جست‌وجوی استاد و پژوهشگر

Joey Paquetمشاهده پروفایل

دانشیار

• Design and implementation of programming languages
• Context-driven computing
• Demand-driven computing

Dr. Joey Paquet is a Tenured Associate Professor and Department Chair in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. He holds a PhD and specializes in research areas including Design and Implementation of Programming Languages, Context-Driven Computing, and Demand-Driven Computing. His work focuses on advancing programming paradigms and their applications in cybersecurity, distributed systems, and autonomic computing. Dr. Paquet is actively involved in thesis supervision across Computer Science and Software Engineering programs at both master's and doctoral levels. Research interests are centered around programming language design, particularly in demand-driven and context-aware systems. His contributions include frameworks like GIPSY and OpenISS, enabling scalable data processing and forensic computing. Recent work explores autonomic intent-driven networking, real-time gesture recognition, and IoT forensics. These efforts highlight his expertise in both theoretical constructs and practical implementations. His advising role supports students in MCompSc, MASc, and PhD programs. While specific grants are not detailed, his projects often involve interdisciplinary collaboration. Dr. Paquet is affiliated with platforms like LinkedIn and ResearchGate, reflecting an active academic presence. Key technical contributions include pioneering work on forensic computing backends, intent expression languages, and multimodal interaction systems. His research addresses challenges in cybersecurity, distributed systems, and service composition, with a focus on resilience and scalability.

Peter K. Kitanidisمشاهده پروفایل

استاد

• Groundwater Flow
• Hydrologic Modeling
• Inverse Problems
• +۳ مورد دیگر

Peter K. Kitanidis is a Professor in the Department of Civil and Environmental Engineering and the Institute for Computational and Mathematical Engineering at Stanford University . His research focuses on groundwater flow , hydrologic forecasting , and stochastic inverse modeling , with applications to pollutant remediation and CO₂ storage monitoring . Education : Diploma, National Technical University of Athens (1974) M.S., MIT (1976) Ph.D., MIT (1978) Research Interests : Groundwater modeling and contaminant transport Hydraulic tomography and aquifer characterization Stochastic methods for uncertainty quantification Bioremediation and enhanced in-situ pollutant decay Dilution and mixing processes in heterogeneous media Real-time river flow forecasting Scientific Awards : L.G. Straub Award (1979) W.L. Huber Research Prize (1994) ISI Highly Cited Researcher (2001) AGU Hydrologic Sciences Award (2011) ASCE Pioneers in Groundwater Lecturer (2011) Advising and Grants : Advised 20+ PhD and MS students (1978–2018) Principal investigator on NSF, EPA, and DOE-funded projects Developed software for groundwater data analysis and CO₂ monitoring Contributed to bioremediation protocols and hydraulic tomography algorithms Labs and Teams : Kitanidis Laboratory for groundwater crisis solutions Collaborated with Oak Ridge National Laboratory and Stanford Hydrogeology Group Mentored postdocs (2000–2017) in reactive transport and inverse modeling

David R. Koesمشاهده پروفایل

دانشیار

• Computational Drug Discovery
• Machine Learning in Biology
• Open Source Software
• +۳ مورد دیگر

David R. Koes is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh, affiliated with the School of Medicine. He holds roles such as Associate Director of the Joint CMU-Pitt Computational Biology PhD Program (CPCB) and is involved in multiple graduate programs including Intelligent Systems and Computational Biomedicine. His research focuses on developing computational algorithms and systems for drug discovery, emphasizing open-source software and machine learning applications in biomedical data. Koes teaches courses like MSCBIO2025 (Bioinformatics Programming in Python) and MSCBIO2065 (Scalable Machine Learning for Big Data Biology). He has secured NIH funding (R35GM140753) and collaborated on projects with institutions like NVIDIA and Google Cloud. His lab develops tools such as GNINA, Pharmit, and 3Dmol.js, and actively contributes to open drug discovery initiatives. Education: PhD in Computer Science from Carnegie Mellon University (CMU). Research Interests: Leveraging computation and AI for drug design, molecular docking, pharmacophore modeling, and open science. Specific areas include developing scalable machine learning pipelines, virtual screening systems, and tools for 3D molecular analysis. Grants and Funding: Current NIH R35 grant and prior support from NSF, Relay Therapeutics, and others. His work emphasizes translating computational methods into practical drug discovery solutions. Lab and Teams: Directs a lab focused on computational drug discovery, collaborating with multiple academic and industry partners. Supervises graduate students and postdocs in projects spanning AI-driven drug design, molecular modeling, and software development.

