Darrin Rasberry is an Assistant Professor at Mercy College of Health Sciences, where he has been teaching since 2016. He holds a PhD in Mathematics from Iowa State University (2017), an M.S. in Mathematics from Texas Tech University (2006), and a B.S. in Mathematics from Midwestern State University (2000). Member of IEEE and American Mathematical Society (AMS) Research focuses on compressive sensing, differential geometry, mathematical modeling, and applied statistics His recent publications include a dissertation on undetermined linear systems (2017) and a 2018 presentation on statistical consulting for nurse residency programs. His work bridges abstract mathematics with practical applications in healthcare and physical sciences. Rasberry teaches foundational courses in mathematics and statistics, including Biostatistics, College Algebra, and General Studies Math. He is affiliated with the Liberal Arts and Sciences (LAS) Department.
Dustin Richmond is an Assistant Professor in the Department of Computer Science and Engineering at the Baskin School of Engineering, University of California, Santa Cruz. His work focuses on secure, usable hardware systems with applications in FPGA acceleration, RISC-V architectures, and side-channel analysis. Email: drichmond@ucsc Office: Engineering 2, Room 221 Research Interests: Secure hardware systems FPGA-based computing Manycore processors High-level synthesis Side-channel vulnerabilities Notable Article Trends: Recent publications emphasize cloud FPGA security, manycore design optimization, and hardware security. Earlier works focus on RISC-V acceleration, OpenCL compiler enhancements, and heterogeneous computing systems. GitHub Contributions: Maintains open-source projects like RISC-V-On-PYNQ and PYNQ-HLS, addressing FPGA programming challenges and RISC-V integration. Active in resolving community issues related to toolchain compatibility and hardware-software interfaces.
University of California, Los AngelesUnited States
Deanna Needell is a Professor of Mathematics at UCLA and holds the Dunn Family Endowed Chair in Data Theory. She serves as Executive Director of the Institute for Digital Research and Education (IDRE). Her work spans numerical linear algebra, machine learning, and signal processing with applications in healthcare, criminal justice, and community projects. Her research interests focus on stochastic iterative methods , compressed sensing , and optimization algorithms , with significant contributions to data completion, deep learning theory, and nonlinear signal recovery. She emphasizes inclusive research practices and community-engaged applications. Awards: Dunn Family Endowed Chair in Data Theory Needell has mentored over 35 postdocs, PhD students, and REU participants since 2012. Her leadership in UCLA's Applied Mathematics REU program has produced projects on AI for Lyme Disease, Holocaust Studies, and Criminal Justice. She maintains an inclusive workspace valuing diversity in race, gender, health status, and disability.
Huaijin Chen is an Assistant Professor in the Department of Information and Computer Science at the University of Hawaii at Manoa (UHM), where he directs the Computational Imaging and Robotic Perception Lab. He joined UHM in August 2024, bringing extensive experience from both industry and academic research in imaging and perception systems. His research interests lie at the intersection of computer vision, computational imaging, and robotics. He specializes in developing physics-aware deep learning models for imaging systems, advancing technologies in 3D ToF imaging, neural image compression, and under-display cameras. His work has practical applications in autonomous vehicles, smartphones, and wearable devices. He is particularly interested in bridging the gap between optical physics and machine learning to enhance perception capabilities. Huaijin Chen is actively contributing to the academic community through leadership roles such as serving as the local chair for ICCV 2025 in Honolulu and as a guest editor for the ACM Journal on Autonomous Transportation Systems, focusing on UAVs. He is currently recruiting motivated PhD students with backgrounds in optics or imaging to join his lab in 2025. Huaijin Chen has co-founded SenseWatch, a startup focused on gesture recognition and vital sign monitoring via wearable devices. His prior industry experience includes leading computational imaging efforts at Vayu Robotics, backed by Khosla Ventures and advised by Geoffrey Hinton, and contributing to smartphone computational photography at SenseBrain Technology. He received his Ph.D. in Electrical and Computer Engineering from Rice University in 2019, where his work centered on physics-aware deep learning in computational imaging. Earlier, he earned a B.S. in Imaging Science from the Rochester Institute of Technology (RIT) in 2013. During his studies, he interned at NVIDIA Research, IBM, and Light Labs, and visited research groups at Cornell, Northwestern, and Brown Universities.
