جست‌وجوی استاد و پژوهشگر

Tak-Wah Lamمشاهده پروفایل

استاد

• Algorithms
• Bioinformatics
• Big Data Analytics
• +۳ مورد دیگر

Professor Tak-Wah Lam is a leading academic at the University of Hong Kong (HKU), serving as Deputy Director of the School of Computing & Data Science and former Head of the Computer Science Department (2018–2023). He joined HKU in 1988 after earning his PhD in Computer Science from the University of Washington. His research spans algorithm design, bioinformatics, and big data analytics, with a focus on applications in health informatics and genomics. He has published over 200 articles, including influential work on bioinformatics software like MEGAHIT and Clair3. He is a Charivate Highly Cited Researcher (2017) and has received multiple grants, including ITF-funded projects totaling over HKD 33 million for research in genomic databases, AI-driven RegTech, and bioinformatics solutions for clinical genetics. Education: BSc from The Chinese University of Hong Kong (CUHK), MS and PhD from the University of Washington. Research interests include algorithms, bioinformatics, and big data analytics. His work bridges theoretical computer science with practical applications in healthcare, such as developing tools for variant calling, genome assembly, and disease risk prediction. He co-founded a startup in 2014 to advance bioinformatics software for clinical DNA sequencing. Key grants include: ITF Tier-2 Project (2021–2023): HKD 7 million for automated karyotype analysis ITF Tier-2 Project (2019–2021): HKD 5 million for AI-driven RegTech ITF Tier-2 Project (2015–2020): HKD 7.6 million for precision medicine genomic databases He is also recognized for teaching excellence, having received awards from HKU's Faculty of Engineering and the Department of Computer Science.

Zhu-Tian Chenمشاهده پروفایل

استادیار

• Data Visualization
• Human-Computer Interaction
• Augmented Reality
• +۷ مورد دیگر

Zhu-Tian Chen is an Assistant Professor in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities, where he leads research in data visualization, human-computer interaction, and augmented reality. Prior to this, he held postdoctoral positions at Harvard University and UC San Diego, working with leading researchers in visual computing and interactive design. Ph.D. in Computer Science, Hong Kong University of Science and Technology B.Eng. in Software Engineering, South China University of Technology His research focuses on augmenting human intelligence through hybrid human-AI systems, particularly in everyday and outdoor environments. He specializes in designing intelligent AR interfaces, embedded visualizations, and language-oriented interactions for applications in sports analytics, education, and data analysis. His work integrates human-centered design with applied machine learning to create intuitive and effective visualization tools. The recent trend in his publications shows a strong emphasis on intelligent AR systems for dynamic scenes, LLM-based code generation interfaces, and real-time augmentation of sports videos using natural language and gaze-based interactions. His work frequently appears in top-tier venues such as IEEE VIS, ACM CHI, and UIST. Best Paper Award, ACM CHI'23 Best Short Paper Honorable Mention, EuroVis'23 Best Paper Honorable Mention, IEEE VIS'22 (twice) Certificate of Distinction and Excellence in Teaching, Harvard University Hong Kong Ph.D. Fellowship Dr. Chen actively mentors undergraduate, master’s, and PhD students, as well as visiting scholars and interns, and is building a new research lab focused on visualization for intelligent AR systems. He has served on program committees for major conferences including ACM CHI, IEEE VIS, and EuroVis, and has been invited to speak at institutions such as Apple, JP Morgan, and multiple universities worldwide. He also contributes to the academic community through grant reviewing for NSF and the Department of Energy. He leads research projects in intelligent AR systems for sports, language-oriented interactions with LLMs, and immersive data visualization, often in collaboration with institutions like Harvard, UC San Diego, and HKUST. His lab welcomes students and collaborators interested in visualization, HCI, and applied AI.

