Research

The Department of Electrical & Systems Engineering has a unique and long tradition of excellence in advancing basic science and solving cutting-edge engineering problems relevant to society. The second-oldest electrical engineering department in the country, it is dedicated to providing high-quality education and research.


 

 


Research Highlights

 

 

 

https://engineering.wustl.edu/news/Pages/An-emerging-understanding-of-smell.aspx1342An emerging understanding of smell<img alt="" src="/news/PublishingImages/iStock-locust-Shinung.jpg?RenditionID=1" style="BORDER:0px solid;" /><div id="__publishingReusableFragmentIdSection"><a href="/ReusableContent/36_.000">a</a></div><p>​How does the brain detect smells?</p><p>To find out, you could rely on biological sciences, using high tech imaging methods, or studying anatomical diagrams. You could even get philosophical and ask, “What is smell, anyway?”</p><p>Or, you could turn to engineering.<br/></p><p>That’s what <a href="/Profiles/Pages/ShiNung-Ching.aspx">ShiNung Ching</a>, an associate professor in the McKelvey School of Engineering at Washington University in St. Louis, did.</p><p>Ultimately, Ching, who is in the Preston M. Green Department of Electrical & Systems Engineering, and doctoral student Sruti Mallik developed computational models of neural circuits that mimic the sensory act of smelling. They found the models also manifest certain properties analogous to those observed in olfactory sensory processing in insect brains.</p><p>The research was carried out in collaboration with Baranidharan Raman, professor of biomedical engineering. It was published earlier this spring in <a href="https://www.jneurosci.org/content/40/17/3408.abstract">The Journal of Neuroscience</a>.<br/></p><p>In order to build a model that mimicked the process of smelling, the research team first had to mathematically describe the process of sensory detection. Then they built computational models of neural circuits that would best satisfy those mathematical constraints.</p><p>Researchers found that the model developed emergent properties — properties that are more than the sum of their parts, so to speak — similar to properties seen in an insect’s antennal lobe, which is important for its sense of smell.</p><p>“What we did was to ask, as engineers, how might we think about building a brain network that enables the detection of different smells. In pursuing this question, what came out was something that looked remarkably biological in nature,” Ching said.</p><p>“This was exciting since it gave us a new hypothesis, grounded in engineering theory, about how the brain achieves this type of sensory processing.”</p><p>The team plans to extend this framework to study olfactory processing in other organisms, as well as other forms of neural information processing.</p><SPAN ID="__publishingReusableFragment"></SPAN><p>This research was supported by Grants EF-1724218 (S.C., B.R.), IIS-1453022 (B.R.), and CMMI-1653589 (S.C.) from the National Science Foundation.<br/></p><div><div class="cstm-section"><h3>ShiNung Ching<br/></h3><div style="text-align: center;"> <strong><a href="/Profiles/Pages/ShiNung-Ching.aspx"><img src="/Profiles/PublishingImages/Ching_ShiNung.jpg?RenditionID=3" alt="ShiNung Ching" style="margin: 5px;"/></a> <br/></strong></div><ul style="text-align: left;"><li>Associate Professor of Electrical & Systems Engineering</li><li>Research: lies at the interface between systems and control engineering and neural medicine. His research projects will be interdisciplinary, focusing on questions in systems theory as well as basic science and clinical applications.<br/></li></ul><p style="text-align: center;"> <a href="/Profiles/Pages/ShiNung-Ching.aspx">>> View Bio</a><br/></p></div></div>Brandie Jeffersonhttps://source.wustl.edu/2020/07/an-emerging-understanding-of-smell/2020-07-07T05:00:00ZResearchers learn about sensory experience by modeling bug brains<p>​Researchers learn about sensory experience by modeling bug brains<br/></p>
