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Kihong ParkView profile

Associate Professor

• Networking and Operating Systems
• Distributed Systems
• Information Security and Assurance
• +4 more

Dr. Kihong Park is an Associate Professor in the Department of Computer Science at Purdue University, affiliated with CERIAS and the Santa Fe Institute. His research focuses on high-speed networks, network security, distributed systems, and stochastic cellular automata. He joined Purdue in 1996 after earning his PhD from Boston University in 1996, where his thesis explored cellular automata dynamics under noise. He has authored influential works on self-similar network traffic modeling, noncooperative game theory in QoS allocation, and proactive defenses against DDoS attacks. Education: BA in Management (Seoul National University, 1988), MS in Computer Science (University of South Carolina, 1990), PhD in Computer Science (Boston University, 1996). Research interests include traffic control in IP networks, scalable network security, robust distributed systems, and cellular automata theory. His work bridges networking, security, and theoretical computer science, with contributions to both foundational understanding and practical implementations like Cisco router scheduling algorithms. He has received the NSF CAREER Award and served on editorial boards of Computer Networks and IEEE Communications Letters. Key publications include seminal work on self-similar traffic origins (SIGCOMM '96), route-based DDoS defense mechanisms (SIGCOMM '01), and noncooperative game theory in network resource allocation (JCSS 2004). His lab, the Network Systems Lab, investigates complex system behaviors in networks and distributed computing.

Anurag AnshuView profile

Assistant Professor

• Quantum Computing
• Theoretical Computer Science
• Quantum Complexity Theory
• +3 more

Anurag Anshu is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). His primary research interests lie in quantum complexity theory, quantum many-body systems, quantum Shannon theory, and quantum learning theory. He holds a PhD from the National University of Singapore (2018) and completed postdoctoral research at UC Berkeley (2020) and the Perimeter Institute for Theoretical Physics (2020). His academic journey includes: Bachelor's in Computer Science from IIT Guwahati (2013) PhD in Quantum Technologies from NUS (2018), awarded the Dean's Graduate Research Excellence Award for his thesis on quantum communication protocols Postdoctoral roles at UC Berkeley's Simons Institute and the Perimeter Institute Research focuses on foundational aspects of quantum computing, including quantum algorithms, complexity theory, and the interplay between quantum information and many-body physics. His work bridges theoretical computer science with quantum physics, addressing challenges in quantum communication, error correction, and learning theory. Notable contributions include studies on NLTS Hamiltonians, quantum Gibbs state learning, and the computational power of quantum circuits. He has received grants such as the NSF CAREER Award (2023) for research on quantum many-body systems. His awards include the 2018 Dean's Graduate Research Excellence Award for his thesis on quantum communication protocols. He advises no listed students but collaborates extensively in quantum information theory and complexity research.

• VLSI and Circuit Design
• Wireless Sensing
• Battery Diagnostics
• +4 more

Byunghoo Jung is a Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering, located at the Materials and Electrical Engineering Building in West Lafayette, Indiana. He holds a BS from Yonsei University (1990), an MS from KAIST (1992), and a PhD from the University of Minnesota (2005). His research focuses on VLSI and circuit design for wireless sensing applications, including battery diagnostics, magnetic tracking systems, and energy harvesting. He leads projects in automotive electronics, biomedical instrumentation, and low-power embedded systems. Education: PhD, University of Minnesota at Twin Cities, 2005 MS, Korea Advanced Institute of Science and Technology, 1992 BS, Yonsei University, 1990 Research interests span circuits and systems for wireless sensing , with emphasis on: Non-intrusive load monitoring Battery management systems Magnetic tracking for medical/surgical applications Low-power sensor networks CMOS integration of optical and ferroelectric devices His work bridges theoretical circuit design with practical implementations in automotive, biomedical, and IoT domains. Recent publications (2020-2025) emphasize advancements in: High-precision current sensing systems Magnetic tracking for VR/AR and surgical navigation Integrated photonic circuits in advanced CMOS nodes Ferroelectric-based oscillator design Notable innovations include: Urine-powered battery systems for diaper sensors Wireless chip-to-chip communication in 3D ICs Self-healing CMOS designs for deep-scaled technologies His research lab develops both hardware prototypes and algorithmic frameworks for next-generation sensor systems.