Na Duمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Human Factors in Smart City Technologies
• Human-Centered Computing
• +۴ مورد دیگر

Na Du is an Assistant Professor in the Department of Informatics and Networked Systems at the University of Pittsburgh's School of Computing and Information. She holds a PhD in Industrial & Operations Engineering from the University of Michigan (2021) and a Graduate Certificate in Data Science. Her research focuses on human factors in smart cities, human-centered computing, and user experience design. She is affiliated with the Intelligent Systems Program, Pitt Cyber, and the Center for Governance and Markets. Education: PhD in Industrial & Operations Engineering (University of Michigan, 2021); Undergraduate in Psychology (Zhejiang University). Research emphasizes explainable AI, human-AI teaming, and smart technologies. Recent grants include funding from Honda Research Institute and Pitt Cyber Accelerator for projects on emotions in Human-AI interaction and Metaverse privacy awareness. Her work has been recognized with awards like the HFES Best Paper Award and the IOE Outstanding Student Award. Advising includes PhD students and researchers in human factors and UX design. The HAT Lab under her leadership explores interdisciplinary challenges in human-computer interaction and smart systems.

• Railway Engineering
• Transportation Systems
• Structural Engineering
• +۳ مورد دیگر

Albert Lau is an Associate Professor of Civil and Environmental Engineering at the Norwegian University of Science and Technology (NTNU), located in Trondheim, Norway. He specializes in railway engineering, structural dynamics, and transportation systems. Lau holds leadership roles as the Study Program Leader for the MSc in Road, Railway, and Transportation Engineering, overseeing curriculum development and program coordination. His research focuses on railway track design, dynamic modeling of train-track interactions, and infrastructure maintenance, with projects such as the MeTinT initiative (Measurement with Train in Regular Traffic). He has extensive experience supervising master’s and PhD students, and his work emphasizes innovation in rail infrastructure and sustainable transportation solutions. Education and Professional Background: Lau earned his PhD from NTNU in 2018, focusing on numerical simulations of railway turnouts. Prior roles include Postdoc (2018–2020) and Assistant Professor (2017–2018) at NTNU, and teaching at Oslo Metropolitan University (2020). His industry experience includes roles as a Design Engineer (2010–2012) and Project Engineer (2013–2014) in Malaysia, where he managed construction projects and structural design. Research Interests: Lau’s work spans railway track dynamics, infrastructure health monitoring, and machine learning applications in transportation. Key projects include developing digital twins for railway test sites and analyzing ground displacement impacts on track anomalies. His contributions to the Road, Railway and Transport Group at NTNU aim to advance rail safety and efficiency through interdisciplinary approaches. Teaching and Outreach: Lau coordinates courses such as TBA4225 (Railway Engineering) and BA6012 (Fundamental Railway Technology). His outreach includes expert commentary on railway incidents, such as an interview on NRK (2024) discussing potential causes of a train accident. Current initiatives focus on revitalizing regional rail services and optimizing train positioning systems.

