Find Academic Supervisors

• Materials Science
• Corrosion Engineering
• Electrochemistry
• +5 more

Prof. Robert Meißner is a Professor at the Department of Surface Physics and Technology at TUHH. His research focuses on molecular simulation techniques applied to corrosion processes, energy storage systems, and nanomaterials. He develops computational tools like ELECTRODE and i-PI for electrochemical and advanced molecular dynamics simulations. His work addresses challenges in magnesium battery performance, structural health monitoring of composite materials, and interfacial phenomena in nanoscale systems. Education details are not explicitly provided in the text, but his professional trajectory reflects extensive academic and industrial experience in materials science. Research interests span from fundamental studies (e.g., water imbibition in nanopores, magnetite oxidation dynamics) to applied innovations (e.g., corrosion protection via layered double hydroxides, data-driven electrolyte design). His recent publications highlight trends in data-driven materials discovery, structural health monitoring via vibro-acoustic methods, and computational prediction of corrosion inhibitors. He collaborates on projects involving graphene-based supercapacitors, epoxy resin curing dynamics, and peptide-surface interactions. Advising and grants: While student names are not listed, his research group actively explores corrosion engineering, battery technology, and nanomaterials. Projects include EU-funded initiatives and industry partnerships. Technical expertise includes ATR-FTIR spectroscopy, molecular dynamics, and machine learning for sparse data scenarios. He leads teams focused on surface science and energy storage, maintaining lab facilities for in situ electrochemical analysis and advanced computational modeling. His work bridges theoretical insights with practical applications in materials durability and energy systems.

Stephen StrainView profile

Assistant Professor

• Cognitive Science
• Artificial General Intelligence
• Computational Modeling of Medical Diagnosis
• +3 more

Dr. Stephen F. Strain is an Assistant Professor of Teaching in the Department of Biomedical Engineering at the University of Memphis. His research focuses on cognitive science, artificial general intelligence, computational modeling of medical diagnosis, and chess-related cognition. He holds a BA in Physics (Columbia University, 1987), MD (ETSU Quillen College of Medicine, 1999), and MS in Biomedical Engineering (University of Memphis, 2009). His doctoral studies under Dr. Stan Franklin explored applications of the LIDA cognitive model to medical diagnosis and chess cognition, though progress was paused following Dr. Franklin’s 2023 passing. Dr. Strain is the faculty sponsor of the UM Chess Club and has an Erdős number of 3 due to collaborative publications with Dr. Franklin. Education: BA, Physics, Columbia College at Columbia University, 1987 MD, ETSU Quillen College of Medicine, 1999 MS, Biomedical Engineering, University of Memphis, 2009 Research Interests: Dr. Strain’s work bridges cognitive science and biomedical engineering, emphasizing computational models of medical diagnosis and chess cognition. He explores how brain rhythms influence cognition and advocates for integrating biological principles into cognitive science. His LIDA Model-based research aims to create intelligent systems for medical decision-making and cognitive tasks. Recent Publications: His recent work includes syllabi for biomedical engineering courses (2021–2024) and advancements in the LIDA model (2011–2018), focusing on cognitive architecture and medical applications. His publications reflect a blend of pedagogical innovation and foundational cognitive science research. Awards & Recognition: No formal awards listed, but his Erdős number of 3 highlights collaborative contributions to interdisciplinary research. Grants & Advising: While no grants are explicitly noted, his doctoral work with LIDA and MAX demonstrates engagement in funded research. He advises the UM Chess Club, fostering academic-community integration. Labs & Teams: Collaborates with the Cognitive Computing Research Group (CCRG) at the University of Memphis, leveraging their LIDA model infrastructure for interdisciplinary projects.

