جست‌وجوی استاد و پژوهشگر

• Human-Computer Interaction
• Design Theory and Methodology
• Interaction Design
• +۶ مورد دیگر

David Ribeiro Lamas is a Professor of Human-Computer Interaction at Tallinn University's School of Digital Technologies, where he heads the Human-Computer Interaction group. He also serves as the chair of the Estonian chapter of ACM's SIGCHI and as an expert member of IFIP's TC13. With an extensive international career spanning the USA, UK, Portugal, Cape Verde, Mozambique, Afghanistan, and Estonia, he has developed deep expertise in designing organizations, communities, and human technologies. His educational background includes a PhD in Human Computer Interaction from Portsmouth University (1998), an MSc in Computer Science from Minho University (1994), and an Honours BSc in Informatics/Applied Mathematics from Portucalense University (1989). He also completed specialized studies in Strategic Management at the Polytechnic University of Catalunya and a postdoc in Augmented and Virtual Environments at Michigan State University. Lamas' research primarily focuses on design theory and methodologies, with recent work emphasizing trust in technology, facial recognition systems, and vibrotactile interfaces. He has pioneered academic programs including the Master in Human-Computer Interaction and the Masters in Interaction Design (run online with Cyprus University of Technology). His approach combines theoretical rigor with practical application, particularly in cross-cultural contexts. His publication record shows a strong trend toward understanding human trust in technology systems, with significant work on facial recognition, vibrotactile feedback systems, and trust frameworks. His research spans both theoretical contributions to HCI methodology and practical applications addressing real-world challenges in digital accessibility and user experience. 2025 HCI Pioneer Award from IFIP TC13 2025 Tallinn Conference Ambassador 2024 IFIP Service Award 2015 Badge of Merit from Tallinn University 2011 Best Paper Award for work on Estonia's M-Government Services 2000 Best Paper Award for research on Web navigation guidance Lamas has successfully supervised forty-eight master students, seven doctoral students, and two post-doc researchers, and currently supervises twelve doctoral students. His leadership extends to numerous research projects including COST Actions on Interactive Narrative Design and Human-Computer Interaction methodologies. He founded and leads STARTS.EE, Tallinn University's initiative promoting encounters between science, technology, and the arts. He has been instrumental in building the Estonian HCI community through seasonal courses on Experimental Interaction Design, Research Methods in HCI, and the Design of Human Technologies since 2010. His World Usability Day events bring together over 600 researchers and practitioners annually from the Baltics, Nordic countries, and beyond. Lamas has chaired major international conferences including INTERACT 2019, NordiCHI 2020, AfriCHI 2021, and ICIDIS 2021.

• Bayesian Statistics
• Machine Learning
• Approximate Inference Methods
• +۸ مورد دیگر

Dennis Prangle is an Associate Professor in Statistics at the University of Bristol, conducting research at the intersection of Bayesian statistics and machine learning. His academic profile demonstrates expertise in developing novel computational inference methods with applications across multiple scientific domains. Dr. Prangle's primary research interests include: Approximate inference methods such as simulation-based inference and variational techniques Likelihood-free inference through Approximate Bayesian Computation (ABC) Experimental design for high-dimensional problems Applications in population genetics, physics, ecology, and epidemiology Stochastic differential equations and composite likelihood approaches His publication record shows consistent methodological contributions with increasing integration of machine learning techniques. Recent work focuses on normalizing flows with flexible tails for improved density estimation, Bayesian emulation of complex systems, and optimal combination of composite likelihoods. His research bridges theoretical statistics with practical applications in financial modeling, infrastructure engineering, and fair classification algorithms. Dr. Prangle maintains an active academic blog where he discusses technical aspects of Bayesian statistics, experimental design, and computational methods, demonstrating his commitment to scholarly communication. His detailed posts on topics like Fisher information gain versus Shannon information gain in experimental design highlight his theoretical contributions to the field.

