جست‌وجوی استاد و پژوهشگر

• Bioprocess Engineering
• Nanobiotechnology
• Bioreactor Design
• +۴ مورد دیگر

Asst. Prof. Gülizar ÇALIŞKAN BİLGİN is affiliated with the Faculty of Engineering's Genetics and Bioengineering Department at Ege University. Her research focuses on bioprocess optimization, nanobiotechnology, and sustainable applications of microalgae. She holds a B.Sc., M.Sc., and Ph.D. in Bioengineering from Ege University (2011–2020). She has contributed to TÜBİTAK-funded projects on biogenic nanoparticle synthesis and microalgae biotechnology. Education: B.Sc. Bioengineering, Ege University (2011) M.Sc. Bioengineering, Ege University (2014) Ph.D. Bioengineering, Ege University (2020) Research Interests: Dr. Bilgin explores bioreactor design, nanoparticle biosynthesis via microalgae, and machine learning-driven bioprocess optimization. Her work emphasizes sustainability, such as converting food waste into biomaterials and developing eco-friendly nanoparticle synthesis methods. Projects & Grants: She leads a project on sustainable microalgae use and has participated in projects like the TÜBİTAK 1001 initiative. Her research bridges academia and education, including creating learning methodologies for biomaterial development in secondary schools. Labs & Teams: Her research is rooted in the NanoBioprocess and Process Analytical Technology Laboratory, where she developed nanoparticle synthesis techniques during her Ph.D.

Marijn Heuleمشاهده پروفایل

دانشیار

• Satisfiability (SAT) Solving
• Formal Verification
• Combinatorial Mathematics
• +۵ مورد دیگر

Marijn Heule is an Associate Professor in the Computer Science Department at Carnegie Mellon University. He received his PhD from Delft University of Technology in the Netherlands. His academic career has been focused on advancing the theory and practice of satisfiability (SAT) solving and its applications. Dr. Heule's research primarily centers on solving hard combinatorial problems in formal verification, number theory, and extremal combinatorics. His work has made significant contributions to both theoretical foundations and practical implementations of SAT solvers. He has developed award-winning SAT solvers, and his preprocessing techniques are incorporated into state-of-the-art SAT solvers used worldwide. His current research focuses on two major challenges: exploiting high-performance computing for SAT solving and validating results from SAT solvers and related tools. Dr. Heule pioneered the "cube-and-conquer" paradigm, a novel parallel SAT solving approach that enables linear time speedups on many hard problems. His first publication on this topic won the best paper award at HVC 2011. He also designed a new proof format with a corresponding fast proof checker for SAT and QBF solvers. This proof logging format has been mandatory for SAT Competitions since 2013, significantly increasing confidence in tool correctness. His work on the Boolean Pythagorean Triples problem, which produced a 200 TB proof that was validated, demonstrated that proof logging and verification is feasible even for the most complex problems. Dr. Heule is one of the editors of the 900+ page "Handbook of Satisfiability," which has become a standard reference in the SAT community. He serves as an Associate Editor of the Journal on Satisfiability, Boolean Modeling and Computation and previously co-chaired the SAT 2015 conference in Austin. Best paper award at HVC 2011 for cube-and-conquer publication Multiple best paper awards/nominations at major conferences including IJCAR, SAT, TACAS Work featured in New York Times, Quanta Magazine, Nature, and other major media outlets Dr. Heule has advised several PhD students including Emre Yolcu, Joseph Reeves, Bernardo Subercaseaux, Cayden Codel, James Gallicchio, and Amar Shah. He has developed important software tools including DRAT-trim (a proof checking utility), QRAT-trim, and implementations of the cube-and-conquer paradigm that have become standard in the SAT solving community.

