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Sofiya MilmanView profile

Professor

• Longevity Research
• Aging Mechanisms
• Endocrinology
• +3 more

Sofiya Milman, M.D., M.S., is a Professor in the Departments of Medicine (Endocrinology) and Genetics at Albert Einstein College of Medicine. She serves as Interim Vice Chair for Research in the Department of Medicine and Director of Human Longevity Studies at Einstein’s Institute for Aging Research. Her research focuses on genomic mechanisms underlying healthy aging, particularly in centenarian populations. Dr. Milman’s work emphasizes the role of hormonal pathways, especially the somatotropic axis involving IGF-1, in regulating longevity and age-related diseases. Her studies demonstrate that lower IGF-1 levels correlate with extended survival and cognitive health in individuals with exceptional longevity. Research interests include translational studies on longevity genes, protective endocrine parameters, and interventions to combat age-related diseases. Her work integrates genetic data with phenotypic information from longevity families to uncover molecular mechanisms driving healthspan extension. Key findings include the antagonistic pleiotropy of IGF-1, where lower IGF-1 levels may protect against cardiovascular disease and diabetes while promoting longevity. Dr. Milman’s articles span topics like IGF binding proteins’ mortality prediction, proteomic changes across lifespan, and genetic insights into extreme longevity. Grants supporting her research include NIH grants (R01 AG057909, R01 AG044829) focused on aging mechanisms. She collaborates widely, with co-authors such as Nir Barzilai and Aviv Bergman. Her lab is part of the Institute for Aging Research, dedicated to translating basic discoveries into clinical interventions for healthy aging.

Elliot J. CrowleyView profile

Senior Lecturer

• Machine Learning
• Automated Machine Learning
• Low-Resource Deep Learning
• +3 more

Dr Elliot J. Crowley is a Senior Lecturer in Electronics and Electrical Engineering at the University of Edinburgh, serving as Discipline Programme Manager. He co-leads the Bayesian and Neural Systems research group. His research focuses on simplifying machine learning, automated ML, low-resource deep learning, and engineering applications. He holds an MEng in Engineering Science and a DPhil (PhD) from the University of Oxford, with postdoctoral experience at Edinburgh's School of Informatics. He leads the EPSRC New Investigator Award and participates in the dAIEdge Horizon Network. Notable contributions include foundational work in neural architecture search (NAS), probabilistic methods for model efficiency, and applications in computer vision. His courses, such as the Data Analysis and Machine Learning module, emphasize practical Python-based learning for engineering students. Key awards include an EPSRC grant and recognition through distinguished papers at ASPLOS 2021. His team includes current PhD students (Linus Ericsson, Miguel Espinosa) and former advisees (Chenhongyi Yang at Meta, Jack Turner at Qualcomm). Research spans from NAS algorithms to ethical machine learning practices, with a focus on bridging theoretical advances and real-world engineering challenges.

Lu YinView profile

Assistant Professor

• AI Efficiency
• AI for Science
• Large Language Models
• +3 more

Lu Yin is an Assistant Professor in the School of Computer Science and Electronic Engineering at the University of Surrey. He holds affiliations as a long-term visiting researcher at Eindhoven University of Technology (TU/e) and collaborator with the Visual Informatics Group (VITA) at the University of Texas at Austin. Previously, he served as a Postdoctoral Fellow at TU/e and worked as a research scientist intern at Google's New York City office. His work bridges academic and industrial research, focusing on AI Efficiency, AI for Science, and Large Language Models. His research emphasizes optimizing neural networks through sparsity techniques, including pruning strategies for LLMs and vision models. Notable contributions include the OWL method for LLM pruning and Lottery Pools for improving sparse network performance. Yin actively collaborates with institutions like TU/e, Google Research, and Intel Research, and has organized conferences such as CAPBS 2025 and CAI 2025 Workshops. Yin has secured significant grants, including a 10,000,000 NWO-funded grant for NVIDIA A100 GPU resources. He has delivered invited talks at prestigious institutions like Carnegie Mellon University and City University of Hong Kong. His work has been recognized with the Best Paper Award from LoG 2022.

Arno SiebesView profile

Professor

• Data Mining
• Pattern Mining
• Minimum Description Length Principle
• +2 more

Arno Siebes is Professor of Algorithmic Data Analysis in the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. His research focuses on data mining methodologies, particularly pattern mining and Minimum Description Length (MDL) principles. Key research areas include: Developing efficient algorithms for pattern discovery Applying MDL to data characterization Creating interpretable models for complex datasets Addressing challenges in data science education Recent publications demonstrate applications in diverse domains including mobility analysis, genomic screening, and pandemic response. His work combines theoretical foundations with practical implementations for knowledge discovery.

