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Jack JewsonView profile

Senior Lecturer

• Bayesian Inference
• Model Misspecification
• Robust Statistics
• +7 more

Jack Jewson is a Senior Lecturer in the Department of Econometrics and Business Statistics at Monash University’s Faculty of Business and Economics, a position he has held since April 2024. He previously conducted postdoctoral research and held a Juan de la Cierva Research Fellowship at Universitat Pompeu Fabra, Barcelona. He is actively accepting PhD students and supervising research in advanced statistical methodologies. Education: PhD in Statistics, University of Warwick (awarded June 2020), in collaboration with the University of Oxford via the Oxford-Warwick Statistics Programme (OxWaSP). Integrated Master’s in Mathematics, Operational Research, Statistics, and Economics, University of Warwick (awarded July 2015). His research focuses on Bayesian inference under model misspecification, particularly in the M-open world, where no true model is assumed to exist. He develops robust computational methods for variable and model selection, and investigates statistical inference under differential privacy constraints. His work integrates loss functions into Bayesian updating and explores graphical and structural modeling applications in economics and biology. These interests are driven by the need for reliable inference in complex, real-world scenarios where models are inherently imperfect. His recent publications span high-impact journals such as The Annals of Applied Statistics , Biometrics , and Bayesian Analysis , as well as top machine learning venues like NeurIPS. The research demonstrates a strong trend toward robust, privacy-aware Bayesian methods with applications in biostatistics, signal processing, and network modeling. His work bridges theoretical statistics with practical computational solutions for modern data challenges. Scientific Awards: No specific awards or fellowships mentioned in the provided text. Jack Jewson has not received explicit mention of grants or funding in the text, but his prior Juan de la Cierva Fellowship indicates competitive research support. He is actively mentoring PhD students and expanding his research group at Monash. His work involves collaboration with leading statisticians such as David Rossell, Piotr Zwiernik, Jim Q. Smith, and Chris Holmes. While no formal lab name is provided, his research group focuses on robust Bayesian computation and privacy-preserving inference.

Maryam Fouad AbdelatyView profile

Researcher

• Deep Learning
• Medical Ultrasound Imaging
• Harmonic Imaging
• +3 more

Maryam Fouad Abdelaty is a researcher in the Department of Medical Engineering at Ruhr University Bochum, where she completed her PhD in September 2024. Her work focuses on applying deep learning techniques to medical ultrasound imaging, including harmonic imaging, synthetic data generation, and contrast-enhanced imaging. She holds a bachelor's degree in electronics engineering from the German University in Cairo (2015), a master's degree in bio-impedance spectroscopy for non-invasive glucose monitoring (2017), and a Dr.-Ing. degree from Ruhr University Bochum (2024). Bachelor's: German University in Cairo (2015) Master's: Thesis on joint NIR and bio-impedance spectroscopy for glucose monitoring (2017) PhD: Focused on deep learning in medical ultrasound (2024) Her research emphasizes improving imaging resolution, reducing invasiveness, and enhancing diagnostic accuracy through AI-driven methodologies. Recent work includes comparative studies of deep learning vs. traditional amplitude modulation in contrast-enhanced ultrasound, as well as semi-supervised approaches for needle localization. Her publications span conferences like IEEE Ultrasonics Symposium and IEEE ISBI, with a focus on deep learning applications in biomedical imaging. Notable contributions include synthetic ultrasound signal generation using GANs and single-shot harmonic imaging techniques. While no formal advising or grants are explicitly listed, her research demonstrates significant contributions to medical imaging innovation. No lab affiliations or teams are mentioned in the provided text.

