Find Academic Supervisors

Dorival PedrosoView profile

Senior Lecturer

• Computational Mechanics
• Numerical Methods for Partial Differential Equations
• Porous Media Modeling
• +8 more

Dr. Dorival Pedroso is a Senior Lecturer at the School of Civil Engineering, The University of Queensland , Australia. His expertise lies in numerical and computational methods for solid mechanics, porous media modeling, and geotechnical engineering. Research spans porous media dynamics , molecular dynamics , and evolutionary algorithms . Recipient of the Argyris Lecture Award (2016) , recognizing contributions to finite element method (FEM) for modeling porous media. Developed open-source software libraries (Gosl, Russell) for scientific computing and optimization. Research Themes include: Computational mechanics for unsaturated soils and granular materials . Development of Hencky bar-grid models and isogeometric shell formulations . Multi-objective optimization via parallel evolutionary algorithms. Recent Trends in publications focus on finite element methods , porous media dynamics , and reliability analysis using genetic algorithms. Scientific Awards Argyris Lecture Award (2016): Top honor for FEM innovation in porous media modeling. Grants & Collaborations ARC-DECRA (2012): Highly competitive early-career award. Advance Queensland Project: $2M+ funding as Chief Investigator. ARC Discovery Projects, Linkage Grants, and industry sponsorships. Teaching includes Advanced Soil Mechanics (CIVL4230) and Ground Improvement (CIVL6215) , covering topics like consolidation, liquefaction, and reinforcement techniques.

José Luis Lázaro GalileaView profile

Professor

• Electronics Engineering
• Intelligent Systems
• Positioning Systems
• +3 more

José Luis Lázaro Galilea is a Professor in the Department of Electronics at the University of Alcalá. He is affiliated with the GEINTRA research group, focusing on Electronic Engineering applied to Intelligent Spaces and Transport. His doctoral thesis (1998) explored infrared modeling for mobile robot guidance. Key research areas include visible light positioning systems, FPGA-based hardware architectures, and remote laboratory development. He has contributed to vehicular VLC systems, indoor navigation algorithms, and biomedical applications like closed-loop insulin delivery systems. Notable projects include the ALCOR initiative for optimizing robot guidance in smart environments and the Helpresponder system for emergency intervention safety. Education: PhD in Electronics from University of Alcalá (1998). Supervised by Dr. Manuel Ramón Mazo Quintas. Academic focus spans digital electronics, embedded systems, and sensor networks. Active in educational innovation, including adapting digital electronics practices for blended learning models. Research emphasizes practical applications of electronic systems in smart spaces, with over 70 publications since 1998. Recent work addresses multipath effects in optical positioning, reconfigurable hardware platforms, and human-centered lighting systems. His interdisciplinary approach integrates robotics, telecommunications, and biomedical engineering. Notable contributions include: Development of high-precision indoor positioning systems using PSD sensors Optimization of sensor deployment via evolutionary algorithms Design of FPGA-based real-time processing architectures Innovative remote laboratory frameworks for STEM education Current projects explore vehicular visible light communication, wearable medical devices, and intelligent transportation systems. His work bridges theoretical advancements with practical implementations in both academic and industrial contexts.

Erkan O. BuzbasView profile

Associate Professor

• Metascience
• Reproducibility in Science
• Computational Statistical Methods
• +2 more

Erkan O. Buzbas is an Associate Professor in the Department of Mathematics and Statistical Science at the University of Idaho's College of Science. He teaches courses including Stat 427, 501, 514, 535, and 565. His educational background includes a B.S. in Chemistry and M.S. in Environmental Sciences from Bogazici University, followed by an M.S. in Statistics and Ph.D. in Bioinformatics and Computational Biology from the University of Idaho. Dr. Buzbas's research focuses on metascience, reproducibility, computational statistical methods, and Bayesian statistics. He employs stochastic modeling of population-level phenomena and develops inference methods for complex systems. His current work investigates scientific knowledge accumulation, the role of reproducibility, and epistemic diversity using statistical theory and simulations. His publications emphasize reproducibility, population genetics, and Bayesian methodologies, with recurring themes in scientific reform, model robustness, and evolutionary inference. Awards include recognition for interdisciplinary collaborations spanning natural, social, and mathematical sciences. Buzbas mentors students in statistical and computational research and has secured grants for metascience investigations. He collaborates with researchers across disciplines to advance quantitative understanding of scientific processes.