Joseph M. Hellersteinمشاهده پروفایل

استاد

• Database Systems
• Distributed Computing
• Data Visualization
• +۴ مورد دیگر

Joseph M. Hellerstein is a Professor of Computer Science at the University of California, Berkeley, College of Engineering. His research focuses on data-centric systems and their impact on computing, spanning database systems, distributed computing, data visualization, machine learning, and programming languages. Ph.D., Computer Science, University of Wisconsin-Madison (1995) MS, Computer Science, UC Berkeley (1992) AB, Computer Science, Harvard University (1990) Key research areas include database management, distributed systems, cloud programming foundations, and human-data interaction. He has developed systems like Hydro (for distributed programming), Trifacta (cloud data engineering), and Anna (distributed key-value store). Recent work emphasizes progressive data processing, consistency models, and compiler-driven distributed systems. Notable awards: ACM Fellow (2009), Alfred P. Sloan Research Fellow (1998), multiple ACM-SIGMOD Test of Time Awards (2002, 2007, 2013, 2023), Jim and Donna Gray Teaching Award (2015), and IEEE VIS Test of Time Award (2022). He has advised PhD students and contributed to industry through co-founding RunLLM and Trifacta (acquired by Alteryx). Current affiliations include the Berkeley Center for Responsible, Decentralized Intelligence (RDI) and EPIC Data Lab.

• Artificial Intelligence
• Game Theory
• Stochastic Games
• +۴ مورد دیگر

James R. Glenn is a Senior Lecturer in the Department of Computer Science at Yale University's Faculty of Arts and Sciences. He teaches multiple computer science courses including systems programming, algorithms, data structures, and computational intelligence for games. His research focuses on stochastic games , particularly on computing optimal strategies and using artificial intelligence techniques to create near-optimal strategies when optimal solutions are infeasible. He also investigates mathematical problems related to '3-free sets' and methods for constructing large sets. His work bridges theoretical computer science with practical applications in game theory and algorithm design. Dr. Glenn teaches courses including CPSC 223 (Data Structures), CPSC 323 (Systems Programming), CPSC 365 (Algorithms), and CPSC 474/574 (Computational Intelligence for Games). His teaching spans foundational computer science concepts to advanced topics in game AI. Current research focuses on stochastic game strategies Expertise in algorithms and computational intelligence Extensive teaching experience across various CS topics His publications reflect his interests in game theory, algorithms, and educational tools, with work spanning from theoretical mathematics to practical software implementations. Dr. Glenn maintains an active teaching schedule with regular office hours in Dunham Lab 429 at Yale University.

Burak Tagtekinمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Optimization Algorithms
• +۴ مورد دیگر

Burak Tagtekin is a researcher in computer science with significant contributions to optimization algorithms, recommender systems, and machine learning applications. His work spans both theoretical and practical domains, including evolutionary algorithms for compiler flag tuning, Bayesian personalized ranking models for recommendation systems, and genetic algorithm-based approaches to job scheduling and resource allocation challenges. Key Research Areas : Optimization Algorithms, Recommender Systems, Machine Learning, Compiler Engineering, Scheduling Problems Collaborations : Frequently collaborates with researchers like Tuna Çakar, Mahiye Uluyagmur Öztürk, and M. Sezer Recent Publications (2021-2024) demonstrate expertise in genetic algorithms, particle swarm optimization, and Bayesian modeling, achieving notable improvements in execution time and resource efficiency across diverse applications such as C++ compilation, job prioritization, and implicit feedback-based recommendation systems. Co-Author Network includes 18+ collaborators across computer science and engineering fields, with institutional connections to IEEE conferences and academic research communities.