Hassan Sajjadمشاهده پروفایل

دانشیار

• Natural Language Processing
• Deep Learning
• Safe and Trustworthy AI
• +۷ مورد دیگر

Hassan Sajjad is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He is also the Director of HyperMatrix, a research group focused on AI and deep learning. His work centers on Natural Language Processing, Safe and Trustworthy AI, interpretability, and robustness of language models. PhD - University of Stuttgart, Germany (2012) Masters - National University of Computer and Emerging Sciences, Pakistan (2007) BSc - National University of Computer and Emerging Sciences, Pakistan (2005) Hassan Sajjad's research focuses on making AI systems more interpretable, robust, and safe. He investigates how deep learning models, particularly transformers, encode linguistic and conceptual knowledge. His work spans language generation , model editing , interpretability , and generalization . He develops tools like NeuroX and NxPlain to analyze neuron-level behavior in NLP models. His research also extends to crisis informatics and multilingual NLP, especially Arabic and Urdu. The recent publications show a strong trend in analyzing and interpreting deep NLP models, with a focus on neuron interpretation , latent concept discovery , and model robustness . His work appears in top-tier venues like NeurIPS, ICLR, ACL, and EMNLP, indicating high impact in the AI and NLP communities. There is a clear emphasis on developing practical tools and frameworks for model analysis. No formal scientific awards are mentioned in the provided text. Hassan Sajjad mentors students and has fellowship opportunities available. While specific grants are not listed, his extensive publication record and leadership roles suggest active research funding. He advises students in AI, NLP, and deep learning, and collaborates widely across institutions. He leads the HyperMatrix research group at Dalhousie University, which focuses on AI, deep learning, and NLP. The group develops tools for model interpretation and works on safe and trustworthy AI. Collaborations extend to institutions like MBZUAI, NYU Abu Dhabi, and various international research centers.

Yingfei Xiongمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages

Yingfei Xiong is an active Associate Professor at Peking University, China, specializing in software engineering and programming languages. With a consistent research trajectory from 2013 through 2026, Xiong has established themselves as a prominent figure in the software engineering research community, regularly contributing to top-tier conferences including SPLASH, ICSE, ASE, and PLDI. Dr. Xiong's research primarily focuses on program synthesis, automated program repair, and software analysis techniques. Their work bridges theoretical programming language concepts with practical software engineering applications, particularly in developing novel approaches for code generation, bug fixing, and program optimization. The research demonstrates strong interdisciplinary connections between traditional software engineering, programming languages theory, and emerging AI techniques. Analysis of Xiong's publication trends reveals a clear evolution in research focus, beginning with foundational work in API transformations and program adaptation around 2013-2016, shifting toward program repair techniques from 2017-2020, and most recently incorporating machine learning and neural approaches into program synthesis and repair (2021-2026). The work consistently addresses practical challenges in software development while maintaining theoretical rigor, with increasing integration of AI techniques in recent years. Dr. Xiong has served in various leadership roles across the software engineering conference ecosystem, including program committee membership and session chair positions at major conferences. Their extensive service demonstrates recognition by peers as a subject matter expert in software engineering and programming languages research. While specific grant information isn't detailed in the provided text, the sustained publication record suggests successful research funding.

Shuvendu K. Lahiriمشاهده پروفایل

پژوهشگر

• Formal Verification
• Smart Contracts
• Program Synthesis
• +۵ مورد دیگر

Shuvendu K. Lahiri is a researcher at Microsoft Research, focusing on formal verification, program synthesis, and software testing. His work bridges artificial intelligence with formal methods, particularly in blockchain security and automated code generation. 2025 : Published LLM-Vectorizer (verified loop vectorizer) and neural synthesis for SMT-assisted proof-oriented programming 2024 : Explored LLM-based test-driven code generation and natural precondition inference 2023 : Developed resource management specifications and contributed to test generation with pre-trained models 2022 : Advanced Solidity type systems and merge conflict resolution using language models His research combines large language models with formal verification tools to improve software correctness. He actively contributes to conferences like ICSE, PLDI, and ISSTA as author and committee member.

