جستجوی استاد راهنمای دانشگاهی

Michela Becchiمشاهده پروفایل

دانشیار

• Computer Architecture
• Systems Software
• Heterogeneous Computing
• +۵ مورد دیگر

Michela Becchi is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. She specializes in computer architecture, systems software, and applications, with a focus on heterogeneous systems, parallel algorithms, and acceleration techniques for bioinformatics, pattern recognition, and quantum computing. Her work spans multi-core CPUs, GPUs, FPGAs, and distributed clusters, emphasizing the boundary between hardware and software design. Dr. Becchi holds a Ph.D. and Master’s degree in Computer Engineering from Washington University in St. Louis (2009) and a Bachelor’s degree in Computer Engineering from Politecnico di Milano, Italy (2000). Her research has been recognized with prestigious awards, including the NSF CAREER Award (2015) and the University of Missouri System President Award for Early Career Excellence (2016). Her research interests include compiler and runtime techniques for heterogeneous systems, acceleration of bioinformatics algorithms, and high-speed networking applications. She has pioneered frameworks for efficient data transformation, GPU-accelerated compression, and memory-efficient graph algorithms for quantum computing. Her work also explores thread coarsening, mixed-precision auto-tuning, and secure multi-core processor design. Key contributions include the PILOT runtime system for GPU memory management, the GPU-FPtuner auto-tuner for floating-point applications, and innovative approaches to automata processors for genomic analysis. Her publications emphasize reproducible accuracy in scientific simulations and the optimization of irregular applications on many-core platforms.

• Autonomous Driving
• Sensor Fusion
• Intelligent Transportation Systems
• +۵ مورد دیگر

Prof. Dr.-Ing. Christoph Stiller is a full professor at the Karlsruher Institut für Technologie (KIT) and serves as the director of the Institute of Measurement and Control Technology (Institut für Mess- und Regelungstechnik, MRT). His work focuses on autonomous driving, sensor fusion, probabilistic estimation, HD mapping, motion planning, and intelligent transportation systems. Education: Details on his academic degrees are not provided in the text, but he holds the title of Dr.-Ing. indicating a doctoral degree in engineering. Research Interests: Prof. Stiller's research spans a wide array of topics critical to the development of autonomous vehicles. His work includes: Sensor Fusion: Integrating data from LiDAR, cameras, and radar to create robust perception systems. HD Mapping & Localization: Developing high-definition maps and precise localization techniques for urban and highway environments. Motion Planning & Decision Making: Creating algorithms for safe and efficient trajectory planning under uncertainty. Machine Learning & AI: Applying deep learning and reinforcement learning to perception, prediction, and control tasks. Publication Trends: His recent publications (2023–2025) emphasize robust traffic light detection, image stitching for panoramic views, motion prediction using redundancy reduction, and safety-enhanced model predictive control. The work increasingly integrates learning-based methods with classical control and estimation theory. Scientific Awards: No specific awards are listed in the provided text. Teaching & Supervision: Prof. Stiller teaches foundational and advanced courses in measurement and control systems, probabilistic estimation, and autonomous driving. He holds regular office hours during both summer and winter semesters and is actively involved in advising students and researchers. Labs & Teams: He leads the Institute of Measurement and Control Technology (MRT) at KIT, which is engaged in cutting-edge research in autonomous systems. The institute collaborates with industry and academia on large-scale projects such as UNICARagil and various European initiatives.

