جست‌وجوی استاد و پژوهشگر

• Neuromuscular Control
• Biomechanics
• Motor Control
• +۵ مورد دیگر

Professor Ian Loram is a leading academic in neuromuscular control and human movement science at Manchester Metropolitan University's Institute for Biomedical Research into Human Movement and Health (IRM). He holds roles as Academic Director of IRM and Academic Lead of the Biomechanics and Motor Control Research Group. His research focuses on sensorimotor control, postural stability, and applications of deep learning in medical imaging. Key contributions include studies on neuromuscular disorders, balance control mechanisms, and automated analysis of muscle function using ultrasound and neural networks. Education: PhD from University of Birmingham (2003) Awards: Leverhulme Early Career Fellowship (2004-2005) Grants: EPSRC Grants EP/F068514/1, EP/F069022/1, and EP/F06974X/1 ("Intermittent Control of Man and Machine") His research interests span neuromuscular control, biomechanics, postural dynamics, and clinical applications of imaging technologies . Recent work emphasizes automated analysis of muscle structure via ultrasound and deep learning, with clinical relevance to conditions like spinal muscular atrophy and cervical dystonia. Publications highlight interdisciplinary approaches, integrating control theory, neurophysiology, and computational methods to understand human movement. His lab develops tools for objective assessment of trunk control in children with cerebral palsy and explores the role of intermittent control in motor learning and balance. Labs/Teams: Biomechanics and Motor Control Research Group Healthcare Science Research Institute

James B D Joshiمشاهده پروفایل

استاد

• Cybersecurity
• Privacy-Preserving Technologies
• Access Control
• +۵ مورد دیگر

James B D Joshi is a Professor in the Department of Informatics and Networked Systems (DINS) at the University of Pittsburgh’s School of Computing and Information (SCI). He joined Pitt in 2003 after earning his PhD from Purdue University. From 2019 to 2023, he served as a Program Director for the NSF’s Secure and Trustworthy Cyberspace (SaTC) program. Currently, he is an intermittent Expert in NSF’s TIP Directorate and led the NSF PDaSP program. His research focuses on cybersecurity and privacy, including access control, AI/ML security, cloud/edge security, and privacy-preserving techniques. Education: PhD, Computer Engineering, Purdue University (2003) MS, Computer Science, Purdue University (1998) BE, Computer Science & Engineering, Motilal Nehru NIT, India (1993) Research Interests: Privacy, security, trust, attribute-based encryption, cloud security, insider threat detection, and privacy-preserving AI/ML. His work emphasizes trustworthy systems and secure distributed computing environments. Recent Contributions: His articles span privacy-preserving federated learning, insider threat mitigation, and secure edge computing. Recent projects include PPFL-RDSN (2025), TAPFed (2024), and blockchain-based transparency frameworks (2022). Awards & Honors: NSF Director’s Award (2023), IEEE Fellow (2023), ACM Distinguished Member (2017), NSF CAREER Award (2006), and SIRI Research Leadership Award (2018). Service & Leadership: Founding director of LERSAIS lab, co-chair of IEEE conferences (TPS, CIC, CogMI), and former Editor-in-Chief of IEEE Transactions on Services Computing. Active in NSF and NITRD policy initiatives, including privacy and digital assets R&D strategies.

