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Sushil PrasadView profile

Professor

• Data Intensive Computing
• Energy-Efficient Deep Learning
• Parallel Algorithms
• +3 more

Sushil Prasad is a Professor of Computer Science at the University of Texas at San Antonio (UTSA), affiliated with the College of Sciences. His research focuses on data-intensive computing, energy-efficient deep learning models, parallel algorithms, and high-performance software systems. He holds a Ph.D. from the University of Central Florida, an M.S. from Washington State University, and a B.Tech. from the Indian Institute of Technology, Kharagpur. His work emphasizes integrating parallel and distributed computing into early computer science curricula. Key research interests include geospatial data analysis using ICESat-2 and Sentinel-2 imagery, edge device-optimized neural networks, and scalable polygon processing algorithms. He has contributed to frameworks like MPI-GIS and Crayons for high-performance geospatial computing. His educational initiatives include NSF-funded curriculum modernization efforts in parallel computing education. Recent work trends show a focus on climate science applications (e.g., polar sea ice classification), energy-efficient AI, and GPU/OpenMP parallelization. He has organized workshops like EduHPC and EduPar to advance HPC education strategies. Notable recognition includes the TCPP Outstanding Service Award (2012). His projects span cloud-based GIS systems, distributed ML training, and big spatial data processing. Collaborations include NSF-funded research on colocation mining, trajectory analysis, and curriculum development for undergraduate HPC education.

• Artificial Intelligence
• Neuroscience
• Cognitive Sciences
• +4 more

Dr. Pedro Mediano is a Lecturer in Computing at Imperial College London's Department of Computing (Faculty of Engineering). His research focuses on complex systems, information theory, and their applications in neuroscience, artificial intelligence, and cognitive science. He is affiliated with the Artificial Intelligence Network and leads interdisciplinary projects exploring synergistic interactions in brain dynamics, psychedelic neurodynamics, and causal emergence. Key research areas include quantifying high-order interactions in complex systems, developing information-theoretic tools for analyzing neural data, and modeling consciousness through integrated information theory. Mediano has pioneered frameworks like the Shannon invariants for scalable information decomposition and developed software tools such as THOI for analyzing higher-order interactions. Recent work examines how psychedelics alter brain entropy, the role of metastability in cognitive processes, and the computational principles underlying causal emergence in machine learning models. His studies integrate mathematical rigor with empirical neuroscience, bridging theoretical and applied domains. Mediano has collaborated on whole-brain models of psychedelic-induced neural complexity and explored the interplay between oxygen metabolism and brain evolution. He holds affiliations with Imperial's AI Network and regularly publishes in top journals across computational neuroscience and complexity science. Current projects include developing open-source tools for information decomposition and investigating the neural correlates of consciousness under altered states.

Maria KolokotroniView profile

Professor

• Urban Heat Island Mitigation
• Energy Efficiency in Buildings
• Nearly Zero Energy Buildings (nZEB) Retrofit
• +4 more

Professor Maria Kolokotroni is a full academic staff member at Brunel University's Department of Mechanical and Aerospace Engineering, within the College of Engineering, Design and Physical Sciences. She holds a Professorial rank and has extensive experience in urban environmental engineering and building energy systems. Her academic career began at UCL (MSc 1992, PhD 1995), followed by postdoctoral research on environmental design guidance and moisture management. She joined Brunel University in 1998 and contributed to the Building Research Establishment's indoor environment division prior to her academic appointment. Education MSc in Environmental Design and Engineering, Bartlett School, UCL (1992) PhD in Thermal Performance of Housing, UCL (1995) Postdoctoral Research: Environmental Design Guidance (UCL, 1996-1998) EPSRC-funded Postdoc: Moisture in Residential Buildings (University of Westminster, 1998) Research Interests Her work focuses on urban heat island mitigation, energy-efficient building technologies, ventilative cooling systems, and urban microclimate analysis. She actively collaborates on EU-funded projects like the IEA’s Annex 80 (Resilient Cooling) and Annex 62 (Ventilative Cooling), emphasizing practical applications of sustainable energy strategies. Key themes include: Urban albedo computation and reflective materials Thermal performance of residential and non-residential buildings Integration of renewable energy systems (photovoltaics, hydrogen) Building energy demand modeling Grants & Projects Recent projects include: PRELUDE: Real-time building energy optimization (EU, 2020-2024) PVadapt: Smart building-integrated photovoltaic systems (EU, 2018-2022) ReCO2ST: Near-zero energy retrofit platform (EU, 2018-2021) Urban albedo studies in high-latitude cities (EPSRC, 2017-2020) Advising & Collaborations Professor Kolokotroni leads interdisciplinary teams and collaborates with experts in mechanical engineering, environmental science, and urban planning. Her work bridges academic research with industry applications through projects like COOL ROOFS EU initiative and the IEA’s energy efficiency annexes. Labs/Teams Active in the International Energy Agency’s EBC Annex 80 (Resilient Cooling) and part of Brunel’s Institute for the Environment (IEF). Her research group focuses on building sustainability and climate resilience through advanced simulation tools and experimental validation.

