جست‌وجوی استاد و پژوهشگر

Guy Evenمشاهده پروفایل

پژوهشگر

• Computer Science
• Mathematics
• Approximation Algorithms
• +۴ مورد دیگر

Guy Even is a prominent researcher in Computer Science and Mathematics, with significant contributions to approximation algorithms, graph theory, and network design. His work spans theoretical and applied domains, including algorithmic complexity, Steiner tree problems, and IEEE floating-point arithmetic optimization. Research Interests: His research focuses on combinatorial optimization, generalized connectivity in graphs, and efficient algorithm design. He has advanced methodologies for divide-and-conquer strategies, feedback set approximation, and spreading metrics in undirected and directed graph problems. Key Article Trends: His publications from 1992 to 2013 highlight advancements in approximation algorithms for NP-hard problems, with a particular emphasis on Steiner networks (undirected/directed), Boolean network observability, and hardware-efficient rounding techniques for floating-point arithmetic. These works bridge theoretical insights with practical applications in computing. Collaborations: Guy has collaborated with notable researchers such as Chandra Chekuri, Anupam Gupta, and Danny Segev. His co-authorship network includes Moti Medina (71 publications), Nissim Halabi (14 publications), and other leading scholars in algorithmic and computational fields. Notable Affiliations: While specific institutions are not explicitly stated in the provided text, his work is cited in prestigious venues like ACM, IEEE, and Springer, indicating strong ties to academia and research communities.

• Theoretical Computer Science
• Circuit Design
• Computational Complexity
• +۳ مورد دیگر

Günter Hotz is a full Professor at the Department of Computer Science (Fachrichtung Informatik), Universität des Saarlandes , Germany. His academic career spans over five decades, including roles as Director of the Computer Center (1972-1974) Spokesperson for SFB 100 (1982-1984) and SFB 124 (1991-1992) . Research Interests include Theoretical Computer Science Circuit Design Computational Complexity Information Theory Geometric Motion Planning Formal Verification . His work focuses on analytic machines, hardware verification, and motion planning algorithms, with applications in linguistics and VLSI design. Publications emphasize formal methods for hardware, computational models over real numbers, and efficient parsing algorithms. Key trends involve integrating mathematical theory with practical circuit design and motion planning solutions. Scientific Awards include Leibniz Prize (1986) Konrad Zuse Medal (1999) Grand Cross of Merit (1998) . Students : Supervised 40 dissertations, with over a third of his advisees becoming professors in mathematics and computer science. Labs & Projects : Led major research initiatives like SFB 100 (COMSKEE system) and SFB 124 (VLSI design methods). Collaborative works with international institutions in France, the U.S., and Georgia.

• Mathematical Statistics
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Jimmy Olsson is a Professor in the Department of Mathematical Statistics at KTH Royal Institute of Technology. His research focuses on generative models, statistical computational methods, and dynamic systems analysis, particularly in the context of artificial intelligence and machine learning. He specializes in state-space models, sequential Monte Carlo methods, and Markov chain Monte Carlo techniques. His work emphasizes foundational contributions to hidden Markov models, computational algorithms for statistical inference, and the intersection of mathematical statistics with modern data science challenges. Key research areas include developing efficient computational tools for analyzing complex, time-dependent data streams, with applications in AI and machine learning. Olsson’s publications span advanced topics such as diffusion models, particle filtering, and online variational learning, reflecting his expertise in bridging theoretical statistics with practical computational methods. He collaborates with researchers across disciplines, including engineering, computer science, and applied mathematics.

Fredrik Lindstenمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Machine Learning
• Approximate Bayesian Inference
• +۷ مورد دیگر