Abhraneel Sarmaمشاهده پروفایل

پژوهشگر

• Uncertainty Visualization
• Data Analysis
• Multiverse Analysis
• +۳ مورد دیگر

Abhraneel Sarma is a PhD candidate in Computer Science at Northwestern University, advised by Professors Matthew Kay and Jessica Hullman. His research focuses on addressing uncertainty in data analysis through tools like multiverse (an R package for sensitivity analysis) and Milliways (a visualization system for multiverse analysis validation). Uncertainty visualization Multiverse analysis Bayesian inference Reproducibility in research His work combines empirical studies and system development to improve data-driven decision-making. Publications include best paper awards at CHI 2019 and honorable mentions at CHI 2024, CHI 2023, and VIS 2022. 2025 : CHI 2024 : CHI (Best Paper Honorable Mention) 2023 : CHI (Best Paper Honorable Mention) 2022 : VIS (Best Paper Honorable Mention) 2019 : CHI (Best Paper Award) Email: abhraneel@u.northwestern.edu

Victor Greiffمشاهده پروفایل

استاد

• Computational Immunology
• Adaptive Immune Receptor Repertoires
• Machine Learning in Antibody Design
• +۵ مورد دیگر

Victor Greiff is a Professor at the University of Oslo's Institute of Clinical Medicine , leading the Computational and Systems Immunology group. His research focuses on integrating high-throughput single-cell sequencing , artificial intelligence , and high-dimensional statistics to advance therapeutic antibody design, diagnostics, and vaccine development for infectious diseases, autoimmunity, and cancer. Key Research Areas : Computational immunology, adaptive immune receptor repertoires, machine learning in antibody discovery, bioinformatics, structural biology, and immune diagnostics. Funding : European Research Council (ERC) Grant AB-AG-INTERACT (Project ID: 101125630). Recent Publications highlight interdisciplinary work in: Machine learning for antibody design and immune repertoire classification . Structural analysis of antibody-antigen interactions and evolutionary immunogenetics . Development of bioinformatics tools (e.g., ExpoSeq, TCRpower, CompAIRR) for immune sequencing data analysis. Scientific Contributions include: Establishing computational frameworks for synthetic immune repertoire generation. Exploring genetic constraints on TCR and BCR diversity. Advancing AI-driven antibody engineering for improved therapeutic developability.

Massimo Mecellaمشاهده پروفایل

استاد

• Service-Oriented Computing
• Business Process Management
• Process Mining
• +۱۲ مورد دیگر

Massimo Mecella serves as Full Professor at Sapienza University of Rome's Faculty of Information Engineering, Computer Science and Statistics, where he leads the Processes, Services and Software Management research group and participates in the CINI National Cyber Security Lab. His academic foundation stems from a PhD in Computer Engineering earned at Sapienza in 2002. His research expertise spans service-oriented computing, business process management, and cyber-physical systems with emphasis on service composition, process mining, and adaptive distributed architectures. Key technical domains include IoT integration, digital twin development, and security frameworks for complex software ecosystems. Recent publications (2024-2025) demonstrate a pronounced shift toward Large Language Model (LLM) integration in business processes and smart manufacturing. Dominant themes include LLM-driven process modeling, multimodal quality control systems, and digital twin implementations for energy management and production optimization. Methodological innovations focus on retrieval-augmented generation (RAG) for service discovery and fuzzy cognitive maturity assessments. His distinguished recognition includes: ICSOC 2013 Most Influential Paper Award (2003-2012 decade) 2017 Best Paper Award As research group leader, Mecella directs projects in smart manufacturing, process mining, and cyber-physical security, with funding typically sourced from EU initiatives and industry partnerships in aerospace, healthcare, and energy sectors. His team maintains active collaborations with aerospace manufacturers through the MICS SPOKE8 project and develops frameworks like SAMBA for human-in-the-loop manufacturing systems. The Processes, Services and Software Management group operates within Sapienza's engineering faculty infrastructure, utilizing specialized labs for CPS/IoT testing and digital twin simulation, with strong ties to the CINI Cyber Security Lab for security validation.

• Computer Vision
• Image Analysis
• Machine Learning
• +۱ مورد دیگر

Puneet Sharma is an Associate Professor at the Department of Automation and Process Technology, UiT The Arctic University of Norway. His primary research areas include computer vision, image analysis, machine learning, and wearables technology, with active contributions to atmospheric science data processing and maritime navigation AI applications. Current role: Associate Professor (Automation) Teaching: Industrial data communication (bachelor), Machine Vision (master) Research: Machine Learning Group member, focus on visual attention models, deep learning for PMSE segmentation, and wearable training systems Recent publications highlight his work in applying machine learning to Polar Mesospheric Summer Echoes (PMSE) analysis, noctilucent cloud classification, and biosignal-based maritime navigation studies. He participated in the Horizon 2020 WEKIT project for wearable-based industrial training.