Xiaofeng Liuمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Medical Imaging
• +۵ مورد دیگر

Xiaofeng Liu is an Assistant Professor at Yale University School of Medicine in the Departments of Radiology & Biomedical Imaging and Biomedical Informatics & Data Science. He is also an Associate Member at the Broad Institute of MIT and Harvard. Previously, he held faculty positions at Harvard Medical School and research roles at Massachusetts General Hospital and Beth Israel Deaconess Medical Center. PhD in Mechatronics from University of Chinese Academy of Sciences Dual Bachelor's degrees in Automation (Wang-Daheng Elite Class) and Communication from University of Science and Technology of China His research integrates trustworthy AI, medical imaging, and data science to improve diagnosis, prognosis, and treatment monitoring for neurological disorders, cancer, and cardiovascular diseases. Key focus areas include domain adaptation techniques, diffusion models, and interpretable AI systems. Led special issues in IEEE Transactions on Pattern Analysis and Medical Image Analysis Developed novel frameworks like Ordinal UDA and Memory-Consistent Adaptation Scientific accolades include the Trailblazer R21 Award (NIBIB), OpenAI Research Award, and National Artificial Intelligence Research Resource Pilot Award. He serves as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and actively contributes to MICCAI and NIH review panels. His lab at Yale (XLiu Lab) investigates neural basis of intelligence to inspire AI development, with applications in brain tumor segmentation, cardiac imaging, and cross-modal medical diagnostics.

Babak Moaveniمشاهده پروفایل

استاد

• Structural Health Monitoring
• Bayesian Inference
• Earthquake Engineering
• +۷ مورد دیگر

Babak Moaveni is a Professor in the Department of Civil and Environmental Engineering at Tufts University, serving as the Associate Chair since September 2024. He also holds a joint appointment as a Professor in Electrical and Computer Engineering. His research focuses on structural health monitoring, Bayesian inference, earthquake engineering, and offshore wind energy systems. Moaveni earned his Ph.D. in Structural Engineering from the University of California San Diego (2007), following an M.S. (2001) and B.S. (1999) from Sharif University of Technology in Tehran, Iran. His research interests span probabilistic system identification, signal processing, uncertainty quantification, and verification/validation of computational models. Notable grants include leadership in the PIRE project on offshore wind energy digital twins and the Coastal Virginia Offshore Wind Pilot Project. He has supervised multiple Ph.D. and M.S. students, with current advisees including Mehdi Akhlaghi and Nasim Partovi-Mehr. Moaveni has received the Best Presentation Award at the 2022 EDGE Symposium and serves on editorial boards for journals like Structural Health Monitoring and Frontiers in Built Environment . His lab, the Structural Health Monitoring Lab, specializes in infrastructure management and offshore wind energy systems. Key professional activities include membership in the American Society of Civil Engineers (ASCE) and roles on Tufts' Tenure and Promotion Committee. His teaching includes courses on structural health monitoring, numerical methods, and structural reliability.

Lateef Akanjiمشاهده پروفایل

مدرس ارشد

• Petroleum Engineering
• Multiphase Flow in Porous Media
• Enhanced Oil Recovery (EOR)
• +۷ مورد دیگر

