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Stefan LindskogView profile

Professor

• Cybersecurity
• Network Security
• Intrusion Detection Systems
• +7 more

Stefan Lindskog is a full Professor at the Department of Computer Science, Karlstad University, Sweden. His research focuses on tunable security services, network security protocols, intrusion detection systems, and encryption methodologies. He has held academic positions at Karlstad University since 1990 and holds a PhD in Computer Engineering from Chalmers University (2005) and a Docent degree in Computer Science from Karlstad University (2008). His work emphasizes practical cybersecurity solutions, including firewall usability, multipath TCP security, and privacy-preserving technologies. Lindskog has authored over 60 peer-reviewed publications, including studies on encrypted network traffic analysis, Tor anonymity vulnerabilities, and firewall rule optimization. He collaborates extensively with industry partners and academic networks, contributing to projects like the EU-funded NEWCOM++ initiative. Teaching: Real-Time Systems (7.5 ECTS) Key Research Themes: Security service tunability, adaptive intrusion detection, and secure communication protocols Collaborations: HITS project (High Quality Networked Services in a Mobile World) Publications span topics from malware detection in encrypted traffic to systematic reviews of firewall usability. Lindskog’s work bridges theoretical security models and operational system implementation, with a focus on real-world applicability in industries like offshore energy and mobile communications.

Panos J. AntsaklisView profile

Professor

• Cyber-Physical Systems
• Networked Embedded Systems
• Hybrid Dynamical Systems
• +9 more

Panos J. Antsaklis is the H. Clifford and Evelyn A. Brosey Professor of Electrical Engineering at the University of Notre Dame, with concurrent appointments in the Department of Computer Science and Engineering and the Department of Applied and Computational Mathematics and Statistics. He is a leading figure in systems and control theory, with a sustained focus on autonomy, cyber-physical systems, and networked control. His research interests center on Cyber-Physical Networked Embedded Systems, hybrid and discrete event dynamical systems, and the quest for autonomy in engineered systems. He investigates control strategies for complex, intelligent, and reconfigurable systems using mathematical models and data, with applications in transportation, manufacturing, power systems, and communication networks. His recent work emphasizes passivity, dissipativity, and passivity indices as foundational tools for robust and resilient design. The 15 most recent publications highlight a strong trend in decentralized and distributed control of networked systems, particularly vehicular platoons and supply chains, using dissipativity-based methods and topological co-design. There is a growing integration of machine learning (e.g., graph neural networks) and formal methods (e.g., signal temporal logic) with classical control theory, reflecting the interdisciplinary nature of modern cyber-physical systems. His scientific awards include: IEEE Fellow (1991) IFAC Fellow (2010) AAAS Fellow (2011) IEEE Third Millennium Medal (2000) Brown University Engineering Alumni Medal (2006) University of Lorraine Honorary Doctorate (2012) Notre Dame Faculty Award (2013) Notre Dame Research Achievement Award (2020) Antsaklis has played a pivotal role in the control systems community, serving as Editor-in-Chief of the IEEE Transactions on Automatic Control (2010–2017) and President of the IEEE Control Systems Society (1997). He has supervised numerous PhD students and authored over 600 publications, including foundational textbooks on linear and hybrid systems. He has also been involved in high-level advisory roles, such as on the NSF Cyber-Physical Systems PI Meeting and the President’s Council of Advisors for Science and Technology (PCAST). He leads the Antsaklis Group at Notre Dame, which focuses on autonomy, control, and resilience in networked systems. The group is active in both theoretical development and practical applications, contributing to national initiatives on control for societal-scale challenges.

• Computational Linguistics
• Natural Language Processing (NLP)
• Language Model Interpretability
• +4 more

Dr. Dan Jurafsky is a Professor at the Department of Linguistics within Stanford University's School of Humanities and Sciences. His work spans computational linguistics, natural language processing, and AI ethics, with a particular focus on language model behavior, speech recognition, and ethical implications of anthropomorphism in AI systems. His recent research explores HumT for measuring human-like tone in LLMs, AnthroScore for anthropomorphism detection, and methods for improving low-resource language support through data augmentation and multilingual representation learning. He has also developed open-source tools like string2string for string algorithms. Jurafsky's publications address critical issues in NLP, including grounding gaps in conversational models, causal interpretability in linguistic tasks, and representational biases in multilingual models. His work emphasizes interdisciplinary applications, from educational NLP tools to Sumerian transliteration datasets, while advocating for rigorous statistical power analysis and ethical model evaluation.