Benjamin Rechtمشاهده پروفایل

استاد

• Machine Learning
• Optimization
• Statistics
• +۵ مورد دیگر

Benjamin Recht is a Professor in the Department of Electrical Engineering and Computer Sciences and Department of Statistics at the University of California, Berkeley. Previously, he was an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. Recht received his BS in mathematics from the University of Chicago and his MS and PhD from the MIT Media Laboratory, followed by a postdoctoral fellowship at Caltech's Center for the Mathematics of Information. His research interests span Machine Learning, Optimization, Control Theory, and Statistics , with a focus on both theoretical foundations and practical applications. Recht's work addresses fundamental questions in reproducibility, generalization, and robustness of machine learning systems, while also developing novel methods for control, computer vision, and data analysis. Recht's recent publications reveal a strong focus on reproducibility in machine learning , with papers like "The Mechanics of Frictionless Reproducibility" (2024), alongside continued contributions to statistical learning theory ("Interpolating Classifiers Make Few Mistakes", 2023) and computer vision ("Plenoxels", 2022; "K-planes", 2023). His work increasingly addresses societal implications of AI , including papers on systemic harm detection and post-deployment evaluation. NSF Career Award Alfred P. Sloan Research Fellowship 2012 SIAM/MOS Lagrange Prize in Continuous Optimization Presidential Early Career Award for Scientists and Engineers 2014 Jamon Prize 2015 William O. Baker Award for Initiatives in Research 2017 and 2020 NeurIPS Test of Time Awards Recht has advised numerous PhD students who have gone on to faculty positions at top universities and research roles at leading technology companies. His work on optimization algorithms has been widely influential, including the development of methods like HOGWILD! for parallel stochastic gradient descent. He co-founded the Conference on Learning for Decision and Control and has served on editorial boards for the Journal of Machine Learning Research and Mathematical Programming. His research group spans both theoretical and applied work, with connections to healthcare (adaptive medication tapering), computer vision (radiance fields), and social impact (systemic harm detection in deployed systems).

Niina Zuberمشاهده پروفایل

پژوهشگر

• Ethical Software Design
• Digital Ethics
• Agile Software Development
• +۶ مورد دیگر

Niina Zuber is a Research Coordinator at the Bavarian Institute for Digital Transformation (bidt), focusing on ethical software design, agile development processes, and the intersection of digital technologies with democratic systems. Her work emphasizes integrating ethical principles into technical systems through frameworks like EDAP (Ethical Deliberation for Agile Processes). PhD in Ethics and Software Development (LMU Munich) Former roles: Research Consultant (Cognostics AG), LMU Munich, Center for Digitalization Bavaria (ZD.B) Key projects: EDAP, ReCREATIV, AI Gender Bias Dialogue Her research spans: ethical requirements management, value-sensitive design, AI's impact on creativity, facial recognition regulation, and digital responsibility frameworks. She regularly contributes to academic publications and public discourse on digital ethics. Scientific Contributions: Co-developed EDAP framework for ethical agile software processes Investigated ethical challenges in large language models (Vox ChatGPT) Explored gender bias in AI systems Studied generative AI's impact on creative industries Contributed to regulatory discussions on facial recognition Zuber collaborates across disciplines, connecting philosophy with technical implementation through projects like EDAP and publications in journals such as Philosophy & Technology and Informatics Spectrum.

• Financial Mathematics
• Actuarial Mathematics
• Stochastic Processes
• +۷ مورد دیگر

