Find Academic Supervisors

Ioannis D SchizasView profile

Associate Professor

• Signal Processing
• Machine Learning
• Data Mining
• +9 more

Ioannis D. Schizas is an Associate Professor in the Department of Electrical and Computer Engineering at The University of Texas at Arlington (UTA), with a primary appointment in the Nedderman College of Engineering. He joined UTA in 2011, progressing from Assistant to Associate Professor before being promoted to Full Professor (effective September 2024). His research focuses on machine learning, signal processing, distributed systems, and data mining, with applications in sensor networks, remote sensing, and graph-based learning. Education: He holds a Diploma in Computer Engineering and Informatics (University of Patras, Greece, 2004), an M.Sc. (2007), and a Ph.D. (2011) in Electrical and Computer Engineering from the University of Minnesota. Research Interests: Schizas' work spans machine learning algorithms, statistical signal processing, and distributed optimization. Key areas include clustering techniques, kernel methods, dimensionality reduction, and applications in wireless sensor networks, hyperspectral imaging, and multi-target tracking. Publications: His recent work emphasizes scalable machine learning (e.g., kernel-based clustering, neural network pruning for IoT), distributed sensor data processing, and graph filtering for data reduction. Notable trends include fusion of modalities (e.g., hyperspectral unmixing) and real-time systems (e.g., video tracking). Awards: He received the Outstanding Advisor Award (2024) and Advisor Excellence Award (2023) at UTA. His research has been supported by grants from the Army Research Office, AFOSR, and NSF, totaling over $1M in funding. Advising & Grants: Schizas has advised numerous PhD/Master’s students on topics like kernel learning, distributed tracking, and data-driven techniques. His grants include projects on dynamic data-driven systems (e.g., multi-threat tracking, DDROSSIL framework). Labs/Teams: His research group collaborates on projects involving distributed algorithms, sensor networks, and machine learning applications. Active collaborations exist with organizations like Adobe Systems and the Air Force Office of Scientific Research.

• Computational Hydrodynamics
• Computational Fluid Dynamics
• Scientific Computing
• +3 more

Anargiros Delis is an Associate Professor of Computational Mathematics at the School of Production Engineering & Management, Technical University of Crete since 2003. He holds a B.Sc. in Mathematics from the University of Crete (1993), an M.Sc. in Numerical Analysis and Computing from the University of Manchester (1994), and a Ph.D. in Applied and Computational Mathematics from the University of the West of England (1998). Education: B.Sc. Mathematics, University of Crete (1993) M.Sc. Numerical Analysis & Computing, University of Manchester (1994) Ph.D. Applied and Computational Mathematics, University of the West of England (1998) His research focuses on computational hydrodynamics, computational fluid dynamics, and scientific computing, with notable contributions to free surface flows, finite volume methods, and applications in coastal engineering, traffic dynamics, and environmental modeling. Recent work includes advancements in numerical simulations for epidemics transport dynamics, optimization of wind turbines using RANS-BEM solvers, and PDE-based traffic flow control systems. Key research themes include macroscopic modeling of epidemics, unstructured grid discretizations for Boussinesq-type models, and the development of high-resolution numerical schemes. His work bridges theoretical advancements with practical applications in renewable energy, urban planning, and hydraulic infrastructure. Delis is affiliated with the Applied Mathematics and Computers Laboratory under the Science Division at the Technical University of Crete, contributing to interdisciplinary projects at the intersection of computational science and engineering.

Farhad A KamangarView profile

Professor

• Artificial Intelligence
• Computer Vision
• Neural Networks
• +3 more

Dr. Farhad A Kamangar is a Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), part of the College of Engineering. He holds a PhD in Electrical Engineering from UTA (1980), an MS from UTA (1977), and a BS from the University of Tehran (1975). His research focuses on Neural Networks, Computer Vision, Artificial Intelligence, and Computer Graphics. He has been recognized with awards such as the 2018 President's Award for Transformative Online Education and induction into UTA's Academy of Distinguished Teachers. Dr. Kamangar's research spans domains like machine learning, image processing, and medical applications. His recent publications address challenges in GAN-based data augmentation, deep subspace clustering, and unsupervised pose estimation. He has advised numerous graduate students and contributed to funded projects in swarm motion planning, telemedicine, and sensor networks. He teaches courses like Neural Networks and Computer Graphics and serves on academic committees including the Ph.D. Admissions Committee and Tenure & Promotion. Grants: Includes NSF-funded projects on telemedicine adherence systems and collaborative robotics. Awards: 2018 President's Award, Robert Q. Lee Award (1997), and multiple teaching honors. Service: Committee roles in curriculum, faculty recruitment, and student appeals.

