Find Academic Supervisors

• Numerical Analysis
• Optimal Control
• Finite Element Methods
• +9 more

Enrico Bertolazzi is an Associate Professor in Numerical Analysis at the University of Trento's Department of Industrial Engineering. He specializes in numerical methods for optimal control, finite element methods, sparse matrix computations, and symbolic computation. His work integrates advanced programming skills in C/C++, MATLAB, and other languages to develop computational tools for engineering and mathematical challenges. Bertolazzi's research addresses complex systems such as vehicle dynamics, trajectory optimization, and real-time simulations, with applications in automotive engineering, renewable energy, and robotics. Research Interests: Numerical Optimal Control and Trajectory Planning Finite Element/Finite Volume Methods for PDEs Sparse Matrix Algorithms Clothoid Curve Modeling Symbolic-Numeric Hybrid Techniques Vehicle Dynamics and Autonomous Systems Publications: Recent work focuses on real-time tire models, DAE index reduction, and autonomous vehicle path planning. His contributions span algorithm development, computational toolboxes (e.g., TrussMe-Fem), and interdisciplinary applications combining mathematics with engineering. Grants & Advising: Advises on optimal control, robotics, and simulation projects. Active in developing educational software like the virtual racing car championship platform. Labs/Teams: Involved in research groups focused on intelligent vehicles, computational mechanics, and numerical methods for industrial applications.

Linda DeBrunnerView profile

Professor

• Digital Signal Processing Hardware
• Low Power Implementations
• Special-Purpose Digital Systems
• +3 more

Dr. Linda DeBrunner is a Professor of Electrical and Computer Engineering at the FAMU-FSU College of Engineering, Florida State University since 2006. Previously, she was a Professor at the University of Oklahoma (1990-2006). She holds a Ph.D. (1991), M.S. (1986), and B.E.E. (1984) from Virginia Tech and Auburn University, respectively. Her research focuses on digital signal processing hardware, low-power implementations, and special-purpose digital systems. Notable projects include low-power DSP designs, multiplier optimization, and bridge displacement estimation using modal analysis. She has secured over $6 million in external funding from NSF, ONR, FHWA, and industry. DeBrunner has published >50 refereed papers and serves on editorial boards (e.g., Journal of Circuits, Systems, and Computers) and conference committees (Asilomar). She received 'Favorite Professor' accolades twice and is a Senior IEEE Member. Her work emphasizes FPGA-based systems, multiplier architectures, and real-time signal processing applications. Recent projects explore the discrete Hirschman transform for convolution optimization and bridge health monitoring via accelerometer data analysis.

Victor DeBrunnerView profile

Professor

• Signal Processing
• Image Processing
• Pattern Recognition
• +6 more

Victor DeBrunner is a Professor in the Department of Electrical & Computer Engineering at Florida A&M University–Florida State University. He holds a Ph.D. in Electrical Engineering from Virginia Tech (1990), an M.S. from Virginia Tech (1986), and a B.S. from Auburn University (1984). His research focuses on signal processing, image processing, and pattern recognition, with a strong emphasis on hardware-efficient implementations of convolution algorithms and transforms like the Hirschman Transform. His work often explores FPGA-based optimizations for real-time applications, low-latency systems, and efficient digital signal processing architectures. DeBrunner’s recent publications (2018–2025) highlight trends in convolution algorithm design, transform-based signal analysis, and hardware-software co-design for DSP systems. He has contributed to advancements in filter sensitivity prediction, sparse algorithms for DFT computations, and reduced-complexity convolution methods. Notable topics include FPGA-optimized architectures for 2D convolution, efficient neural network implementations using Hirschman transforms, and latency-reduction strategies in linear convolution systems. His research extends to pedagogical innovations in introductory circuits education, emphasizing student engagement through active learning methods. While no awards are explicitly listed in the provided texts, his prolific publication record indicates sustained academic impact in signal processing and hardware design. DeBrunner’s work frequently involves collaborations on structural health monitoring systems, bridge displacement measurement via accelerometers, and adaptive filtering techniques for noise cancellation. He has also explored applications in weather radar signal processing and biomedical imaging quality metrics.

