جستجوی استاد راهنمای دانشگاهی

Alexandru G. Bardasمشاهده پروفایل

دانشیار

• Cybersecurity
• Systems Security
• Moving Target Defenses
• +۹ مورد دیگر

Alexandru G. Bardas is an Associate Professor at the University of Kansas in the Department of Electrical Engineering & Computer Science (EECS) and the Institute for Information Sciences (I2S) . He received his PhD from Kansas State University under advisors Xinming (Simon) Ou and Scott A. DeLoach. His research focuses on cybersecurity from a systems perspective , including moving target defenses, security operations center (SOC) metrics, DevOps security, power grid cybersecurity, and defensive technologies for political activists. He explores UDP-based DDoS detection, DNS traffic analysis, and the intersection of AI with cybersecurity, emphasizing foundational knowledge over tool-specific training. Key research areas: Cybersecurity, Systems Security, Moving Target Defenses, SOC Metrics, DevOps Security Recent publications in ACSAC 2024 , USENIX Security 2024/2023 , and IEEE Security & Privacy 2022 Dr. Bardas has received significant recognition including: NSF CAREER Award (2022) for SOC automation Bellows Scholar (2021) at KU NSA SoS Honorable Mention (2023) He actively advises students across disciplines, with graduates now at Sandia National Laboratories , Blue Cross Blue Shield , and Pacific Northwest National Laboratory . Dr. Bardas participates in NSF grant reviews , serves on program committees for SOUPS and MILCOM , and leads outreach initiatives like the GenCyber Summer Camp .

Roman Samulyakمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Magnetohydrodynamics
• Plasma Physics
• +۲ مورد دیگر

Roman Samulyak is a Professor in the Department of Applied Mathematics and Statistics at Stony Brook University. He holds a Ph.D. from NJIT in Applied and Computational Mathematics with specializations in Hydro- and Electrodynamics. His research develops advanced numerical algorithms for modeling complex physical systems in high-energy physics and fusion energy. Research spans computational methods for magnetohydrodynamics, plasma physics, nuclear fusion/fission systems, and particle accelerator design. Current applications include disruption mitigation in tokamaks and laser-driven particle acceleration. Recent publications demonstrate strong focus on plasma-based accelerators and fusion reactor modeling, particularly pellet ablation dynamics, laser wakefield acceleration, and MHD simulations of tokamak plasmas. Research utilizes high-performance computing resources for large-scale simulations. Office location is Math Tower 1-108 at Stony Brook University.

• Hardware Security
• Machine Learning
• 3D IC Design
• +۴ مورد دیگر

Wafi Danesh is an Assistant Professor in the Department of Engineering Programs at SUNY New Paltz, part of the School of Science & Engineering. He holds a PhD in Electrical and Computer Engineering from the University of Missouri Kansas City (2022). Prior to academia, he served as a Senior Engineer I - Design at Microchip Technology Inc. (2022-2023). His research focuses on hardware security, leveraging machine learning for FPGA Trojan detection and secure 3D IC design. Teaching interests include System-on-Chip Design, Digital Logic Fundamentals, and Computer Architecture. Education: PhD in Electrical and Computer Engineering, University of Missouri Kansas City, 2022 Research Interests: Dr. Danesh explores cutting-edge methods to enhance hardware security, including AI-driven approaches for IoT device protection and thermal management in 3D integrated circuits. His work bridges machine learning and physical hardware vulnerabilities, emphasizing FPGA security and PUF-based solutions for wireless systems. Publications Trends: His articles span FPGA Trojan detection via NLP and unsupervised learning, thermal challenges in 3D ICs, and neuromorphic computing innovations. Recent work highlights automated security tools and multi-valued computing for energy efficiency. Awards: None explicitly listed in the provided materials. Advising & Grants: No formal advisees or grants are mentioned. His professional activities center on research and teaching.