• Numerical Analysis
• High Performance Parallel Computing
• Algorithm Design
• +5 more

Carl C Kjelgaard Mikkelsen is an Associate Professor at the Department of Computing Science, Umeå University, Sweden. His research focuses on developing robust numerical algorithms that cannot fail, particularly in the context of finite precision arithmetic used by computers. His work bridges theoretical numerical analysis with practical high-performance computing applications. Dr. Mikkelsen's research interests center around numerical analysis and high performance parallel computing, with special emphasis on the worst case analysis of algorithms. He investigates the profound differences between exact arithmetic used by humans and finite precision arithmetic used by computers. His current projects include developing parallel constraint solvers for constrained molecular dynamics and parallel solvers for ultra-sparse linear systems. He is a co-author of the StarNEig library for solving dense nonsymmetric standard and generalized eigenvalue problems in parallel. His recent publications demonstrate a strong focus on accuracy requirements in numerical methods, particularly Newton's method, and the development of robust parallel algorithms for linear algebra problems. His work spans from theoretical analysis of numerical stability to practical implementation in high-performance computing environments. Dr. Mikkelsen teaches numerical analysis and scientific computing, emphasizing algorithm design, analysis, and implementation. He focuses on teaching students the critical differences between exact and finite precision arithmetic, and invests significant time in teaching software development practices and technical writing.

Teng ZhangView profile

Associate Professor

• Statistics
• Machine Learning
• Signal Processing
• +7 more

Dr. Teng Zhang is an Associate Professor in the Department of Mathematics at the College of Sciences, University of Central Florida. His research focuses on statistics, machine learning, and signal processing, with a strong emphasis on robust covariance estimation, matrix analysis, and phase retrieval. Robust statistical methods Machine learning algorithms Signal processing for complex systems Optimization in non-convex models Applications in cybersecurity and bioinformatics Recent publications highlight his work on low-rank matrix models, adversarial robustness in deep learning, and secure distributed estimation for cyber-physical systems. His research spans theoretical guarantees and practical implementations, with applications in computer vision, network security, and biomedical data analysis.

Ali ShahsavariView profile

Lecturer

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

Ali Shahsavari is a Lecturer in the Department of Mechanical, Aerospace and Nuclear Engineering at Rensselaer Polytechnic Institute (RPI). His academic focus is on materials science and mechanics, particularly in the area of fiber network mechanics and composite materials. He has contributed to understanding the mechanical behavior of sparsely cross-linked fiber networks, size effects in material properties, and multiscale modeling of fibrous structures. His work bridges theoretical frameworks with experimental validation, emphasizing practical applications in material design and failure analysis. Shahsavari's research interests span computational mechanics, composite material optimization, and elasticity modeling. He has explored topics such as wave propagation in fiber networks, stiffness enhancement in composites, and homogenization techniques for matrix-embedded systems. His studies often address scalability and structural stability, with implications for advanced material engineering. His publications reflect a consistent focus on fiber networks' mechanical properties, including dynamic response analysis, cross-link effects, and continuum representation approaches. Notable contributions include investigations into exceptional stiffening phenomena and size-dependent mechanical behaviors. While no scientific awards or grants are explicitly mentioned, his work demonstrates rigorous interdisciplinary collaboration with researchers like R. C. Picu and Katia Bertoldi. No lab affiliations or teams are specified in the provided information. His advising record remains undisclosed, though his research output suggests active engagement in academic mentorship.

John MitchellView profile

Professor

• Optimization
• Integer Programming
• Conic Optimization
• +6 more

John Mitchell is a Professor and Graduate Program Director in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute. His research focuses on optimization theory and applications, including integer programming, conic optimization, and complementarity constraints. He holds an additional affiliation with the Department of Industrial and Systems Engineering. Key research interests include sparse/low-rank optimization with applications in compressed sensing, genome-wide association studies, and financial optimization. His work extends to practical domains like disaster management (resource allocation, humanitarian logistics) and resilient supply chain design. Mitchell's methods address both theoretical challenges (e.g., exact formulations) and real-world problems such as network interdiction and infrastructure restoration. Publications from 2015-2025 highlight interdisciplinary applications: from ridesharing algorithms and trafficking network disruption to community detection in graphs and neuromorphic computing optimization. His work bridges mathematical rigor with societal impact in areas like emergency evacuation modeling and fair division approaches for humanitarian logistics. His research emphasizes algorithm development (e.g., branch-and-cut methods) and convex reformulations for nonconvex problems. Current projects explore quantitative resilience metrics for multi-echelon supply chains and ground-truth community detection via modularity optimization.

