Find Academic Supervisors

Ali JannesariView profile

Associate Professor

• High-Performance Computing
• Machine Learning
• Software Analytics
• +5 more

Ali Jannesari is an Associate Professor and Director of the Laboratory for Software Analytics and Pervasive Parallelism (SwAPP Lab) at the Department of Computer Science, Iowa State University. His research focuses on the intersection of High-Performance Computing (HPC) and AI, aiming to advance reliable and efficient software for modern parallel computing platforms. He has held academic and research roles including Senior Research Fellow at UC Berkeley and leadership positions in Germany's Technical University of Darmstadt and RWTH Aachen University. He holds a Habilitation in Computer Science (2016, TU Darmstadt), PhD (2010, KIT), and M.S. (2005, University of Stuttgart). Education: Habilitation in Computer Science, Technical University of Darmstadt, Germany (2016) Ph.D. in Computer Science, Karlsruhe Institute of Technology (2010) M.S. in Computer Science, University of Stuttgart (2005) Research Interests: High-Performance Computing (HPC), Machine Learning, Parallel Computing, Software Analytics, AI-driven compiler optimization, federated learning, and cross-language code analysis. His work bridges HPC and AI to enhance computational efficiency and scalability in data-driven applications. Recent Contributions: Leading research in GNN-based code parallelization (AutoParLLM), federated multimodal learning, and HPC performance optimization via graph-based methods. His lab's work has been published in top venues like NAACL, ICS, and NeurIPS. Lab & Team: The SwAPP Lab develops tools for software analytics and pervasive parallelism, with a focus on HPC-AI integration. Current projects include optimizing distributed training systems and improving code migration with LLMs.

• Operational Research
• Scheduling
• Production Planning
• +3 more

Mr. Jean-Charles Billaut is a Professor at the Polytechnic School of Tours (EPU) within the University of Tours, affiliated with the Computer Science Department and the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT). His primary research focuses on Operational Research, particularly in scheduling theory, production planning, and logistics optimization, with notable contributions to healthcare and food supply chain systems. He has held leadership roles, including Director of the Computer Science Laboratory since 2007 and Editor-in-Chief of the European Journal of Operational Research since 2007. His work bridges theoretical advancements and real-world applications, addressing challenges in multi-agent scheduling, robust production systems, and emergency logistics. Key collaborations include optimizing chemotherapy production and medical sample dispatching, reflecting his commitment to impactful operational research. His research often employs metaheuristics and exact methods to solve complex scheduling and routing problems, emphasizing sustainability and resilience in supply chains.

• Analogue and Mixed Signal IC Design
• Digital System Design (FPGA)
• CMOS Lab-on-Chip Platforms
• +4 more

Junming Zeng is a Researcher at the Department of Electrical and Electronic Engineering, Faculty of Engineering at Imperial College London. His work focuses on advanced CMOS technologies for biomedical applications, including lab-on-chip platforms and ion imaging systems. He holds a PhD from Imperial College London (2022), following a Master's in Analogue and Digital Integrated Circuit Design (2017) and a Bachelor's in Electronic Engineering (2016), both from UK institutions. His research interests span analogue/mixed-signal IC design, FPGA-based digital systems, and ultra-high-speed ion sensing solutions. He has pioneered CMOS lab-on-chip platforms for real-time chemical monitoring and developed compressed sensing techniques for optimizing sensor array performance. His work integrates deep learning for applications like diabetes glucose prediction and drift compensation in ISFET sensors. Zeng has received the Best Student Paper Award (1st Prize) at ISCAS 2018 and Imperial's Department PhD Scholarship. His research bridges electrical engineering and biomedical engineering, with a focus on scalable, energy-efficient systems for healthcare and diagnostics. He leads projects involving edge computing, temporal fusion transformers, and microfluidic integration, demonstrating expertise in both hardware innovation and algorithmic development. His lab develops cutting-edge systems such as 1000fps ISFET SoCs with programmable gain and high-throughput digital readout architectures. Recent work includes live demonstrations of real-time pH monitoring in 3D-printed microfluidic systems and spatio-temporal ion membrane characterization platforms.

