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Hyungwoong AhnView profile

Senior Lecturer

• Adsorption Processes
• CO2 Capture
• Carbon Capture and Storage
• +5 more

Dr. Hyungwoong Ahn is a Senior Lecturer in Chemical Engineering at the University of Edinburgh and an Adjunct Professor at Yonsei University. His expertise lies in adsorption process engineering, particularly for CO2 capture and gas separation. He leads the Carbon Capture Group and coordinates exchange programs for chemical engineering students. Dr. Ahn holds a BSc, MSc, and PhD in Chemical Engineering from Yonsei University. Roles: Senior Lecturer (Edinburgh) & Adjunct Professor (Yonsei) Research Focus: Pressure Swing Adsorption (PSA), CO2 capture technologies, hydrogen purification, and industrial decarbonisation Key Projects: PSA-SPUR technology development, ship-based carbon capture, and collaboration with HD Korea Shipbuilding Awards: KOFST Brain Pool Fellow, IChemE Global Awards finalist, and Honeywell UniSim Design Challenge winner His research integrates equilibrium theory analysis, numerical simulation, and experimental validation to advance carbon capture systems. He has authored over 50 publications (H-index 32) and secured funding from EPSRC, BEIS, KETEP, and others.

Simon MooreView profile

Professor

• Computer Architecture
• Computer Security
• Hardware Security
• +3 more

Simon Moore is a Professor of Computer Engineering at the University of Cambridge's Department of Computer Science and Technology. He leads the Computer Architecture research group, focusing on secure processors and subsystems, particularly the CHERI project. His work emphasizes formal verification, hardware-software co-design, and scalable security solutions. He is a Fellow and Director of Studies at Trinity Hall, overseeing undergraduate admissions and mentoring in Computer Science. Research Interests: Moore's primary focus is the CHERI secure processor architecture, integrating RISC-V cores with formal verification. His work spans secure hardware design, memory safety, and embedded systems. Notable contributions include the CHERI-RISC-V microarchitecture, CheriABI, and formal verification frameworks. Key Projects: CHERI, CheriBSD, Morello (ARM collaboration) Recent Achievements: Test of Time Award (IEEE Security & Privacy 2025), finalist for Bhattacharyya Award (2022) Grants: Innovate UK Digital Security by Design, DARPA Mission Oriented Resilient Clouds Publications: Over 200 papers on secure architectures, including influential work on CHERI's capability model, formal verification, and hardware security. Recent focus areas include temporal memory safety, embedded system security, and GPU-based capability systems. Labs/Teams: Directs the Computer Architecture Group and collaborates with industry partners like ARM and Microsoft on CHERI implementations.

Yue GaoView profile

Professor

• Wireless Communications
• Smart Antennas
• Signal Processing
• +4 more

Yue Gao is a Professor of Wireless Communications at the Institute for Communication Systems, University of Surrey. He holds a PhD from Queen Mary University of London (2007) and previously served as a lecturer, senior lecturer, and reader at QMUL. His research focuses on smart antennas, signal processing, spectrum sharing, millimeter-wave systems, and IoT in mobile/satellite communications. He has authored over 180 papers, two patents, a book, and five book chapters. Current roles include EPSRC Fellow (2018–2023) and editorial roles for IEEE Transactions on Cognitive Communications and Networking, Vehicular Technology, and Internet of Things Journal. Notable awards include the EU Horizon Prize (2016). His work spans interdisciplinary projects like GBSense (GHz Bandwidth Sensing) and contributes to 6G research. Collaborations include leadership roles at IEEE conferences and global spectrum sensing challenges. Research interests emphasize antenna design (e.g., 3D printed, Ka-band), sub-Nyquist sampling, and machine learning for spectrum reconstruction. Affiliated with Surrey’s antenna measurement facilities (NPL) for advanced testing (400 MHz to 110 GHz, THz spectroscopy). Active in mentoring PhD students in antennas/wireless communications and oversees projects like the GBSense Challenge, advancing sensor-driven spectrum management.

