, and altering if necessary, the contents of the general-purpose registers of the SEV guest’s CPU cores, gradually revealing or messing with whatever workload the guest may be executing. The hypervisor can access the registers, which typically hold temporary variables of whatever software is running, by briefly pausing the guest and inspecting its saved state. Efforts by AMD to prevent this from happening, by hiding the context of a virtual machine while the hypervisor is active, can also, it is claimed, be potentially thwarted.

SEV is supposed to safeguard sensitive workloads, running in guest virtual machines, from the prying eyes and fingers of malware and rogue insiders on host servers, typically machines located off-premises or in the public cloud.

The techniques, specifically, undermine the data confidentiality model of guest virtual machines by enabling miscreants to “recover data transferred over TLS connections within the encrypted guest, retrieve the contents of sensitive data as it is being read from disk by the guest, and inject arbitrary data within the guest,” according to the study.

As a result, the paper calls into question the confidentiality promises of cloud service providers. Pulling off these techniques, in our view, is non-trivial, so if anyone does fancy exploiting these weaknesses in SEV in real-world scenarios, they’ll need to be determined and suitably resourced.

In 2016, AMD introduced two memory encryption capabilities to protect sensitive data in multi-tenant environments, Secure Memory Encryption (SME) and Secure Encrypted Virtualization (SEV). The former protects memory against physical attacks like cold boot and direct memory access attacks. The latter mixes memory encryption and virtualization, allowing each virtual machine to be protected from other virtual machines and underlying hypervisors and their admins.

Other vendors have their own secure enclave systems, like Intel SGX, which offers a different set of potential attack paths.

SEV, says AMD, protects customers’ guest VMs from one another, and from software running on the underlying host and its administrators. Whatever happens in these virtual machines should be off limits to other customers as well as the host machine’s operating system, hypervisor, and admins. However, the researchers have demonstrated that this threat model fails to ward off register inference attacks and structural inference attacks by malicious hypervisors.

“By passively observing changes in the registers, an adversary can recover critical information about activities in the encrypted guest,” the researchers explain in their paper.

A variant technique even works against Secure Encrypted Virtualization Encrypted State (SEV-ES), an extended memory protection technique that not only encrypts RAM but encrypts the guest’s virtual machine control block: this is an area of memory that stores a virtual machine’s CPU register contents when it is forced to yield to the hypervisor. This encryption should thus stop the hypervisor from making any sense of the paused VM’s context, though its contents can still be inferred, we’re told.

“We show how one can use data provided by the Instruction Based Sampling (IBS) subsystem (e.g. to learn whether an executed instruction was a branch, load, or store) to identify the applications running within the VM,” the paper says. “Intuitively, one can collect performance data from the virtual machine and match the observed behavior to known signatures of running applications.”

To conduct their work, the boffins used a Silicon Mechanics aNU-12-304 server with dual AMD Epyc 7301 processors and 256GB of RAM, running Ubuntu 16.04 and a custom 64-bit Linux kernel v4.15. Guest VMs received a single vCPU with 2GB of RAM, running Ubuntu 16.04 with the same kernel as the host.

While the security implications of accessing encrypted data and injecting arbitrary data are obvious, even exposing what applications are running in a guest VM has potentially undesirable consequences. Service providers could use the technique for application fingerprinting and banning unwanted software; malicious individuals could conduct reconnaissance to target exploits, to developing return-oriented programming (ROP) attacks or to undermine Address Space Layout Randomization (ASLR) defenses.

The researchers recommend the IBS subsystem be changed so that guest readings are discarded when secure encrypted virtualization is enabled.

The Register asked AMD for comment, and we’ve not heard back.

Bansal receives Google Focused Research Award in NLP

July 8, 2019
Mohit Bansal

Mohit Bansal

Mohit Bansal, an assistant professor in the Department of Computer Science and director of the UNC-NLP Lab, received a Google Focused Research Award in natural language processing to fund exploration of spatial language understanding.

The award is worth $1.5 million, which will be split evenly between Bansal and fellow principal investigator Yoav Artzi, an assistant professor in the Department of Computer Science and Cornell Tech at Cornell University.

The awarded project seeks to study both spatial language comprehension and generation in interactive settings using resources that provide real-life visual input and environment configurations. We use and interpret language dealing with our immediate environment every day. But most research in spatial language takes place in environments with constrained mobility or visibility or limited interactivity, which limits the utility of the results. Bansal and Artzi hope that the use of real-life visual input and environment configurations will enable better study and model development of the language in real-life environments.

The Focused Research Awards program is a means by which Google supports a small number of multi-year research projects in areas of study that are of key interest to Google as well as the research community. The awards are invitation-only and typically last for two to three years, and the recipients gain access to Google tools, technologies and expertise. These unrestricted gift awards are highly prestigious, and usually considered as a significantly larger and more selective version of the Google Faculty Award program–only a handful of professors have received these awards since its establishment nearly a decade ago in 2010.

For more information, please visit the award page.