جستجوی استاد راهنمای دانشگاهی

Mohsen Lesaniمشاهده پروفایل

دانشیار

• Reliability of software systems
• Security of software systems
• Concurrent systems
• +۱۲ مورد دیگر

Mohsen Lesani is an Associate Professor in the Computer Science and Engineering Department at the University of California, Santa Cruz's Baskin School of Engineering. His research focuses on reliability and security of software systems, particularly concurrent and distributed systems, with recent emphasis on secure replicated systems and distributed machine learning. Dr. Lesani received his PhD from UCLA, MS in artificial intelligence from Sharif University of Technology, and BS in software engineering from University of Tehran. He was previously a postdoc at MIT. His educational background provides a strong foundation for his interdisciplinary research spanning programming languages, distributed systems, and security. His research interests center on creating reliable and secure distributed systems. Current projects include resilient and secure distributed systems, heterogeneous and reconfigurable secure distributed systems, automatic analysis and synthesis of replicated objects, verification of distributed systems, data analytics, secure exchange across blockchains, machine learning for performance models, domain-specific languages and type systems, and automatic fence insertion for concurrent systems. His work bridges theoretical foundations with practical implementations to address real-world challenges in distributed computing. Lesani's research has been recognized with several prestigious awards including the NSF CAREER award in 2020 and DARPA YFA award in 2022. His work has also received the SIGPLAN Research Highlight in 2019, a distinguished paper award at OOPSLA 2018, and a best paper award at ISSRE 2015. These accolades reflect the impact and quality of his contributions to the field. He actively mentors PhD students in the Safe and Secure Software (S3) lab, including Xiao Li, Eric Chan, Javad Saber-Latibari, and Tejas Mane. His research has been supported by multiple NSF grants, demonstrating sustained funding for his innovative work. Lesani serves on program committees for major conferences including POPL, PLDI, OOPSLA, and DISC, contributing to the academic community. Lesani leads the Safe and Secure Software (S3) lab at UC Santa Cruz, where his team works on cutting-edge research in distributed systems, programming languages, and security. The lab fosters a collaborative environment where theoretical insights are translated into practical systems that address real-world challenges in reliability and security of distributed applications.

Panagiotis Hadjidoukasمشاهده پروفایل

دانشیار

• Parallel Computing
• Distributed Systems
• High Performance Computing
• +۳ مورد دیگر

Panagiotis Hadjidoukas is an Associate Professor and Head of the Laboratory for Computing at the Computer Engineering and Informatics Department, University of Patras, within the School of Engineering. His work focuses on high-performance computing systems and parallel programming models. His research spans parallel and distributed computing , runtime support for parallel programming models , and automation of AI/ML workloads . Key contributions include developing the torc runtime system for task parallelism and pioneering work in extreme-scale scientific simulations. His interests bridge theoretical computer science with practical applications in scientific computing and AI acceleration. Notable achievements include the ACM Gordon Bell Prize Winner (2013) for 11 PFLOP/s cloud cavitation simulations and Finalist (2015) for in-silico lab-on-a-chip microfluidics. His software tools ( torc_lite , torcpy ) enable efficient parallelism across diverse architectures. Doctor of Philosophy (2003), University of Patras Master of Science (2001), University of Patras Diploma in Computer Engineering (1998), University of Patras As Head of the Laboratory for Computing, he leads infrastructure development while maintaining active research collaborations with IBM Research and ETH Zurich. His teaching portfolio includes graduate courses on high-performance computing for data sciences and parallel processing principles.

