Find Academic Supervisors

Jørgen Arendt JensenView profile

Professor

• Medical Ultrasound
• Biomedical Signal Processing
• Digital Signal Processing
• +4 more

Jørgen Arendt Jensen is a Professor of Biomedical Signal Processing at the Technical University of Denmark (DTU), with dual affiliations in the Department of Health Technology and the Department of Electrical Engineering (DTU Elektro). He leads the Center for Fast Ultrasound Imaging (CFU), a collaborative initiative involving DTU, BK Medical, Rigshospitalet, and DTU Nanotech. His research focuses on advanced medical ultrasound technologies, including synthetic aperture imaging, vector flow imaging, and ultrasound simulation, aiming to improve clinical image acquisition efficiency and accuracy. He teaches medical imaging courses and co-initiated the joint biomedical engineering program between DTU and the University of Copenhagen. Jensen’s work contributes to UN Sustainable Development Goals related to health and innovation. His research interests span algorithm development for fast ultrasound imaging, blood velocity characterization, and simulation of ultrasound systems. He supervises multiple PhD students and collaborates on projects involving transducer design, real-time imaging systems, and microvascular pathology analysis. Recent publications emphasize advancements in super-resolution ultrasound imaging, transducer optimization, and pressure gradient estimation. His lab, CFU, develops cutting-edge imaging solutions for clinical applications. Jensen’s contributions include patents on ultrasound imaging techniques and collaborative ventures to enhance diagnostic capabilities through interdisciplinary engineering.

John R AndersonView profile

Professor

• Cognitive Neuroscience
• Cognitive Science
• Computational Psychology
• +3 more

John R Anderson is the Richard King Mellon University Professor of Psychology and Computer Science at Carnegie Mellon University (CMU), affiliated with the Department of Psychology within the Dietrich College of Humanities and Social Sciences. His research focuses on understanding higher-level cognition, particularly mathematical problem-solving, through the development of the ACT-R cognitive architecture—a computational framework simulating human cognitive processes. This architecture integrates behavioral, neural, and educational data to model learning and decision-making. Anderson’s work bridges cognitive science, neuroscience, and educational technology. He investigates how brain imaging (e.g., fMRI, EEG) can reveal the temporal dynamics of cognitive processes and improve instructional methods. His research emphasizes analyzing brain activity time courses to uncover underlying mechanisms of problem-solving and skill acquisition. Key Research Themes: Cognitive architectures, neural correlates of learning, computational models of memory, and intelligent tutoring systems. Notable Contributions: Development of the ACT-R architecture, integration of neuroimaging with cognitive modeling, and studies on skill transfer and learning strategies. Anderson’s publications include seminal books like Cognitive Psychology and Its Implications and How Can the Human Mind Occur in the Physical Universe? His work has advanced understanding of associative memory, strategic decision-making, and the application of cognitive models in educational technology. His lab, the ACT-R Research Group, collaborates across disciplines to model complex cognitive tasks and their neural foundations. Current projects analyze real-time brain activity to refine educational interventions and improve human-machine interaction.

Patrick SturtView profile

Associate Professor

• Psycholinguistics
• Syntactic processing
• Language comprehension
• +5 more

Patrick Sturt is a Reader in Psychology at the University of Edinburgh's School of Philosophy, Psychology and Language Sciences. His research focuses on syntactic processing in language comprehension, computational models of incremental parsing, anaphor resolution, and eye movements in reading. With over 100 publications and more than 3,300 citations, he is a recognized expert in psycholinguistics and language processing. Dr. Sturt's research interests span multiple areas of language processing. He investigates how humans comprehend sentences in real-time, with particular focus on syntactic structures, agreement phenomena, and anaphoric reference. His work often employs eye-tracking methodologies to examine the moment-by-moment processing of linguistic information. He has made significant contributions to understanding how readers handle syntactic ambiguities, garden-path sentences, and the role of prediction in language comprehension. His recent publications demonstrate a strong focus on cross-linguistic studies, particularly examining language processing in Mandarin Chinese and Korean. Many of his studies investigate how syntactic and semantic information interact during comprehension, and how linguistic structures like honorifics, classifiers, and non-canonical word orders are processed. His work bridges theoretical linguistics with experimental psycholinguistics, providing empirical evidence for models of sentence processing. Dr. Sturt actively supervises PhD students including Carine Abraham, Wenjia Cai, Chiuchou Hao, Ruomeng Zhu, and Christy Gu. He teaches Psychology of Language 1 and 2 at the MSc level, as well as Data Analysis for Psychology in R for first-year undergraduates. His teaching reflects his research expertise, providing students with both theoretical knowledge and practical analytical skills. Based in Room G29 of the Psychology Building at 7 George Square, Edinburgh, Dr. Sturt maintains regular office hours on Tuesdays from 3-4pm, providing accessibility to students and colleagues. His email address is patrick.sturt@ed.ac.uk.