Dr. Lateef Akanji is a Senior Lecturer in the Department of Petroleum Engineering at the School of Engineering, University of Aberdeen, where he has been contributing since 2014. He previously served as Lecturer and Head of the Petroleum Technology Research Group at the University of Salford, Assistant Professor at King Saud University, and Visiting Lecturer at the University of Leoben. His academic journey includes a PhD from Imperial College London and degrees from the University of Ibadan. University: University of Aberdeen School: School of Engineering Position: Senior Lecturer, Petroleum Engineering Email: l.akanji@abdn.ac.uk Education: PhD, Petroleum Engineering, Imperial College London M.Sc., Petroleum Engineering, University of Ibadan B.Sc. (Honours), Petroleum Engineering, University of Ibadan DIC (Diploma of Imperial College) Research Interests: Dr. Akanji's research centers on multiphase flow in porous and permeable media, with applications in enhanced oil recovery (EOR) in clastic, carbonate, and unconventional shale reservoirs. His work integrates theoretical, experimental, and computational fluid dynamics, utilizing platforms like Python, C++, and Fortran. He is pioneering the application of artificial intelligence in petroleum engineering, particularly in EOR screening and production optimization. His research includes pore-scale modeling, gas-lift systems, and nuclear reactor flow dynamics. Publication Trends: His recent publications (2025–2021) reflect a strong focus on fluid displacement in porous media, shale reservoir characterization, AI applications in energy, and nuclear safety. Notable themes include computational modeling of multiphase flow, biosurfactant EOR, and advanced numerical methods for reservoir simulation. Scientific Awards and Honors: Fellow of the Higher Education Academy (FHEA) Chartered Engineer (CEng) Chartered Petroleum Engineer European Engineer (Eur Ing) Member of the Energy Institute (MEI) Advising and Grants: Dr. Akanji supervises numerous PhD students in areas such as AI-based production optimization, permeability upscaling, and biosurfactant EOR. He leads research funded by PTDF, TETFUND, Sonangol, and Elphinstone, focusing on high-pressure high-temperature flow loops, gas-lift pilot rigs, and neuro-fuzzy screening systems. His collaborative projects involve institutions in the UK, Austria, and Australia. Laboratories and Research Platforms: He contributes to the development of the Complex System Modelling Platform (CSMP++), a C++-based API for simulating multi-physics flow in porous systems, co-developed with ETH Zurich and Montanuniversität Leoben. He also leads a technology innovation platform for EOR, including experimental rigs for biosurfactant screening and gas-lift stability testing.

Brian Maggiمشاهده پروفایل

استاد

• Coastal Engineering
• Geotechnical Engineering
• Coastal Resiliency
• +۵ مورد دیگر

Brian Maggi, Ph.D., P.E., is a Professor and Department Head at the U.S. Coast Guard Academy's Department of Civil and Environmental Engineering. He has military, academic, and research experience in geotechnical engineering, coastal engineering, and construction project management. Education: Ph.D. in Civil Engineering from the University of Rhode Island (2022) M.S. in Civil Engineering from the University of Connecticut (2005) B.S. in Civil Engineering from the U.S. Coast Guard Academy (2000) Maggi's research focuses on coastal resiliency, reinforced dunes, and erosion modeling. He has conducted field-monitoring programs using drones, LiDAR, and advanced equipment to develop fragility curves for coastal protection systems. His publications include case studies on geosynthetic sand containers for dune reinforcement and educational strategies for integrating coastal resiliency into engineering curricula. Maggi collaborates with colleagues to incorporate Short Range – Unmanned Aircraft Systems (SR-UAS) into the USCGA academic and mission programs. Professional Affiliations: American Society of Civil Engineers (ASCE) Society of American Military Engineers (SAME) American Society of Engineering Education (ASEE) Maggi teaches courses such as Civil Engineering Design, Construction Project Management, and Short Range – Unmanned Aircraft Systems. He is certified as an SR-UAS Instructor Pilot and contributes to multidisciplinary research and mission support for the Coast Guard.

Constantin Grigoمشاهده پروفایل

پژوهشگر

• Machine Learning
• Uncertainty Quantification
• Stochastic Differential Equ://grigo.userweb.mwn.de/ueber-mich/
• +۲ مورد دیگر

Constantin Grigo is a PhD researcher at the Technical University of Munich (TU Munich), actively engaged in the Continuum Mechanics group. His work focuses on Uncertainty Quantification (UQ) and Machine Learning (ML), particularly for applications in maritime safety, bicycle traffic modeling, and stochastic systems. He has presented his research at major conferences like SIAM UQ and WCCM, and has been recognized with Student Travel Awards from SIAM UQ 2018 and SIAM CSE 2019. Education: Master of Science in Physics, LMU Munich (2015) Bachelor of Science in Physics, LMU Munich (2012) Year abroad at Grenoble INP (2010-2011) Research Interests: Probabilistic machine learning for coarse-graining high-dimensional systems Bayesian model and dimension reduction Stochastic differential equations in heterogeneous media Microscopic traffic simulation for bicycles and autonomous vehicles Digital twin applications for maritime and urban mobility Reduced-order modeling of random materials Selected Awards: SIAM UQ 2018: Student Travel Award Winner SIAM CSE 2019: Student Travel Award Winner His publications span topics such as data-driven scenario specification for autonomous vehicles, bicycle maneuver prediction using neural networks, and physics-constrained surrogates for UQ. He also contributes to open-source simulation tools like SUMO for traffic modeling.

