جست‌وجوی استاد و پژوهشگر

Saman Amarasingheمشاهده پروفایل

استاد

• Programming Languages
• Compilers
• Domain-Specific Languages
• +۷ مورد دیگر

Professor Saman Amarasinghe is a full Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), and Principal Investigator at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Commit compiler research group, which focuses on programming languages and compilers that maximize application performance on modern computing platforms. His work spans multiple academic departments and research centers, with strong affiliations to both MIT's School of Engineering and CSAIL. Professor Amarasinghe's research interests center around high-performance domain-specific languages and compiler technology . His work combines language design with sophisticated compilation techniques to deliver unprecedented performance for targeted application domains. His research spans multiple areas including image processing (Halide), sparse tensor algebra (TACO), graph analytics (GraphIt), stream computations (StreamIt), and bioinformatics (Seq). A significant thread throughout his work is the application of machine learning for compiler optimizations, from Meta optimization in 2003 to the OpenTuner autotuner framework. Analysis of Professor Amarasinghe's recent publications reveals a strong focus on sparse computing , compiler vectorization , and domain-specific language implementation . His work consistently bridges theoretical compiler concepts with practical performance gains across diverse application domains. The progression from earlier work on StreamIt and Halide to more recent projects like GraphIt and TACO shows an evolution toward more specialized, high-performance DSLs targeting specific computational patterns. His 2020-2025 publications particularly emphasize sparse tensor operations, GPU acceleration, and machine learning integration with compiler technology. ACM Fellow (2019) Professor Amarasinghe has made significant contributions to academic entrepreneurship and student development. He founded Determina, Inc. (acquired by VMware) based on security research from his MIT lab and co-founded Lanka Internet Services, Ltd., Sri Lanka's first ISP. As faculty director of MIT Global Startup Labs, his programs across 17 countries have helped create over 20 successful startups. His teaching includes the popular Performance Engineering of Software Systems (6.172) course with Professor Charles Leiserson, as well as innovative project-based courses like the Open Source Software Project Lab and Bring Your Own Software Project Lab. His educational approach emphasizes hands-on experience with compiler and language design concepts. Professor Amarasinghe leads the Commit compiler research group at MIT CSAIL, which has produced numerous influential domain-specific languages and compilers including Halide, TACO, Simit, StreamIt, and GraphIt. The lab maintains strong industry connections through projects like OpenTuner and Determina, and collaborates with researchers worldwide on compiler technology. The group's work spans both theoretical compiler research and practical implementation, with a consistent focus on bridging the performance gap between high-level programming abstractions and hardware capabilities.

• Machine Learning
• Bioinformatics
• Medical Imaging
• +۵ مورد دیگر

Paul J. Kennedy is a Professor at the University of Technology Sydney's Centre for Artificial Intelligence. He holds a PhD from the same institution (1999). His research focuses on machine learning applications in healthcare, bioinformatics, medical imaging, and data mining. Key areas include developing algorithms for genomic data analysis, healthcare pathway modeling, and edge-cloud frameworks for omics data. Education: PhD in Artificial Intelligence (1999, UTS). Research interests span machine learning, health informatics, and data compression. Notable work includes studies on administrative health records, lung nodule detection, and virtual reality-based cancer cohort analysis. He has co-authored over 100 publications across journals like BMC Bioinformatics, IEEE Transactions, and Artificial Intelligence in Medicine. Advising: Collaborates extensively with students/researchers but no explicit student list provided. Grants and labs: Active in interdisciplinary projects involving medical and computational teams, though specific grants are not detailed here.

Hai (Helen) Liمشاهده پروفایل

استاد

• Neuromorphic Computing
• Machine Learning
• Emerging Memory Technologies
• +۳ مورد دیگر