Bhuvana Krishnaswamy is an Assistant Professor at the Department of Electrical and Computer Engineering, University of Wisconsin–Madison, with an affiliation in Biomedical Engineering. She is an award-winning researcher and educator specializing in Communications, Networks, Privacy, and Security , blending wireless systems with biological applications. PhD, Georgia Institute of Technology (2018) MS, Georgia Institute of Technology (2013) BS, College of Engineering (2011) Her research spans Wireless Networks , Low-Power Systems , IoT , and Molecular Communication , focusing on scalable, efficient, and innovative network designs. She has pioneered frameworks like Cloud-LoRa and BYOG for LoRaWAN optimization, alongside bio-inspired systems such as PACT and molecular network protocols. Recent publications emphasize AI-driven bandwidth adaptation , battery-less communication , and satellite interference mitigation . Her work also intersects with environmental monitoring (e.g., soil CO2 sensing) and biomedical engineering. Scientific Awards : IEEE Senior Member (2024) NSF CAREER Award (2022) WARF Innovation Award Finalist (2020, 2019) Early Career Innovator Award (2023) N2Women Rising Star (2022) Bhuvana teaches courses like Introduction to Computer Engineering and Communication Networks , emphasizing the integration of research into pedagogy. Her lab collaborates on projects involving wireless systems, IoT, and molecular networks, supported by grants such as the NSF CAREER award.
Professor Yi Ma is a faculty member at the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley, and holds the Chair Professor in AI at the School of Computing and Data Science (CDS), University of Hong Kong. He serves as Director of both CDS and the Institute of Data Science (IDS) at HKU since 2024 and 2023, respectively. His research spans artificial intelligence , computer vision , compressive sensing , and machine learning , with a focus on low-dimensional models for high-dimensional data analysis . He has pioneered work in robust principal component analysis , sparse representation , and deep learning theory , as reflected in his textbook High-Dimensional Data Analysis with Low-Dimensional Models (Cambridge, 2021) and recent position papers. Key contributions include organizing conferences like the Conference on Parsimony and Learning (CPAL) and developing course EECS 208 at Berkeley, which explores computational principles for AI and data analysis. His work is affiliated with research hubs such as the Berkeley Artificial Intelligence Research (BAIR) , Center for Augmented Cognition , and Vive Center for Enhanced Reality . Scientific Honors: Society for Industrial & Applied Mathematics (SIAM) Fellow (2020) Association for Computing Machinery (ACM) Fellow (2017) Institute of Electrical & Electronics Engineers (IEEE) Fellow (2013) Office of Naval Research Young Investigator (2005) NSF CAREER Award (2004) CS TCPAMI ICCV Best Paper Award/David Marr Prize (1999) He has held leadership roles at institutions including ShanghaiTech University and Microsoft Research Asia, blending theoretical and applied research in structured data modeling and vision-based robotics .
Dr. Gene Kim is a Professor of Biomedical Engineering in Radiology at Weill Cornell Medicine . His research focuses on developing advanced magnetic resonance imaging (MRI) techniques for cancer diagnosis and treatment monitoring, particularly in breast and head/neck cancers. The Kim Laboratory investigates tumor microenvironment through dynamic contrast-enhanced (DCE-MRI) and diffusion MRI (dMRI) methodologies. Ph.D. in Biomedical Engineering, University of Southern California Postdoctoral Training in Cancer Imaging, University of Pennsylvania Key research areas include: Tumor vascular properties and water exchange kinetics Quantitative dMRI for cell viability assessment Adipose-tissue interactions in breast cancer Development of active contrast encoding (ACE-MRI) for comprehensive imaging Recent publications demonstrate expertise in high-resolution 3D imaging , deep learning applications , and temporal resolution optimization . The lab has received continuous funding from the National Cancer Institute through grants like R01CA219964 and UG3/UH3CA228699 . Notable methodological contributions include: Golden-angle radial sparse parallel (GRASP) MRI POMACE framework for cell size measurement ACE-MRI for integrated T1/flip angle quantification
