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Pietro PietriniView profile

Professor

• Clinical Neuroscience
• Cognitive Neuroscience
• Molecular Biology
• +6 more

Pietro Pietrini is a Full Professor and Director of the IMT School for Advanced Studies Lucca since 2015. Previously, he held roles including Chair of Clinical Biochemistry and Molecular Biology at the University of Pisa Medical School. He earned an MD from the University of Pisa and a Ph.D. in Neuroscience from Scuola Superiore Sant'Anna, with board certification in Psychiatry. His research integrates neuroimaging (MRI, PET, TMS) and neuropsychological tools to study brain function in sensory deprivation, mental disorders, and social behavior. He pioneered studies on cortical reorganization in the blind and developed a multidisciplinary approach spanning neuroscience, ethics, and linguistics. Education: MD (1986, University of Pisa), Ph.D. Neuroscience (Scuola Superiore Sant'Anna), Board Certification in Psychiatry (University of Pisa). Research focuses on: neuroplasticity in sensory-deprived brains, affective neuroscience, and the neural basis of antisocial behavior. Key projects include studying empathy in incarcerated populations and the ethics of neuroscience in legal contexts. Scientific Awards: Commander of the Order of Merit of the Italian Republic (2015), International Recognition in Biological Psychiatry and Neuroscience. Labs/Teams: Leads the MOMILAB, focusing on translational research at the intersection of neuroscience and social sciences.

Wei LeView profile

Associate Professor

• AI
• Machine Learning
• Software Engineering
• +5 more

Dr. Wei Le is an Associate Professor in the Department of Computer Science at Iowa State University. His primary affiliations include the Program Analysis & AI Lab and collaborative work with Mayo Clinic. He holds a Ph.D. in Computer Science from the University of Virginia (2010). His research focuses on the intersection of AI and software engineering, particularly in trustworthy AI systems, vulnerability detection, and program analysis. Key areas include AI for medicine, analyzing AI models, and improving software reliability through techniques like dataflow analysis and symbolic execution. Recent work emphasizes efficient vulnerability detection using deep learning (e.g., ICSE 2024), numerical instability in ML applications (FSE 2025), and GPU numerical testing (SC 2024). His research has been supported by NSF, Google, and DARPA. Notable awards include the Distinguished Paper Award at FSE 2016 and Best Presentation at FSE 2008. He has advised over 10 students and collaborates with institutions like Columbia University and UCLA. Teaching responsibilities include courses on program analysis (COM S 413/513), principles of programming languages, and advanced AI topics. He actively contributes to conferences as a PC member and area chair (e.g., ICSE, FSE).

Gordon KindlmannView profile

Associate Professor

• Biomedical Image Analysis
• Scientific Visualization
• Systems
• +5 more

Gordon Kindlmann is an Associate Professor of Computer Science at the University of Chicago, affiliated with the Systems Group research community. His work bridges computational imaging science and visualization theory, focusing on biomedical applications and machine learning integration. He leads projects in diffusion MRI analysis, surgical planning tools like SlicerDMRI, and theoretical advancements in visualization design. Research Interests: Biomedical Image Analysis Scientific Visualization Theory High Performance Computing Medical Imaging Algorithms Machine Learning Applications Recent Articles Trends: His work emphasizes cardiovascular modeling (e.g., aortic dissection prediction) and visualization validation techniques. Recent collaborations include optimizing visualization tools for scalability and accuracy in threaded data exploration. Awards: None explicitly listed in provided text. Grants/Advising: Involved in CDAC Discovery Grants (2019) and actively supports student research, though specific advisees are not listed here. His lab develops open-source tools like Diderot for tensor field visualization. Labs & Teams: Member of the Systems Group, a collaborative environment advancing systems research, programming languages, and software engineering.

Robert RandView profile

Assistant Professor

• Quantum Computing
• Formal Verification
• Quantum Programming
• +3 more

Robert Rand is an Assistant Professor of Computer Science at the University of Chicago, affiliated with the Programming Languages Research Group and the Chicago Quantum Exchange . His research bridges programming languages, formal verification, and quantum computation. Dr. Rand focuses on Quantum Computing , Formal Verification , and Quantum Programming . He develops tools like QWIRE for quantum circuit representation and VOQC for verified quantum compilers. Current work includes a textbook on verified quantum programming. His publications span quantum compiler verification, algebraic quantum languages, and formal methods in quantum computing. Recent projects involve error-correction, type systems, and quantum-classical interoperability through Qunity . Scientific Awards 2023 AFOSR Young Investigator Research Program Award France and Chicago Collaborating in the Sciences Award 2022 Keynote Speaker, Compiler Construction Distinguished paper awards: PLDI 2021, POPL 2019 Victor Basili Postdoctoral Fellowship (UMD) Dr. Rand actively collaborates on NSF-funded EPiQC (Enabling Practical-Scale Quantum Computing) and teaches advanced topics in quantum programming languages. He is currently working on a textbook on verified quantum programming and maintains active research groups in both Programming Languages and Quantum Computing.