Zhenjiang Huمشاهده پروفایل

استاد

• Programming Languages
• Software Engineering
• Functional Programming
• +۷ مورد دیگر

Zhenjiang Hu is a Chair Professor and Dean of the School of Computer Science at Peking University. He serves as Director of the Programming Languages Laboratory and has held significant academic positions including Professor at the National Institute of Informatics and University of Tokyo. BS and MS from Shanghai Jiaotong University (1988, 1991) PhD from University of Tokyo (1996) Lecturer/Assistant Professor at University of Tokyo (1997) Associate Professor at University of Tokyo (2000) Full Professor at National Institute of Informatics (2008) Full Professor at University of Tokyo (2018-2019) Professor Hu's research primarily focuses on programming languages and software engineering, with special emphasis on functional programming, bidirectional transformation, and software adaptation. His work explores transformational programming approaches for automatic program optimization, systematic parallelization of sequential programs, efficient manipulation of structured documents, and bidirectional model transformation for software development. His research has significantly advanced the field of bidirectional programming, developing foundational theories and practical applications that enable more reliable and maintainable software systems. His recent publications demonstrate a strong trajectory in bidirectional programming, program synthesis, and graph processing. The research shows increasing sophistication in handling program transformations, with growing emphasis on practical applications in software engineering contexts. His work increasingly integrates formal methods with practical programming language design, creating systems that maintain theoretical soundness while addressing real-world software development challenges. The research spans multiple venues including top conferences like PLDI, POPL, ICFP, and OOPSLA, reflecting its broad impact across programming language research. Fellow of JFES (Japan Federation of Engineering Society, 2016) ACM Distinguished Scientist (2016) Member of Academia Europaea (2019) IEEE Fellow (2020) Member of Engineering Academy of Japan (2020) Professor Hu actively mentors students and has welcomed excellent candidates to join his group through Peking University's International Elite PhD Program and Boya Postdoctoral Fellowship Program. He serves on numerous program committees for major conferences including PLDI, POPL, ICFP, and OOPSLA, and holds editorial positions for prestigious journals such as Journal of Functional Programming and Science of Computer Programming. His leadership extends to conference organization, having served as PC Chair for CNCC 2024 and General Co-Chair for SoICT 2019. As Director of the Programming Languages Laboratory at Peking University, Professor Hu leads a research team focused on advancing programming language theory and practice. His lab has developed influential frameworks like BiGUL for bidirectional programming and Fregel for graph processing. The laboratory maintains strong international collaborations and contributes to both theoretical foundations and practical implementations in programming languages and software engineering.

Michael Carbinمشاهده پروفایل

دانشیار

• Programming Systems
• Probabilistic Programming
• Quantum Computing
• +۳ مورد دیگر

Michael Carbin is an Associate Professor at MIT in the Department of Electrical Engineering and Computer Science (EECS), where he leads the Programming Systems Group at the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research centers on developing programming systems that handle uncertainty through probabilistic programming, quantum computing, and neural networks. Carbin's work spans programming languages, systems, and machine learning, with themes including uncertainty management, efficiency optimization, and formal verification. His publications demonstrate a strong focus on probabilistic inference methods, neural network optimization, and quantum programming frameworks. Awards and Honors: Sloan Research Fellowship (2020) Multiple Best Paper Awards (OOPSLA 2013, 2014; ICLR 2019) NSF CAREER Award (2018) Google Faculty Research Award (2018) As the head of the Programming Systems Group, he advises 10+ graduate students and postdocs, focusing on cutting-edge systems research. He has secured grants including Facebook Research Awards and NSF funding.

• Biomedical Engineering
• Biomedical Signal Processing
• Entropy Analysis
• +۷ مورد دیگر