Armin Alaghi serves as a Research Scientist at Oculus Research (Redmond, WA) and holds an Affiliate Assistant Professor position at the University of Washington. His dual affiliation enables valuable knowledge transfer between cutting-edge industrial research and academic pursuits in computer systems engineering. Dr. Alaghi's research spans the intersection of embedded systems, digital circuits, and mathematics. His primary focus involves building low-power augmented reality (AR) and virtual reality (VR) systems while developing novel computation methods for unreliable beyond-CMOS technologies. His previous research contributions include significant work in stochastic computing (where he developed the STRAUSS synthesis methodology), reliable Network on chip (NoC) design, FPGA testing methodologies, NoC testing techniques, artificial neural networks implementations, asynchronous circuit design, and multi-valued logic systems. He has made his spectral-transform-based synthesis tool publicly available on GitHub, demonstrating commitment to open research. Analysis of Dr. Alaghi's publication record reveals a clear research trajectory from foundational circuit-level work toward practical applications in AR/VR systems. His publications from 2020-2025 demonstrate expertise spanning computer architecture, security for immersive technologies, neural network compression techniques, and homomorphic encryption methods. A recurring theme throughout his work is the exploration of quality-energy tradeoffs and error-resilient computing approaches, with increasing focus on security aspects of AR/VR systems in his most recent work. Dr. Alaghi maintains active connections with the broader research community, as evidenced by his Erdős number of 3 (Armin Alaghi John P. Hayes Frank Harary Paul Erdős) and his ongoing contributions to open-source research tools. His GitHub repository for stochastic computing synthesis shows community engagement with multiple contributors. At Oculus Research, Dr. Alaghi applies his theoretical expertise to practical challenges in next-generation AR/VR system development. His work bridges academic research with real-world product development, particularly in addressing energy efficiency challenges for wearable computing platforms through innovative circuit design approaches.
Rongrong Wang serves as Associate Professor in both the Department of Computational Mathematics, Science and Engineering (CMSE) and Department of Mathematics at Michigan State University, based in the Engineering Building with contact email wangron6@msu.edu . Her academic journey includes: B.S. in Mathematics and B.A. in Economics from Peking University, Beijing Ph.D. in Applied Mathematics from University of Maryland College Park under John Benedetto and Wojciech Czaja Postdoctoral fellowship at University of British Columbia with Ozgur Yilmaz and Felix Herrmann Her research spans Applied and Computational Harmonic Analysis , Machine Learning , and Compressed Sensing with focus areas including neural network training dynamics, learning theory, tensor analysis, and inverse problems. She investigates theoretical foundations of deep learning while developing applications for medical imaging and signal processing. Recent publications (2024-2025) demonstrate strong interdisciplinary work at the intersection of deep learning theory and medical imaging, particularly exploring edge-of-stability phenomena in neural networks and diffusion-guided reconstruction techniques. Her work also advances tensor decomposition methods and in-context learning mechanisms in language models. Professor Wang actively recruits self-motivated graduate and undergraduate students with backgrounds in mathematics, computer science, or electrical engineering for research opportunities in her lab.
Massachusetts Institute of TechnologyUnited States
Gregory Wornell serves as the Sumitomo Electric Industries Professor in Engineering within MIT's Department of Electrical Engineering and Computer Science (EECS), part of the School of Engineering. He maintains key affiliations with the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Institute for Data, Systems, and Society (IDSS), while leading the Signals, Information, and Algorithms Laboratory in the Research Laboratory of Electronics (RLE). Education: BASc from the University of British Columbia SM and PhD from MIT His research program integrates theoretical foundations with practical systems across signal processing, information theory, and statistical inference. Current work explores architectures for sensing, learning, and communication systems alongside computational imaging, vision, and perception frameworks. Neuroscience applications form an emerging thread in his interdisciplinary approach, particularly regarding information processing in biological systems. Analysis of recent publications (2021-2025) reveals three dominant trends: 1) Uncertainty quantification and calibration methods for machine learning systems, 2) Fairness frameworks for AI with uncertain sensitive attributes, and 3) Novel signal processing techniques for RF communications and acoustic imaging. His work consistently bridges information-theoretic principles with deep learning implementations. Scientific awards: None specified in source materials. Advising and grants: Source materials indicate active PhD supervision through publications with students like Shah, Shen, and Sattigeri, though formal advisee lists aren't provided. Research appears supported by MIT-IBM Watson AI Lab collaborations and institutional resources from RLE/CSAIL. He directs the Signals, Information, and Algorithms Laboratory (SIAL), which focuses on developing mathematical frameworks for information extraction from complex systems. The lab maintains strong connections with MIT's wireless communications and computational imaging communities through RLE and CSAIL collaborations.
