Find Academic Supervisors

Bahram GharabaghiView profile

Professor

• Water Resources Engineering
• Environmental Modeling
• Climate Change Impact Assessment
• +4 more

Bahram Gharabaghi, PhD, P.Eng., is a Professor in the Department of Water Resources Engineering at the University of Guelph. His career spans over three decades, beginning with a postdoctoral fellowship at the University of Guelph in 2001, followed by roles as Assistant Professor (2003–2008), Associate Professor (2008–2016), and his current position since 2016. His research focuses on environmental modeling, climate change impacts, and sustainable water management. He holds degrees from Iran University of Science and Technology (BSc, 1989), Sharif University of Technology (MSc, 1992), and the University of Guelph (PhD, 1999). Research interests include hydrodynamic modeling of river flows, stormwater and aquatic habitat monitoring, and the development of tools for water quality protection. He integrates remote sensing, machine learning, and AI to address challenges like flood forecasting and nutrient management. Courses taught span finite element methods, water quality measurement, and urban water systems design at both undergraduate and graduate levels. His work emphasizes real-time data applications for resilient infrastructure, such as road de-icing optimization and flood susceptibility mapping. He has contributed to over 50 peer-reviewed articles, focusing on environmental data analysis and predictive modeling techniques. Collaborative projects include transboundary water management in the Kabul River Basin and climate resilience strategies in urban areas.

• Biosurveillance of communicable diseases
• Infectious disease informatics
• Genomic epidemiology
• +3 more

Professor Vitali Sintchenko is a public health microbiologist and informatician at The University of Sydney, affiliated with the School of Medical Sciences , Sydney Infectious Diseases Institute , and Westmead Institute for Medical Research . As a Clinical Microbiologist with NSW Health Pathology , he develops genomic epidemiology tools for communicable disease surveillance, focusing on tuberculosis, pertussis, and Salmonella. His work integrates pathogen genomics with clinical data to improve outbreak detection and control. MBBS, PhD, FRCPA, FACHI Member, Commonwealth Public Health Laboratory Network and Communicable Diseases Genomics Network Leadership in Mycobacterium Reference Laboratory and Enteric & Pneumococcal Reference Laboratories Research spans: Genomic surveillance of epidemic bacterial pathogens Development of decision support systems for outbreak response Integration of syndromic surveillance with molecular epidemiology Microbiome analysis in respiratory infections Antimicrobial resistance genomics Machine learning applications in pathogen tracking Recent publications demonstrate leadership in Salmonella and Tuberculosis genomic epidemiology, with Nature , Lancet , and PLoS contributions. His team's work on SARS-CoV-2 variants and Shigella sonnei evolution highlights real-time surveillance applications. Scientific contributions include: World's first book on Infectious Disease Informatics (Springer, 2010) Development of MGTdb genomic database Creation of DODGE outbreak detection algorithm Advancements in CRISPR-based diagnostics Key role in Australian Pathogen Genomics Program As an academic leader, he supervises students working on: Ellen Donnan : TB control strategies in NSW David Pham : Metagenomic pathogen detection Sarah Smith : Pneumococcal disease genomics Michael Wehrhahn : Dientamoeba fragilis epidemiology His laboratory at Westmead Hospital develops: Genomic outbreak detection systems AI-driven surveillance tools Pathogen transmission mapping Drug resistance prediction models

• Computational Fluid Dynamics (CFD)
• Turbulent Combustion
• High Performance Computing (HPC)
• +5 more

Professor Karl Jenkins is a Professor of Computational Engineering at Cranfield University , where he leads the Centre for Computational Engineering Sciences . His expertise spans Computational Fluid Dynamics (CFD) , Turbulent Combustion , High Performance Computing (HPC) , and Multiphase Flow Modeling . Jenkins has published over 100 papers and received the Gaydon Prize for his contributions to combustion research. His research focuses on reacting flows , turbulence modeling , and compressible multiphase flows , with recent work addressing green hydrogen production , aircraft component segmentation , and virtual reality applications in aviation safety. He has developed high-order numerical methods for shock wave analysis and interface-capturing in unstructured mesh environments . A former Sir Arthur Marshall Research Fellow at Cambridge University, Jenkins combines academic rigor with industrial collaboration , having worked with companies like Rolls-Royce plc , Airbus SE , and Siemens AG . He mentors research students including Yiren Tong and actively contributes to LES/DNS computational frameworks for aerospace and environmental applications .