Thorsten Schmidt is Professor of Mathematical Stochastics at the University of Freiburg, succeeding Prof. Ernst Eberlein in the summer semester of 2015. He also serves as Senior Financial Engineer at MathFinance. Previously, he held professorships at Chemnitz University of Technology (2008-2015), Technical University Munich (2008), and University of Leipzig (2004 onwards). From 2017-2019, he was a Research Fellow at the Freiburg Institute for Advanced Studies (FRIAS) in a joint research group with the University of Strasbourg and USIAS on the topic of Linking Finance and Insurance. His research focuses primarily on financial and actuarial mathematics, stochastic processes, and statistics, with recent work on machine learning methods and their applications in financial mathematics and AI regulation. In Freiburg, his goal with his young team is to tackle complex challenges with improved mathematical models and apply these methodologies to various fields. Key Research Areas: Financial mathematics and credit risks Pricing and hedging of derivative financial products Statistics of stochastic processes Energy markets and nonlinear filter theory Machine learning applications in finance and insurance His recent publications show a strong trend toward integrating machine learning with traditional mathematical finance, particularly in risk management, insurance-finance arbitrage, and robust financial modeling. His work increasingly addresses ethical considerations in AI applications within finance, reflecting his broader interest in responsible AI development. Notable Awards: IDA Award Finance (2015) FRIAS-USIAS Research Fellow (2017/2018) IDA Award Machine Learning and AI (2020) MAPFRE Research Grant (2020) Luis Bachelier Fellow (2021) As Editor-in-Chief of Statistics and Risk Modeling and Associate Editor for Mathematical Finance and International Journal of Theoretical and Applied Finance, Schmidt plays a significant role in academic publishing. He leads the CRC 'Small Data' research center with Harald Binder, focusing on medical problems where disease progression must be estimated with few data points per patient. His LeanAI project, funded by the Vector Foundation, explores the connection between machine learning and theorem-proving software LEAN, aiming to develop AI that can translate between mathematics and formal proof systems. His laboratory work centers around the application of stochastic methods combined with machine learning to solve problems in finance and insurance where data is limited ('Small Data' initiative), with significant funding from DFG (€12 million for CRC Small Data) and the Carl Zeiss Foundation.

Ting Sunمشاهده پروفایل

دانشیار

• Urban climate modelling
• Climate change impacts and adaptation
• Extreme weather events in cities
• +۵ مورد دیگر

Dr Ting Sun is an Associate Professor in Climate & Meteorological Hazard Risks at University College London , Department of Risk and Disaster Reduction. He earned his BEng (2009) and PhD in Hydrology (2013) from Tsinghua University , followed by a visiting period at Princeton University (2011–2012). After postdoctoral appointments at Tsinghua and the University of Reading , he held a NERC Independent Research Fellowship at Reading (2017–2022) before joining UCL in May 2022. Education PhD in Hydrology, Tsinghua University, 2013 BEng in Hydraulic Engineering, Tsinghua University, 2009 Visiting PhD Student, Princeton University, 2011–2012 Research Interests Dr Sun’s work converges on urban climate modelling across scales —from neighbourhood blocks to global grids—focusing on the impacts of weather and climate extremes such as heat waves and extreme rainfall in cities. He is the lead developer of the Surface Urban Energy and Water balance Scheme (SUEWS) and its Python wrapper SuPy , developed in collaboration with Prof Sue Grimmond’s micromet group. He also contributes as a core member of the Urban Multi-scale Environmental Predictor (UMEP) development team. His multidisciplinary expertise integrates hydro-climate dynamics, computational modelling, machine learning, built-environment processes, and public-health linkages . Research Trends from Recent Publications Across the 15 most recent articles, a clear trajectory emerges from high-resolution urban-process modelling toward integrated socio-environmental assessments . Studies published in 2024–2025 couple atmospheric models (WRF-SUEWS) with global building-morphology datasets (GLAMOUR) to quantify how cities alter rainfall patterns, temperature sensitivity, and heat-related mortality. Earlier works progressively refined SUEWS’s physical parameterisations and Python accessibility, while recent outputs leverage deep-learning remote-sensing tools (SHAFTS) and hybrid hydrological-neural architectures to deliver actionable insights for urban planning and climate adaptation. Scientific Awards & Fellowships NERC Independent Research Fellowship , University of Reading, 2017–2022 HEA Fellowship , University College London, 2023 Professional Service & Editorial Roles Topic Editor , Geoscientific Model Development (from 2025) Editorial Board Member , Scientific Data (from 2024) Peer review and consultancy for journals, conferences, and policy bodies Supervision of taught-course projects and research degrees External examining and mentoring Labs, Teams & Collaborations Dr Sun leads and collaborates within the UCL Department of Risk and Disaster Reduction , working closely with the micromet group at the University of Reading (Prof Sue Grimmond) on SUEWS/SuPy development. He is an active member of the UMEP consortium and maintains extensive international collaborations spanning Tsinghua University, Princeton, and numerous European research centres, underpinning a vibrant, interdisciplinary research network focused on urban climate resilience.