Stefan Hendricksمشاهده پروفایل

پژوهشگر

• Geophysics
• Remote Sensing
• Sea Ice Thickness
• +۲ مورد دیگر

Dr. Stefan Hendricks works as a Senior Scientist at the Alfred Wegener Institute for Polar and Marine Research, specializing in Sea Ice Physics within the Climate Sciences division. His primary research focuses on the global sea ice mass balance using satellite remote sensing, particularly with CryoSat-2 and Sentinel-3 radar altimetry systems. Key Responsibilities: Lead PI for CryoSat-2 thematic sea ice products (Cryo-TEMPO), Advisor for Sentinel-3 and future CRISTAL missions, Developer of the Python-based sea ice radar altimetry toolbox (pysiral) Expertise: Satellite radar altimetry, Algorithm development, Data validation, Arctic/Antarctic ice dynamics His research involves extensive algorithm development for converting altimeter waveforms into sea ice thickness metrics, fieldwork participation in polar expeditions, and integration of multi-sensor data for climate data records. Stefan maintains active collaborations with international institutions and contributes to ESA projects like SIN’XS. Recent publications highlight innovations in FMCW radar deconvolution techniques, sub-ice platelet layer mapping, and decadal-scale sea ice classification using machine learning. He also leads efforts in creating drift-aware sea ice thickness maps and validating SMOS-derived Antarctic ice datasets.

Pallav Kumar Shresthaمشاهده پروفایل

پژوهشگر

• Hydrological modeling
• Flood forecasting systems
• Reservoir management
• +۴ مورد دیگر

Pallav Kumar Shrestha is a postdoctoral researcher at the Helmholtz Centre for Environmental Research (UFZ) since 2017, focusing on Computational Hydrosystems in Leipzig, Germany. His work centers on resolving challenges in global hydrological modeling, particularly for small catchments and reservoir systems. Developed subgrid catchment conservation method for gridded hydrological models Created new reservoir module for mHM Contributor to Nature Communications flood early warning system paper Research Themes : Flood forecasting at global/regional scales Reservoir modeling in regulated basins Climate change impacts on water resources Computational hydrosystems development Environmental informatics for global hydrology Key Projects : ULYSSES (Copernicus), SaWaM (BMBF), 4DHydro (ESA), and state of global water resources (WMO). His 2025 ASCE-EWRI award paper on Great Lakes runoff intercomparison highlights his collaborative impact. Modeling Expertise : Active member of the mHM development team , contributing to debugging, user support, and training across Europe and Asia. His technical skills bridge Fortran programming for skill assessment tools and ecFlow automation systems.

Debin GAOمشاهده پروفایل

استاد

• Cybersecurity
• Software Engineering
• Human-Machine Collaborative Systems
• +۹ مورد دیگر

Debin GAO is a Full-time Professor of Computer Science at the Singapore Management University , affiliated with the School of Computing and Information Systems (SCIS) . He serves as Co-Director of the Centre on Security, Mobile Applications & Cryptography and Faculty Manager for the SMU BSc (IS)-CMU Fast-Track Programme . His research focuses on Android security , trusted execution environments , and malware detection . PhD from Carnegie Mellon University (2006) Supervisor to SCIS undergraduate instructors Research Advisor to EE Fook Ming GAO's research explores security vulnerabilities in mobile platforms , with emphasis on cache side-channel attacks and Android app debloating . His recent work investigates LLM-driven malware classification and secure code partitioning for smart contracts . His publications demonstrate a focus on mobile security (15/15), including malware analysis (9/15), trusted execution environments (5/15), and side-channel attack mitigation (4/15). Notable contributions include DynDebloater (2025), AutoTEE (2025), and CacheAlarm (2025). As Co-Director of the Centre on Security, Mobile Applications & Cryptography , GAO leads initiatives in trustworthy app delegation (AGChain, 2024) and user-centric security (OTO, 2012). His teaching covers Information Security & Trust , Networking , and Software Engineering .