Hoomaan MaskanView profile

Researcher

• Signal Processing
• Statistical Learning
• Compressive Sensing
• +5 more

Hoomaan Maskan is a doctoral student at the Department of Mathematics and Mathematical Statistics, Umeå University. His research focuses on Signal Processing , Statistical Learning , and Compressive Sensing with applications in sparsity and spatio-temporal data analysis. His recent publications highlight advancements in high-resolution ODEs for accelerated optimization, nonconvex programming, and super-resolution DOA estimation. Key themes include mathematical modeling, sparse signal recovery, and algorithm design for spatio-temporal data. Research projects include " Compressive Sensing and Statistical Learning with Sparsity " (2019-2024). He is a member of the Mathematical Programming and Statistical Learning and Inference for Spatio-Temporal Data research groups.

Victor ChurchillView profile

Assistant Professor

• Computational Mathematics
• Scientific Machine Learning
• Image Reconstruction
• +5 more

Victor Churchill is an Assistant Professor of Mathematics at Trinity College since 2023. He holds a Ph.D. and A.M. from Dartmouth College, an M.S. from New York University's Courant Institute, and a B.A. from Boston College. His research focuses on computational mathematics, scientific machine learning, and image reconstruction, particularly in Bayesian uncertainty quantification for synthetic aperture radar imaging and learning unknown dynamical systems using neural networks. He has held a postdoctoral position at The Ohio State University under Dr. Dongbin Xiu and previously worked at Dartmouth under Dr. Anne Gelb. Research Highlights: His work includes deep learning of PDEs, ensemble prediction for robust neural network training, and chaotic system learning from partial observations. Recent contributions address coarse time-scale observations and uncertainty quantification in SAR imaging. He was awarded the SIAM Science Policy Fellowship (2023-2024) to engage with federal science policy advocacy. Teaching: He teaches computational science courses at both undergraduate and graduate levels, integrating his research into lectures through case studies and data-driven examples. His pedagogical approach emphasizes applied computational mathematics and real-world problem-solving. Affiliations: Previously affiliated with The Ohio State University as a Visiting Assistant Professor of Scientific Computation. Active in computational math communities, including SIAM policy engagement. Personal Interests: An avid runner with marathon personal bests, he also enjoys bonsai cultivation, architectural design, and animal care. His unconventional hobbies include experimenting with hair color transformations.

Ying LiuView profile

Associate Professor

• Computer Vision
• Deep Learning
• Image and Video Coding
• +4 more

Dr. Ying Liu is an Associate Professor in the Department of Computer Science and Engineering at Santa Clara University's School of Engineering. She serves as the Secretary/Treasurer of the APSIPA US Chapter and holds multiple leadership roles including APSIPA Distinguished Lecturer (2025-26) and Vice Chair of the APSIPA US Local Chapter. Her research focuses on deep learning applications for visual data processing and compression. Education: Ph.D., Electrical Engineering, SUNY at Buffalo, 2012 M.S., Electrical Engineering, SUNY at Buffalo, 2008 B.S., Telecommunications Engineering, Beijing University of Posts and Telecommunications, 2006 Dr. Liu's research spans deep learning-based image/video processing , coding for machines , and generative AI . Her work integrates convolutional neural networks, transformers, and generative models to advance video compression and machine vision systems. Current projects include vision-language models and point cloud coding, with emphasis on computational efficiency for real-world deployment. Analysis of her 15 most recent publications reveals a strong focus on neural video compression (40%), image coding for machines (30%), and generative AI applications (20%), with increasing emphasis on transformer architectures and multi-task frameworks since 2022. Her research bridges theoretical innovation with practical industrial applications in manufacturing and autonomous systems. Scientific Awards: Researcher of the Year Award, School of Engineering, Santa Clara University (2024) APSIPA Distinguished Lecturer Appointment (2025-26) Dr. Liu actively mentors PhD students in the Video and Image Processing (VIP) Laboratory, with four current advisees including Pengli Du (first PhD graduate in 2024). Her research is supported by significant grants including an NSF ERI award ($500K, 2022-2025), NVIDIA Academic Hardware Grant, and multiple industry-funded projects with Kwai Inc. totaling over $300K. She also secures internal university funding through Kuehler Undergraduate Research Grants and School of Engineering Research Grants. The VIP Laboratory develops deep learning solutions for the projected 13 billion global cameras by 2030, focusing on applications for mobile video sharing, surveillance, and autonomous vehicles. Current projects utilize CNNs, GANs, RNNs, and transformers for visual coding efficiency.