• Quantum Information Science
• Quantum Computing
• Quantum Error Correction
• +۶ مورد دیگر

Dr. Thinh Le is a Postdoctoral Research Fellow at the School of Computer Science , University of Technology Sydney , where he works at the Centre for Quantum Software and Information (since 2023). His academic journey includes research fellowships at the Institute for Quantum Optics and Quantum Information Vienna (2020-2023) and Leibniz University Hannover (2019-2020). He earned his PhD in Physics (2015) and BSc in Physics (2011) from the National University of Singapore . Thinh's research focuses on quantum information science , bridging theoretical foundations and experimental implementations. His work addresses critical challenges in quantum computing (error correction, fault tolerance), quantum tomography (device-independent characterization), and quantum software stack development. He investigates quantum randomness generation, Bell nonlocality, and entanglement distillation for quantum networks, with applications in quantum cryptography and secure communication. His recent publications examine advanced topics like trapped-ion lattice surgery (2025), quantum correlation boundaries (2025), and quantum backflow phenomena (2023). Earlier work established foundational limits for real quantum theory (2021) and developed practical entanglement distillation protocols (2018). Scientific awards include the Lise Meitner Postdoctoral Fellowship (2020-2023) and Humboldt Postdoctoral Fellowship (2019-2020). His funded research projects focus on quantum computing error correction and device-independent quantum tomography , aligning with his theoretical expertise guided by experimental applications.

• Medical Informatics
• Statistical Science
• Bioinformatics
• +۵ مورد دیگر

Dr. Masato Inoue is a Professor at the Faculty of Science and Engineering , School of Advanced Science and Engineering at Waseda University. He holds a Doctor of Medical Science from Kyoto University. Education: 2003 - Kyoto University Graduate School of Medicine 2003 - Kyoto University His research spans multiple disciplines at the intersection of Medical Informatics , Bioinformatics , and Statistical Mechanics . Key areas include: Medical Imaging : Developing Bayesian super-resolution algorithms and Prior Ensemble Learning for improved MRI reconstruction Voice Analysis : Creating innovative voice quality quantification systems for clinical diagnostics Genetic Analysis : Advancing haplotype inference methods and gene network modeling Signal Processing : Applying statistical mechanics to diverse problems from coding theory to neuroscience His recent publications (2021-2012) demonstrate consistent contributions to medical imaging algorithms , voice disorder classification , and genetic data analysis . Notable collaborations include work with Kyoto University researchers , Swedish medical institutions , and cross-disciplinary teams in bioengineering.

Kibaek Kimمشاهده پروفایل

پژوهشگر

• Computational Optimization
• Stochastic Programming
• Integer Programming
• +۵ مورد دیگر

Kibaek Kim is a Computational Mathematician in the Laboratory for Applied Mathematics, Numerical Software, and Statistics at Argonne National Laboratory's Mathematics and Computer Science Division. He also serves as a Senior Scientist at-Large at the University of Chicago Consortium for Advanced Science and Engineering. His educational background includes a Ph.D. in Industrial Engineering and Management Sciences from Northwestern University (2014) under the supervision of Dr. Sanjay Mehrotra. Dr. Kim's research focuses primarily on computational optimization, including stochastic programming and integer programming, with applications to complex systems. His recent work has centered on developing algorithms for solving stochastic mixed-integer programs using high-performance computing clusters. He is particularly known for his contributions to federated learning algorithms and software development, as well as modeling and numerical algorithms for large-scale optimization on high-performance computing systems and GPUs. His work finds applications in electric grid systems, healthcare, and key scientific domains of interest to the Department of Energy. Dr. Kim's publications demonstrate a strong focus on federated learning frameworks, with significant contributions to privacy-preserving techniques and distributed training algorithms. His work bridges theoretical optimization with practical implementation on high-performance computing infrastructure. DOE Early Career Research Program award (2019) IEEE Senior Member (2022) Multiple IMPACT Argonne Awards (2021) George L. Nemhauser Best Student Paper (2014) Throughout his career, Dr. Kim has mentored numerous postdoctoral researchers, predocs, and Ph.D. interns from top institutions including MIT, UIUC, Georgia Tech, and Northwestern University. He serves as associate editor for Mathematical Programming Computation and Naval Research Logistics, and as a board member for COIN-OR Foundation and IISE Energy Systems. Dr. Kim leads research on the APPFL (Advanced Privacy-Preserving Federated Learning) framework, which provides a comprehensive environment for implementing, testing, and validating privacy-preserving federated learning algorithms across distributed computing environments.