Georgios Manis is an Associate Professor in the Department of Computer Science and Engineering at the School of Engineering, University of Ioannina, Greece. He holds a PhD from the National Technical University of Athens and has been a faculty member at the University of Ioannina since 2002, progressing from Lecturer to Associate Professor in 2018. He has also served as temporary teaching staff at the University of Patras, University of Crete, and University of Ioannina in the late 1990s and early 2000s. Education: B.Sc. in Computer Engineering (Diploma), National Technical University of Athens (NTUA), 1987–1992 MSc in Advanced Methods in Computer Science (Distributed and Parallel Systems), Queen Mary, University of London, 1992–1993 PhD in Computer Engineering, NTUA, School of Electrical and Computer Engineering, 1993–1997 His research interests lie at the intersection of Biomedical Engineering and Computing Systems , with a strong emphasis on Biomedical Signal Processing , Entropy Analysis , and Machine Learning . He has pioneered work in Bubble Entropy —a parameter-free entropy measure—and developed fast algorithms for entropy computation. His work also extends to compiler design and parallel computing, particularly in the automatic parallelization of recursive functions and loops. The trends in his recent publications reflect a dual focus: (1) biomedical applications involving entropy, heart rate analysis, and disease diagnosis using machine learning (especially Random Forests and SVMs), and (2) high-performance computing, including parallelization techniques and compiler optimizations for multi-core and SVP architectures. His research is highly interdisciplinary, combining signal processing, algorithm design, and clinical applications. Scientific Leadership and Recognition: Guest Editor, Special Issue on “Entropy in Biomedical Engineering”, Entropy (MDPI) Member of the IPAN Laboratory, University of Ioannina Active contributor to IEEE, Elsevier, and MDPI journals He has supervised several graduate students and is involved in funded research projects such as Palimpsest and Homore , focusing on smart systems for cultural interaction and elderly monitoring. His advising contributions are evident in co-authored papers with students like Evanthia Tripoliti and Aristeidis Mastoras. He teaches both undergraduate and postgraduate courses, including Compilers I/II and Biomedical Data Analysis . Laboratories and Teams: He is a member of the IPAN lab at the University of Ioannina, which supports interdisciplinary research in informatics and biomedical applications. His collaborative network includes researchers from Greece and abroad, particularly in the fields of biomedical signal analysis and entropy-based methods.

Dominik Helmمشاهده پروفایل

استاد

• Program Analysis
• Static Analysis
• Call Graph Algorithms
• +۵ مورد دیگر

Dominik Helm serves as an interim professor at the University of Duisburg-Essen and is a researcher at the Software Technology Group, Technical University of Darmstadt since 2018. He holds a Dr.-Ing. degree and serves as the lead maintainer of the OPAL static analysis framework. Helm is also affiliated with ATHENE (National Research Center for Applied Cybersecurity) and CRISP, where he contributes to cybersecurity research focusing on 'Security at Large' for comprehensive systems. Dr. Helm's research centers on modularization and automatic parallelization of collaborative static analyses to improve precision, soundness, and performance. His work specifically targets the OPAL framework for Java VM bytecode, with expertise spanning purity and immutability analyses, modular call graphs, and bug/security vulnerability detection. His research bridges theoretical foundations with practical implementation, addressing real-world challenges in static analysis. His publication record shows consistent contributions to top-tier conferences (PLDI, ISSTA, ESEC/FSE, ASE) with recent work focusing on cross-language analysis, modular call graph algorithms, and evaluation of static analysis precision. Helm has demonstrated particular interest in making static analysis more practical through modularity and parallelization. As an educator, Helm teaches courses including Software Engineering, Type Systems, Quality Assurance, and Program Analysis at the University of Duisburg-Essen. He has supervised multiple student teams through bachelor and master projects, guiding research on call graphs for dynamic languages, IDE solvers, alias analysis, and immutability analysis. Dr. Helm actively contributes to the academic community through program committee service for ICSE, ISSTA, ASE, and other major conferences. His work with the OPAL framework represents a significant contribution to the static analysis community, providing a platform for developing and composing modular analyses for Java bytecode processing and analysis.