Tobias GlasmachersView profile

Professor

• Machine Learning
• Artificial Intelligence
• Optimization Algorithms
• +6 more

Prof. Dr. Tobias Glasmachers is a Full Professor at the Institut für Neuroinformatik , Ruhr-Universität Bochum, Germany, specializing in the Theory of Machine Learning . He leads the Optimization of Adaptive Systems group and holds appointments in both Computer Science and Interdisciplinary AI research. Key Research Areas : Optimization algorithms, evolutionary computation, reinforcement learning, supervised learning, and neural networks Technical Focus : Gradient-based methods, support vector machines, and adaptive coordinate descent Applications : Robotics, waste sorting facilities, 3D game environments (e.g., Doom/Minecraft), and human-centered AI design Notable Contributions : Development of LM-MA-ES evolution strategy, Hessian Estimation Evolution Strategy, and tachAId tool for ethical AI design. His work bridges theoretical analysis with practical implementations across diverse domains. Teaching : Offers courses in Informatik 1 - Programmieren, Machine Learning: Supervised Methods, and Evolutionary Algorithms. Supervises numerous Bachelor's and Master's theses on AI/ML applications.

• Computer Architecture
• Domain-Specific Architectures
• Reconfigurable Computing
• +4 more

Trevor Mudge is a Professor in the Department of Computer Science and Engineering at the University of Michigan. His work focuses on domain-specific architectures, reconfigurable computing, and energy-efficient processors. He leads a research group emphasizing collaborative innovation, with weekly meetings to discuss progress and foster interdisciplinary collaborations with institutions like MIT and Technion. His research interests include hardware-software co-design, near-memory processing, and accelerators for graph analytics and machine learning. He advocates for student autonomy in research direction, encouraging them to explore novel ideas while providing mentorship through frequent one-on-one interactions and 'extreme editing' sessions for manuscript refinement. Dr. Mudge actively supports student internships aligned with their research, particularly in industry partnerships that enhance their academic work. His publications reflect a strong emphasis on specialized architectures for wireless communication, sparse matrix computations, and low-power embedded systems. His group's approach to advising prioritizes flexibility, with communication channels like email, Slack, and virtual whiteboards enabling continuous feedback. Vacations and time away are accommodated with advance notice, emphasizing student well-being and productivity balance.

Kevin LinView profile

Assistant Professor

• Single-cell Biology
• Statistical Genetics
• Biostatistics
• +3 more

Kevin Lin is an Assistant Professor in the Department of Biostatistics at the University of Washington, joining in Fall 2023. His research focuses on developing statistical methods for analyzing single-cell data to uncover cellular mechanisms in diseases like Alzheimer’s and immune resistance. He holds a PhD from Carnegie Mellon University and completed postdoctoral training at the University of Pennsylvania. His work bridges computational methods with biological questions, emphasizing matrix factorization, network modeling, and changepoint detection. Education: PhD in Statistics & Data Science (Carnegie Mellon University, 2020) Previous Role: Postdoctoral Researcher at University of Pennsylvania (Wharton Statistics) Research interests include high-dimensional data analysis, statistical genetics, and single-cell RNA-Seq. Notable contributions include methods like Tilted-CCA for multimodal data integration and eSVD-DE for cohort-wide differential expression analysis. His work has been recognized with awards such as the Wikimedia Foundation Research Award (2023). Lin’s lab collaborates on projects involving endolysosomal dysfunction in Alzheimer’s, yeast cell division dynamics, and lineage-aware machine learning. Key achievements include over 20 publications in journals like Nature Biotechnology , PNAS , and Biometrics . He advises on grants related to single-cell technologies and serves on the EDI subcommittee for mental health initiatives at UW. Outside academia, Lin enjoys zumba and cooking.