Veronika Eyringمشاهده پروفایل

استاد

• Climate science
• Climate modelling
• Machine learning for climate models
• +۷ مورد دیگر

Veronika Eyring serves as Head of the Earth System Model Evaluation and Analysis Department at the German Aerospace Center (DLR) Institute of Atmospheric Physics and Professor of Climate Modelling at the University of Bremen. She holds dual appointments at these leading institutions, directing cutting-edge research at the intersection of climate science and artificial intelligence. Education: 2008: Habilitation in Environmental Physics at the University of Bremen 1999: PhD in Physics from the University of Bremen 1994: Diploma in Physics from the University of Erlangen Professor Eyring's research program focuses on improving climate models and projections through innovative integration of machine learning techniques and spaceborne Earth observations. Her work spans process-oriented modeling, development of observationally-based performance metrics, and understanding systematic biases in climate models. She has pioneered approaches to weighting model projections based on their performance using machine learning, significantly advancing the field of climate model evaluation. Her research has critical applications across multiple sectors including aeronautics, space research, transportation, and energy systems. Analysis of her recent publications reveals a clear trajectory toward deeper integration of machine learning with traditional climate modeling approaches. Her work has increasingly focused on developing community tools like the Earth System Model Evaluation Tool (ESMValTool) and leading major international initiatives such as the USMILE project (Understanding and Modelling the Earth System with Machine Learning). The publications span climate science, machine learning, Earth system modeling, and remote sensing, with specific emphasis on climate model evaluation, parameterization techniques, and improved climate projections. Scientific Awards: AGU Ambassador Award (2024) TUM Distinguished Affiliated Professor (2024) Gottfried Wilhelm Leibniz Prize (2021) ERC Synergy Grant (2019) Thomson Reuters Highly Cited Researcher (2016-2021) Top female researchers award, Helmholtz-Society (2015) Professor Eyring actively supervises a large research group comprising PhD students working on ML-based sea ice parameterizations, causal model evaluation for air-sea interactions, and machine learning-based detection of droughts in climate projections. She leads the prestigious ERC Synergy Grant USMILE and secured significant funding through the DFG Gottfried Wilhelm Leibniz Prize. Her research group at DLR includes multiple postdocs, research scientists, and software engineers working collaboratively on climate informatics projects. Professor Eyring leads the Earth System Model Evaluation and Analysis Department at DLR, which encompasses research groups focused on CMIP model evaluation, ESMValTool development, and machine learning applications in climate science. She founded and supervises the 'Climate Informatics' Group at the DLR Institute for Data Science in Jena. Her department maintains strong international collaborations, particularly with the National Center for Atmospheric Research (NCAR) in Boulder, Colorado, where she serves as an Affiliate Scientist.

Knut Reinertمشاهده پروفایل

استاد

• Algorithmic Bioinformatics
• Genomic Sequence Analysis
• Proteomics
• +۷ مورد دیگر

Professor Knut Reinert is a leading figure in algorithmic bioinformatics at the Free University of Berlin, where he holds a professorship in the Department of Mathematics and Computer Science. He also maintains a significant affiliation with the Max Planck Institute for Molecular Genetics in Berlin, where he leads the Efficient Algorithms for Omics Data group. His research spans both institutions through the Reinert Lab, which focuses on developing novel computational approaches for biological data analysis. Reinert's educational background includes a Diploma in Computer Science (1994) and a Doctorate (Dr. Ing./Ph.D., 1999, with honors) from the Max-Planck-Institut for Computer Science and Universität des Saarlandes in Saarbrücken. Prior to his professorship, he worked as a computer scientist under Prof. Gene Myers at Celera Genomics in Rockville, USA (1999-2002). His primary research interests center on algorithmic bioinformatics with specific focus on developing novel algorithms and data structures for biomedical mass data analysis. This includes creating mathematical models for genomic sequence analysis and algorithms for mass spectrometry data to detect differential protein expression between normal and diseased samples. His work bridges the gap between computational tool development and practical biological applications, with particular emphasis on NGS and proteomics data. The publications and projects led by Prof. Reinert demonstrate a consistent focus on advancing computational methods in bioinformatics. His research spans genomic sequence analysis, RNA research (particularly long non-coding RNAs), parallel computing applications, and GPU acceleration for biological data processing. The work shows increasing sophistication in handling large-scale biological datasets through innovative algorithmic approaches. Intel® Parallel Computing Center designation for his lab CUDA Research Center status DFG funding of 530 thousand Euros for RNA research de.NBI funding of 2 million Euros BMBF funded projects 'LIVE-DREAM' and 'EssBar' Prof. Reinert leads multiple significant research projects and has established strong collaborations with international partners including Texas A&M, Kings College London, Eberhardt-Karls Universität Tübingen, Robert-Koch-Institute, and various Turkish institutions. His lab receives funding from major organizations including DFG, BMBF, and Intel. The Reinert Lab maintains active teaching responsibilities at FU Berlin, offering courses at BSc, MSc, and PhD levels using both traditional and innovative learning concepts like e-learning and inverted classrooms. The Reinert Lab consists of two interconnected research groups that work closely with experimental biologists and medical researchers to develop practical computational solutions for real-world biological problems. The lab has established itself as a key player in the German and international bioinformatics community through its development of the widely-used SeqAn library and participation in national infrastructure initiatives.