Fabrice Rastelloمشاهده پروفایل

پژوهشگر

• Automatic Parallelization
• Compiler Back-End Optimization
• Static Single Assignment (SSA)
• +۴ مورد دیگر

Fabrice Rastello is a leading researcher at Inria, France, where he serves as the leader of the CORSE (Compiler Optimization and Runtime SystEms) research team. His expertise spans automatic parallelization and compiler back-end optimization, with significant contributions to Static Single Assignment (SSA) form theory and register allocation techniques. Affiliated with University Grenoble Alpes - Inria - CNRS - Grenoble INP - LIG, he maintains an active research profile with continuous publications in top-tier conferences including PLDI, CGO, and PPoPP. His research interests focus on combining theoretical foundations with practical applications in compiler design. Key areas include automatic parallelization techniques (building on his PhD work on tiling as a loop transformation), compiler back-end optimization, and the emerging field of hybrid compilation that bridges static compilation with runtime techniques. He has made significant contributions to register allocation for JIT compilation in the context of SSA properties, advising three PhD students on related topics. Analysis of his recent publications reveals a strong trend toward data movement optimization, with numerous papers on I/O complexity bounds, cache modeling, and microarchitectural performance analysis. His work spans both theoretical computer science and practical applications, including contributions to healthcare technology during the pandemic with the eSpiro ventilator project. He has also explored intersections between compiler techniques and machine learning, particularly in using ML for performance modeling. Fabrice Rastello serves on program committees for major conferences in the field, including PLDI, CGO, and CC, demonstrating his standing in the research community. His 2022 book 'SSA-based Compiler Design' as editor further establishes his authority in compiler theory and practice. His recent work shows continued innovation in compiler techniques, with publications in 2024 demonstrating ongoing research activity across multiple domains including cache modeling, program analysis, and machine learning applications.

• Logic Programming
• Fixpoint Theory
• Non-Monotonic Reasoning
• +۵ مورد دیگر

Panos Rondogiannis is a Professor in the Department of Informatics and Telecommunications at the National and Kapodistrian University of Athens, focusing on Theoretical Informatics. His research spans logic programming semantics, fixpoint theory, and non-monotonic reasoning with contributions to higher-order constructs and formal frameworks for preference representation. His research interests include logic programming semantics, fixpoint theory, non-monotonic reasoning, and the application of higher-order constructs in programming. He explores many-valued logics for preference representation and has contributed to game-theoretic and categorical approaches in formal semantics. Rondogiannis’s recent work (2020–2024) addresses recursive functions in TensorFlow, non-monotonic fixpoint theories, and stable model semantics for higher-order logic programs. Earlier contributions (2017–2019) include advancements in approximation fixpoint theory, extensional semantics for logic programs, and the expressive power of higher-order datalog systems. No scientific awards mentioned. No advising or grant information provided in the available texts. No specific labs or collaborative teams are detailed in the provided information.

Christos Tsironisمشاهده پروفایل

استادیار

• Electromagnetic Wave Propagation
• Plasma Physics
• Controlled Thermonuclear Fusion
• +۵ مورد دیگر

Christos Tsironis is an Assistant Professor at the National Technical University of Athens (NTUA), working in the School of Electrical and Computer Engineering within the Division of Electromagnetics, Electrooptics and Electronic Materials. His research focuses on plasma physics and electromagnetic wave propagation with applications to fusion energy and space physics. Dr. Tsironis earned his educational degrees as follows: B.Sc. in Physics from the National and Kapodistrian University of Athens (1999) M.Sc. in Physics from the National and Kapodistrian University of Athens (2002) Ph.D. in Physics from the Aristotle University of Thessaloniki (2007) His research spans several interconnected fields: Electromagnetic Wave Propagation in Ionized Media, Physics and Technology of Controlled Thermonuclear Fusion via Magnetic Confinement, Electromagnetic Phenomena in Space Physics, and Dynamical Systems and Automatic Control. His work bridges theoretical plasma physics with practical engineering applications for fusion energy development. Dr. Tsironis has published extensively on electron cyclotron resonance heating (ECRH) systems, wave-particle interactions in plasmas, and electromagnetic wave propagation in fusion-relevant environments. Dr. Tsironis's publication record demonstrates a progression from fundamental plasma physics research to applied engineering solutions for fusion energy. His recent work (2022-2025) shows increasing involvement in large international fusion projects like ITER and DEMO, with emphasis on developing practical heating systems and operational scenarios for future fusion reactors. His research has evolved from theoretical studies of wave-plasma interactions to applied solutions for fusion reactor technology, particularly in the area of electron cyclotron heating systems. Dr. Tsironis leads the Plasma, Electron Beam and Nonlinear Optics Laboratory at NTUA, where he supervises research in plasma physics and electromagnetic phenomena. He teaches undergraduate courses including Electromagnetic Fields I and II, Applied and Computational Electromagnetics, Propagation of electromagnetic waves in plasmas, and Physics and Technology of the Controlled Thermonuclear Fusion. His technical expertise includes computational methods for plasma physics simulations, programming in Fortran, C, and MATLAB/Simulink, and collaboration with major fusion research institutions worldwide through EUROfusion programs.