Saleh AshkboosView profile

Researcher

• Deep Learning
• Artificial Intelligence
• Machine Learning
• +5 more

Saleh Ashkboos is a Ph.D. student in the Computer Science Department at ETH Zurich, advised by Professors Torsten Hoefler and Dan Alistarh. He is also a Research Assistant at the Scalable Parallel Computing Lab and an affiliated doctoral student of the ETH AI Center. His research focuses on accelerating deep neural network training and developing systems for large-scale graph processing. Prior to ETH Zurich, he earned his Master's degree in Computer Science from Sharif University of Technology, advised by Professor Amir Daneshgar. His work has led to notable contributions, including the best paper award at SC22 for 'ProbGraph.' Recent research emphasizes efficient LLM training and quantization techniques, with publications on topics like 4-bit inference, quantization-aware training frameworks, and scalable meteorological modeling. He has interned at Apple and Microsoft, and his work is accessible via Google Scholar and GitHub. Key projects include GPTQ (post-training quantization for transformers), SliceGPT (LLM compression), and ProbGraph (high-performance graph mining). His technical contributions span distributed systems, neural network optimization, and climate-related machine learning.

• Neuroscience
• Neuroimaging
• Brain Connectivity
• +4 more

Kangjoo Lee is an Associate Research Scientist in Psychiatry at Yale School of Medicine, specializing in neuroimaging and computational neuroscience. Their research focuses on understanding brain dynamics, particularly in mental health conditions like schizophrenia and mood disorders, using advanced neuroimaging techniques and AI-driven analysis. Education: PhD in Neuroscience from McGill University (2019), Postdoctoral Research Associate at Yale University (2023). Research interests include brain connectivity, sleep deprivation effects, and developing AI tools for neuroscience. Lee has contributed to studies on ketamine's neural mechanisms, sleep recovery networks, and large language models' predictive capabilities in neuroscience. Notable achievements include the Merit Abstract Award (2021) and leadership roles in the Organization for Human Brain Mapping's Diversity Committee. Their work bridges clinical neuroscience with computational methods, emphasizing reproducibility and inclusivity in scientific practices. Current roles include Guest Editor for Biological Psychiatry and committee memberships in neuroimaging and diversity initiatives. Lee collaborates across disciplines, advancing translational research in mental health biomarkers and neurotechnology.

Shahin ShahrampourView profile

Assistant Professor

• Machine Learning
• Optimization and Control
• Distributed Learning
• +5 more

Shahin Shahrampour is an Assistant Professor in the Department of Mechanical & Industrial Engineering at Northeastern University, with a courtesy appointment in Electrical & Computer Engineering. He holds a Ph.D. in Electrical & Systems Engineering from the University of Pennsylvania, an M.A. in Statistics from The Wharton School, and a B.S. in Electrical Engineering from Sharif University of Technology. Prior to Northeastern, he was at Texas A&M University and held a postdoctoral fellowship at Harvard University. Research Interests: Machine learning, optimization and control, distributed and sequential learning, with a focus on computationally efficient methods for data analytics. Specific areas include manifold optimization, online and reinforcement learning, non-convex optimization, and statistical signal processing. His work bridges theory and applications in networked systems and dynamic environments. Awards & Grants: Received a $500,000 NSF grant (2023) for distributed optimization in non-convex environments. Best Paper Award at IEEE ICASSP 2022 for contributions to signal processing. TEES Engineering Genesis Award (2020) for multidisciplinary research at Texas A&M. Principal Investigator on NSF-funded projects in collaborative online optimization and decentralized learning. Labs & Teams: Leads research in machine learning and control systems at Northeastern University, focusing on interdisciplinary challenges in distributed optimization and real-time learning. His lab emphasizes theoretical foundations and practical implementations in networked and dynamic systems.