Angel Santamaria Navarroمشاهده پروفایل

دانشیار

• Mobile Robotics
• Autonomous Systems
• Human-Robot Interaction
• +۳ مورد دیگر

Angel Santamaria-Navarro serves as Associate Professor at Polytechnic University of Catalonia (UPC) and Robotics Researcher at the Institute of Robotics and Industrial Informatics (IRI), a CSIC-UPC joint center in Barcelona. His work bridges academic research and real-world deployment in mobile robotics, with current leadership in European TRIFFID (autonomous first-responder systems) and national LENA (lifelong navigation learning) projects spanning urban logistics and emergency response applications. His research centers on Mobile Robotics and Autonomous Systems with emphasis on human-robot collaboration in unstructured environments . Key focus areas include robot navigation in crowded urban settings, manipulation of deformable objects, and deployment of delivery systems like the LogiSmile project piloted across European cities. His work uniquely integrates machine learning with field robotics to solve practical challenges in last-mile logistics and disaster response. Recent publications (2022-2025) reveal a concentrated shift toward real-world robotic deployment , particularly in emergency response (TRIFFID) and urban logistics. Dominant themes include communication-aware multi-robot coordination, probabilistic perception for dynamic environments, and human acceptance studies – reflecting his commitment to transitioning lab innovations to operational field systems through Horizon Europe and national projects. Key recognitions include: 1st place at DARPA Subterranean Challenge, urban circuit (2020) 2nd place at DARPA Subterranean Challenge, tunnel circuit (2019) Beatriu de Pinós research fellowship (2021) Georges Giralt PhD award finalist (2018) R3 accreditation as consolidated researcher (2023) He actively mentors next-generation researchers as supervisor of PhD candidate Hafsa Taher (deep learning for autonomous vehicles) and Master's student Joan Tur Ruiz (object pose tracking). His research portfolio includes €5M+ in competitive funding spanning Horizon Europe projects (TRIFFID, TORNADO), national initiatives (LENA, AUDEL), and industry partnerships like the Vaive Logistics spin-off co-founded in 2023. As core member of UPC's robotics team, he contributed to the NeBula framework that won DARPA's Subterranean Challenge and currently leads development of TRIFFID's autonomous first-responder systems. His work with the LogiSmile consortium demonstrates practical urban deployment of delivery robots across Barcelona, Lisbon, and Milan, while the SOCIAL PIA project pioneers cybernetic avatars for cooperative human-robot teams.

Jeffrey M. Chungمشاهده پروفایل

دانشیار

• Epilepsy
• Neural Signal Processing
• Neurosurgery
• +۴ مورد دیگر

Jeffrey M. Chung, MD, FAAN is an Associate Professor of Neurology at Cedars-Sinai Medical Center. He serves as Director of both the Epilepsy Program and the Neurophysiology Laboratory since 2013. His clinical practice focuses on epilepsy and neurophysiology, with 26 years of experience in neurology and clinical neurophysiology. Northwestern University Medical School (1999) - MD Evanston Hospital (2000) - Internship UCLA (2003) - Residency UCLA (2005) - Fellowship Dr. Chung's research explores epilepsy mechanisms, neural signal processing , multi-modal probabilistic mapping for surgical outcomes, and device-oriented therapies . His recent publications focus on theta phase precession in memory , single-neuron correlates of seizures , and neuroimaging in post-stroke conditions . He leads clinical trials on novel anti-seizure agents and neuromodulation techniques. Circle of Friends – Grateful Patient Donation Honoree (Cedars-Sinai) Southern California Super Doctors (Los Angeles Magazine) Falks Medical Scholar (Arthritis Foundation) Golden Apple Teaching Award (Cedars-Sinai) Dr. Chung directs the Epilepsy Program and Neurophysiology Laboratory at Cedars-Sinai, while also mentoring through journal clubs and clinical teaching. He has received multiple industry grants from companies like Marinus Pharmaceuticals and Vertex Pharmaceuticals for trials on treatment-resistant epilepsy. His work bridges clinical care with neurophysiological research and advanced neural signal processing .