• Control Theory
• Optimization
• Machine Learning
• +۷ مورد دیگر

Michael M. Zavlanos is the Yoh Family Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University's Pratt School of Engineering. He also holds secondary appointments in the Department of Computer Science and the Department of Electrical and Computer Engineering. Currently serving as the Director of the Healthcare Systems Optimization program with Duke AI Health and as an Amazon Scholar with Amazon Robotics, his academic career spans control theory, optimization, and artificial intelligence with applications across multiple domains. Dr. Zavlanos received his educational foundation from prestigious institutions: Diploma in Mechanical Engineering from the National Technical University of Athens (NTUA), Greece (2002) M.S.E. in Electrical and Systems Engineering from the University of Pennsylvania (2005) Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2008) His research program spans multiple interconnected domains, with a strong foundation in control theory, optimization, and learning methodologies . This theoretical work directly enables applications in robotics and autonomous systems , where his team develops algorithms for multi-robot coordination, motion planning under complex constraints, and network connectivity maintenance. A significant portion of his work addresses networked and distributed control systems , focusing on how multiple agents can coordinate effectively with limited communication. More recently, he has expanded his research into cyber-physical systems with healthcare applications, leveraging his expertise to optimize healthcare delivery systems through the Duke AI Health initiative. Dr. Zavlanos' work demonstrates a consistent trajectory from theoretical foundations to real-world applications. His early work established fundamental principles for maintaining connectivity in mobile robot networks, which evolved into more sophisticated approaches for temporal task planning and risk-averse decision making in uncertain environments. The most recent phase of his research integrates machine learning with traditional control theory to address complex healthcare system optimization problems. His significant contributions to the field have been recognized through prestigious awards: Office of Naval Research Young Investigator Program (YIP) Award (2014) National Science Foundation Faculty Early Career Development (CAREER) Award (2012) National Science Foundation Faculty Early Career Development (CAREER) Award (2011) Duke University Distinguished Faculty Rank (2019) Duke University Distinguished Professor designation (2018) As an educator, Dr. Zavlanos has taught courses including ME 627: Linear System Theory, ME 592: Research Independent Study, ECE 391/291: Projects in Electrical and Computer Engineering, and CEE 627: Linear System Theory. His research program has been supported by multiple grants from the National Science Foundation and the Office of Naval Research, enabling him to mentor numerous graduate students and postdoctoral researchers in the development of cutting-edge control and optimization algorithms. Dr. Zavlanos leads research efforts at the intersection of control theory, optimization, and artificial intelligence, with particular focus on translating theoretical advances into practical applications. His recent work with Duke AI Health represents a strategic expansion of his research portfolio into healthcare systems optimization, where he applies his expertise in algorithmic decision making to improve patient scheduling, resource allocation, and operational efficiency in medical settings. Through his Amazon Scholar role, he also contributes to advancing robotics technologies for real-world applications.

Charlene Kalleمشاهده پروفایل

دانشیار

• Ergodic Theory
• Dynamical Systems
• Number Theory
• +۳ مورد دیگر

Charlene Kalle is an Associate Professor at the Mathematical Institute of Leiden University, where she also serves as the programme director of the bachelor Mathematics program. She earned her PhD in mathematics from Utrecht University and held postdoctoral positions at Warwick University and the University of Vienna before joining Leiden University. Charlene Kalle's academic journey began with her PhD studies at Utrecht University. After completing her doctorate, she pursued postdoctoral research at Warwick University and the University of Vienna, which provided her with diverse international research experiences before settling at Leiden University. Dr. Kalle's research focuses on ergodic theory , particularly examining the long-term behavior of dynamical systems. Her work explores intermittency in random dynamics , number expansions , and the fractals that emerge naturally in these contexts. She has made significant contributions to understanding random dynamical systems, β-expansions, and continued fractions. Her research bridges pure mathematics with applications in probability theory and number theory, creating connections between seemingly disparate mathematical fields. Dr. Kalle has co-authored the textbook A First Course in Ergodic Theory (2021), which has become a standard reference in the field. An analysis of Dr. Kalle's recent publications reveals a strong focus on random dynamical systems and their applications to number theory. Her work frequently examines invariant measures, matching properties, and approximation techniques within various number expansion systems. A recurring theme is the study of intermittency and critical phenomena in random interval maps, which has implications for understanding complex dynamical behaviors. Her research often combines theoretical insights with practical applications, particularly in the realm of number theory and fractal geometry. Dr. Kalle has received several prestigious recognitions for her work: TOP-grant from the Dutch Research Council (NWO) Senior Teaching Qualification (awarded February 5, 2024) Dr. Kalle has supervised numerous PhD students to completion, including Niels Langeveld (2019), Marta Maggioni (2021), and Benthen Zeegers (2023). She currently supervises Jonny Imbierski and Sven van Dijk, and has collaborated with international students like Yan Huang from Chongqing University. Her research has been supported by significant grants from the Dutch Research Council, including two prestigious TOP-grants that have enabled her to pursue innovative research directions in ergodic theory and dynamical systems. Dr. Kalle is actively involved in the mathematical community through her service roles. She serves as the secretary and board member of both the Foundation Compositio Mathematica, which oversees several prestigious mathematical journals, and the Royal Dutch Mathematical Society (Koninklijk Wiskundig Genootschap), the oldest national mathematical society in the world, founded in 1778.