• Mechanical Engineering
• Artificial Intelligence
• Tribology
• +۳ مورد دیگر

Hassan Shirvani is a Professor of Engineering Design and Simulation at the School of Engineering and the Built Environment, Anglia Ruskin University. He serves as Director of the Engineering Analysis Simulation and Tribology (EAST) Research Group, focusing on industry collaborations to solve engineering challenges. PhD in Mechanical Engineering, University of Bath MSc in Mechanical Engineering, University of Birmingham Member, Institute of Mechanical Engineers (IMechE) His research spans mechanical engineering, artificial intelligence, and biomedical applications, including: Thermal system optimization Machine learning in clinical decision-making Composite metal foil manufacturing Virtual reality medical training systems Flow dynamics in heat exchangers and nozzles AI-assisted diagnostics Hybrid manufacturing processes Hassan's publications reflect expertise in computational modeling, multi-physics simulations, and industrial applications. Notable areas include deep learning for suicide prediction, thermodynamic analysis of sustainable energy systems, and tribology in mechanical components.

• Robust Machine Learning
• Trustworthy AI
• Bayesian Uncertainty Propagation
• +۵ مورد دیگر

Dimah Dera is an Endowed Assistant Professor at the Chester F. Carlson Center for Imaging Science, College of Science, Rochester Institute of Technology (RIT). She holds a Ph.D. and M.S. in Electrical and Computer Engineering, and an M.A. in Mathematics from Rowan University. Her research focuses on robust and trustworthy machine learning, integrating Bayesian theory and statistical signal processing into modern ML frameworks for healthcare, remote sensing, and surveillance systems. She is an NVIDIA Deep Learning Institute University Ambassador and active in IEEE Signal Processing and ACM SIGHPC. Dr. Dera has received prestigious awards including the NSF CRII Award (2023), NSF REU Supplement (2024), and IEEE Benjamin Franklin Key Award (2021). Her work emphasizes Bayesian uncertainty propagation for robust AI systems, with applications in sequential time-series analysis and medical imaging. Her scholarly contributions span robust image classification, uncertainty-aware neural networks, and Bayesian vision transformers. Teaching includes courses like Mathematical Methods for Imaging and Image Processing & Computer Vision II . She actively mentors students and leads research initiatives funded by NSF and industry collaborations.

Sunil Aryalمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۱۰ مورد دیگر

Sunil Aryal is an Associate Professor of Data Science at the School of Information Technology, Faculty of Science Engineering and Built Environment, Deakin University, Australia. He received his PhD and Master by Research degrees from Monash University Australia and has published over 70 papers in top-tier international venues in Artificial Intelligence, Machine Learning and Data Mining. Dr. Aryal's educational background includes: Graduate Certificate of Higher Education Learning and Teaching, Deakin University (2020) PhD in Computer Science, Monash University (2017) Master of Information Technology (Research), Monash University (2012) Master of Information Technology (Coursework), University of Southern Queensland (2008) Bachelor of Information Technology, Purbanchal University, Nepal (2005) His primary research interests focus on making Machine Learning and Data Mining algorithms robust and flexible to handle heterogeneous, noisy and uncertain data in real-world problems. His work spans across several specific areas including anomaly detection, clustering, kernel/similarity-based learning, ensemble methods, learning from limited data, reinforcement learning, natural language processing, and computer vision. Dr. Aryal is particularly interested in applying these techniques to solve challenges in Defence, National Intelligence, Engineering, Manufacturing, Healthcare and Education. Dr. Aryal co-leads the Machine Learning for Decision Support (MLDS) Research Group at Deakin University and has secured over AUD 4.5 million in external research funding. His research is supported by diverse organizations including US and Australia Defence Agencies, the Australian Office of National Intelligence, Worksafe Victoria, the Victorian State Department of Education and Training, the Technology Innovation Institute (TII) UAE, and Table Tennis Australia (TTA). His notable awards include multiple Deakin University research and teaching awards, the Australian Postgraduate Award for his PhD studies, and several student travel awards during his doctoral candidature. Dr. Aryal actively supervises numerous PhD and Master's students and has contributed significantly to teaching in various courses at Deakin University and previously at Federation University. He serves on several university committees and contributes to the research community as a reviewer, program committee member, and editor for various journals and conferences.