• Privacy Engineering
• Privacy-by-Design
• Constructive Alignment
• +3 more

Shan Chen is a Lecturer (Teaching and Leadership) at the School of Computing, Macquarie University , affiliated with the Future Communications Research Centre and the Ethics and Agency Research Centre . Their work bridges privacy engineering principles with information systems design, focusing on machine learning applications in cybersecurity, educational technology, and privacy-by-design frameworks. Research Interests : Privacy Engineering and Privacy-by-Design methodologies Constructive Alignment in educational systems Cybersecurity solutions leveraging machine learning Data analysis for crowd anomaly detection Information systems for expansive learning and mobile collaboration Ontology-driven privacy policy enforcement Awards : Winner of the Faculty of Science and Engineering Award for Inter-School Collaboration (2023) Research Contributions : Recent work includes advancing crowd anomaly detection techniques (2024), evaluating machine learning for cybersecurity threats (2020), and designing privacy-aware systems for social networks (2010–2013). Their interdisciplinary approach spans disaster alert systems, cloud ERP implementations, and privacy requirements engineering. Labs/Teams : Active contributor to the Future Communications Research Centre (exploring communication technologies) and Ethics and Agency Research Centre (ethical frameworks for information systems).

George AzzopardiView profile

Associate Professor

• Pattern Recognition
• Image Processing
• Machine Learning
• +5 more

George Azzopardi is an Associate Professor in Pattern Recognition at the University of Groningen, affiliated with the Bernoulli Institute. He leads the PRISMA team, focusing on robust AI, medical imaging, and interdisciplinary applications in astronomy and forensics. His roles include coordinating the Applied AI theme at the Jantina Tammes School and leading WG4 of the EU GLITSS COST Action. Education: PhD in Computer Science (University of Groningen, 2013), MSc in Advanced Methods in Computer Science (Queen Mary University London, 2008), BSc in Computer Science (Goldsmiths College London, 2006). Research Interests: Brain-inspired algorithms, image processing, predictive analytics, and applications in radio astronomy, forensics, and medical diagnostics. His work emphasizes robustness in AI models and cross-domain collaborations. Publications span advancements in COSFIRE filters, deep learning robustness, and interdisciplinary applications like radio galaxy classification and biometric systems. Recent trends include enhancing model explainability and combating adversarial attacks. Awards: Ben Feringa Impact Award (2023), ENLIGHT Impact Award (2024) Advising: Supervises PhD students in AI-driven medical imaging, radio astronomy, and computer vision. Collaborates with institutions like ASTRON, UMCG, and industry partners Ziel NL and KAIOS. Labs/Teams: PRISMA team develops robust AI tools for medical and astrophysical data analysis, with projects on biostructure segmentation and camera forensics.

• Teaching Chinese as a Second Language
• Language Teaching Materials Development
• Online Language Education
• +2 more

Chao Li is a Lecturer of Chinese at the Georgia Institute of Technology's School of Modern Languages, part of the Ivan Allen College of Liberal Arts. He has been with Georgia Tech since 2001, focusing on teaching Chinese language at all proficiency levels and co-directing the Chinese Language for Business and Technology (LBAT) Program since 2006. His expertise includes developing online Chinese language courses, creating multimedia content, and compiling grammatical notes for effective language learning materials. Chao Li holds a Master of Arts in International Relations from Beijing Institute of Foreign Affairs and a Bachelor of Arts in Economics and Chinese from Yunnan University, China. He is a key figure in advancing online Chinese language education at Georgia Tech, emphasizing the integration of technology into language pedagogy. His work bridges language instruction with business and technology contexts, reflecting his commitment to practical, industry-relevant language training. His professional contributions include pioneering efforts in online course development, particularly in designing interactive and adaptive learning environments. While no formal awards are listed, his role in shaping Georgia Tech's Chinese language program highlights his significant impact on language education innovation. Chao Li’s teaching philosophy centers on student engagement through culturally immersive and technologically enhanced methodologies.