Rayan ChikhiView profile

Researcher

• Bioinformatics
• Algorithmics
• Computational Biology
• +7 more

Rayan Chikhi is a Researcher and Structure Manager Responsible at the Institut Pasteur, affiliated with the UMR3569 – Virology unit and the Advanced Molecular Virology department. His work lies at the intersection of bioinformatics, algorithmics, and computational biology, with a strong focus on developing efficient data structures and analytical tools for genome and metagenome analysis. His research interests include: Bioinformatics and algorithm development for biological sequences Pangenomics and variation graph construction k-mer and minimizer-based data structures Genome and metagenome assembly Ancient DNA analysis and decontamination Microbial source tracking Rayan Chikhi’s recent publications demonstrate a consistent trend toward creating scalable, efficient, and open-source computational tools that address bottlenecks in genomic data analysis. His work on unitig matrices (MUSET), quotient filters, and pangenome graphs reflects a focus on reducing computational resource demands while preserving biological accuracy. These tools are widely applicable in metagenomics, ancient DNA studies, and large-scale genomic association studies. Scientific contributions and software developments include: MUSET: for constructing abundance-preserving unitig matrices Backpack Quotient Filter: a dynamic, space-efficient k-mer index decOM: for microbial source tracking in ancient samples aKmerBroom: for decontaminating ancient oral DNA Contributions to K-mer File Format standardization He actively mentors and collaborates with junior researchers, including PhD students Francesco Andreace and Timothé Rouzé, and postdoctoral fellows such as Camila Duitama González and Kristen Curry. He is involved in major institutional initiatives such as the INCEPTION Convergence Institute and the Artificial Intelligence at the Pasteur Institute cross-functional project, highlighting his role in interdisciplinary research. His work is supported by collaborative grants and contributes to advancing open science in computational genomics. Rayan Chikhi is a key member of the bioinformatics team at the Institut Pasteur, contributing to both methodological innovation and practical applications in virology and microbial genomics. His lab and team focus on building robust, open-source software tools that empower large-scale biological discovery.

• Algorithms on strings
• Small-space algorithms
• Data structures
• +4 more

Tatiana Starikovskaya is an Assistant Professor in the Talgo team at the Computer Science Department of École normale supérieure (ENS Paris), France. Her work focuses on the design and analysis of algorithms for string processing, space-efficient computation, and complexity theory, with applications in bioinformatics and data security. Education : PhD in Mathematics (2013) and MSc in Mathematics (2009) from Lomonosov Moscow State University; MSc in Data Science (2009) from Moscow Institute of Physics and Technology and Yandex School of Data Analysis. Her research explores the intersection of algorithm theory and practical applications, particularly in streaming models , probabilistic text indexing , and clustering of sequence data . Key themes include handling wildcards , k-mismatch problems , and edit distance under space constraints. Current projects include PARSe (ANR-20-CE48-0001) , aiming to develop ultra-efficient algorithms for noisy string data, and AlgoriDAM (ANR-19-CE48-0016) , focusing on algorithmic theory for modern data models. She co-supervises PhD students Gabriel Bathie and T. El Ghazi, with collaborations spanning institutions like University of Wrocław and IRISA. She has served on program committees for major conferences (STACS, ESA, ICALP, CPM) and organized the CPM Summer School (2023) and New Horizons of Stringology (2024) workshop at Cirm. Teaching roles include courses at ENS Paris (Algorithmique, Programming for Non-CS Students) and Higher School of Economics (Moscow).

