جست‌وجوی استاد و پژوهشگر

• Medical Imaging
• Nuclear Medicine
• Biomedical Engineering
• +۵ مورد دیگر

Spencer L. Bowen, Ph.D., serves as Assistant Professor of Radiology at UT Southwestern Medical Center where he leads PET research within the Radiology Research section. His work develops nuclear tomographic imaging tools to advance precision medicine for oncology, neurology, and cardiology applications through innovative scanner technologies and quantitative imaging methodologies. Dr. Bowen earned his bachelor's degree in biomedical engineering from the University of Washington and doctoral degree from the University of California at Davis, followed by a research fellowship at Massachusetts General Hospital. His academic journey includes prior appointment as Research Assistant Professor at the Fralin Biomedical Research Institute and Virginia Tech-Wake Forest University School of Biomedical Engineering. His research program investigates advanced acquisition techniques, reconstruction algorithms, and post-processing methods for quantitative hybrid PET-CT/MR systems. Key focus areas include attenuation correction methodologies, partial volume correction, dedicated breast imaging systems, and cardiac PET quantification. This work bridges engineering innovation with clinical applications to improve diagnostic accuracy and treatment monitoring across multiple disease domains. Dr. Bowen's publication record demonstrates consistent contributions to medical imaging science, with recent emphasis on quantitative cardiac PET, attenuation correction techniques, and dedicated breast imaging systems. His 2016 study on partial volume correction methods in aging research and 2023 work on cardiac PET attenuation correction represent significant methodological advances in the field. As an active scientific contributor, Dr. Bowen serves as reviewer for leading journals including the Journal of Nuclear Medicine, Medical Physics, Physics in Medicine and Biology, and IEEE Transactions on Nuclear Science and Medical Imaging. His work has received notable recognition including a cover feature in the Journal of Nuclear Medicine. The Bowen Lab maintains active collaborations across medical imaging disciplines and currently recruits PhD graduate students to advance nuclear tomography research. Dr. Bowen's mentorship extends through his role as faculty advisor and his participation in graduate training programs focused on biomedical imaging technologies.

• Cybersecurity
• Network Security
• Critical Infrastructure Security
• +۳ مورد دیگر

Gail-Joon Ahn serves as a Professor of Computer Science and Engineering at Arizona State University, specializing in cybersecurity solutions for critical infrastructure with a focus on energy delivery systems. Research interests include: Cybersecurity Network Security Critical Infrastructure Security Energy Systems Security Security Assessment Adaptive Security His work develops frameworks like EDSGuard for security requirement enforcement and OntoEDS for collaborative security analysis in energy systems. Publication trends demonstrate concentrated efforts in applying ontological engineering and proactive assessment methodologies to protect energy infrastructure, advancing critical infrastructure resilience through formal security policy implementation. Scientific Awards: No awards mentioned Advising and Grants: The provided text contains no information regarding student advising or research funding. Labs and Teams: Dr. Ahn is affiliated with the Center for Risk and Economic Analysis of Threats and Exploits (CREDC) at Arizona State University, contributing to energy delivery system security research.

Timothy Holyمشاهده پروفایل

استاد

• Neuroscience
• Olfaction
• Neural Coding
• +۶ مورد دیگر

Timothy Holy, PhD is the Alan A. & Edith L. Wolff Professor of Neuroscience and Vice Chair of Research at Washington University School of Medicine. He leads the Holy Lab, which focuses on the neural mechanisms of olfactory coding and the development of innovative imaging technologies for neuroscience research. His educational background includes a BA in Mathematics and Physics (summa cum laude) from Rice University (1991), an MA in Physics from Princeton University (1992), and a PhD in Physics from Princeton University (1997) under thesis advisor Stanislas Leibler. Dr. Holy's research spans multiple domains of neuroscience with particular emphasis on the olfactory system of mice. His lab has pioneered light sheet microscopy for calcium imaging, enabling simultaneous recording from tens of thousands of neurons. More recently, they developed PhOTseq, a technique for optically tagging neurons for later sequencing. His work bridges physics, neuroscience, and computational approaches to understand how sensory systems process information. He is also among the world's foremost creators of the Julia programming language, which has gained exponential adoption in scientific computing. Analysis of his recent publications reveals a strong focus on neural coding in decision-making circuits, olfactory processing, and the development of computational tools for biological research. His work increasingly integrates machine learning approaches with traditional neuroscience techniques. Distinguished Teaching Service Award (2005, 2008, 2014) McKnight Technological Innovation in Neuroscience Award (2007) St. Louis Academy of Sciences Innovation Award (2009) NIH Director's Pioneer Award (2009) Society for Neuroscience Research Award for Innovation in Neuroscience (RAIN) (2009) Dr. Holy has mentored numerous students and postdoctoral researchers who have gone on to establish their own research programs. His lab has secured significant grant funding supporting technology development and fundamental neuroscience research. Current projects include investigating cellular mechanisms of individuality and plasticity, navigation using olfactory cues, and developing new mathematical tools for optimization and machine learning. The Holy Lab combines a focus on understanding neural circuits and behavior with a willingness to pioneer new technologies. It maintains strong collaborations across disciplines, particularly in the development and application of the Julia programming language for biological research.