• Planetary Science
• Geophysics
• Computational Fluid Dynamics
• +4 more

Julian Lowman is a Professor at the University of Toronto Scarborough (UTSC), specializing in planetary interiors, mantle convection, and computational fluid dynamics. His research focuses on understanding the thermal and structural evolution of planetary mantles, core-mantle interactions, and high-performance numerical modeling techniques. He holds a Ph.D. from York University (1996) and contributes to advancing geodynamic simulations for terrestrial planets and moons. Key research interests include the mechanics of mantle convection, the role of viscosity and compositional variations, and the application of high-performance computing to model planetary processes. He has explored topics such as stagnant-lid convection, plate tectonic dynamics, and the thermal evolution of planetary cores and mantles. His work bridges computational methods with geophysical observations to address questions in Earth science and planetary science. Lowman’s publications span over three decades, addressing topics from Mercury’s mantle dynamics to exoplanet tectonics. His methodologies include advanced numerical models that simulate 2D and 3D convection patterns, fluid dynamics under Arrhenius viscosity regimes, and the influence of curvature on planetary interiors. He collaborates on projects involving mantle plumes, supercontinent cycles, and the interplay between surface tectonics and deep mantle structure. Despite his extensive contributions, no specific scientific awards or grants are explicitly listed in the provided texts. He advises no named students in the available data but likely contributes to graduate training at UTSC. His research aligns with interdisciplinary themes in computational geosciences and planetary evolution.

Pierre YaoView profile

Assistant Professor

• Energy Storage
• Electrochemical Materials
• Battery Technology
• +4 more

Pierre Yao is an Assistant Professor in the Department of Mechanical Engineering at the University of Delaware, part of the College of Engineering. His research focuses on energy materials for advanced batteries, particularly lithium-oxygen and lithium-ion systems, leveraging operando electrochemical tools like synchrotron diffraction and spectroscopy. He holds a PhD from MIT (2016) and a BS from the University of Delaware (Summa Cum Laude), with postdoctoral experience at Argonne National Laboratory. Education: PhD in Mechanical Engineering, Massachusetts Institute of Technology, 2016 BS in Mechanical Engineering, University of Delaware, Summa Cum Laude Research Interests: Development of operando tools for real-time material analysis Stabilization of conversion anodes in lithium-ion batteries Electrolyte systems to mitigate polysulfide shuttling in Li-S batteries Data-driven approaches to energy material discovery Professional Highlights: Recipient of five years of research funding from Toyota Motor Europe R&D Advanced Technologies Alumni of the University of Delaware Mechanical Engineering Department Key contributions to understanding battery degradation mechanisms and solid-electrolyte interphase (SEI) dynamics Labs & Affiliations: Office located at 334 Spencer Lab, Newark, DE. Active in the University of Delaware’s Energy Materials and Devices research cluster.

Vivienne SzeView profile

Professor

• Energy-Efficient Machine Learning
• Computer Vision
• Video Compression/Processing
• +7 more

Vivienne Sze is a Professor at MIT's Department of Electrical Engineering and Computer Science (EECS), part of the School of Engineering. Her research focuses on energy-efficient computing systems for machine learning, computer vision, and video compression, with applications in autonomous systems, healthcare, and IoT. She leads projects integrating algorithmic innovations with hardware design to achieve low-power solutions for embedded and mobile devices. Her work has been recognized through prestigious awards, including the Primetime Engineering Emmy Award for co-developing the HEVC video compression standard and multiple faculty awards from tech giants like Google and Qualcomm. She co-authored the book *Efficient Processing of Deep Neural Networks*, emphasizing practical hardware-software co-design strategies. Research Interests: Energy-Efficient Machine Learning Accelerators Video Coding and Compression Standards Embedded Systems and Mobile Computing Processing-in-Memory (PIM) Architectures AI for Health Monitoring and Digital Health Sustainability in AI Infrastructure Publications highlight trends in: Optimizing DNNs for edge devices Innovations in entropy coding and CABAC Memory-efficient Gaussian-based algorithms Energy-aware design for photonic computing Awards include IEEE conference best paper awards and industry recognitions for her contributions to video coding and hardware acceleration. Her lab's collaborative efforts span academia and industry, aiming to bridge theoretical research with real-world deployable systems.