Işıl DilligView profile

Associate Professor

• Programming Languages
• Static Analysis
• Program Verification
• +4 more

Işıl Dillig is an Associate Professor in the Department of Computer Science at the University of Texas at Austin, leading the UToPiA research group. She focuses on programming languages, with emphasis on static analysis, verification, and synthesis to enhance software reliability and security. Education: BS, MS, PhD in Computer Science from Stanford University Her research addresses critical challenges in: Program analysis for security and correctness Automated program synthesis for complex tasks Verification of concurrent and database-driven systems Recent publications span neurosymbolic synthesis, database integration, and smart contract optimization. Trends include hybrid AI-formal methods approaches and domain-specific language design. Scientific recognition includes: Sloan Fellowship NSF CAREER award She actively contributes to academic leadership as committee member, program chair, and keynote speaker in top conferences like PLDI, OOPSLA, and POPL.

Rupak ChatterjeeView profile

Assistant Professor

• Quantum Information and Computation
• Quantum Machine Learning
• Quantum Optimization
• +4 more

Rupak Chatterjee is an Industry Assistant Professor in the Department of Applied Physics at New York University's Tandon School of Engineering. His research bridges quantum information/computation and mathematical physics, with a focus on quantum algorithms for machine learning, optimization, operator algebras, and quantum mechanical systems. Education: Postdoctoral Scholar, Physics (James Franck Institute, University of Chicago) Ph.D. & M.S., Physics (Stony Brook University) M.Math., Mathematics (University of Waterloo) B.Sc., Physics (University of Calgary) Chatterjee's research explores quantum systems for machine learning, quantum optimization protocols, and mathematical frameworks like C∗-algebras and supersymmetric quantum mechanics. His work integrates theoretical rigor with applications in quantum computing and complex physical systems. His recent publications (2019–2024) demonstrate a strong emphasis on quantum entanglement, chaos in optomechanical systems, adiabatic quantum optimization, and relativistic quantum mechanics. Several publications also apply quantum methods to finance and diffusion modeling. No awards, student advising, or grant information is documented in the provided text.

• Data Science
• Human-Computer Interaction
• Information Visualization
• +4 more

Dr. Taoxin Peng is a Lecturer at the School of Computing Engineering and the Built Environment, Edinburgh Napier University. He is affiliated with the Centre for Algorithms, Visualisation and Evolving Systems where he conducts research in data science and human-computer interaction. His primary research focuses on: Data Science : Developing synthetic data generation tools and data quality frameworks Human-Computer Interaction : Creating innovative input methods like laser-based interaction systems Information Visualization : Designing techniques for online dataset exploration Artificial Intelligence : Foundational work in causal reasoning and knowledge representation Publication analysis shows consistent focus on data systems (65% of works) with recent expansion into HCI applications. Early career publications centered on AI fundamentals, transitioning to data quality research in the 2000s, and more recently to practical applications in synthetic data and interactive systems. Dr. Peng has supervised PhD candidate Andreas Steyven (2013-2018) on evolutionary swarm robotics research. He collaborates with researchers including Prof. Emma Hart and Prof. Ben Paechter through the Centre for Algorithms, Visualisation and Evolving Systems.

• Applied Mathematics
• Optimization
• Computational Mathematics
• +3 more

Yair Censor is a distinguished Professor in the Department of Mathematics at the University of Haifa. His work focuses on applied mathematics, particularly in optimization, projection methods, and computational algorithms with applications in medical physics and engineering. He has developed foundational techniques like the superiorization method, which balances feasibility-seeking and objective function reduction in iterative algorithms. Research Interests: Optimization theory, convex feasibility problems, superiorization, medical imaging algorithms, and parallel computing. Collaborations: Extensive work with Gabor T. Herman, Aviv Gibali, Reinhard Schulte, and others in computational methods for radiation therapy and tomography. His recent publications (2023–2025) emphasize feasibility-seeking algorithms, superiorization in floorplanning, and perturbation resilience. While no explicit awards or students are listed in the provided text, his contributions to iterative methods and medical physics remain influential.

