جستجوی استاد راهنمای دانشگاهی

Joseph Ramseyمشاهده پروفایل

پژوهشگر

• Causal Inference
• Machine Learning
• Computational Epistemology
• +۴ مورد دیگر

Joseph Ramsey is a Researcher in the Department of Philosophy at Carnegie Mellon University , affiliated with the Dietrich College of Humanities and Social Sciences . He serves as Director of Research Computing and has been instrumental in developing computational infrastructure and algorithms for causal inference. Core projects: Tetrad (causal search algorithms), AProS (proof generator for logic), Causality Lab , and Laboratory for Symbolic and Educational Computing . His research spans causal modeling, algorithm design, and applications in neuroscience, bioinformatics, and education. He has contributed to software tools like Causal-learn and Py-Tetrad , enabling scalable causal discovery in high-dimensional datasets. He has received funding from NASA, NSF, and the University of Pittsburgh for projects ranging from Martian rover software to glaucoma detection models. His work integrates philosophy, computer science, and applied statistics.

• Biotechnology
• Systems Engineering in Bioprocessing
• Model-Based Tools
• +۴ مورد دیگر

Professor Cleo Kontoravdi is a Professor of Biological Systems Engineering at the Department of Chemical Engineering, Imperial College London, within the Faculty of Engineering. Her roles include Director of Postgraduate Studies (2021–present) and Postgraduate Admissions Tutor (2018–2021). She holds affiliations with key research centers such as the Centre for Process Systems Engineering, Centre for Synthetic Biology, and Future Vaccine Manufacturing Research Hub. Her research focuses on applying systems engineering principles to bioprocessing, integrating model-based tools like sensitivity analysis and optimization with experimental work on mammalian cell cultures and vaccine production. Key areas include metabolic flux analysis, media optimization, and multiscale modeling. Her recent publications highlight advancements in hybrid modeling frameworks, mRNA vaccine process design, and metabolic engineering of CHO cells. She actively contributes to vaccine manufacturing strategies and sustainable biopharmaceutical supply chains. Education: PhD (2007) and MEng (2002) in Chemical Engineering from Imperial College London. Professional experience spans academic roles (since 2007) and industry R&D at Lonza Biologics (2006–2007). Her work bridges computational biology, bioprocess engineering, and systems biology, addressing challenges in vaccine development and production efficiency. Awards: Not explicitly listed in the provided text. Research grants and collaborations are implied through her involvement in high-impact projects like the Future Vaccine Manufacturing Hub. She advises on bioprocess optimization, glycoengineering, and biomanufacturing sustainability.

Zachary E. Rossمشاهده پروفایل

استاد

• Seismology
• Geophysics
• Machine Learning
• +۷ مورد دیگر

Zachary E. Ross is a Professor of Geophysics at the California Institute of Technology (Caltech) and holds the William H. Hurt Scholar distinction since 2021. His research integrates machine learning, computational mathematics, and seismology to analyze earthquakes and fault zones using large seismic datasets. Education B.S., University of California, Davis (2009) M.S., California Polytechnic State University, San Luis Obispo (2011) Ph.D., University of Southern California (2016) His research focuses on high-resolution imaging of fault zones, understanding earthquake sequences in space and time, and applying artificial intelligence to seismic data analysis. He develops scalable algorithms for waveform inversion, ground-motion synthesis, and real-time seismic monitoring. Recent publications highlight his work on neural operators for wave propagation, AI-driven seismicity analysis, and induced earthquake dynamics. He teaches advanced courses including Ge 264 – Machine Learning in Geophysics and Ge 271 – Dynamics of Seismicity . Scientific Awards William H. Hurt Scholar (2021–present)

Damian Arellanesمشاهده پروفایل

مدرس

• Theoretical Computer Science
• Algebraic Composition
• Models of High-Level Computation
• +۵ مورد دیگر