Vasileios PaschalidisView profile

Assistant Professor

• Gravitational Physics
• Theoretical Astrophysics
• Numerical Relativity
• +5 more

Vasileios Paschalidis is an Assistant Professor in the Department of Astronomy and Department of Physics at The University of Arizona . He serves as one of the 5 core faculty members in the Theoretical Astrophysics Program and leads research at the intersection of gravitational physics and theoretical astrophysics , with a focus on compact object binaries and multimessenger astronomy . Department of Astronomy, The University of Arizona Department of Physics, The University of Arizona Theoretical Astrophysics Program, The University of Arizona His research investigates strong-field gravitation through simulations of black hole-black hole , neutron star-neutron star , black hole-neutron star , and white dwarf-neutron star binaries. Key areas include gravitational wave modeling , electromagnetic signatures , neutrino emissions , and equation of state constraints for nuclear matter. Recent work explores eccentric mergers in globular clusters , spin-flip mechanisms in X-shaped radio galaxies, and planet formation around pulsars . The 15 most recent publications highlight his expertise in numerical relativity , general relativistic MHD , and radiation transport . These works examine charged black hole mergers , ultrarelativistic particle dynamics , accretion disk variability , and novel periodicities in jet power from binary black hole systems. His simulations underpin gravitational wave detection by LIGO/Virgo/KAGRA and future missions like eLISA .

Ruth BrenkView profile

Professor

• Structure-Based Drug Design
• Computational Chemistry
• Drug Discovery
• +9 more

Ruth Brenk is a Professor at the Department of Biomedicine, Faculty of Medicine, University of Bergen (UiB), and an associated member of the Computational Biology Unit. She leads the Brenk Lab, which focuses on advancing structure-based drug design through the integration of computational and experimental approaches. Research Interests: Her work centers on improving methods for structure-based drug design, particularly targeting enzymes with biological significance. Key areas include RNA-ligand interactions (especially riboswitches as antibiotic targets), protein-ligand interactions in conserved binding sites, and druggability prediction for both protein and RNA targets. Her lab emphasizes the synergy between theoretical modeling and experimental validation. Recent Publications Trend: Analysis of her latest publications (2021–2025) reveals a strong focus on antibiotic discovery, particularly against Pseudomonas aeruginosa via FabF/FabB inhibition using fragment screening and X-ray crystallography. There is a growing emphasis on RNA as a drug target, including tools like DrugPred_RNA and participation in the TargetRNA EU network. Her work also explores metalloenzyme inhibitors and the quality of screening libraries (e.g., EU-OPENSCREEN). Scientific Awards: While no personal awards are listed, her research group has achieved recognition, including a prize for the second-best oral presentation at the 34th Molecular Modeling Workshop 2020 awarded to group member Illimar Rekand. Advising and Grants: Ruth Brenk actively mentors multiple PhD candidates and postdoctoral researchers. She leads significant funded projects, including the EU Horizon-funded TargetRNA MSCA network, a Centre for Digital Life Norway project on responsible innovation in drug discovery for antibiotics and COPD, and work on Ribotacs funded through EU-OPENSCREEN. These projects highlight her leadership in interdisciplinary, collaborative research. Labs and Teams: The Brenk Lab is part of the 'Structural Biology and Drug Discovery' research unit at UiB. The lab collaborates closely with researchers from the Department of Chemistry, including Nathalie Reuter and Bengt-Erik Haug, and participates in international consortia such as EU-OPENSCREEN and Digital Life Norway.