Sang-Woo Junمشاهده پروفایل

استاد

• Big Data Analytics
• Hardware Acceleration
• Non-Volatile Memory
• +۳ مورد دیگر

Professor Sang-Woo Jun is a leading researcher in systems and software for big data analytics, focusing on FPGA-based hardware acceleration and non-volatile memory (NVM) storage. His work spans applications such as graph analytics and bioinformatics, with a strong emphasis on cost-effective, high-performance computing architectures. He advises PhD students like Shengquan Ni and Yicong Huang, both of whom have achieved notable milestones (e.g., thesis defense, fellowship awards). Research Interests: Hardware Acceleration for Big Data FPGA-Based System Architectures Non-Volatile Memory Systems Graph Analytics and Bioinformatics Edge Computing and Low-Power Systems Recent Contributions: His articles highlight innovations in edge accelerators (e.g., IceSpy, Eciton), genomics acceleration (Bancroft), and scalable graph processing (Durin, Sting). These works emphasize reconfigurable systems, privacy-preserving techniques, and energy-efficient designs. Lab & Team: As part of the Intelligent Systems Group (ISG), he collaborates on events like the Southern California Database Day. His research bridges hardware-software co-design with real-world applications in IoT, environmental monitoring, and genomics.

Christopher Chenمشاهده پروفایل

دانشیار

• Space Plasma Physics
• Solar Wind
• Plasma Turbulence
• +۵ مورد دیگر

Dr. Christopher Chen is a Reader in Space Plasma Physics at Queen Mary University of London, holding a UKRI Future Leaders Fellowship. He is affiliated with the School of Physical and Chemical Sciences within the Department of Physics and Astronomy. Chen earned his PhD in Space Physics from Imperial College London in 2011 and has held research positions at the University of California, Berkeley and Imperial College before joining QMUL in 2017, where he progressed from Lecturer to Senior Lecturer in 2021 and then to Reader in 2022. Chen's research focuses on space plasma physics, particularly solar wind turbulence and its fundamental properties. His work bridges theoretical models, spacecraft data analysis, and laboratory experiments to understand complex plasma behavior. He is actively involved in several major space missions including Parker Solar Probe (as a member of FIELDS and SWEAP teams), Interstellar Probe mission study, and Magnetospheric Multiscale mission (as a member of the Science Working Team). His publication record shows a strong focus on solar wind turbulence, Alfvén wave interactions, and plasma dynamics in the inner heliosphere. The most recent work emphasizes forecasting techniques for solar wind parameters, laboratory experiments of wave interactions, and detailed analysis of turbulence properties using data from Parker Solar Probe. His research demonstrates a clear progression toward understanding the kinetic-scale processes that govern energy transfer in space plasmas. Royal Astronomical Society Fowler Award (2017) American Geophysical Union Macelwane Medal (2021) Conferred Fellow of the American Geophysical Union (2021) American Physical Society Landau-Spitzer Award (2022) Chen actively supervises a large research group including multiple postdoctoral researchers and PhD students, and has secured significant research funding through an STFC Ernest Rutherford Fellowship (2016-21), UKRI Future Leaders Fellowship (2022-26), and multiple STFC Consolidated Grants. His teaching responsibilities include organizing and lecturing the Astrophysical Plasmas module and supervising Masters and undergraduate research projects. He is also engaged in extensive public outreach through media interviews, public talks, and participation in science festivals.

Ivan De Oliveira Nunesمشاهده پروفایل

استادیار

• Security & Privacy
• Computer Networking
• Embedded Systems
• +۶ مورد دیگر

Prof. Ivan De Oliveira Nunes is an Assistant Professor of Cybersecurity at the University of Zurich (UZH), affiliated with the Department of Informatics (IfI) and the Digital Society Initiative (DSI). Previously, he held a similar position at the Rochester Institute of Technology (2021–2025). He earned his Ph.D. from the University of California, Irvine (UCI) in 2021, advised by Prof. Gene Tsudik, and was part of the Sprout Lab. His academic journey includes a B.S. in Computer Engineering from the Federal University of Espirito Santo (Brazil) and an M.Sc. in Computer Science from the Federal University of Minas Gerais (Brazil). His research focuses on Security & Privacy, Computer Networking, Embedded Systems, and Applied Cryptography. Key areas include IoT/CPS Security, System Security, and Provable Execution in resource-constrained systems. Notable contributions include work on remote attestation, secure software updates, and formally verified security protocols. Prof. Nunes advises multiple Ph.D., M.Sc., and undergraduate students, including current Ph.D. candidates Antonio Joia Neto, Liam Tyler, and Alexandra Lengert. Former advisees include Adam Caulfield (now at U. of Waterloo) and Spencer Roth (M.Sc. 2023). He leads the SPINS Research Group, exploring secure systems design, and has secured grants such as the CRII: SaTC grant (2023). His work bridges theoretical foundations and practical implementations, emphasizing real-world applicability in embedded and IoT systems.