Fredrik Lindsten is a Senior Associate Professor in Machine Learning and Head of the Division of Statistics and Machine Learning at Linköping University's Department of Computer and Information Science (IDA). His research focuses on statistical machine learning, emphasizing probabilistic modeling and uncertainty quantification in methods such as approximate Bayesian inference, representation learning, and graph-based approaches. Applications span weather forecasting, materials science, biochemistry, and automotive industry challenges. Education: MSc (2008), PhD (2013) in Automatic Control from Linköping University; Postdoctoral roles at University of Cambridge, UC Berkeley, and University of Oxford. Affiliations: WASP (Wallenberg AI, Autonomous Systems and Software Program), ELLIIT (Lab for Information and Communication Technology). Research Interests: Lindsten’s work bridges statistical methodology and machine learning, particularly in quantifying uncertainty in predictions. His team explores method development across diverse applications, including spatio-temporal models and graph-based techniques. Recent projects include probabilistic weather forecasting with graph neural networks and cryo-EM reconstruction techniques. Publications: Over 25+ peer-reviewed articles, with recent highlights in Nature Methods , NeurIPS , and Physical Review Materials , focusing on Bayesian methods, generative models, and computational statistics. Awards: Ingvar Carlsson Award (Swedish Foundation for Strategic Research), Benzelius Award (Royal Society of Sciences in Uppsala). Grants & Teams: Supervises 7+ PhD students; active in collaborative projects funded by WASP, ELLIIT, and VR (Swedish Research Council). Labs/Teams: Leads the Division of Statistics and Machine Learning (STIMA) at IDA, fostering interdisciplinary research in probabilistic machine learning.

• Computer Science
• Medical Imaging
• Machine Learning
• +۳ مورد دیگر

Dr. Benjamin Mora is an Associate Professor in the Department of Computer Science at Swansea University, affiliated with the School of Mathematics and Computer Science within the Faculty of Science and Engineering. He is actively involved in postgraduate supervision, specializing in topics such as medical imaging, machine learning, and neural network optimization. His research focuses on interdisciplinary applications of computer science, including medical data analytics, data compression techniques for volumetric medical images, and the development of explainable AI tools. Notable contributions include advancements in Fourier Transform Infrared spectroscopy for early cancer detection and the MedZip algorithm for lossless 3D medical image compression. Dr. Mora teaches modules such as Data Visualization (CSCM37) and Operating Systems and Architectures (CSCM98). He collaborates on projects like ExMed, an AI tool for medical data analysis, and has supervised research on pancreatic cancer detection and protein-ligand design. His work spans decades, with publications in journals like IEEE Transactions on Neural Networks and Learning Systems, Analytical Chemistry, and ACM Transactions on Graphics. His research emphasizes practical applications in healthcare and computational efficiency.

James Demmelمشاهده پروفایل

استاد

• Numerical Analysis
• Linear Algebra
• Scientific Computing
• +۴ مورد دیگر

James Demmel is a Professor of Computer Science and Mathematics at the University of California, Berkeley, with joint appointments since 1990. His research focuses on numerical linear algebra, high-performance computing, and parallel algorithms. He co-developed widely used libraries like LAPACK and ScaLAPACK. Demmel holds ACM, SIAM, and IEEE Fellowships, and is a member of both the National Academy of Engineering and Sciences. Education: Ph.D. in Computer Science, UC Berkeley, 1983 B.S. in Mathematics, Caltech, 1975 Research Interests: Demmel’s work emphasizes numerical methods for linear algebra, including algorithms for eigenvalue problems, matrix factorizations, and high-performance computing architectures. His contributions bridge theory and practice, addressing challenges in floating-point arithmetic and algorithm scalability. Publications & Awards: Over 150+ publications, including foundational papers on LAPACK and iterative methods. Recipient of the SIAM Activity Group Linear Algebra Best Paper Prize (1988, 1991), Wilkinson Prize (1993), and ACM Supercomputing Test of Time Award (2019). Labs & Teams: Demmel collaborates with groups like Berkeley Benchmarking and Optimization Group (BeBOP) and CLIMB, focusing on algorithmic efficiency and exascale computing.