Zubin JelvehView profile

Assistant Professor

• Data Science
• Public Policy
• Criminal Justice
• +3 more

Zubin Jelveh is an Assistant Professor at the University of Maryland’s College of Information Studies (iSchool). He specializes in data science for public policy, record linkage, and the science of science. Prior to joining UMD in 2021, he served as a Research Director at Crime Lab New York, where he collaborated with civic leaders to design interventions targeting violence reduction and criminal justice reform. Dr. Jelveh holds a BA in Economics from the University of Chicago, an MA in Quantitative Methods in the Social Sciences from Columbia University, and a PhD in Computer Science from New York University. His research bridges machine learning and social sciences, focusing on prediction models in criminal justice (e.g., domestic/gun violence) and record linkage algorithms tailored for justice data. He also examines scientific incentives and how research is presented. His work emphasizes ethical AI applications, particularly in judicial systems, and includes projects like assessing pretrial risk in Maryland using CLUE data and targeting interventions for high-risk domestic violence victims. He explores themes such as algorithmic fairness, data quality in randomized experiments, and the impact of specialized prosecution strategies. Before academia, Jelveh was a journalist covering economics for outlets like The New York Times. His interdisciplinary approach integrates computational methods with societal challenges, aiming to improve policy outcomes through rigorous data analysis.

• Neuroimaging
• Functional Magnetic Resonance Imaging
• Language
• +7 more

Professor Katie McMahon is a leading academic at Queensland University of Technology (QUT), holding dual roles as Deputy Director of the Herston Imaging Research Facility and Academic Lead for Research at the School of Clinical Sciences. With a PhD from the University of Queensland, her expertise spans neuroimaging and cognitive neuroscience. Research Focus: Neurobiology of language, genetics of brain development, and neuroimaging applications in stroke, epilepsy, and pediatric language disorders (e.g., autism). Collaborations: Works with Queensland Health and philanthropic organizations on brain imaging and musculoskeletal studies. Teaching: Contributes to radiation therapy and MR-guided radiation therapy courses, emphasizing clinical sciences education. Recent Research Trends: Her work bridges neuroimaging techniques (fMRI, DTI) with cognitive deficits, exploring lexical processing, aphasia recovery, and genetic influences on brain structure. Recent studies address stroke rehabilitation, epilepsy language deficits, and fatty liver imaging protocols. Grants & Projects: Lead investigator on grants like “Predicting and Promoting Aphasia Recovery” (2016) and “Enhancing Language Learning in Ageing with Exercise” (2016). Labs/Teams: Active in the Herston Imaging Research Facility and National Imaging Facility committees.

Paulius RaubaView profile

Lecturer

• Machine Learning
• Data Science
• Artificial Intelligence
• +3 more

Paulius Rauba is a Visiting Lecturer at ISM University of Management and Economics and a PhD candidate in Machine Learning at the University of Cambridge. He holds a BSc from ISM and an MSc from the University of Oxford, where he is also an Oxford Shirley Scholar. His research focuses on fundamental machine learning, model evaluation, robustness, and quantitative social science. He teaches courses in Econometrics, Data Science, and Elements of Artificial Intelligence. Paulius leads Gauss, a company specializing in deploying machine learning models, and has advised the European Commission on AI systems. His work spans academia, industry, and international organizations, emphasizing data-driven solutions for business and social challenges. Education: BSc, ISM University of Management and Economics MSc, University of Oxford His research explores model robustness, AI applications in healthcare, and conflict analysis through statistical and neural methods. Recent articles address topics like self-healing ML frameworks and context-aware model testing. Paulius is committed to advancing AI ethics and practical deployment. Awards: Oxford Shirley Scholar As a leader in Gauss, he bridges academic innovation with industry needs, focusing on scalable AI solutions. His advisory roles and grants underscore his interdisciplinary impact.

• System-on-Chip (SoC) Design
• FPGA-Based High-Level Synthesis
• Real-Time Embedded Systems
• +5 more