Somil BansalView profile

Assistant Professor

• Autonomous Systems
• Robotics
• Control Theory
• +3 more

Somil Bansal is an Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University, part of the School of Engineering. Previously, he served as an Assistant Professor in the Electrical and Computer Engineering (ECE) department at the University of Southern California. He holds a B.Tech. from IIT Kanpur, an MS, and a Ph.D. from UC Berkeley’s EECS department. Research Focus: Development of mathematical tools and algorithms for safety-critical autonomous systems, emphasizing learning-enabled systems’ safety. Key Collaborations: Waymo, Skydio, Google, Boeing, NASA AMES/JPL. Awards: NSF CAREER Award, Eli Jury Award, RSS Pioneer Award, and Outstanding Graduate Instructor Award. His research integrates control theory and machine learning to ensure safety in autonomous systems, focusing on safe learning frameworks, anomaly detection, and real-time safety guarantees. He leads the Safe and Intelligent Autonomy (SIA) Lab, which explores applications in robotics, autonomous driving, and aerospace systems. Teaching: Courses include Introduction to Control Design Techniques and Principles of Safety-Critical Autonomy. He advises doctoral students and supervises research projects in his lab.

Wojciech JaroszView profile

Associate Professor

• Computer Graphics
• Rendering
• Light Transport Simulation
• +3 more

Wojciech Jarosz is an Associate Professor of Computer Science at Dartmouth College, affiliated with the College of Engineering and Computer Science. His research focuses on computer graphics, particularly light transport simulation, rendering algorithms, and digital fabrication. He co-founded the Visual Computing Lab and previously led the rendering group at Disney Research Zürich. Jarosz holds a Ph.D. and M.S. from UC San Diego and a B.S. from the University of Illinois Urbana-Champaign. His educational background includes studies in computer science and engineering, with a strong emphasis on graphics and rendering. Research interests span light transport simulation, Monte Carlo methods, appearance capture, and fabrication. Notable achievements include the Eurographics Young Researcher Award (2013) and the NSF CAREER Award (2019). Jarosz's work integrates theoretical rigor with practical applications, such as real-time rendering techniques and volumetric light transport. His lab develops tools for artistic authoring, including intuitive metaphors for volumetric lighting in animated films. Recent projects explore wave-optics BSDF models, optical heterodyne rendering, and unifying radiative transfer models. Key awards include the SIGGRAPH 2024 Best Paper Award and Neukom Institute prizes. His teaching includes courses on rendering algorithms, computer graphics, and computational photography. Jarosz collaborates with industry (e.g., Disney, NVIDIA) and advocates for diversity in computer graphics research.

Graham HemingwayView profile

Professor

• Model Integrated Computing
• Software Integrated Systems
• Cyber-physical Systems
• +5 more

Graham Hemingway is a Professor of the Practice of Computer Science and General Engineering at Vanderbilt University's School of Engineering. His research focuses on model-integrated computing, software systems integration, and cyber-physical systems, with applications in embedded control and electric grid simulations. He holds dual appointments in Computer Science and General Engineering within the School of Engineering, reflecting his interdisciplinary work. His research emphasizes model-based integration techniques to address challenges in complex systems like real-time control and energy infrastructure. Dr. Hemingway's publications span collaborative infrastructure design, electric grid simulation methodologies, and real-time embedded systems. His work often intersects simulation tools, distributed systems architecture, and high-confidence system design. While no awards or grants are explicitly listed in the provided text, his contributions to model-driven engineering and simulation frameworks demonstrate significant impact in computational and systems engineering domains.