Martin SzummerView profile

Researcher

• Deep Learning
• Probabilistic Models
• Reinforcement Learning
• +9 more

Martin Szummer is a Researcher at the University of Cambridge, UK, and part of the Spoken Dialogue Systems group. Previously, he worked at Microsoft Research in Cambridge (Machine Learning and Perception Group). He holds a Ph.D. in Machine Learning from MIT (2002) and an M.S. from the MIT Media Lab. Research Focus: Szummer’s work spans probabilistic models, deep learning, reinforcement learning, and natural language processing. He explores methods to improve interactions with users through dialogue systems and develops techniques to manage complex data via Bayesian inference, semi-supervised learning, and structured prediction. His contributions include probabilistic programming frameworks, big data processing on cloud clusters, and applications in text mining, image recognition, and computational advertising. Notable Achievements: Recipient of the SIGDIAL 2013 Best Paper Award for dialogue system adaptation research. Developed the Temporal Texture database, foundational for video analysis and synthesis. Collaborations & Mentoring: Advised prominent students like Percy Liang (Stanford) and Volodymyr Mnih (DeepMind). His work at Microsoft Research led to innovations in POMDP-based dialogue systems and semi-supervised learning. Technical Contributions: Publications on dialogue systems, probabilistic modeling, and web-scale data mining. Authored datasets like the Temporal Texture collection, widely used in computer vision research.

• Computational Physics
• Theoretical Astrophysics
• Radio Astronomy
• +6 more

Ross Turner is a Lecturer in Mathematics and Physics at the University of Tasmania, working within the School of Natural Sciences, Department of Physics. He leads the analytical modeling research stream in the University's Theoretical and Computational Astrophysics Group (Active Galactic Nuclei; AGN) and collaborates with the Australian Centre for Excellence in Antarctic Science (ACEAS). His research spans computational physics with applications in astronomy, theoretical astrophysics, applied mathematics, and computational earth sciences, aligning with the University's themes of Data, Knowledge and Decisions, and Marine, Antarctic and Maritime. Ross received his Doctor of Philosophy in Astrophysics from the University of Tasmania in 2018 and a Bachelor of Science (First Class Honours) in Physics from the same institution in 2013. His research primarily focuses on developing the statistical tools to analyze radio astronomy observations from next-generation large sky surveys, including by the Square Kilometre Array. He created the highly-cited RAiSE (Radio AGN in Semi-Analytic Environments) software package to simulate the physics of jet-inflated lobes emanating from active supermassive black holes. His work in computational earth sciences relates to interpreting large datasets from active glaciers using complex dynamical models and machine learning techniques. Turner's publication record demonstrates strong expertise in both astrophysics and computational geophysics, with recent work spanning AGN jet dynamics, radio galaxy evolution, and innovative applications of machine learning to cryoseismology. His research bridges theoretical modeling with observational astronomy and geophysical applications, showing a consistent trajectory of increasingly sophisticated computational approaches to complex physical systems. Vice Chancellor's Award 2022, Early Career Researcher Award College of Science and Engineering Early Career Researcher Award 2022 Eureka Prize Finalist, Australian Museum, 2022 The Bok Prize, Astronomical Society of Australia, 2014 Multiple Teaching Commendations and Unit Commendations Student-nominated Teaching Appreciation Certificate Ross has been awarded over $2 million (equivalent value) in competitive research funding, including more than 2000 hours of observing time to study AGNs using terrestrial and satellite telescopes. He supports the learning of Honours and higher degree by research students in both applied mathematics and theoretical astrophysics; 70% of his primary supervised Honours students have received a University Medal. He serves as primary or co-supervisor for six current PhD candidates at the University of Tasmania and the International Centre for Radio Astronomy Research (ICRAR). Turner leads the analytical modeling research stream within the University's Theoretical and Computational Astrophysics Group and works with the Compute Antarctic Group as part of ACEAS. His work involves developing computational frameworks that bridge theoretical astrophysics with observational data analysis, creating tools that enable more precise interpretation of complex astronomical phenomena and geophysical processes.