Dr. Quang Ngoc Vinh Tran serves as an Assistant Professor in the Department of Civil, Environmental, and Geospatial Engineering at Michigan Technological University. Previously, he conducted three years of postdoctoral research at Harvard Medical School, Harvard-affiliated hospitals, and the University of Illinois Bioacoustics Research Lab. With over 30 peer-reviewed publications, he actively contributes to the American Concrete Institute (ACI) and American Society of Civil Engineers (ASCE). His academic credentials include: PhD in Civil and Environmental Engineering from the University of Illinois at Urbana-Champaign MS in Civil and Environmental Engineering from California State University, Fullerton BS in Industrial and System Engineering from Ho Chi Minh University of Technology, Vietnam Dr. Tran's research integrates nondestructive evaluation with advanced sensing technologies across civil and biomedical domains. His core expertise spans pressure sensing, ultrasound/microbubble cavitation, computer vision, and deep learning algorithms for noninvasive characterization of construction materials and biological tissues. This interdisciplinary approach enables innovative solutions for concrete technology and cardiac pressure monitoring. Analysis of his recent publications reveals dual-track innovation: civil engineering work focuses on non-contact concrete assessment (setting times, joint activation, durability) using ultrasonic and computer vision techniques, while biomedical research pioneers ultrasound-based pressure sensing for cardiac applications and tumor microstructure characterization. Both tracks demonstrate sophisticated integration of physical sensing with computational analysis. He instructs Structural Analysis Laboratory and previously assisted with Infrastructure NDE Methods, emphasizing project-based learning and student potential development. His research is executed through the Nondestructive Research Lab (NRL), where he leads projects on early-age concrete sensing systems, high-performance concrete durability modeling, cardiac pressure sensing via focused ultrasound, and ultrasound-based tissue characterization for disease staging.
Dr. Yogesh Rathi is an Associate Professor of Psychiatry and Radiology at Harvard Medical School and Brigham and Women's Hospital. His research focuses on computational magnetic resonance imaging (MRI) techniques to analyze brain structure and function, particularly in psychiatric and neurological disorders. Associate Professor of Psychiatry and Radiology Brigham and Women's Hospital Harvard Medical School His research spans advanced diffusion MRI for faster imaging, ultra-high-resolution tractography, and harmonization of multi-scanner MRI data. He applies these methods to study white matter connectivity in humans and primates, alongside clinical applications for deep brain stimulation (DBS) and transcranial magnetic stimulation (TMS) in OCD, Parkinson’s, and depression. Dr. Rathi's work includes biophysical modeling of axon diameter estimation, functional connectivity analysis via fMRI, and development of real-time tools for precision targeting in neurosurgical interventions. His team secured significant NIH funding for harmonizing clinical diffusion MRI data. US Patent 10302727: Rapid Diffusion MRI Scanning NIH R01 Grant (4th percentile): MRI Harmonization Collaborator in $33M NIMH/FNIH Grant Key techniques developed by Dr. Rathi include real-time TMS targeting visualization and joint relaxation-diffusion MRI sequences for tissue characterization. These innovations are used in biomarker discovery and treatment monitoring.
Demba Ba is an Associate Professor of Electrical Engineering and Bioengineering at Harvard University's School of Engineering and Applied Sciences (SEAS). He serves as the Dean of Undergraduate Studies for Bioengineering since 2020 and joined SEAS in 2015 after a postdoctoral fellowship at MIT (2007-2014). His research bridges computational neuroscience and artificial intelligence, focusing on sparse signal representations, interpretable AI, and neural network theory. Fluent in Wolof, Fulani, French, Spanish, English, and Arabic, he also contributes to signal processing, statistical learning, and dynamic systems. PhD in EECS from MIT (2011) MS in EECS from MIT (2006) BS in Electrical Engineering from University of Maryland (2004) His work explores connections between sparse coding and neural networks through publications in top venues like NeurIPS, ICML, and IEEE Transactions. Articles emphasize convolutional dictionary learning, Bayesian frameworks, and applications to neural data analysis. He has received the 2016 Sloan Fellowship in Neuroscience and 2021 Roslyn Abramson Award for undergraduate teaching excellence. 2021: Gaussian process convolutional dictionary learning (Submitted) 2020: Deep residual auto-encoders for dictionary learning 2018: Multitaper time-frequency analysis for neuroscience Demba Ba leads the CRISP research group and holds advisory roles at Harvard. His awards include: 2021 Roslyn Abramson Award 2016 Alfred P. Sloan Foundation Fellow 2010 ICME Best Student Paper Award He teaches courses like ES 201 (Decision Theory) and ES 157 (Biomedical Signal Processing), while maintaining interdisciplinary collaborations in neuroscience, machine learning, and signal processing.