David Landa Marbanمشاهده پروفایل

پژوهشگر

• Mathematical Modelling
• Subsurface Processes
• Carbon Capture and Storage (CCS)
• +۴ مورد دیگر

David Landa Marban is a Researcher at NORCE Research AS, affiliated with the Energy and Technology division. His primary focus is on mathematical modeling and simulation of subsurface processes, including applications in CO2 storage (CCS), microbial enhanced oil recovery (MEOR), and microbially induced calcite precipitation (MICP). He has expertise in scientific computing, software development for multi-phase flow and reactive transport, and numerical simulations at pore, core, and field scales. Education: PhD in Applied Mathematics from the University of Bergen (2019), followed by a postdoctoral position at NORCE (2019-2021). Current role as a Research Scientist since 2022. Research interests emphasize computational geosciences, with projects such as MuPSI (multiscale pressure-stress impacts on CO2 storage) and CSSR (sustainable subsurface resources). He develops open-source tools like pyopmspe11 and contributes to frameworks like OPM Flow for reservoir simulation and history matching. Key contributions include modeling pressure interference in CO2 storage, machine-learned near-well models, and data-driven predictions for CO2 EOR. His work bridges lab-scale experiments with field-scale applications, addressing challenges in subsurface energy systems and leakage remediation.

Phillip Raffeckمشاهده پروفایل

پژوهشگر

• Real-Time Systems
• Embedded Systems
• Energy-Aware Computing
• +۷ مورد دیگر

Phillip Raffeck is a researcher at the Department of Computer Science (INF) within the Chair of Computer Science 4 (System Software) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). His work focuses on real-time systems, embedded systems, and energy-aware computing, with a particular emphasis on worst-case execution time (WCET) and energy consumption (WCEC) analysis. He has contributed to projects like the Invasive Run-Time Support System (iRTSS) and tools for energy-neutral system design. Department: Department of Computer Science (INF) University: Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Position: Researcher His research spans real-time systems , multi-core optimization , and power-aware computing . Key contributions include migrative synchronization protocols , energy-neutral operating systems , and code metrics for timing analysis . Recent work explores carbon-aware co-design and predictable migration in embedded environments. Publications highlight collaborations with teams across Germany and international symposia like RTSS, WCET, and EMSOFT. His article trends reflect advancements in intermittent execution models , transactional networking , and static analysis toolchains . He serves as a secondary reviewer for conferences including RTAS and ISORC. Phillip supervises graduate theses on topics such as DMA-based OS offloading , dynamic migration , and interrupt latency analysis . He contributes to teaching courses like Betriebsystemtechnik and Systemnahe Programmierung in C at FAU.

DJEMAI Mohamedمشاهده پروفایل

استاد

• Control Theory
• Hybrid Systems
• Multi-Agent Systems
• +۹ مورد دیگر

Dr. Mohamed Djemai is a Full Professor at École Nationale Supérieure de l'Électronique et de ses Applications (ENSEA), Cergy, and INSA Hauts-de-France. He is affiliated with the Quartz Laboratory (EA 7393) and LAMIH UMR CNRS 8201 at University Polytechnic Hauts-de-France. His research focuses on nonlinear control systems theory, with emphasis on hybrid and variable structure systems, sliding mode approaches, fault detection, and applications to power systems, robotics, and vehicle dynamics. IEEE Senior Member Associate Editor for Nonlinear Analysis: Hybrid Systems Co-Facilitator of National Working Group GT-SDH (2014–present) Member of IFAC-TC-1.3 (Discrete Event and Hybrid Systems) since 2001 Member of IFAC-TC-2.1 (Control Design) since 2005 His recent publications address fractional-order control of multiagent systems, stability analysis on time scales, fault-tolerant satellite attitude control, and robust consensus algorithms for nonlinear systems. Key methodologies include sliding mode control, event-triggered control, and observer-based fault detection. Current teaching activities encompass diagnostics, linear systems, signal processing, and sensor conditioning. The trend in Dr. Djemai's research since 2022 involves advanced control strategies for cyber-physical systems, distributed fault detection mechanisms, and time scale theory applications to intermittent communication problems. Notable collaborations include work with Michael Defoort, Stefano Di Gennaro, and international institutions like Kyungpook National University and University of Reims. Scientific contributions include: IEEE Senior Member recognition Development of robust exact filtering differentiators Innovations in fixed-time consensus protocols Leadership in IFAC technical committees Editorial role in hybrid systems analysis His laboratory work at Quartz and LAMIH supports applications in aerospace systems, renewable energy conversion, and industrial risk management architectures.