• Wireless Communication
• Networking
• Military & Secure Communication
• +5 more

John M. Shea is a Professor and Associate Chair for Faculty Affairs in the Department of Electrical & Computer Engineering at the University of Florida. His research focuses on wireless communication & networking, military & secure communication systems, cross-layer design, and networked autonomous vehicles. He has collaborated with organizations like AFOSR, AFRL, DARPA, and industry partners Harris and Samsung Electromechanics. Education: PhD, MS, and BSEE in Electrical Engineering from Clemson University (1993-1998). Awards include the Summer Faculty Fellow (ONR, 2012/2014), MILCOM Technical Achievement Award (2012), and the Fred W. Ellersick Award (1996). Recent research trends emphasize cybersecurity in autonomous systems, resilient consensus algorithms for multi-agent networks, and privacy-preserving localization techniques. His work on jammer placement strategies and dynamic spectrum sharing has been supported by defense-related grants. Advising and grants: Extensive industry-academic partnerships, with grants from AFOSR, NSF, and DoD. His research group explores cutting-edge applications in military communication systems and autonomous vehicle networks.

Qilin LiView profile

Lecturer

• Machine Learning
• Computer Vision
• Structural Engineering
• +3 more

Qilin Li is a Lecturer in Computing at Curtin University, where he researches machine learning and computer vision applications for structural engineering. His work focuses on developing data-driven models for simulating physical processes in structural dynamics and health monitoring. Li integrates AI methodologies with engineering principles to improve infrastructure safety and reliability. His research interests include machine learning algorithms for structural analysis, computer vision-based monitoring systems, graph neural networks for physical simulations, and AI applications in safety engineering. He develops techniques for predicting structural responses to extreme events like explosions. Li's publications demonstrate innovation in combining computational mechanics with deep learning. His work on graph neural networks for blast fragmentation and transformer networks for structural segmentation advances predictive modeling for engineering applications.

Koen TorremansView profile

Assistant Professor

• Structural Geology
• Mineral Deposits
• Basin Analysis
• +5 more

Koen Torremans is an Assistant Professor at the School of Earth Sciences, University College Dublin (UCD), and a Funded Investigator at iCRAG (Irish Centre for Research in Applied Geosciences). He specializes in structural geology, mineral deposit geology, and basin analysis, with a focus on sedimentary-hosted ore systems in regions like the Central African Copperbelt, Irish Orefield, and Namibian Gariep Belt. His work integrates structural analysis, numerical modeling, and geochemistry to understand fluid flow and ore genesis. Dr. Torremans holds a PhD from KU Leuven and has held postdoctoral roles at UCD. His research explores fault segmentation, fluid migration pathways, and structural controls on mineral systems. He co-leads UCD's Fault Analysis Group and teaches 3D mapping and modeling courses. Awards include the Best Student Presentation at the 2013 SGA Meeting and the Midland Valley Student Structure Prize. His recent publications emphasize fluid mixing processes in Irish-type Zn-Pb deposits, U-Pb dating of continental successions, and structural modeling of ore systems. Key themes include the interplay between tectonics, fluid dynamics, and mineralization in extensional basins. Current grants focus on 3D subsurface characterization and fault-controlled fluid pathways in Ireland's Lower Carboniferous.

