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Martin MorenoView profile

Assistant Professor

• Compiler Optimizations
• High-Performance Computing
• Quantum Computing
• +3 more

Martin Moreno is a tenure-track Assistant Professor in the Department of Computer Science and Engineering at The Ohio State University. His office is located in Dreese Laboratories at 2015 Neil Ave, Columbus, OH. His research focuses on the intersection of compilers, programming languages, high-performance computing, and quantum computing, with goals to enhance program execution speed, reliability, and portability across architectures. Research Interests: Dr. Moreno specializes in polyhedral compilation, GPU optimizations, quantum computing abstractions, distributed-memory algorithms, and energy-aware computing. His work bridges theoretical compiler design with practical performance improvements for modern hardware, including GPUs and quantum processors. Recent Research Trends: Analysis of his 15 most recent publications reveals strong emphasis on: 1) Automated code generation for distributed systems and tensor computations, 2) Quantum program optimization through affine transformations, 3) Energy-efficient GPU computing via polyhedral models, and 4) Novel compiler frameworks for task-based parallelism. His work consistently integrates formal methods (e.g., SMT solvers) with practical runtime systems. Student Advising: Actively seeks motivated students for research projects in compiler optimizations and quantum computing. Applicants to OSU are encouraged to list him as a potential advisor and mention PLSE/HPC interests in statements of purpose.

Ni TrieuView profile

Assistant Professor

• cryptography
• security
• secure computation
• +5 more

Ni Trieu is an Assistant Professor of Computer Science at Arizona State University, specializing in cryptography and security with a focus on secure computation and its applications. Her research has significant implications for privacy-preserving technologies in various domains including healthcare, data sharing, and machine learning. Dr. Trieu received her PhD and Master's degrees from Oregon State University under the supervision of Professor Mike Rosulek, followed by a postdoctoral position at UC Berkeley with Professor Dawn Song. During her graduate studies, she gained industry experience through research internships at Bell Labs, Visa Research, and Google. She completed her undergraduate education at St. Petersburg State Polytechnic University. Her research interests center around cryptography and security , with specific expertise in secure computation and its practical applications including private set intersection, private database queries, and privacy-preserving machine learning. Her work bridges theoretical cryptography with real-world security challenges, developing protocols that balance security guarantees with computational efficiency. Dr. Trieu has maintained an impressive publication record in top-tier security conferences including CCS, PETS, EuroS&P, and CRYPTO. Her recent work shows a clear trajectory toward more complex and practical secure computation scenarios, with increasing focus on multi-party settings, efficiency improvements, and applications to real-world problems like genomic privacy, contact tracing, and machine learning. The publications demonstrate consistent innovation in secure computation techniques while addressing practical constraints of real implementations. Dr. Trieu actively contributes to the academic community through conference service, having served on program committees for major security conferences and participated in NSF panel reviews. Her recent news indicates she's been invited to speak at prestigious venues including Simons Institute, VIASM, and NIST.

• Secure Multi-Party Computation
• Privacy-Preserving Technologies
• Cryptography
• +5 more

Thomas Schneider is a Professor at Darmstadt University of Technology (TU Darmstadt) in the Department of Computer Science, with strong affiliations to the European Center for Security and Privacy by Design in Germany. His academic journey began with a PhD from Ruhr-Universität Bochum's Horst Görtz Institute for IT-Security in 2012, establishing his foundation in security research. Dr. Schneider's research spans multiple critical areas in computer security and privacy, with particular emphasis on secure multi-party computation, privacy-preserving technologies, cryptography, and federated learning. His work bridges theoretical cryptographic foundations with practical applications in real-world systems. He has made significant contributions to encrypted search, privacy-preserving machine learning, mobile security, and oblivious database systems. Analysis of his recent publications reveals a strong trend toward practical implementations of privacy-enhancing technologies that maintain both security guarantees and computational efficiency. His research consistently addresses the tension between privacy protection and system performance, developing innovative protocols that minimize overhead while maintaining strong security properties. Key themes include secure computation frameworks, leakage-resilient protocols, and privacy-preserving analytics for sensitive domains like healthcare and government data processing. Dr. Schneider actively collaborates with researchers across multiple institutions, contributing to the advancement of cryptographic techniques that have practical relevance in today's data-driven world. His work demonstrates a consistent focus on developing solutions that are not only theoretically sound but also deployable in real systems.