Qi TangView profile

Research Fellow

• Hydrogeology
• Data Assimilation
• Earth System Modeling
• +3 more

Qi Tang is a Research Fellow in the Department of Environmental Science at the University of Basel and concurrently serves as Coordinator of the Swiss Water Earth Systems PhD School at the University of Neuchâtel. His expertise spans hydrogeology, data assimilation, and Earth system modeling. He holds a PhD in Hydrogeology from RWTH Aachen University (2017) and has held postdoctoral positions at institutions including the Alfred Wegener Institute (Germany), the University of Basel, and the Chinese Academy of Sciences. Education: PhD in Hydrogeology, RWTH Aachen University, Germany (2012–2017) MSc in Hydrology and Water Resources, Beijing Normal University (2009–2012) BSc in Applied Mathematics, China Agriculture University (2005–2009) Research Interests: Qi Tang focuses on advancing coupled Earth system models through data assimilation techniques. His work integrates hydrological, oceanographic, and climatic processes to improve predictive accuracy. Key areas include river-aquifer interaction dynamics, satellite data integration in ocean-atmosphere models, and cloud computing for real-time water resource management. His research bridges theoretical modeling with practical applications in environmental monitoring and climate prediction. Publications: His articles emphasize data-driven approaches to environmental systems. Recent work highlights coupled model improvements using satellite data (e.g., ocean-atmosphere interactions), ensemble Kalman filtering for flood simulations, and Bayesian networks for precipitation modeling. These studies underscore his expertise in both computational methods and field applications. Advising & Grants: While no formal advisees are listed, his postdoctoral roles suggest involvement in mentoring junior researchers. No specific grants are mentioned in the provided texts. Labs/Teams: Affiliated with the Hydrogeological Processes group at the Center for Hydrogeology and Geothermal Energy (CHYN), University of Neuchâtel. This group specializes in geothermal energy, hydrochemistry, and stochastic hydrogeology.

• Numerical Linear Algebra
• Matrix Theory
• Numerical Analysis
• +6 more

Davide Palitta is an Assistant Professor (tenure track) at the Department of Mathematics of the University of Bologna. His research focuses on numerical linear algebra, matrix equations, and their applications in fields like data assimilation, deep learning, and parallel computing. He holds a PhD in Mathematics from the University of Bologna (2018) and has held postdoctoral positions at the Max Planck Institute in Magdeburg (2018–2021) and a visiting fellowship at Brown University (2020). His work emphasizes the development of efficient numerical algorithms for large-scale matrix equations, including Sylvester, Lyapunov, and Riccati equations. Key contributions include Krylov subspace methods, randomized techniques, and time-parallel integration strategies. His recent publications span topics such as sketched Krylov methods, tensor-based solvers, and preconditioning strategies for weak-constraint data assimilation. Palitta collaborates actively with international institutions and organizes seminars like the Scube series on numerical linear algebra. He is involved in upcoming conferences on matrix equations, data assimilation, and parallel computing. No scientific awards are explicitly mentioned in the provided materials.

• Parallel and Distributed Computing
• Database Systems
• Network-Centric Computing
• +2 more

Professor Ajit Singh is a faculty member in the Department of Electrical and Computer Engineering at the University of Waterloo , affiliated with the Faculty of Engineering . His expertise spans Parallel and Distributed Computing , Database Systems , Network-Centric Computing , and Software Architecture and Design . Education: BSc (BIT, India) MPhil (Jawaharlal Nehru University, New Delhi) MSc and PhD (University of Alberta) Research & Industrial Work: Founded SlipStream Data (acquired by Research in Motion/BlackBerry) to accelerate internet content delivery on low-bandwidth networks. Consulted for IBM Canada, Bell Canada, Nortel, and other tech firms. Current projects include Energy Efficient Mobile Computing and Processing Data Streams . Advising & Students: Current advisees: Liton Chakraborty, Mohammad Mursalin Akon, Rajesh Palit, Tan Wang. Past students include Dhrubajyoti Goswami, Afzal Mawji, and others. Research Themes: Focuses on scalable caching, distributed systems, and energy-efficient computing. Explores disk-based optimization for stream joins and parallel architectures.

Lap Chi LauView profile

Professor

• Algorithms
• Optimization
• Spectral Graph Theory

Lap Chi Lau is a Professor at the University of Waterloo's Cheriton School of Computer Science. His primary research explores algorithms, optimization, and spectral graph theory. His recent publications focus on graph algorithms, spectral methods, and combinatorial optimization, with consistent applications to network design and experimental methodologies. Publications demonstrate advanced techniques in graph sparsification, eigenvalue methods, and efficient algorithm design for complex computational problems. His work frequently intersects theoretical computer science and applied mathematics, contributing to foundational improvements in graph partitioning, spectral clustering, and randomized algorithms.