Ahmed M. A. SayedView profile

Senior Lecturer

• Distributed Systems
• Machine Learning Systems
• Federated Learning
• +7 more

Dr. Ahmed M. A. Sayed is a Senior Lecturer (equivalent to Associate Professor) and Director of the MSc Big Data Science Programme at Queen Mary University of London's School of Electronic Engineering and Computer Science. He leads the SAYED Systems Group and focuses on distributed systems, federated learning, edge computing, and network optimization. His research bridges system design and machine learning, emphasizing scalability and efficiency. Education: PhD in Computer Science (HKUST, 2017), M.Sc. and B.Sc. (Assiut University, 2012 and 2007). Prior roles include Research Scientist at KAUST and Senior Researcher at Huawei's Future Network Lab. Research Interests: Systems for ML, federated learning, edge/Cloud computing, network congestion control, and IoT. He has secured £730K+ in grants, including a UKRI-EPSRC grant for the KUber project (2024–2027). Awards: 2024 Best Student Paper (IJCAI FL Workshop), Hong Kong PhD Fellowship (2013–2017), and numerous travel grants. Actively supervises PhD/MSc students and postdocs. Grants & Leadership: PI of UKRI-EPSRC KUber project, Co-I in HKRGC and KAUST grants. Organizes workshops at venues like MobiSys and serves on TPC for ICML, EuroSys, and NeurIPS. Labs: Leads SAYED Systems Group, affiliated with Networks Group and DT4SGD Lab at Queen Mary.

Ferrante NeriView profile

Professor

• Machine Learning
• Artificial Intelligence
• Optimization
• +5 more

Professor Ferrante Neri is a faculty member at the University of Surrey, holding the positions of Professor of Machine Learning and Artificial Intelligence and Associate Dean (International) for the Faculty of Engineering and Physical Sciences (FEPS). He is affiliated with the Nature Inspired Computing and Engineering Research Group, Surrey Institute for People-Centred AI (PAI), and the Computer Science Research Centre within the School of Computer Science and Electronic Engineering. His research focuses on optimization, explainable AI, and machine learning, with contributions to memetic computing and differential evolution. Since 2010, he has chaired the IEEE Task Force on Memetic Computing. He advises PhD students in topics like dynamic multi-objective optimization and AI-driven applications. His teaching expertise includes mathematical foundations for computer science. He has supervised students such as Aisha E S E Saeid and Pengjin Wu. Notable research areas include evolutionary algorithms, neural architecture search, and applications in robotics and environmental monitoring. Labs and teams include the Nature Inspired Computing group, which explores AI-driven solutions for complex problems. His work bridges theoretical advancements and practical applications in fields like autonomous systems and deep learning.

Antonio PellegrinoView profile

Lecturer

• Mechanical Engineering
• Materials Science
• High Strain Rate Mechanics
• +4 more

Antonio Pellegrino is a Lecturer in the Department of Mechanical Engineering at the University of Bath. His research focuses on high strain rate mechanics of materials, including lightweight polymers, titanium alloys, and biological materials. He develops novel experimental apparatus like the combined tension-torsion Hopkinson Bar. Key areas include AI in materials modelling, failure stress loci of syntactic foams, and dynamic fracture analysis. He holds a PhD in Mechanical Engineering from the University of Catania (2012). Recent projects include an EPSRC-funded study on syntactic foams using in-situ microscale tomography and X-ray scattering. Collaborations span institutions globally, with emphasis on advanced materials testing and data-driven modelling. Awards include election to the Governing Board of DYMAT (2024). He supervises doctoral students and serves as a peer reviewer for journals like International Journal of Mechanical Sciences and Engineering Fracture Mechanics . Research highlights include: development of a tension-torsion Hopkinson Bar; studies on strain rate-dependent behavior of composites and metals; biomechanical investigations of eye lens mechanics. His work contributes to UN SDGs through sustainable materials research and education initiatives. Education: PhD Mechanical Engineering (University of Catania, 2012) Key Skills: High-strain rate testing, data-driven modelling, experimental apparatus design Recent Activities: 16 peer review and committee roles (2023-2025)

Paul FranzonView profile

Professor

• Quantum Information Science
• Machine Learning & AI Hardware
• 3D Integration
• +4 more