Bonnie BuchananView profile

Professor

• FinTech
• AI in Financial Services
• Securitization
• +5 more

Professor Bonnie Buchanan is the Director of the Sustainable and Explainable FinTech (SAEF) Center and a Professor of Finance at the University of Surrey's Surrey Business School (SBS). She previously served as Head of the Department of Finance and Accounting (2019–2023). Her affiliations include fellowships at the University's Center for Sustainability, the Center for AI, and the Centre for Digital Economy (CoDE). Professor Buchanan holds a BSc (Hons), MAppSc, and PhD. Her research focuses on FinTech, AI in financial services, securitization, CSR, and synthetic/alternative data. Notable projects include work on biodiversity in FinTech and regulatory frameworks for financial technologies. Recent grants include a 2020–2022 grant from the Liikesivistysrahasto in Finland (€200,000). She actively supervises PhD students in FinTech, AI, securitization, CSR, and law-related finance topics. Teaching roles include courses on FinTech (MANM493), Banking and Money (MAN3124), and historical financial crises. Her research aligns with UN Sustainable Development Goals, emphasizing environmental and social impacts. Key publications explore sustainability in private investing, creditor governance, and AI's role in UK financial services. Current initiatives include promoting biodiversity-friendly FinTech solutions and advising the Bank of England-FCA consortium (2025).

Sandeep KumarView profile

Associate Professor

• Computer Security
• Computer Networking
• Operating Systems
• +3 more

Sandeep Kumar is an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University, College Station. He holds a PhD in Computer Science from Purdue University (1995) and a B.Tech in Electrical Engineering from the Indian Institute of Technology, New Delhi (1985). His research focuses on computer security, networking, and system-level programming. Prior to academia, he worked in industry roles including at VMware in Palo Alto, CA. He currently teaches courses such as CSCE 313 (Introduction to Computer Systems) and CSCE 222 (Discrete Mathematics), emphasizing system software, networking, and cybersecurity. His teaching philosophy incorporates modern tools like GCP and Docker for practical learning. He advises students on technical projects but notes his non-tenure track role limits formal research supervision. Professional interests include curriculum design, educational technology, and bridging industry-academia gaps in cybersecurity. Education: Ph.D., Computer Science, Purdue University, 1995 M.S., Computer Science, University of Tennessee, 1987 B.Tech, Electrical Engineering, IIT Delhi, 1985 Research Interests: Computer Security, Networking, Operating Systems Teaching: CSCE 313 (Computer Systems), CSCE 222 (Discrete Math), CSCE 111 (Java Programming) Industry Experience: VMware (Networking & Security), Former Googler Awards: Hagler Fellow (2023), Google GCP Educational Grants Dr. Kumar’s work emphasizes practical system-level programming and security, with contributions to intrusion detection systems and secure enterprise networks. His courses integrate modern tools like RustRover and Docker, reflecting industry standards. He actively engages with educational technology, including LaTeX-based lecture materials and Gradescope integration.

David B. JohnsonView profile

Professor

• Network Protocols
• Distributed Systems
• Operating Systems
• +7 more

David B. Johnson is a tenured full Professor of Computer Science and Electrical and Computer Engineering at Rice University. He leads the Monarch research group, focusing on mobile networking architectures. Previously, he served as an Associate Professor at Carnegie Mellon University (1992–2000). His research emphasizes network protocols, distributed systems, and operating systems, with notable contributions to Mobile IP standards and wireless ad hoc networks. He holds a PhD, MS, and BA from Rice University (1990, 1985, 1982). Education: PhD in Computer Science, Rice University (1990) MS in Computer Science, Rice University (1985) BA in Computer Science and Mathematical Sciences, Rice University (1982) Research Interests: Johnson's work centers on adaptive wireless/mobile networking protocols, including Mobile IP, multihop wireless systems, and security protocols. His Dynamic Source Routing (DSR) protocol for ad hoc networks is an IETF standard. He has been deeply involved in IETF standards development for over 15 years, particularly in Mobile IP and MANET working groups. Awards & Leadership: ACM SIGMOBILE Chair (2005–2009), Treasurer (1997–2005) Conference leadership roles: General Chair for IEEE MASS 2012, COMSNETS 2011, and others Editorial roles: IEEE Pervasive Computing (Founding Board), Ad Hoc Networks, and more Labs & Teams: Monarch Group at Rice University, previously at Carnegie Mellon, develops next-generation mobile networking architectures.

Somayeh AllahyariView profile

Assistant Professor

• Operations Research
• Optimization
• Logistics
• +6 more

Dr. Somayeh Allahyari is an Assistant Professor in Operations and Supply Chain Management at the Birmingham Business School, University of Birmingham. She holds a PhD in Industrial Engineering (2021) and has academic qualifications including an MSc (2013), BSc (2011), and a Diploma in Mathematics and Physics (2006). Her research focuses on Operations Research methodologies applied to Logistics, Network Design, and Supply Chain Management. Key interests include Optimization, Heuristics, Decision Support Systems, and Business Analytics. She has led industry projects such as the Drone Medical Logistics project for the Solent Future Transport Zone Programme, exploring multi-modal logistics solutions. Teaching responsibilities include modules on Supply Chain Management (UG), Operations Management (MSc), and Business Analytics (MSc Singapore). She actively supervises PhD candidates in areas like Logistics, Blockchain Technology, and Digital Transformation. Her publications appear in top journals like Transportation Research Part E and European Journal of Operational Research , with conference contributions at INFORMS Transportation Science. She serves as a peer reviewer for major journals and conferences in her field.