Ali José MashtizadehView profile

Associate Professor

• Operating Systems
• Distributed Systems
• Storage Systems

Ali José Mashtizadeh is an Associate Professor at the Cheriton School of Computer Science, University of Waterloo. His research focuses on operating systems, distributed systems, and storage, with expertise in system reliability, network optimization, and concurrent programming. Education: Ph.D., Computer Science, Stanford University (2017) M.S., Computer Science, Stanford University (2017) M.Eng., Electrical Engineering and Computer Science, MIT (2007) B.S., Electrical Engineering, MIT (2006) His research centers on designing scalable and reliable systems, with recent publications exploring TCP network frameworks, in-memory data persistence, and microsecond-scale scheduling. Key themes include optimizing tail latency, neutralization-based memory reclamation, and fault-tolerant distributed services. His articles consistently demonstrate innovations in low-latency networking, operating system architecture, and cloud infrastructure, with recent emphasis on serverless benchmarks and processor customization. No scientific awards or advising relationships are detailed in the provided materials.

Yaqub JonmohamadiView profile

Researcher

• Computer Vision
• Deep Learning
• Multimodal Data Fusion
• +3 more

Yaqub Jonmohamadi is a former Associate Investigator at the QUT Centre for Robotics. He holds a PhD in neuroimaging and has conducted postdoctoral research since 2015 in computer vision, biomedical engineering, and neuroimaging. His research interests span computer vision, deep learning, multimodal data fusion, and signal processing. He has contributed to advancements in medical imaging, robotic surgery, and neuroimaging through interdisciplinary collaborations. His work includes 3D semantic mapping for surgical robotics, illumination control in arthroscopy, and EEG-fMRI data fusion techniques. Publications focus on applying machine learning and computer vision to healthcare challenges, such as surgical scene restoration and knee arthroscopy segmentation. His research demonstrates expertise in both theoretical and applied aspects of robotics and biomedical engineering. Jonmohamadi’s GitHub repository hosts code related to his projects, reflecting a commitment to open science. He has collaborated with institutions like Elsevier and Springer on works advancing robotic surgery and medical imaging. Despite no listed awards, his academic contributions are evident through his peer-reviewed publications and technical innovations in medical robotics. His work bridges computer science and healthcare, addressing real-world surgical and diagnostic challenges.

• Systems
• Concurrency
• Memory Management
• +5 more

Christoph Kirsch is a Professor and Chair of the Department of Computer Science at the University of Salzburg. He also serves as Chair of the Programming Research Laboratory at the Faculty of Information Technology, CTU Prague. His research focuses on systems, concurrency, memory management, and formal methods, with notable contributions including the Selfie educational software project and work on symbolic execution. He is a prolific author with over 50 publications in top venues like LCTES and EMSOFT. Education: Ph.D. in Computer Science (details not specified). His teaching includes courses on elementary computer science concepts and curricula development. He advises students such as Anna Bolotina and has graduated over a dozen PhD students. Research highlights include the Selfie system (self-referential C compiler and emulator), work on concurrency primitives like Scal and Timestamped Stack, and contributions to real-time systems (Logical Execution Time, Variable-Bandwidth Servers). His recent focus includes teaching digital thinking and evaluating eval in R programs. He has organized conferences like MPLR’24 and served on program committees for EuroSys and RTNS. His book Elementary Computer Science emphasizes foundational concepts for broad audiences.

Josep CasanovasView profile

Professor

• Modelling and Simulation
• Internet and Information Systems
• Urban Mobility
• +4 more

Josep Casanovas is a Full Professor at the Statistics and Operations Research Department of the Technical University of Catalonia (UPC), affiliated with the Barcelona School of Informatics. He previously served as head of inLab FIB (2012-2020) and as dean (1998-2004) and vice-rector (2006-2011) of UPC, leading strategic initiatives in university governance and ICT policies. His research focuses on Modelling and Simulation , Internet and Information Systems , and Urban Mobility . He has led projects for the European Union, including C-ROADS Spain, REMEDiAL, and ECHORD++, addressing intelligent transport, software automation, and robotic innovation. Recent publications highlight his work on agent-based simulation for urban health, deep learning applications in traffic and energy savings, and wildfire management tools . He co-directs LogiSim and coordinates the Severo Ochoa Research Excellence Program at the Barcelona Supercomputing Center (BSC-CNS).