Yongjing MaoView profile

Academic

• Remote Sensing
• Coastal Geomorphology
• Shoreline Monitoring
• +4 more

Dr. Yongjing Mao serves as an Associate Lecturer at the Water Research Laboratory within the School of Civil and Environmental Engineering at the University of New South Wales (UNSW). His academic journey includes a PhD in Remote Sensing and Coastal Geomorphology from The University of Queensland (2022), an MSc in Coastal and Marine Engineering and Management from Delft University of Technology (2018), and a BSc in Harbour, Coastal and Offshore Engineering from Hohai University (2016). Mao specializes in integrating coastal morphology with advanced remote sensing and machine learning techniques to address critical shoreline monitoring challenges. His research focuses on developing SAR-Optical satellite image fusion methods for cloud-contaminated imagery, implementing data assimilation for shoreline modeling, and creating deep learning spatio-temporal models (such as ConvLSTM and PredRNN) for predicting future shoreline changes at regional to global scales. His work significantly contributes to understanding coastal responses to sea-level rise and improving satellite-derived shoreline mapping accuracy. Mao's publication portfolio demonstrates consistent high-impact contributions to coastal remote sensing, with recent work benchmarking shoreline prediction models and developing innovative fusion techniques. His research bridges theoretical advancements with practical applications for coastal management, particularly in vulnerable regions like the Great Barrier Reef catchments. UQ Dean's Award for Outstanding HDR Theses (2022) Erasmus Mundus Partner Country Scholarship (€49,000) (2016) Mao actively contributes to coastal research through the Water Research Laboratory at UNSW, where he applies cutting-edge computational methods to real-world coastal challenges. His teaching responsibilities include CVEN9620 Rivers, Estuaries and Wetlands and ENGG2500 Fluid Mechanics for Engineers, where he integrates his research expertise into curriculum development. Current projects focus on spatio-temporal deep learning applications for shoreline change prediction and satellite-derived environmental monitoring systems.

Salvatore BarbaView profile

Professor

• Architectural Drawing
• Conservation and Restoration
• Remote Sensing
• +8 more

Salvatore Barba is a Full Professor in the Department of Civil Engineering at the University of Salerno, Italy, where he conducts research and teaching in architectural surveying, cultural heritage documentation, and digital reconstruction. He also serves as a Contract Professor at the Faculty of Architecture, University of Basilicata, Matera. His work bridges engineering, architecture, and heritage science, focusing on advanced digital methodologies. Research interests: His expertise lies in photogrammetry , remote sensing , 3D laser scanning , GIS-BIM integration , and augmented reality for cultural heritage. He investigates degradation diagnostics, low-cost survey tools, and micro-photogrammetry, emphasizing interdisciplinary and methodologically rigorous approaches to digital documentation. Publications and technology: His recent articles highlight the application of UAVs, wearable SLAM scanners, and handheld microscopes in heritage contexts. He validates instruments like the Shining 3D EinScan-Pro and develops protocols for high-accuracy documentation. His work consistently underscores the importance of expert interpretation over purely automated processes in surveying. Scientific Awards: No specific awards mentioned in the provided text. Advising and grants: While specific students and grants are not listed, he collaborates on international projects, participates in technology transfer agreements (e.g., with Dino-Lite), and contributes to methodological advancements in heritage science. His role as a faculty member implies advising graduate students in civil engineering and architectural documentation. Labs and teams: He is involved in research activities related to digital heritage documentation, likely within laboratory facilities at the University of Salerno. His collaborative work with researchers across Europe and on diverse heritage sites indicates active participation in interdisciplinary research teams focused on advanced surveying and conservation technologies.

Thanos E GoltsosView profile

Senior Lecturer

• Operations Management
• Forecasting
• Inventory Control
• +11 more

Dr Thanos E Goltsos is a Senior Lecturer (Associate Professor) in Management Science at Cardiff Business School, Cardiff University. His academic work bridges operations management, forecasting, and inventory control, with a strong emphasis on reverse logistics and the circular economy. He holds a PhD in Operational Research from Athens University of Economics and Business, an MSc in Business Analysis and Management from Loughborough University, and an MEng in Mechanical Engineering from Aristotle University of Thessaloniki. Education: PhD in Operational Research, Athens University of Economics and Business MSc in Business Analysis and Management (with honours), Loughborough University MEng in Mechanical Engineering and Industrial Management, Aristotle University of Thessaloniki High School (Pierce College, American College of Greece) His research focuses on statistical and judgmental forecasting, inventory control, intermittent demand, and decision support systems, particularly in remanufacturing and circular economy contexts. He applies simulation and optimization techniques to improve supply chain performance under uncertainty. The recent publications highlight a strong trend toward integrating forecasting and inventory systems, analyzing temporal aggregation methods, and modeling complex dynamics in reverse supply chains. His 2024 work extends into healthcare with machine learning-based forecasting of pandemic admissions, demonstrating cross-domain applicability of his methodologies. Scientific Awards: Cardiff University Innovation and Impact Award (2015) – ASTUTE Project Cardiff University Innovation and Impact Award (2019) – Qioptiq KTP Dr Goltsos has secured and contributed to externally funded projects, including a Knowledge Transfer Partnership with QIOPTIQ Ltd co-funded by EPSRC and Innovate UK. He has advised on and developed bespoke forecasting and inventory solutions for industrial applications. He is also actively involved in supervising PhD research in forecasting, inventory management, and circular economy strategies. He is affiliated with the Logistics and Operations Management Section at Cardiff Business School and has collaborated extensively with industry partners, contributing to impactful, applied research. His work is supported by professional memberships in the International Institute of Forecasters and the Technical Chamber of Greece.

