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Linghao SongView profile

Assistant Professor

• Accelerator Architecture
• FPGAs
• Hardware Acceleration Systems
• +4 more

Linghao Song is an Assistant Professor in the Department of Electrical & Computer Engineering at Yale University. His research focuses on accelerator architecture design, FPGA-based acceleration systems, and ReRAM-based computing. He is affiliated with the School of Engineering & Applied Science and holds expertise in sparse matrix processing, high-level synthesis, and task-parallel programming frameworks. Education: Ph.D. and M.S. from Duke University and University of Pittsburgh, respectively, with a B.S.E. from Shanghai Jiao Tong University. Research Interests: Song's work spans FPGA-accelerated computing, resistive memory (ReRAM) architectures for deep learning and graph processing, and high-performance sparse matrix operations. His recent projects include the TAPA framework for FPGA programming, ReFloat for iterative linear solvers, and the Sextans/Serpens accelerators for sparse matrix computations. Key Contributions: Over 20 peer-reviewed publications on topics like ReRAM-based accelerators (e.g., GraphR, PipeLayer), FPGA optimization frameworks (TAPA, RapidStream), and novel hardware designs for neural networks and graph analytics. His work emphasizes energy efficiency, scalability, and cross-domain applicability of hardware accelerators. Awards: Recipient of the Duke ECE Outstanding Dissertation Award (2021), EDAA Outstanding Dissertation Award (2020), and National Scholarship of China (2012). Labs/Teams: Leads research in FPGA acceleration and memory-based computing, collaborating on projects involving HBM integration, resistive memory architectures, and task-parallel dataflow frameworks.

Henning Bruhn-FujimotoView profile

Professor

• Optimization
• Graph Theory
• Operations Research
• +3 more

Prof. Henning Bruhn-Fujimoto is a faculty member at the Institute for Optimization and Operations Research at Ulm University. His research focuses on graph theory, combinatorial optimization, and discrete mathematics , with notable contributions to Erdős-Pósa properties, cycle packing, and algorithmic graph theory. He teaches courses in mathematical foundations of machine learning and combinatorics. Academic Background: - Habilitationsschrift : Graphs and their Circuits (2009) - PhD Thesis: Infinite circuits in locally finite graphs (2005) - Diploma Thesis: Generating the cycle space by induced non-separating cycles (2001) Research Interests: - Structural graph theory and algorithm design - Optimization in discrete systems - Applications of combinatorial mathematics Thesis Supervision: He regularly oversees bachelor's and master's theses in optimization, graph theory, and related fields. Notable past thesis topics include elevator system optimization, Erdős-Posa properties in graphs, and container ship unloading algorithms. Labs/Teams: His work is centered within the Institute's optimization group, collaborating with researchers on projects involving graph algorithms and combinatorial optimization.

• Mathematical Logic
• Proof Theory
• Set Theory
• +5 more

Juan P. Aguilera is a researcher at the Institute of Discrete Mathematics and Geometry at TU Wien, Austria. His research focuses on mathematical logic, particularly in proof theory, set theory, reverse mathematics, and infinitary logics. He actively participates in academic events, including organizing workshops and giving invited talks globally. His research interests span foundational questions in logic, including Gödel logics, determinacy axioms, and structural reflection principles. He contributes to interdisciplinary areas such as the model theory of non-classical logics and applications of infinitary proof systems. Aguilera has been involved in major logic events like the Logic Colloquium 2025 (TU Wien) and co-organized the Philosophical Transactions of the Royal Society special issue on proof theory (2023). He has given talks at institutions like Ghent University, the University of Hamburg, and ITAM (Mexico City). No scientific awards are explicitly listed, but his prolific engagement in academic conferences and editorial work highlights his contributions to the field. He has secured grants and leads collaborative projects, though specific grant details are not provided. He is affiliated with TU Wien's discrete mathematics group and collaborates internationally on logic and set theory research.