Xiaolei FangView profile

Associate Professor

• Statistical Learning
• Deep Learning
• Federated Learning
• +3 more

Xiaolei Fang is Associate Professor in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University. His research develops advanced statistical learning, deep learning, and optimization methods for industrial applications involving high-dimensional data, with particular focus on condition monitoring, failure prognostics, and system performance optimization. He holds a PhD in Industrial Engineering and MS in Statistics from Georgia Tech. Professor Fang's research integrates machine learning with industrial engineering to solve complex problems in predictive maintenance, quality control, and energy systems. His methodological innovations include federated learning approaches for privacy-preserving prognostics, distributionally robust machine learning models, and tensor-based statistical methods for manufacturing quality diagnostics. He has received multiple prestigious awards including the ISE Outstanding Research Award (2024), Sigma Xi Best PhD Thesis Award (2019), and SAS Data Mining Best Paper Award (2016). His research has been funded by NSF, Cisco Systems, and the US Department of Energy. Professor Fang teaches courses in Quality Design & Control, Statistical Models for Systems Analytics, High-Dimensional Data Analytics, and Optimization Models. He has supervised 9 PhD students to completion and currently advises 7 graduate students working on projects spanning federated learning for prognostics, tensor-based quality control, and machine learning applications in manufacturing and energy systems.

Shirin Saeedi BidokhtiView profile

Assistant Professor

• Information Theory
• Networking
• Data Compression
• +2 more

Shirin Saeedi Bidokhti is an Assistant Professor at the University of Pennsylvania's School of Engineering and Applied Science with primary appointment in Electrical and Systems Engineering and secondary appointment in Computer and Information Science. She is affiliated with the Warren Center for Network and Data Sciences. She holds M.Sc. and Ph.D. degrees from EPFL and completed postdoctoral work at Stanford and Technical University of Munich. Her research focuses on information theory, networking, data compression, and machine learning. Her recent publications demonstrate strong emphasis on neural compression algorithms, network optimization during the COVID-19 pandemic, and age-of-information theory. Awards include: 2023 IEEE Communications Society & Information Theory Society Joint Paper Award 2021 NSF CAREER Award 2019 NSF-CRII Award Swiss National Science Foundation Fellowships She advises PhD students including Xingran Chen. Current research involves developing data compression algorithms for IoT applications and network strategies for pandemic response.

Cheng, Reynold C.K.View profile

Professor

• Data Science
• Big Graph Analytics
• Uncertain Data Management
• +3 more

Professor Reynold Cheng is a faculty member at the University of Hong Kong (HKU), specifically within the Department of Computer Science in the School of Computing and Data Science (CDS). He currently serves as the Division Head of the AI & Data Science Division at CDS and is part of the Steering Committee of the Musketers Foundation Institute of Data Science. His academic journey includes a BEng and MPhil from HKU (1998–2000) and an MSc and PhD from Purdue University (2003–2005). Prior to HKU, he was an Assistant Professor at the Hong Kong Polytechnic University (HKPU) from 2005 to 2008. Cheng’s research focuses on data science, big graph analytics, and uncertain data management. He has received numerous awards, including the SIGMOD Research Highlights Reward 2020, HKICT Awards 2021, and HKU Knowledge Exchange Award (Engineering) 2021. His work has been recognized through grants such as the HKU-TCL Joint Research Centre for AI-funded project (HKD 1M, 2020–2022) and a CRF-funded project for real-time monitoring of infectious diseases (HKD 6.5M, 2021–2022). Cheng actively contributes to academic service, including serving as PC co-chair for IEEE ICDE 2021 and editorial roles in journals like IS and DAPD. His publications span top venues like SIGMOD, VLDB, and KDD, emphasizing algorithm design for large graphs and probabilistic data systems.