Alexander HermansView profile

Researcher

• Computer Vision
• Deep Learning
• Robotics
• +7 more

Dr. Alexander Hermans is a researcher at the Institute for Vision and Graphics, Faculty of Electrical Engineering and Information Technology, RWTH Aachen University. He is actively engaged in cutting-edge research at the intersection of computer vision, deep learning, and robotics, with a focus on 3D perception, segmentation, and anomaly detection. Research Interests: His primary research areas include Computer Vision , Deep Learning , Robotics , 3D Scene Understanding , Semantic and Instance Segmentation , and LiDAR-based Perception . His work often addresses the practical challenges of deploying vision systems in real-world robotic applications. Publication Trends: Dr. Hermans' recent publications demonstrate a strong trend towards leveraging transformer architectures for 3D and video understanding, developing robust methods for anomaly detection, and creating unified frameworks for diverse vision tasks. His research on diffusion models and self-supervised learning also highlights his engagement with the latest advancements in AI. Scientific Awards: Best Vision Paper Award at ICRA 2014 Advising and Grants: While no specific students or grants are listed, his role as a senior author on numerous publications and his leadership in creating benchmarks (like OoDIS) and software indicate a significant mentoring and project leadership role. His work is often supported by large-scale datasets and collaborations, suggesting involvement in substantial research grants and projects (e.g., the STRANDS project). Labs and Teams: He is a core member of the research group led by Prof. Bastian Leibe at RWTH Aachen, a group renowned for its work in computer vision and robotics. His research is closely tied to this lab, which focuses on developing robust, real-world applicable vision systems for autonomous agents.

• Numerical Analysis
• Approximation Theory
• Multiresolution Analysis
• +7 more

Francesc Arandiga Llau is a Professor in the Department of Mathematics at the Faculty of Mathematics, Universitat de València, Spain. He is affiliated with the ANIMS (Numerical Analysis, Images, Multiresolution and Simulation) research group, where he conducts research in applied mathematics with a focus on numerical methods and their applications. Education: PhD from Universitat de València (1992), thesis on operator approximation and spectral radius continuity, supervised by Dr. Vicent Caselles Costa. His research interests center on Numerical Analysis , Approximation Theory , and Multiresolution Methods , with significant contributions to WENO schemes , nonlinear interpolation , and image and signal compression . His work often bridges theoretical developments with practical implementations in computational mathematics and engineering. He has made notable advances in the stability, accuracy, and adaptability of reconstruction techniques for piecewise smooth and discontinuous functions. The analysis of his recent publications reveals a consistent focus on high-order numerical methods, particularly in the context of image processing and data compression . His work leverages multiresolution analysis , radial basis functions , and adaptive interpolation to improve accuracy and efficiency. Themes across his articles include monotonicity preservation, error control, and the design of nonlinear schemes that avoid spurious oscillations near discontinuities. There are no scientific awards explicitly mentioned in the provided text. Francesc Arandiga has extensive collaborative research, particularly with scholars such as Rosa Donat, Dionisio F. Yáñez, Pep Mulet, and Antonio Baeza. His work has been supported through various research projects, though specific grants are not detailed in the text. He has advised students, including those who have completed theses under his supervision, although a full list is not provided. He is a key member of the ANIMS research group, which focuses on Numerical Analysis, Images, Multiresolution, and Simulation. This team works on developing and analyzing advanced computational methods for scientific and engineering applications, particularly in the areas of data representation, image processing, and numerical solutions to differential equations.