Thomas Lembergerمشاهده پروفایل

پژوهشگر

• Software Verification
• Code Analysis
• Software Testing
• +۵ مورد دیگر

Dr. Thomas Lemberger is a researcher at the Software and Computational Systems Lab in the Department of Computer Science at Ludwig-Maximilians-Universität München (LMU Munich). He holds a PhD and is actively involved in software verification research, particularly with the CPAchecker framework. His work spans multiple aspects of formal methods and software testing. Dr. Lemberger's research focuses on software verification, code analysis, software testing, fault localization, and program repair. His work explores cooperative approaches across test generation and formal verification, aiming to combine the strengths of different verification techniques. He has made significant contributions to distributed verification, witness-based validation, and fault localization techniques. His research often addresses practical challenges in scaling verification to real-world software systems. His publication record shows a strong focus on decomposing verification tasks into manageable components, improving verification efficiency through parallelization, and creating practical tools that bridge the gap between theoretical verification techniques and practical software development workflows. Recent work emphasizes cooperative verification approaches that combine multiple techniques to overcome individual limitations. Dr. Lemberger has been nominated for best paper at SPIN 2024 for his work on fault localization with verification witnesses, highlighting the recognition of his contributions to the field. He actively mentors numerous BSc and MSc students on various verification-related topics, covering areas from microservice verification to automatic program repair. His research is supported by German Research Foundation (DFG) projects including DFG-CONVEY, DFG-COOP, and DFG-IDEFIX. Dr. Lemberger is a key contributor to the CPAchecker verification framework and has developed tools like CoVeriTeam GUI that enable no-code composition of verification workflows. His work bridges theoretical verification research with practical tool development for real-world software engineering challenges.

Saday Sadayappanمشاهده پروفایل

استاد

• Compiler Optimization
• High-Performance Computing
• Sparse/Dense Matrix Computations
• +۳ مورد دیگر

P. (Saday) Sadayappan is a Professor at the University of Utah's School of Computing, specializing in high-performance computing and compiler optimizations. His research focuses on performance optimization for parallel systems, particularly for tensor computations, sparse matrix operations, and machine learning workloads. He leads multiple NSF and DARPA-funded projects focused on GPU optimization, tensor computations, and scalable machine learning frameworks. Research Interests: Dr. Sadayappan's work spans compiler optimizations for high-performance systems, optimization of sparse/dense matrix/tensor computations, scalable machine learning, and algorithm-architecture co-design. His recent projects include developing performance-portable frameworks for tensor applications and optimizing data locality for scientific computing. Publication Trends: His recent publications (2020-2022) predominantly focus on GPU acceleration of machine learning workloads (especially CNNs), automated I/O complexity analysis, and optimization techniques for sparse matrix/tensor operations. Earlier work (2018-2019) established foundations in GPU code generation for tensor contractions and cache optimization. Awards and Honors: ACM SIGPLAN Most Influential PLDI Paper Award (2018) for A Practical Automatic Polyhedral Parallelizer and Locality Optimizer Active Grants and Projects: NSF: A Comprehensive Framework for Efficient, Scalable, and Performance-Portable Tensor Applications (2022-2027) NIH SBIR: Enabling next generation machine learning for large scale image analysis (2023-2025) NSF: AI Institute for Intelligent CyberInfrastructure (ICICLE) (2021-2026) NSF: Data Locality Optimization for Sparse Matrix/Tensor Computations (2020-2024) DARPA SBIR: Performance Portable Framework for Developing Graph Applications (2017-2022) Teaching: He currently teaches CS 4230/6230 (Parallel and High-Performance Computing) at the University of Utah.