Sean LukeView profile

Professor

• Evolutionary Computation
• Genetic Programming
• Multi-agent Systems
• +13 more

Sean Luke is a Professor in the Department of Computer Science at George Mason University (GMU) and serves as Associate Director of the Center for Social Complexity at the Krasnow Institute. He leads the GMU Evolutionary Computation Laboratory (ECLab) and is actively engaged in research at the intersection of artificial intelligence, evolutionary computation, and multi-agent systems. His work spans robotics, music technology, simulation, and knowledge representation. Position: Professor, Department of Computer Science, GMU Leadership: Associate Director, Center for Social Complexity Laboratory: ECLab – GMU’s Evolutionary Computation Laboratory Research Focus: Multi-agent systems, evolutionary algorithms, swarm robotics, computational creativity in music, agent-based modeling Sean Luke's research interests center on evolutionary computation, genetic programming, and multi-agent simulation. He has made foundational contributions to the development of MASON, a widely used Java-based multi-agent simulation toolkit, and ECJ, a general-purpose evolutionary computation framework. His work in computational creativity explores how AI can assist in music synthesis and patch design, culminating in the open-access textbook Computational Music Synthesis . He also investigates swarm robotics, human-agent collaboration, and optimization in complex systems such as supply chains. His most recent publications (2020–2024) reflect a strong trend toward interdisciplinary applications of AI and simulation. These include co-creative music systems, planner-guided robot swarms, and agent-based modeling for supply chain resilience. His work frequently appears in top venues such as GECCO, AAMAS, and IJCAI, and emphasizes open science through freely available software and textbooks. Sean Luke has mentored numerous students and researchers, including William Squires, Drew Wicke, Ermo Wei, and Matteo D'Auria, who have co-authored significant works in multi-agent learning, swarm robotics, and model calibration. He has also been involved in the RoboPatriots RoboCup team, demonstrating practical applications of AI in robotics competitions. Key Projects: MASON, ECJ, RoboPatriots, Computational Music Synthesis Open Resources: Essentials of Metaheuristics , Computational Music Synthesis Luke’s work is supported by a strong track record of publications and technical reports, with recent efforts focusing on improving fairness, scalability, and usability in AI systems. He continues to advance the state of the art in evolutionary algorithms and multi-agent learning, with a growing emphasis on real-world applications and human-AI collaboration.

Jose EspirituView profile

Professor

• Reliability Engineering
• Risk Assessment
• Data Analytics
• +4 more

Dr. Jose Espiritu serves as Vice President for Research and Innovation at Texas A&M University-Kingsville, leading all research initiatives across the institution. His office is located in the Memorial Student Union Building (MSC 201) at 700 University Blvd., Kingsville, TX 78363, with contact number 361-593-3344. His educational background includes: Ph.D. in Industrial & Systems Engineering from Rutgers University M.S. in Industrial & Systems Engineering from Rutgers University M.S. in Industrial Engineering - Quality from Instituto Tecnológico de Celaya B.S. in Biochemical Engineering from Instituto Tecnológico de Zacatepec Dr. Espiritu's research focuses on critical engineering challenges through the lenses of Reliability Engineering , Risk Assessment , and Sustainability Engineering . His work bridges theoretical optimization with practical power systems applications, particularly through Distributed Flexible AC Transmission Systems (D-FACTS) technology. A significant portion of his publications addresses renewable energy integration and environmental impact mitigation , demonstrating consistent evolution from foundational reliability studies toward contemporary sustainability challenges. His 2025-2022 publications show increased emphasis on multi-objective optimization frameworks that balance economic, environmental, and operational constraints. His scientific contributions reveal strong patterns in power systems optimization (60% of recent work), sustainability engineering (30%), and interdisciplinary applications (10%). Key recurring themes include transmission flexibility enhancement, biofuel feedstock optimization under environmental constraints, and stochastic modeling for grid resilience. The research consistently employs evolutionary algorithms and multi-objective optimization techniques to address complex engineering problems. As Vice President for Research, Dr. Espiritu oversees institutional research strategy, compliance, and funding initiatives. His leadership extends to promoting sustainability engineering education through programs like the Building Expertise on Energy Sustainability (BEES) initiative mentioned in his 2011 publications. While specific grant details aren't provided in the source materials, his extensive publication record suggests substantial research funding in power systems and sustainability domains.