Arjun Radhakrishnaمشاهده پروفایل

پژوهشگر

• Program Synthesis
• Formal Methods
• Concurrency
• +۳ مورد دیگر

Arjun Radhakrishna is a researcher at Microsoft working on program synthesis and formal methods. Previously, he was a post-doctoral researcher at the University of Pennsylvania under Prof. Rajeev Alur and completed his PhD at IST Austria under Prof. Thomas A. Henzinger. His work focuses on developing tools for reliable systems with applications in concurrency and embedded systems. Education: PhD in Computer Science from IST Austria (2014) Postdoctoral research at University of Pennsylvania Research Interests: Arjun's research lies at the intersection of program synthesis , formal methods , and quantitative system analysis . He develops tools that address soft specifications (e.g., performance, energy consumption) for concurrent and reactive systems , combining techniques from programming languages and formal verification. Key projects include performance-aware concurrency synthesis and quantitative specification frameworks for reactive systems. Scientific Awards: 2014 ACM SIGBED Paul Caspi Memorial Dissertation Award Best Paper Nomination at EMSOFT 2012 Service and Contributions: Arjun has served on program committees for conferences including CAV, PLDI, POPL, and VMCAI. He co-developed EUSolver, a tool that won multiple SyGuS competition tracks (2016). His dissertation introduced techniques for quantitative verification and synthesis with applications in embedded systems and concurrent programming.

Yingfei Xiongمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages
• Program Synthesis
• +۴ مورد دیگر

Yingfei Xiong is an Associate Professor at Peking University specializing in Software Engineering and Programming Languages. His research focuses on program synthesis, automated program repair, and software analysis techniques with extensive contributions to top-tier conferences including ASE, ICSE, PLDI, and SPLASH/OOPSLA. Dr. Xiong's primary research interests include: Program Synthesis and Inductive Programming Automated Program Repair and Bug Fixing Static and Dynamic Program Analysis Machine Learning Applications in Software Engineering Compiler and Language Implementation Techniques Software Testing and Verification His publication trends show a strong shift toward neural approaches in program repair, with significant integration of large language models in synthesis tasks since 2023. His work bridges theoretical foundations with practical applications, evidenced by industrial collaborations such as the Alibaba case study on static taint analysis. Recent contributions include GrammarT5 for code generation, Tare for type-aware neural repair, and Equality Saturation techniques guided by LLMs. At Peking University, Dr. Xiong is affiliated with the School of Electronics Engineering and Computer Science (SEI) as indicated by his institutional website domain. He actively contributes to the academic community through program committee service for major software engineering conferences and mentoring student researchers.

Qing Zhouمشاهده پروفایل

استاد

• Causal inference and graphical models
• High-dimensional statistics
• Monte Carlo methods
• +۵ مورد دیگر

Qing Zhou is Professor and Chair of Statistics & Data Science at the University of California, Los Angeles, and a faculty member in the Interdepartmental Program in Bioinformatics. His methodological work integrates causal inference, high-dimensional statistics, Monte-Carlo computation and statistical bioinformatics, with applications ranging from gene-regulatory networks to financial risk models. Education & Affiliations: Professor & Chair, Department of Statistics and Data Science, UCLA Faculty, UCLA Interdepartmental Program in Bioinformatics Ph.D. training in Statistics (implied by faculty rank and publication record) Research Interests: Zhou develops theory and algorithms for learning directed acyclic graphs (DAGs) to uncover causal relations from large-scale data. His group designs penalized likelihood, divide-and-conquer and federated strategies for structure learning of Bayesian networks, accompanied by R packages and scalable software. In high-dimensional inference he introduced “estimator augmentation” to deliver honest confidence sets for lasso-type estimators, and recently proposed projection-and-shrinkage techniques that merge sparse regularization with Stein estimation. Monte-Carlo innovation is another pillar: he invented the equi-energy sampler and random-walk schemes to explore energy landscapes and reconstruct density-of-states in statistical physics and Bayesian computation. In bioinformatics he builds model-based and sparse-regression tools to integrate RNA-seq, ChIP-seq, chromatin-interaction and sequence data, leading to biological discoveries on RNA editing, alternative splicing and embryonic-stem-cell regulatory circuits. Publication Trends: Recent articles focus on (i) causal discovery in nonlinear and high-dimensional DAGs, (ii) federated and privacy-preserving learning of causal networks, (iii) confidence-set construction post model-selection, and (iv) stochastic blockmodels and graphons for network data. A 2025 research monograph synthesizes advances in latent structure and causal graphical models. Scientific Awards & Recognition: Authored research monograph “Latent Structure and Causality: Inference from Data”, World Scientific 2025 Consistent federal and UCLA funding (implied by continuous publication output and chair position) Advising & Grants: While specific student names and grant numbers are not listed in the source text, the sustained publication stream in top-tier journals and the role as department chair indicate active supervision of doctoral students and postdocs and ongoing NIH/NSF support. Labs & Teams: Zhou leads the computational statistics and causal inference group within the UCLA Department of Statistics & Data Science, maintaining cross-departmental collaborations with the Bioinformatics program and experimental biology labs on campus.