• Algorithms
• Complexity
• Sorting
• +7 more

Riko Jacob is an Associate Professor and Head of Section in Theoretical Computer Science at the IT University of Copenhagen. His research focuses on algorithms, complexity, sorting, differential privacy, and combinatorial optimization. He leads projects such as DIREC (Digital Research Centre Denmark) and DISTRUST (Distributed business process execution under partial trust), emphasizing interdisciplinary collaboration. Research Interests: Jacob's work spans algorithm design, competitive analysis, randomized algorithms, and matrix operations. His recent studies address bichromatic sorting, saddlepoint detection, and parallel sorting with comparison errors, contributing to foundational advancements in theoretical computer science. Projects: Jacob is the Principal Investigator (PI) in key initiatives including DIREC (2020–2025), focusing on digital research innovation, and DISTRUST (2020–2025), addressing secure distributed systems. He also contributed to SSS (Scalable Similarity Search) under EU funding. Awards: No scientific awards explicitly listed, though his extensive publication record reflects scholarly impact. Advising & Grants: Supervised 1 student (name unspecified). Secured funding through Innovation Fund Denmark and EU grants, totaling millions in research support. Labs/Teams: Affiliated with the Center for Information Security and Trust and collaborates on global networks in algorithmic research.

• Computer Networking
• Networked Systems
• Datacenter Networks
• +5 more

Vishal Shrivastav is an Assistant Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering. He is affiliated with Max W and Maileen Brown Family Hall in West Lafayette, Indiana. His research focuses on Computer Networking and Networked Systems , with a primary emphasis on Computer Engineering . His work spans areas including programmable switches, datacenter networks, and privacy-preserving reconfigurable systems. Shrivastav's recent research trends highlight advancements in ultra-low latency networking fabrics, scalable oblivious reconfiguration mechanisms, and machine learning-driven traffic analysis. His projects often involve hardware-software co-design for high-performance networked systems. He maintains an active research webpage at https://engineering.purdue.edu/~vshriva/ and collaborates with industry on real-time IoT analytics and next-generation network platforms. His office is located in BHEE 334B at the West Lafayette campus.

Andrew DannerView profile

Professor

• Geographic Information Systems (GIS)
• Computer Science
• Parallel Computing
• +6 more

Andrew Danner is a Professor in the Department of Computer Science at Swarthmore College. His research focuses on I/O-efficient algorithms, geographic information systems (GIS), and parallel computing education. He holds a Ph.D. in Computer Science from Duke University (2006) and a B.S. in Physics and Mathematics from Gettysburg College (1999). Current research emphasizes scalable solutions for large geospatial datasets, including lidar point interpolation and watershed hierarchies. His TerraStream project developed I/O-efficient algorithms for elevation data analysis. He also created TPIE, a C++ library for developing I/O-efficient applications. Teaching includes introductory and advanced courses such as CS 21 (Introduction to Computer Science), CS 40 (Computer Graphics), and CS 46 (Theory of Computation). He integrates parallel computing concepts across the curriculum, publishing work on CUDA visualization tools (ParaVis) and pedagogical strategies for distributed systems. His work addresses challenges in large-scale data processing, including terrain modeling, grid DEM construction, and efficient watershed decomposition algorithms. Recent efforts focus on improving introductory CS education through support programs that enhance retention of underrepresented groups.

Jeremy T. FinemanView profile

Professor

• Parallel Algorithms
• Cache-Oblivious Algorithms
• Distributed Computing
• +2 more

Jeremy T. Fineman is Professor and Wagner Chair in Computer Science at Georgetown University. He holds a Ph.D. from MIT and previously held positions at Carnegie Mellon University. His research develops efficient algorithms for parallel and distributed systems. Fineman's work spans cache-oblivious persistence, asymmetric read/write cost optimization, and contention resolution in dynamic networks. He has pioneered new approaches to topological sorting and reachability problems in large-scale systems. Honors include an IEEE Fellowship and best paper awards. He currently directs graduate studies and leads research on memory-optimized computing architectures.