• Cloud Computing
• Distributed Systems
• Data Processing
• +4 more

Luis Antunes Veiga is an Associate Professor and Senior Researcher at INESC-ID Lisbon, affiliated with the Distributed Systems Group and the Computing Systems and Communication Networks Laboratory. His research focuses on Cloud Computing, Edge Computing, Distributed Systems, and Big Data processing. He teaches courses such as 'Cloud Computing and Virtualization' and 'Operating Systems, Virtualization and Cloud Computing.' His work emphasizes scalable systems, network-aware workflows, and resource-efficient data processing. Research Interests: His primary areas include distributed systems architectures, edge computing frameworks, graph processing algorithms, and software-defined systems. He explores topics like latency-aware network design, resource auction mechanisms for edge environments, and interoperable service workflows. His contributions span theoretical frameworks and practical implementations, such as the RATEE system for edge resource trading and the VeilGraph incremental graph processing framework. Publications: His recent work addresses challenges in distributed systems, such as elastic scaling of stream processing, efficient graph processing in Spark, and latency optimization in internet-scale workflows. These publications reflect a trend toward integrating software-defined approaches with edge and cloud infrastructures. Notable Awards: Best Young Researcher INESC-ID, Excellence in Teaching (IST 2012), and a Best-Paper Award at ACM/IFIP/Usenix Middleware 2007. Labs & Teams: Active in the Computing Systems and Communication Networks Laboratory, leading projects on edge computing and distributed systems.

Oskar MencerView profile

Professor

• Numerical methods and simulations
• Imaging
• Discretization of space, time and value
• +7 more

Oskar Mencer is a Professor in the Department of Computing at Imperial College London , where he has been a member of academic staff since 2000. He was also a Consulting Professor at Center for Computational Earth and Environmental Science , Stanford University (2009-2010). His research focuses on Multiscale Dataflow Computing , exploring the interaction of hardware and software systems through domain-specific representations, parallel programming, VLSI design, and compiler methodologies. He has contributed to fields including high-performance computing, FPGA optimization, and computational finance. Dr. Mencer's work has been recognized with a Special Award from Com.sult (2012), Imperial College Research Excellence Award (2007), and a Top EPSRC Advanced Fellowship (2001). He has received multiple best paper awards, including at ICFPT'08 and ASAP 2008, and his 2000 paper on stream architectures was recognized as historically significant in 2015. His publications demonstrate a strong emphasis on FPGA acceleration , custom architectures , and parallel processing across geophysics, finance, and neuroscience applications. Special Award for Dataflow Innovation (2012) Best Paper Award - ICFPT'08 Best Paper Award - ASAP 2008 He has served on technical committees for conferences such as FPL , DATE , and FPT , and leads the Computer Architecture Research Group (currently inactive).

• Materials Science
• Mechanical Engineering
• Computational Modeling
• +3 more

Dr. Simone Falco is a Research Fellow at the Department of Engineering Science, University of Oxford, and a Lecturer at Oriel College. He holds a DPhil (Oxon) in Engineering Science. His research focuses on numerical modeling of polycrystalline materials, micromechanical behavior of ceramics, and multi-scale approaches. He has contributed to projects involving high-rate testing, explosive materials simulation, and flash sintering techniques. His work bridges computational methods with experimental validation, emphasizing practical applications in materials engineering. Education: Bachelor’s and Master’s in Aerospace Engineering, University of Naples Federico II (2006, 2009) PhD in Engineering Science, University of Oxford (2015) Research Interests: Generation of numerical models for real polycrystalline microstructures Numerical simulation of brittle material failure Multi-scale homogenization techniques High-rate material testing Modeling of material manufacturing processes Key Awards: Ross Coffin Purdy Award, American Ceramic Society (2019) JCS 2018 Award, Journal of the Ceramic Society of Japan (2019) Excellence Award, University of Oxford (2019) Lab/Team Affiliations: Impact Engineering and Materials Engineering groups at the University of Oxford.

Selin AhipasaogluView profile

Professor

• Operational Research
• Convex Optimization
• Robust Optimization
• +5 more

Prof. Selin Damla Ahipasaoglu is a Professor in Operational Research at the University of Southampton's School of Mathematical Sciences . She serves on the management team of the UKRI CDT SustAI (Artificial Intelligence for Sustainability) as Senior Tutor and Co-Lead for the Transportation and Logistics Theme . Her work bridges mathematical optimization with practical applications in sustainability, finance, and transportation systems. Research Interests : Convex Optimization Robust Optimization Discrete Choice Theory Experimental Design Machine Learning Current Research : Focused on robust optimization and its applications in discrete choice modeling, portfolio optimization, and transportation systems. She explores theoretical frameworks alongside real-world implementations, particularly through interdisciplinary projects like the UKRI CDT SustAI. Teaching : In the 2025/2026 academic year, she teaches MATH3017: Mathematical Programming and MATH2013: Operational Research II . She supervises PhD students in Mathematical Sciences, including Kexin Lai, Samuel Jericho Ward, and others.