Artur CzumajView profile

Professor

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Randomized Algorithms
• +9 more

Artur Czumaj is a Professor in the Department of Computer Science at the University of Warwick and serves as the Director of the Centre for Discrete Mathematics and its Applications (DIMAP). He is a member of the Division of Theory and Foundations (FoCS) and holds affiliations with the Alan Turing Institute, the Warwick Data Science Institute (WDSI), and the Warwick Centre for Doctoral Training in Mathematics of Real-world Systems (MathSys). Previously, he served as Head of Department and President of the European Association for Theoretical Computer Science (EATCS) from 2020 to 2024. His research lies at the core of theoretical computer science, focusing on the design and analysis of algorithms, particularly randomized, sublinear, parallel, and distributed algorithms. His work spans graph theory, combinatorics, computational geometry, algorithmic game theory, and property testing. He has led major research initiatives funded by EPSRC, IBM, the Royal Society, and Weizmann-UK grants. The trends in his recent publications highlight a strong emphasis on sublinear algorithms, dynamic graph algorithms, and property testing, with recurring themes in randomized methods, graph processing, and efficient data structures. His work bridges foundational theory with applications in data summarization, network analysis, and computational geometry. EPSRC grants: EP/D063191/1, EP/G064679/1, EP/G069034/1, EP/J021814/1, EP/N011163/1, EP/V01305X/1, EPSRC studentship IBM Faculty Award Royal Society International Exchanges Scheme Weizmann-UK Making Connections Grants on combinatorial and algorithmic primitives and the interplay between algorithms and randomness Peter Davies received the 2020 Warwick Faculty of Science Thesis Prize under his supervision Artur Czumaj has supervised numerous PhD students, including Anna Adamaszek, Michal Adamaszek, Sam Coy, Peter Davies, Michail Fasoulakis, Jan Hladky, Wang Xin, and Hairong Zhao. His research has been supported by sustained grant funding, reflecting his leadership in theoretical computer science. He has organized major workshops at Dagstuhl, Oberwolfach, Simons Institute, and the University of Warwick, and has served on the steering committees of HALG and as PC Chair for SODA 2018 and ICALP 2020. He is actively involved in organizing key research events, including the Simons Institute Special Semester on Sublinear Algorithms (2024), the Workshop on Sublinear Graph Simplification (2024), and the Computational Complexity Conference (CCC 2023) at Warwick. He also co-organizes the Warwick-Weizmann workshops and the IGAFIT Algorithmic Postdocs Workshop, fostering international collaboration in algorithms research.

• Biomedical Engineering
• Control Systems
• Brain-Computer Interfaces
• +7 more

Andrzej Majkowski is an Associate Professor at the Institute of the Theory of Electrical Engineering, Measurement and Information Systems, Faculty of Electrical Engineering, Warsaw University of Technology. His career spans over two decades of research in biomedical engineering, focusing on brain-computer interfaces, signal processing, and emotion recognition. Active in both teaching and research, he contributes to advancing methodologies in electrophysiological signal analysis. Warsaw University of Technology Institute of the Theory of Electrical Engineering, Measurement and Information Systems Faculty of Electrical Engineering Specializing in biomedical engineering , Majkowski's research bridges control systems and information technologies with neuroscience applications. His work explores brain-computer interfaces , EEG/EMG signal processing , and emotion recognition using multimodal physiological data. Recent studies focus on deep learning architectures for artifact removal and classification tasks. Recent publications highlight trends in CNN-LSTM hybrid models for signal denoising, convolutional networks for seizure detection, and machine learning applications in visual evoked potential analysis. His work spans both clinical applications (epilepsy monitoring) and human-computer interaction (emotion recognition, sign language detection). With over 98 documented publications and significant bibliometric indicators (h-index 13 in Scopus), Majkowski has supervised 95 promoted theses. His research includes one funded project and collaborations in biomedical instrumentation, though specific award details remain unspecified in available records.

Anthony SteedView profile

Professor

• Virtual Reality
• Augmented Reality
• Interactive Virtual Environments
• +7 more

Anthony Steed is a Professor in the Department of Computer Science at University College London, where he leads the Virtual Environments and Computer Graphics group and serves as deputy head of department. His work focuses on real-time interactive virtual environments and related technologies. His research interests include: Virtual Reality and Augmented Reality Real-time Graphics and Interactive Virtual Environments Networking for Tightly Synchronous Telecollaboration 3D User Interfaces and Novel Forms of Interaction Perceptual and Cognitive Issues in Mixed Reality Haptics and Telepresence Professor Steed has made significant contributions to the field through his co-authored books "Networked Graphics" (2010) and "Computer Graphics and Virtual Environments: From Realism to Real-Time". He has also contributed to puzzle design, co-authoring "The Gentle Art of Filling Space" with Michael Dowle. His notable recognition includes the IEEE VGTC Technical Achievement Award (2016). Professor Steed supervises several doctoral students and post-doctoral fellows, and teaches courses including Research Methods, Virtual Environments, and Perception and Interfaces at both undergraduate and MSc levels.