Mark GondreeView profile

Associate Professor

• Security Pedagogy
• Applied Cryptography
• Computer Science Education
• +1 more

Mark Gondree is an Associate Professor in the Computer Science Department at Sonoma State University, with research expertise in security pedagogy, applied cryptography, and secure computation. He holds a PhD from UC Davis and has received multiple teaching awards including the CSU Student Success Analytics Certificate and POGIL Activity Clearinghouse recognition. His research explores cybersecurity education methods, cloud data geolocation, and industrial control system vulnerabilities. Recent publications focus on curriculum development for computer security concepts and efficient cryptographic protocols. Awards: CSU Student Success Analytics Certificate (2024) POGIL Activity Clearinghouse Publications (2022) QuARRY Repository Award (2020) He actively advises graduate and undergraduate researchers on projects ranging from fuzz testing to pseudorandom number generator analysis. Current grants include NSF funding for CS teacher preparation programs and cloud security research. As department chair, he oversees curriculum development and leads the Computer Science Colloquium series.

Andrea LincolnView profile

Assistant Professor

• Theoretical Computer Science
• Fine-Grained Complexity
• Average-Case Complexity
• +3 more

Andrea Lincoln is an Assistant Professor at Boston University's Department of Computer Science within the College of Arts & Sciences. Her research focuses on theoretical computer science, particularly average-case and fine-grained complexity. She holds a PhD from MIT (2020) and completed a postdoctoral fellowship at UC Berkeley with Barna Saha (2020-2021). Her work explores algorithmic complexity through reduction networks and dynamic systems analysis. Research interests include computational problem complexity, algorithm design for dynamic graphs, and average-case scenario analysis. She has contributed to understanding hardness proofs for problems like k-SUM and Orthogonal Vectors, as well as developing techniques for evaluating Boolean formulas and subgraph counting. Her recent work (2022-2025) emphasizes algorithmic efficiency under predictions, compression impacts on string distance measures, and cache-adaptive performance analysis. Notable publications address hypercycle database problems and delegation protocols for search problems. While no specific awards are listed, her prolific publication record reflects sustained contributions to theoretical computer science. Dr. Lincoln advises no listed students and has no documented grants in the provided text. Her work is associated with foundational algorithmic research rather than applied lab settings.

Edith CohenView profile

Visiting Professor

• Algorithms Design
• Data Mining
• Big Data Analytics
• +10 more

Edith Cohen is a Research Scientist at Google and a Visiting Full Professor in the School of Computer Science at Tel Aviv University, where she contributes to academic research and teaching. Her career spans leading industrial research labs, including AT&T Bell Labs, Microsoft Research, and Google Research. Undergraduate and M.Sc.: Tel Aviv University (1985, 1986) Ph.D.: Stanford University, Computer Science (1991) Her research centers on scalable algorithms for massive data, with key contributions in sketching (e.g., MinHash, HyperLogLog), sampling techniques, graph mining (centrality, influence, similarity), data streams, and differential privacy. She designs principled algorithmic solutions that balance accuracy, speed, and storage in large-scale systems. The recent articles highlight a strong focus on robustness and privacy in streaming and learning systems, particularly under adversarial or adaptive settings. Her work bridges theoretical rigor with practical impact, applying advanced statistical and algorithmic methods to real-world data challenges. ACM Fellow (2017) IEEE Communications Society William R. Bennett Prize (2007) ISF and NSF Research Grants She has advised several students and interns at AT&T and Google, and has served on program committees and editorial boards of top computer science venues. Her work has led to numerous patents in data summarization, network routing, and query processing. She actively collaborates with researchers across institutions and continues to publish in premier conferences such as STOC, KDD, ICML, and PODS.

