About

Pamela Abshire is a Professor in the Department of Electrical and Computer Engineering and the Institute for Systems Research at the University of Maryland, College Park. Her areas of specialty include VLSI circuit design and bioengineering. Dr. Abshire's research focuses on better understanding the tradeoffs between performance and resources in natural and engineered systems. Her interests encompass information theory for physical systems, noise theory for electronic, photonic, and biological systems, analysis and design of sensory information processing systems, and algorithm, VLSI circuit, and microsystem design, especially for low power applications. Prior to her appointment at the University of Maryland in November 2001, she was a graduate student in the Sensory Communications and Microsystems Lab in the Department of Electrical and Computer Engineering at The Johns Hopkins University. Her dissertation was on Sensory Information Processing Under Physical Constraints. Dr. Abshire's educational background includes a B.S. degree in Physics with Honor from the California Institute of Technology and advanced degrees in Electrical and Computer Engineering from Johns Hopkins University. Her professional experience includes work as a Research Engineer at Medtronic, Inc. Her research interests also include the analysis and design of sensory information processing systems, and her work spans electronic, photonic, and biological systems.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Electronic engineering
• Computer hardware
• Geology
• Materials science
• Optoelectronics
• Electrical engineering
• Optics
• Physics
• Chemistry
• Computer vision
• Environmental science
• Nanotechnology
• Mathematics
• Remote sensing

Selected publications

• A fast and simple algorithm for accurate spike detection in HD-MEA recordings

  Journal of Neuroscience Methods · 2026-03-19

  articleOpen access

  High density microelectrode arrays (HD-MEAs) provide a strong platform to study individual neuronal activity and neuronal network dynamics. However, the analysis of high volume and complex data present several challenges. Common spike detection methods based on Root-Mean-Square (RMS) threshold crossing underestimate the number of spikes during neuronal bursting, which frequently occurs in neuronal cultures. In addition, the detection of action potentials by multiple electrodes makes spike sorting a computationally expensive task. We optimized a previously described detection method, based on the scaled median of absolute deviations (MED) that is more accurate during high rates of neuronal firing. In addition, we added a step to de-duplicate (DP) spikes recorded on multiple electrodes, which enhanced the accuracy of MED. The combined method of detection and de-duplication (DP-MED) is less computationally expensive and easier to implement than popular sorting alternatives like Kilosort-4. During burst periods, the MED-based method detected over half of spikes that were undetected by the RMS-based method. To evaluate the performance of DP-MED, we simulated data that emulates neuronal activity recorded with HD-MEA. Across increasing firing rates, DP-MED shows more precision than Kilosort-4 but is slightly less accurate. After inducing high firing rate in cortical cultures with pharmacological stimulation, DP-MED detected a similar number of spikes than Kilosort-4, however, the analysis in Kilosort-4 was 40-fold more time-consuming. These results highlight the effectiveness of the DP-MED method in the context of drug screening using HD-MEAs. • We developed DP-MED, a fast and computationally efficient algorithm for precise spike detection for high density multi electrode array (HD-MEA) recordings. • DP-MED is more precise than commonly used RMS-based methods and its precision is less sensitive to neuronal bursting. • The performance of DP-MED is comparable to KiloSort-4, a widely used open-source spike sorting algorithm. • DP-MED is approximately 40 times faster than KiloSort-4 in processing high volume HD-MEA data.

  PublisherDOI

• A fast and simple algorithm for accurate spike detection in HD-MEA recordings

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-01-22

  articleOpen access

  Abstract Background High density microelectrode arrays provide a strong platform to study individual neuronal activity and neuronal network dynamics. However, the analysis of high volume and complex data present several challenges. Common spike detection methods based on Root-Mean-Square (RMS) threshold crossing underestimate the number of spikes during neuronal bursting, which frequently occurs in neuronal cultures. In addition, the detection of action potentials by multiple electrodes makes spikes sorting a computationally expensive task. New Method We optimized a previously described detection method, based on the scaled median of absolute deviations (MED) that is more accurate during high rates of neuronal firing. In addition, we added a step to de-duplicate (DP) spikes recorded on multiple electrodes, which enhanced the accuracy of MED. The combined method of detection and de-duplication (DP-MED) is less computationally expensive and easier to implement than popular sorting alternatives like Kilosort-4. Results and Conclusions During burst periods, the MED-based method detected over half of spikes that were undetected by the RMS-based method. To evaluate the performance of DP-MED, we simulated data that emulates neuronal activity recorded with HD-MEA. Across increasing firing rates, DP-MED shows more precision than Kilosort-4 but is slightly less accurate. After inducing high firing rate in cortical cultures with pharmacological stimulation, DP-MED detected a similar number of spikes than Kilosort-4, however, the analysis in Kilosort-4 was 40-fold more time-consuming. These results highlight the effectiveness of the DP-MED method in the context of drug screening using HD-MEAs.