• Database Systems
• Stream Processing
• Internet of Things (IoT)
• +۷ مورد دیگر

Steffen Zeuch is a researcher at Humboldt University of Berlin, Germany, with a strong research focus on database systems, stream processing, and Internet of Things (IoT) data management. He is a key contributor to the NebulaStream platform, an extensible, high-performance system for multi-modal edge applications. His work spans query optimization, GPU acceleration, fault tolerance, and distributed state management in stream processing environments. Research interests include database systems, stream processing, IoT data management, query optimization, GPU computing, and hardware-aware execution. His research addresses challenges in real-time analytics, efficient data placement, adaptive compilation, and complex event processing in distributed and edge environments. The publication trends in his recent articles highlight a consistent focus on stream processing systems—especially NebulaStream—with increasing attention to GPU acceleration, adaptive optimization, fault tolerance, and complex event processing. His work bridges theoretical query optimization with practical system implementation, emphasizing performance, scalability, and deployment in real-world IoT and edge infrastructures. Steffen Zeuch has made significant contributions to top-tier venues such as VLDB, SIGMOD, EDBT, and DEBS. His collaborative work, primarily with Volker Markl and other members of the database group at Humboldt University, demonstrates strong research leadership and technical depth in data-intensive systems. He has led and contributed to projects involving system design, performance benchmarking, and real-world deployment of stream processing platforms. His work on tutorials and system demonstrations indicates active engagement in community education and dissemination. While no specific lab or team name is mentioned, his research is centered around the NebulaStream project, a comprehensive platform for managing and analyzing data across fog, edge, and cloud environments. This platform supports complex analytics beyond traditional cloud boundaries, enabling scalable and efficient IoT applications.

Isabel Valeraمشاهده پروفایل

استاد

• Machine Learning
• Fairness in AI
• Causal Inference
• +۷ مورد دیگر

Isabel Valera is a full Professor in the Department of Computer Science at Saarland University, Saarbrücken, Germany. She also holds an adjunct faculty position at the Max Planck Institute for Software Systems (MPI-SWS) and is a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), contributing to the Robust Machine Learning Program and the Saarbrücken AI & Machine Learning (Sam) Unit. Research Interests: Her work lies at the intersection of machine learning, fairness, and causality. She focuses on developing methods for algorithmic recourse , fair decision-making , causal modeling , and robust learning . She is particularly interested in designing models that provide actionable explanations and ensure equitable outcomes in AI systems. Her research leverages probabilistic modeling, variational inference, and deep generative architectures. Publication Trends: Her recent publications (2021–2023) show a strong emphasis on algorithmic recourse , fairness under uncertainty , causal representation learning , and multimodal and graph-based generative models . She frequently publishes in top-tier venues such as NeurIPS, ICML, ICLR, AAAI, and FAccT, often in collaboration with leading researchers like Bernhard Schölkopf and Zoubin Ghahramani. Scientific Awards and Fellowships: Humboldt Post-Doctoral Fellowship Minerva Fast Track Fellowship (Max Planck Society) ELLIS Fellow Advising and Grants: She has mentored several early-career researchers who are now active contributors in machine learning, including Adrián Javaloy and Amir-Hossein Karimi. While specific grant details are not listed, her leadership roles and fellowships indicate sustained funding support from major research organizations. She has served as a co-editor for major conferences such as AISTATS and ECML PKDD, demonstrating her active role in the academic community. Labs and Research Groups: She leads or has led research groups at both Saarland University and the Max Planck Institutes (MPI for Intelligent Systems and MPI for Software Systems), focusing on foundational and applied aspects of machine learning with societal impact.

Christian Hochbergerمشاهده پروفایل

استاد

• Reconfigurable Computing
• FPGA Architecture
• Embedded Systems
• +۳ مورد دیگر

Prof. Dr.-Ing. Christian Hochberger is a Professor at Technische Universität Darmstadt, affiliated with the Department of Computer Science. His research focuses on reconfigurable computing, FPGA architecture, embedded systems, and hardware/software co-design. He teaches courses such as 'Rechnersysteme I/II' and 'High-Level Synthese.' University: Technische Universität Darmstadt Department: Department of Computer Science Research interests span FPGA-based acceleration, CAD tools for reconfigurable systems, and energy-efficient computing. His work frequently addresses challenges in hardware design, parallelization, and fault tolerance. Publications from 2022-2024 highlight contributions to memristive devices, genetic circuit design automation, and CGRA optimization. His recent work emphasizes experimental methodologies and novel techniques in resistive switching and memristor-based FPGAs. Awards: None explicitly listed in the provided text.

