Justin Quinn is a Senior Lecturer and Director of the Centre for Engineering and Renewable Energy at Ulster University's School of Computing, Engineering and Intelligent Systems, based at the Derry~Londonderry campus. His work bridges computational intelligence with advanced manufacturing, focusing on defect detection in 3D printing and haptic feedback systems. Current active projects include Hartree Northern Ireland (Principal Investigator, 2023–2026) and Local Industrial Decarbonisation Plan for NI (Co-Investigator, 2024). His research interests span: Smart manufacturing and IoT applications Finite element analysis and defect detection Spiking neural networks for FPGA deployment Visio-tactile sensors in haptic feedback Composite material optimization Recent publications highlight trends in industrial technology, including collaborations with researchers like Coleman, Kerr, and Magee. His scientific awards include the Innovation Voucher (IV1013087) for pressure ulcer prevention innovation. As an educator and researcher, he contributes to Ulster University's leadership in engineering and renewable energy, aligning projects with UN Sustainable Development Goals.
Professor Teng Long is a Professor of Power Electronics at the University of Cambridge , where he leads the Advanced Power Electronics Laboratory (The Long Group) . His research focuses on power electronics for transport electrification , renewable energy , and healthcare applications . PhD, University of Cambridge (2013) MEng, University of Birmingham (2009) BEng, Huazhong University of Science and Technology (2009) His work spans power semiconductor packaging , high-frequency magnetics , and multiscale energy conversion systems . He has secured over £3 million in research grants, with half from the UK government and half from industrial sponsors like STMicroelectronics and Siemens . Recent publications highlight advancements in SiC power modules for low thermal stress , self-adaptive switching technologies , and high-efficiency converter designs for AI accelerators and renewable energy systems . He is a Chartered Engineer with over 70 academic papers and 5 international patents. His group includes 3 postdoctoral researchers and 7 PhD students, fostering innovation in power electronics and transport electrification .
Tommi S. Jaakkola is the Thomas Siebel Professor of Electrical Engineering and Computer Science at MIT, with appointments in the School of Engineering and the Institute for Data, Systems, and Society. He leads a research group at the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research advances machine learning for efficient, principled, and interpretable learning, prediction, and control. Key interests include: Statistical inference and estimation Generative modeling for molecular design and natural language Game-theoretic interactions and strategic modeling Applications in biomedical domains and drug discovery Recent publications demonstrate strong focus on diffusion models and flow-based methods for protein structure generation and molecular optimization, with increasing integration of physical principles and symmetries. His group actively publishes at top AI conferences (ICML, NeurIPS, ICLR) with significant biomedical applications. Jaakkola advises numerous PhD students and has mentored graduates now at companies like Boltz PBC, DE Shaw, and Xaira. His CSAIL laboratory fosters interdisciplinary collaboration between computer science, biology, and chemistry to address complex biomedical challenges.
Jiaqi Gu is an Assistant Professor at the School of Electrical, Computer and Energy Engineering at Arizona State University. His work bridges photonics, quantum computing, and machine learning to develop next-generation hardware for efficient computing. PhD in Electrical and Computer Engineering, University of Texas at Austin (2023) His research focuses on emerging hardware design (photonics, post-CMOS electronics, quantum), hardware-algorithm co-design , AI/ML algorithms , and electronic-photonic design automation . He explores how photonic and quantum systems can be optimized for AI workloads, with recent work on differentiable photonic simulation, compact optical neurons, and quantum component placement tools. His publications include 20+ papers in 2025 on topics like photonic tensor cores, optical neural networks, and quantum-aware design. Key trends: integrating machine learning with photonic device simulation, optimizing photonic circuits for adversarial robustness, and advancing quantum computer compilation. Scientific awards include: Best Paper at ASP-DAC 2020 Best Poster at NSF Workshop on Machine Learning Hardware 2020 Margarida Jacome Dissertation Prize 2023 Outstanding Dissertation Award 2024 Dr. Gu advises students through EEE 490/590/790 courses and leads the ScopeX research group , which develops tools for photonic and quantum hardware design.