Dr. Damian Arellanes is a Lecturer (Assistant Professor) in Computer Science at Lancaster University, UK, affiliated with the Software Engineering Group and Lancaster Centre for Intelligent, Robotic and Autonomous Systems (LIRA). He holds a PhD from The University of Manchester (2020) and a Postgraduate Certificate in Academic Practice from Lancaster University (2023). His research focuses on theoretical foundations of algebraic composition for high-level computation models, including emergent/self-organising systems and software composition. He has contributed to areas such as category theory, control-flow separation, and compositional programming for IoT systems. Education: PhD in Computer Science, University of Manchester (2020) Postgraduate Certificate in Academic Practice, Lancaster University (2023) MSc in Computer Science, supported by CONACYT (2012–2014) BEng in Computer Engineering, supported by PRONABES (2009–2012) Research Interests: Damian’s work emphasizes algebraic semantics, compositional models for software, and theoretical computer science principles. He explores how abstract mathematical frameworks (e.g., category theory) can formalize computational systems and enable scalable IoT solutions. Publications: Damian has published extensively on algebraic composition, IoT systems, and formal methods. Key themes include compositional programming, self-organizing software, and scalable service architectures. Awards: Official Nominator for the VinFuture Prize (2024) Honourable Mention for Most Outstanding Mexican Student in STEM (2021) Nick Sanders Kickstarter Fund (2019) Outstanding Doctoral Paper Award (2019) Best MSc Thesis in AI (2015) Advising & Grants: Damian supervises PhD students, such as Mina Yavari, and actively reviews for journals like IEEE TSC and conferences like TASE. He has secured scholarships and fellowships from CONACYT and the Mexican government. Labs/Teams: Member of LIRA’s Fundamentals Section and the Software Engineering Group at Lancaster University.

James Saundersonمشاهده پروفایل

مدرس ارشد

• Convex Optimization
• Semidefinite Programming
• Polynomial Optimization
• +۴ مورد دیگر

Dr. James Saunderson is a Senior Lecturer and Director of Education in the Department of Electrical and Computer Systems Engineering at Monash University. He holds a PhD in Electrical Engineering and Computer Science from MIT and has held postdoctoral roles at Caltech and the University of Washington. His expertise spans convex optimization, semidefinite programming, and quantum information theory. Education : PhD in EECS, MIT (2015) MS in EECS, MIT (2011) Bachelor of Engineering (Honours) and Bachelor of Science (Honours), University of Melbourne (2008) Research Interests : Convex optimization, quantum information theory, signal processing, and algorithm design. Focuses on algebraic and geometric aspects of optimization, with applications in engineering and quantum systems. Recent Projects : Exploiting duality in quantum relative entropy optimization Hyperbolic programming and conic optimization Applications in nanotechnology and bioinformatics Teaching : Courses include Control System Design, Signals and Systems, and Optimization for Engineers. Awards : SIAM Optimization Best Paper Prize (2020) Grants and Collaborations : Australian Research Council Discovery Early-Career Research Fellow (2020–2024) Leading projects in quantum optimization and bioengineering applications.

• Nonlinear analysis
• Scattering theory
• Real and complex analysis
• +۷ مورد دیگر

Ronald Coifman is the Sterling Professor of Mathematics and Professor of Computer Science at Yale University. His research focuses on nonlinear analysis, scattering theory, complex analysis, numerical analysis, and their applications in data science, signal processing, and biomedical imaging. He holds the National Medal of Science and is a member of the National Academy of Sciences and the American Academy of Arts and Sciences. Coifman's work bridges pure mathematics and applied sciences, emphasizing harmonic analysis, manifold learning, and data-driven modeling. His contributions include foundational advancements in wavelet theory, diffusion maps, and nonlinear dimensionality reduction techniques. Key innovations include the development of empirical intrinsic geometry for analyzing complex systems and the use of Wasserstein distances in high-dimensional data analysis. His academic portfolio includes over 250 publications since the 1960s, spanning topics from theoretical mathematics to practical medical diagnostics. Notable applications include methods for stroke detection, medical imaging analysis, and anomaly detection in dynamic systems. Coifman collaborates across disciplines, integrating computational methods with domain-specific challenges in biology, chemistry, and engineering. Education: Ph.D. in Mathematics from the University of Geneva (1965) Awards: National Medal of Science (2001), Member of NAS (1993), Member of AAAS (2006) Key Projects: Development of diffusion maps, manifold learning algorithms, and empirical geometry frameworks Coifman's current research explores the intersection of machine learning and mathematical analysis, with recent focus on intrinsic data organization, emergent dynamical models, and scalable computational methods for large datasets.