https://engineering.wustl.edu/news/Pages/alumnus-wang-joins-community-of-accomplished-scholars-as-a-hertz-fellow.aspx1343Alumnus Wang joins community of accomplished scholars as a Hertz Fellow<p>​A good researcher knows the importance of networking and developing relationships with mentors. Maxwell Wang, who earned a bachelor’s degree in electrical engineering in 2016, learned this lesson earlier than most.<br/></p><img alt="" src="/news/PublishingImages/wang-maxwell-portrait.jpg?RenditionID=2" style="BORDER:0px solid;" /><p>While in elementary school, he would sit in on classes at his local community college and flip through mathematics textbooks.<br/></p><p>"Eventually, I began to meet helpful mentors who helped guide me to the next path, and I soon started taking courses at the University of Illinois at Urbana-Champaign," he said.</p><p>This path would take him from studying calculus and applied mathematics as a fifth grader to one of the most prestigious fellowships in the engineering field.</p><p>In May, Wang was named one of the 16 recipients of the 2020 Hertz Fellowship. For more than 57 years, the Fannie and John Hertz Foundation has supported the research of doctoral students who "demonstrate the greatest potential to tackle the most urgent problems facing society."</p><p>Wang was honored for his work studying the mechanics of the brain to better personalize treatments for conditions such as depression and Parkinson's disease.</p><p>According to Wang, current treatments for these conditions involve a lot of trial and error, with patients going through years of alternating diagnoses before finding a therapy that works.</p><p>"My goal is to figure out how can we understand what is fundamentally going on in the brain when patients have these diseases and what changes occur when we apply a treatment," he said. "Instead of trial and error, can we look at a brain beforehand and decide which treatment is going to work and how can we tailor treatments for specific patients?<em>"</em></p><p>Wang credits his passion for studying the brain to another inspiring mentor. The former Langsdorf Scholar had originally planned to study robotics at the engineering school at WashU. He changed course after taking a class with ShiNung Ching, associate professor in the Preston M. Green Department of Electrical & Systems Engineering. Ching's research interests include systems and control engineering and neural medicine.</p><p>"Prior to him, my experience was primarily in the engineering domain," Wang said. "It was exciting to see how the very practical concepts we talk about in engineering can be applied to discover new and exciting knowledge, especially about the brain<em>."</em></p><p>Wang's previous research experience was mostly unmanned aerial vehicles and image processing.</p><p><strong>"</strong>Being exposed to how little we know about the brain and all the great mysteries that are out there to uncover was really illuminating for my future interest," he said.</p><p>"Maxwell absorbs and synthesizes ideas in a way that is rare, and it was a pleasure working with him during his undergraduate years," Ching said. "His research in my lab ended up being presented at one of the leading conferences in our field, a remarkable achievement. I'm delighted to see him named as a Hertz fellow, a well-earned honor we as a department aspire to see our graduates achieve, and look forward to following his future contributions to science."</p><p>Wang also credited Joseph O'Sullivan, the Samuel C. Sachs Professor of Electrical Engineering, and Joseph Culver, professor of radiology at the School of Medicine, of physics in Arts & Sciences and of biomedical engineering, for introducing him to imaging technologies that allow researchers to more comprehensively study the brain.</p><p>"That's what got me thinking: With all that we know of electrical engineering systems and this amazing technology that allows us to see what is going on in the brain, what are the opportunities to combine these two fields to create something amazing?"</p><p>Wang is at Carnegie Mellon University pursuing a doctoral degree in machine learning and neuroscience. As a Hertz Fellow, he'll continue to learn from a community of hundreds of other researchers and scholars.</p><p>"After I entered college, there's been almost a magical chain of awesome mentors that I have run into," he said. "I'm just really grateful for all of it."<br/></p>Danielle Lacey2020-07-07T05:00:00ZAlumnus Maxwell Wang, who was recently named a Hertz Fellow, credits the community of mentors he’s met throughout his research career with helping him to become the accomplished scholar he is today.