Taejoon Kim is an Associate Professor at the School of Electrical, Computer and Energy Engineering, Arizona State University, with research focusing on Wireless Communications, Statistical Signal Processing, and Networked Systems. His work bridges Information Theory, Machine Learning, and Optimization for applications in 5G/6G Security, mmWave/THz Communication, and Distributed Fusion. Education: PhD in Electrical and Computer Engineering from Purdue University, MS from KAIST (South Korea), BS from Sogang University (South Korea). Research interests span Machine Learning for Physical Layer design, Feature Learning, Distributed Interference Management, and Algorithms on Manifolds. Recent projects examine learnability in AI/ML-based physical layer techniques, fast beam alignment in mmWave systems, and optimization of network resource allocation. His article portfolio reveals trends in 5G/6G Security, Federated Learning, and Channel Estimation. Notable awards include the Kansas Board of Regents Faculty of the Year (2024), Miller Professional Award (2023), and IEEE Transactions on Communications Best Paper Award (2016). Scientific Honors Kansas Board of Regents Faculty of the Year (2024) Miller Professional Award for Research (2023) Harry Talley Excellence in Teaching (2022) IEEE Stephen O. Rice Prize (2016) CityU Hong Kong President's Award (2017) Active grants include NSF Convergence Accelerator Track G, ONR projects, and NASA collaborations. His research group has produced 29 US patents and supervised numerous PhD students, including Hadi Ghauch (now Assistant Professor at Telecom Paris) and Wei Zhang (now at Harbin Institute of Technology Shenzhen).
Marios D. Dikaiakos is Professor of Computer Science at the University of Cyprus where he serves as the Founding Director of the Laboratory for Internet Computing. He previously served as founding Director of the Center for Entrepreneurship (2015-2021) and Head of the Computer Science Department (2010-2014). His academic journey includes a Ph.D. from Princeton University (1994) and a Dipl.-Ing. from the National Technical University of Athens (1988). Dr. Dikaiakos' educational background: Ph.D. in Computer Science from Princeton University (1994) M.A. degree from Princeton University (1991) Dipl.-Ing. degree from National Technical University of Athens (summa cum laude, 1988) His research spans Internet Computing with a focus on Cloud Computing, Online Social Networks, and Vehicular Computing. Recent work explores Edge Computing, Fog Computing, and AI applications in entrepreneurship and social media analysis. His interdisciplinary approach combines technical computing expertise with insights into human behavior and business applications, particularly examining how technology influences entrepreneurship, polarization, and misinformation detection. His recent publications reveal a strong trend toward energy-efficient computing, AI-driven social media analysis, and the intersection of computing with entrepreneurship. The work spans technical domains like Edge and Fog Computing while addressing societal challenges including misinformation detection, polarization analysis, and sustainable computing practices. Many publications demonstrate collaborative research across computer science subfields. Dr. Dikaiakos has received several prestigious awards: Best paper award of the 14th IEEE CloudCom conference (2023) Best student paper award for BenchPilot (2022) Best paper award for 5G-Slicer (2022) Best Demo Award at the ACM/IEEE Symposium on Edge Computing (2020) As an academic advisor, Dr. Dikaiakos has successfully guided PhD students including Demetris Paschalides (2024) and Moysis Symeonidis (2022). He has been principal or co-principal investigator for 25 projects funded by the European Union and the Research Promotion Foundation of Cyprus. His service includes editorial roles at ACM Computing Surveys and Computing journals, and leadership positions in major conferences like EuroPar 2023 and CCGrid 2019. Dr. Dikaiakos founded and directs the Laboratory for Internet Computing at the University of Cyprus, which focuses on cutting-edge research in distributed systems, cloud and edge computing, and social network analysis. The lab has developed multiple research software systems released internationally and maintains active collaborations with institutions across Europe. Current projects address challenges in energy-aware computing, misinformation detection, and AI applications in entrepreneurship.