John WalshView profile

Professor

• Structural Geology
• Geophysics
• Fault Analysis
• +5 more

John Walsh is a Professor in the School of Earth Sciences at University College Dublin (UCD). He is a Co-PI and founding Director of iCRAG (Irish Centre for Research in Applied Geosciences), a SFI-funded research center, and a member of the Fault Analysis Group and Sustainable GeoEnergy Research Group. His academic journey includes a BSc from UCD and a PhD from NUI Galway (1980-1985). He has held roles at the University of Liverpool (1985–2000) before joining UCD in 2000. Research Interests : Focus on fault geometry, earthquake mechanics, fluid flow in fault systems, numerical modeling, and applied geoscience for energy and resource exploration. Specific areas include geothermal energy localization, induced seismicity, and fault zone evolution. Grants & Awards : Received prestigious awards such as the Royal Irish Academy Gold Medal (2018), William Smith Medal (2017), and Honorary Fellow of the Geological Society (2015). Active in professional societies including AAPG, EAGE, and the Royal Irish Academy. Teaching : Coordinates modules like Introduction to Earth Sciences , Geological Mapping , and contributes to advanced courses on natural hazards, structural geology, and field research. Labs & Teams : Leads the Fault Analysis Group (founded 1985) and directs iCRAG, focusing on applied geoscience for energy, mineral resources, and environmental sustainability.

Rodney HowellView profile

Associate Professor

• Real-Time Systems
• Self-Stabilization
• Petri Nets
• +5 more

Rodney Howell is an Associate Professor and Undergraduate Programs Director in the Department of Computer Science at Kansas State University (KSU). He holds a Ph.D. (1988) and B.S. (1984) in Computer Science from the University of Texas at Austin and Wichita State University, respectively. His research expertise includes Real-Time Systems, Self-Stabilization, Petri Nets, and Computational Complexity. He authored the textbook Algorithms: A Top-Down Approach (2023) and has directed seven Master’s theses. Key awards include the Kansas State Scholarship (1980–1984), McGregor Scholarship (1980–1984), and Microelectronics and Computer Development Fellowship (1984–1986). He has organized the KSU High School Programming Contest since 1992 and contributed to grants such as the NSF-funded Software Control Laboratory (1993–1995). His advising includes students like Muralidhar Venkatrao, Thiagarajan Rajagopalan, and Vineet Tadakamalla. His work spans over 30 years at KSU, including roles like Special Assistant to the Dean (2019–2020) and involvement in academic outreach.

Po ChenView profile

Associate Professor

• Seismology
• Computational Geophysics
• 3D Seismic Tomography
• +3 more

Po Chen is an Associate Professor in the Department of Geology and Geophysics at the University of Wyoming , affiliated with the College of Engineering and Physical Sciences and the School of Energy Resources . His research bridges computational methods with seismology for advanced Earth structure and earthquake source modeling. Education : PhD in Seismology (University of Southern California, 2005), BS in Geophysics and Economics (Beijing University, 2000). Po Chen's research focuses on Full-3D Waveform Tomography (F3DT) , leveraging high-performance computing to solve nonlinear structural inversion problems. He develops and applies numerical methods for seismic wave propagation, fault-plane ambiguity resolution, and ground-motion estimation. Recent work integrates machine learning for real-time earthquake detection (e.g., RED-PAN, RockNet) and landslide parameter inversion. His publications highlight advancements in seismic source inversion, GPU-accelerated algorithms, ambient-noise tomography, and critical zone imaging. Key collaborations include tools like UCVM (3D velocity model querying) and pSIN (seismic interferometry). Teaching emphasizes active learning and the synergy between research and education, with graduate advisees contributing to projects in seismic imaging and hazard analysis.