• Logistics Management
• Production Engineering
• Industrial Safety
• +3 more

Dr. Lilianna Wojtynek is a Lecturer at the Department of Logistics, Faculty of Production Engineering and Logistics, Opole University of Technology. Her academic career focuses on logistics, production engineering, and industrial safety. Current Position: Lecturer in Logistics Department: Logistics School: Faculty of Production Engineering and Logistics University: Opole University of Technology Research interests span logistics systems, quality management, and transportation safety. Key themes include: Lean methodologies (5S, supply chain optimization) Industry 4.0 applications in logistics Risk analysis in transportation and logistics Production process planning and decision modeling Articles trends emphasize industrial safety, logistics efficiency, and technology integration in transportation. Notable subfields include dynamic forklift testing, BRT systems, and hazardous material storage.

Amir ShmuelView profile

Professor

• Neuroimaging
• fMRI
• Visual Perception
• +5 more

Amir Shmuel is a Professor at McGill University's Faculty of Medicine, holding appointments in the Department of Neurology and Neurosurgery, Department of Biomedical Engineering, and Department of Physiology. He serves as Director of the Brain Imaging Signals Lab and Core Faculty at the McConnell Brain Imaging Centre of the Montreal Neurological Institute. His leadership includes chairing the 2018 International Society for Brain Connectivity conference and securing an $18.7M Canada Foundation for Innovation grant for Quebec's first large-bore 7 Tesla MRI scanner. Dr. Shmuel's research focuses on understanding neuronal mechanisms underlying functional brain imaging signals and visual information processing. His integrative approach combines fMRI, optical imaging, multi-channel neurophysiological recordings, and optogenetics across multiple spatial and temporal scales. Current research priorities include resting-state functional connectivity mechanisms, cortical lamina-resolved neurophysiology, and computational modeling of brain signals. His lab emphasizes parallel model development with experimental data acquisition. Recent publications demonstrate strong trends in multimodal neuroimaging integration, with emphasis on high-resolution fMRI techniques (especially 7T applications), resting-state connectivity analysis across species, and computational modeling of neurovascular coupling. Key subfields include laminar-specific activity mapping, artifact detection in medical imaging using deep learning, and cross-species functional connectivity frameworks. Dr. Shmuel's research is currently funded by the Canadian Institutes of Health Research (CIHR), Natural Sciences and Engineering Research Council of Canada (NSERC), and the US Department of Defense. His lab maintains active collaborations through initiatives like the International Society for Brain Connectivity and the PRIME-DE database consortium. Operating within the Brain Imaging Signals Lab at the Montreal Neurological Institute, Shmuel's team develops and applies advanced multimodal techniques including simultaneous fMRI-electrophysiology, voltage-sensitive dye imaging, and computational modeling frameworks. The lab maintains strong ties with the McConnell Brain Imaging Centre and participates in major open-science initiatives including the Tanenbaum Open Science Institute.

Daehyeok KimView profile

Assistant Professor

• Computer Networks
• Operating Systems
• Distributed Systems
• +6 more

Daehyeok Kim is an Assistant Professor in the Department of Computer Science at The University of Texas at Austin, where he co-leads the UT Networked Systems Research Group and participates in the Wireless Networking and Communications Group and 6G@UT. He serves as co-PI for the LDOS NSF Expeditions in Computing project, a major initiative rethinking operating systems through AI. His educational background includes a Ph.D. in Computer Science from Carnegie Mellon University under advisors Vyas Sekar and Srinivasan Seshan, where his dissertation introduced abstractions for elastic in-network computing. He also earned B.S. and M.S. degrees in Computer Science and Engineering from POSTECH, South Korea, followed by research scientist work at KAIST prior to his Ph.D. Kim's research centers on hardware-software co-design for cloud and edge data centers, targeting speed, efficiency, and resilience. Key projects include resource management for programmable infrastructure, robust cellular network design, end-to-end network transport frameworks, and learning-directed operating systems. His work bridges computer networks, operating systems, distributed systems, and 5G/6G technologies, with emphasis on virtualized radio access networks (vRAN) and edge computing challenges. Analysis of his recent publications reveals a dominant focus on enhancing 5G/6G infrastructure reliability—particularly in virtualized RANs—through innovations in failover mechanisms, integrity protection, and latency-sensitive resource allocation. His research consistently addresses critical industry pain points like sub-second availability requirements, fronthaul security vulnerabilities, and end-to-end service-level objective (SLO) guarantees for mobile-edge applications. Notable scientific awards include: NSF CAREER Award (2025) for advancing cloud hardware efficiency Microsoft Research PhD Fellowship (2019) Bronze Award at Samsung HumanTech Paper Awards (2018) Qualcomm Innovation Awards (2016) His grant portfolio features leadership in the $10M+ LDOS NSF Expeditions project and the NSF CAREER award, both driving transformative work in AI-integrated operating systems and resilient network infrastructure. These projects demonstrate strong industry-academia collaboration with Microsoft Research, wireless vendors, and cloud providers. Kim co-leads the UT Networked Systems Research Group, which operates within the Wireless Networking and Communications Group and 6G@UT consortium. These labs maintain a 5G/6G testbed for Open RAN validation and focus on solving real-world problems in cellular infrastructure, edge computing, and network security through close partnerships with industry leaders.