Xue LinView profile

Associate Professor

• Robust and Secure Machine Learning
• Deep Learning on the Edge
• Cyber-Physical Systems
• +3 more

Xue Lin is an Associate Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a courtesy appointment in Khoury College of Computer Science. She joined Northeastern in 2017 and holds a PhD from the University of Southern California (2016) and a bachelor’s from Tsinghua University. Her research focuses on robust and secure machine learning, deep learning on edge devices, and cyber-physical systems. She leads the High Energy-Efficiency & Performance System Lab, which develops efficient algorithms and systems for applications like autonomous vehicles and medical AI. Dr. Lin’s work is supported by NSF, DARPA, and the U.S. Department of Transportation, among others. Notable achievements include a $1M DARPA grant for adversarial diagnosis systems, a 1st Place ISLPED 2020 Design Contest win, and multiple best paper awards. She has advised students such as Kaidi Xu (PhD’21), Mengshu, and Siyue, who have contributed to impactful projects like adversarial T-shirt attacks and FPGA-based DNN accelerators. Her research also addresses security in autonomous systems and inclusive design challenges for older and visually impaired passengers. Key grants include NSF CPS Small Awards, SaTC Medium Awards, and collaborations with institutions like the University of Maine and Michigan State University. Awards include the 2024 Faculty Fellow Award and recognition in Stanford’s top 2% cited scientists. Her lab’s projects span secure autonomous systems, energy-efficient inference frameworks (e.g., GRIM), and robust neural network verification techniques.

Sergiy VorobyovView profile

Professor

• Optimization
• Signal Processing
• Multi-Linear Algebra
• +9 more

Sergiy Vorobyov is a Professor at the Department of Signal Processing and Acoustics , Aalto University , Finland. He has held academic and research positions at multiple institutions, including the University of Alberta (Canada), Kharkiv National University of Radio Electronics (Ukraine), RIKEN (Japan), McMaster University (Canada), Duisburg-Essen University and Darmstadt University of Technology (Germany), and Heriot-Watt University (UK). His expertise spans optimization, signal processing, and multi-antenna systems. Dr. Vorobyov holds a Doctoral degree in Natural Sciences from the National Technical University Kharkiv Polytechnical Institute, awarded on January 15, 2002. His research interests focus on optimization and multi-linear algebra applied to signal processing challenges, including statistical and array signal processing, sparse signal processing, estimation and detection theory, and sampling theory. He explores multi-antenna, large-scale, cooperative, and cognitive systems, contributing to advancements in wireless communications and radar engineering. His work aligns with UN Sustainable Development Goals, emphasizing education and innovation. In recent years (2025), his publications emphasize cutting-edge advancements in wireless communications and signal processing. Topics include millimeter-wave MIMO channel estimation, optimization algorithms with momentum-based techniques, vehicular network communications, and robust covariance matrix estimation in challenging noise environments. These contributions highlight his expertise in developing efficient and adaptive methods for modern communication systems. He has received prestigious awards, including: 2004 IEEE Signal Processing Society Best Paper Award 2007 Alberta Ingenuity New Faculty Award 2011 Carl Zeiss Award for teaching and innovative methods 2012 NSERC Discovery Accelerator Award 1st Price Best Paper Award (2015) 1st Price Best Student Paper Award at CAMSAP 2015 As a researcher, Vorobyov has supervised seven theses and led multiple funded projects, such as: AI Based RAN (2023–2025): Scalable AI solutions for 5G/6G networks. MASSIVE AND SPARSE ANTENNA ARRAY PROCESSING FOR MILLIMETERWAVE COMMUNICATIONS (2019–2021): Advanced antenna design and processing techniques. M-CUBE SPA (2017–2021): EU-funded sparse antenna array research. Transmit beamspace for active compressive sensing and communication with multiple waveforms (2016–2020): Radar and MIMO system optimization. He leads the Sergiy Vorobyov Group , focusing on real-time signal processing algorithms and their applications in next-generation wireless systems. His research addresses practical challenges such as efficient channel estimation, robust detection in massive access scenarios, and improving network performance in urban environments.