Glen Bauerمشاهده پروفایل

استاد

• Mathematics
• Calculus
• Differential Equations
• +۲ مورد دیگر

Glen Bauer serves as Associate Dean for the College of Arts and Sciences and Professor of Mathematics at Lawrence Technological University, where he has been a faculty member since 1982. He earned both undergraduate and graduate degrees in Mathematics from Wayne State University, establishing his foundational expertise in quantitative disciplines. His academic focus centers on core mathematical instruction, particularly the engineering calculus sequence, differential equations, and probability/statistics. He pioneered the Calculus I Workshop program from 1997 to 2003, integrating mathematical software to enhance pedagogical effectiveness. His primary research and teaching interests include: Calculus Education Differential Equations Applications Probability Theory Statistical Methods As Associate Dean since 1998, he manages comprehensive academic operations including student recruitment/retention, curriculum development, academic policy formulation, and strategic planning, while periodically serving as Acting or Interim Dean. Prof. Bauer actively mentors students through his role as faculty advisor for LTU Cru and contributes to campus spiritual life via Bible study leadership and children's ministry programs.

Yipeng Huangمشاهده پروفایل

استادیار

• Quantum Computing
• Computer Architecture
• Analog Computing
• +۵ مورد دیگر

Yipeng Huang is an Assistant Professor in the Department of Computer Science at Rutgers University, School of Arts and Sciences. His research focuses on building and helping programmers use quantum, analog, and other emerging computer architectures for the post-Moore's Law era of computing. He teaches undergraduate and graduate courses including Computer Architecture (CS 211) and Quantum Computing: Programs and Systems (CS 558/443). Huang actively mentors graduate students and recruits undergraduates for research in quantum computing and computer architecture. His PhD advisees include Zirui Li, Jonathan Garcia-Mallen, Adeeb Kabir, Haoyan Luo, Enhyeok Jang, and Seungwoo Choi, with undergraduate researchers including Pooja Kedia and Winston Li. Huang's research spans quantum computing, computer architecture, and analog computing. His work examines quantum error correction, quantum software development, and hybrid analog-digital systems for scientific computation. He has published extensively on quantum error correction, quantum compilation frameworks, and analog accelerators for solving differential equations and linear algebra problems. Huang's recent publications show a strong focus on practical quantum computing systems, with several 2025 papers on high-dimensional quantum error correction, qudit simulation, and optimized quantum program generation. His work bridges theoretical quantum computing with practical implementation challenges. 2024 ISCA Distinguished Artifact Award for Tetris: A Compilation Framework for VQA Applications in Quantum Computing 2021 MICRO Top Picks honorable mention 2017 MICRO Top Picks honorable mention 2016 MICRO Top Picks Huang serves on program committees for major computer architecture conferences including ISCA, ASPLOS, HPCA, and MICRO. He is actively involved in science education outreach, organizing the Workshop on Broadly Accessible Quantum Computing and serving on committees for the Summer Science Program and FIRST Robotics Competition.

• Mathematical Biology
• Computational Modeling
• Biophysics
• +۳ مورد دیگر

Sabine Fischer is a Professor for Supramolecular and Cellular Simulations at the University of Würzburg’s Center for Computational and Theoretical Biology (CCTB). She holds a PhD in Mathematics from the University of Nottingham (2009) and completed postdoctoral research at the University of Cambridge (2009-2011) and the University of Frankfurt (2011-2017). Her work focuses on mathematical modeling and data-driven simulations of biological processes across subcellular, multicellular, and multi-tissue scales. Key research areas include agent-based modeling of parasite collective behavior (e.g., Project 7 of SPP 2332 PoP), cell-cell interactions, and morphological modeling with Blender. Her group develops tools for image analysis and applies methods ranging from classical statistics to machine learning. Education: Diploma in Mathematics, University of Würzburg PhD in Mathematical Biology, University of Nottingham (2009) Professional Experience: Postdoc, University of Cambridge (2009-2011) Postdoc, University of Frankfurt (2011-2017) Development Engineer, h.a.l.m. Elektronik GmbH (2017-2018) Professor, University of Würzburg (since 2018) Her research integrates experimental data with computational models to study processes like mouse blastocyst differentiation, tissue-level organization, and parasitic locomotion. Current projects emphasize agent-based modeling of Trypanosoma collective behavior and the development of open-source tools like VESNA for 3D vessel analysis.