Daehyeok Kimمشاهده پروفایل

استادیار

• Computer Networks
• Operating Systems
• Distributed Systems
• +۶ مورد دیگر

Daehyeok Kim is an Assistant Professor in the Department of Computer Science at The University of Texas at Austin, where he co-leads the UT Networked Systems Research Group and participates in the Wireless Networking and Communications Group and 6G@UT. He serves as co-PI for the LDOS NSF Expeditions in Computing project, a major initiative rethinking operating systems through AI. His educational background includes a Ph.D. in Computer Science from Carnegie Mellon University under advisors Vyas Sekar and Srinivasan Seshan, where his dissertation introduced abstractions for elastic in-network computing. He also earned B.S. and M.S. degrees in Computer Science and Engineering from POSTECH, South Korea, followed by research scientist work at KAIST prior to his Ph.D. Kim's research centers on hardware-software co-design for cloud and edge data centers, targeting speed, efficiency, and resilience. Key projects include resource management for programmable infrastructure, robust cellular network design, end-to-end network transport frameworks, and learning-directed operating systems. His work bridges computer networks, operating systems, distributed systems, and 5G/6G technologies, with emphasis on virtualized radio access networks (vRAN) and edge computing challenges. Analysis of his recent publications reveals a dominant focus on enhancing 5G/6G infrastructure reliability—particularly in virtualized RANs—through innovations in failover mechanisms, integrity protection, and latency-sensitive resource allocation. His research consistently addresses critical industry pain points like sub-second availability requirements, fronthaul security vulnerabilities, and end-to-end service-level objective (SLO) guarantees for mobile-edge applications. Notable scientific awards include: NSF CAREER Award (2025) for advancing cloud hardware efficiency Microsoft Research PhD Fellowship (2019) Bronze Award at Samsung HumanTech Paper Awards (2018) Qualcomm Innovation Awards (2016) His grant portfolio features leadership in the $10M+ LDOS NSF Expeditions project and the NSF CAREER award, both driving transformative work in AI-integrated operating systems and resilient network infrastructure. These projects demonstrate strong industry-academia collaboration with Microsoft Research, wireless vendors, and cloud providers. Kim co-leads the UT Networked Systems Research Group, which operates within the Wireless Networking and Communications Group and 6G@UT consortium. These labs maintain a 5G/6G testbed for Open RAN validation and focus on solving real-world problems in cellular infrastructure, edge computing, and network security through close partnerships with industry leaders.

David A. Plaistedمشاهده پروفایل

استاد پژوهشی

• Mechanical theorem proving
• Term rewriting systems
• Logic programming
• +۳ مورد دیگر

David A. Plaisted is a Research Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill. He joined UNC-Chapel Hill as a full professor after serving on the faculty of the Computer Science Department at the University of Illinois at Urbana-Champaign until 1984. His academic career spans several decades with significant contributions to automated reasoning and computational logic. Bachelor's degree in Mathematics from the University of Chicago (1970) Ph.D. in Computer Science from Stanford University (1976) Professor Plaisted's research focuses on mechanical theorem proving, term rewriting systems, logic programming, and algorithms. His work in term-rewriting systems investigates methods of combining them with first-order theorem provers, including techniques for applying efficient permutation group algorithms to equational theorem proving. In mechanical theorem proving, he has developed a sequence of methods including clause linking with semantics and ordered semantic hyper-linking. His research in logic programming includes developing tests to eliminate the occurrence check in Prolog while maintaining semantics. His work spans theoretical foundations to practical applications in program verification and generation. His recent publications demonstrate continued innovation in automated reasoning, particularly in semantic guidance for theorem proving. His work shows a consistent focus on improving the efficiency and effectiveness of automated deduction systems, with recent contributions to SGGS (Semantically-Guided Goal-Sensitive) theorem proving and analysis of the relationship between semantics and unification in proof systems. Professor Plaisted has served on numerous program committees and editorial boards including the Journal of Symbolic Computation, Information Processing Letters, Mathematical Systems Theory, and Fundamenta Informaticae. He is currently on the editorial board of ACM Transactions on Computational Logic and the electronic Journal of Functional and Logic Programming. He has organized significant conferences including serving as co-chair of the Second International Conference on Rewriting Techniques and Applications in 1987. He has spent several sabbaticals at prestigious institutions including SRI in Menlo Park (1982-1983), the Max-Planck Institute and University of Kaiserslautern in Germany (1993-1994), and research visits to groups in Grenoble and Nancy, France (1998).