Adnan TahirovicView profile

Professor

• Nonlinear control theory
• Multi-agent systems
• Mobile robotics
• +6 more

Prof. Adnan Tahirovic is a Professor in the Department of Automatic Control at the University of Sarajevo's Faculty of Electrical Engineering. He leads the Lab for Cooperative Artificial Intelligence and Advanced Control Systems. His academic background includes a PhD from Politecnico di Milano and research stints at Imperial College London, NASA-JPL, and Caltech. Education: M.Sc. (Control Theory, 2006) and Ph.D. (Information Technology, 2011). His research focuses on nonlinear control, multi-agent systems, mobile robotics, computational neuroscience, and AI. Key projects include MORUS (NATO-funded maritime security), AeroSTREAM (EU-funded autonomous aerial systems), and MARBLE (blue economy robotics). Research Interests: Nonlinear systems, optimal control, multi-agent reinforcement learning, motion planning in robotics, and AI applications in medicine. His work addresses the curse-of-dimensionality in optimal control, yielding breakthroughs in nonlinear systems and multi-agent coordination. Publications: Over 30 journal/conference papers, including IEEE Transactions and high-impact robotics conferences. Books include works on MPC-based mobile vehicle navigation and motion planning algorithms. Awards: Silver Plaque award, Microsoft Azure Research Award, DAAD Fellowship, and Golden Plaque for academic excellence. He has supervised over 40 master’s/PhD students, many advancing to prestigious institutions. Labs/Teams: Directs the Cooperative AI & Control Lab, collaborating with institutions like Imperial College London and University of Zagreb. Current projects span maritime robotics, blue economy applications, and computational neuroscience modeling.

• Electrical Engineering
• Power Systems
• Partial Discharge Detection
• +3 more

Antonella Ragusa is a Senior Lecturer in Electrical and Electronic Engineering at the University of Derby, affiliated with the College of Science and Engineering. Her research focuses on advancing partial discharge detection techniques in power cables using electromagnetic time reversal methods. She has recently presented work on localizing multiple partial discharges and addressing interference challenges in cable circuits. Her publications from 2023 highlight contributions to power systems diagnostics, emphasizing practical applications for improving electrical infrastructure reliability. No scientific awards or grants are explicitly mentioned in the provided materials. She currently has no listed advisees or collaborators in student supervision roles. Ragusa's work bridges theoretical electromagnetic principles with real-world engineering problems, particularly in high-voltage systems maintenance and fault localization technologies.

Zakirul Alam BhuiyanView profile

Associate Professor

• Cybersecurity
• Privacy-Preserving Systems
• Trustworthy AI/ML
• +7 more

Dr. Md Zakirul Alam Bhuiyan is an Associate Professor in the Department of Computer and Information Sciences at Fordham University, New York. He leads the DependSys Lab (Dependable and Secure Systems Lab) and is affiliated with the Fordham Center for Cybersecurity. His research focuses on cybersecurity, data-driven dependability, trustworthy AI/ML, and IoT/CPS applications. He has authored over 250 publications, including top-tier journals and conferences like IEEE Transactions on Industrial Informatics and IEEE INFOCOM. Dr. Bhuiyan has held adjunct roles at Harvard University and Temple University. His work emphasizes securing healthcare monitoring systems, event detection in CPS, and privacy-preserving data collection. Education details are not explicitly stated, but his career trajectory includes prior positions at Temple University and Harvard. He has secured grants and awards, including the IEEE TCSC Award for Excellence, ScholarGPS recognition, and being named one of the world's top 2% scientists. His professional activities include editor roles for IEEE Transactions and ACM journals, and organizing major conferences like IEEE DASC and TrustCom. Research interests span cybersecurity, IoT, and AI/ML, with a focus on real-world applications like structural health monitoring and smart city infrastructure. His lab, DependSys, addresses challenges in dependable and secure systems. He has advised numerous students and collaborates globally, delivering keynote speeches at events like IEEE ISPA and ICECI. Key achievements include 25+ ESI Highly Cited Papers, 7 Best Paper Awards, and leadership in over 50 conferences. Current projects involve federated learning, edge computing, and blockchain for healthcare privacy. His lab explores innovative uses of Wi-Fi signals for sensing and security, including contactless health monitoring and intrusion detection.