Dr. Aijun Song is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Alabama College of Engineering. He is affiliated with the Center for Water Quality Research and the Alabama Water Institute. His work bridges engineering, environmental science, and technology, with a focus on developing innovative underwater communication systems and autonomous vehicle technologies for water monitoring applications. Dr. Song's educational background includes: Ph.D. in Electrical and Computer Engineering from University of Delaware M.S. in Electrical and Computer Engineering from Xidian University, Xi'an, China B.S. in Electrical and Computer Engineering from Xidian University, Xi'an, China Dr. Song's research primarily focuses on underwater acoustic communications, signal processing, and autonomous vehicle technologies. His work addresses challenges in underwater wireless communications including channel modeling, equalization techniques, and full-duplex communication systems. He has made significant contributions to the development of underwater sensor networks, acoustic transceivers, and communication protocols for autonomous underwater vehicles. His research has important applications in environmental monitoring, ocean exploration, and water quality assessment. Dr. Song's recent publications demonstrate a strong trend toward practical implementations of underwater communication technologies, with increasing emphasis on reconfigurable intelligent surfaces, energy-efficient systems, and real-world testbed validation. His work spans theoretical development, simulation, and extensive field testing in lake and river environments. The research addresses critical challenges in underwater communication including channel variability, interference cancellation, and long-range transmission. Dr. Song has received notable recognition for his work: NSF CAREER Award from the National Science Foundation (2021) Outstanding Faculty/Staff-Initiated Engagement Effort by the Council on Community-Based Partnerships at the University of Alabama (2019) Outstanding Service Award from the 8th ACM International Conference on Underwater Networks & Systems (2013) Dr. Song actively mentors students and collaborates on interdisciplinary research projects. He serves as co-principal investigator for the USGS FLOW Academy, working with Dr. Lisa Davis and Dr. Steven Burian to provide hands-on water science education. His leadership in the Tuscaloosa MATHCOUNTS program has significantly impacted local STEM education, particularly for underserved populations and female students. Dr. Song's research has been supported by significant funding including an NSF CAREER award and collaborations with the US Geological Survey. Dr. Song leads a research team focused on underwater robotics and wireless communication technologies. His lab develops and tests autonomous underwater vehicles including JaiaBots and EcoMapper systems. The team is advancing underwater swarming technologies to enhance water monitoring capabilities, with an emphasis on creating open-source, low-cost solutions for automated water data collection and rapid flood disaster response. Recent field demonstrations at the Black Warrior River and Lake Tuscaloosa showcase the practical applications of his research.
Mehmet Akcakaya is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. His research focuses on machine learning for medical imaging, especially MRI reconstruction using deep learning and compressed sensing. He leads NIH-funded projects and is accepting PhD students. His research interests include physics-informed deep learning , compressed sensing , and MRI reconstruction . He develops algorithms for high-resolution, accelerated MRI with applications in cardiac and brain imaging. Key challenges addressed are reference-free reconstruction and robustness in inverse problems. Recent work shows a trend toward self-supervised and unsupervised deep learning for MRI, with innovations in cycle-consistent learning and diffusion models for inverse problems. There is increasing emphasis on few-shot and zero-shot adaptation to handle limited training data. Dr. Akcakaya leads the NIH-funded project Robust and Efficient Learning of High-Resolution Brain MRI Reconstruction and collaborates on the Center for Mesoscale Connectomics . He is accepting PhD students and mentors research in medical imaging and machine learning. His work is conducted at the University of Minnesota's Center for Magnetic Resonance Research, leveraging interdisciplinary collaborations in biomedical engineering and neuroscience.