• Bioinformatics
• Computational Biology
• Machine Learning
• +3 more

Semih Kurt is a researcher at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science (EECS) and the Department of Computer Science's Computational Science and Technology (CST) division. He is also associated with Science for Life Laboratory (SciLifeLab), contributing to Sweden's national molecular biosciences infrastructure through computational genomics research. Education: PhD in Computer Science, KTH Royal Institute of Technology, 2024. Dissertation: "Methods for rapid phylogenetic inference and copy number variation detection from transcriptomics data". Research Interests: Kurt specializes in bioinformatics and computational biology , developing machine learning frameworks for genomic data analysis. His work addresses critical challenges in copy number variation (CNV) inference , phylogenetic reconstruction , and intratumoral heterogeneity using single-cell and spatial transcriptomics. By leveraging variational autoencoders and scalable algorithms , he creates tools that decode cancer progression mechanisms and evolutionary relationships with unprecedented resolution. Publication Trends: Kurt's 2022-2024 publications reveal a strategic focus on accelerating genomic analysis through algorithmic innovation. His CopyVAE and decoST frameworks demonstrate deep learning's power in CNV detection without prior biological knowledge, while Sparse Neighbor Joining reduces phylogenetic computation from quadratic to near-linear complexity. This trajectory shows consistent advancement in computational efficiency and biological interpretability , bridging computer science with cancer genomics and evolutionary biology. Scientific Awards: No scientific awards were mentioned in the provided text. Advising and Grants: The available documentation does not specify student supervision or research funding details. His collaborative work with Jens Lagergren (supervisor) and international co-authors suggests active participation in funded research consortia, though specific grants remain unreported. Labs and Teams: Kurt operates within KTH's Computational Science and Technology (CST) division, which develops computational methods for scientific challenges. His SciLifeLab affiliation connects him to Sweden's premier molecular biosciences hub, facilitating access to high-throughput sequencing facilities and cross-institutional cancer genomics initiatives.

Tyler CassidyView profile

Lecturer

• Mathematical Biology
• Viral Dynamics
• Structured Population Models
• +6 more

Dr. Tyler Cassidy is a Lecturer in Mathematical Biology at the School of Mathematics, University of Leeds. His research focuses on the intersection of mathematics and medicine, using structured population models and delay differential equations to study disease progression and treatment resistance. PhD in Applied Mathematics, McGill University (2019) Postdoctoral work at Los Alamos National Laboratory with Alan Perelson NSERC Postdoctoral Fellowship awardee (deferred) Current research themes: viral dynamics, cancer treatment resistance, oncolytic viruses His mathematical expertise includes developing numerical and analytical techniques for structured models, with applications to HIV, hepatitis B/C, and tumor heterogeneity. Recent work involves virtual clinical trials and multiscale modeling of antiviral therapies. Scientific awards include the NSERC Postdoctoral Fellowship. He serves on the editorial board of PLOS Computational Biology and organizes symposia on delay dynamics in infection modeling. Outside academia, he enjoys running, cycling, rock climbing, and hockey. Pronouns: he/him/his.

• Intelligence Partitioning
• IoT
• Deep Learning
• +5 more

Irida Shallari is a Lecturer in the Department of Computer and Electrical Engineering (DET) at Mid Sweden University, specializing in Intelligence Partitioning for IoT systems. Her research spans Deep Learning , Embedded Systems , and Data Fusion , with applications in Edge Computing , 6G , and Smart Agriculture . Education: Doctoral Thesis (2021): "Intelligence Partitioning for IoT : Design Space Exploration for a Data Intensive IoT Node" (Mid Sweden University) Licentiate Thesis (2019): "Intelligence Partitioning for IoT : Communication and Processing Inter-Effects for Smart Camera Implementation" (Mid Sweden University) Her research focuses on optimizing IoT node architectures, balancing Communication vs. Computation trade-offs, and deploying Deep Learning in resource-constrained environments. Recent work includes Time-dependent Clustering for neural network quantization, ArUco Marker Calibration , and Generative AI in Education ethics. The 15 most recent publications (2025-2024) highlight trends in Distributed CNN Optimization , Model Evaluation for industrial IoT, and cross-disciplinary applications in Environmental Monitoring and Academic Integrity . No scientific awards are explicitly mentioned in the provided text.