Ali Vakilianمشاهده پروفایل

عضو هیئت علمی

• Algorithmic Foundations of Machine Learning
• Data Science
• Streaming Algorithms
• +۱۸ مورد دیگر

Ali Vakilian is a Research Assistant Professor at the Toyota Technological Institute at Chicago (TTIC), with a strong academic background in theoretical computer science and algorithms. He will join the Department of Computer Science at Virginia Tech as an Assistant Professor in Fall 2025. His research bridges algorithmic theory and machine learning, focusing on scalable, fair, and efficient algorithms for massive data. Education: Ph.D. in EECS, Massachusetts Institute of Technology (MIT), advisors: Erik Demaine and Piotr Indyk M.S. in Computer Science, University of Illinois at Urbana-Champaign (UIUC), advisor: Chandra Chekuri B.S. in Computer Engineering, Sharif University of Technology Research Interests: Ali Vakilian's work centers on the algorithmic foundations of machine learning and data science. He develops streaming, sketching, and sublinear-time algorithms for massive datasets, and pioneers learning-augmented algorithms that use machine learning predictions to improve performance while maintaining worst-case guarantees. His research in trustworthy ML includes algorithmic fairness, fair clustering, and learning with strategic agents. He also contributes to combinatorial optimization and approximation algorithms for network design, set cover, and low-rank approximation. His recent publications (2023–2025) show a consistent focus on fair clustering (individual and group fairness), streaming graph algorithms , learning-augmented methods , and frequency estimation . These works appear in top venues such as NeurIPS, ICML, SODA, and ICALP, often with recognitions like oral or spotlight presentations. Scientific Awards: Outstanding Student Paper Highlight Award, AISTATS 2024 Notable-top-25% paper, ICLR 2023 Oral presentation, AISTATS 2024 Spotlight presentation, NeurIPS 2023 Advising and Grants: Ali Vakilian mentors several students and interns, including summer interns at TTIC and Fatima Fellows. His research is supported by the National Science Foundation (TRIPODS program), as noted in the press coverage of his work on LearnedSketch. He actively contributes to the academic community through advising, organizing workshops (e.g., Algorithms with Predictions, Learning-Augmented Algorithms), and serving on program committees (e.g., NeurIPS, ICML, AISTATS). Labs and Teams: He is affiliated with the theory and algorithms group at TTIC and collaborates with researchers at MIT, UIUC, and other institutions. His work on learning-augmented algorithms has led to influential workshops and collaborations with leading figures such as Piotr Indyk and Erik Demaine.