• History of Mathematics
• Algebraic Structures
• Geometric Resolution
• +4 more

Cinzia Cerroni is a Full Professor at the University of Palermo , affiliated with the Department of Mathematics and Computer Science under the School of Basic and Applied Sciences . She serves as Delegate for the Coordination of the University Orientation and Tutoring Center (COT) since November 4, 2021, and holds regular office hours in Room 105 of her department. Research focuses on the history of mathematics , algebraic structures , and mathematical education Key contributions to cryptographic history , non-Archimedean geometries , and gender representation in STEM Active in teacher collaboration projects and interdisciplinary education Her 15 recent publications span topics from tessellations and bicomplex numbers to wartime impacts on mathematical collaboration and educational methodologies. Subfields include group theory , historical algorithms , geometric patterns , and cryptographic theory . Contact: cinzia.cerroni@unipa.it | Phone: +3909123891092

Nate FosterView profile

Professor

• Programming Languages
• Networking
• Formal Verification
• +3 more

Nate Foster is a Professor of Computer Science at Cornell University and a Visiting Researcher at Jane Street . During the 2023-24 academic year, he served as a Visiting Professor at EPFL in the Data Center Systems Laboratory. His research bridges Programming Languages and Networking , focusing on formal verification, data plane programming, and language design for networked systems. His recent work includes developing symbolic automata for network verification (e.g., Active Learning of Symbolic NetKAT Automata , StacKAT ), creating efficient verifiers like KATch , and advancing type safety in data plane programming (e.g., SafeP4 ). These efforts span formal methods, algorithm optimization, and practical networked systems. Notable scientific awards include: NSF CAREER Award Sloan Research Fellowship ACM SIGCOMM Rising Star Award ACM SIGPLAN Robin Milner Award He actively contributes to PLDI, POPL, OOPSLA, and ICFP, serving as author, editor, and committee member. His GitHub repositories include tools for academic website templates and programming language research codebases.

Valentino SantucciView profile

Associate Professor

• Artificial Intelligence
• Evolutionary Computation
• Machine Learning
• +7 more

Valentino Santucci is an Associate Professor of Computer Engineering at the University for Foreigners of Perugia, Italy, affiliated with the Department of International Human and Social Sciences (SUSI). Since 2021, he has served as the Rector's Delegate for Technological Innovation and Information Flows, highlighting his leadership in digital transformation within the institution. His research spans key areas in Artificial Intelligence, particularly Evolutionary Computation, Natural Language Processing, Machine Learning applications in e-learning and sustainability, and digital technologies in education. He employs algebraic techniques to analyze combinatorial search spaces and evolutionary algorithm dynamics, contributing to both theoretical and applied advancements. The most recent publications reflect a strong trend in combinatorial optimization using algebraic frameworks, hybrid evolutionary-swarm algorithms, and applications in text complexity classification and educational technology. His work bridges computer science with humanities and sustainability, reflecting a multidisciplinary approach. PhD in Computer Science and Mathematics, University of Perugia (2012) He teaches courses in Artificial Intelligence, Computer Science for Humanities, Cybersecurity, and Sustainability across various degree programs. His editorial roles include contributing to WoS/Scopus-indexed journals, and he has taught at the University of Perugia and Hong Kong Baptist University. Dr. Santucci actively mentors through teaching and research supervision. While specific student names are not listed, his involvement in academic projects and publications suggests advisory roles. He has no explicitly mentioned grants, but his research output and leadership position indicate active project engagement. He is involved in institutional innovation through his role in technological advancement and has contributed to projects integrating soft skills and learning technologies. His work emphasizes practical applications of AI in education and sustainability, positioning him at the intersection of technical innovation and societal impact.