  PublisherOA PDFDOI

• Advancing Healthcare Through Wearable Technology: Key Takeaways From the IEEE CASS Workshop on Medical Wearables [CASS Conference Highlights]

  IEEE Circuits and Systems Magazine · 2025-01-01 · 1 citations

  article

  The IEEE CASS Workshop on Medical Wearables, held on October 27, 2024, in Xi’an, China, focused on the impact of wearable technologies in healthcare. Experts gathered to discuss advancements in sensor design, power management, and data transmission for wearable devices, highlighting how they enable continuous health monitoring and improve patient care. Key topics included challenges in miniaturization, energy efficiency, and the need for standardized data protocols for interoperability. The presentations featured several recent innovations like galvanic body-coupled power transfer, non-invasive blood gas monitoring, and AI-driven EEG and auscultation diagnostics. The workshop also addressed commercialization barriers, including regulatory requirements, intellectual property, and user-centered design. Emphasizing interdisciplinary collaboration, the event explored how wearables can be integrated into healthcare systems for proactive, personalized care. Emerging AI, telemedicine, and bio-sensing trends are poised to revolutionize patient management, making continuous, remote monitoring more accessible and practical. The workshop concluded with a note of optimism about the future potential of medical wearables to reshape healthcare delivery globally.

  PublisherDOI

• Tree Search for Efficient Target Detection in FMCW Radar

  2025-08-10

  articleSenior author

  This paper evaluates a tree-search algorithm for transmitter beam forming as an approach for efficient target detection in cognitive radar systems. Key performance metrics such as detection probability, false alarm rate, and energy efficiency are derived to evaluate the efficacy of this adaptive search strategy in the application scenario of a drone performing target detection so that it can find open spaces and avoid collisions. Results indicate that the tree-search approach exhibits improved computational efficiency and faster response times compared to traditional scanning methods. The findings underscore the importance of algorithmic design considerations when addressing trade-offs between accuracy and resource utilization in radar systems. This analysis establishes that tree search in transmitter beam forming is a fast and power-efficient method for detecting targets when the downstream computational costs are high or when the environment is sparse.

  PublisherDOI

• Power-Performance Tradeoffs in a Time of Flight Lidar Readout Chain

  2025-08-10

  articleSenior author

  Direct time-of-flight (ToF) light detection and ranging (LiDAR) is a powerful imaging technique for depth sensing and 3D mapping. Single-photon avalanche diodes (SPADs) serve as high-sensitivity detectors, enabling precise photon-based measurements. Power consumption is a critical factor in SinglePhoton Avalanche Diode (SPAD) readout, especially for pulsed LiDAR applications. This work explores the power trade-offs in the readout chain, focusing on the quench and reset (QAR) block. We model the SPAD and use circuit-level simulations to evaluate the power-performance characteristics of the QAR. We found that the active quench and reset (AQAR) saves more than 75 percent of power compared to a passive quench circuit. The insights from our study serve to guide design choices for optimizing SPAD-based ToF LiDAR systems.

  PublisherDOI

• A Novel Dual Path Detection Approach for Single Pixel FMCW LiDAR Imaging

  2025-08-10

  articleSenior author

  We consider the problem of target ranging at very short distances using free space frequency-modulated continuous wave (FMCW) LiDAR with single pixel imaging. This is a challenging problem due to the presence of phase noise and environmental factors that reduce precision of measurements made at short ranges. We present a novel technique for free space FMCW LiDAR with single pixel imaging that uses a dual path-based setup to mitigate the noise. Experimental results from laboratory setups with different target ranges show that this technique is able to perform precise measurements at short ranges while introducing very little noise.

  PublisherDOI

• Functionalized ImmunoFET for Detection of Phosphatidyl-L-serine

  2025-05-25

  articleSenior author

  This paper presents an ion-sensitive field-effect transistor (ISFET)-based biosensor for detecting phosphatidylserine (PS), a key apoptosis marker. The sensor, featuring a silicon nitride (Si<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>N<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf>) membrane functionalized with Annexin V, achieved sensitivities of 20 mV/decade for Ag/AgCl electrodes and 52 mV/decade for gold electrodes, with a limit of detection down to 10 nM. Surface modification was validated via fluorescence microscopy, showing a twofold increase in signal intensity upon PS binding. Compared to fluorescence microscopy and enzyme-linked immunosorbent assay (ELISA), which require extensive processing, the ISFET-based approach offers a rapid, label-free, and miniaturizable alternative. Optimized surface treatment enhanced performance, making it suitable for real-time apoptotic marker detection and integration into portable diagnostics.