• Computer Architecture
• High-Performance Computing
• Hardware Transactional Memory
• +۷ مورد دیگر

Adria Armejach Sanosa is a Senior Lecturer in the Department of Computer Architecture at the Faculty of Computer Science of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC). He is also affiliated with the Barcelona Supercomputing Center (BSC-CNS), a leading institution in high-performance computing in Europe. His research spans computer architecture, high-performance computing, memory systems, and hardware acceleration for genomics and machine learning. PhD from UPC His research interests focus on optimizing computer systems for performance and efficiency, particularly in the areas of hardware transactional memory, cache optimization, RISC-V architectures, and acceleration of bioinformatics workloads. He investigates how to improve data movement, prefetching, and parallelism in large-scale heterogeneous systems. His work combines architectural innovations with practical implementations on real-world HPC platforms. The most recent articles reflect a strong trend towards high-performance computing for genomics, sparse data handling, and efficient hardware/software co-design. Topics include genomics benchmarking on ARM processors, tensor marshaling, RTL simulation scalability, and low-precision training for deep neural networks. These works demonstrate a consistent focus on bridging architectural research with real-world applications in science and AI. HiPEAC Paper Award 2024 HiPEAC Paper Award Armejach has advised several doctoral students, including J. Pavón, G. López, and J. Osorio. He has been involved in numerous competitive R&D+i projects such as Digital Autonomy for RISC-V in Europe, Laboratorio Zettaescala de Barcelona, and Genome Analysis Acceleration on HPC Architectures. These projects are often funded by national and European programs, indicating strong recognition and support for his research. He collaborates extensively within the CAP (High-Performance Computing) research group and with key figures like Miquel Moreto, Mateo Valero, and Osman Unsal. He is a member of the CAP research group and contributes to initiatives like the Laboratory for Open Computer Architecture and systems (RISC-V Chip Development) and the Barcelona Zettascale Lab. These labs focus on open hardware, European technology sovereignty, and next-generation supercomputing. His work on Metro-MPI for RTL simulation and hardware accelerators for databases highlights his contributions to both design automation and data-intensive computing.

Joel S. Emerمشاهده پروفایل

استاد

• Accelerator Architectures for Sparse Computation
• Accelerator Architectures for Deep Learning
• Spatial Processing Architectures
• +۵ مورد دیگر

Joel S. Emer is a Professor of the Practice in MIT's Department of Electrical Engineering and Computer Science (EECS) and a Senior Distinguished Research Scientist at NVIDIA. His research focuses on computer architecture, processor micro-architecture, and performance modeling. He has contributed to advancements in simultaneous multithreading, cache optimization, and reliability analysis. Emer holds over 25 patents and has published over 60 papers, earning awards like the IEEE Rau Award and induction into the National Academy of Engineering. Education: Ph.D., Electrical Engineering, University of Illinois Urbana-Champaign, 1979 M.S., Electrical Engineering, Purdue University, 1975 B.S., Electrical Engineering, Purdue University, 1974 (highest honors) Research interests include accelerator architectures for sparse computation and deep learning, spatial processing, memory hierarchy design, and reliability analysis. His work on Eyeriss and other accelerators has shaped energy-efficient neural network hardware. Recent projects explore hierarchical structured sparsity (HSS) and compute-in-memory (CIM) techniques. Key awards include the ISCA Best Paper Session (2024), IEEE Micro Top Picks (2024), and the SIGMICRO Test of Time Award (2022). He co-advises students with Prof. Vivienne Sze, focusing on sparse tensor acceleration and energy-efficient designs. Awards: 2023 IEEE Rau Award 2022 IASED Lifetime Achievement Award 2020 National Academy of Engineering Membership 2009 Eckert-Mauchly Award Grants and collaborations span industry partnerships (e.g., NVIDIA) and academic initiatives. Emer leads the Emze Group, exploring hardware-software co-design for emerging architectures. Current work includes sparse tensor accelerators (e.g., HighLight, Tailors) and modeling tools like Sparseloop and Accelergy.