Barbara WohlmuthView profile

Professor

• Numerical Simulation
• Partial Differential Equations
• Discretization Techniques
• +4 more

Prof. Barbara Wohlmuth is a full professor in Numerical Mathematics at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. She leads the International Graduate School of Science and Engineering at TUM and has held professorships at Stuttgart, Darmstadt, and Berlin universities. Her research focuses on numerical simulation of partial differential equations, multiscale solvers, and coupled multi-field problems with applications in engineering. Education: Studied mathematics at TUM and Université Joseph Fourier in Grenoble, received her doctorate from TUM in 1995, and completed habilitation in Augsburg. Visiting professorships in USA, France, and Hong Kong. Research interests include discretization techniques, predictive modeling, and interdisciplinary collaboration with engineering disciplines. Notable achievements: 2012 Gottfried Wilhelm Leibniz Prize (Germany’s highest academic honor in sciences), 2005 Sacchi-Landriani Prize. Publications emphasize advanced numerical methods in fluid dynamics, geophysics, and biomedical engineering. Active in editorial roles for international journals and scientific committees across Europe and USA. Elected member of Bavarian and European Academies of Sciences. Key contributions include: Development of robust numerical algorithms for exascale simulations Pioneering work in coupled multi-physics modeling Innovative methods for computational contact mechanics Leadership in graduate education initiatives

Babak AyazifarView profile

Teaching Professor

• Signal Processing
• Graph Signal Processing
• Education Technology
• +4 more

Babak Ayazifar is a Teaching Professor at UC Berkeley's EECS department since 2005. He holds a BS from Caltech (1989) and SM/PhD from MIT (2003). His work focuses on signal processing, education innovation, and curriculum development. He has won prestigious teaching awards including the Goodwin Medal (1999) and IEEE’s Mac Van Valkenburg Award (2012). His research bridges graph signal processing and pedagogical methodologies, with notable contributions to scalable educational tools like Jupyter notebooks. Education: BS Electrical Engineering, California Institute of Technology (1989) SM/PhD Electrical Engineering and Computer Science, MIT (2003) Key Roles: Visiting Senior Lecturer at MIT (2013–2014) Faculty co-advisor for Tau Beta Pi since 2009 Co-inventor of U.S. Patent 5,387,940 (video compression) Teaching Contributions: Developed EECS 16A curriculum Pioneer of interactive lab tools for signal processing education Advocate for TA mentoring and teaching excellence His research integrates signal processing fundamentals with educational innovation, emphasizing practical applications and student engagement. Recent work includes leveraging graph theory for signal analysis and Fourier pedagogy reform.

Morten BrunView profile

Associate Professor

• Topological Data Analysis
• Computational Topology
• Persistent Homology
• +3 more

Morten Brun is an Associate Professor at the Department of Mathematics, University of Bergen. His research spans computational topology, persistent homology, and applications in biology and data science. Email: morten.brun@uib.no Research Interests: He specializes in topological data analysis, focusing on sparse nerves, relative persistent homology, and computational geometry. His work applies topological methods to biological problems, including drug resistance modeling in tuberculosis and immune profiling in multiple sclerosis. Recent Publications: His 2025 work includes hypercubic modeling of tuberculosis drug resistance and high-dimensional immune profiling post-stem cell transplantation. Earlier articles explore computational topology techniques (2017-2024) and interdisciplinary applications in toxicology and systems biology.