https://engineering.wustl.edu/news/Pages/Artificial-intelligence-identifies-locates-seizures-in-real-time.aspx1339Artificial intelligence identifies, locates seizures in real-time<img alt="" src="/news/PublishingImages/SeizureDetectionAI.jpg?RenditionID=1" style="BORDER:0px solid;" /><div id="__publishingReusableFragmentIdSection"><a href="/ReusableContent/36_.000">a</a></div><div id="ctl00_PlaceHolderMain_pnlNewsImage"> <video controls="controls" style="width: 100%; max-width: 854px; height: auto;"><source src="/news/Documents/AI-Two-seizures.mp4" type="video/mp4"></source></video> <br/></div> <sub>This gif was recorded during two seizures, one at 2950 seconds, the other at 9200. The top left animation is of EEG signals from three electrodes. The top right is a map of the inferred network. The third animation plots the Fiedler eigenvalue, the single value used to detect seizures using the network inference technique. (Courtesy: Walter Bomela, Li Lab)</sub> <div> <span style="font-size: 12px;"><br/></span> <p>​Researchers from Washington University in St. Louis’ McKelvey School of Engineering have combined artificial intelligence with systems theory to develop a more efficient way to detect and accurately identify an epileptic seizure in real-time.</p><p>Their results were published May 26 in the journal <a href="https://www.nature.com/articles/s41598-020-65401-6">Scientific Reports</a>.<br/></p><p>The research comes from the lab of <a href="/Profiles/Pages/Jr-Shin-Li.aspx">Jr-Shin Li</a>, professor in the Preston M. Green Department of Electrical & Systems Engineering, and was headed by Walter Bomela, a postdoctoral fellow in Li’s lab.<br/></p><p>Also on the research team were Shuo Wang, a former student of Li’s and now assistant professor at the University of Texas at Arlington, and <span style="caret-color: #000000; color: #000000; font-size: medium;">Chu-An Chou</span> of Northeastern University.</p><p>“Our technique allows us to get raw data, process it and extract a feature that’s more informative for the machine learning model to use,” Bomela said. “The major advantage of our approach is to fuse signals from 23 electrodes to one parameter that can be efficiently processed with much less computing resources.”</p><p>In brain science, the current understanding of most seizures is that they occur when normal brain activity is interrupted by a strong, sudden hyper-synchronized firing of a cluster of neurons. During a seizure, if a person is hooked up to an electroencephalograph — a device known as an EEG that measures electrical output — the abnormal brain activity is presented as amplified spike-and-wave discharges.</p><p>“But the seizure detection accuracy is not that good when temporal EEG signals are used,” Bomela said. The team developed a network inference technique to facilitate detection of a seizure and pinpoint  its location with improved accuracy.</p><p>During an EEG session, a person has electrodes attached to different spots on his/her head, each recording electrical activity around that spot.</p><p>“We treated EEG electrodes as nodes of a network. Using the recordings (time-series data) from each node, we developed a data-driven approach to infer time-varying connections in the network or relationships between nodes,” Bomela said. Instead of looking solely at the EEG data — the peaks and strengths of individual signals — the network technique considers relationships. “We want to infer how a brain region is interacting with others,” he said.<br/></p><p>It is the sum of these relationships that form the network.</p><p>Once you have a network, you can measure its parameters holistically. For instance, instead of measuring the strength of a single signal, the overall network can be evaluated for strength. There is one parameter, called the Fiedler eigenvalue, which is of particular use. “When a seizure happens, you will see this parameter start to increase,” Bomela said.</p><p>And in network theory, the Fiedler eigenvalue is also related to a network’s synchronicity — the bigger the value the more the network is synchronous. “This agrees with the theory that during seizure, the brain activity is synchronized,” Bomela said.</p> <p>A bias toward synchronization also helps eliminate artifact and background noise. If a person, for instance, scratches their arm, the associated brain activity will be captured on some EEG electrodes or channels. It will not, however, be synchronized with seizure activity. In that way, this network structure inherently reduces the importance of unrelated signals; only brain activities that are in sync will cause a significant increase of the Fiedler eigenvalue.