Tommaso Cucinotta is an Associate Professor at the Real-Time Systems Laboratory (ReTiS) within the TECIP Institute of Scuola Superiore Sant'Anna, Pisa, Italy. He earned a MSc and PhD in Computer Engineering from University of Pisa and Scuola Superiore Sant'Anna, respectively. His career spans academic and industrial roles, including researcher positions at Alcatel-Lucent Bell Labs (2012-2014) and Software Development Engineer at Amazon DynamoDB (2014-2016). He coordinates real-time and embedded systems research at ReTiS since 2019. Born in 1974, Potenza, Italy MSc in Computer Engineering, University of Pisa (2000) with 110 cum laude PhD in Computer Engineering, Scuola Superiore Sant'Anna (2004) His research focuses on real-time systems in cloud environments, including adaptive resource management, AI-driven performance monitoring, secure computing, and scalable NoSQL databases. He explores operating system innovations for many-core architectures, network function virtualization (NFV) optimization, and kernel-level enhancements for latency control. His work integrates formal methods with practical implementations, such as autonomic QoS control and high-performance container communication frameworks. Recent publications analyze predictive elasticity in cloud infrastructures, real-time DAG optimization on heterogeneous platforms, and AI applications for system-level performance tuning. He actively contributes to open-source tools like ARSim and AQuoSA, while mentoring MSc thesis projects on topics like Kubernetes optimization, fault-tolerant replication logs, and machine unlearning techniques for LLMs. Collaborations with industry leaders (Ericsson, Red Hat, Vodafone) bridge academic research with real-world scalability challenges. Scientific awards include the Best Paper Award at CLOSER 2020 for his work on high-performance inter-container communication frameworks. He participates in program committees of major conferences and contributes to the evolution of Linux real-time scheduling mechanisms through projects like SCHED_DEADLINE enhancements for multimedia applications.
Willie John Padilla serves as the Dr. Paul Wang Distinguished Professor in the Department of Electrical and Computer Engineering at Duke University's Pratt School of Engineering. His research spans electromagnetic metamaterials and metasurfaces with applications across microwave, terahertz, and infrared frequencies. Dr. Padilla leads the Padilla Lab, which specializes in THz, infrared, optical and magneto-optic properties of novel materials using various spectroscopic methods. Ph.D. from University of California, San Diego (2004) Dr. Padilla's research focuses on theoretical, computational, and experimental investigation of electromagnetic metamaterials with particular emphasis on artificial intelligence and deep learning applications. His work explores the tailoring of thermal radiation beyond natural materials limitations, developing metamaterial emitters based on MEMS, graphene, and liquid crystals for controlled thermal emission. His lab investigates high-temperature metamaterials capable of withstanding extreme environments for thermal photovoltaic systems. Padilla's research bridges fundamental electromagnetic theory with practical applications in energy harvesting, computational imaging, and sensing technologies. His recent publications demonstrate a strong trend toward integrating machine learning with metamaterial design and characterization, particularly physics-informed learning approaches that combine domain knowledge with neural networks. This represents a significant shift in the field toward more efficient design methodologies that overcome traditional computational limitations in electromagnetic simulation. IEEE Fellow (2025) Optica Fellow (2013) Presidential Early Career Award for Scientists and Engineers (2009) Dr. Padilla actively mentors doctoral students including Yang Deng, Rixi Peng, and Natalie A Rozman, with research spanning from fundamental metamaterial physics to practical applications. His team includes Visiting Researcher Omar Khatib and Adjunct Assistant Professor Evan Runnerstrom, creating a multidisciplinary research environment that bridges electrical engineering, materials science, and computational methods. The Padilla Lab maintains strong focus on both fundamental research and practical applications, with particular emphasis on developing metamaterial solutions for energy harvesting, thermal management, and advanced imaging systems. Current projects include developing metamaterial emitters for controlled thermal radiation beyond the Stefan-Boltzmann law and exploring high-temperature metamaterial designs for extreme environment applications.
Charlotte Frenkel is a Tenure-Track Assistant Professor in the Microelectronics Department at Delft University of Technology (TU Delft), where she leads research in neuromorphic engineering and low-power AI hardware. Her work bridges the gap between biological intelligence and artificial neural networks, focusing on energy-efficient computing at the edge. Dr. Frenkel's research spans digital and mixed-signal IC design, computer architecture, learning algorithms, and neuroscience. She directs the Cognitive Sensor Nodes and Systems (CogSys) lab, which develops neuromorphic processors like ODIN, MorphIC, SPOON, and ReckOn that demonstrate competitive advantages over conventional neural network accelerators. Her publications reveal a strong focus on spiking neural networks, event-based processing, and on-chip learning. Key trends include developing hardware that leverages sparsity for energy efficiency, creating bio-inspired learning algorithms that solve weight transport and update locking problems, and establishing frameworks for benchmarking neuromorphic systems through initiatives like NeuroBench. Scientific Awards: IBM Innovation Award 2021 Nokia Bell Labs Scientific Award 2021 IEEE ISCAS 2020 Best Paper Award NEUROTECH/NICE Best Early Researcher Presentation 2021 AiNed Fellowship Grant Dr. Frenkel is actively expanding her research group through PhD and postdoc positions. She serves as Associate Editor for IEEE Transactions on Biomedical Circuits and Systems and Frontiers in Neuroscience, and has held numerous leadership roles in conference organization including Program Chair for tinyML Research Symposium 2024 and Neuro-Inspired Computational Elements conference 2023-2024. Her service includes extensive reviewing activities for top IEEE journals and conferences in her field.