Ran LeviView profile

Professor

• Algebraic Topology
• Homotopy Theory
• Neuro-Topology
• +7 more

Professor Ran Levi is a Chair in Mathematical Sciences at the University of Aberdeen, affiliated with the School of Natural and Computing Sciences and the Department of Mathematics. His research bridges pure mathematics and neuroscience, focusing on algebraic topology and its applications to neural systems. BSc in Mathematics, Hebrew University, Jerusalem (1988) PhD in Mathematics, University of Rochester (1993) His research interests include Algebraic Topology , Homotopy Theory of classifying spaces , Combinatorial Topology , and Neuro-Topology —the application of topological methods to neuroscience. He leads the Aberdeen Neuro-Topology Research Group and collaborates with the Blue Brain Project at EPFL. His work explores how topological and geometric frameworks can model and predict neural structures, potentially inspiring new mathematical concepts. Recent publications reveal a strong trend in topological data analysis of neural circuits , p-local groups , and combinatorial constructions in topology. His interdisciplinary work appears in journals such as iScience , eLife , PLOS ONE , and Algebraic & Geometric Topology . His research has been supported by grants including: EPSRC: Topological Analysis of Neural Systems (EP/P025072/1) Collaboration with École Polytechnique Fédérale de Lausanne (Blue Brain Project) Professor Levi has advised several researchers, including Dejan Govc, Janis Lazovskis, Henri Riihimaki, and Jason Smith. He is actively involved in international collaborations with mathematicians and neuroscientists across Europe and the US. He is a key member of the Institute of Pure and Applied Mathematics (IPAM) at Aberdeen and contributes to the AberdeenML and Cybersecurity and Privacy research groups. His work exemplifies the growing synergy between mathematics and computational neuroscience.

Nikolaos SidiropoulosView profile

Professor

• Signal Processing
• Machine Learning
• Wireless Communications
• +5 more

Nikolaos Sidiropoulos is the Louis T. Rader Professor in the Department of Electrical and Computer Engineering at the University of Virginia, School of Engineering and Applied Science. He has previously held faculty positions at the University of Minnesota and the Technical University of Crete, Greece. His research bridges signal processing, machine learning, and communications, with a strong emphasis on tensor decomposition and optimization. His research interests include: Signal Processing Machine Learning Wireless Communications Optimization Tensor Decomposition Cyber-Physical Systems Data Science His educational background includes a Diploma in Electrical Engineering from Aristotle University of Thessaloniki (1988), and M.S. and Ph.D. degrees from the University of Maryland, College Park (1990, 1992). His recent publications focus on tensor methods, deep learning integration with clustering, and physical layer multicasting, reflecting a trend toward interdisciplinary methodologies that combine classical signal processing with modern machine learning. Notable scientific awards include: NSF/CAREER Award (1998) IEEE SPS Best Paper Awards (2001, 2007, 2011, 2022) IEEE SPS Donald G. Fink Overview Paper Award (2022) IEEE SPS Claude Shannon–Harry Nyquist Technical Achievement Award (2022) EURASIP Technical Achievement Award (2022) Distinguished Research Award, University of Virginia (2023) Fellow of IEEE (2009) and EURASIP (2014) Distinguished Lecturer, IEEE SPS (2008–2009) ADC Endowed Chair, University of Minnesota (2015) Students have won five best student paper awards at IEEE conferences (SPAWC 2012, ICASSP 2014, CAMSAP 2015, DSW 2019, CAMSAP 2023) He has advised numerous students and postdoctoral researchers, many of whom have gone on to publish influential work. His research has been supported by the National Science Foundation and other major funding agencies. He leads an active research lab at Thornton A012, University of Virginia, focusing on next-generation signal processing and machine learning algorithms.

Ramin JavadiView profile

Associate Professor

• Graph Theory
• Combinatorics
• Discrete Mathematics
• +3 more

Ramin Javadi is an Associate Professor of Mathematics at Isfahan University of Technology and a Visiting Professor at the Laboratoire de l'Informatique du Parallélisme (LIP) at ENS Lyon from June to July 2025. His research focuses on graph theory and combinatorics, with notable contributions to structural graph theory and algorithm design. He holds a Ph.D. in Mathematics from Sharif University of Technology (thesis: 'Isoperimetric problems on graphs'), and an M.Sc. from Isfahan University of Technology (thesis: 'Chip-firing game and b-coloring of graphs'). His collaboration with LIP aims to strengthen scientific exchange in structural graph theory and expand institutional ties. Key research areas include parameterized complexity, Ramsey numbers, and graph coloring. Despite no explicit mention of awards, his extensive publication record reflects impactful contributions to discrete mathematics. He has advised multiple students from Isfahan University of Technology, though specific names are not listed. His work bridges theoretical computer science and pure mathematics, particularly through interdisciplinary studies in graph algorithms and combinatorial optimization.