Daniel Boleyمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Data Mining
• Machine Learning
• +۵ مورد دیگر

Daniel Boley is a Professor and Distinguished University Teaching Professor at the University of Minnesota, within the College of Science and Engineering, Department of Computer Science and Engineering. He serves as the Director of Graduate Studies for the Graduate Program in Data Science, which offers a Master's of Science and a Post-Baccalaureate Certificate. His office is located in Kenneth H. Keller Hall at 4-225C. Professor Boley's research spans computational methods in linear algebra, scalable data mining algorithms, and applications in systems biology and bioinformatics. His work focuses on scalable algorithms for convex optimization in machine learning, analysis of networks and graphs from metabolic biochemical networks, and wireless device networks. He has made significant contributions to numerical linear algebra methods for control problems, parallel algorithms, and iterative methods for matrix eigenproblems. His research interests also include algebraic models in systems and evolutionary biology, and biochemical metabolic networks. His recent publications demonstrate a strong focus on applying graph theory and network analysis to diverse domains including robot swarms, medical imaging (particularly for glioblastoma and COVID-19 diagnosis), and metabolic network analysis. His work bridges theoretical computer science with practical applications in biology and medicine, with a consistent emphasis on developing scalable computational methods. The trend shows increasing interdisciplinary collaboration, particularly with medical researchers. Distinguished Member by the ACM Top university award for post baccalaureate, graduate and professional education Distinguished University Teaching Professor title Professor Boley has advised numerous PhD students including Tatiana Lenskaia (2021), Shaozhe Tao (2018), Ham Ching Lam (2014), and others dating back to 1994. His research has been supported by various grants enabling work on scalable computation of elementary pathways through metabolic networks, Markov models of viral evolution, and scalable data mining algorithms for text analysis. He has developed software tools for clustering, dot plot visualization, and educational graphics. Professor Boley directs the Graduate Program in Data Science and has been involved in projects such as the Principal Direction Divisive Partitioning (PDDP) Project. His research group develops practical implementations of theoretical advances, including the PDDP clustering algorithm, Dot.py genome viewer, and various educational graphics tools for introductory programming courses. He maintains active collaborations across disciplines, particularly in bioinformatics and medical imaging applications.

• Data management and data science
• Artificial intelligence
• Distributed computing and systems software
• +۶ مورد دیگر

Thomas C Henderson is a tenured Professor at the School of Computing within the College of Engineering at the University of Utah. His research focuses on autonomous systems , computational models , and intelligent machine systems , with specific interests in biosystem simulation, distributed systems, and adaptive algorithms. He has contributed to Bayesian sensor networks , UAS traffic management , and probabilistic logic frameworks for intelligent agents. Current academic appointments since 1989 Adjunct roles in Biomedical Engineering Active in IEEE committees (2024-2025) Research trends in recent publications emphasize autonomous aircraft coordination , probabilistic reasoning , and multi-sensor integration . Key subfields include lane-based airspace modeling, reinforcement learning for UAS, and pseudogradient navigation techniques. His work has received recognition through a Best Paper Award (IEEE 2008) and the US Air Force Summer Faculty Fellowship . Strategic deconfliction protocols Dynamic data-driven applications Structural health monitoring systems He actively mentors undergraduate research through courses like Deep Learning Capstone and Senior Capstone Design . Current grants include "Deep Learning in AI and Robotics" (2024-2029) and "Robust Reasoning using Geometric SAT/PSAT" (2022-2023).