Christian AlmederView profile

Professor

• Supply Chain Management
• Operations Research
• Production Planning and Scheduling
• +3 more

Prof. Dr. Christian Almeder serves as Professor and Head of the Chair of Supply Chain Management within the Faculty of Business Administration and Economics at Viadrina European University (Frankfurt (Oder), Germany). His research focuses on operations research applications in production planning, logistics, and supply chain optimization, with particular expertise in lot sizing, scheduling, and perishable goods management. He maintains active research output with publications spanning from 1997 to 2023. Almeder's research centers on mathematical modeling of complex production and logistics systems. His primary contributions involve developing heuristic and metaheuristic solutions for capacitated lot sizing problems, multi-level scheduling, and integrated production-distribution planning. Key specialties include handling perishability constraints, lead time uncertainties, and batch processing requirements using genetic programming, simulation-based optimization, and clearing function approaches. His work bridges theoretical operations research with industrial applications in supply chain management. Analysis of his 15 most recent publications (2013-2023) reveals a consistent focus on lot sizing and scheduling, with increasing emphasis on integrated supply chain problems and perishable goods logistics. Methodologically, he combines metaheuristics (genetic programming, simulated annealing) with simulation techniques to address real-world complexities like stochastic processing times and limited buffers. His work demonstrates strong application in production planning parameter tuning, vehicle routing integration, and robust operational planning under uncertainty.

Kevin LederView profile

Professor

• Stochastic Modeling
• Cancer Evolution
• Rare Event Simulation
• +2 more

Kevin Leder is Professor and Director of Graduate Studies in the Department of Industrial and Systems Engineering at the University of Minnesota. His research spans stochastic modeling of cancer evolution and rare event simulation methodologies. His cancer research develops mathematical frameworks to understand evolutionary dynamics in tumors, including mechanisms of treatment resistance, recurrence patterns, and intratumoral heterogeneity. He applies optimization techniques to design improved therapeutic regimens and develops statistical methods for inferring tumor parameters from clinical data. A second research focus involves rare event analysis for stochastic systems including queuing networks and reflecting diffusions. Leder's publications demonstrate expertise in developing probabilistic models for cancer recurrence timing, inferring cellular dynamics from drug screen data, and optimizing treatment schedules using Bayesian approaches. His interdisciplinary work integrates methods from applied probability, statistics, and operations research. He has received research funding from NSF, NCI, and Fulbright Foundation. As Graduate Studies Director, he oversees academic programs and mentors students in mathematical oncology and stochastic processes.

Larry WassermanView profile

Professor

• nonparametric inference
• causal inference
• machine learning
• +6 more

Larry Wasserman is a UPMC University Professor at Carnegie Mellon University, jointly appointed in the Department of Statistics and Data Science and the Machine Learning Department. He received his Ph.D. from the University of Toronto in 1988 and is recognized as one of the leading statisticians of his generation. His research spans theoretical and applied statistics, with core interests in: Foundational inference : Nonparametric methods, asymptotic theory, causal frameworks Modern applications : Machine learning, high-dimensional statistics, astrostatistics Interdisciplinary domains : Bioinformatics, genomics, physical sciences via the STAMPS group His recent publications demonstrate strong emphasis on causal methodology, optimal transport, and robust inference, with applications ranging from particle physics to genomic analysis. Articles frequently develop novel nonparametric techniques with minimax optimality guarantees. Award highlights include: COPSS Presidents' Award (1999) - Top honor for statisticians under 40 CRM-SSC Prize (2002) - Landmark contributions to statistics Fellowships: American Statistical Association, Institute of Mathematical Statistics, AAAS He leads the Statistical Machine Learning Theory Group and founded STAMPS (Statistical Methods for Physical Sciences). His textbooks All of Statistics and All of Nonparametric Statistics are widely used in graduate programs globally.

• Multi-agent systems
• Artificial intelligence
• Machine learning
• +8 more

Ho-fung Leung is a Professor at the Department of Computer Science and Engineering, Faculty of Engineering, Chinese University of Hong Kong. With a prolific publication record spanning over three decades, his research has significantly contributed to the fields of artificial intelligence, multi-agent systems, and natural language processing. His educational background, though not explicitly stated in the provided text, likely includes advanced degrees in computer science or a related field, given his extensive research contributions and faculty position at a prestigious university. Professor Leung's research interests span multiple areas within artificial intelligence, with a particular focus on multi-agent systems, reinforcement learning, natural language processing, and human-computer interaction. His work often explores the intersection of theoretical foundations and practical applications, developing novel algorithms and frameworks that address real-world challenges in AI systems. He has made significant contributions to constraint satisfaction problems, trust and reputation systems in multi-agent environments, and more recently to deep learning applications in NLP and human activity recognition. His recent publications demonstrate a strong trend toward applying advanced machine learning techniques to complex problems in natural language understanding, knowledge representation, and human activity recognition. Many of his papers focus on improving the efficiency, robustness, and interpretability of AI systems through innovative architectural designs and learning paradigms. Key research themes include few-shot learning, knowledge-enhanced models, and theoretical analysis of reinforcement learning dynamics. Professor Leung has received recognition for his work through numerous publications in top-tier conferences and journals, though specific awards are not detailed in the provided information. He has supervised numerous students throughout his career, with many of his publications featuring junior researchers in first-author positions. His research group appears to focus on cutting-edge problems in AI, with current projects spanning reinforcement learning theory, knowledge graph applications, and multimodal learning systems. Collaborators include researchers from across CUHK and international institutions. Professor Leung is actively involved in multiple research projects, with recent work focusing on human activity recognition using wearable sensors, knowledge-enhanced language models, and theoretical aspects of reinforcement learning. His research continues to evolve while maintaining strong connections to foundational AI principles, demonstrating remarkable adaptability in a rapidly changing field.