</p><p>Currently this technique works for an individual patient. The next step is to integrate machine learning to generalize the technique for identifying different types of seizures across patients.</p><p>The idea is to take advantage of various parameters characterizing the network and use them as features to train the machine learning algorithm.</p><p>Bomela likens the way this will work to facial recognition software, which measures different features — eyes, lips and so on — generalizing from those examples to recognize any face.</p><p>“The network is like a face,” he said. “You can extract different parameters from an individual’s network — such as the clustering coefficient or closeness centrality — to help machine learning differentiate between different seizures.”</p><p>That’s because in network theory, similarities in specific parameters are associated with specific networks. In this case, those networks will correspond to different types of seizures.</p><p>One day, a person with a seizure disorder can wear a device analogous to an insulin pump. As the neurons begin to synchronize, the device will deliver medication or electrical interference to stop the seizure in its tracks.</p><p>Before this can happen, researchers need a better understanding of the neural network.</p><p>“While the ultimate goal is to refine the technique for clinical use, right now we are focused on developing methods to identify seizures as drastic changes in brain activity,” Li said. “These changes are captured by treating the brain as a network in our current method.”</p> <SPAN ID="__publishingReusableFragment"></SPAN> <p>This collaboration was supported in part by Faculty Science and Technology Acquisition and Retention (STARs) Program, project ID: AR91084L-51, Northeastern Seed Grant Program and Burroughs Wellcome Fund 2020 Collaborative Research Travel Grant (#1019976), the National Science Foundation under the awards CMMI-1763070 and CMMI-1933976, and by the NIH grant R01GM131403-01.<br/></p></div><span> <div class="cstm-section"><h3>Jr-Shin Li<br/></h3><div><p style="text-align: center;"> <a href="/Profiles/Pages/Jr-Shin-Li.aspx"> <img src="/Profiles/PublishingImages/Li_Jr-Shin.jpg?RenditionID=3" class="ms-rtePosition-4" alt="" style="margin: 5px;"/></a> <br/></p><div style="text-align: center;"><div style="text-align: center;"><ul style="text-align: left;"><li> <span style="font-size: 1em;">Professor Jr-Shin Li’s research group has extensive and close collaboration with biologists, chemists and applied physicists.</span><br/></li></ul></div> <br/> <a href="/Profiles/Pages/Jr-Shin-Li.aspx">View Bio</a><br/></div></div></div></span> <div class="cstm-section"><h3>Media Coverage<br/></h3><div> <strong>AZoRobotics: </strong> <a href="https://www.azorobotics.com/News.aspx?newsID=11414">AI Enables a More Efficient Way to Detect Epileptic Seizure in Real Time</a><br/></div><div> <br/> </div><div> <strong>Futurity: </strong> <a href="https://www.futurity.org/seizures-tracking-artificial-intelligence-2397942/">AI Tracks Seizures in Real Time</a><br/></div><div><br/></div> <strong>Medgadget: </strong> <a href="https://www.medgadget.com/2020/07/smart-algorithm-for-seizure-detection-and-classification.html">Smart Algorithm for Seizure Detection and Classification</a><br/></div>Brandie Jeffersonhttps://source.wustl.edu/2020/06/artificial-intelligence-identifies-locates-seizures-in-real-time/2020-06-29T05:00:00ZTreating the brain as a network allows researchers to extract more meaningful data from EEGs<p>​Treating the brain as a network allows researchers to extract more meaningful data from EEGs<br/></p>Y
https://engineering.wustl.edu/news/Pages/New-microscopy-method-provides-unprecedented-look-at-amyloid-protein-structure.aspx1325New microscopy method provides unprecedented look at amyloid protein structure<img alt="" src="/news/PublishingImages/LewFlyover.jpg?RenditionID=1" style="BORDER:0px solid;" /><div id="__publishingReusableFragmentIdSection"><a href="/ReusableContent/36_.000">a</a></div><p>Neurodegenerative diseases such as Alzheimer’s and Parkinson’s are often accompanied by amyloid proteins in the brain that have become clumped or misfolded. At Washington University in St. Louis, a newly developed technique that measures the orientation of single molecules is enabling, for the first time, optical microscopy to reveal nanoscale details about the structures of these problematic proteins.