Roland N. Horneمشاهده پروفایل

استاد

• Geothermal reservoir engineering
• Energy production optimization
• Well test interpretation
• +۸ مورد دیگر

Roland N. Horne is the Thomas Davies Barrow Professor of Earth Sciences at Stanford University and Senior Fellow at the Precourt Institute for Energy. He holds positions in the Department of Energy Science & Engineering and is an Affiliate at the Stanford Woods Institute for the Environment. With degrees from the University of Auckland (BE, PhD, DSc), Horne has established himself as a leading expert in geothermal reservoir engineering and energy production optimization. His research focuses on inverse problems in reservoir modeling, including tracer analysis of fractures, computer-aided well test analysis, production schedule optimization, and automated history matching. Horne has made significant contributions to understanding geothermal reservoir engineering and multiphase flow of boiling fluids through porous materials and fractures. The analysis of his recent publications (2023-2025) reveals a strong emphasis on enhanced geothermal systems (EGS), with particular focus on flexible operations, economic modeling, and advanced characterization techniques. His work increasingly incorporates machine learning approaches for reservoir analysis and has expanded into microbial tracing methods for interwell connectivity assessment. There's also significant attention to US geothermal resource potential and integration into the broader energy transition. Honorary Member of the Society of Petroleum Engineers Member of the US National Academy of Engineering Multiple SPE Distinguished Lecturer appointments (1998, 2009, 2020) John Franklin Carl Award recipient Five Best Paper awards from Geothermal Resources Council Patricius Medal from German Geothermal Society Core Values Award from Women in Geothermal (2023) Horne has supervised 60 PhD and 135 MS students throughout his career. His current teaching includes undergraduate and graduate courses in Fundamentals of Energy Processes, Geothermal Reservoir Engineering, Mass and Energy Transport in Porous Media, and Well Test Analysis. He previously served as President of the International Geothermal Association (2010-2013) and Technical Program Chair for multiple World Geothermal Congress events. Horne maintains active research collaborations worldwide, including with the University of Tokyo (where he was a Fellow of the School of Engineering in 2016) and China University of Petroleum. His current research group focuses on advancing EGS technologies and developing more accurate reservoir characterization methods for geothermal applications.

Bhuvana Srinivasanمشاهده پروفایل

استاد

• Fusion Energy
• Plasma-based Propulsion
• High-Energy-Density Plasmas
• +۵ مورد دیگر

Bhuvana Srinivasan is a Professor in the Department of Aeronautics and Astronautics at the University of Washington, directing the PLASMAWISE Laboratory. Previously, she held the rank of Associate Professor and served as Director of the Plasma Dynamics Computational Laboratory at Virginia Tech, supported by the Crofton Faculty Fellowship. Her research focuses on fusion energy, plasma-based propulsion, and computational plasma physics, with an emphasis on plasma-material interactions and instabilities across diverse plasma regimes. She has authored over 30 peer-reviewed publications and secured grants from the NSF, DOE, and AFOSR. Education: Ph.D. in Aeronautics and Astronautics, University of Washington (specializing in computational plasma physics) M.S. in Aeronautics and Astronautics, University of Washington B.S. in Aerospace Engineering and Mechanical Engineering, Illinois Institute of Technology Research Interests: Her work spans fusion energy concepts, plasma propulsion systems, high-energy-density plasma instabilities, and ionospheric dynamics. Key areas include plasma-surface interactions in fusion devices, magnetic field effects on plasma mixing, and algorithm development for fluid-kinetic models. She emphasizes high-fidelity multi-fluid simulations using discontinuous Galerkin methods. Awards & Recognition: NSF CAREER Award (2019-2024) Crofton Faculty Fellow (Virginia Tech, 2021-2023) Dean’s Outstanding Assistant Professor (Virginia Tech, 2017) Amelia Earhart Fellowship (Zonta International, 2007-2009) Advocacy & Leadership: She chairs DEI initiatives in academic departments and serves on national committees including the DOE Fusion Energy Sciences Advisory Committee and the APS Division of Plasma Physics Executive Board. Her work bridges computational plasma physics with societal impact, including fusion energy democratization and space exploration propulsion systems.