Michael StillmanView profile

Professor

• Computational Algebraic Geometry
• Commutative Algebra
• String Theory
• +3 more

Michael Stillman is a Professor of Mathematics at Cornell University, affiliated with the Nonlinear Algebra research group at the Max Planck Institute for Mathematics in the Sciences (MPI MiS). His work focuses on computational algebraic geometry, commutative algebra, and their applications to theoretical physics, particularly string theory. He is a principal author of the open-source computer algebra system Macaulay2, which underpins much of his research in algorithm development and computational methods for algebraic structures. Stillman's research includes tackling computationally intensive problems in string theory, such as calculating nef cones and semigroups for Calabi-Yau varieties linked to toric geometry, and optimizing cohomology computations for line bundles on toric varieties. His recent collaboration with Mastroeni and Schenck explored quadratic Gorenstein algebras and the Koszul property.

Maximilian WiesmannView profile

Research Fellow

• Algebraic Geometry
• Computational Geometry
• Combinatorics
• +4 more

Maximilian Wiesmann is a mathematician working as an ELBE Postdoctoral Fellow at the Center for Systems Biology Dresden, Germany. Previously, he completed his PhD at the Max-Planck-Institute for Mathematics in the Sciences in Leipzig under the supervision of Eliana Duarte and Bernd Sturmfels. Mentored by Aida Maraj and Ivo Sbalzarini at CSBD, his work bridges pure mathematics with applications in computational biology and quantum information. His educational background includes: PhD in Mathematics, Max-Planck-Institute for Mathematics in the Sciences, Leipzig (2025) MASt in Pure Mathematics, University of Cambridge (2022) B.Sc. in Mathematics, University of Bonn (2021) Wiesmann's research focuses on the intersection of algebraic geometry and computational mathematics, particularly in nonlinear algebra applications. His work spans theoretical investigations in toric geometry and combinatorics to practical implementations in quantum information and machine learning. He has developed significant software tools including the EulerStratifications.jl Julia package, which accompanies his research on hypersurface families. His publications demonstrate a consistent trajectory from foundational mathematical structures to their applications in statistical modeling and computational science. Analysis of his 11 most recent publications reveals a strong focus on algebraic statistics and geometric approaches to statistical problems, with increasing attention to machine learning applications. His work connects pure mathematical structures with practical computational frameworks, particularly through the lens of nonlinear algebra. The development of specialized software like EulerStratifications.jl demonstrates his commitment to making theoretical advances accessible for practical computation. Wiesmann has served as a tutor in linear algebra and foundational mathematics at the University of Bonn during winter terms 2019/20 and 2020/21. He also completed a Quantum Science Summer Internship at Riverlane in Cambridge, UK in 2021, and was a visiting student at the Institute for Mathematical and Statistical Innovation at the University of Chicago in September and October 2023. At CSBD, Wiesmann is part of a vibrant research environment focused on systems biology, where he applies his expertise in algebraic geometry to biological modeling problems. His work with the Nonlinear Algebra Community on Zenodo demonstrates his commitment to open science and community building in his field.

Sam SalemView profile

Lecturer

• Electronic Music
• Computer Music
• Generative Models
• +5 more

Sam Salem is a PRiSM Lecturer in Composition at the Royal Northern College of Music (RNCM), specializing in audiovisual works that blend instrumental performance with electronics, video, and computational techniques. His interdisciplinary work spans electronic/computer music, visual art, computer science, and English literature, with a focus on exploring ambiguity and the Uncanny through machine learning and AI. He is a founding member and co-artistic director of Distractfold Ensemble, which received the Kranichstein Music Prize for Interpretation (first British ensemble to do so). His compositions have been performed globally and honored with awards such as the HearSay Prize (2015), Prix Palma Ars Acustica nomination (2015), and Concours Luc Ferrari (2012). Recent works include THIS IS FINE (2021), Midlands (2019), and The Lovers (2017), which was praised in the New York Times for its 'chamber-orchestra pandemonium with accomplished video work.' Current research focuses on machine-learning audio synthesis in collaboration with PRiSM’s Research Software Engineer, Christopher Melen.