Hai (Helen) Li is a Professor and Clare Boothe Luce Associate Chair at Duke University's Electrical and Computer Engineering department. She was a TUM-IAS Hans Fischer Fellow (2017) hosted by Prof. Ulf Schlichtmann in the Neuromorphic Computing focus group. Education: B.S./M.S. from Tsinghua University, Ph.D. from Purdue University Positions: Qualcomm, Intel, Seagate, Polytechnic Institute of New York University, University of Pittsburgh Her research spans neuromorphic computing systems , machine learning acceleration , emerging memory technologies , and low-power circuits . Publications demonstrate expertise in ReRAM/memristor-based accelerators, sparse neural networks, and processing-in-memory architectures. Key contributions include cross-layer optimization frameworks and robust neuromorphic designs. Her awards include: 9 Best Paper Awards (ASPDAC, ICMLA, ISVLSI, etc.) NSF Career Award DARPA Young Faculty Award IEEE Fellow (2019) ACM Distinguished Member (2017) IEEE TCSDM Outstanding Leadership Award (2021)

• Computational Biology
• Bioinformatics
• Genomics
• +۴ مورد دیگر

Dr. Roland Wittler is a researcher at the Faculty of Engineering , Bielefeld University , Germany, specializing in Genome Informatics . He has held key roles such as Coordinator of Doctoral Studies (since 2024), Scientific Coordinator for the Graduate School 'Digital Infrastructure for the Life Sciences' (DILS), and coordinator for bioinformatics PhD programs. PhD in Computational Biology (Bielefeld University, 2010) DFG scholarship recipient (2006–2009) Prior research assistant at Simon Fraser University (2010) His research focuses on phylogenomics , gene cluster analysis , and alignment-free sequence comparison . He pioneered methods using colored de Bruijn graphs for reference-free phylogeny reconstruction and DARRC for pan-genome compression. His work spans from gene order evolution to high-throughput sequencing data structures . The 15 most recent publications show trends in phylogenomic clustering k-mer-based pangenome modeling and graph-based genomic alignment He contributes to computational methods for genome analysis, including algorithms for detecting overlapping deletions and optimizing pan-genome storage via Bloom filter trunks. His work bridges theoretical algorithm design and practical bioinformatics applications .

• Relational Machine Learning
• Deep Learning
• Complex Network Science
• +۴ مورد دیگر

Dr. Rebekka Burkholz is a tenured faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany, leading the Relational Machine Learning Group . Her research bridges machine learning and complex network science to develop robust, data-efficient models with applications in molecular biology. Previously, she held positions at Harvard T.H. Chan School of Public Health and ETH Zurich. PhD in Systems Design (2016) from ETH Risk Center Mathematics and Physics BSc/MSc from TU Darmstadt Her work focuses on sparse training methods and theoretical deep learning , addressing challenges like computational efficiency and adversarial robustness. Recent publications explore: Sparse training via implicit sparsification GNN optimization through rescaling and rewiring Integration of domain knowledge in biomedical modeling Theoretical guarantees for batch normalization and lottery tickets Scientific awards include: Zurich Dissertation Prize (2016) CSF Best Contribution Award (2016) She actively advises PhD students and collaborates with interdisciplinary teams in biostatistics and systems biology.

Jens Stoyeمشاهده پروفایل

استاد

• Bioinformatics
• Computational Biology
• Comparative Genomics
• +۳ مورد دیگر

Jens Stoye is a Professor of Genome Informatics at the Faculty of Engineering , Bielefeld University, where he has held this position since 2002. He leads the Genome Informatics Working Group at CeBiTec and serves as Managing Director of the Bielefeld Institute for Bioinformatics Infrastructure (BIBI). Additionally, he is a member of the Board of Directors at the Center for Interdisciplinary Research (ZiF) and held numerous administrative roles, including Dean of the Faculty of Technology and Speaker of the DFG Research Training Group. Education: Diploma in Informatics in the Natural Sciences (1995) and PhD (1997) from Bielefeld University Current Roles: Full University Professor, Managing Director of BIBI, Executive Director of ZiF (2023-2025) His research focuses on computational genomics , including genome rearrangement, comparative genomics, and pangenomics. Recent work explores the double distance problem , natural genome phylogeny reconstruction, and efficient pangenome storage techniques. He has developed algorithms for genome assembly benchmarking (GABenchToB), rearrangement epidemiology (pling), and core genome detection. Scientific Contributions include best paper awards and editorial roles at IEEE/ACM Transactions on Computational Biology and Bioinformatics , BMC Bioinformatics , and Discrete Applied Mathematics . He has served on over 20 conference committees (WABI, ISMB, RECOMB) and advised 165+ theses (36 PhD, 71 Master/Diploma, 58 Bachelor). Leadership Roles: Vice-Speaker of German Bioinformatics Society (2002-2014) Dean of Faculty of Technology (2007-2009) Speaker of DFG Research Training Group (2013-2019) Member of DAAD Postdoc Fellowship Committee (2014-2025)