Venkatesh Murthyمشاهده پروفایل

استاد

• Neuroscience
• Olfaction
• Neural Circuits
• +۳ مورد دیگر

Venkatesh Murthy serves as the Raymond Leo Erikson Life Sciences Professor of Molecular & Cellular Biology at Harvard University and co-directs the Harvard Brain Science Initiative. His laboratory is housed within the Faculty of Arts and Sciences at Harvard's Biological Laboratories in Cambridge, Massachusetts. His research investigates the neural and algorithmic basis of odor-guided behaviors in terrestrial animals, primarily using mouse models. The Murthy Lab develops naturalistic behavioral paradigms that enable simultaneous electrophysiological recordings, high-resolution optical imaging, and optogenetic manipulation. Key research areas include neural circuit dynamics in the olfactory system, modification of circuits through behavioral state and learning, and development of computational models to explain neural observations. The lab maintains strong interdisciplinary collaborations with theoretical neuroscientists. Recent publications (2023-2025) reveal consistent focus on olfactory navigation mechanisms, neural signal processing algorithms, circuit-level analysis of social behaviors, and biomimetic applications for electronic sensing systems. Work spans experimental techniques including multi-animal pose estimation (using DeepLabCut), neural deconvolution methods, and analysis of fluctuating odor environments. The Murthy Lab operates within Harvard's Department of Molecular & Cellular Biology as part of the broader Harvard Brain Science Initiative ecosystem, contributing to research in Cognitive and Behavioral Neuroscience, Theory and Computation, and Sensory and Motor Systems.

• Atmospheric Water Harvesting
• Building Energy Design
• Computational Fluid Dynamics (CFD)
• +۶ مورد دیگر

Marco Simonetti is an Associate Professor at the Department of Energy (DENERG) of Politecnico di Torino, with affiliations to the CleanWaterCenter@PoliTo and Energy Center Lab. He serves as a Course Instructor for Solar Thermal Technologies and Thermotechnical System Design in the Energy and Nuclear Engineering department, and as a Lecturer for Energy Transition and Low-Carbon Architecture modules. Research Interests: Atmospheric water harvesting, Building energy simulation, Natural ventilation, Solar cooling Skills: Computational Fluid Dynamics (CFD), Environmental Engineering, Sustainable Building Physics His recent research focuses on atmospheric water harvesting systems using biocomposite hydrogels, airborne disease mitigation through ventilation strategies, and climate-responsive architectural design. He leads the SMAP project on alginate-bentonite hydrogel for water vapor capture and participates in international collaborations like PRINCETO with Princeton University. Marco holds multiple patents including Bio-Stopper for infection control, ventilation systems with biofilters, and water harvesting technologies. He supervises PhD student Hamed Rasam, who analyzes aerosol spread in indoor environments.

Pirathayini Srikanthaمشاهده پروفایل

دانشیار

• Game Theory
• Large-scale Optimization
• Distributed Control
• +۵ مورد دیگر

Pirathayini Srikantha serves as Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering, holding a Tier 2 Canada Research Chair in Reliable and Secure Power Grid Systems with critical national security implications for Canada's power infrastructure. Her academic foundation includes: B.A.Sc. in Systems Design Engineering from University of Waterloo (2009) M.A.Sc. in Electrical and Computer Engineering from University of Waterloo (2013) Ph.D. from University of Toronto's Edward S. Rogers Sr. Department of Electrical and Computer Engineering (2017) Professor Srikantha's research centers on game theory, large-scale optimization, and distributed control to enable adaptive power grid operations. Her work addresses cybersecurity vulnerabilities in smart grids through distributed coordination frameworks and develops transactive energy market mechanisms that incentivize grid support during congestion. By integrating AI breakthroughs, she focuses on creating systems that automatically infer, predict, and respond to power disturbances while enhancing infrastructure resilience against evolving threats. Her publication trajectory reveals consistent innovation in grid security and optimization, with increasing emphasis on AI-driven solutions for grid resilience since 2015. Key thematic evolution shows progression from foundational distributed control (2015-2017) to sophisticated cybersecurity applications (2018-2021), consistently appearing in premier venues like IEEE Transactions on Smart Grid. Her scholarly impact is recognized through: Best paper award at IEEE Smart Grid Communications Runner-up best poster award at ACM Women in Computing As Canada Research Chair holder, she directs a federally funded research program addressing critical grid vulnerabilities, while serving as Associate Editor for IEEE Transactions on Smart Grid. Her work bridges theoretical advances in game-theoretic security with practical grid applications, though specific student mentorship details aren't publicly enumerated in source materials. Her research group operates at the intersection of electrical engineering and computer science, developing next-generation tools for grid operators through collaborations with Canadian energy infrastructure stakeholders.