Pedram Johariمشاهده پروفایل

پژوهشگر

• Intra-body Communications
• Wireless Networking
• Internet of Medical Things
• +۴ مورد دیگر

Pedram Johari serves as a Principal Research Scientist at Northeastern University's Institute for Wireless Internet of Things, working under Prof. Tommaso Melodia in the Department of Electrical and Computer Engineering within the College of Engineering. His research focuses on next-generation wireless communications with particular emphasis on medical applications and nanoscale networks. Dr. Johari earned his Ph.D. in Electrical Engineering from the University at Buffalo, State University of New York in 2018 under the supervision of Prof. Josep M. Jornet. Prior to joining Northeastern, he served as CTO of an IoT-tech startup in New York (2018-2019) and held academic positions at University at Buffalo as Adjunct Instructor and Research Assistant Professor (by courtesy appointment). His research spans four interconnected domains: Intra-body Communications and Networking focusing on nanoscale electromagnetic and optical communications within biological tissues; Internet of Medical Things developing wireless systems for healthcare applications; Low Power Wireless IoT creating energy-efficient communication protocols; and Vehicular Communications advancing cooperative driving systems. His work bridges theoretical modeling with practical implementation, often incorporating AI techniques for network optimization. Analysis of his recent publications reveals a strong trend toward digital twin technology for wireless networks, AI-enabled communication systems, and medical applications of nanoscale communications. His research increasingly integrates machine learning with traditional communication theory, particularly in Open RAN systems and seizure prediction technologies, demonstrating cross-disciplinary impact across engineering, computer science, and biomedical fields. Best Paper Award at IEEE Global Communications Conference (GLOBECOM) Best Short Paper Award at IEEE Vehicular Networking Conference (VNC) Best Presentation Award at IEEE Conf. on Computer Communications Workshops (INFOCOM WKSHPS) Dr. Johari actively contributes to academic service as a reviewer for numerous prestigious journals including IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing, and IEEE Internet of Things Journal. He has served on technical program committees for major conferences including IEEE/ACM CHASE and IEEE SECON. His teaching experience includes courses in programming, circuit analysis, and digital principles at University at Buffalo. As a key member of the Wireless Networks and Embedded Systems Lab at Northeastern University, Dr. Johari contributes to the Colosseum wireless network emulator project - recognized as the world's largest wireless network emulator. His work with the Institute for Wireless Internet of Things involves collaboration with industry partners to translate research into practical wireless communication solutions.

Guannan Liuمشاهده پروفایل

استادیار

• Cybersecurity
• Network Security
• System Security
• +۴ مورد دیگر

Guannan Liu is an Assistant Professor of Computer Science at the Colorado School of Mines, specializing in cybersecurity and network security. He holds a Ph.D. in Computer Engineering from Virginia Tech (2023) and a BS from Purdue University (2016). His research focuses on system/network security, human-factor security, cloud computing security, and user authentication mechanisms. He has identified critical vulnerabilities in online services, collaborating with major tech firms to address resource mismanagement issues. Dr. Liu's work has been published in top-tier security conferences. He actively seeks motivated students for cybersecurity research projects. His honors include a Student Travel Grant for DSN 2022 and academic recognition through Dean’s List distinctions. His research bridges theoretical insights with practical impact, particularly in identifying real-world system vulnerabilities through comprehensive measurements. Key areas of exploration include container registry typosquatting, cloud gaming service misuse, DNS query analysis, and acoustic side-channel attacks on IoT devices. His interdisciplinary work integrates software engineering, artificial intelligence, and network measurement techniques to enhance security across diverse systems.