Matthew Osmanمشاهده پروفایل

استادیار

• Climate Science
• Paleoclimatology
• Climate Modeling
• +۴ مورد دیگر

Matthew Osman is an Assistant Professor of Climate Science in the Department of Geography at the University of Cambridge. He leads the Cambridge Computational Climate and PaleOceanography (C3PO) group and serves as a supervisor for the Cambridge NERC Doctoral Landscape Awards (DLA). Dr. Osman's research focuses on understanding climate dynamics across various timescales by integrating geochemical proxy records, modern observations, and climate model simulations. His work centers on: Developing quantitative tools to reconstruct past climates and constrain future projections Using data assimilation and modeling methods for key climate intervals (mid-Pliocene, Last Ice Age, interglacials) Creating probabilistic frameworks combining proxies with climate simulations Developing statistical proxy system models for ice cores and marine records Investigating cryosphere-climate feedbacks, particularly ice sheets and sea ice His research spans sub-seasonal to millennial time intervals, specializing in bridging climate proxies with global climate models. He works closely with the international PMIP/CMIP community on projects spanning Arctic sea ice sensitivity, AMOC weakening, and carbon cycle feedbacks. Dr. Osman actively supervises PhD students through the Department of Geography PhD Program and encourages students interested in quantitative climate science to develop projects in: Using paleo data to constrain future climate projections Developing fingerprinting techniques for proxy records Building probabilistic proxy system models Applying paleoclimate data assimilation during ice sheet collapse Climate risk modeling using physics-informed statistics The C3PO group maintains a strong commitment to diversity and inclusion, welcoming researchers from all backgrounds to address the climate crisis through quantitative, multidisciplinary approaches.

• Security Engineering
• Science of Security
• Human-Computer Interaction
• +۴ مورد دیگر

Janne Lindqvist is an Associate Professor at the Department of Computer Science, Aalto University. His research bridges security engineering, human-computer interaction (HCI), and privacy, with a focus on making security systems usable and user-centric. University: Aalto University Department: Department of Computer Science Rank: Associate Professor Email: janne.lindqvist@aalto.fi Lindqvist’s work spans security engineering , privacy systems , and user research , emphasizing practical authentication methods, password management, and human behavior in security contexts. He explores how users interact with systems like TPM APIs, gesture passwords, and mobile authentication mechanisms. Recent publications highlight trends in authentication systems , ubiquitous computing security , and mobile user behavior . Key themes include biometric authentication, gesture-based security, and balancing usability with cryptographic robustness. CHI'25 Honorable Mention Award CHI'24 Best Paper Award His research integrates empirical studies with technical implementations, such as analyzing password forgetting patterns and developing acoustic sensing for vehicle detection (e.g., Auto++, BO-Ear). Collaborative efforts span machine learning, psychology, and embedded systems.

Suchi Sariaمشاهده پروفایل

دانشیار

• Machine Learning
• Computational Health Informatics
• Probabilistic Methods
• +۶ مورد دیگر

Suchi Saria is the John C. Malone Associate Professor at Johns Hopkins University , with appointments in the Whiting School of Engineering (Computer Science), the Bloomberg School of Public Health (Health Policy & Management), and the Whiting School (Applied Math & Statistics). She directs the Machine Learning and Healthcare Lab and co-founded the Bayesian Health startup. Education: PhD in Computer Science from Stanford University (advisor: Daphne Koller), NSF Computing Innovation Fellowship at Harvard (2011), prior research at UMass (Barto, Madhavan), and industry experience at Aster Data Systems (acquired by Teradata). Research Focus: Saria develops statistical machine learning tools to extract insights from heterogeneous clinical data (structured/unstructured EHRs, sensor streams). Her work enables counterfactual reasoning for personalized treatment plans, dataset shift mitigation in healthcare AI, and weak supervision frameworks for mobile health apps. Key applications include sepsis prediction , Parkinson’s symptom tracking , and critical care optimization . Article Trends: Recent publications emphasize AI safety (2024-2025), addressing racial bias , transparency frameworks , and dynamic monitoring for clinical deployments. Her work spans conformal prediction , causal modeling , and policy guidelines for health AI. Scientific Awards: Sloan Research Fellowship (2018) DARPA Young Faculty Award (2016) MIT Technology Review TR35 Innovator (2017) Popular Science Brilliant 10 (2016) IEEE Intelligent Systems AI’s 10 to Watch (2015) NSF Computing Innovation Fellowship (2011) Rambus Fellowship (2004-2010) Best Paper Awards in ML, Informatics, and Medicine venues Advising & Grants: Saria mentors PhD students/postdocs in machine learning and health informatics , including funded projects like the NSF Smart and Connected Health Grant (2014) and Google Research Award (2014). Her lab’s TREWScore system (Science Translational Medicine 2015) is deployed in hospitals, while her Bayesian Health startup commercializes AI solutions for provider experience.