Paul Franzon is the Cirrus Logic Distinguished Professor and Associate Department Head for Graduate Affairs at the Department of Electrical and Computer Engineering, North Carolina State University. He holds a PhD and Bachelor's in Electrical Engineering and a Bachelor's in Physics/Mathematics from the University of Adelaide, Australia. His research focuses on quantum information science, machine learning-driven hardware design, 3D integration, and high-speed systems. Education: PhD in Electrical Engineering, University of Adelaide (1988) Bachelor's in Electrical Engineering, University of Adelaide (1984) Bachelor's in Physics and Mathematics, University of Adelaide (1982) Research Interests: Quantum computing and algorithm optimization AI-driven design automation for 3D integrated circuits High-speed communication systems Hardware security and FPGA acceleration Awards & Honors: IEEE Fellow (2006) Alcoa Foundation Distinguished Engineering Research Award (2005) NC State Alumni Distinguished Undergraduate Professor Award (2003) NSW Australia Expatriate Scientist Award (2003) Advising & Grants: Advised PhD student Priyank Kashyap (2023 graduate) Recipient of NSF Young Investigators Award (1993) Labs & Collaborations: Center for Advanced Electronics Through Machine Learning (CAEML) IEEE EPS Society (Associate Editor)

Milica OrlandicView profile

Associate Professor

• Hyperspectral Imaging
• Remote Sensing
• Digital Design
• +7 more

Milica Orlandic is an Associate Professor in the Department of Electronic Systems at NTNU. She holds an MSc from the University of Montenegro (2009) and a PhD from NTNU (2015). Her research focuses on hyperspectral imaging, remote sensing, FPGA-based systems, and embedded computing for aerospace applications. She is actively involved in the HYPSO CubeSat mission, developing onboard processing systems for Earth observation. Education: MSc in Electrical Engineering, University of Montenegro (2009) PhD in Electronics, NTNU (2015) Research Interests: Her work spans hyperspectral data processing , including compression, anomaly detection, and onboard computing for satellites. She also explores reconfigurable hardware (FPGAs) for real-time signal processing, cyber-physical systems, and spaceborne sensor systems. Publications Trends: Recent work emphasizes lightweight machine learning for anomaly detection, FPGA acceleration of hyperspectral compression (CCSDS 123), and algorithm co-design for CubeSat missions. Key contributions include robust onboard processing frameworks for HYPSO-1 and adaptive hardware-software systems. Advising & Teams: She supervises a dynamic team of over 40 PhD and MSc students working on FPGA implementations, satellite systems, and hyperspectral algorithms. Notable collaborations include the HYPSO CubeSat project, which aims to deliver high-resolution Earth observation data with low latency. Labs & Infrastructure: Her research leverages NTNU’s facilities for embedded systems prototyping, FPGA development, and CubeSat payload testing. The HYPSO mission integrates her team’s hardware-software co-design innovations for space applications.

• Theorem Proving
• Automated Reasoning
• Formal Methods
• +14 more

Jasmin Blanchette is a Professor of Theoretical Computer Science and Theorem Proving at the Institute for Informatics, Ludwig-Maximilians-Universität München (LMU), where he also serves as Dean of Studies for Computer Science since January 2024. He is additionally affiliated as a guest researcher with the VeriDis group at Loria in Nancy, France. His research lies at the intersection of automated and interactive theorem proving, with a focus on higher-order logic and proof automation. Key projects include the development of tools like Sledgehammer, Nitpick, and Zipperposition, and foundational work on (co)datatypes and higher-order superposition. His recent publications reflect a strong trend in formalizing and verifying automated reasoning techniques, especially in higher-order logic, with applications in proof automation, SMT solving, and logical verification. Articles frequently appear in top venues such as CADE, ITP, and the Journal of Automated Reasoning. CADE 2023 Best Paper Award for 'Verified given clause procedures' FroCoS 2023 Best Paper Award (with Visa Nummelin and Sander Dahmen) IPA Dissertation Award (awarded to his student Petar Vukmirović) Dutch 'cum laude' distinction (awarded to his student Anne Baanen) Dutch Prize for ICT Research 2022 Blanchette has advised numerous PhD and postdoctoral researchers, many of whom are now active contributors to the formal methods community. He has received significant research grants through projects like Matryoshka and Nekoka. He is also the editor-in-chief of the Journal of Automated Reasoning and plays a central role in organizing key conferences such as ITP, CADE, and CPP. He leads an active research group at LMU, consisting of postdocs and PhD students working on topics such as higher-order superposition, formalization of voting systems, categorical logic, and proof search heuristics. The team collaborates closely with international groups, including those at Inria and TU Wien.