Timo Hämäläinen is a Professor of Computer Engineering at Tampere University, leading the Unit of Computing Sciences within the Faculty of Information Technology and Communication Sciences. He is a core member of the System-on-Chip research group and the Computer Engineering Team, actively contributing to the System-on-Chip Hub initiative. His research focuses on System-on-Chip (SoC) design methodologies, FPGA-based high-level synthesis, real-time embedded systems, and open-source hardware-software co-design frameworks like RISC-V and HEVC encoding. He has pioneered work on agile SoC development processes and validation techniques for large-scale hardware systems. Key research areas include: High-level synthesis (HLS) optimization for FPGAs Real-time processor architectures and context-switching latency reduction Formal verification of IP-XACT-compliant SoC designs Resilient RISC-V MPSoC implementations Hardware-accelerated algorithms for signal processing and networking His recent publications (2023-2025) emphasize agile SoC development frameworks, hardware-software co-design for real-time systems, and validation methodologies for large-scale embedded systems. He has contributed to open-source projects like Kvazaar HEVC encoder and the Kactus2 IP-XACT toolchain. Academic advising includes students such as Santéri Mäki-Äijö (formal verification), Arto Oinonen (RISC-V tooling), and Sakari Lahti (processor modeling). His work integrates academic research with industrial collaboration through frameworks like the Fault-slip-Through quality assessment methodology.

• Deep Learning
• Partial Differential Equations
• Physics-Informed Neural Networks
• +3 more

Katsiaryna Haitsiukevich is a Visitor (Faculty) and Doctoral Researcher in the Department of Computer Science at Aalto University. Her research focuses on deep learning applications in science and engineering, particularly at the intersection of partial differential equations and neural networks. She holds a doctoral degree (Tekn. toht., tietotekniikka) awarded in May 2025. Her work includes advancing physics-informed deep learning frameworks, improving neural network training through ensemble methods, and leveraging large language models for symbolic regression. Key contributions span dynamical systems analysis, parameter space exploration, and Hamiltonian trajectory learning. Since 2021, she has published six peer-reviewed articles in top conferences like ICANN, IJCNN, and ACL. Notable collaborations include projects with researchers like Prof. P. Marttinen and Prof. A. Ilin. Her research emphasizes practical applications in computational physics, mathematical modeling, and engineering systems.

• Human-Computer Interaction (HCI)
• Human-AI Interaction
• Information Visualization
• +4 more

Professor Damien Masson is an Assistant Professor in Human-Computer Interaction (HCI) at the Université de Montréal , affiliated with Mila and IVADO . He leads the Montréal HCI group and focuses on interactive systems for AI, document interaction, and software learning. His research bridges HCI principles with AI to enhance user experience in complex applications. Education : PhD in Computer Science (University of Waterloo, 2019-2023), MSc in Computer Science (Université de Lille, France), BSc in Computer Science (Université de Lille, France). Research Interests : Designing intelligent systems (e.g., DirectGPT), visualizing information (e.g., Charagraph/ChartDetective), and augmenting digital documents (e.g., Chameleon). He explores how interface metaphors and direct manipulation can improve interactions with AI and data. Recent Work : His 2025 CHI papers introduced Textoshop (drawing-inspired text editing) and linked writing tools to user needs. In 2024, DirectGPT demonstrated faster LLM interactions via direct manipulation. Earlier work includes Statslator (statistical reporting) and ChartDetective (data extraction from vector charts). Awards : 2024 Bill Buxton Award (best HCI dissertation), 2023 CHI Best Paper (ChartDetective), and 2020 CHI People's Choice Award (Chameleon demo). Lab & Collaborations : Based in the André-Aisenstadt building, his group collaborates with institutions like Mila and UWaterloo. He actively mentors students in HCI and AI-related projects.

• Artificial Intelligence in Medicine
• Data Science
• Machine Learning
• +5 more

Andrew Taylor, MD, MHS is an Adjunct Associate Professor of Biomedical Informatics and Data Science, Emergency Medicine, and Biostatistics at Yale University. He leads the Yale Interdisciplinary AI & Medicine Lab (Y-IAML), focusing on integrating AI into healthcare delivery through cross-disciplinary collaboration. His work spans AI ethics, implementation science, and decision theory to create practical, patient-centered solutions. Education: MD from Emory University (2007), Emergency Medicine residency at University of Connecticut (2011), and Informatics Fellowship/MHS from Yale (2015). He also completed a point-of-care ultrasound fellowship. Research interests include machine learning for predictive analytics, EHR data mining, decision support tools for emergency care, and health equity. His team develops AI-driven solutions to reduce diagnostic errors, optimize clinical workflows, and address disparities in emergency department care. Recent work highlights AI's role in triage equity, opioid use disorder treatment, and deprescribing optimization. Key publications focus on AI-based decision support systems, lung nodule detection via NLP, and CMS quality measures. Notable Awards: University of St Andrews Global Fellow (2023) Grants: NIH (NIDA/NIA/NIMHD/NLM), AHRQ, SIDM, Gordon and Betty Moore Foundation, and industry partnerships fund his research. Current projects include ADAPT trial for buprenorphine initiation and EHR-driven causal analyses. Labs/Teams: Director of Artificial Intelligence and Data Science in Emergency Medicine, leading Y-IAML with 15+ collaborators across informatics, ethics, and clinical domains.