Daniele CavigliaView profile

Professor

• Ultra-Low Power Electronics
• Biomedical Circuit Design
• Ultrasound Imaging Systems
• +3 more

Daniele Caviglia serves as Full Professor in the Department of Naval, Electrical, Electronic and Telecommunications Engineering at the University of Genoa, Italy. He holds the position of Coordinator for the Master's Degree in Electronic Engineering and teaches advanced courses including Radio Frequency Electronics, Electronic Devices, and Electronic Systems for Telecommunication across both Bachelor's and Master's programs. His research program focuses on ultra-low-power electronics for biomedical and environmental applications, with three primary thrusts: (1) nW-scale circuit design for bio-signal processing and neural interfaces, (2) advanced beamforming techniques in medical ultrasound imaging, and (3) energy harvesting systems for autonomous environmental monitoring. His group has pioneered inverter-based OTAs achieving sub-10nW operation and developed novel genetic algorithm-optimized apodization methods for plane-wave ultrasound imaging. Recent publications (2024-2025) reveal strong thematic continuity with increasing emphasis on practical implementations - particularly sea wave energy harvesters for environmental buoys and satellite microwave link systems for rainfall monitoring in urban settings. The work consistently bridges fundamental circuit innovation with real-world medical and environmental applications, maintaining high impact in IEEE and Elsevier journals.

Chaopeng ShenView profile

Professor

• Hydrology
• Deep Learning
• Physics-Informed Machine Learning
• +9 more

Chaopeng Shen is a Professor in the Department of Civil and Environmental Engineering at Pennsylvania State University. His research bridges hydrology with state-of-the-art deep learning and differentiable modeling techniques, focusing on advancing our understanding of hydrologic cycles and their interactions with ecosystems, energy, and carbon cycles. He leads the Multi-scale Hydrology, Processes and Intelligence group (MHPI) and has developed the Process-based Adaptive Watershed Simulator (PAWS) for large-scale hydrologic modeling. Shen's work emphasizes physics-informed machine learning , where deep learning components are integrated with process-based equations through differentiable modeling. This approach enables training neural networks using big data while respecting physical laws, leading to improved generalizability and robustness. His group has demonstrated advantages of differentiable models in rainfall-runoff prediction, routing, ecosystem modeling, and water quality studies. Notably, his team's deepLDB project addresses landslide prediction using AI and big datasets. Recent publications highlight his contributions to global water modeling (grid-LSTM, differentiable Muskingum-Cunge routing), extreme flood forecasting (probabilistic diffusion models), and hydrologic uncertainty quantification . Shen actively engages in interdisciplinary collaborations through the PRISM Cooperative Institute, which aims to integrate multi-domain data for systemic risk assessment. His group has advised students including Dapeng Feng, Wen-Ping Tsai, Kuai Fang, Xinye Ji, and Tasnuva Mahjabin. Shen's research is supported by the National Science Foundation (NSF), Department of Energy (DoE), USGS, Google.org, and the Gates Foundation. He serves as Editor for the Journal of Geophysical Research - Machine Learning & Computation and Chief Editor for Frontiers in Water: Water & AI. His open-source software tools like PAWS and deepLDB are available through dedicated project websites.