Scott YamView profile

Associate Professor

• Photonics
• Optical Communications
• Fiber Optics
• +4 more

Dr. Scott Yam is an Associate Professor in the Department of Electrical and Computer Engineering at Queen's University, Canada. He holds a B.Eng. from the University of Waterloo (1998) and M.Sc./Ph.D. from Stanford University (2000/2004). His research focuses on fiber and opto-electronic devices, with applications in optical communications, sensing, and detection. Key areas include multimode fiber systems, fiber-based sensors, and high-speed signal processing. Education: B.Eng., Electrical and Computer Engineering, University of Waterloo, Canada (1998) M.Sc., Electrical Engineering, Stanford University, USA (2000) Ph.D., Electrical Engineering, Stanford University, USA (2004) Research Interests: Explores the physics of fiber-optic devices and their practical applications. Current projects include: Low-cost high-data-rate transceivers using multimode fibers Fiber-based sensors for strain, refractive index, and industrial applications High-speed digital signal processing in optical systems Molecular optical imaging of biological tissues Awards: Youth Scientist Award (IEEE LEO Japan Chapter) Student Paper Award (IEEE LEO Japan Chapter) Advising & Grants: Supervises research in optical communications and sensing. Has authored over 50 journal/conference papers and holds 2 patents. Active reviewer for journals like Journal of Lightwave Techologies and Optics Express . Labs/Teams: Research integrates photonics, optoelectronics, and biomedical applications through collaborations in optical networking and sensor development.

• Environmental Science
• Air Quality Modeling
• Machine Learning Applications
• +3 more

Dimple Pruthi is a Research Fellow in the Gangarosa Department of Environmental Health at Emory University. Her work focuses on advanced computational methods for air quality prediction, integrating machine learning, satellite data analytics, and environmental modeling. She specializes in PM2.5 forecasting, wildfire impact analysis, and sustainable pollution monitoring systems. Her research interests include developing hybrid machine learning models for environmental applications, analyzing transient weather effects on pollution, and optimizing neuronal networks for air quality indices. She has extensively studied Delhi's air quality dynamics and California wildfires' particulate matter emissions. Key trends in her publications (2016–2025) reveal a focus on satellite-based PM2.5 prediction using GOES-R data, low-cost sensor systems, and fractal/wavelet analysis for pollutant variability. She combines computational techniques with environmental science to address urban pollution challenges and climate change impacts. No scientific awards or grants are explicitly listed. Her advising record is not provided, but her work contributes to environmental health policy through predictive modeling frameworks.

• Biological Sensors
• Semiconductor Device Technology
• Infrared Sensing and Imaging
• +3 more

Brett Nener is a Professor in the School of Engineering (Electrical, Electronic and Computer Engineering) at the University of Western Australia. With an h-index of 23 and 24 h-index citations, he has contributed to 189 publications and 27 grants. His expertise spans III-nitride-based sensors, semiconductor devices, and infrared imaging, aligning with UN Sustainable Development Goals for sustainable technology innovation. Research Interests : Biological sensors using III-nitrides, semiconductor device technology, atmospheric optical propagation, and infrared sensor systems. His work emphasizes sensor stability, material characterization, and adaptive optical systems. Grants & Collaborations : Leads projects on missile detection via multispectral imaging, laser communication ground stations, and cloud climatology for satellite communications. Collaborates with institutions like the Defence Science and Technology Group and SmartSat CRC. Labs & Teams : Involved in MEMS spectrometer development and interdisciplinary teams focusing on sensor integration and optical communication systems. His research bridges material science, robotics, and bioinformatics.