</p><p>Research from the lab of <a href="/Profiles/Pages/Matthew-Lew.aspx">Matthew Lew</a>, assistant professor in the Preston M. Green Department of Electrical & Systems Engineering at the McKelvey School of Engineering, describing this new approach was published in <a href="https://www.osapublishing.org/optica/abstract.cfm?uri=optica-7-6-602">Optica</a>, The Optical Society’s journal for high impact research.<br/></p><p>“Neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases are leading causes of death all over the world,” said Tianben Ding, a PhD student in Lew’s lab and a co-author of the new paper. “We hope our single-molecule orientation imaging approach can provide new insights into amyloid structure and possibly contribute future development of effective therapeutics against the diseases.”</p><p>Biological and chemical processes are driven by complicated movements and interactions between molecules. Although most amyloid proteins may be non-toxic, the misfolding of even a few could eventually kill many neurons.</p><p>“We need imaging technologies that can watch these molecular movements in living systems to understand the fundamental biological mechanisms of disease,” Lew said. “Amyloid and prion-type diseases like Alzheimer’s, Parkinson’s and diabetes are our first targets for this technology, but we see it being applied in many other areas too.”</p><figure class="wp-caption alignright" style="box-sizing: inherit; display: inline; margin: 0px 0px 1.5em 1.5em; float: right; max-width: 100%; padding: 0px; border: none; background-image: none; caret-color: #3c3d3d; color: #3c3d3d; font-family: "source sans pro", "helvetica neue", helvetica, arial, sans-serif; font-size: 19.200000762939453px; width: 430px;"><img src="https://media.giphy.com/media/H7HuNXZtmrPq0b8E4w/giphy.gif" alt="" style="box-sizing: inherit; border-width: 0px; width: 430px; margin: 5px;"/><figcaption class="wp-caption-text" style="box-sizing: inherit; margin-bottom: 0px; font-size: 1rem; font-style: italic; line-height: 1.333; color: #626464; margin-top: 0.25em;">This “flyover” animation shows the intricate structural details of amyloid aggregates captured by single-molecule orientation localization microscopy. Well-ordered regions, disorganized areas and areas of relatively recent growth can all be seen. <a href="https://osf.io/pe3qu/wiki/Visualizations/" style="box-sizing: inherit;">See more visualizations online</a>.  (Courtesy: Lew Lab)</figcaption></figure><h4>Selecting the best microscope</h4><p>Lew’s lab has developed several microscopy methods that measure the orientation and location of fluorescent molecules attached to single proteins. The orientation information is obtained by measuring not only the location of fluorescence in the sample but also characteristics of that light, such as polarization, which are typically ignored in most other microscopy approaches.</p><p>“By measuring the orientations of single molecules bound to amyloid aggregates, this microscope enabled us to map differences in amyloid structure organization that cannot be detected by standard localization microscopes,” said Tingting Wu, a co-author of the paper.</p><p>The researchers used the powerful microscopy approach to measure the orientations of single fluorescent molecules bound to amyloid aggregates. Because there is no artificial linker between the fluorescent probes and amyloid surfaces, the probes’ binding orientation to the amyloid surfaces conveys information about how the amyloid protein itself is organized.</p><p>The researchers quantified how the orientations of each fluorescent molecule varied each time one attached to an amyloid protein. Differences in these binding behaviors can be attributed to structure differences between amyloid aggregates. Because the method provides single-molecule information, the researchers could observe nanoscale differences between amyloid structures without averaging out details of local features.