Michele AlbanoView profile

Associate Professor

• Internet of Things (IoT)
• Energy Engineering
• Quality of Service (QoS)
• +5 more

Affiliations & Roles Michele Albano is an Associate Professor at the Department of Computer Science, Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design, focusing on research in IoT, Cyber-Physical Systems, and Edge Computing. He leads the Productive4.0 project (2017–2020), funded by Horizon Europe, addressing Industry 4.0 challenges in product lifecycle management. His work integrates formal verification tools like Uppaal with real-world applications in robotics, energy systems, and blockchain-based platforms. Research Interests Albano's research spans IoT architecture optimization , energy-efficient systems , and model-driven engineering . He develops tools for autonomous exploration algorithms (MAES), edge-cloud resource orchestration, and fault-tolerant computation offloading. His work bridges theoretical models (e.g., Uppaal SMC) with practical implementations in smart grids and robotic systems. Recent projects include blockchain-based crowdsourcing for machine learning and energy-aware thermal dynamics estimation in buildings. Collaborations & Impact He collaborates with the European Industry 6.0 community, contributing to the Arrowhead Framework for interoperable IoT systems. His research outputs include 112 publications, with 2025 highlights in human-inspired robotics and cognitive cloud frameworks. Media coverage in 2024–2025 highlights his work on green IT and secure API generation. Albano advises students on system modeling (e.g., ACSmt plugin development) and edge computing optimization. Labs & Teams His research group focuses on Cyber-Physical Systems and Smart Grids , with contributions to tools like RoutesMobilityModel and FlexHousing. He actively participates in workshops on New Trends in Software Architecture (SATrends '24) and IEEE conferences on Industrial Informatics.

Lynsey O'RourkeView profile

Lecturer

• Educational Psychology
• Dyslexia Research
• Writing Process Analysis
• +2 more

Lynsey O'Rourke is a Lecturer in Public Health at Oxford Brookes University's School of Psychology, Social Work and Public Health. She serves as Module Lead for Independent Studies (HESC7019) while contributing to multiple public health modules including Evidence-Based Practice (HESC7013) and Advanced Health Promotion (CMNR7007). PhD research on spellcheck impacts for dyslexic writers Member of the writing research group Recipient of international research grants Her research focuses on writing processes, particularly how spellcheck affects cognitive flow in individuals with dyslexia. Studies show spellcheck's red underline disrupts thought patterns more significantly for dyslexic students, with 45% interrupting writing flow for corrections versus 10% in non-underlined errors. Key publications: 2023 - Comparative handwriting analysis in Developmental Language Disorder 2020 - Quantitative study on digital writing tools Scientific awards include: Santander Continuation Fee Grant (2018) European Literacy Network Training Support (2016) Multiple international conference funding awards

Amy FinnView profile

Associate Professor

• Developmental Cognitive Neuroscience
• Learning and Memory Systems
• Child Development
• +3 more

Amy Finn is an Associate Professor and Director of the Finn Learning and Neural Development Lab (LAND Lab) at the University of Toronto. Her research investigates how brain and cognitive development shape learning outcomes in children, focusing on memory systems, neural plasticity, and environmental influences. The LAND Lab explores developmental cognitive neuroscience, emphasizing age-related learning patterns, critical periods, and brain-environment interactions. Current research areas include implicit memory development, attentional modulation of learning, socioeconomic impacts on neural systems, and computational models of cognition. Notable publications span topics like attention-memory dynamics, scene perception in children, and socioeconomic status effects on hippocampal development. Collaborations involve institutions such as MIT, Emory University, and international labs. Developmental Cognitive Neuroscience Learning and Memory Systems Neural Plasticity Environmental Influences on Learning

Katarzyna Oszust-PolakView profile

Assistant Professor

• Foreign Language Education
• Translation Studies
• Applied Linguistics
• +3 more

Katarzyna Oszust-Polak is an Assistant Professor at the Center of Polish Language and Culture for Polonia and Foreigners at Maria Curie-Skłodowska University. With a background in Russian philology, she has focused her career on Polish language teaching methodology for non-native speakers and translation-based language acquisition. PhD in Humanities (2012) with dissertation on translation exercises in Russian language acquisition Postgraduate qualifications in teaching Polish as a foreign language (2016-2017) Research Focus: Her work examines translation's role in foreign language education, emphasizing: Translation didactics Intercultural competence development Polish language acquisition strategies Language exercise design Multimedia teaching techniques Student feedback analysis Publication Trends: Spanning 2007-2017, her 15 most recent works analyze: Translation's pedagogical value Language learning error patterns Cultural mediation in translation Language warm-up techniques Translation exercise typologies Intercultural communication frameworks Teaching Activities: She offers courses in: Practical Polish language (all proficiency levels) Multimedia techniques for language teaching Integrated language skills for Russian philology students