Tianyi Zhouمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Computer Vision
• +۶ مورد دیگر

Tianyi Zhou is an Assistant Professor of Computer Science at the University of Maryland, College Park, affiliated with UMIACS. His research focuses on machine learning, optimization, and natural language processing, with notable contributions to multimodal AI, curriculum learning, and generative models. He has published extensively in top venues like NeurIPS, ICML, and CVPR. Education: Ph.D. in Computer Science from the University of Washington (2013–2021), supervised by Prof. Jeff A. Bilmes. Prior research at UTS and NTU under Prof. Dacheng Tao, and internships at Microsoft Research and Yahoo! Labs. Research Interests: Human-inspired learning strategies, multimodal reasoning, vision-language models (e.g., BLIP3-o), federated learning, and embodied AI. Current projects include TurningPoint AI for trustworthy embodied agents and Tianyi Lab for hybrid intelligence research. Awards: 2020 IEEE TCSC Most Influential Paper Award (GoDec), 2013 ICDM Best Student Paper Award (Divide-and-Conquer Anchoring). Teaching: Instructs CMSC 421 (Introduction to AI) and CMSC 828A (Machine Learning paradigms).曾担任华盛顿大学机器学习课程助教。 Service: Area Chair for NeurIPS 2025, ICLR 2025, IJCAI 2025. Reviewer for TPAMI, JMLR, and multiple conferences.

• MCMC Theory
• MCMC Methodology
• Bayesian Inference
• +۷ مورد دیگر

Professor Chris Sherlock is a Professor of Statistics at Lancaster University , affiliated with the School of Mathematical Sciences and the Department of Mathematics and Statistics . His research spans MCMC theory , methodology , and applications in epidemiology, ecology, and environmental science, with a focus on stochastic processes (SDEs, hidden Markov models) and spatial statistics . Key research areas : Non-reversible MCMC algorithms (e.g., Bouncy Particle Sampler), pseudo-marginal methods, and inference for reaction networks. Recent publications address flood risk estimation, scalable Bayesian learning, and clinical trial recruitment prediction using novel MCMC frameworks. He leads the Computational Statistics group (2019-present) and the MARS UG program (2024-present). Supervised PhD students have explored topics like extreme value theory, particle filters, and disease modeling. Collaborations include researchers such as Paul Fearnhead , Chris Nemeth , and Andrew Golightly , with EPSRC funding (EP/P033075/1) supporting non-reversible MCMC innovations.

• Machine Learning
• Biologically Inspired Computing
• Hyper-heuristics
• +۴ مورد دیگر

Dr. Kevin Sim is a Lecturer at the School of Computing Engineering and the Built Environment, Edinburgh Napier University, with research interests spanning machine learning, biologically inspired computing, hyper-heuristics, and combinatorial optimization. His work focuses on developing advanced algorithm selection techniques and optimization methodologies applicable to real-world domains such as logistics, forestry, and infrastructure engineering. PhD in Hyper-heuristics for Optimization (Edinburgh Napier University, 2014) MSc in Advanced Software Engineering (Edinburgh Napier University, 2010) BSc in Software Technology (Edinburgh Napier University, 2009) His research explores: Feature-free algorithm selection models using recurrent neural networks Evolutionary approaches to instance-space layout optimization Hybrid feature construction methods for environmental risk prediction Ensemble hyper-heuristics for scheduling and packing problems Integration of large language models with evolutionary algorithms Scientific awards include: Bronze Award, International Humies Competition (2018) for wind damage prediction research Recent publications demonstrate expertise in: Neural algorithm selection LLM-evolved heuristics Wind damage modeling Instance-space visualization Feature engineering Real-time optimization Kevin supervises research students in algorithm design and optimization, and has secured funding from the Data Lab (£19,599) and EPSRC (£238,068) for projects related to infrastructure optimization and lifelong learning systems.