• Numerical Analysis
• Numerical Solution of Partial Differential Equations
• Computer Science
• +۶ مورد دیگر

Arvanitis Christos is a Lecturer at the School of Electronic & Computer Engineering of the Technical University of Crete . He holds a PhD in Mathematics (2002) and a Master's in Computational & Applied Mathematics (1997) from the University of Crete, alongside a 2-year military service (1995–1997). His academic career includes teaching roles at multiple universities (2002–2008), a hiatus in Secondary Education (2008–2017), and current research-teaching activities since 2017. Education: PhD in Mathematics (2002) Master's in Computational & Applied Mathematics (1997) BSc in Mathematics (1992) His research spans Numerical Analysis and Computer Science , focusing on automatic spatial partitioning in numerical differential equation solutions , with applications in Neural Networks , Human-Machine Interfaces , GPU Computing , and Machine Vision . His 5 publications in reputable journals highlight these interdisciplinary themes. He has participated in applied research programs such as Symmetries in Neural Networks , Underwater Acoustics , and Adaptive Algorithms for Optical Flow , demonstrating expertise in both theoretical and computational domains. Technical Skills: CPU/GPU Programming Distributed Computing GUI Development

Shachar Itzhakyمشاهده پروفایل

دانشیار

• Program Synthesis
• Automated Reasoning
• Functional Programming
• +۵ مورد دیگر

Shachar Itzhaky is an Associate Professor in the Department of Computer Science at Technion - Israel Institute of Technology, Haifa. His research spans multiple areas of programming languages, formal methods, and software engineering, with a focus on making program development and verification more accessible and efficient. He has served on program committees for numerous prestigious conferences including PLDI, POPL, SPLASH, and ICFP. Dr. Itzhaky's research interests center around program synthesis, automated reasoning, and formal verification. His work in program synthesis explores techniques for automatically generating programs from high-level specifications, with applications in end-user programming and software development. In automated reasoning, he has made significant contributions to e-graph based reasoning, invariant inference, and property-directed verification. His research in formal methods focuses on practical applications for program verification, particularly for data structures and security properties. An analysis of his recent publications reveals a strong focus on leveraging advanced formal techniques for practical program understanding and generation. His work consistently bridges theoretical foundations with practical applications, particularly in program synthesis, verification, and end-user programming tools. The trend shows increasing integration of machine learning techniques with traditional formal methods, as well as expanding applications to security and privacy domains. ACM SIGPLAN John C. Reynolds Doctoral Dissertation Award Dr. Itzhaky has been actively involved in the programming languages research community, serving on numerous program committees and contributing to the advancement of formal methods and program synthesis. His work has practical implications for software development tools, security analysis, and end-user programming environments. While specific grant information isn't detailed in the provided text, his extensive publication record in top-tier venues suggests successful funding for his research endeavors. His work on projects like Object Spreadsheets and Lifty demonstrates a commitment to creating practical tools that address real-world programming challenges. Dr. Itzhaky's research is conducted within the vibrant programming languages and formal methods group at Technion's Computer Science department. His work intersects with multiple research threads including program synthesis, verification, and security, suggesting collaboration across these areas within the department. His tools like EPR-based Verification, PDR∀, and VeriCon represent significant technical contributions that likely form the basis of ongoing research projects with students and collaborators.