Rena HuangView profile

Associate Professor

• Optoelectronic Devices
• Integration and Packaging
• 3-D Integrated Microsystems
• +7 more

Rena Huang is an Associate Professor in the Department of Electrical, Computer and Systems Engineering at Rensselaer Polytechnic Institute (RPI) with additional affiliations in Physics, Applied Physics, and Astronomy. She joined RPI in October 2004 after completing her Ph.D. in Electrical Engineering at Georgia Institute of Technology (2003) and serving as a postdoctoral fellow at the Microsystem Packaging Research Center (PRC) at Georgia Tech. Her research focuses on: Optoelectronic devices and integration/packaging 3-D integrated microsystems and lightwave circuits Integrated slow wave structures Photodetectors and electro-optic modulators Laser diodes and silicon photonics She is affiliated with the Center for Materials, Devices, and Integrated Systems (CMDIS), conducting cutting-edge work in photonic computing architectures. Analysis of her 90+ publications shows strong emphasis on: Silicon photonic modulators with slow-light enhancement Foundry-based fabrication of photonic integrated circuits Photonic tensor cores for AI acceleration Reservoir computing for biomedical applications Inverse design and optimization methodologies No scientific awards or student advisees are mentioned in the source material. Her research demonstrates consistent focus on hardware-efficient photonic computing solutions with recent work exploring co-design approaches across device, circuit, and architecture levels.

• Sparse Matrix Computation
• Optimization Algorithms
• High-Performance Computing
• +2 more

Shahadat Hossain is Professor and Program Chair of Computer Science at the University of Northern British Columbia. His research develops optimization algorithms for sparse matrix problems, complex networks, and high-performance computing. Key contributions include: DSJMToolkit for sparse Jacobian matrices Graph-based methods for matrix estimation Quantum-classical optimization hybrids He has supervised over 20 graduate students and secured NSERC Discovery Grants for optimization research. Hossain holds a Dr.Scient. from University of Bergen and completed visiting positions at Cornell University and Humboldt University. His 15 most-cited publications demonstrate consistent focus on sparse matrix computation, graph algorithms, and computational optimization techniques with applications in scientific computing and network analysis.

Jing QinView profile

Associate Professor

• Mathematical Optimization
• Variational Image Processing
• Compressive Sensing
• +3 more

Jing Qin 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 Optimization, Variational Image Processing, Compressive Sensing, and computational analysis of imaging data. His work addresses challenges in signal recovery, tensor decomposition, hyperspectral imaging, and machine learning applications. Recent publications emphasize advancements in tensor recovery algorithms, hyperspectral band selection, and graph-based signal processing. These contributions highlight innovations in iterative methods, regularization techniques, and deep learning models for structural analysis and robotic systems. Qin’s research bridges theoretical optimization frameworks with practical applications in remote sensing, medical imaging, and computer vision. His articles reflect a strong focus on interdisciplinary solutions, combining mathematical rigor with computational efficiency. Despite no explicitly listed awards or grants, his prolific publication record underscores sustained scholarly impact. Further details are available via his faculty webpage: http://ms.uky.edu/~jqi229/ .

Amin Coja-OghlanView profile

Professor

• Probabilistic Combinatorics
• Mathematical Physics
• Disordered Systems
• +2 more

Amin Coja-Oghlan is Professor of Efficient Algorithms and Complexity Theory at TU Dortmund University's Department of Computer Science. His research integrates probabilistic combinatorics, information theory, and statistical physics to solve fundamental problems in theoretical computer science. Education includes a doctorate in Mathematics (University of Hamburg, 2002) and habilitation in Computer Science (Humboldt University Berlin, 2005). Research advances understanding of phase transitions in constraint satisfaction problems, optimization landscapes, and random structures. Recent publications analyze SAT thresholds, group testing, and sparse matrix properties. Academic appointments include professorships at Goethe University Frankfurt and lectureships at Edinburgh and Warwick. Research contributions bridge discrete mathematics with computational complexity.