Mark GatesView profile

Academic

• High Performance Computing
• Scientific Computing
• Linear Algebra
• +3 more

Mark Gates is a Research Assistant Professor at the Innovative Computing Laboratory (ICL) at the University of Tennessee, Knoxville. His work focuses on high performance computing (HPC), scientific computing, and linear algebra, particularly leveraging multi-core and GPU architectures. He contributes to projects like SLATE, PLASMA, and MAGMA, advancing dense and sparse linear algebra libraries for modern parallel systems. His research emphasizes scalable algorithms, communication-avoiding techniques, and exascale computing. Education: Ph.D., M.S., and B.S. in Computer Science from the University of Illinois Urbana-Champaign (2011, 2007, 1998). Research interests include parallel algorithms, numerical stability, and GPU acceleration. Recent work highlights include task-based polar decomposition, mixed-precision algorithms, and efficient sparse linear algebra operations. His articles address challenges in distributed computing, library development, and performance optimization. Labs/Teams: Active in ICL, collaborating on HPC software for exascale systems. No scientific awards explicitly listed, though contributions to foundational HPC libraries are notable.

Rishabh DabralView profile

Researcher

• 3D Computer Vision
• Computer Graphics
• Multi-Modal Interactions
• +6 more

Rishabh Dabral is a Research Group Leader at the Max Planck Institute for Informatics since August 2024, leading the "3D Visual Intelligence" group. He is also affiliated with the Research Training Group on Neuro-Explicit Models of Language, Vision, and Action at Saarland University. Expertise: 3D computer vision, computer graphics, human-object interaction modeling, and motion synthesis. Leadership: Conducts cutting-edge research on 3D human performance capture and physical plausibility in motion. His research focuses on: 3D human pose estimation under gravity constraints Multi-modal gesture synthesis using neural architectures Quantum auto-encoding for 3D representations Wearable robotics informed by human behavior Temporal dynamics in human-object interaction Recent publications at top venues like SIGGRAPH , CVPR , and ICCV demonstrate his work on: Music-driven motion synthesis Egocentric motion capture systems Reactive two-person interaction models Diffusion-based gesture generation Object-aware motion prediction Wearable robotic limb design

Michael FriedlanderView profile

Professor

• Convex Optimization
• Machine Learning
• First-Order Methods
• +3 more

Michael Friedlander is a Professor of Computational Mathematics at the University of British Columbia (UBC), holding joint appointments in the Departments of Computer Science and Mathematics. He serves as Director of the UBC Institute of Applied Mathematics and is a Sauder School Distinguished Scholar. His research focuses on optimization theory, algorithms, and applications, particularly in convex optimization, first-order methods, and machine learning. Friedlander is a SIAM Fellow (2024) and has received multiple teaching awards, including the 2013 UBC CS Teaching Award. Education: PhD (2002) in Operations Research from Stanford University; BA (1993) in Physics from Cornell University. Research Interests: Friedlander’s work spans computational optimization, including first-order methods, gauge duality, federated learning, and applications in signal processing and quantum computing. His contributions include SPGL1, a widely used convex optimization solver, and foundational work on atomic decomposition and polar alignment in structured optimization. Key Contributions: His research bridges optimization with practical applications, such as compressed sensing and data science. Notable areas include dual methods for federated learning, low-rank spectral optimization, and algorithms for sparse recovery. He has advised numerous PhD students, including work on topics like gauge duality and federated learning contribution valuation. Awards & Honors: SIAM Fellow (2024) 2013 UBC CS Department Teaching Award CACS Outstanding Young Researcher Prize AI & Statistics 2009 Best Paper Award Advising & Grants: Supervised over 20 graduate students and postdocs. Active in grant-funded research, including projects on optimization algorithms and their industrial applications. Collaborates with institutions like Pacific Institute for Mathematical Sciences (PIMS) and the Data Science Institute at UBC. Labs & Affiliations: Affiliated with the Institute for Computing, Information and Cognitive Systems (ICICS), Data Science Institute, and Quantum Computing Research Cluster at UBC. Leads interdisciplinary efforts in applied mathematics and computational science.