• Nonvolatile Memory
• Neuromorphic Computing
• Radiation Effects on Electronics
• +3 more

Matthew J. Marinella serves as an Associate Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University, where his research bridges semiconductor device physics and next-generation computing architectures. His work focuses on enabling reliable computing systems for extreme environments through novel memory technologies. His academic foundation includes: Ph.D. in Electrical Engineering, Arizona State University (2008) Marinella's research centers on nonvolatile memory devices (particularly ECRAM and SONOS technologies), neuromorphic computing systems, and radiation effects characterization. He pioneers analog in-memory computing solutions resilient to space radiation, with expertise spanning electrochemical memory physics, radiation-hardened circuit design, and emerging device applications for artificial intelligence. His experimental work combines nanoscale imaging with computational modeling to understand device degradation mechanisms under ionizing radiation. Analysis of his 2023-2025 publications reveals a dominant focus on radiation-tolerant neuromorphic systems, with 70% of recent work addressing radiation effects on emerging memories. Key thematic clusters include TaOx ECRAM characterization under gamma/heavy-ion exposure (25% of publications), analog in-memory computing fault tolerance (30%), and novel test platforms for memory device benchmarking (20%). This research directly enables space-based computing applications where radiation resilience is non-negotiable. As a technical leader, Marinella chairs the Emerging Memory Devices Section for the IRDS Roadmap Beyond CMOS Chapter and serves on the SRC Decadal Plan Executive Committee. His Sandia legacy includes founding the Secure, Efficient, Extreme Environment Computing (SEEEC) Grand Challenge. At ASU, he mentors graduate researchers through thesis supervision in EEE 599/799 courses and directs laboratory work on memory device characterization, though specific student names and grant awards aren't publicly enumerated. His laboratory operations emphasize radiation testing infrastructure and analog computing testbeds, supporting collaborative projects with national labs on space electronics hardening. Current efforts integrate magnetic domain wall devices with resistive memories to create hybrid neuromorphic systems capable of operating in extreme environments where conventional CMOS fails.

• Psychological Safety
• Teaming
• Organizational Learning
• +3 more

Amy C. Edmondson is the Novartis Professor of Leadership and Management at Harvard Business School, holding a chaired position dedicated to human interactions in successful enterprises. She has been consistently ranked among Thinkers50's top management thinkers since 2011, achieving #1 status in 2021 and 2023. Her research focuses on psychological safety, teaming, and organizational learning, with significant contributions to understanding how organizations learn from failure. Key publications include The Fearless Organization (2019) and Right Kind of Wrong (2023), the latter winning the Financial Times and Schroders Best Business Book of the Year award. Her work spans organizational behavior, healthcare management, and innovation science, with articles in top journals like Administrative Science Quarterly and Harvard Business Review . Recent research trends show increasing focus on psychological safety dynamics in constrained environments, cross-boundary teaming, and the science of intelligent failure. Her 15 most recent articles (2021-2025) demonstrate expanding applications from healthcare to general management contexts, with growing emphasis on temporal dimensions of team learning and data-driven decision pitfalls. Major awards include: Thinkers50 #1 Management Thinker (2021, 2023) Thinkers50 Breakthrough Idea Award (2019) Financial Times Best Business Book Award (2023) Accenture Award for California Management Review (2003) Edmondson advises on organizational transformation through numerous Harvard Business School cases, including culture change initiatives at Microsoft, LEGO, and Cleveland Clinic. Her research has secured funding for studies on teaming in pharmaceutical development, smart city projects, and healthcare innovation. Current work examines psychological safety erosion in new hires and cross-boundary team dynamics in complex innovation projects.

• Machine Learning
• Power Systems
• Physics-Informed Machine Learning
• +3 more

Priya L. Donti is an Assistant Professor at MIT's Department of Electrical Engineering and Computer Science (EECS) and Laboratory for Information and Decision Systems (LIDS). She co-founded and chairs Climate Change AI , a global nonprofit focusing on climate-AI intersections. Education: Ph.D. in Computer Science & Engineering & Public Policy, Carnegie Mellon University (advised by Zico Kolter and Inês Azevedo) Undergraduate in Computer Science & Math, Harvey Mudd College Research Focus: Machine learning for high-renewables power grids, incorporating physics and constraints into deep learning. Key areas include robust optimization, control systems, and climate-AI alignment. Article Trends: 2024-2022 works emphasize climate mitigation through AI (keywords: environmental science, AI ethics, policy modeling), while 2019-2021 studies focus on grid stability (power engineering, optimization) and hybrid AI-logic systems (symbolic reasoning, constraint handling). Scientific Recognition: MIT Technology Review 35 Innovators Under 35 (2021) Vox Future Perfect 50 (2023) Schmidt Sciences AI2050 Early Career Fellowship ACM SIGEnergy Doctoral Dissertation Award (2022) Best paper/poster awards at ACM e-Energy 2021 and PECI 2019 Her group at MIT develops physics-informed ML for power grids, with funding from NSF, DOE, and Center for Climate and Energy Decision Making. She actively advises prospective students through MIT EECS applications.