Andrés Goensمشاهده پروفایل

استادیار

• Programming Languages
• Formal Methods
• Theorem Proving
• +۳ مورد دیگر

Andrés Goens is an Assistant Professor at the University of Amsterdam since 2023. He holds a Ph.D. (Dr.rer.nat.) in Computer Science from TU Dresden (2021) and an M.Sc. in Mathematics from RWTH Aachen University (2014). His research focuses on programming languages, formal methods, and theorem proving, with applications to compilers and heterogeneous systems. He investigates efficient execution of concurrent programs in multicore architectures and explores machine learning techniques for compiler optimization. Key research areas include concurrency, weak memory models, and bridging abstract mathematics with practical compiler design. His work often employs theorem provers like Lean to formalize program behavior. Recent publications address e-graphs for variable handling, guided equality saturation, and optimizing virtual networks in distributed systems. Publications span venues like PLDI, POPL, and ISCA, reflecting contributions to programming language theory and compiler infrastructure. His research bridges theoretical foundations with practical system design, aiming to improve both programmer productivity and computational efficiency. Contact details: a.goens@uva.nl (professional) and andres@goens.org (personal).

• High-performance computing
• Parallel programming
• Computer architecture
• +۷ مورد دیگر

Enrique Morancho Llena is a Professor in the Department of Computer Architecture at the Barcelona School of Informatics (FIB), Polytechnic University of Catalonia (UPC). He is an active member of the UPC PM - Programming Models research group and has been affiliated with the CAP - Grup de Computació d'Altes Prestacions (High-Performance Computing Group), one of UPC's most productive research teams with over 120 publications. His academic career spans more than 30 years, with continuous research output from 1994 to the present. Dr. Morancho Llena's research spans multiple areas in computer systems, with particular expertise in high-performance computing, parallel programming models, computer architecture, operating systems, and compiler optimization. His work has evolved from early research in instruction scheduling and memory access optimization to current work on heterogeneous computing, RISC-V architecture, hardware security, and malware detection. He has made significant contributions to OpenMP programming models, GPU acceleration, and virtualization technologies, with publications in top-tier venues including IEEE Transactions on Parallel and Distributed Systems and the Journal of Parallel and Distributed Computing. His recent publications (2021-2024) show a clear trend toward heterogeneous computing architectures, security aspects of computer systems, and RISC-V based technologies. He has published extensively on OpenMP extensions for heterogeneous systems, hypervisor implementations for RISC-V, and security applications including malware detection using opcode analysis. His work bridges theoretical computer architecture concepts with practical implementations for scientific computing and security applications. Dr. Morancho Llena has supervised PhD students, with R. Gran's thesis on "Non-speculative enhancements for the scheduling logic" being one documented example. He has participated in numerous competitive research projects funded by Spanish and Catalan governments, including multiple iterations of the "Computació d'Altes Prestacions" (High-Performance Computing) projects and RISC-V related initiatives under the RIS3CAT program. His collaborations extend across UPC departments and include partnerships with the Barcelona Supercomputing Center. He is an active member of the CAP research group and the PM - Programming Models group, focusing on advanced programming techniques for high-performance systems. His work on the UPC-Computación de Altas Prestaciones series demonstrates long-term leadership in establishing UPC as a center of excellence in high-performance computing research. Current projects include work on RISC-V based accelerators, security in the computing continuum, and trustworthy development environments for cloud services.