• Future of Education
• Broadband Technology
• Social Networking
• +۴ مورد دیگر

Lev Gonick is the University Chief Information Officer at Arizona State University and holds the academic rank of Professor of Practice in the School of Public Affairs. He also serves as a Senior Global Futures Scientist with the Global Futures Scientists and Scholars initiative, and is affiliated with the Center on Technology, Data and Society. His career spans over 25 years in education, technology, and smart city development, including roles as CIO at Case Western Reserve University (2001-2013) and co-founder/CEO of DigitalC, a broadband innovation nonprofit. Education: Ph.D. in International Political Economy, York University (Canada), 1992 M.A. in World Politics, Binghamton University, 1983 B.A. in Philosophy, Political Science, and Religion, The Ohio State University, 1981 Research Interests: Focuses on the intersection of technology and society, including the future of education, broadband infrastructure, smart cities, IoT, programmable urban systems, and the political economy of information technology. His work emphasizes community engagement through technology and digital equity initiatives. Notable Achievements: Recipient of Inside Business Magazine's Power 100 (2015) Government Technology's Top 25 Doers, Dreamers and Drivers (2011) Broadband Properties Cornerstone Award (2011) NATOA Visionary of the Year (2010) ComputerWorld Premier 100 IT Leader CIO Magazine CIO 100 Award Professional Contributions: Pioneered projects like the Case Connection Zone, foundational to national initiatives like US Ignite and Gig.U. Developed ClevelandPlus in Second Life and OneCleveland digital city initiatives. Advocates for fiber-to-home networks and inclusive technology policies.

Eugene Bagdasarianمشاهده پروفایل

استادیار

• Artificial Intelligence Security
• Privacy-Preserving Technologies
• Federated Learning
• +۴ مورد دیگر

Eugene Bagdasarian is an Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst. His research focuses on security and privacy in AI systems, particularly adversarial machine learning, federated learning vulnerabilities, and contextual integrity frameworks. He holds a PhD from Cornell Tech and a prior engineering degree from Bauman University, with industry experience at Cisco as a software engineer. Research interests include backdoor attacks in federated learning (developing frameworks like Backdoors101 and Mithridates), privacy-preserving technologies such as AirGapAgent and Ancile, and mitigating vulnerabilities in multi-modal systems. His work has addressed instruction injections, adversarial illusions, and bias in generative models, with coverage in media like The Economist and VentureBeat. Notable achievements include the Usenix Security Distinguished Paper Award, Apple Scholars in AI/ML Fellowship, and Digital Life Initiative Fellowship. His contributions span technical domains like differential privacy, secure aggregation, and policy-based federated learning frameworks. Advising and grants: No specific advising/grants listed. However, his research collaborations include developing systems like Ancile (use-based privacy enforcement) and Mithridates (backdoor auditing). Current work trends focus on advancing security in LLM agents, dynamic network firewalls, and ethical AI governance.

• Neuroscience
• Spatial Omics
• Transcriptomics
• +۳ مورد دیگر

Graham Su is a Postdoctoral Associate at the Yale School of Medicine , specializing in Neuroscience . His research bridges spatial omics , epigenomics , and transcriptomics to unravel complex biological systems. His recent work focuses on: High-resolution spatial mapping of chromatin accessibility in glioblastoma Multi-omics profiling of hippocampal inflammation in depression Development of DBiTplus for integrated imaging-sequencing analysis Single-cell CAR T-cell dynamics in leukemia remission Techniques include spatial transcriptomics, epigenomic taxonomies, and computational workflows like SPACEc. His publications highlight collaborations across neuroscience, oncology, and bioinformatics. Contact: graham.su@yale.edu

Xuan Zhangمشاهده پروفایل

دانشیار

• Artificial Intelligence Hardware
• Machine Vision Sensors
• Electronic Design Automation
• +۳ مورد دیگر