Alexander Korotin is an Assistant Professor at the Skolkovo Institute of Science and Technology (Skoltech) where he heads the Generative AI research group. He is also a senior research scientist at the Artificial Intelligence Research Institute (AIRI), leading the "Foundations of Generative AI" group. His academic journey includes a PhD in Math & Physics from Skoltech (2023), an MSc in Computer Science from the Higher School of Economics (HSE), and a BSc in Mathematics also from HSE. Dr. Korotin's research focuses on generative modeling, with particular emphasis on developing novel algorithms based on Optimal Transport and Schrodinger Bridges. His work bridges theoretical mathematics with practical machine learning applications, contributing significantly to the field of generative artificial intelligence. He has pioneered approaches to make Schrodinger Bridge solvers more efficient and practical, most notably with his "Light Schrödinger Bridge" framework that simplifies complex computational procedures while maintaining theoretical rigor. His publication record shows a clear progression toward making advanced generative modeling techniques more accessible and computationally efficient. Recent work demonstrates increasing sophistication in handling complex distribution matching problems through physics-inspired approaches (like electrostatic field matching) and novel distillation techniques that accelerate inference. The research spans from theoretical foundations to practical applications in image processing, semi-supervised learning, and reinforcement learning. Dr. Korotin has received recognition for his contributions to neural optimal transport and Schrodinger Bridges, with his papers frequently appearing in premier machine learning venues. His work on efficient computational methods for optimal transport has established him as a rising expert in these specialized areas of machine learning. As an academic leader, Dr. Korotin advises research students and collaborates extensively with colleagues across institutions, contributing to the advancement of generative AI through both theoretical developments and practical implementations. His work continues to push the boundaries of what's possible in generative modeling, with recent publications focusing on making advanced mathematical approaches more computationally efficient for real-world AI applications.
Paul Campbell is an Assistant Research Scientist associated with the ZEUS Laser Facility at the University of Michigan, Ann Arbor. His work focuses on particle acceleration and magnetic field generation during intense laser-plasma interactions, with applications in high-energy-density physics and laboratory astrophysics. He holds a PhD in Applied Physics from the University of Michigan and previously held a DOE Fusion Energy Sciences postdoctoral fellowship. Education: PhD in Applied Physics, University of Michigan; BS in Physics, University of the South (Sewanee, Tennessee). Research interests include: Laser-plasma accelerators Neutron source optimization Magnetized plasmas Ultrafast diagnostics Pulsed power systems Recent publications span experimental and computational domains, including: Advancements in neutron generation and imaging via dense plasma focus (2024-2025) Multi-messenger diagnostics for laser-driven shocks (2025) Thermonuclear fusion yield improvements in Z-pinch systems (2024) Analysis of plasma stagnation and instability mechanisms (2023-2024) Interdisciplinary work on digital mental health regulation (2024-2025) He contributes to the development and operation of high-power laser systems like MJOLNIR, focusing on diagnostics, stability, and energy coupling efficiency. His contact details include campbpt@umich.edu, and he is affiliated with the High Field Science research group.
Guangyao Chen is an Assistant Professor in the Department of Computer Science within Cornell University's College of Engineering, where he leads research at the intersection of computer vision, machine learning, and artificial intelligence. His work focuses on advancing open-world visual understanding systems capable of handling unknown classes and real-world complexity. His primary research interests include: Computer Vision and Open-Set Recognition Few-Shot Learning and Cross-Domain Adaptation LLM-Visual Integration and Symbolic Reasoning Neuromorphic Computing and Spiking Neural Networks Multi-Modal Learning and Real-Time Systems Analysis of his 2021-2025 publications reveals a strategic evolution toward solving open-world perception challenges. His recent work demonstrates how large language models can unlock complex event understanding from object detectors, while his G-OSR benchmark establishes new standards for graph-based open-set recognition. Notable contributions include real-time multimodal anomaly detection frameworks, retina-inspired saliency models, and Autoagents for automatic agent generation - all addressing critical gaps in deploying AI systems in dynamic, uncontrolled environments. Though specific awards and advising details aren't documented in available sources, his prolific publication output (including 9 papers in 2025) indicates an active research program with significant community impact. His work bridges theoretical advances in representation learning with practical applications in robotics, medical imaging, and industrial systems where handling unknown classes is critical.