Morten BrunView profile

Associate Professor

• Topological Data Analysis
• Computational Topology
• Persistent Homology
• +3 more

Morten Brun is an Associate Professor at the Department of Mathematics, University of Bergen. His research spans computational topology, persistent homology, and applications in biology and data science. Email: morten.brun@uib.no Research Interests: He specializes in topological data analysis, focusing on sparse nerves, relative persistent homology, and computational geometry. His work applies topological methods to biological problems, including drug resistance modeling in tuberculosis and immune profiling in multiple sclerosis. Recent Publications: His 2025 work includes hypercubic modeling of tuberculosis drug resistance and high-dimensional immune profiling post-stem cell transplantation. Earlier articles explore computational topology techniques (2017-2024) and interdisciplinary applications in toxicology and systems biology.

Georgi Dimitrov MengovView profile

Professor

• Computational Neuroscience
• Behavioral Economics
• Decision Science
• +4 more

Professor Georgi Dimitrov Mengov serves as a full-time faculty member at the Faculty of Economics, Sofia University "St. Kliment Ohridski" since 2005, heading the Center for Modeling Socioeconomic Systems. His interdisciplinary work bridges computational neuroscience and economic decision-making through affiliations including the editorial board of the International Journal of Neural Networks and Advanced Applications and advisory roles at Swiss Innovation Valley. His research focuses on human-oriented decision science , examining neural correlates of economic choices, emotional memory dynamics, and socioeconomic forecasting. Mengov develops computational models predicting intuitive economic behavior using neural network architectures, with particular emphasis on Behavioral economics and risk analysis Neurocomputational modeling of social systems Mathematical-psychological frameworks for decision processes His publication trends reveal consistent interdisciplinary integration: neural network methodologies applied to socioeconomic problems (2006-2018), behavioral economics of digital networks (2021-2025), and foundational work in human decision theory through monographs like Decision Science: A Human-Oriented Perspective (Springer, 2015). Recent work increasingly addresses turbulent economic environments and virtual social networking impacts. Scientific contributions include editorial leadership for major journals and program committee participation in the International Joint Conference on Neural Networks (2013-2019). Notable recognitions include ORCID registration (0000-0001-8157-3246) and ResearchGate profile. As academic advisor and center director, Mengov mentors emerging researchers while leading projects on socioeconomic modeling. His work demonstrates strong integration of theoretical neuroscience with practical economic applications, securing consistent publication output in high-impact journals including Neural Networks and Journal of Behavioral and Experimental Economics . Mengov maintains active research laboratories focused on neuroeconomic modeling and socioeconomic forecasting, with recent work exploring fractal social structures and virtual network economics. Current projects examine motivation dynamics in turbulent economic environments through the ACCESS Press publications.

• Computer Architecture
• High-Performance Computing
• Multicore and Manycore Systems
• +7 more

Miquel Moreto Planas is a Senior Lecturer in the Department of Computer Architecture at the Barcelona School of Informatics, Universitat Politècnica de Catalunya (UPC). He is also affiliated with the Barcelona Supercomputing Center (BSC-CNS), a leading institution in high-performance computing. His academic profile is deeply rooted in computer architecture and high-performance computing, with a strong emphasis on practical and theoretical advancements in multicore systems, memory management, and hardware acceleration. His research interests span a wide range of topics including computer architecture, high-performance computing, multicore and manycore systems, cache and memory management, hardware acceleration for genomics and AI, RISC-V processor design, processing-in-memory, interconnection networks, and real-time systems. These interests are reflected in his extensive publication record and collaborative projects. The most recent articles highlight a significant trend toward interdisciplinary research, particularly the application of advanced computer architecture techniques to bioinformatics and healthcare. Key themes include the acceleration of genomic sequence alignment using novel hardware such as processing-in-memory, the development of benchmarks for ARM-based HPC systems in genomics, and the creation of AI-based 3D decision support tools for neurosurgical applications. His work also continues to advance core computer architecture topics like cache management, power-aware resource allocation in heterogeneous systems, and the design of secure, post-quantum cryptographic hardware based on RISC-V. Fulbright Award 2011 HiPEAC Paper Award HiPEAC Paper Award 2024 HiPEAC Paper Award Moreto has been a principal investigator or key contributor to multiple competitive R&D+i projects, such as the STRATUM project for neurosurgical tools, REDIOH for open hardware, and the Laboratorio Zettaescala de Barcelona. He has advised several doctoral students, including López, G., Kostalampros, I., and Haghi, A., and is a core member of the CAP (High Performance Computing) research group at UPC. His work is characterized by strong collaborations with leading researchers like Mateo Valero, Eduard Ayguadé, and Jesús Labarta, often bridging the gap between UPC and BSC-CNS. His laboratory and team affiliations are centered around the CAP group and the Barcelona Supercomputing Center, where he contributes to cutting-edge research in high-performance and embedded computer architectures. His recent work on the BIMSA accelerator and the STRATUM project demonstrates a clear future direction toward applying high-performance computing solutions to critical problems in genomics and medicine.