Anna-Lena HorlemannView profile

Associate Professor

• Algebraic Coding Theory
• Post-Quantum Cryptography
• Network Coding Theory
• +3 more

Anna-Lena Horlemann is an Associate Professor for Foundations of Computation at the School of Computer Science, University of St. Gallen. Her research focuses on algebraic coding theory, post-quantum cryptography, and network coding. She leads the Coding Theory and Cryptography research group, actively contributing to NIST's post-quantum standardization efforts. Her work addresses quantum computing threats to current cryptosystems and develops error-correcting codes for reliable data transmission and storage. Education: PhD in Mathematics from the University of Zurich (2013), supervised by Prof. Joachim Rosenthal. Diploma in Mathematics from Ruhr-Universität Bochum. Research interests span coding theory (e.g., rank-metric codes, subspace codes), cryptography (code-based systems, privacy protocols), and neuroinformatics (connectome analysis). Recent publications emphasize Lee metric applications, MDS code classification, and cryptanalysis of Gabidulin-based systems. She mentors doctoral researchers such as Nadja Willenborg and Violetta Weger, focusing on topics like code densities and quantum-resistant algorithms. Her team's work bridges theoretical foundations and practical implementations, with contributions to error-correction resilience and cryptographic security in post-quantum scenarios. No formal awards listed, but actively participates in international conferences (IEEE, CBCrypto) and journal reviews.

• Cryptography
• Elliptic Curve Cryptography
• Public-Key Cryptosystems
• +5 more

Dr. Chloe Martindale is a Senior Lecturer at the University of Bristol , affiliated with the School of Computer Science . Her research focuses on cryptography, particularly in isogeny-based systems, public-key cryptosystems, and computational mathematics. Her recent work includes parametrizing maximal orders along supersingular isogeny paths and analyzing attacks on the SIDH protocol. She has contributed to homomorphic encryption security using abelian groups. Scientific Awards Best Paper Honorable Mention (2023) Dutch Cyber Security Research Paper Award (2019)

Yulong LiView profile

Assistant Professor

• Fractional Calculus
• Fractional Differential Equations
• Fractional Sobolev Spaces
• +7 more

Yulong Li is an Assistant Professor in the Department of Mathematics at the University of Dayton, part of the College of Arts and Sciences. He is a full-time faculty member with a strong research focus in mathematical analysis, particularly in fractional calculus and related differential equations. His academic journey includes a Ph.D. from the University of Wyoming, postdoctoral experience at the University of Nevada, Reno, and a research fellowship at the Singapore University of Technology and Design. Ph.D., Mathematics, University of Wyoming, 2019 M.S., Mathematical Physics, Capital Normal University, 2015 B.A., Mathematics and Applied Mathematics, Jilin Normal University, 2012 Dr. Li's research centers on fractional calculus , fractional differential equations , fractional Sobolev spaces , singular integral equations , and special functions . His work explores the theoretical foundations of nonlocal operators, boundary value problems, and spectral properties of fractional elliptic equations. He investigates regularity, existence, and uniqueness of solutions in fractional settings, contributing to both pure and applied mathematical analysis. The trend in his recent publications (2019–2025) shows a consistent focus on analytical and functional aspects of fractional diffusion, advection-reaction equations, and nonlocal elliptic operators. His articles frequently appear in specialized journals such as Fractional Calculus and Applied Analysis and Communications on Pure and Applied Analysis , emphasizing rigorous mathematical proofs, integral representations, and spectral analysis. Topics include maximum principles, eigenvalue problems, and solution decomposition in fractional frameworks. Although no scientific awards are mentioned in the provided text, his publication record indicates active scholarly contributions. There is no information available about grants or student advising in the current materials. Dr. Li has been involved in research collaborations, notably with V. Ginting and others, focusing on computational and theoretical aspects of fractional equations. His work bridges abstract analysis with potential applications in physics and engineering, particularly in modeling anomalous diffusion processes.