Mustafa Demirمشاهده پروفایل

مدرس

• Human-Machine Teaming
• Human-AI Teaming
• Advanced Statistical Modeling
• +۱۱ مورد دیگر

Dr. Mustafa Demir serves as an Associate Research Scientist at Arizona State University's Biodesign Center for Applied Structural Discovery and Faculty Associate in the Ira A. Fulton Schools of Engineering. His interdisciplinary work integrates cognitive science and engineering to optimize human-AI collaborative systems across healthcare, transportation, and defense domains through human-centered design principles. Education: Ph.D. in Simulation, Modeling, and Applied Cognitive Science, Arizona State University (2017) Dr. Demir's research centers on human-machine teaming dynamics, employing advanced statistical and nonlinear dynamical systems modeling. His expertise includes quantum cognitive approaches to decision-making, team cognition analysis, and machine learning applications for real-time physiological monitoring. Current projects focus on AI-powered stress management tools, curiosity-driven STEM education systems, and human-autonomy coordination in driving and command environments using eye-tracking and biometric sensing. Analysis of his 2023-2025 publications reveals methodological innovation in dynamical systems analysis (DSA Toolbox) and quantum probability modeling applied to trust calibration in autonomous vehicles, educational technology, and digital health interventions. His work consistently bridges theoretical modeling with real-world implementation in complex sociotechnical systems. Dr. Demir mentors students in cognitive engineering and applied data science while leading multi-institutional research initiatives funded by NSF, AFRL, and DARPA. His grant portfolio supports experimental work across simulated and operational environments including remotely piloted aircraft systems and urban search-and-rescue scenarios. He contributes to the Biodesign Center for Applied Structural Discovery and HLA-Inception research group, developing computational models of team interaction and adaptive AI systems for healthcare and education applications.

• Control Systems
• Energy Management
• Distributed Optimization
• +۴ مورد دیگر

Yutaka Iino serves as an Associate Professor at Waseda University's Advanced Collaborative Research Organization for Smart Society (ACROSS) and Research Institute for Advanced Network Technology (RIANT). With over 35 years of professional experience, he transitioned from a long career at Toshiba Corporation (1984-2018) where he worked on energy management and energy-saving technologies, to his current academic position focusing on smart society element technologies including power, transportation, and information systems. Doctor of Engineering from Tokyo Institute of Technology (2015) Master's in Electrical Engineering from Waseda University (1984) Bachelor's in Electrical Engineering from Waseda University (1982) Professor Iino's research focuses on control and system engineering with particular emphasis on energy management systems and distributed optimization. His work bridges theoretical control systems with practical applications in smart grids, electric transportation, and distributed energy resources. He has pioneered approaches in decentralized optimization, modeling techniques for energy systems, and demand science applications in power networks. His research interests include Di-centralized and distributed optimization, Distributed Energy Resource management, System Control Theory, and Demand Science. Analysis of his recent publications reveals a strong focus on practical applications of distributed energy resources, particularly in the context of electric buses, photovoltaic integration, and grid congestion management. His work demonstrates a consistent trajectory toward developing market-oriented solutions for energy systems, with increasing emphasis on machine learning techniques for parameter optimization and federated learning for smart inverter control. The research spans multiple domains including electrical engineering, transportation systems, and control theory, with a clear practical orientation toward solving real-world energy challenges. SICE Paper Award, No.1995 for 'Cyber-physical Optimal Traffic Signal Control Based on a Macroscopic Traffic Model and Its Verification Using Microscopic Traffic Simulator' Best Paper Award of IEEJ Transactions on Electrical and Electronic Engineering 2021 for 'Evaluation of Mutual Effect between Power and Traffic Aiming Electrified Urban Transportation System Using Integrated Light Rail Transit and Distribution System Model' Professor Iino has secured research funding through Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research, including current projects on cooperative next-generation distribution system operation methods for mass introduction of photovoltaic power generation (2023-2027) and hybrid model analysis methods with data and physical models for automatic energy analysis (2020-2023). His committee memberships include roles in IEEE, Japan Society of Energy and Resources, The Institute of Electrical Engineers of Japan, and The Society of Instrument and Control Engineers (where he holds Fellow status). At Waseda University, Professor Iino contributes to the research ecosystem through the Advanced Collaborative Research Organization for Smart Society, where he leads efforts in developing practical energy management solutions that bridge theoretical control systems with real-world applications in smart grids and transportation electrification. His work exemplifies the integration of academic research with industry applications, leveraging his extensive industrial experience at Toshiba to inform his current academic pursuits.