• Visual Analytics
• Big Data
• Software Engineering
• +7 more

Adriano Lopes is an Invited Assistant Professor at Iscte - Instituto Universitário de Lisboa, affiliated with the Department of Information Science and Technology within the School of Technologies and Architecture. He is also an Associate Researcher at ISTAR-IUL, the university's research center in Information Sciences, Technologies, and Architecture. His academic work spans software systems engineering, visual analytics, and big data technologies. PhD in Computer Science – University of Leeds, UK (1999) Master’s in Computer Science – University of Coimbra (1993) Bachelor’s in Electrical Engineering (Computer Science branch) – University of Coimbra (1986) Postgraduate studies in Financial Analysis – Technical University of Lisbon, ISEG (2013) Adriano Lopes’ research focuses on visual analytics, big data, software engineering, and computer graphics, with a strong emphasis on data visualization and its applications in domains such as smart tourism and urban planning. His work integrates human-centric design with advanced computational techniques to support decision-making in complex systems. His recent publications highlight a shift toward applied research in digital transformation for sustainable tourism, particularly through spatiotemporal visualization of tourism crowding and carrying capacity modeling. These works demonstrate a consistent trend in leveraging data-driven platforms for real-world societal challenges, especially in the context of post-pandemic recovery and sustainable development. Adriano has supervised numerous Master’s students at both Iscte and Universidade Nova de Lisboa, with thesis topics covering sentiment analysis, GPU-based rendering, anomaly detection, and tourism flow forecasting. He has participated in EU-funded research projects such as RESETTING, which aims to relaunch sustainable tourism models through digitalization. His academic service includes organizing roles in major visualization conferences like EUROVIS 2006 and the Eurographics UK Conference. He has been actively involved in research labs and teams including ISTAR-IUL and CITI (research center at FCT/UNL), contributing to interdisciplinary projects that bridge computer science with architecture, telecommunications, and tourism. His work reflects a strong commitment to collaborative, applied research with societal impact.

Lei ChenView profile

Assistant Professor

• Computational Mechanics
• Fracture Mechanics
• Phase-Field Modeling
• +12 more

Lei Chen is an Assistant Professor in the Department of Mechanical Engineering at Mississippi State University, College of Engineering. His research focuses on advanced computational modeling techniques applied to materials and mechanical systems. Education: Ph.D., Mechanical Engineering, National University of Singapore (NUS), Singapore, 2012 M.E., Materials Processing, Huazhong University of Science & Technology (HUST), China, 2007 B.E., Materials Processing, Huazhong University of Science & Technology (HUST), China, 2005 His research interests lie at the intersection of computational mechanics and materials science, particularly in fracture modeling, multi-scale simulations, and phase-field methods. He develops and applies advanced numerical techniques to model phenomena such as dendrite growth in batteries, microstructure evolution in alloys, and mechanical behavior of biological and composite materials. His work integrates finite element methods, smoothed finite element methods (S-FEM), and phase-field models with crystal plasticity and multi-physics coupling. The recent publications highlight a strong trend in computational modeling of energy materials and structural integrity. Key themes include phase-field simulations of lithium dendrite formation, multi-scale modeling of polycrystal grain growth, and advanced fracture mechanics using edge-based strain smoothing techniques. These works span disciplines from battery technology to biomaterials, demonstrating a versatile and impactful research program. Scientific Awards and Honors: Travel Fellowship, Enabling Methods for Materials Innovation, University of Florida, 2015 Travel Grant, USNCCM13, 2015 Z. Hsu Scientific Paper Award, 2015 Vice-Chancellor's Research Fellowship, QUT, AUS Chinese Excellent Self-financed Student Abroad Scholarship, 2012 President Graduate Fellowship (top 5%), NUS, 2009–2011 Research Graduate Scholarship, NUS, 2007–2011 Outstanding Graduate Student Award, HUST, 2006 Excellent Bachelor Graduate Award, HUST, 2005 First-Class Scholarship, HUST, 2002–2004 Dr. Chen has advised research students and collaborated extensively with leading researchers such as Long-Qing Chen. His work is supported by academic grants and institutional fellowships. He has contributed to significant advancements in computational methods for fracture and microstructure modeling. He has also published in high-impact journals and presented at major international conferences. He is actively involved in research related to energy storage systems, additive manufacturing, and biological materials, often leveraging high-performance computing and image-driven simulations. His lab focuses on developing robust and accurate numerical frameworks for predicting material behavior across scales.