• Energy and Power Systems
• Renewable Energy Integration
• Power System Stability
• +13 more

Evan Franklin is an Associate Professor in Energy and Power Systems within the School of Engineering at the University of Tasmania. He also serves as Associate Head of Research, reflecting his leadership in advancing engineering research at the institution. His academic work is centered on modern power systems with a strong emphasis on renewable integration, grid stability, and sustainable energy technologies. His primary research interests include energy and power systems, renewable energy integration, grid frequency control, harmonic analysis, distributed energy resources (DER), battery and compressed air energy storage, agrivoltaics, and hydrogen integration. His work bridges engineering fundamentals with real-world applications in sustainable energy systems, contributing to Australia's transition toward clean energy. The recent publications of Dr. Franklin span high-impact journals such as Energies , IEEE Transactions on Industry Applications , Renewable and Sustainable Energy Reviews , and Journal of Energy Storage . The research trends reflect a strong focus on power system stability, microgrid control, harmonic mitigation, and innovative energy storage solutions. His work increasingly integrates AI and machine learning techniques for power quality and system monitoring, while also exploring interdisciplinary applications like agrivoltaics and offshore energy systems. Dr. Franklin has successfully supervised both PhD and Master’s students, including Ahmadreza Eslami and Md Ruhul Amin, with research topics ranging from harmonic analysis to frequency control using battery storage. He has secured substantial research funding from major national and international bodies, including the Australian Research Council (ARC), Australian Renewable Energy Agency (ARENA), CSIRO, and the Blue Economy CRC. Notable projects include the ARC Training Centre in Energy Technologies for Future Grids, MoorPower wave energy projects, and studies on hydrogen integration and black-start capabilities. He leads and participates in research teams focused on renewable energy systems, including the Centre for Renewable Energy and Power Systems at UTAS. His collaborative network includes key researchers such as Professor Michael Negnevitsky, industry partners like Carnegie Clean Energy and TasNetworks, and government agencies including Hydro Tasmania and Aurora Energy. His work is instrumental in shaping resilient, sustainable, and intelligent power systems for the future.

• Algorithms
• Data Structures
• Pattern Matching
• +3 more

Simon J. Puglisi is a Professor in the Department of Computer Science at the University of Helsinki. His research focuses on algorithms, data structures, pattern matching, and data compression, with applications in bioinformatics and genomics. He has collaborated extensively with researchers in the field, including Travis Gagie, Juha Kärkkäinen, and Andrew Turpin. His work spans theoretical computer science and practical applications in genomic data processing and efficient indexing techniques. Key research interests include genome sequencing algorithms, efficient compression methods (e.g., Lempel-Ziv and relative Lempel-Ziv), and the development of succinct data structures for handling large genomic datasets. His contributions to suffix arrays, wavelet trees, and de Bruijn graphs have advanced computational methods in bioinformatics and information retrieval. Puglisi's articles frequently address challenges in text indexing, error correction in short-read sequencing, and optimizing algorithms for scalability. He is known for his work on self-indexing techniques and the SHREC error correction method for genomic data. His research bridges theoretical algorithm design with real-world applications in high-throughput sequencing and large-scale data management.

• Scientific Workflow Management
• Distributed Computing
• Numerical Linear Algebra
• +3 more

Wilfried Gansterer is a Professor at the Faculty of Computer Science, University of Vienna, leading the Theory and Applications of Algorithms research group. His work focuses on numerical algorithms, distributed computing, and machine learning, with notable contributions to graph neural networks and fault-tolerant systems. Active in projects such as Algorithmic Data Science for Computational Drug Discovery (2020–2028) and REPEAL (Resilience vs. Performance in Numerical Linear Algebra, 2016–2020). Research Interests: Dr. Gansterer’s expertise spans graph neural networks, matrix compression, adversarial defense mechanisms, and high-performance computing. His work addresses challenges in efficient computation, resilience against node failures, and optimizing distributed systems. Projects : Algorithmic Data Science for Computational Drug Discovery (2020–2028) REPEAL: Resilience vs. Performance in Numerical Linear Algebra (2016–2020) Verteiltes Rechnen (Distributed Computing, 2007–2014) Awards : 2023 Best Paper Award for work on Crossfire: An Elastic Defense Framework for Graph Neural Networks. Labs/Teams : Directs the Theory and Applications of Algorithms group, focusing on algorithmic innovation in distributed and high-performance computing environments.