• Distributed Computing
• High-Performance Computing
• Parallel Systems
• +۷ مورد دیگر

Tore Brox-Larsen is an Associate Professor in the Department of Informatics at UiT The Arctic University of Norway. He is actively involved in research and teaching, with a focus on distributed computing, high-performance systems, and visualization technologies. His work is centered on scalable systems for large displays, sensor networks, and remote data visualization, often in collaboration with interdisciplinary teams. His research interests include distributed and parallel computing, remote visualization over wide-area networks, large-scale display systems, health informatics, and Arctic monitoring technologies. He has contributed significantly to the development of systems for tiled display walls, genomics visualization, and sensor-based observatories. His work bridges theoretical computer science with practical applications in healthcare, environmental monitoring, and collaborative research environments. The most recent publications reflect a strong trend in applying computing technologies to real-world challenges, particularly in Arctic research and healthcare. His work emphasizes performance optimization, usability, and scalability in distributed systems. Topics span from low-level communication latency analysis to high-level collaborative visualization frameworks. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: There is no explicit information about students advised or research grants received. However, his extensive publication record and collaborative projects suggest active participation in funded research initiatives and potential mentorship of junior researchers and students. Labs and Teams: Tore Brox-Larsen is part of a long-standing research group at UiT involving Otto Anshus, John Markus Bjørndalen, and others, focusing on high-performance distributed systems. He has contributed to the development of the MultiStream system, scalable display walls, and the Arctic observatory sensor network. His work indicates involvement in both software and systems research teams, likely associated with UiT’s informatics infrastructure and high-performance computing initiatives.

• string algorithms
• data structures
• graph algorithms
• +۵ مورد دیگر

Shay Golan is a Senior Lecturer in the Computer Science department at Ariel University, where he teaches core algorithms and data structures courses. Previously, he held postdoctoral positions at Reichman University, Haifa University (hosted by Shay Mozes and Oren Weimann), and was a Fulbright Postdoctoral Fellow at UC Berkeley's EECS department (hosted by Jelani Nelson). He earned his Ph.D. in Computer Science from Bar-Ilan University under the supervision of Ely Porat and Tsvi Kopelowitz. Dr. Golan's primary research focuses on string algorithms with emphasis on streaming, small space, and dynamic settings. His work spans multiple subfields including data structures, graph algorithms, and discrete algorithms. He has made significant contributions to pattern matching, edit distance problems, and string compression techniques, with applications extending to bioinformatics for DNA sequence analysis. His recent publication trajectory (2022-2026) reveals a strong focus on string algorithmic challenges across multiple dimensions: theoretical complexity analysis (LZ77 compression, string covers), specialized string problems (2D string matching, hairpin structures), graph algorithm applications (planar graph distance oracles), and bioinformatics implementations (DNA minimizers). His work consistently appears in top theoretical computer science venues including STOC, SODA, and CPM, demonstrating both theoretical depth and practical relevance. Fulbright Postdoctoral Fellow at UC Berkeley As an educator, Dr. Golan has consistently taught foundational computer science courses including Algorithms 1, Algorithms 2, Data Structures, and Computability since 2016. His research is supported through collaborations with leading institutions including Reichman University, Haifa University, and UC Berkeley. He maintains active research partnerships with prominent computer scientists including Itai Boneh, Ely Porat, and Tsvi Kopelowitz. Dr. Golan participates in the broader theoretical computer science community through conference presentations and workshop participation, with recorded talks available for several of his publications. His work bridges theoretical computer science with practical applications, particularly in computational biology and genomic sequence analysis.