• Control Theory
• Distributed Control
• Networked Systems
• +۱۲ مورد دیگر

Emma Tegling is a Senior Lecturer (Associate Professor) at the Department of Automatic Control, Faculty of Engineering (LTH), Lund University, Sweden. She joined the department in January 2021 and holds a prestigious WASP (Wallenberg AI, Autonomous Systems and Software Program) professorship. Her research focuses on the analysis and control of large-scale networked systems, with applications in distributed electric power networks and socio-epidemiological networks. She is actively involved in multiple research projects, supervises several PhD students, and contributes to major academic events in control theory. Education: Ph.D. in Electrical Engineering, KTH Royal Institute of Technology (2019) M.Sc. in Engineering Physics, KTH Royal Institute of Technology (2013) B.Sc. in Engineering Physics, KTH Royal Institute of Technology (2011) Emma Tegling's research centers on the fundamental limitations of distributed control, particularly in large-scale and non-normal network systems. Her work addresses critical challenges in vehicular formations, power grids, and social networks. She develops scalable control designs, consensus protocols, and optimal control strategies for complex networked environments. Her recent publications highlight breakthroughs in string stability, transient performance, and distributed optimization. The trend in her articles shows a strong focus on mathematical control theory, network dynamics, and real-world applications in socio-technical systems. Scientific Awards: WASP professorship (Wallenberg AI, Autonomous Systems and Software Program) Emma Tegling leads and co-leads several significant research grants, including WASP NEST: Learning in Networks and Dynamics of Complex Socio-Technological Network Systems. She actively supervises PhD students such as Jonas Hansson and David Ohlin, whose work has led to novel consensus protocols and optimal control formulations. Her academic leadership extends to organizing the European Control Conference and co-organizing interdisciplinary workshops on power and democracy in modern societies. She is also involved in public engagement and academic service through supervision and project coordination. Emma Tegling is a key member of the Department of Automatic Control at Lund University, contributing to research teams focused on networked systems, control theory, and AI integration. She collaborates extensively within ELLIIT (the Linköping-Lund initiative on IT and mobile communication) and participates in cross-disciplinary labs working on AI, digitalization, and natural/artificial cognition. Her work is aligned with UN Sustainable Development Goals related to sustainable energy and resilient infrastructure.

Ruben Loaiza Mayaمشاهده پروفایل

دانشیار

• Copula Modelling
• Bayesian Estimation Methods
• Time Series Analysis
• +۴ مورد دیگر

Ruben Loaiza-Maya is an Associate Professor (Research) in the Department of Econometrics and Business Statistics at Monash University. He holds a PhD in Econometrics from the University of Melbourne and an undergraduate degree in Economics from Universidad Nacional de Colombia (Medellin). His research focuses on Copula Modelling, Bayesian Estimation Methods, Time Series Analysis, and Macroeconomic/Financial Forecasting. Key contributions include advancements in variational inference, state space models, and robust forecasting techniques under model misspecification. He leads the active project 'Variational Inference for Intractable and Misspecified State Space Models' (2023–2026), funded as a Primary Chief Investigator. His work contributes to UN Sustainable Development Goals through methodological advancements in economic and financial analysis. Recent research emphasizes scalable Bayesian methods, hybrid variational approaches, and efficient computational techniques for high-dimensional models. Publications span prestigious journals like the International Journal of Forecasting, Journal of Econometrics, and Journal of Business and Economic Statistics. Notable collaborations include studies on copula-based time series forecasting and robust approximate Bayesian computation. His work bridges theoretical econometrics with practical applications in risk management and macroeconomic policy.