Farzaneh Derakhshanمشاهده پروفایل

استادیار

• Concurrency
• Formal Methods
• Programming Languages
• +۴ مورد دیگر

Farzaneh Derakhshan is an Assistant Professor in the Computer Science Department at Illinois Institute of Technology. She received her Ph.D. in Pure and Applied Logic from Carnegie Mellon University in 2021 under Prof. Frank Pfenning and completed a postdoctoral fellowship at CMU with Profs. Limin Jia and Stephanie Balzer. Her research focuses on formal methods for concurrent and secure systems. Education: Ph.D. in Pure and Applied Logic, Carnegie Mellon University (2021) Dr. Derakhshan's research explores logical foundations for concurrent program design, emphasizing correctness guarantees (safety, reactivity) and security properties (fault tolerance, side-channel protection). Current projects include modal logics for under/over-approximation, relational logic for GPU security, type systems for intermittent computing, and behavioral types for security enforcement. Her work bridges theoretical logic with practical systems challenges. Recent publications (2021-2025) reveal strong thematic continuity in concurrency, security, and formal verification. Key trends include session-typed process models for secure communication, modal type systems for intermittent computing, and logical frameworks for program assurance. These contributions demonstrate consistent innovation at the intersection of programming languages, logic, and cybersecurity. Scientific Awards: No scientific awards are mentioned in the provided text. Advising and Research Funding: Current Students: PhD: Godha Pallavi Bhogadi, Myra Dotzel, Lang Liu; Master's: Akash Madhu Former Students: Narasimha Karthik G. (MS), Griffin Colomer (BSc) Grants: NSF SaTC CORE #2350217: "Mixed Assurance Reasoning via Modal Logic" (2024) Dr. Derakhshan leads an active research group focused on programming language security, collaborating with Carnegie Mellon University researchers. She co-organizes community initiatives including the 2026 Dagstuhl Seminar on Behavioural Types for Resilience and serves on multiple program committees for top programming languages conferences.

Yidi Wangمشاهده پروفایل

استادیار

• Real-Time Systems
• Embedded Systems
• Cyber-Physical Systems
• +۵ مورد دیگر

Yidi Wang is an Assistant Professor in the Department of Computer Science and Engineering at Santa Clara University (SCU). She holds a Ph.D. in Electrical and Computer Engineering from the University of California, Riverside (2023), and previously worked as a Postdoctoral Scholar at UCR (2023–2024). Her research focuses on real-time, embedded, and cyber-physical systems, particularly addressing challenges in GPU scheduling, energy efficiency, and batteryless device operation. She teaches courses such as Introduction to Embedded Systems and Operating Systems at SCU. Education: Ph.D. in Electrical and Computer Engineering, UC Riverside (2023) M.S. in Electrical and Computer Engineering, UC Riverside (2019) B.S. in Electrical Engineering, Huazhong University of Science and Technology (2018) Research Interests: Real-time scheduling for GPU-accelerated applications, energy-efficient computing, heterogeneous platforms, and reliable systems for intermittently powered devices. Her work spans system-level implementations and theoretical analysis, including novel scheduling algorithms and mathematical models for performance optimization. Awards: None explicitly listed in the provided texts. Advising & Grants: Currently mentoring 3 students (2 MS, 1 undergraduate) and recruiting PhD/MS candidates. She serves on technical committees for RTSS and RTAS conferences and has reviewed for journals like Transactions on Computers and Real-Time Systems. Labs/Teams: Leads research on GPU scheduling and batteryless systems, collaborating with industry and academic partners.