• Earthquake Seismology
• Seismic Tomography
• Subduction Zone Processes
• +۴ مورد دیگر

Andreas Rietbrock is Professor and Director of the Geophysical Institute (GPI) at the Karlsruhe Institute of Technology (KIT) , Germany, where he also serves as Dean of Studies for Geophysics . He is a leading expert in earthquake seismology, seismic tomography, and subduction zone dynamics, with a strong focus on integrating advanced observational techniques and computational methods. Education: While specific degrees are not listed in the provided text, his extensive publication record and leadership roles indicate advanced academic training in geophysics and seismology. Research Interests: His work spans a wide range of topics including: Seismic imaging of subduction zones (e.g., Nazca, Lesser Antilles) Earthquake rupture dynamics and fault mechanics Volcanic seismology and magma transport Full waveform inversion and AI-enhanced seismic analysis Distributed Acoustic Sensing (DAS) applications Induced seismicity and reservoir monitoring Research Trends: His recent publications (2022–2025) emphasize the use of dense seismic arrays, AI-based data processing, and multi-method tomography to study complex tectonic environments. Key themes include high-resolution imaging of slab structures, fluid migration in subduction zones, and the integration of DAS and machine learning for seismic monitoring. Scientific Contributions: Andreas has led major international projects such as the ANTICS Large-N deployment in Albania and the VoiLA project in the Lesser Antilles. He has published extensively in top-tier journals like Nature , Geophysical Research Letters , and Journal of Geophysical Research , with over 200 peer-reviewed articles. Teaching and Supervision: He teaches courses such as "Introduction to Geophysics II", "Seismology", and "Current Topics in Seismology and Risk". While specific student names are not listed, his role as Dean and principal investigator on numerous projects indicates active supervision of graduate students and postdocs. Labs and Teams: He leads the seismology group at GPI, coordinating large-scale deployments of seismic instruments, including ocean-bottom seismometers and fiber-optic DAS systems. His team collaborates globally with institutions in Europe, South America, and Asia.

Charles Rahalمشاهده پروفایل

دانشیار

• Social Science Methodology
• Applied Social Data Science
• High-dimensional Econometrics
• +۸ مورد دیگر

Charles Rahal is an Associate Professor in Data Science and Informatics at the University of Oxford, with additional affiliations as an Associate Member of Nuffield College and Researcher at the Gradel Institute, New College. He serves as a Co-Investigator at the ESRC Centre for Care and sits on the Steering Group of Reproducible Research Oxford. His academic career includes previous roles as a Senior Departmental Research Lecturer at the Leverhulme Centre for Demographic Science and a British Academy Postdoctoral Fellow. Dr. Rahal completed his PhD in 2016 and has established himself as a prominent social science methodologist and applied social data scientist with expertise in high-dimensional econometrics. His research spans multiple domains, focusing particularly on unique Big Data origination processes and their relationship to social inequality, mobility, and stratification. He is deeply engaged in machine learning methods, civic technology, spatial and time series econometrics, model uncertainty, and scientometrics. His recent publications reveal a strong trend toward computational social science, with significant contributions to understanding prediction limits, pandemic impacts, healthcare systems, and environmental sustainability. The articles demonstrate his interdisciplinary approach, bridging traditional social science with cutting-edge computational methods, particularly in the analysis of large-scale datasets and development of novel metrics like the InterModel Vigorish for model comparison. Dr. Rahal is actively involved in teaching and mentoring, co-convening courses in Demographic Analysis, Life Course Research, and the Oxford Partner site of the Summer Institute in Computational Social Sciences. He has developed workshops on machine learning, command line interfaces, and LaTeX, reflecting his commitment to methodological training in social science. He leads the Metrics and Models lab and maintains several open-source projects including the GWAS Diversity Monitor and RobustiPy. His editorial roles include Associate Editor-in-Chief at the Journal of Social Computing and Associate Editor at ACM Transactions on Social Computing, highlighting his influence in shaping computational social science methodology.