Chiu LawView profile

Associate Professor

• Optical sensors
• Nonlinear optical devices
• Optical communication
• +2 more

Dr. Chiu Law is an Associate Professor in Electrical Engineering and Computer Science at the University of Wisconsin-Milwaukee's College of Engineering & Applied Science. His research focuses on magnetostrictive materials, optical sensors, and advanced composite technologies for energy applications. Education includes: PhD in Electrical Engineering, Purdue University (1987) MS in Electrical Engineering, Purdue University (1985) BS in Electrical Engineering, West Virginia University (1983) Research interests span optical sensors, nonlinear devices, wave propagation, and magnetostrictive composites with applications in sensing, actuation, and energy harvesting. Recent work explores nanocellulose-based smart materials and infrared thermography for material characterization. Publications demonstrate consistent focus on optical and magnetostrictive technologies, evolving from fundamental wave propagation studies to advanced composite applications. The work shows progressive integration of materials science with photonic and electronic systems. Awards and honors: Outstanding Research Award (1998) NSF CAREER Award (1995) Leads the Smart Composites and Sensors Laboratory in collaboration with Dr. El-Hajjar, with projects funded by NSF, DARPA, and Department of Defense.

Jason RifeView profile

Professor

• navigation
• safety-critical transportation systems
• state estimation
• +3 more

Jason Rife is a Professor and Chair of Mechanical Engineering at Tufts University's School of Engineering, also holding a joint appointment in Electrical and Computer Engineering. He leads the Automated Systems and Robotics Lab, focusing on safe human-machine interactions, autonomous vehicle navigation, and control systems. His research emphasizes validating navigation performance for critical automated systems, such as automated ground vehicles and aircraft landing. Education: Ph.D. (Stanford University, 2004), M.S. (Stanford, 1999), B.S. (Cornell University, 1996). Prior to joining Tufts in 2007, he was a Research Associate at Stanford's GPS Laboratory, studying aircraft navigation systems. Research Interests : Navigation, autonomous systems, state estimation, human-robot interaction, safety-critical transportation, and control systems. His work bridges robotics, aerospace, and automotive engineering, with applications in autonomous vehicle safety and precision navigation. Grants & Awards : Recipient of the Lillian and Joseph Leibner Award (2020), Best Paper awards, and grants from NSF, FAA, and the U.S. Department of Transportation. He has led projects on software-state observability, adaptive system identification, and trusted human-robot teamwork. Teaching : Courses include Advanced Dynamics, Optimal Control, and Collaborative Robotics. He has coordinated graduate and undergraduate engineering programs, including the Human-Robot Interaction joint PhD. Professional Activities : Served on the Institute of Navigation's Satellite Division Board, organized conferences, and reviewed for journals like IEEE Transactions on Aerospace and the Journal of Aircraft.

Robert WhiteView profile

Associate Professor

• MEMS/NEMS
• Acoustic Microsystems
• Microfabrication
• +5 more

Robert White is an Associate Professor in the Department of Mechanical Engineering at Tufts University's School of Engineering. He leads the Microscale Sensors and Systems Lab and advises the Tufts Micro and Nano Fabrication Facility. His expertise spans acoustic microsystems, MEMS/NEMS, microfabrication, and aerodynamic measurement technologies. White holds a PhD from the University of Michigan and MS/BS degrees from MIT. Education: Ph.D., University of Michigan, Mechanical Engineering (2005) M.S., Massachusetts Institute of Technology, Mechanical Engineering (1999) B.S., Massachusetts Institute of Technology, Mechanical Engineering (1999) Research focuses on acoustic sensors for planetary science (e.g., Mars missions), quench detection in superconductors, and high-altitude anemometry. Key contributions include fluid-filled MEMS hydrophones and sonic anemometers. He has over 120 publications and holds multiple patents in sensor design. Awards include AIAA Associate Fellow status and grants from NASA, DOE, and industry partners. Teaching responsibilities include courses in acoustics, mechanics, and MEMS systems. Current projects involve developing sensors for extreme environments and improving aerospace instrumentation. Labs/Teams: Microscale Sensors and Systems Lab Tufts Micro/Nano Fabrication Facility