Sarah Masud Preumمشاهده پروفایل

استادیار

• Human-AI Interaction
• Natural Language Processing
• Applied Machine Learning
• +۴ مورد دیگر

Sarah Masud Preum is an Assistant Professor of Computer Science at Dartmouth College, with adjunct roles in the Department of Biomedical Data Science at Geisel School of Medicine and as Faculty Affiliate at the Center for Technology and Behavioral Health (CTBH). She also serves as Technical Associate Director of the Dartmouth Center for Precision Health and Artificial Intelligence. Her work focuses on machine learning for computational health, including natural language processing, temporal modeling, and human-AI interaction to develop personalized decision support systems in healthcare. Education includes a B.Sc. from Bangladesh University of Engineering and Technology, followed by M.Sc. and Ph.D. degrees from the University of Virginia. Previously, she was a postdoctoral research scholar at Carnegie Mellon University's School of Computer Science, recognized as a Rising Stars in EECS (2020) for her academic excellence and contributions to equity in STEM. Her research interests span Human-AI Interaction, Natural Language Processing, Mobile Health, and Cyber-Physical Systems. Over 2020–2023, her publications emphasize AI-driven solutions for healthcare challenges like conflict detection in health information and cognitive assistants for emergency response. Earlier work includes behavioral prediction models (MAPer) and spatial database optimizations (Maximum Visibility Queries). Awards: Rising Stars in EECS (2020) In teaching, she offers courses like Transforming Healthcare through Machine Learning and Machine Learning and Statistical Data Analysis. Her affiliations with multidisciplinary centers reflect her commitment to bridging technology and healthcare.

Emily Whitingمشاهده پروفایل

دانشیار

• Computer Graphics
• Computational Fabrication
• Architectural Geometry
• +۴ مورد دیگر

Emily Whiting is an Associate Professor of Computer Science at Boston University and Director of the Shape Design & Computation Lab. She also serves as Director of PhD Admissions and Co-Director of the BU Computer Graphics Lab. Her research focuses on computational fabrication, architectural geometry, and computer-aided design, bridging digital geometry processing, engineering mechanics, and rapid prototyping. She holds a PhD from MIT (2012), an SM in Design & Computation from MIT (2006), and a BASc in Engineering Science from the University of Toronto (2004). Previously, she was faculty at Dartmouth and a Marie Curie Postdoctoral Fellow at ETH Zurich. Her research interests include 3D printing optimization, structural design for fabrication, and tools for functionally-valid object creation. Notable projects include work on elastic garments, climbing experience replication, and print-wind instrument design. Her work has been featured on TEDx and PBS NOVA, and she has received awards such as the NSF CAREER Award and Sloan Research Fellowship. Education: PhD (MIT), SM (MIT), BASc (University of Toronto) Labs: Shape Design & Computation Lab, BU Computer Graphics Lab Key Projects: Knitting 4D garments, Environment-Scale Fabrication, Thermal-comfort casts Recent professional activities include program committee roles at SIGGRAPH 2025 and UIST 2024, and serving as Program Co-Chair for Pacific Graphics 2024. She advises a team of PhD and MS students, with alumni now in academia and tech industries.