Yuke WangView profile

Assistant Professor

• Deep Learning Systems
• High-Performance Computing
• Parallel Programming
• +3 more

Yuke Wang is an incoming Assistant Professor at Rice University's Department of Computer Science starting Fall 2025. He earned his Ph.D. in Computer Science from the University of California, Santa Barbara (2024) and B.E. in Software Engineering from the University of Electronic Science and Technology of China (2018). His research focuses on optimizing deep learning systems through compiler and hardware co-design, with expertise in GPU acceleration, parallel computing, and distributed training. Education: Ph.D., UC Santa Barbara (2024); B.E., UESTC (2018). Professional experience includes postdoctoral research at Amazon AWS AI and internships at NVIDIA, Microsoft Research, and Alibaba DAMO Academy. Research interests include accelerating graph neural networks (GNNs), recommendation systems, and large language models (LLMs). He has developed frameworks like GNNAdvisor, MGG, and ZEN to enhance efficiency and scalability in deep learning workloads. His work has been recognized with awards such as the NVIDIA Graduate Fellowship and ACM PACT Student Research Competition. Awards include NVIDIA Graduate Fellowship (2022-2023), UCSB Dissertation Fellowship (2023), and multiple best paper nominations. He actively serves on program committees for top conferences like OSDI, ASPLOS, and ICS. Current hiring: Seeking graduate/undergraduate students for projects in deep learning systems, compiler optimization, and GPU acceleration. Collaborates closely with industry partners including NVIDIA and Amazon.

• Aerodynamics
• Turbulent Flow Analysis
• Particle Image Velocimetry (PIV)
• +9 more

Stefano Discetti is a Full Professor in the Department of Aerospace Engineering at Universidad Carlos III de Madrid, leading the Aerospace Engineering Research Group. His work bridges experimental fluid dynamics, machine learning, and flow control. Key research themes include turbulence characterization, heat transfer enhancement, and advanced measurement techniques like PIV/PTV. He has developed data-driven methods for flow estimation, sensor placement optimization, and physics-informed neural networks. Research Trends from his publications show a focus on: Machine learning for turbulent flow reconstruction (GANs, CNNs, KNN) Non-intrusive sensing from wall measurements Manifold learning and reduced-order modeling Heat transfer control via plasma actuators and passive structures Time-resolved diagnostics using hybrid experimental/numerical approaches Projects include principal roles in EU and national grants like NEXTFLOW (2021-2026) and EXCALIBUR (2023-2026), with industry collaborations at Airbus and TU Delft.

Pekka ParviainenView profile

Associate Professor

• Machine Learning
• Bayesian Networks
• Graphical Models
• +7 more

Pekka Parviainen is an Associate Professor in the Department of Informatics at the University of Bergen, within the Faculty of Mathematics and Natural Sciences. His research spans machine learning, probabilistic modeling, and AI theory, with a focus on Bayesian and Markov networks, adversarial robustness, fairness, and energy forecasting. He is affiliated with the Center for Data Science (CEDAS), an active research center at the university. His research interests include: Structure learning in graphical models Probabilistic forecasting using graph neural networks Adversarial robustness and defense mechanisms Fairness in clustering and machine learning Optimization and approximation in learning algorithms Applications in renewable energy and quantum sensing His recent publications (2020–2025) reflect a strong theoretical grounding combined with real-world applications, particularly in energy systems and AI safety. The works trend toward scalable and interpretable models, with increasing focus on fairness and robustness. Key themes include Bayesian network learning, metric learning, and causal graph modeling. Scientific contributions include: Development of novel adversaries (e.g., Voronoi-epsilon) for measuring robustness Scalable algorithms for learning large DAGs and Bayesian networks Integration of continuous optimization with combinatorial heuristics Applications in electricity demand forecasting and gas sensing Parviainen advises PhD students, including Hyeongji Kim (2023 thesis on distance in machine learning), and collaborates extensively with researchers in Norway and internationally. He has received computational support via Sigma2 (NN9884K) and is part of the CEDAS project, which fosters interdisciplinary data science research. While no specific grants are detailed, his involvement in funded projects and high-impact publications indicates active grant engagement. He is associated with the Center for Data Science (CEDAS), where he contributes to advancing data-driven methodologies across domains. The team emphasizes scalable, robust, and fair AI systems, aligning with national and international research priorities in trustworthy machine learning.