• High Performance Computing
• Parallel Computing
• Matrix Algorithms
• +3 more

Joao Nuno Estevao Fidalgo Ferreira Alves is a dedicated researcher within the Faculty of Computer Science, actively contributing to the Research Group Scientific Computing with expertise in high-performance computing and matrix algorithm optimization. His work focuses on developing innovative solutions for European exascale supercomputing challenges. His educational background includes: Bachelor of Science (BSc) Master of Science (MSc) Mr. Alves specializes in cache-oblivious algorithms, space-filling curves, and matrix transposition techniques, addressing critical data locality challenges in large-scale parallel systems. His research bridges theoretical computer science and practical supercomputing applications, with publications in premier venues including ACM Transactions on Mathematical Software and IEEE IPDPS. Analysis of his 2022-2023 publications reveals a concentrated research trajectory in matrix operations, where he pioneered triangular space-filling curves and Hilbert curve-based blocking schemes to achieve cache-efficient in-place transposition. These contributions significantly advance scientific computing methodology for modern parallel architectures. He currently serves as co-Principal Investigator for the project 'Innovative Algorithms for Applications on European Exascale Supercomputers' (2024-2025), securing active research funding for next-generation algorithm development in the European supercomputing ecosystem. Within the Research Group Scientific Computing, Mr. Alves maintains strong international collaborations, notably with Prof. Benkner at the University of Vienna, driving cooperative research on exascale algorithm design and implementation.

Charles E. LeisersonView profile

Professor

• Parallel Computing
• Algorithms
• Digital Circuit Optimization
• +4 more

Charles E. Leiserson is a Professor of Computer Science and Engineering at the Massachusetts Institute of Technology (MIT) , affiliated with the Department of Electrical Engineering and Computer Science (EECS) and leading the Supertech research group at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His career spans groundbreaking contributions to parallel computing, algorithms, and education. Educated at Yale University (B.S., 1975) and Carnegie Mellon University (Ph.D., 1981). Research Interests include: Parallel Computing: Systolic architectures, fat-tree interconnection networks, and work-stealing schedulers. Algorithm Design: Retiming for digital-circuit optimization, cache-oblivious algorithms, and Cilk multithreaded programming technology. Engineering Impact: Development of Akamai Technologies' content-distribution network and Cilk Arts, Inc. Scientific Awards & Honors IEEE Computer Society Taylor L. Booth Education Award (2014) ACM Paris Kanellakis Theory and Practice Award (2013) ACM Doctoral Dissertation Award (1982) Margaret MacVicar Faculty Fellow (MIT's highest undergraduate teaching honor) Fellow of ACM, AAAS, IEEE, and SIAM Educational Contributions include co-authoring the influential textbook Introduction to Algorithms , developing MIT courses on algorithms and software performance engineering, and pioneering remote education through the Singapore-MIT Alliance (SMA) program. His work has impacted hundreds of faculty and students via leadership workshops and the Undergraduate Practice Opportunities Program (UPOP).

Kanat TangwongsanView profile

Researcher

• Algorithms
• Parallel Computing
• Streaming Algorithms
• +6 more

Kanat Tangwongsan is a researcher and educator in computer science, with a PhD from Carnegie Mellon University and prior roles at IBM T.J. Watson Research Center. He has extensive experience in algorithm design, parallel computing, and self-adjusting computation, with publications in top-tier venues like DEBS, ICDE, SPAA, and TOCS. Education: PhD in Computer Science (Carnegie Mellon University), B.S. in Computer Science and Mathematics (Carnegie Mellon University) Research Interests: His work spans algorithms , parallel computing , streaming data structures , and self-adjusting programs , focusing on theoretical improvements and practical implementations for graph problems, convex hulls, and network design. Article Trends: His publications emphasize low-latency streaming , parallel SDD solvers , k-means clustering , and triangle enumeration , with applications in large-scale graph analysis, numerical systems, and cache-oblivious methods. Advising and Collaborations: He has collaborated with prominent researchers like Guy E. Blelloch, Martin Hirzel, and Anupam Gupta, contributing to open-source algorithm frameworks and educational materials.