• Materials Science
• Computational Chemistry
• Density Functional Theory
• +3 more

Michael Herbst is an Assistant Professor (tenure-track) at EPFL, holding a joint appointment in the School of Basic Sciences (SB) and the School of Engineering (STI). He leads the Mathematics for Materials Modelling (MatMat) research group, focusing on error control in atomistic simulations, density-functional theory (DFT), and interdisciplinary computational methods. His work bridges mathematics, materials science, and computer science, emphasizing robust algorithms and Julia-based software development. Herbst holds a PhD from Heidelberg University and has held postdoctoral positions at RWTH Aachen and Inria Paris. He is a core member of the MARVEL and CESMIX research centers. Education: 2018: Dr. rer. nat. (magna cum laude), Heidelberg University 2009–2013: BA and MSci (1st class) in Natural Sciences, University of Cambridge 2008–2009: Studies in Mathematics/Physics, TU Kaiserslautern Research Interests : Herbst's research centers on developing reliable computational methods for materials modeling, including error estimation in DFT, black-box SCF algorithms, and Julia-based tools like the Density-Functional Toolkit (DFTK). His work addresses challenges in high-throughput simulations, numerical stability, and interdisciplinary collaboration across mathematics, physics, and computer science. Grants & Projects : MARVEL Center for Computational Design (EPFL) CESMIX Center for Extreme-Scale Simulations (MIT) EMC² Project (Sorbonne/Inria/École des Ponts) Awards : HGS MathComp PostDoc Fellowship (2018–2021) DAAD Travel Funding (2018) Exploratory Research Space Fund (RWTH Aachen, 2022) Labs & Teams : Head of the MatMat group at EPFL, focusing on error-controlled simulations and open-source software development.

Edoardo SerraView profile

Associate Professor

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

Edoardo Serra is an Associate Professor in the Department of Computer Science at Boise State University (BSU), a role he has held since July 2021. He previously served as an Assistant Professor at BSU from 2015 to 2021 and holds a joint appointment as a Senior Researcher at Pacific Northwest National Laboratory (PNNL) since June 2021. Since January 2023, he has co-directed the Computing Ph.D. Program at BSU and serves as General Chair of the 2024 ACM CIKM Conference. His academic journey includes a Ph.D. in Computer Science Engineering from the University of Calabria, Italy (2012), followed by postdoctoral positions at the University of Calabria and the University of Maryland. He also served as a Visiting Researcher at UCLA (2010–2011). His research focuses on AI/ML applications in cybersecurity, graph representation learning, generative AI, and robust AI systems. Notable projects include: NSF-funded cybersecurity curriculum integration Department of Defense-funded analysis of terrorist networks Idaho Department of Commerce precision agriculture initiatives Key research areas include graph neural networks, adversarial robustness, and ML-driven security solutions. His work has been recognized with awards such as Best Application Paper (2021) and Best Paper Award (2018). He actively contributes to professional service roles, including program chairs and editorial boards. Current projects emphasize AI ethics, generative models, and scalable graph algorithms. He advises on applied AI consulting for industry and government, focusing on model interpretability and cybersecurity implications.

Enzo MastinuView profile

Associate Professor

• Biomedical Engineering
• Prosthetics and Neuroprosthetics
• Embedded Systems
• +4 more

Enzo Mastinu is an electronic engineer specialized in embedded systems for biomedical applications, holding an Associate Professor qualification in Bioengineering. He earned his bachelor's and master's degrees in electronic engineering from the University of Cagliari and a PhD in biomedical signals and systems from Chalmers University of Technology, Sweden. His research focuses on advanced prosthetics and neuroprostheses for upper limb amputations, incorporating embedded systems design, control algorithms, sensory feedback, signal processing, AI, and osseointegration. Key projects include the HAND and HAND2 initiatives, funded by the EU and the Italian Ministry of Research, aiming to develop semi-autonomous prosthetic hands. He has published 25 journal articles (75% in Q1) and 21 conference papers, contributing to a PCT patent. Mastinu is a Senior Member of IEEE EMBS and RAS, reviews for ~90 journals/conferences, and edits Transactions on Medical Robotics and Bionics (IEEE) and Scientific Data (Nature). He has supervised ~40 students across PhD, master's, internships, and postdocs, and teaches courses in biomedical engineering and STEM education. His scientific awards include the Marie Skłodowska-Curie Fellowship (2021), National Qualification as Associate Professor (2024), and a Young Researcher Grant (2025). Research emphasizes clinical implementation of prosthetics with neural feedback and intuitive control, as highlighted in high-impact journals like the New England Journal of Medicine.