Sudha Mokkapatiمشاهده پروفایل

دانشیار

• Semiconductor Physics
• Photonics
• Nanotechnology
• +۳ مورد دیگر

Dr. Sudha Mokkapati is an Associate Professor in the Department of Materials Science and Engineering at Monash University. Her research focuses on semiconductor nano-photonics, nano-lasers, and nanostructured solar cells. She holds a PhD from the Australian National University (2008) and has held academic positions at Cardiff University (2016–2019) and postdoctoral roles at ANU's Centre for Sustainable Energy Systems and Research School of Physics and Engineering. Education: M.Sc. Physics, University of Hyderabad M.Tech. Materials Science and Engineering, Indian Institute of Technology Kanpur Ph.D. Physics, Australian National University Research Interests: Semiconductor nanostructures for optoelectronics Nanowire-based lasers and solar cells Photon management in thin-film solar cells Plasmonic and nanophotonic device engineering Her recent publications emphasize advancements in gas sensing technologies, photonic resonators, and nanoscale optoelectronic devices. She leads projects on chemical detection platforms and wafer-scale 2D heterostructure synthesis. Collaborations span international institutions, addressing sustainable energy and nanotechnology challenges aligned with UN Sustainable Development Goals. Grants/Projects: All-electronic platform for real-time toxic gas detection (2024–2025) Low-cost wireless sensors for chemical hazards (2021–2023) van der Waals Epitaxy for flexible optoelectronics (2017–2020) Labs/Teams: Engaged in nanophotonics and materials engineering research groups at Monash, focusing on device fabrication and characterization for energy and sensing applications.

Parinaz Naghizadehمشاهده پروفایل

استادیار

• Network Economics
• Game Theory
• Ethics and Economics of AI
• +۳ مورد دیگر

Parinaz Naghizadeh is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California San Diego (UCSD), affiliated with the Design Lab. She holds a PhD from the University of Michigan and has prior roles at Ohio State University and postdoctoral positions at Purdue and Princeton. Her research focuses on network economics, game theory, AI ethics, optimization, and cybersecurity. She received the NSF CAREER Award (2022), Rising Stars in EECS (2017), and Barbour Scholarship (2014). Education: PhD in Electrical Engineering (University of Michigan), M.Sc. in Mathematics and Electrical Engineering (University of Michigan), B.Sc. in Electrical Engineering (Sharif University of Technology, Iran). Research Interests: She develops mathematical models to analyze decision-making in complex networks, with emphasis on AI ethics, multi-agent systems, and cybersecurity. Recent work explores biases in AI, strategic classification, and incentive mechanisms for security investments. Article Trends: Her recent publications (2023-2025) address strategic classification challenges, multiplex network equilibria, federated learning fairness, and robust control in cyber-physical systems. Themes include ethical AI, game-theoretic security design, and optimization under uncertainty. Awards: NSF CAREER Award (2022), Rising Stars in EECS (2017), Barbour Scholarship (2014) Advising & Grants: No student advisees listed, but active in securing research grants (e.g., NSF CAREER). Works with interdisciplinary teams in UCSD's Design Lab. Labs/Teams: Affiliated with UCSD's Design Lab, focusing on innovative engineering solutions for societal challenges.

Christian Jacobمشاهده پروفایل

استاد

• Algorithms gleaned from nature
• Biocomputing
• Bioinformatics
• +۶ مورد دیگر

Christian Jacob is a Professor in the Department of Computer Science within the Faculty of Science at the University of Calgary . He holds a B.S. in Computer Science and a Doctor of Engineering Science from Erlangen University . His research focuses on nature-inspired algorithms, biocomputing, and agent-based simulations applied to biological systems and education. Key initiatives include the LINDSAY Virtual Human Project , which uses immersive virtual reality to explore human anatomy and physiology. He contributes to the university's strategic priorities in Digital Worlds and Health and Life initiatives. His work integrates evolutionary algorithms, cellular automata, and swarm intelligence into creative and medical applications. Notable achievements include the ASTech Award (2015) from Alberta Science and Technology. His projects emphasize interactive education through tools like LeukemiaSIM , Eukaryo , and the Giant Walkthrough Gut . Jacob also explores visualization techniques, such as evoVision3D and LifeBrush , to enhance scientific understanding. His research bridges computational methods with real-world applications in healthcare, architecture, and game design. Collaborative efforts include developing agent-based models for immune systems, nervous responses, and crowd behavior. Jacob's work spans interdisciplinary fields, blending computer science with biology, engineering, and the arts.