</p><figure class="wp-caption alignright" style="box-sizing: inherit; display: inline; margin: 0px 0px 1.5em 1.5em; float: right; max-width: 100%; padding: 0px; border: none; background-image: none; caret-color: #3c3d3d; color: #3c3d3d; font-family: "source sans pro", "helvetica neue", helvetica, arial, sans-serif; font-size: 19.200000762939453px; width: 269px;"><img data-attachment-id="398237" data-permalink="https://source.wustl.edu/2020/06/new-microscopy-method-provides-unprecedented-look-at-amyloid-protein-structure/singlemicroscopy/" data-orig-file="https://source.wustl.edu/wp-content/uploads/2020/06/singlemicroscopy.jpg" data-orig-size="269,141" data-comments-opened="0" data-image-meta="{"aperture":"0","credit":"","camera":"","caption":"","created_timestamp":"0","copyright":"","focal_length":"0","iso":"0","shutter_speed":"0","title":"","orientation":"0"}" data-image-title="singlemicroscopy" data-medium-file="https://source.wustl.edu/wp-content/uploads/2020/06/singlemicroscopy.jpg" data-large-file="https://source.wustl.edu/wp-content/uploads/2020/06/singlemicroscopy.jpg" class="wp-image-398237 size-full" src="https://source.wustl.edu/wp-content/uploads/2020/06/singlemicroscopy.jpg" alt="" style="box-sizing: inherit; border-width: 0px; width: 269px; display: block; margin: 5px;"/><figcaption class="wp-caption-text" style="box-sizing: inherit; margin-bottom: 0px; font-size: 1rem; font-style: italic; line-height: 1.333; color: #626464; margin-top: 0.25em;"><a href="https://osf.io/pe3qu/wiki/Visualizations/" style="box-sizing: inherit;">Click here to see single-molecule orientation localization microscopy in action</a>. (Image: Tianben Ding, Tingting Wu and Matthew Lew)</figcaption></figure><h4>Opportunities for long-term studies </h4><p>“We plan to extend the method to monitor nanoscale changes within and between amyloid structures as they organize over hours to days,” said Ding. “Long-term studies of amyloid aggregates may reveal new correlations between how amyloid proteins are organized and how quickly they grow or spontaneously dissolve.”</p><p>The researchers note that the set-up they used for orientation-localization microscopy consisted of commercially available parts that are accessible to anyone performing single-molecule super-resolution microscopy. Their analysis code is available online at: <a href="https://github.com/Lew-Lab/RoSE-O">https://github.com/Lew-Lab/RoSE-O</a>.</p><p>“In optical microscopy and imaging, scientists and engineers have been pushing the boundaries of imaging to be faster, probe deeper and have higher resolution,” Lew said. “Our work shows that one can shed light on fundamental processes in biology by, instead, focusing on molecular orientation, which can reveal details about the inner workings of biology that cannot be visualized by traditional microscopy.”</p><p><a href="https://www.osa.org/en-us/about_osa/newsroom/news_releases/2020/new_microscopy_method_provides_unprecedented_look/">Read the full release on the OSA website</a>.<br/></p><SPAN ID="__publishingReusableFragment"></SPAN><p>T. Ding, T. Wu, H. Mazidi, O. Zhang, M. Lew. Single-molecule orientation localization microscopy for resolving structural heterogeneities between amyloid fibrils. Optica, June 4, 2020. DOI: <a href="https://doi.org/10.1364/OPTICA.388157">doi.org/10.1364/OPTICA.388157</a>.<br/></p><span> <div class="cstm-section"><h3>Matthew Lew<br/></h3><div><p style="text-align: center;"> <img src="/Profiles/PublishingImages/Lew_Matthew_5620.jpg?RenditionID=3" class="ms-rtePosition-4" alt="" style="margin: 5px;"/> </p><p></p><ul style="padding-left: 20px; caret-color: #343434; color: #343434;"><li>Electrical Systems Engineering - <span style="caret-color: #343434; color: #343434;">Assistant Professor</span><br/></li><li>Research: Builds advanced imaging systems to study biological and chemical systems at the nanoscale, leveraging innovations in applied optics, signal and image processing, design optimization, and physical chemistry.<br/></li></ul><div style="text-align: center;"> <a href="/Profiles/Pages/Matthew-Lew.aspx">View Bio</a><br/><br/><br/></div></div></div></span>A new technique developed in the lab of Matthew Lew at the McKelvey School of Engineering measures the orientation of single molecules. (Courtesy: Lew Lab)Nancy D. Lamontagnehttps://source.wustl.edu/2020/06/new-microscopy-method-provides-unprecedented-look-at-amyloid-protein-structure/2020-06-05T05:00:00ZMeasuring location, orientation of single molecules could give new insights into Alzheimer’s, Parkinson’s<p>​Measuring location, orientation of single molecules could give new insights into Alzheimer’s, Parkinson’s<br/></p>

Research Areas

Applied Physics
  • Nano-photonics
  • Quantum Optics
  • Engineered Materials
  • Electrodynamics
Devices & Circuits
  • Computer Engineering
  • Integrated Circuits
  • Radiofrequency Circuits
  • Sensors
Systems Science
  • Optimization
  • Applied Mathematics
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Signals & Imaging
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