• Artificial Intelligence
• Machine Learning
• Air Traffic Management
• +۴ مورد دیگر

Assoc Prof Sameer Alam is an Associate Professor in the School of Mechanical & Aerospace Engineering at Nanyang Technological University (NTU), Singapore, and Director of the Air Traffic Management Research Institute (ATMRI), a joint initiative with the Civil Aviation Authority of Singapore. He leads a research team of 26 scientists and 6 PhD students, focusing on developing intelligent air traffic management systems. His expertise spans AI applications in ATM, human-AI teamwork, and deep reinforcement learning. Alam holds a PhD in Computer Science from UNSW Australia (2008) and has held academic roles including Senior Lecturer at UNSW Canberra. He has served as a visiting scientist at NASA Ames and École Nationale de l’Aviation Civile. His research has garnered prestigious awards, including the ANU Science Medal and multiple Best Paper Awards. Education: PhD in Computer Science, UNSW Australia (2008) Postdoctoral research at Eurocontrol and Airservices Australia Research Interests: AI-driven air traffic control systems Machine learning for airport operations Human-AI collaboration in safety-critical environments Grants & Consulting: Lead researcher in strategic projects like the Saab-NTU Joint Lab Technical consultant for ICAO’s Middle East office and Singapore’s Ministry of Transport Labs & Leadership: Director of ATMRI Organizing Co-Chair for ICRAT 2024

David Murrugarraمشاهده پروفایل

دانشیار

• Mathematical Biology
• Systems Biology
• Computational Biology
• +۳ مورد دیگر

David Murrugarra is an Associate Professor in the Department of Mathematics at the University of Kentucky, within the College of Arts & Sciences. His research focuses on Mathematical Biology, with emphasis on Systems Biology and Computational Biology. He holds a PhD in Mathematics from Virginia Tech and completed a postdoctoral fellowship at the School of Mathematics at Georgia Tech before joining UK in 2014. His contact information includes murrugarra@uky.edu and an office at 771 Patterson Office Tower. Education: PhD in Mathematics, Virginia Tech Postdoctoral Research, School of Mathematics, Georgia Tech Research Interests: Dr. Murrugarra develops theoretical and computational tools for modeling gene regulatory networks, optimal control of probabilistic models using Markov decision processes, and RNA secondary structure prediction via machine learning. His funded projects include an NSF grant on modularity in biological systems and a UK Pilot Grant on RNA structure predictability. Recent Research Trends: His work bridges abstract mathematical frameworks with biological applications, emphasizing modularity, network control, and systems-level analysis. Key themes include modular decomposition of biological networks, intervention strategies for cancer systems, and the integration of machine learning into biological modeling. Grants & Projects: NSF grant: Mathematical Theory of Biological Modularity UK Pilot Grant: RNA Secondary Structure Prediction Labs & Collaborations: He leads a weekly Applied and Computational Mathematics seminar and co-organizes the Mathematics Community and Ethics (MCE) Working Group at UK. His research group actively engages in interdisciplinary projects combining mathematics, biology, and computational science.