Pierre Marquisمشاهده پروفایل

استاد

• Artificial Intelligence
• Knowledge Representation
• Automated Reasoning
• +۶ مورد دیگر

Pierre Marquis is a distinguished Professor of Computer Science at Université d'Artois , affiliated with the Centre de Recherche en Informatique de Lens (CRIL-CNRS, UMR 8188) . Since December 2024, he has served as the vice-president for research and doctoral studies at Université d'Artois. His research focuses on Artificial Intelligence , particularly knowledge representation , automated reasoning , inconsistency handling , and knowledge compilation , with recent emphasis on Explainable AI (XAI) . Research Interests: Marquis's work spans foundational AI topics including abduction , induction , belief revision , and preference modeling . He has pioneered knowledge compilation techniques to optimize AI tasks and developed frameworks for reasoning under inconsistency through paraconsistent logics and argumentation. His EXPEKTATION chair (2020-2026) under France's national AI program drives his current focus on interpretable machine learning models. Scientific Awards: 2025: CNRS Silver Medal 2022: AAIA Fellow 2017: Senior Member of Institut Universitaire de France (IUF) 2009: EurAI (ECCAI) Fellow Doctoral Students: Mentoring Clément Lens (critical patient monitoring systems) and Mehdi Sabiri (data-knowledge integration for AI explanations). Collaborating with students like Louenas Bounia (formal XAI models) and Romain Wallon (pseudo-Boolean constraints). Grants & Projects: Leads the EXPEKTATION research chair (2020-2026) and participates in ANR PING/ACK (2019-2023), ANR THEMIS (2021-2025), CNRS IRP MAKC (2020-2024), and H2020 TAILOR (2020-2024). Previously led PIA4 MAIA (2023-2032) and Pint (2022-2023). Labs & Teams: Active in CRIL-CNRS, contributing to PyXAI (Python XAI library) and d4 (model counting), while mentoring teams on consensus belief merging and dynamic constraint processing .

Chaopeng Shenمشاهده پروفایل

استاد

• Hydrology
• Deep Learning
• Physics-Informed Machine Learning
• +۹ مورد دیگر

Chaopeng Shen is a Professor in the Department of Civil and Environmental Engineering at Pennsylvania State University. His research bridges hydrology with state-of-the-art deep learning and differentiable modeling techniques, focusing on advancing our understanding of hydrologic cycles and their interactions with ecosystems, energy, and carbon cycles. He leads the Multi-scale Hydrology, Processes and Intelligence group (MHPI) and has developed the Process-based Adaptive Watershed Simulator (PAWS) for large-scale hydrologic modeling. Shen's work emphasizes physics-informed machine learning , where deep learning components are integrated with process-based equations through differentiable modeling. This approach enables training neural networks using big data while respecting physical laws, leading to improved generalizability and robustness. His group has demonstrated advantages of differentiable models in rainfall-runoff prediction, routing, ecosystem modeling, and water quality studies. Notably, his team's deepLDB project addresses landslide prediction using AI and big datasets. Recent publications highlight his contributions to global water modeling (grid-LSTM, differentiable Muskingum-Cunge routing), extreme flood forecasting (probabilistic diffusion models), and hydrologic uncertainty quantification . Shen actively engages in interdisciplinary collaborations through the PRISM Cooperative Institute, which aims to integrate multi-domain data for systemic risk assessment. His group has advised students including Dapeng Feng, Wen-Ping Tsai, Kuai Fang, Xinye Ji, and Tasnuva Mahjabin. Shen's research is supported by the National Science Foundation (NSF), Department of Energy (DoE), USGS, Google.org, and the Gates Foundation. He serves as Editor for the Journal of Geophysical Research - Machine Learning & Computation and Chief Editor for Frontiers in Water: Water & AI. His open-source software tools like PAWS and deepLDB are available through dedicated project websites.