Man-Ki YoonView profile

Assistant Professor

• Trustworthy and Accountable Computing
• Autonomous Systems
• Real-Time Embedded Systems
• +4 more

Man-Ki Yoon serves as an Assistant Professor in the Department of Computer Science at North Carolina State University's College of Engineering. His academic journey includes a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (2017), an M.S. from the same institution (2011), and a B.S. in Computer Science and Engineering from Seoul National University in Korea (2009). Prior to joining NC State, he worked as a research scientist in Computer Science at Yale University. His educational background demonstrates a strong foundation in computer systems with progressive specialization in real-time and embedded computing. The transition from his undergraduate studies in Korea to doctoral research in the United States reflects an international academic trajectory focused on increasingly specialized aspects of computer systems. Yoon's research centers on trustworthy and accountable computing for autonomous systems, with particular emphasis on autonomous vehicles and unmanned aerial vehicles. His work bridges theoretical formal methods with practical security applications in real-time embedded systems and the Internet of Things. This research agenda addresses critical challenges in verifying temporal behavior of real-time applications while maintaining strong security guarantees against timing-based side channel attacks. His approach combines insights from operating systems, security, and distributed systems to develop novel solutions that enable reasoning about fault events, component responsibility, and evidence trustworthiness in complex cyber-physical environments. Analysis of his recent publications reveals a clear evolution in research focus from foundational real-time scheduling problems toward increasingly sophisticated security mechanisms for autonomous systems. His work demonstrates consistent progression from theoretical verification techniques to practical implementations addressing accountability in distributed systems and security challenges in autonomous vehicle ecosystems. The publications show growing emphasis on AI/ML integration with traditional real-time systems, particularly in safety-critical applications where accountability and verifiability remain paramount. NCDOT Transportation Center of Excellence: Sustainable and Resilient Infrastructure – Cyber–Resilient Traffic Management Centers Faculty Research and Professional Development Award IBM Workforce Development Initiative Award SaTC: CORE: Small: Partition-Oblivious Real-Time Hierarchical Scheduling Yale Course-based Undergraduate Research Experience (CURE) Grant Qualcomm Roberto Padovani Scholarship Intel PhD Fellowship Qualcomm Innovation Fellowship Yoon actively mentors students through research projects and teaching, with numerous publications featuring student co-authors spanning topics from autonomous vehicle security to real-time system verification. His current NSF-funded research on partition-oblivious scheduling demonstrates significant grant acquisition capability, addressing fundamental security challenges in real-time hierarchical scheduling where conventional time-partitioning mechanisms fail to achieve strong temporal isolation from a security perspective. This work develops algorithmic solutions enabling non-interference-based security among partitions, allowing systems to employ advanced hardware and software technologies for high-end real-time applications in secure environments. His laboratory environment emphasizes hands-on experimentation with autonomous systems, evidenced by his self-driving car course where students build software systems for miniaturized autonomous vehicles. This practical approach complements theoretical research with tangible implementations, creating a comprehensive research ecosystem that spans from formal verification to physical system deployment.

• Computer Security
• Machine Learning
• Hardware Design
• +6 more

Farinaz Koushanfar is a Professor at the University of California, San Diego (UCSD), with a former affiliation at the University of California, Berkeley. Her research focuses on advancing security, machine learning, and hardware design through interdisciplinary approaches. Key areas include adversarial defense mechanisms, cryptographic systems, federated learning, and zero-knowledge proofs. She has collaborated extensively with institutions and researchers globally, contributing to over 360 publications. Her work emphasizes practical security solutions, such as watermarking for intellectual property protection and methods to counteract adversarial attacks in neural networks. Recent trends in her publications highlight innovations in cache compression, robust watermarking for large language models, and securing wireless communication systems against modality-agnostic attacks. Collaborations with industry and academia underscore her commitment to real-world applications of theoretical advancements. Awards and grants are not explicitly listed here, but her prolific publication record and leadership in high-impact projects indicate significant recognition in her field. Advising and mentoring students and junior researchers are central to her academic contributions, though specific student names are not detailed in the provided text. Her lab’s work often intersects with emerging technologies like blockchain, edge computing, and privacy-preserving machine learning.