Scot M. MillerView profile

Associate Professor

• Atmospheric Science
• Greenhouse Gases
• Air Pollution
• +11 more

Scot M. Miller is an Associate Professor in the Department of Environmental Health and Engineering at the Whiting School of Engineering, Johns Hopkins University. He leads the Greenhouse Gas Research Group, focusing on quantifying emissions of greenhouse gases and air pollutants using satellite, aircraft, and tower observations. His research spans global scales, from Arctic ecosystems to urban and industrial sources in the U.S. and China. His research interests include atmospheric science, greenhouse gas emissions, inverse modeling, big data analytics, and climate policy. He integrates tools from statistics, high-performance computing, and satellite remote sensing to improve emission estimates and inform environmental regulations. Recent publications highlight trends in methane, ethane, and sulfuryl fluoride emissions, carbon cycle dynamics, and innovative methods for analyzing massive satellite datasets. His work increasingly leverages OCO-2 and OCO-3 satellite data to study carbon dioxide and methane fluxes across diverse ecosystems. Scientific awards include the NSF CAREER Award, JHU Catalyst Award, and the Carnegie Distinguished Postdoctoral Fellowship. He was also recognized with Harvard’s Certificate of Excellence in Teaching. Miller advises multiple PhD students, including Mingyang Zhang, Dylan Gaeta, and Leyang Feng, and has secured grants from NASA, NSF, NOAA, and JHU. He is a member of the NASA OCO Science Team and collaborates with institutions such as Northern Arizona University, Carnegie Institution, and NOAA. His lab is involved in urban environmental monitoring in Baltimore and interdisciplinary climate policy research.

Dipanwita ThakurView profile

Assistant Professor

• Green Artificial Intelligence
• Federated Learning
• Smart Healthcare
• +4 more

Dr. Dipanwita Thakur serves as Assistant Professor at the Department of Computer Engineering, Modeling, Electronics and Systems (DIMES) at the University of Calabria, Italy since July 2023. She is an active member of the European Cooperation in Science & Technology (COST Action CA22104) focusing on cybersecurity and serves in the IEEE Future Networks Working Group for Artificial Intelligence/Machine Learning. Previously, she held a 15-year Assistant Professor position at Banasthali University, Rajasthan, and has industry experience at TechMahindra and C-DAC. Education: Ph.D. in Smart Healthcare from West Bengal University of Technology, Kolkata M.Tech. in Software Engineering from Banasthali Vidyapith MCA from NIELIT, Government of India B.Sc. from University of Calcutta Her research pioneers Green Artificial Intelligence with emphasis on energy-efficient federated learning and smart healthcare applications. She develops privacy-preserving human activity recognition systems using multimodal data fusion, focusing on performance evaluation and environmental sustainability. Her work bridges theoretical machine learning with practical healthcare solutions, optimizing AI systems for reduced carbon footprint while maintaining clinical efficacy through hardware-algorithm co-design and quantization techniques. Recent publications reveal a strong trajectory toward sustainable AI, with increasing focus on energy-aware federated learning frameworks, multimodal medical segmentation, and non-IID data handling. Her work consistently addresses the critical balance between model accuracy, convergence speed, and energy consumption across edge devices, with growing emphasis on quantization techniques and hardware-algorithm co-design for real-world deployment. Scientific Awards: Elevated to IEEE Senior Member (2024) Dr. B.C. Roy Memorial Scholarship for outstanding 10th Board results (1992) Student Science Seminar Award by West Bengal Government (1990) Dr. Thakur actively mentors students as evidenced by her congratulations to advisee Farwa for paper acceptances. She serves as Associate Editor for Information Fusion (Elsevier) and IEEE Sensors Journal, and holds editorial roles at Scientific Reports. Her research is advanced through COST Action CA22104 and IEEE working groups, though specific grant details aren't listed in the source material. She has organized key workshops including Green-Aware AI 2024 and Green Federated Learning at IJCNN 2025. She leads research within the MONAI community on data quality and federated learning, and contributes to IEEE IoT and Future Networks initiatives. Her work with the COST Action CA22104 Behavioral Next Generation in Wireless Networks connects cybersecurity with sustainable AI development, while her Missouri S&T visiting scholar position focuses on energy optimization for federated learning systems.