Anne CondonView profile

Professor

• Computational Complexity
• Algorithms
• Molecular Programming
• +3 more

Dr. Anne Condon is a Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Faculty of Science. Her research focuses on computational complexity, algorithms, and molecular programming, particularly in nucleic acid structure prediction and bioinformatics. She is an ACM Fellow and a Fellow of the Royal Society of Canada. Education: Bachelor's degree in Computer Science from University College Cork, Ireland (1982) Ph.D. in Computer Science from the University of Washington (1987) Research Interests: Dr. Condon's work bridges computer science and molecular biology, addressing challenges in algorithm design for nucleic acid systems, RNA secondary structure prediction, and energy parameter estimation. She explores computational models such as Chemical Reaction Networks (CRNs) and DNA strand displacement systems for molecular programming. Her research emphasizes practical applications in synthetic biology and biotechnology. Publications: Her recent work includes studies on chemical reaction networks for approximate majority consensus, efficient DNA kinetics modeling, and interpretable dimensionality reduction of single-cell transcriptome data. Key contributions span algorithm design, computational biology, and interdisciplinary collaborations. Awards & Affiliations: ACM Fellow Fellow of the Royal Society of Canada Member of the Institute for Computing, Information and Cognitive Systems (ICICS) and the Institute of Applied Mathematics at UBC Supervision & Grants: She actively supervises doctoral and master's students in areas like nucleic acid kinetics, bioinformatics, and molecular programming. Her grants and funding support interdisciplinary research at the intersection of computer science and molecular biology. Labs & Teams: Condon's affiliations with UBC's ICICS and Institute of Applied Mathematics facilitate collaborative research in computational methods for systems biology and molecular computing.

Pablo GalazView profile

Lecturer

• Music Composition
• Sound Art
• Live Electronics
• +3 more

Dr. Pablo Galaz is a Lecturer in Composition and Analysis at the University of Southampton. His work bridges music composition, live electronics, and interdisciplinary collaboration. He actively participates in research projects such as 'Sound and mental health' and 'Machine Learning-Driven Gestural Control for Expressive Sound Synthesis', collaborating with researchers like Dr. Chaitanya Paruchuri and Dr. Richard Polfreman. His creative output includes multimedia installations and instrumental works with live electronics, reflecting interests in interactive performance and technological integration. Research Focus: Sound art, machine learning in music, and interdisciplinary collaboration Notable Projects: Human-like inclinations (AI-driven multimedia installation), Transnodal (cello/electronics) Publications: Focused on creative media artifacts blending acoustic and electronic elements His recent works (2023-2025) emphasize real-time interaction between performers and technology, while earlier pieces (e.g., Roren) explore structural composition in chamber music. No awards are listed but shows active grant-funded research activities.

Gregory ValiantView profile

Associate Professor

• Algorithms
• Machine Learning
• Statistics
• +3 more

Gregory Valiant is an Associate Professor of Computer Science at Stanford University , specializing in Algorithms, Machine Learning, Statistics, and Information Theory. He holds a PhD from UC Berkeley and a BA in Mathematics from Harvard University. His research focuses on designing efficient algorithms for inferring information from limited data, addressing challenges in computation, memory, communication, and data quality. He advises multiple PhD students and collaborates with institutions like Microsoft Research New England. Educations: PhD, Computer Science, UC Berkeley (2012) BA, Mathematics, Harvard University (2006) Research Interests: Gregory’s work centers on the interplay between algorithms and statistical inference, particularly in high-dimensional settings. His lab explores topics like distribution learning, sample amplification, and adversarial robustness. Recent projects include developing algorithms for trace reconstruction, matrix completion, and transformer-based in-context learning. His methodologies often combine theoretical rigor with practical applications in machine learning and data science. Key Article Trends: His publications span theoretical foundations (e.g., sample amplification, convex optimization complexity) and applied machine learning (e.g., transformer capabilities, adversarial testing). Notable work includes contributions to distribution testing, statistical estimation under constraints, and algorithmic lower bounds. Advising: Current advisees include Annie Marsden, Steven Cao, and Chirag Pabbaraju. Former students have pursued roles at institutions like Harvard, Berkeley, and companies like Google, Waymo, and Facebook.