Eric Rotenbergمشاهده پروفایل

استاد

• Computer Architecture
• High-Performance Computing
• Low-Power Design
• +۳ مورد دیگر

Dr. Eric Rotenberg is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University. His research focuses on high-performance, low-power, and reliable processor architectures, with contributions to 3D stacked circuits, heterogeneous multi-core systems, and adaptive microarchitecture design. Education: Ph.D. in Computer Sciences, University of Wisconsin-Madison (1999) M.S. in Computer Sciences, University of Wisconsin-Madison (1996) B.S. in Electrical Engineering, University of Wisconsin-Madison (1991) Research Interests: Computer Architecture and Systems 3D-Stacked Integrated Circuits Heterogeneous Multi-Core Processors Microarchitecture Optimization Energy Efficiency in Processors Key Awards: IEEE Fellow (2015) NSF CAREER Award (2001) NC State Outstanding Teacher Award (2004) 2015 Micro Test-of-Time Award (co-authored) Professional Contributions: Developed the AnyCore adaptive processor framework and the H3 3D-stacked heterogeneous multi-core processor. Explored post-silicon microarchitecture adaptation and slipstream processors for improved performance and fault tolerance.

• Industrial Ecology
• Material Flow Analysis
• Urban Metabolism
• +۵ مورد دیگر

Daniel Beat Müller serves as Professor at the Industrial Ecology Programme within the Department of Energy and Process Engineering at the Norwegian University of Science and Technology (NTNU), Trondheim. His office is located at Realfagbygget Gløshaugen (E4-120) with contact details daniel.mueller@ntnu.no and +4791897755. His research centers on analyzing human needs in relation to material/energy flows and environmental impacts, with two primary focus areas: (i) urban evolution and associated material flows for managing building/infrastructure stocks, and (ii) national/global metal cycles to identify supply constraint reduction strategies. His methodology integrates design, modeling, and decision-making through transdisciplinary stakeholder engagement. Müller teaches Material Flow Analysis and Systems Analysis of the Built Environment for Industrial Ecology and Civil Engineering Master's students. His research outputs demonstrate strong trends in circular cities, critical mineral management, and urban metabolism, with recent publications emphasizing building information modeling, electric vehicle battery systems, and phosphorus cycling. His work consistently addresses resource criticality within energy transition contexts. As (ad interim) chair of the International Society of Industrial Ecology’s MFA-ConAccount section, he contributes to methodological standardization. He previously served on the U.S. National Research Council’s Committee on Defense Stockpiles and remains active in Switzerland's National Research Programme 65 "New Urban Quality". Müller supervises numerous Master's and doctoral students, with thesis topics spanning lithium-ion battery recycling, building stock dynamics, and urban resource flows. His projects frequently involve industry collaboration for practical implementation of material stewardship strategies.

Kan Wuمشاهده پروفایل

دانشیار

• Data interpretation and forward modeling of distributed fiber optic strain sensing
• Hydraulic fracture modeling, monitoring, and optimization
• Subsurface monitoring of carbon storage and enhanced geothermal systems
• +۲ مورد دیگر

Kan Wu is an Associate Professor in the Harold Vance Department of Petroleum Engineering at Texas A&M University, part of the College of Engineering. He holds the Class of ’75 DVG Associate Professorship and is a Chancellor EDGES Fellow. His research focuses on advanced modeling techniques for subsurface systems, including data interpretation of fiber optic sensing, hydraulic fracture optimization, and carbon storage monitoring. He earned his Ph.D. from the University of Texas at Austin and degrees from China University of Petroleum (East). Education : Ph.D., Petroleum Engineering, University of Texas at Austin M.S., Petroleum Engineering, China University of Petroleum (East) B.S., Petroleum Engineering, China University of Petroleum (East) Research Highlights : Dr. Wu’s work bridges physics-based and data-driven approaches, addressing challenges in fracture propagation, subsurface monitoring, and multi-scale modeling. His team develops real-time data interpretation tools for distributed fiber optic measurements and investigates fracture interactions in unconventional reservoirs. Key Awards : 2024: Completions Optimization and Technology Award (SPE Gulf Coast) 2023: Best of Paper Award (URTEC) 2023: Karen E. Olson & Louis H. Turner Research Award Grants & Leadership : He leads the Advanced Geomechanics Fracture & Reservoir Application Consortium (AGFRAC), focusing on subsurface engineering systems. His Chevron Corporation Faculty Fellowship (2017–2022) supported collaborative industry research. Office: RICH 501Q.