Maria Francesca Spadeaمشاهده پروفایل

استاد

• Medical Image Processing
• Artificial Intelligence in Healthcare
• Radiomics
• +۵ مورد دیگر

Prof. Dr.-Ing. Maria Francesca Spadea serves as Director of the Institute of Biomedical Engineering (IBT) at Karlsruhe Institute of Technology (KIT), part of the Helmholtz Association. Her leadership role includes overseeing research initiatives, teaching activities, and administrative responsibilities within the institute. Located in space 512, she maintains regular consultation hours on Wednesdays from 10:30-11:30 am by appointment. Professor Spadea's research spans several cutting-edge areas in biomedical engineering, with particular focus on medical image processing, artificial intelligence applications in healthcare, and radiomics. Her work bridges computational techniques with clinical applications, emphasizing practical solutions for medical imaging challenges. She has pioneered approaches in federated learning for medical image translation, particularly in CT/MRI synthesis for radiation therapy applications. Her research also extends to cancer cell analysis, vascular biomechanics, and medical robotics, demonstrating a broad yet cohesive research portfolio that addresses critical challenges in modern healthcare. Analysis of Professor Spadea's recent publications reveals a strong emphasis on AI-driven medical imaging solutions, particularly in the translation between different imaging modalities (like MRI-to-CT) using federated learning approaches that preserve patient privacy. Her work demonstrates growing specialization in radiation therapy applications, with multiple publications addressing synthetic CT generation for treatment planning. There's also a clear trajectory toward multi-institutional collaboration, as evidenced by her involvement in projects spanning multiple research centers across Europe. Professor Spadea actively mentors numerous students, including M. Krohmer Zabaleta, N. Skupien, and M. Destito, who have completed bachelor's and master's theses under her supervision. Her research group appears well-integrated within the broader Institute of Biomedical Engineering, collaborating extensively with colleagues like P. Zaffino and C.B. Raggio on multiple projects. The group maintains strong connections with clinical partners, as evidenced by publications addressing real-world medical challenges in radiation therapy, cardiology, and neurosurgery. The research activities of Professor Spadea's team are centered within the Institute of Biomedical Engineering at KIT, with particular focus on medical imaging processing and AI applications. Her laboratory appears to specialize in developing computational tools for medical image analysis, with recent work emphasizing privacy-preserving federated learning frameworks that enable multi-institutional collaboration without sharing sensitive patient data. The team maintains active collaborations with clinical departments, particularly in radiation oncology, as evidenced by numerous publications addressing CT synthesis for radiation therapy planning.

Atrisha Sarkarمشاهده پروفایل

استادیار

• Human-centric multiagent systems
• Human-AI interaction
• Human-robot interaction
• +۴ مورد دیگر

Atrisha Sarkar is an Assistant Professor in the Department of Electrical and Computer Engineering at Western University , Canada, and heads the Humans and Autonomous Agents Lab . She is also a faculty member of the Rotman Institute of Philosophy and a Faculty Affiliate at the Schwartz Reisman Institute for Technology and Society . Her research integrates empirical and behavioral game theory with software engineering to design human-centric AI systems that prioritize safety and societal well-being. Education: Atrisha holds a PhD and has previously served as a postdoctoral fellow at the Schwartz Reisman Institute for Technology and Society at the University of Toronto under the supervision of Prof. Gillian Hadfield. Research Focus: Her work centers on human-centric multiagent systems , combining methods from: Behavioral and empirical game theory Software engineering Human-AI and human-robot interaction AI safety and reliability She applies these to domains such as autonomous driving, cooperative AI, and social media dynamics, aiming to ensure AI systems align with human values and societal norms. Publications and Impact: Atrisha has published extensively in top-tier venues including AAAI , AAMAS , ICRA , NeurIPS , and EC . Her work spans from theoretical models of strategic behavior to practical frameworks for validating autonomous systems, with a strong emphasis on real-world applicability. Labs and Teams: She leads the Humans and Autonomous Agents Lab at Western University, where her team focuses on designing AI agents that can cooperate effectively with humans in complex, dynamic environments.