Gilbert Bernsteinمشاهده پروفایل

استادیار

• Computer Graphics
• Programming Languages
• High-Performance DSLs
• +۷ مورد دیگر

Gilbert Bernstein is an Assistant Professor in the Computer Science & Engineering department within the College of Engineering at the University of Washington. His research bridges computer graphics and programming languages, with a focus on high-performance domain-specific languages. Previously, he was a post-doctoral scholar at UC Berkeley and MIT working with Jonathan Ragan-Kelley, and received his PhD from Stanford University under Pat Hanrahan. His research interests span Computer Graphics, Programming Languages, High-Performance DSLs, Physical Simulation, Geometry & Topology, Differentiable Programming, Hardware DSLs, Tools for Artists, Fabrication, and Human-Computer Interaction. Bernstein develops languages and compilers that enable efficient computation for creative applications, physical simulations, and graphics rendering systems. His recent publications reveal strong trends in differentiable programming for graphics applications, domain-specific languages for hardware acceleration, and computational approaches to traditional crafts like quilting and knitting. His work consistently combines formal language theory with practical applications in graphics and fabrication. Bernstein actively mentors students across multiple institutions including current advisees Felix Hahnlein (UW Postdoc), Ryan Zambrotta (UW PhD), Haoran Peng (UW PhD), and previous students including Alex Reinking (UC Berkeley PhD 2022, now at Qualcomm) and MacKenzie Leake (Stanford PhD 2021, now at Adobe Research). His lab works on diverse projects including debugging CAD programs, compilers for finite element methods, semantics for knitting machines, algebraic scheduling of tensor programs, and exocompilers for hardware accelerators. Bernstein also collaborates on DSLs for networking, Counterstrike bots, gradient-based optimization, memory management, hardware design, and garment design tools.

Laura Dietzمشاهده پروفایل

دانشیار

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +۶ مورد دیگر

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire, where she leads the TREMA lab. Her academic journey began with a PhD from the Max Planck Institute for Informatics in Saarbruecken, Germany (2011), followed by postdoctoral positions at the University of Massachusetts Amherst (2010-2015) and University of Mannheim (2015-2016). Her educational background includes PhD studies at both the Max Planck Institute for Informatics (2007-2011) under Prof. Gerhard Weikum and Prof. Tobias Scheffer, and earlier research at Humboldt University in Berlin. She has built a distinguished career bridging theoretical computer science with practical applications in information retrieval and machine learning. Dietz's research primarily focuses on the intersection of information retrieval, natural language processing, and knowledge graphs, with a parallel research initiative in watershed data science. She is particularly known for her work on entity-aspect linking, complex answer retrieval, and the vision of automatic Wikipedia construction. Her approach integrates fine-grained knowledge annotations with text understanding to create comprehensive information systems that go beyond traditional 10-blue-links search paradigms. In watershed data science, she applies similar machine learning techniques to environmental data streams, focusing on solute transport analysis during storm events. Her recent publications reveal a strong trend toward fine-grained semantic understanding, particularly in entity-oriented search tasks. She has pioneered methods for entity-aspect linking that significantly improve retrieval accuracy by capturing different contexts in which entities appear. Her work increasingly integrates knowledge graphs with neural architectures, showing sophisticated understanding of how to leverage both structured and unstructured information for better search experiences. Best paper award at JCDL 2018 for work on entity-aspect linking NSF CAREER Award (2019-2023) for "Utilizing Fine-grained Knowledge Annotations in Text Understanding and Retrieval" OSSI Award 2013 from UMass ICB3 for open-source hardware/software Dietz actively mentors PhD and Masters students through the TREMA lab, with current research focusing on entity ranking, topic extraction, conversational search, and watershed forecasting. Her grant portfolio includes the NSF CAREER award and funding from the Northeast Big Data Innovation Hub for forecasting salinity in rivers during storm events. She has also coordinated the TREC Complex Answer Retrieval track (2017-2019), creating important benchmarks for the IR community. The TREMA lab (Text Retrieval, Entity Modeling, and Applications) serves as the hub for Dietz's research activities, bringing together students and collaborators to work on cutting-edge problems in information access. The lab's work spans both theoretical contributions to information retrieval and practical applications in domains ranging from environmental science to scientific publication analysis.