• Artificial Intelligence
• Social Robotics
• Human-Robot Interaction
• +3 more

Joana Campos is an Assistant Professor in the Department of Computer Engineering at Instituto Superior Técnico, University of Lisbon. Her research focuses on Artificial Intelligence, Social Robotics, and Human-Robot Interaction, with a particular emphasis on conflict modeling and resolution in multi-agent systems. Artificial Intelligence Social Robotics Human-Robot Interaction Conflict Modeling Multi-Agent Systems Her publications explore interdisciplinary topics such as acoustic-prosodic personality assessment, mixed-motive games for conflict analysis, and computational approaches to conflict resolution in serious games. Collaborations with Ana Paiva, Carlos Martinho, and other researchers are central to her work. Joana's projects include My Dream Theater and MAY: My Memories Are Yours , which blend AI with immersive storytelling and emotional engagement. She also investigates applications of evolutionary psychology in conflict resolution training for children.

Cornelia FernerView profile

Lecturer

• Machine Learning
• Natural Language Processing
• Data Science
• +3 more

Cornelia Ferner serves as a Lecturer in the Department of Information Technologies and Digitalisation at Salzburg University of Applied Sciences, based at Campus Urstein. Her office is located in room Urstein-430, and she can be reached via email at cornelia.ferner@fh-salzburg.ac.at or by telephone at +43-50-2211-1329. Education: Bachelor of Science (BSc) Her research expertise lies at the intersection of artificial intelligence and real-world applications, with primary focus areas including machine learning (particularly deep learning and generative models), natural language processing (topic modeling, sentiment analysis), and data science methodologies. She applies these techniques to energy systems analysis, social media mining for refugee movement tracking, and process optimization in business contexts, demonstrating strong interdisciplinary capabilities across technical and societal domains. Analysis of her publication trends (2017-2022) reveals consistent methodological innovation in neural networks and probabilistic modeling, applied to energy data (smart grids, tariff structures) and social dynamics (event detection, refugee movements). Her work bridges theoretical AI advancements with practical European industry challenges, particularly in digital transformation of energy markets and social media analytics. No scientific awards were mentioned in available sources. Information regarding graduate student supervision or specific research funding was not documented in current materials, though her multi-case studies indicate collaborative industry engagement. Her publications suggest involvement in cross-institutional energy sector projects and social impact research initiatives. Details about laboratory facilities or dedicated research teams were not available in provided documentation.

• Scientific Visualization
• Data Visualization
• Computer Graphics
• +4 more

Pere Pau Vázquez Alcocer is an Associate Professor in the Department of Computer Science at Universitat Politècnica de Catalunya (UPC), affiliated with the ViRVIG research group and the School of Informatics (FIB). With a PhD in Software from UPC (2003), he specializes in Scientific Visualization , Data Visualization , and Virtual Reality applications. His work bridges Computer Graphics with Medical Data Visualization and Molecular Visualization , emphasizing AI applications to visualization . His research spans two decades, focusing on Interactive volume rendering VR-based biomedical analysis Perceptual optimization in visualizations Urban mobility data modeling with recent publications in Computer Graphics Forum , IEEE CG&A , and arXiv on AI-driven visualization frameworks and molecular interaction techniques. His 15 most recent publications (2025-2023) demonstrate trends in AI integration for data visualization, immersive analytics for biomedical applications, and optimization of rendering techniques on mobile and VR platforms, with keywords spanning Computer Science , Neuroscience , and Urban Planning . Awarded the Best PhD thesis (2003) and Best Paper at international conferences, he has supervised 8 PhD students including Pedro Hermosilla (now Assistant Professor at TU Wien) and Jesús Díaz (now Associate Professor at Universitat de Vic). Teaching for over 20 years at UPC across five schools, he currently leads courses in Data Visualization , Scientific Visualization , and Virtual Reality .