• Time series analysis
• Changepoint analysis
• Signal processing
• +6 more

Xueheng Shi is an Assistant Professor in the Department of Statistics at the University of Nebraska-Lincoln (UNL), with a joint appointment in Biological System Engineering . He joined UNL in August 2022 after postdoctoral positions at UC Santa Cruz (2020–2021) and UC Davis (2021–2022) under mentors Robert Lund, Alexander Aue, and Thomas Lee. Dr. Shi teaches graduate courses including mathematical statistics, asymptotic theory, probability, stochastic processes, and time series analysis. Research Interests: Dr. Shi's work focuses on developing statistical methodologies for time series analysis , changepoint detection , and high-dimensional data . Key applications include climate science, signal processing, and machine learning. His research emphasizes computational algorithm development and theoretical foundations across domains like climate modeling and stochastic optimization. Publications: Recent articles (2019–2024) concentrate on climate change detection , particularly global warming trends and temperature series analysis. Methodological innovations include autocovariance estimation around changepoints, comparative studies of detection algorithms, and reviews of best practices in climatological statistics. Dominant themes are changepoint techniques, environmental data diagnostics, and statistical computing. Advising: Dr. Shi actively mentors graduate students in statistics and invites prospective applicants interested in his research areas to contact him directly.

Igor JankovicView profile

Associate Professor

• Groundwater Hydrology
• Environmental Engineering
• Contaminant Transport
• +2 more

Igor Jankovic is an Associate Professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo's School of Engineering and Applied Sciences. His research focuses on groundwater flow and contaminant transport in heterogeneous aquifers, with particular emphasis on the impact of aquifer heterogeneity on solute movement and transport modeling. Education: PhD in Civil Engineering, University of Minnesota (1997) MS in Civil Engineering, University of Minnesota (1993) BS in Civil Engineering, University of Split, Croatia (1990) His work addresses critical issues in groundwater hydrology including: Advective transport mechanisms in heterogeneous media Breakthrough curve prediction and analysis Effective hydraulic conductivity modeling Upscaling of flow and transport parameters Application of the Analytic Element Method (AEM) for complex aquifer simulations Comparison of transport models (CTRW, MRMT) in heterogeneous environments Research trends in his publications reveal a focus on: Three-dimensional heterogeneous aquifer modeling Non-Fickian and anomalous transport behavior Impact of spatial variability on contaminant migration Development of numerical algorithms for large-scale groundwater simulations Validation of stochastic transport theories against field experiments (e.g., MADE and Borden aquifers) Interaction between physical and chemical heterogeneity in reactive transport

Len GelmanView profile

Professor

• Signal Processing
• Condition Monitoring
• Vibration Analysis
• +4 more

Len Gelman is a Professor and Chair in Signal Processing and Condition Monitoring at the University of Huddersfield's Department of Engineering within the School of Computing and Engineering. He also serves as Director of the Centre for Efficiency and Performance Engineering. His research focuses on advanced signal processing techniques for fault diagnosis in electromechanical systems, vibration analysis, and predictive maintenance. He is actively involved in PhD supervision and has authored over 100 publications, achieving 1499 citations and an h-index of 21. Key research areas include digital twin technology, nonlinear spectral analysis, and machine learning for industrial diagnostics. His work addresses challenges in non-stationary signal processing, motor current signature analysis, and condition monitoring under varying operating conditions. Collaborations include interdisciplinary projects with the Centre for Efficiency and Performance Engineering. Recent studies highlight innovations in fault diagnosis frameworks for rotating machinery, conveyor belt systems, and wind turbines. His contributions bridge theoretical advancements with practical industrial applications, emphasizing explainable AI and adaptive diagnostics. Gelman's research has been presented at major conferences like the World Congress on Engineering and published in specialized journals. Education: Not explicitly stated in the provided text. Awards: High citation count and h-index reflect his significant academic impact. Grants/Advising: Supervised 2 PhD projects; accepting new students in diagnostic engineering and condition monitoring. Labs/Teams: Leads the Centre for Efficiency and Performance Engineering and collaborates with the Department of Engineering's research groups.