Brad KnoxView profile

Associate Professor

• Reinforcement Learning
• Human-Computer Interaction
• Artificial Intelligence
• +4 more

Brad Knox is a Research Associate Professor in the Department of Computer Science at the University of Texas at Austin . His work bridges machine learning, human-computer interaction, and computational cognitive science, with a focus on developing systems that learn from human feedback. Key research areas: Reinforcement Learning, Human-AI Interaction, Reward Design, Autonomous Systems Notable contributions: TAMER framework for human-guided learning, empirical studies on reward misdesign, and human preference modeling for autonomous agents Research Trends : His recent work (2023-2025) emphasizes reward alignment, safety in autonomous systems, and preference-based learning frameworks. Earlier studies (2012-2020) established foundational methods for integrating human feedback into reinforcement learning architectures and exploring behavioral signatures in decision-making. Scientific Honors : Bert Kay Dissertation Award (2013) Victor Lesser Distinguished Dissertation Award (IFAAMAS, Runner-up, 2013) NSF SBIR Grant (PI, 2016) NSF Graduate Research Fellowship (2008-2011) IEEE Intelligent Systems AI 10 to Watch (2013) Teaching & Leadership : Knox served as Principal Lecturer for MIT's Interactive Machine Learning course (2013) and held organizational roles at major conferences including Reinforcement Learning Conference (Scheduling Chair, 2025) and RLDM workshop (Co-chair, 2022).

Aswin SankaranarayananView profile

Professor

• Computational Photography
• 3D Shape Estimation
• Imaging Systems
• +4 more

Aswin Sankaranarayanan is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU) , where he leads the Image Science Lab . His research focuses on computational photography , 3D shape estimation , and novel imaging system design . He earned his Ph.D. in Electrical and Computer Engineering (2009) from the University of Maryland and completed a postdoctoral fellowship at Rice University (2012) . Research Themes: Developing imaging systems that exploit low-dimensional signal models to overcome traditional sensing limitations Co-design of optics and processing algorithms for efficient sensing Application of non-linear signal models to high-dimensional data Advancing compressed sensing and big data processing techniques Scientific Recognition: SIGGRAPH 2023 Best Paper Award (Split-Lohmann Multifocal Displays) CVPR 2019 Best Paper Award (Fermat Paths for NLOS Reconstruction) NSF CAREER Award (2017) Dean’s Early Career Fellowship (2018-2021) Herschel Rich Invention Award (2016) Technical Contributions: His recent publications reveal expertise in non-line-of-sight shape reconstruction , VR/AR display systems , and biomedical imaging . Collaborations span institutions like University College London and University of Toronto.

• Reconfigurable Computing
• Machine Learning Acceleration
• Hardware Acceleration
• +5 more

Aman Arora is an Assistant Professor at Arizona State University's Ira A. Fulton Schools of Engineering, specializing in the School of Computing and Augmented Intelligence. His research focuses on reconfigurable computing, hardware acceleration of machine learning, and non-traditional computing paradigms like Processing-In-Memory. With over a decade of semiconductor industry experience, he bridges academic research and industrial applications. PhD in Computer Science from The University of Texas at Austin Research interests emphasize domain-specific acceleration through FPGA optimization , compute-in-memory architectures , and machine learning for CAD/EDA . His work addresses critical challenges in energy efficiency and throughput for AI workloads. Recent publications demonstrate trends toward compute-in-memory systems , FPGA-based deep learning acceleration , and sustainable hardware design . Key contributions include frameworks like SAF, CSR, and GAMA for dynamic hardware optimization. Laboratory Website: ADVENT Lab Teaching includes courses on digital hardware design (CSE 320) and advanced topics in machine learning acceleration (CEN 524/CSE 524). Industry experience informs his practical approach to research and education.