• Artificial Intelligence
• Machine Learning
• Data Integration
• +۳ مورد دیگر

Prof. Geert-Jan P.M. Houben is a Professor at Delft University of Technology's Web Information Systems Department within the Faculty of Electrical Engineering, Mathematics and Computer Science. His research focuses on AI ethics, machine learning, data integration, and decision support systems. He has published over 165 works and supervised 24 students. Notable contributions include frameworks for meaningful human control in AI systems and methodologies for bias mitigation in data-driven systems. Editorial roles: Editor for CEUR-WS, Springer, and other publishers since 2012 Awards: Royal Honours from TU Delft (2025) Research emphasizes ethical AI, data engineering, and scalable systems. Recent work addresses AI governance, bias in analytics, and interactive data discovery in modern data ecosystems. He actively contributes to conferences like ACM Web Science and VLDB.

• Bioinformatics
• Genomic Epidemiology
• Metagenomics
• +۳ مورد دیگر

Tommi Mäklin is a researcher affiliated with the University of Helsinki, conducting interdisciplinary research at the intersection of bioinformatics, microbiology, and genomic epidemiology. His work focuses on bacterial pathogen analysis, metagenomics, and the application of computational methods to study infectious diseases. Mäklin has contributed to high-impact studies on topics such as colibactin-producing Escherichia coli's link to cancer incidence, hospital-acquired infections during the pandemic, and enhanced metagenomic analysis tools like TRACS and Themisto. Key affiliations include visiting research positions at the University of Oslo (2024) and EMBL European Bioinformatics Institute (2023–2023), where he received the Theory@EMBL Visitor fellowship. His research has been featured in prominent journals like Nature Communications and The Lancet Microbe , with findings highlighted in media such as Yle and HS. Education: Not explicitly stated in provided materials. Research Interests: Genomic epidemiology, bacterial pathogen transmission, metagenomics, antibiotic resistance, and computational methods for microbial analysis. His recent work explores geographical cancer incidence correlations with bacterial exposure, hospital pathogen surveillance, and scalable genomic tools for outbreak analysis. Awards include recognition for methodological advancements in bioinformatics and microbial genomics.

Christian Plesslمشاهده پروفایل

استاد

• High-Performance Computing
• Reconfigurable Computing
• FPGA Acceleration
• +۳ مورد دیگر

Prof. Dr. Christian Plessl is a W3 Professor of High-Performance Computing at the Institute of Computer Science, University of Paderborn. He leads the Paderborn Center for Parallel Computing (PC²), a national HPC center within the NHR alliance. His roles include Director of PC², Board Member of the NHR association, and member of the Sonderforschungsbereich 901. Education: PhD (Dr. sc. ETH) in Computer Engineering, ETH Zürich (2006) MSc in Electrical Engineering, ETH Zürich (2001) Postdoc at ETH Zürich (2007–2011) Research Interests: Architecture and tools for high-performance parallel and reconfigurable computing, FPGA acceleration, quantum chemistry, scientific computing, adaptive systems, and energy-efficient HPC solutions. Key projects include EKI-App (FPGA-based neural networks), FPGA4XPCS (X-ray spectroscopy), and HighPerMeshes (unstructured grid frameworks). Publications: Over 100 peer-reviewed works, focusing on FPGA acceleration, HPC frameworks, and quantum computing. Recent trends emphasize energy-efficient neural networks, FPGA-based quantum computing, and scalable HPC algorithms. Awards: Best Paper Awards at HEART 2023, ReConFig 2012/2014 Paderborn University Research Awards (2018, 2009) SEW-EURODRIVE Student Award (2001) Grants & Projects: Principal investigator in DFG, BMBF, and EU-funded projects. Collaborates with AMD/Xilinx, Intel/Altera, and Fujitsu. Leads initiatives like PerficienCC (custom computing) and HighPerMeshes. Labs/Teams: Directs the High-Performance Computing group at PC², focusing on FPGA supercomputing and HPC infrastructure. Active in the NHR alliance for national HPC coordination.