• Cybersecurity
• Computer Security
• Information Security
• +6 more

Matthew Allen Bishop is a Professor in the Department of Computer Science at the University of California, Davis. His primary affiliation is with the College of Engineering. Bishop's research focuses on cybersecurity, including secure programming, insider threat detection, malware analysis, and cybersecurity education. He has contributed extensively to curricular guidelines (e.g., CSEC 2017) and frameworks for cyber defense. His work spans theoretical advancements (e.g., intrusion detection models) and applied systems (e.g., secure voting platforms). Notable research areas include: Cybersecurity Education: Developing curricula and pedagogical frameworks for secure coding and ethical practices. Insider Threat Mitigation: Declarative approaches and behavioral analysis for detecting and preventing attacks. Malware Mitigation: Techniques leveraging uncertainty principles and defensive programming. Election Security: Analyzing vulnerabilities and designing secure voting systems. Bishop has collaborated with institutions like the Department of Homeland Security (DHS) and National Security Agency (NSA) on critical infrastructure protection. His publications span conferences like IEEE Security & Privacy, HICSS, and NSPW, emphasizing real-world applications of cybersecurity principles.

Danny DolevView profile

Professor

• Distributed Computing
• Fault-Tolerant Systems
• Algorithms
• +4 more

Prof. Danny Dolev is a distinguished academic holding the Berthold Badler Chair in Computer Science at The Hebrew University of Jerusalem's Rachel and Selim Benin School of Computer Science and Engineering. He is an ACM Fellow and IEEE Fellow. His research focuses on distributed computing, fault-tolerant systems, algorithms, and secure protocols. He has held leadership roles, including Director of his school (1999–2002) and Chair of the Israeli National Committee for Information Technology (1994–1998). Education: B.Sc., The Hebrew University of Jerusalem, 1971 M.Sc., Weizmann Institute of Science, 1973 PhD., Weizmann Institute of Science, 1979 Research Interests: Danny Dolev's work spans distributed algorithms, Byzantine fault tolerance, consensus protocols, and hardware algorithms. His contributions include groundbreaking research on self-stabilizing systems, secure communication, and fault-tolerant clock synchronization. His HEX and Chronos protocols exemplify innovations in scalable synchronization and network security. Publications: His recent work emphasizes Byzantine agreement, asynchronous fault tolerance, and game-theoretic distributed systems. Key papers address optimal resilience in consensus algorithms and secure multi-party computation. Awards: ACM Fellow (2010) IEEE Fellow (2004) Grants & Leadership: Member of the Scientific Council, European Research Council (2010–2014) Chair of Israel's National Committee for Information Technology (1994–1998) Leadership roles at IBM Almaden Research Center (1987–1993) and Stanford University (1979–1981) Labs & Teams: His research group at Hebrew University focuses on distributed systems, with collaborations on projects like Steward (wide-area Byzantine replication) and Self-Stabilizing Circuits .

• Human Aspects of Cyber Security
• Cyber Awareness and Hygiene
• Serious Games (Cyber Defence Exercises)
• +5 more

Dr. Kaie Maennel is a Lecturer at the School of Computer and Mathematical Sciences, part of the Faculty of Sciences, Engineering and Technology at the University of Adelaide. Her research focuses on Human Aspects of Cyber Security, including Cyber Awareness and Hygiene, Serious Games (Cyber Defence Exercises), and Learning Analytics in Cyber Training. She also investigates Usable Security, Information Security Culture, and Cybersecurity Risk Management. With over 20 years of corporate experience in Audit and Assurance at Deloitte, she holds professional certifications: ACCA, CIA, and Estonian CPA. Her research interests emphasize bridging cybersecurity education with practical applications, leveraging experiential learning and advanced analytics. She actively participates in high-profile cybersecurity initiatives like the NATO CCDCOE’s Locked Shields workshop, contributing to exercise design and assessment frameworks. Dr. Maennel is eligible to supervise Masters and PhD students as a Co-Supervisor, focusing on cybersecurity education, exercise methodologies, and human-centric security strategies. She prioritizes interdisciplinary approaches and real-world impact through collaborations with industry and academic partners. Her work spans cyber defense exercise ontology development, cultural adaptation of cybersecurity programs, and leveraging behavioral genetics insights. Recent trends in her publications highlight the integration of AI into cybersecurity training and the critical role of human factors in mitigating cyber risks.