• Robot Vision
• Robot Navigation
• Computer Vision
• +7 more

Helder Araujo is a Professor at the Department of Electrical and Computer Engineering, University of Coimbra. His research focuses on Robot Vision , Computer Vision , and Medical Imaging , with applications in Capsule Endoscopy , Autonomous Driving , and Sensor Fusion . Recent publications highlight advancements in 3D Object Detection using transformer networks (e.g., RetSeg3D , DDet3D ), Medical Robotics (e.g., Self-supervised monocular pose estimation ), and Federated Learning (e.g., FAIR-FATE ). Key subfields include Transformer Models , Deep Learning , and Autonomous Navigation . His projects span Medical Imaging (e.g., Multi-Cam Capsule Endoscopy ), Autonomous Robotics (e.g., Learning to Navigate Endoscopic Capsule Robots ), and Edge Computing (e.g., Benchmarking CNN Inference on Low-Power Devices ). Grants include funding from Fundação para a Ciência e a Tecnologia (e.g., PTDC/EEI-ROB/1155/2020) and the European Commission (e.g., AdvanCed Hardware/Software Components for Embedded Vision). He has served as an invited researcher at Université Blaise Pascal (2012–2014). Peer review activities cover journals like IEEE Transactions on Pattern Analysis and Machine Intelligence and Autonomous Robots . His work bridges Medical Imaging , Autonomous Systems , and AI Fairness .

• Applied Mathematics
• Nonlinear Dynamical Systems
• Fluid Dynamics
• +7 more

Nikolas Olson Aksamit is an Associate Professor in the Department of Mathematics and Statistics at UiT The Arctic University of Norway, where he conducts interdisciplinary research at the intersection of applied mathematics and environmental science. His work focuses on understanding coherent structures in fluid flows relevant to the coupling between the cryosphere, atmosphere, and hydrosphere. His research interests include applied mathematics, nonlinear dynamical systems, fluid dynamics, Lagrangian coherent structures, snow science, and climate dynamics. He develops mathematical tools to analyze complex environmental systems, particularly in polar and alpine regions, with applications to sea ice, blowing snow, and ocean turbulence. His work bridges theoretical modeling with field observations and data analysis. The recent articles reflect a strong trend in using Lagrangian methods and nonlinear dynamics to study geophysical flows. Key themes include the detection of coherent structures in turbulent flows, sea ice dynamics, climate modeling, and the application of machine learning to oceanographic data. His publications span high-impact journals in fluid mechanics, geophysics, and environmental science, often in collaboration with leading experts in dynamical systems and climate science. He is a member of the research group Changing Arctic and contributes to the project Development of nonlinear dynamics-based techniques for sea ice diagnosis . While no formal students or awards are listed, his active publication record indicates a vibrant research program. His work has implications for climate prediction, polar monitoring, and environmental engineering.