Jörn Ostermannمشاهده پروفایل

استاد

• Video Coding
• Computer Vision
• Machine Learning
• +۹ مورد دیگر

Prof. Jörn Ostermann is a Full Professor and Head of the Institut für Informationsverarbeitung at Leibniz Universität Hannover since 2003, with prior roles at AT&T Bell Labs and AT&T Labs-Research. He served as Dean of the Faculty of Electrical Engineering and Computer Science (2011–2013) and member of the Senat (since 2020). His research spans video coding, computer vision, machine learning, 3D modeling, and computer-human interfaces , with applications in SAR imaging, predictive maintenance, children's speech analysis, and cochlear implants. Key projects include Next Generation Video Coding , Conditional Coding for Learned Compression , and GreenAutoML4FAS . Notable trends in his recent publications (2025–2023) include Neural network-based video compression Uncertainty estimation in speech recognition Zero-delay coding for cochlear implants Domain adaptation for aerial image segmentation 3D mesh compression standards Error concealment in VVC coding Scientific recognitions: AT&T Standards Recognition Award (1998) ISO Award (1998) IEEE Fellow (2005) Distinguished Lecturer, IEEE CAS Society (2002/2003) MPEG Convenor (2020–2023) He co-authored a graduate textbook on Video Communications , holds >30 patents, and has led >20 research projects. His work bridges academic research and industrial standardization, particularly in MPEG and IEEE committees.

• High-Performance Computing (HPC)
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Murali Emani is a researcher affiliated with Argonne National Laboratory, IL, USA. His work focuses on High-Performance Computing (HPC), machine learning, and artificial intelligence acceleration. Emani holds a PhD in Computer Science from the University of Edinburgh (2015). He specializes in optimizing large-scale systems, including performance evaluation of AI accelerators, transformer models, and neural architecture search. His research bridges HPC infrastructure with AI applications, addressing challenges in resource allocation, inference efficiency, and cross-architecture benchmarking. Key contributions include frameworks for GPU memory optimization (XUnified) and holistic performance evaluation of large language models across diverse hardware (e.g., BaKlaVa, Centimani). He collaborates extensively with institutions like the University of Chicago, NVIDIA, and MLCommons on initiatives such as DeepSpeed4Science and GenSLMs for genome-scale language models. His work frequently appears in top venues like IPDPS, SC, and Euro-Par. Emani’s research emphasizes practical system-level innovations, with applications spanning bioinformatics (SARS-CoV-2 evolutionary analysis), protein design, and exascale computing. He advocates for FAIR principles in HPC data management and contributes to open benchmarks like MLPerf HPC.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Jian Li is a Professor at Tsinghua University's Institute for Interdisciplinary Information Sciences in Beijing, China. With an extensive publication record spanning multiple disciplines including Artificial Intelligence, Machine Learning, Computer Vision, and Medical Imaging, Dr. Li leads a highly productive research program that bridges theoretical computer science with practical applications across various domains. Dr. Li's research interests focus on developing novel AI methodologies with applications in healthcare, engineering, and scientific computing. Their work demonstrates particular expertise in neural network architectures, image processing techniques, and interdisciplinary approaches that combine domain knowledge with machine learning. The research spans both theoretical foundations and practical implementations, with numerous publications in top-tier journals and conferences. Analysis of recent publications reveals a strong trend toward interdisciplinary research, particularly in medical AI applications, scientific computing, and robotics. Dr. Li's work frequently involves developing novel neural network architectures and optimization techniques that address specific challenges in target domains. The publications show consistent collaboration with researchers across China and internationally, indicating an active research network. Dr. Li has contributed significantly to advancements in knowledge distillation, medical image analysis, fluid dynamics modeling, and reinforcement learning applications. The research demonstrates both theoretical rigor and practical relevance across multiple application domains.