Suyash P. AwateView profile

Professor

• Medical image computing
• Machine learning
• Image analysis
• +2 more

Suyash P. Awate serves as the Asha and Keshav Bhide Chair Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (IIT) Bombay, Mumbai, India. His research spans medical image computing, machine learning, image analysis, computer vision, and statistical modeling and inference. His work focuses on developing novel computational methods for medical image analysis, reconstruction, and quality enhancement. Prof. Awate's research interests center on medical image computing, where he develops advanced machine learning techniques for medical image reconstruction, segmentation, and quality enhancement. His work addresses critical challenges in medical imaging such as low-dose PET/CT imaging, uncertainty quantification in segmentation, robustness to out-of-distribution data, and accelerated MRI/fMRI acquisition. He has pioneered methods combining variational inference, expectation maximization, and deep learning to improve medical image quality while reducing radiation exposure and scan times. His research has significant clinical applications in neuroimaging, tumor analysis, and brain function mapping. His recent publications demonstrate a strong trend toward integrating deep learning with classical statistical methods, particularly focusing on uncertainty-aware models for medical image analysis. His work consistently addresses the challenge of robustness in medical imaging, developing methods that maintain performance even with degraded input data or distribution shifts. A significant portion of his research targets clinical applications, particularly in neuroimaging and oncology, with emphasis on practical implementation for real-world medical settings. Prof. Awate actively mentors PhD, MTech, and BTech students at IIT Bombay. His current PhD students include Vatsala Sharma (awarded Microsoft Research India PhD Award), Jimut Bahan Pal (CMInDS Fellow, Prime Minister's Research Fellow), Tejomay Padole (TCS Research Fellow), Subhankar Nag, and Koustav Pal. He also guides MTech/MS students Soumya Mukherjee, Jay Gorakhiya, and Russel Abreo, along with BTech student Varshith Anumalasetty. He teaches several courses including CS 736: Medical Image Computing (inaugurated in 2014), CS 663: Fundamentals of Digital Image Processing, CS 215: Data Analysis and Interpretation, CS 740: Mathematics for Visual Computing, and CS 101: Computer Programming and Utilization. His laboratory focuses on developing computational methods for medical image analysis, with particular emphasis on uncertainty quantification, robust deep learning for medical imaging, and accelerated acquisition techniques. The research group maintains strong collaborations with medical institutions to ensure clinical relevance of their computational methods.

Mohammad RastegariView profile

Adjunct Assistant Professor

• Computer Vision
• Efficient Machine Learning
• Model Compression
• +3 more

Mohammad Rastegari serves as a senior technical manager in Apple's AI/ML organization and holds an affiliate assistant professor position in the Computer Science and Engineering Department at the University of Washington. Previously, he was a research scientist at the Allen Institute for AI (AI2) where he contributed to the PRIOR team, and co-founded XNOR.ai as Chief Technology Officer. He completed his Ph.D. at the University of Maryland under the supervision of Professor Larry S. Davis, focusing on computer vision and machine learning. His research centers on efficient deep learning with major contributions in binary neural networks (notably XNOR-Networks for resource-constrained devices) and model compression . Current work extends to large language model efficiency through weight clustering, token pruning, and memory optimization while maintaining strong foundations in computer vision. Analysis of his recent publications reveals a strategic shift toward optimizing transformer-based architectures across vision and language domains, with consistent emphasis on deployable solutions for edge computing and real-world applications. He co-founded XNOR.ai (acquired by Apple) and contributed to AI2's PRIOR initiative, demonstrating a career trajectory bridging academic innovation and industrial implementation in efficient AI systems.

• Machine Learning
• Neuroscience
• Medical Informatics
• +3 more

Sebastian Niehaus is a Doctoral Researcher affiliated with the Max Planck Institute for Human Cognitive and Brain Sciences and TU Dresden. His work bridges Neuroscience and Machine Learning , focusing on Medical Informatics and Statistical Computing . He develops scalable AI applications for clinical workflows through standardized data harmonization pipelines.