Thomas Hackerمشاهده پروفایل

استاد

• Cloud Computing
• High-Performance Computing
• Operating Systems
• +۳ مورد دیگر

Thomas Hacker is a Professor in the Department of Computer and Information Technology at Purdue Polytechnic Institute, Purdue University. His research focuses on cloud computing, high-performance computing, operating systems, computer networking, and cyber infrastructure . He holds a Ph.D. and M.S. in Computer Science & Engineering from the University of Michigan, along with dual B.S. degrees in Computer Science and Physics from Oakland University. Education: PhD (Computer Science & Engineering), University of Michigan (2004) MS (Computer Science & Engineering), University of Michigan (1993) BS (Computer Science, Mathematics Minor), Oakland University (1989) BS (Physics), Oakland University (1989) Dr. Hacker's research spans cloud and grid computing, operating systems, and distributed systems , with applications in earthquake engineering data systems and AI-driven infrastructure analysis. His recent work explores extended layer 2 networking for bare-metal provisioning ( 2023 IEEE Cloud Summit ) and machine-supported bridge inspection using artificial intelligence ( Transportation Research Record, 2023 ). Notable scientific contributions include 15+ publications on topics like cyberinfrastructure for earthquake engineering, container-based virtualization, and data-intensive systems. His work has been recognized with awards such as the NSF CAREER Award (2010) and multiple Purdue Seed for Success Awards . Key Scientific Awards: NSF CAREER Award (2010) Purdue Seed for Success Awards (2008-2013) ASEE Information Systems Division Best Paper Award (2012) College of Technology Outstanding Faculty in Discovery Award (2010) He has held leadership roles at Purdue, including Department Head (2018-2021) and Interim Department Head (2011-2016) . His career spans academic positions at Indiana University, University of Michigan, and industry roles at Storage Technology Corporation.

• Artificial Intelligence
• Privacy and Data Protection
• Trustworthy AI
• +۴ مورد دیگر

Prof. Dr. Pinar Yolum Birbil is a leading researcher in Responsible AI at the Faculty of Science , Utrecht University . Her work bridges Artificial Intelligence , Privacy and Data Protection , and Software Agents , focusing on Human-Centered AI and Trustworthy Systems . She is part of the AI & Data Science and Responsible AI research groups. PhD in Computer Science, North Carolina State University (2003) MS in Computer Science, North Carolina State University (2000) BSc in Computer Engineering, Marmara University (1998) Her research explores Privacy Preservation in collaborative systems, Computational Theory of Mind for human-agent coordination, and Norm-Based AI Systems . Recent projects include the Hybrid Intelligence Center and tools like PANOLA for privacy support. She investigates how AI can balance user autonomy , ethical norms , and societal values in applications ranging from urban planning to healthcare. Her scientific publications (2023-2025) span topics like Explainable Privacy , Trust in Hybrid Teams , and AI for Diabetes Management , appearing in venues such as JAIR , AAMAS , and ACM TOIT . She emphasizes collaborative AI , with contributions to multiagent simulations and privacy-preserving mechanisms . Scientific Awards : NC State University Alumni Hall of Fame (2017) Woman Entrepreneur of the Year (Microsoft Turkey & KAGIDER, 2017) Bogazici University Academic Encouragement Award (multiple years) Best Paper Award at ESAW 2009 She supervises a dynamic research group with PhD students working on Hybrid Intelligence , Computational Ethics , and Privacy Modeling . Her projects often involve interdisciplinary collaboration with institutions like TNO and the Transforming Cities Hub .