Fatemeh Ganjiمشاهده پروفایل

استادیار

• Hardware Security
• Cryptography
• Machine Learning
• +۳ مورد دیگر

Fatemeh Ganji is an Assistant Professor in the Department of Electrical & Computer Engineering at Worcester Polytechnic Institute (WPI), with an affiliation to the Cybersecurity program. She holds a Ph.D. in Electrical Engineering from the Technical University of Berlin (2017), where she received the BIMoS Ph.D. Award and was nominated for the ACM Dissertation Award. Prior to WPI, she served as a Post Doctoral Associate at the University of Florida (2018–2020) and at Telecom Innovation Laboratories/Technical University of Berlin (2017–2020). Her research focuses on interdisciplinary approaches in hardware security, combining machine learning and cryptography to design and evaluate security-critical hardware systems. Key areas include physically unclonable functions (PUFs), side-channel analysis, and countermeasures against tampering and counterfeiting. Her work is funded by the European Union (Horizon 2020, FP7), German BMBF, NSF, and NIST. Ganji actively contributes to the academic community as a reviewer for IEEE and ACM journals and serves on technical program committees for CHES, FPL, DATE, and SPACE conferences. Her recent projects include developing AI-driven forensic analysis for PCB tamper detection, secure multiparty computation frameworks for chiplet systems, and open-source tools for implementation security testing. Her awards include the BIMoS Ph.D. Award 2018 and recognition from the Technical University of Berlin for her doctoral work on PUF learnability. She has also pioneered methods to detect recycled integrated circuits and enhance hardware trust through reverse engineering and machine learning.

Giovanna Tinettiمشاهده پروفایل

استاد

• Extrasolar Planets
• Space Science
• Molecular Spectroscopy
• +۳ مورد دیگر

Giovanna Tinetti is a Professor of Astrophysics and Vice Dean (Research) at King's College London's Faculty of Natural, Mathematical & Engineering Sciences. She leads the European Space Agency's Ariel mission, a space telescope surveying exoplanet atmospheres, set to launch in 2029. As co-founder of the London Centre for Space Exochemistry Data and Blue Skies Space Ltd, she pioneers satellite technology for scientific data collection. She holds a PhD in Theoretical Physics from the University of Turin, with prior affiliations at Caltech/JPL, the Institute of Astrophysics in Paris, and University College London (UCL), where she was a Royal Society University Research Fellow. Her research focuses on exoplanetary atmospheres, molecular spectroscopy, and advanced data science techniques. With over 300 publications, her 2019 paper on water vapor in K2-18b's atmosphere achieved the highest altmetric score in Physical Sciences that year. She has delivered over 350 international talks and lectures. Education: PhD in Theoretical Physics (University of Turin) Affiliations: King's College London, UCL (past), ESA's Ariel Mission, Blue Skies Space Ltd Research Interests: Exoplanet atmospheres, molecular spectroscopy, space science, data-driven analysis methodologies, and atmospheric modeling. Her work bridges observational astronomy with computational chemistry to interpret exoplanet compositions and climates. Awards: Royal Society University Research Fellow Highest Altmetric Score (2019 Physical Sciences) Grants & Projects: Principal Investigator for ESA's Ariel mission Co-leader of the Ariel Data Challenge 2025 Labs/Teams: London Centre for Space Exochemistry Data, Blue Skies Space Ltd technical team, and the international Ariel collaboration network.