Milad Ghasrikhouzani serves as a Senior Lecturer at UNSW Canberra within the School of Engineering and Technology. His academic position focuses specifically on Travel Behaviour and Transport Modelling, where he contributes to both teaching and research activities in transportation systems. Dr. Ghasrikhouzani specializes in behavioral modeling, discrete choice analysis, and consumer preference research related to transport innovation adoption. His research examines how individuals and communities respond to emerging mobility solutions including autonomous vehicles (AVs), electric vehicles (EVs), and other new transport technologies. A distinctive aspect of his work investigates the role of social influence in facilitating behavioral shifts and accelerating adoption of innovations in the transport sector. His research framework considers both individual decision-making processes and broader social/systemic factors that shape travel behavior in evolving mobility landscapes. Analysis of Dr. Ghasrikhouzani's recent publication record (2022-2025) reveals consistent research output across multiple high-impact transportation journals. His work spans diverse subfields including sustainable transport consumption, demand-responsive transport systems, behavioral modeling of transportation choices, and the psychological drivers of mobility decisions. A notable trend shows increasing focus on integrated transport systems, particularly examining micromobility-public transport integration, trust in navigation technologies, and the impact of neighborhood characteristics on travel behavior. His research methodology frequently employs advanced statistical techniques including survival analysis, hybrid choice modeling, and discrete choice experiments. Dr. Ghasrikhouzani has secured research funding from significant government and industry partners including the National Housing Research Program through the Australian Housing and Urban Research Institute (AHURI), the ACT Government, the NSW Department of Premier and Cabinet, and the NSW Office of Environment and Heritage. These partnerships demonstrate the practical relevance and policy impact of his research in transportation planning and urban development contexts.
Yongjoo Park is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign, affiliated with the Siebel School of Computing and Data Science and the Data and Information Systems (DAIS) Research Lab. He co-founded and serves as Chief Scientist at Keebo, Inc., a startup developing automated data warehouse optimization platforms. Ph.D. in Computer Science and Engineering, University of Michigan, Ann Arbor (2017) M.S. in Computer Science, University of Michigan, Ann Arbor (2013) B.S. in Electrical Engineering, Seoul National University (2009) His research focuses on building intelligent data-intensive systems that integrate statistical and AI techniques for improved reliability, scalability, and usability in data science workflows. Key projects include Kishu (undoable Jupyter notebooks), AirIndex (automated index optimization), and CARE (causal-reasoning data systems). His work bridges database systems and machine learning, emphasizing end-to-end optimization for structured/unstructured data processing. Recent publications highlight trends in computational notebook checkpointing, index tuning through data-aware storage, and AI-driven database learning. His projects have been recognized at top venues including SIGMOD, VLDB, and CHI, with a focus on practical implementations for real-world data challenges. Scientific Awards: NSF CAREER Award (2025) Best Demo Award at SIGMOD 2025 ACM SIGMOD Jim Gray Dissertation Award Runner-up (2018) Engineering Council Outstanding Advising Award (2021) Teaching Excellence Awards (2023, 2024) Yongjoo advises multiple PhD and MS students, including Supawit Chockchowwat (joining CMKL University in Thailand) and Nikhil Sheoran (now at Databricks). His research group maintains strong industry collaborations and open-sources systems like Kishu and VerdictDB through their GitHub organization .
Dr. Alper Demir is a researcher at the Department of Computer Engineering, Izmir University of Economy since 2020. He received his B.S., M.S., and Ph.D. in Computer Engineering from Middle East Technical University in 2014, 2016, and 2019 respectively. His research focuses on Artificial Intelligence and Reinforcement Learning , specifically addressing challenges in Partially Observable Markov Decision Processes . Key contributions include work on memory formation, intrinsic motivation, and landmark-based guidance systems for learning agents. Recent publications analyze urban dynamics in Izmir (water consumption, traffic patterns) and explore technical solutions for voltage control in distribution networks using reinforcement learning frameworks. His methodological work includes innovations in temporal abstraction, reward shaping, and subgoal discovery mechanisms within reinforcement learning paradigms.