Qinglei CaoView profile

Assistant Professor

• High-performance Computing
• Artificial Intelligence
• Parallel Computing
• +2 more

Qinglei Cao is an Assistant Professor in Computer Science at Saint Louis University's School of Science and Engineering, where he leads the Omni Computing Lab. He holds a PhD from University of Tennessee, Knoxville and has industry experience at Cerebras Systems. His research bridges High-Performance Computing and Artificial Intelligence, focusing on parallel algorithms for scientific applications. Key areas include task-based runtime systems, sparse matrix computations, and optimization techniques for large-scale environmental modeling. Recent publications demonstrate innovations in geospatial computation precision, extreme-scale statistical modeling, and energy-efficient algorithms. His work frequently targets climate prediction and computational mathematics challenges. Awards: ACM Gordon Bell Prize for Climate Modelling (2024) Best Paper Award, IEEE CLUSTER (2020) Gordon Bell Prize Finalist (2022, 2024) He advises PhD students in HPC/AI convergence and leads research on GPU/CPU optimization for machine learning workflows.

• Machine Learning
• Artificial Intelligence
• Graph Neural Networks
• +2 more

Maoying Qiao is a Lecturer at Australian Catholic University's Peter Faber Business School, specializing in machine learning and artificial intelligence. Her research spans graph neural networks, computer vision, and probabilistic modeling, with applications in network analysis and 3D vision. Key publications focus on improving graph convolutional networks through negative sampling, adapting stochastic block models for power-law networks, and developing conditional graphical lasso methods for multi-label image classification. Marine science applications include automated catch detection systems for fisheries.

Teseo SchneiderView profile

Assistant Professor

• Computer Graphics
• Geometry Processing
• Numerical Simulations
• +4 more

Teseo Schneider is an Assistant Professor in Computer Science at the University of Victoria, affiliated with the Faculty of Engineering and Computer Science. He holds a PhD from the University of Lugano (USI), where his thesis focused on bijective barycentric mappings. He completed a postdoctoral fellowship at New York University (NYU) under an SNF Early post-doc mobility grant. His research interests include geometry processing, computer graphics, numerical simulations, and differential geometry. Education: PhD in Informatics (2017, USI), MSc in Computer Science (2012, USI), BSc in Computer Science (2010, USI). Professional roles include lecturer for courses like Geometric Modeling and Computer Graphics , and co-supervisor for projects involving mesh generation and simulation. Research highlights include contributions to polyhedral finite elements, contact dynamics, and open-source libraries like PolyFEM and LibHip. His work bridges computational geometry with practical applications in biomechanics and engineering. Awards: SNF Early post-doc mobility fellowship (2017). Publications span high-impact venues such as SIGGRAPH, TOG, and Nano Letters, reflecting his expertise in simulation, meshing, and geometric algorithms.

Aditya BhaskaraView profile

Associate Professor

• Theoretical Computer Science
• Machine Learning
• Algorithm Design
• +7 more

Aditya Bhaskara is an Associate Professor in the School of Computing at the University of Utah, where he is part of the Theory Group and the Utah Center for Data Science. His office is located in MEB 3470. Education: Ph.D. in Computer Science, Princeton University (2012) B. Tech in Computer Science and Engineering, IIT Bombay, India Post-doctoral researcher, Google NYC (2013-2015) Post-doctoral researcher, EPFL (2012-2013) Dr. Bhaskara's research spans theoretical computer science and machine learning. He has a strong focus on algorithm design, particularly approximation and online algorithms. On the machine learning side, he investigates robustness of learning models and domain shifts from a theoretical perspective. His work often blends theory and ML, exploring how to leverage ML-based predictions in classical algorithm design and other beyond worst-case models. His research has significant applications in data streaming, dimensionality reduction, and graph analysis. His recent publications demonstrate a clear trend toward bridging theoretical computer science with practical machine learning applications. Many papers focus on spectral algorithms, robustness in network models, and optimization techniques for large-scale data. There is also significant work in wireless communications and spectrum management, showing how his theoretical work translates to real-world problems in telecommunications and data science. Scientific Awards: NSF CAREER award AF Small grant Grants from NRDZ and FMiTF programs Google Faculty Research Award Dr. Bhaskara actively advises students with strong mathematical backgrounds interested in theoretical computer science and machine learning. He has received significant research funding from the National Science Foundation and Google. He is co-organizing the Data Science Lecture Series at the University of Utah and has served on prestigious program committees including SODA 2024, STOC 2023, ICALP 2023, and ITCS 2022. His teaching portfolio includes advanced courses on algorithms, machine learning theory, and probability. As part of the Theory Group and the Utah Center for Data Science, Dr. Bhaskara collaborates with researchers across disciplines to advance theoretical foundations of computing and their practical applications in data science. His work contributes to both the theoretical understanding of algorithms and their real-world implementation in various domains including wireless networks, data analysis, and machine learning systems.