Susan Howickمشاهده پروفایل

استاد

• System Dynamics Modelling
• Operational Research
• Management Science
• +۱۰ مورد دیگر

Susan Howick is a Professor of Management Science and Vice-Dean (Academic) at Strathclyde Business School, University of Strathclyde. Her academic leadership and research are centered on system dynamics and decision support modelling, with applications in healthcare, energy, and public policy. Research Interests: System dynamics and hybrid modelling (SD & ABM) Integration of multiple modelling methods for decision support Disruption and delay analysis in complex projects Systemic risk evaluation in engineered and social systems Client-oriented modelling processes The recent publications show a strong trend towards interdisciplinary applications, particularly in health systems and Arctic search and rescue, often involving community-driven and culturally sensitive models. Her work increasingly combines Bayesian networks with system dynamics to support decision-making in uncertain, high-stakes environments. Scientific Awards: Operational Research Society Goodeve Medal (2001) EURO Prize for OR for the Common Good Finalist (2022) European Journal of Operational Research Editor's Choice Award (2021) Best Paper Award at 17th International Conference on Group Decision and Negotiation (2017) Elected to General Council of the OR Society (2018) Susan has supervised PhD students and served as co-investigator and principal investigator on multiple research projects, including those funded by EPSRC and GCRF. She has also delivered executive training for organizations such as Bombardier, PwC, and the Scottish Government, demonstrating strong industry engagement. Labs and Research Teams: She is actively involved in research groups focused on system dynamics, risk modelling, and decision support, collaborating with teams in healthcare (e.g., SSM - Health Systems), Arctic resilience (Nunavut Search and Rescue Project), and hybrid simulation. She contributes to the academic community through editorial roles in European Journal of Operational Research and System Dynamics Review .

Graeme Braggمشاهده پروفایل

استاد آموزشی

• Parallel Computing
• Event-driven Systems
• Bioinformatics
• +۵ مورد دیگر

Dr Graeme Bragg is a Senior Teaching Fellow at the University of Southampton within the Department of Electronics and Computer Science . His work spans teaching, research, and technical development with a focus on event-driven computing, bioinformatics, and computational modeling. He actively supervises PhD students and collaborates on interdisciplinary projects. Research Interests: Parallel computing, event-driven systems, genotype imputation, Petri net simulations, subglacial hydrology modeling Teaching: Specializes in hardware description languages and computational methods for engineering students Technical Expertise: RISC-V architecture, FPGA acceleration, bespoke compute fabric development His recent publications demonstrate expertise in applying event-driven computing to diverse problems including: 2025: Automated marking systems for SystemVerilog labs 2025: Seasonal dynamics in subglacial hydrology 2023: Genotype imputation using custom hardware 2022: Optimization algorithms and graph analysis Current research explores: Custom RISC-V FPGA clusters for bioinformatics Event-triggered systems for scientific simulations Parallel computing solutions for molecular modeling Contact: gmb@ecs.soton.ac.uk | +44 23 8059 2784

• Nonvolatile Memory
• Neuromorphic Computing
• Radiation Effects on Electronics
• +۳ مورد دیگر

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.

Joergen Kornfeldمشاهده پروفایل

پژوهشگر

• Connectomics
• Neuroscience
• Machine Learning
• +۳ مورد دیگر

Joergen Kornfeld is a researcher at the University of Cambridge, affiliated with the MRC Laboratory of Molecular Biology (LMB) in the Connectomics of Learned Behaviour group. His work focuses on understanding how learned behaviors are encoded in neural circuits through connectomic analysis. Institution: University of Cambridge Role: Connectomics Researcher Research Interests: • Connectomics and synaptic connectivity mapping • High-throughput 3D electron microscopy • Deep learning applications in neural network analysis • Behavioral memory storage mechanisms • Comparative neuroanatomy of learned behaviors • Computational modeling of neural circuits. Recent publications highlight his expertise in developing deep learning tools (e.g., DeepFocus, SyConn2) for connectomic reconstruction, with applications in zebra finch song learning and larval zebrafish neural circuits. His work bridges advanced imaging techniques, computational methods, and behavioral neuroscience. Techniques: High-throughput 3D electron microscopy, flood-filling networks Model Systems: Zebra finch, larval zebrafish