• Clinical Sciences
• Public Health
• Exercise Physiology
• +7 more

Dr. Christian Brakenridge is a Post-Doctoral Research Fellow at Swinburne University of Technology's School of Health Sciences and collaborates with the Baker Heart and Diabetes Institute (Melbourne) and the South-eastern Finland University of Applied Sciences . His work focuses on 24-hour behaviors (sitting, standing, activity, sleep) and their impact on cardiometabolic health , particularly in type 2 diabetes and obesity populations. PhD from Australian Catholic University Bachelor of Clinical Exercise Physiology from Queensland University of Technology His research leverages wearable sensors (accelerometers, EMG shorts, glucose monitors) to analyze movement patterns and health outcomes. Recent work includes: Optimizing 24-hour time-use for metabolic health Comparative muscle exertion in overweight individuals Neighbourhood walkability effects on stepping behavior Allogeneic stem cell transplant rehabilitation Scientific accolades include an Academy of Finland research grant and leadership in the OPTIMISE Your Health Clinical Trial . He supervises PhD students at Swinburne and Xamk Finland, and his 15 most recent publications span topics from sedentary behavior epidemiology to machine learning applications in activity monitoring.

Murat İSKEFİYELİView profile

Associate Professor

• Anomaly detection
• Critical infrastructure
• Deep learning
• +18 more

Murat İSKEFİYELİ serves as an Associate Professor in the Department of Cyber Security Engineering within the Faculty of Computer and Information Sciences at Sakarya University. His academic career at the institution spans 27 years since 1998, progressing from Research Assistant through Lecturer (2009-2010), Assistant Professor (2010), and current Doctor Lecturer (2018) positions. His educational trajectory includes: Doctorate (2002-2010): Sakarya University Institute of Science, Electronics, thesis on "Modeling and performance analysis of WiMAX (IEEE 802.16)-Profibus interconnection element using petri nets" Master's Degree (1998-2002): Sakarya University Institute of Science, Electrical and Electronics Engineering (MA with Thesis) Bachelor's Degree (1994-1998): Sakarya University Faculty of Engineering, Electrical-Electronics Engineering Dr. İSKEFİYELİ's research integrates cybersecurity with artificial intelligence methodologies, focusing on anomaly detection systems for critical infrastructure and IoT environments. His work develops lightweight intrusion detection mechanisms using deep learning architectures, with specialized applications in facial anti-spoofing and two-factor authentication systems. He also pioneers optimization algorithms for multistage pumping stations and load-shifting operations in industrial control systems through dynamic programming approaches. No scientific awards were documented in the source material. Advising activities and research funding details were not specified in the provided information. Research laboratory affiliations and team structures were not mentioned in the available documentation.

Rosário Serrão CunhaView profile

Researcher

• Mental Health Psychology
• Public Education
• Student Learning
• +5 more

Rosário Serrão Cunha is a Researcher at the Research Center for Human Development (CEDH) in Portugal. Her career spans education and psychology, focusing on school leadership challenges , mental health in educational settings , and adolescent well-being . She collaborates extensively on projects like LETS CARE (2022–2026) and EDUGest (2017–2020), emphasizing educational inclusion and leadership development. Rosário's research explores school leadership , mental health , and parental involvement during crises . Her work includes systematic reviews, psychometric studies, and empirical analyses of educational policies. Recent articles validate tools for universal well-being screening in schools and examine socio-emotional support systems. Her scientific awards include a 2014 Certificate of Pedagogical Competence for Trainers and multiple Merit Scholarships of 100% (2010–2014). Rosário actively participates in conferences, organizes workshops, and serves as a peer reviewer. She supervises research but no formal student advisees are listed. Key projects like LETS CARE and EDUGest highlight her commitment to safe school environments and leadership training . Her network spans Portugal and international collaborators, as indicated by recent conference activities and publications.