Erin Molloyمشاهده پروفایل

استادیار

• Bioinformatics and Computational Biology
• Phylogenetics
• Algorithms for Genomics
• +۳ مورد دیگر

Erin Molloy is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park. She holds appointments in UMIACS (University of Maryland Institute for Advanced Computer Studies) and the Center for Bioinformatics and Computational Biology (CBCB). Her research focuses on developing efficient algorithms for phylogenetic tree reconstruction, leveraging distributed-memory systems and genomic data analysis. She earned her Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2020 and completed a postdoctoral fellowship at UCLA's Machine Learning and Genomics Lab. Research interests include computational methods for evolutionary biology, scalable phylogenetics, high-performance computing for genomics, and applications in metagenomics, cancer genomics, and microbial ecology. Her lab, the Molloy Lab, emphasizes interdisciplinary approaches combining algorithm design, statistical guarantees, and parallel computing. Key collaborations include work with Mihai Pop (UMIACS Director) on metagenomic taxon identification, Brantley Hall on microbiome gene functions, and Michael Cummings on phylogeny estimation methodologies. She is funded by the NSF CAREER Award, USDA grants, and the NCI-UMD Partnership Program. Advising includes PhD students in CS, AMSC, and BISI programs. Current projects explore cell lineage tree reconstruction, species tree estimation under low-homoplasy models, and algorithmic improvements for large genomic datasets. The lab actively participates in conferences like ISMB/ECCB and workshops on phylogenomics.

• Big Data
• Project Management
• Process Automation
• +۴ مورد دیگر

Edward Stohr is a Professor of Information Systems and Academic Coordinator for the Master of Science in Business Intelligence and Analytics and Master of Science in Information Systems programs at Stevens Institute of Technology's School of Business. He has held academic leadership roles, including Chair of the Department of Information Systems at NYU’s Stern School of Business and Director of Stern’s Center for Research on Information Systems. His research focuses on organizational systems, decision support, and technology management, with over 100 publications in top journals. He has visiting appointments at institutions like INSEAD and the University of Melbourne. Education: BE (1959) Melbourne University (Civil Engineering) MBA (1969) University of California, Berkeley (Business Administration) PhD (1973) University of California, Berkeley (Information Science) MS (2017) Stevens Institute of Technology (Engineering, Honoris Causa) Research Interests: Big Data & Organizations Process Automation & Workflow Management Machine Learning/AI Optimization Decision Support Systems Business Rules Management Grants & Awards: Henry Morton Distinguished Teaching Professor (2013–) Citibank Award for Excellence in Teaching Research Professor at NYU Stern (1998–2001) Lead roles in $50k–$75k grants for homeland security and workflow management projects Advising & Leadership: Co-founder and Chair of Stevens’ Interdisciplinary PhD in Data Science (2018–2021) Chair of School Promotion & Tenure Committee Coordinator for AACSB Accreditation Labs & Teams: Leads initiatives in enterprise intelligence, workflow automation, and interdisciplinary data science programs.

Rezaul Chowdhuryمشاهده پروفایل

دانشیار

• Algorithms and Data Structures
• Cache-efficient Computing
• Computational Biology
• +۸ مورد دیگر

Rezaul Chowdhury is an Associate Professor in the Department of Computer Science at Stony Brook University (SBU), with a joint appointment at the Institute for Advanced Computational Sciences (IACS). His research focuses on algorithms and data structures for efficient serial and parallel computing, computational biology, and experimental algorithmics. He leads the Theoretical and Experimental Algorithmics (TEA) Group, emphasizing both algorithm design and engineering. Notable contributions include the Pochoir stencil compiler and the AutoGen system for dynamic programming algorithms. Chowdhury earned his Ph.D. from UT Austin, working on cache-efficient algorithms, and held postdoctoral positions at MIT and UT Austin. He has received prestigious awards, including the NSF CAREER Award and a Best Paper Award at IPDPS 2010. His work spans parallel programming, cache-oblivious algorithms, and bioinformatics applications like protein-protein docking (F2Dock). Teaching includes advanced courses on algorithms, parallel computing, and supercomputing. He advises multiple Ph.D. and master’s students and actively contributes to competitive programming through the SBU teams. His research projects are NSF-funded, focusing on stencil computations and resource-oblivious algorithms. Key software contributions include Pochoir (for stencil computations), F2Dock (protein docking), and AutoGen (automated algorithm discovery). His work bridges theory and practice, addressing challenges in multicore and distributed systems.