  PublisherDOI

• An anhydrobiotic cell line expressing odorant receptors shows odorant responses after dry storage

  Scientific Reports · 2025-10-10

  articleOpen access

  Odorant receptor-expressing cells have been shown to recognize various odors, which has brought them to the interest of the growing field of cell-based olfactory sensors. However, cell cultures are difficult to use outside a laboratory because of their continuous need for controlled conditions. In this study, the odorant receptor DmOr47a, the co-receptor DmOrco, and the calcium-sensing fluorescent protein GCaMP6f were stably expressed in a Pv11 cell line (Pv11-00443-Or47a), which is desiccatable. This cell line not only retained desiccation tolerance, but also showed dose-dependent fluorescence responses to the DmOr47a ligand pentyl acetate that were recovered 12 h after rehydration. Even more importantly, Pv11-00443-Or47a showed a response to the agonist of DmOrco just 1 h after rehydration, even upon inhibition of protein synthesis. This result demonstrates for the first time that a transmembrane protein can be dry-stored in an orthologous cell culture system. This work also constitutes an initial step towards the development of improved desiccatable sensing cells for use in portable devices.

  PublisherOA PDFDOI

• IEEE Circuits and Systems Society Information

  IEEE Transactions on AgriFood Electronics · 2024-03-01

  articleOpen access
  PublisherOA PDFDOI

• Interpretable dimensionality reduction and classification of mass spectrometry imaging data in a visceral pain model via non-negative matrix factorization

  PLoS ONE · 2024-10-10 · 4 citations

  articleOpen accessSenior authorCorresponding

  Mass spectrometry imaging (MSI) is a powerful scientific tool for understanding the spatial distribution of biochemical compounds in tissue structures. In this paper, we introduce three novel approaches in MSI data processing to perform the tasks of data augmentation, feature ranking, and image registration. We use these approaches in conjunction with non-negative matrix factorization (NMF) to resolve two of the biggest challenges in MSI data analysis, namely: 1) the large file sizes and associated computational resource requirements and 2) the complexity of interpreting the very high dimensional raw spectral data. There are many dimensionality reduction techniques that address the first challenge but do not necessarily result in readily interpretable features, leaving the second challenge unaddressed. We demonstrate that NMF is an effective dimensionality reduction algorithm that reduces the size of MSI datasets by three orders of magnitude with limited loss of information, yielding spatial and spectral components with meaningful correlation to tissue structure that may be used directly for subsequent data analysis without the need for additional clustering steps. This analysis is demonstrated on an MSI dataset from female Sprague-Dawley rats for an animal model of comorbid visceral pain hypersensitivity (CPH). We find that high-dimensional MSI data (∼ 100,000 ions per pixel) can be reduced to 20 spectral NMF components with < 20% loss in reconstruction accuracy. The resulting spatial NMF components are reproducible and correlate well with H&E-stained tissue images. These components may also be used to generate images with enhanced specificity for different tissue types. Small patches of NMF data (i.e., 20 spatial NMF components over 20 × 20 pixels) provide an accuracy of ∼ 87% in classifying CPH vs naïve control subjects. This paper presents the novel data processing methodologies that were used to produce these results, encompassing novel data processing pipelines for data augmentation to support training for classification, ranking of features according to their contribution to classification, and image registration to enhance tissue-specific imaging.

  PublisherDOI

Recent grants

• REU Site: Biosystems Internships for ENgineers (BIEN)

  NSF · $372k · 2011–2016

• CAREER: Physical Information Efficiency for Sensing, Communicating, and Computing

  NSF · $522k · 2003–2008

• Cell-Based Olfactory Sensing for Biometrics [48U08UMDabsh]

  NSF · $391k · 2008–2014

• Integrated Transduction, Actuation, and Control for Cell-Based Sensing [UOM_FY05_059]

  NSF · $1.4M · 2005–2015

• REU Site: Biosystems Internships for ENgineers (BIEN)

  NSF · $406k · 2008–2012

Frequent coauthors

• Elisabeth Smela

  University of Maryland, College Park

  37 shared

• Marc Dandin

  30 shared

• Timir Datta

  Feinstein Institute for Medical Research

  22 shared

• Bathiya Senevirathna

  University of Maryland, College Park

  19 shared

• Nicole Nelson

  Madigan Army Medical Center

  17 shared

• M.H. Cohen

  University of Maryland, College Park

  14 shared

• Somashekar Bangalore Prakash

  Intel (United States)

  13 shared

• David Sander

  Virtual Vehicle (Austria)

  12 shared