Sung-Wook BaikView profile

Professor

• Image Processing
• Pattern Recognition
• Video Analytics
• +12 more

Sung-Wook Baik is a Full Professor at the Department of Digital Contents , Sejong University , and serves as Vice President of Research Affairs. He also holds an Adjunct Professor position at Ton Duc Thang University , Vietnam. His career spans over two decades, including leadership roles as Dean of Colleges of Software Convergence and Electronics and Information Engineering, Managing Director of the National Program of Excellence in Software, and Director of the Intelligent Media Laboratory (IM Lab). Education: Ph.D. in Information Technology, George Mason University (1999) M.A. in Computer Science, Northern Illinois University (1992) B.A. in Computational Statistics, Seoul National University (1987) Professor Baik’s research focuses on image processing, pattern recognition, video analytics, and smart energy systems for smart cities. His work integrates deep learning , transformers , and edge computing for applications in surveillance , fire detection , and energy load forecasting . He has published over 150 peer-reviewed papers and led international conferences, emphasizing IoT , IIoT , and multimodal data fusion . His recent publications highlight advancements in anomaly detection for smart surveillance, population projection using graph neural networks, and energy consumption prediction via hybrid deep learning. These works span journals like Pattern Recognition , IEEE Transactions on Industrial Informatics , and Applied Energy , with keywords covering Computer Science , Artificial Intelligence , and Smart City Infrastructure . Scientific Awards: Ministry of Science and ICT Minister's Citation (2020) Daeyang Academic Achievement Award (2018) Aejieon Academic Achievement Award (2016) Outstanding Research Award, Sejong University (2005) Commonwealth of Virginia Awards (2001, 1998) IEEE Contribution Award (2010) In collaboration with global teams, he advances smart surveillance and renewable energy management . His lab, IM Lab , specializes in intelligent media technologies , with patents in fire detection , energy forecasting , and person re-identification systems.

Veronica PiccialliView profile

Professor

• Nonlinear Optimization
• Semidefinite Programming
• Game Theory
• +6 more

Veronica Piccialli is a Full Professor at the Department of Computer, Control and Management Engineering "Antonio Ruberti" (DIAG) at Sapienza University of Rome. She teaches Geometry I for Management Engineering and Optimization Methods for Machine Learning for Data Science. Previously, she was Associate Professor at the University of Rome Tor Vergata (2020-2021) and Researcher there from 2008 to 2020. She serves as Associate Editor for INFORMS Journal on Computing (since 2019) and EURO Journal on Computational Optimization (since 2021). Dr. Piccialli earned her degree in Computer Engineering (summa cum laude) and PhD in Operations Research from Sapienza University of Rome in 2000 and 2004 respectively. In 2006, she completed a postdoc at the Combinatorics & Optimization department of the University of Waterloo, Canada. She obtained Italian national scientific qualifications as Associate Professor in 2013 and as Full Professor in 2017. Her research focuses on the intersection of optimization and machine learning, with particular expertise in nonlinear optimization, semidefinite programming, and mixed integer nonlinear programming. She applies these methods to diverse areas including Brain Computer Interfaces, electric consumption disaggregation, and process engineering for membrane systems. Her work demonstrates how advanced optimization techniques can enhance machine learning algorithms and solve complex engineering problems. Her recent publications show a strong trend toward integrating optimization with machine learning, particularly in clustering algorithms, support vector machines, and neural networks. She has developed exact algorithms for semi-supervised learning problems and applied optimization techniques to real-world challenges in logistics, energy systems, and process engineering. Her interdisciplinary approach bridges theoretical advances in optimization with practical applications across multiple domains. Dr. Piccialli has authored or co-authored over 40 articles in prestigious journals including Mathematical Programming, SIAM Journal on Optimization, IEEE Transactions on Neural Networks and Learning Systems, and Computational Optimization and Applications. She has also contributed 3 refereed book chapters to international publications. As an educator, she supervises student research and teaches advanced courses in optimization methods and geometry. Her teaching materials demonstrate a commitment to connecting theoretical concepts with practical applications, particularly in data science and machine learning contexts. Her current research involves collaborations with Université de Lorraine on membrane systems for gas filtration and with RFI (Rete Ferroviaria Italiana) on optimizing last-mile facilities for freight trains, demonstrating the real-world impact of her optimization expertise.