• Human Ageing
• Genetics of Longevity
• Biomarkers of Ageing
• +۷ مورد دیگر

Joris Deelen is a Research Group Leader at the Max Planck Institute for Biology of Ageing in Cologne, Germany, where he leads research on the genetic and biomarker underpinnings of human longevity. His work bridges molecular epidemiology, functional genomics, and metabolomics to understand why some individuals age more healthily than others. PhD in Molecular Epidemiology, Leiden University (cum laude) MSc and BSc in Biomedical Sciences, Leiden University His research focuses on identifying and functionally characterizing rare genetic variants in long-lived individuals using CRISPR/Cas9 models and large-scale human cohort data. He also investigates metabolic biomarkers predictive of mortality and healthy ageing. His recent work emphasizes integrating multi-omics data to uncover mechanisms of ageing. The trends in his publications reflect a strong focus on human ageing genetics, with key contributions in genome-wide association studies, functional validation of genetic variants, and metabolic profiling for mortality prediction. His work spans high-impact journals such as Nature and Nature Communications , highlighting translational relevance. His scientific awards include: ERC Starting Grant (2022–2027) Longevity Impetus Grant (2022–2026) Humboldt Research Fellowship (2017) Dr. Deelen supervises several PhD students and leads a multidisciplinary team integrating bioinformatics, molecular biology, and clinical data. He has secured major third-party funding and is establishing human ageing studies in Cologne to validate biomarkers in clinical settings. His lab collaborates extensively with international cohorts and leverages data from model organisms. He is actively involved in advancing the biology of ageing through innovative research, aiming to compress morbidity and extend healthspan.

• Bioinformatics
• Cryptography
• Genetic Algorithms
• +۵ مورد دیگر

Mohammad Nassef is an academic researcher with a focus on interdisciplinary applications combining computer science, bioinformatics, and cryptography. His work spans multiple domains, including Developing bio-inspired algorithms for audio and image encryption Optimization techniques in genetic data analysis Automated scoring systems in educational software Edge detection methods for biomedical imaging Research Trends: His publications highlight a consistent integration of biological sequence modeling into computational algorithms, with recent work emphasizing cybersecurity, data compression, and machine learning applications in genetics. Collaborations with researchers like Ibrahim Farag, Amr Badr, and Monagi H. Alkinani indicate a networked approach to solving complex problems in data security and medical informatics.

• Quantum Physics
• Quantum Computing
• Quantum Information
• +۷ مورد دیگر

Mohammad Ansari is a theoretical physicist and Principal Investigator at Forschungszentrum Jülich's Peter Grünberg Institute (PGI-2: Theoretical Nanoelectronics), with a secondary appointment at RWTH Aachen University's Institute for Quantum Information. He leads the Quantum Device Theory group focused on advancing quantum computing technologies through theoretical modeling and innovation. His research spans multiple domains within quantum information science, with particular emphasis on scalable superconducting qubits , quantum thermodynamics , and quantum field theory applications . Ansari's work bridges fundamental physics with practical quantum computing implementations, exploring novel correspondences between information theory and physical phenomena. His publication record shows consistent advancement in quantum computing theory, with recent focus on eliminating parasitic interactions in quantum processors, developing novel quantum gates, and applying quantum techniques to biological systems. The research demonstrates a trajectory from fundamental quantum gravity work to practical quantum computing implementations. John Brodie Prize from Perimeter Institute for Theoretical Physics Novel symmetry in quantum computation verified by IBM Watson Research Center (2020) Pioneering work on quantum fluctuation-dissipation theorem (2015) Early theoretical work on quantum gravitational effects in black hole radiation (2006) Ansari actively supervises a diverse team of researchers, including postdocs, PhD students, and Master's candidates, fostering the next generation of quantum scientists. His group maintains strong collaborations with institutions including MIT, UCSB, and University of Waterloo. The team operates from facilities at both Forschungszentrum Jülich and RWTH Aachen, leveraging resources from Germany's leading quantum research centers.

Christian Rohlfingمشاهده پروفایل

پژوهشگر

• Signal Processing
• Data Compression
• Audio Source Separation
• +۳ مورد دیگر

Dr.-Ing. Christian Rohlfing is a Researcher at the Institute of Communications Engineering , RWTH Aachen University . His work spans genomic data compression , audio source separation , and video coding . Rohlfing’s research focuses on efficient genomic sequence compression using entropy coding, context-based methods, and standardization within MPEG-G . He also advances point cloud compression via graph transforms and adaptive entropy coding, and audio/video coding with non-negative matrix factorization (NMF), dictionary learning, and convolutional neural networks (CNNs). His publications (2011–2023) reflect expertise in signal processing for multimedia and bioinformatics , with applications in spatial audio coding , informed source separation , and gene variation compression . Rohlfing contributes to international standards like ITU-T and ISO/IEC MPEG , particularly in Versatile Video Coding (VVC) and MPEG-G for genomic data.