Xuan Zhang serves as Associate Professor in Electrical and Computer Engineering at Northeastern University, leading the Sensory AI Lab since joining in January 2024. Her research bridges computer architecture, integrated circuits, and artificial intelligence to develop miniaturized AI systems for autonomous physical platforms. She earned her PhD in Electrical and Computer Engineering from Cornell University in 2012. Her educational background forms the foundation for her interdisciplinary work spanning hardware and software co-design. Dr. Zhang's research focuses on artificial intelligence hardware, machine vision sensors, and security for autonomous systems. She pioneers techniques for efficient in-sensor computing, analog circuit optimization via machine learning, and hardware-level privacy preservation. Her work addresses critical challenges in energy efficiency, robustness, and security for edge AI deployment, particularly in resource-constrained environments like medical devices and autonomous vehicles. Analysis of her 2023-2025 publications reveals three dominant trends: (1) hardware-accelerated privacy mechanisms for sensors, (2) machine learning-driven analog circuit design automation, and (3) energy-efficient architectures for neural network inference. These works consistently target real-world applications in healthcare, autonomous systems, and semiconductor design. Her accolades include the prestigious NSF CAREER Award (2020) and leadership in a $10 million federal semiconductor initiative. She contributes to national efforts in AI-powered chip design through the National Center for the Advancement of Semiconductor Technology. Dr. Zhang advises graduate researchers in the Sensory AI Lab, securing significant funding for projects spanning hardware security, in-sensor computing, and autonomous system assurance. Her lab collaborates with federal agencies and industry partners on cutting-edge semiconductor research. The Sensory AI Lab operates at the hardware-software interface, developing novel architectures for intelligent edge devices. Current projects include optical privacy preservation, robust analog design tools, and energy modeling frameworks for in-sensor visual computing systems.

Cheng Zhi Huangمشاهده پروفایل

استادیار

• Artificial Intelligence
• Music Technology
• Computer Science
• +۴ مورد دیگر

Cheng Zhi Huang is the Robert N. Noyce Career Development Professor and Assistant Professor at MIT, holding a shared appointment between the departments of Music and Theater Arts and Electrical Engineering and Computer Science (EECS). His work bridges artificial intelligence, music technology, and computer science to advance human-AI collaboration in musical creativity. Huang leads research in generative models for music composition, real-time interactive systems, and expressive performance synthesis. His contributions include tools like ReaLJam for AI-assisted jamming and the MAESTRO dataset for piano performance modeling. His research interests span AI-driven music generation, human-AI interaction frameworks, and culturally-aware music technologies. Notable projects include The Bach Doodle—an accessible web-based composition tool—and MIDI-DDSP for detailed performance control. Huang’s work emphasizes ethical and creative applications of AI in arts, fostering collaborations between musicians, engineers, and computer scientists. His publications highlight advancements in hierarchical generative modeling, source separation techniques, and co-creation interfaces for novices. Huang’s research has been showcased in venues like TISMIR and IEEE conferences, reflecting his interdisciplinary impact on music technology and machine learning.

Luke Olsonمشاهده پروفایل

استاد

• Numerical Analysis
• High-Performance Computing
• Scientific Computing
• +۴ مورد دیگر

Luke Olson is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), part of the College of Engineering. He holds an affiliate appointment in the Department of Mechanical Science and Engineering. His research focuses on numerical methods, high-performance computing, and parallel algorithms, particularly algebraic multigrid (AMG) solvers and sparse matrix computations. He leads the development of open-source libraries like PyAMG and RAPtor, advancing computational tools for scientific and engineering applications. Education: Ph.D. in Applied Mathematics from the University of Colorado Boulder (2003), M.S. in Mathematics from the University of Iowa (1999), and B.A. in Mathematics and Physics from Luther College (1997). Research Interests: Algebraic multigrid methods and preconditioners High-performance computing and parallel algorithms Numerical solutions to partial differential equations Scientific computing and software development Machine learning integration in numerical simulations Recent Articles Trends: Recent work merges machine learning with traditional numerical methods, such as neural network closures for turbulent combustion and reduced basis approximations using neural networks. Ongoing contributions include optimizing multigrid methods for exascale architectures and enhancing communication efficiency in distributed systems. Awards: Recognized with the NSF CAREER Award (2007), UIUC Campus Award for Excellence in Teaching (2024), and the Donald Biggar Willett Faculty Scholar distinction (2016). Active in conference organization, including the Copper Mountain Conference on Multigrid Methods. Teaching & Grants: Teaches courses like Numerical Methods for PDEs and Scientific Machine Learning. Leads projects funded by NSF and industry collaborations, emphasizing education innovation through the AE3 fellowship (2014–2016). Labs/Teams: Directs the Scientific Computing Group at UIUC and contributes to interdisciplinary initiatives like the Center for Exascale-enabled Scramjet Design (CEESD).

Jim Howardمشاهده پروفایل

مدرس

• Algorithms
• Machine Learning
• Machine Learning System Design and Operations
• +۳ مورد دیگر

Jim Howard is an Instructor in Software Engineering and Data Science at the University of St. Thomas. With over 15 years of industry experience, he currently serves as a Principal Engineer of Machine Learning at Laurel.AI. His expertise spans algorithm development, machine learning system design, and the application of AI in legal contexts and public policy. Howard holds a PhD in Computer Science from the Colorado School of Mines and a BS in Computer Science and Mathematics from Rose-Hulman Institute of Technology. Research Interests: Machine learning infrastructure optimization Generative models for policy creation Legal implications of AI deployment Production system design for ML models Teaching: In Fall 2025, he teaches SEIS 765 Machine Learning Operations (MLOps), focusing on bridging ML development and operational deployment through collaborative practices and tooling. Industry Experience: Previously worked with Fortune 500 firms like 3M, Land O'Lakes, and Meta, specializing in scalable ML implementation.