Tan Chee KeongView profile

Senior Lecturer

• Software-defined networks
• Beyond 5G networks
• Indoor positioning systems
• +8 more

Dr. Tan Chee Keong is a Senior Lecturer at the School of Information Technology, Monash University, Malaysia. He holds a PhD in Information and Communication Technology from Multimedia University (2014). His expertise lies in network system design, with a focus on software-defined networks, beyond 5G networks, indoor positioning systems, game theory, machine learning, and data science. He has secured over RM 800K in research grants as a principal or co-investigator and contributed to over 10 international journal papers. Notably, he received the Best Teaching Award from Multimedia University in 2016. Research activities include projects funded by government and industry, such as AI-Driven Energy Management for Smart Cities and IoT-based energy monitoring systems. His work addresses challenges in network efficiency, indoor positioning, and smart city technologies. Dr. Tan has collaborated on projects involving UAV swarming intelligence, reinforcement learning, and autonomous systems. Education: B.Eng (Hons) in Electronics (Telecommunication), Multimedia University, 2006 M.Eng.Sc (Information and Communication Technology), Multimedia University, 2009 Ph.D (Information and Communication Technology), Multimedia University, 2014 Research Interests: Optimizing wireless networks and protocols Indoor positioning and trajectory tracking Game theory applications in network resource management Machine learning for signal processing and localization Recent publications (2023–2025) emphasize AI-driven solutions for energy efficiency, path planning in disaster environments, and blockchain-based logistics optimization. These reflect a trend towards integrating AI/ML with traditional networking challenges. Awards: Best Teaching Award, Multimedia University (2016) Grants & Projects: AI-Enabled Energy Management (RM 800K+) IoT-Based Energy Monitoring (2022–2025) Smart City IoT Applications He plays a key role in industry-academia collaborations, particularly in Malaysia’s tech sector. His labs focus on developing practical solutions for smart city infrastructure and next-generation communication systems.

Aristidis K. NikoloulopoulosView profile

Associate Professor

• Copula Modeling
• Biostatistics
• Econometrics
• +3 more

Aristidis K. Nikoloulopoulos is an Associate Professor in Statistics at the School of Mathematics, University of East Anglia (since December 2023). Previously, he was an Associate Professor in the School of Computing Sciences (2013-2023) and a Lecturer (equivalent to Assistant Professor) there (2009-2013). He completed his PhD in Statistics at Athens University of Economics and Business in 2007, followed by postdoctoral work at Laval University and the University of British Columbia. He has also held positions at Athens University of Economics and Business and Cyprus University of Technology. Current affiliation: School of Mathematics, University of East Anglia Past affiliations: School of Computing Sciences (UEA), Department of Statistics (AUEB), Medical School (Cyprus UoT) His research focuses on copula modeling, with applications in biostatistics, econometrics, and finance. Key contributions include copula-GARCH models for financial returns, factor copula models for survey data, and copula-mixed models for meta-analysis of diagnostic tests. He has developed CRAN packages like CopulaREMADA and FactorCopula, and his work addresses dependence structures in multivariate discrete data. Recent publications highlight vine copula mixed models for diagnostic accuracy studies, joint meta-analysis of correlated diagnostic tests, and factor tree copula models for psychometric applications. These works emphasize dependence modeling, computational efficiency, and practical implementations in health and social sciences. He actively supervises PhD students and has delivered numerous invited seminars and short courses on copula modeling, including engagements at the University of Geneva and the Vine Copula Workshop. His teaching portfolio includes statistical methods, linear regression, time series, and computational statistics at undergraduate and postgraduate levels.

Edward GebaraView profile

Associate Professor

• Radiation-tolerant electronics
• Extreme temperature integrated circuits
• Self-healing integrated circuits
• +4 more

Dr. Edward Gebara is an Associate Professor of Electrical and Computer Engineering at Michigan State University's College of Engineering. His expertise spans radiation-tolerant electronics, extreme-temperature integrated circuits, and self-healing mixed-signal techniques. He holds B.S., M.S., and Ph.D. degrees in Electrical and Computer Engineering from Georgia Institute of Technology (1996–2003). Research Interests: Radiation-tolerant electronics for aerospace Extreme temperature environment circuits Self-healing and reconfigurable ICs Wireless cancellation and signal integrity solutions Professional Activities: Former Director of Engineering at Electromagnetic Sensor Technologies Inc. (2011–2023) Adjunct Assistant Professor at Georgia Tech (2010–2011) IEEE MTT-S Symposium roles: Vice Chair (2010–2012), Technical Committee Chair (2018–2021), and Workshop Chair (2021) Awards: Best Paper Award at IEEE International Microwave Symposia (2000) Grants & Advising: Directed Ph.D. students in mixed-signal and microwave systems Led industry-academia collaborations in GaAs/GaN technologies Labs & Teams: Mixed-Signal Research Group at Georgia Tech (2003–2011) Electromagnetic Sensor Technologies R&D team