Yifan Sunمشاهده پروفایل

استادیار

• GPU Architecture
• Computer Architecture Simulation
• Multi-GPU Systems
• +۵ مورد دیگر

Yifan Sun is an Assistant Professor in the Department of Computer Science at William & Mary, leading the Scalable Architecture Lab. He holds a Ph.D. in Electrical and Computer Engineering from Northeastern University (2020). His research focuses on GPU architecture, simulation tools, and multi-GPU system design. Recent work includes TrioSim (a lightweight DNN workload simulator) and NetCrafter (optimizing multi-GPU network traffic). He has published extensively at top venues like ISCA, MICRO, and IEEE Vis. Educations: Ph.D. in Electrical and Computer Engineering, Northeastern University (2020); M.S. and B.S. not explicitly stated but implied through academic progression. Research Interests: Developing explainable architecture tools, improving simulation frameworks (Akita/MGPUSim), and addressing challenges in wafer-scale GPU design. His work bridges hardware-software co-design with visualization techniques to enhance human understanding of complex architectures. Grants: Awarded NSF CCRI and CRII grants for simulation-as-a-service and explainable architecture projects. Collaborations include UVA, NUS, and Northeastern University. Labs/Teams: Scalable Architecture Lab (SARCHLAB), focusing on GPU systems, simulation, and visualization. Active in organizing workshops and GitHub repositories (e.g., https://github.com/sarchlab).

Mingxi Zhouمشاهده پروفایل

دانشیار

• Marine Robotics
• Autonomous Underwater Vehicles
• Vehicle Autonomy
• +۳ مورد دیگر

Associate Professor Mingxi Zhou is affiliated with the University of Rhode Island ( URI )'s Graduate School of Oceanography and Department of Oceanography . His research focuses on marine robotics, autonomous underwater vehicles (AUVs), and underwater navigation, with an emphasis on vehicle autonomy and multi-vehicle collaboration. Ph.D., Memorial University of Newfoundland (2017) M.Eng., Memorial University of Newfoundland (2012) B.Eng., Central South University (2009) His work addresses challenges in adaptive formation control, sensor fusion, and accessible unmanned platform development. Recent publications highlight deterministic learning algorithms, underwater pose estimation, and fault isolation in soft robotics. His research trends include advancements in autonomous systems, collaborative AUVs, and robust navigation under dynamic uncertainty, leveraging technologies like sonar, visual-inertial odometry, and distributed learning frameworks. He currently teaches OCG120G: World of Robots and OCE/ELE550: Ocean Systems Engineering . He founded the SOS Lab in 2018 at URI's Narragansett Bay Campus, prioritizing student training on interdisciplinary skills and providing competitive financial support.

Shaddi Hasanمشاهده پروفایل

استادیار

• Network Infrastructure
• ICTD (Information and Communication Technologies for Development)
• Human-Computer Interaction (HCI)
• +۳ مورد دیگر

Shaddi Hasan is an Assistant Professor of Computer Science at Virginia Tech's College of Engineering. His research focuses on building equitable network infrastructure for underserved regions, emphasizing human-centered design through collaborations with users and policymakers. He holds a Ph.D. (2019) from UC Berkeley and a B.S. (2010) from the University of North Carolina at Chapel Hill. Education: Ph.D. in Computer Science, UC Berkeley (2019) M.S. in Computer Science, UC Berkeley (2013) B.S. in Computer Science, UNC Chapel Hill (2010) Research interests span networks, ICTD, HCI, and distributed systems. Key projects include the Magma open-source core network platform (developed at Facebook Connectivity) and CellBricks for modular rural cellular deployment. His work frequently involves real-world deployments in developing regions. Notable awards include the NSDI 2023 Community Award and multiple best paper recognitions. Current projects analyze broadband mapping accuracy and consumer label interpretation, with a focus on policy implications. Advising: Actively mentoring PhD students and undergraduates. Lab: SPIN Lab (Systems, People, Infrastructure, and Networks). Projects include GitHub repositories for Broadband Data Kit and Magma.