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Katherine NewhallView profile

Professor

• Applied Mathematics
• Stochastic Differential Equations
• Dynamical Systems
• +5 more

Katherine Newhall is a Professor in the Department of Mathematics at the University of North Carolina at Chapel Hill, where she maintains an active research program in stochastic modeling and dynamical systems. Her office is located in Phillips Hall 308, and she can be reached at knewhall@unc.edu. She serves as a member at large of the GSNP (Group on Statistical and Nonlinear Physics) board, a position she assumed in April 2024. Dr. Newhall earned her educational credentials from Rensselaer Polytechnic Institute, including a B.S. in Applied Physics and Applied Mathematics (2004), an M.S. in Mechanical Engineering (2006) with thesis entitled 'Turbulent Boundary Layers: A look at Skin Friction, Pressure Gradient and Surface Roughness,' and a Ph.D. in Mathematics (2011) with dissertation 'Synchrony in Stochastically-Driven Neuronal Network Models.' Following her doctoral work, she completed postdoctoral research at New York University's Courant Institute of Mathematical Sciences from 2011 to 2014. Her research focuses on developing new tools for analyzing large and infinite dimensional stochastic systems to understand large-scale and long-time dynamics of physical and biological systems. Rather than relying on traditional Fokker-Planck formulations that become intractable with increasing complexity, her work builds on concepts of statistical mechanics to create macroscopic descriptions from individual unit statistics. This approach extends the usefulness of energy landscapes even in non-gradient systems, enabling explanations of experimentally observable phenomena while exposing fundamental mechanisms responsible for system behavior. Her work spans applications from granular materials and chromosome dynamics to biological systems and metamaterials. Dr. Newhall's publications demonstrate consistent advancement in stochastic modeling techniques, with recent work (2023-2025) focusing on hyperuniformity in biological structures, energy landscape sampling methods, and the role of weak transient interactions in biological systems. Her research shows a clear trajectory from fundamental mathematical developments toward increasingly sophisticated biological applications. Outstanding Referee of the Physical Review journals (2019) NSF grant DMS-1816394 DMREF grant ($2M NSF Grant to Revolutionize Materials, 2023) Member at large of the GSNP board (2024) Dr. Newhall has successfully mentored numerous PhD students to completion, including Anna Coletti (2024), Daftari (2023), Moakler (2021), Ben Walker (2021), and Yuan Gao (2019). Her research is consistently supported by competitive grants, most notably the $2M NSF DMREF grant awarded in 2023. She maintains active collaborations across disciplines, particularly in applying mathematical techniques to biological problems such as chromatin organization and organ transplantation risk assessment. Her laboratory work focuses on developing computational methods for analyzing complex stochastic systems, with particular emphasis on the hydra string method for exploring high-dimensional potential energy surfaces. The research group maintains strong connections with both theoretical and experimental collaborators working on granular materials, chromosome dynamics, and biological systems.

Aravind N.R.View profile

Associate Professor

• Algorithms
• Graph Theory
• Combinatorics
• +5 more

Dr. N.R. Aravind is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad. Holding a Ph.D. in Theoretical Computer Science from The Institute of Mathematical Sciences, Chennai, he is affiliated with the College of Engineering. His research spans algorithms, graph theory, combinatorics, and determinantal complexity, with a focus on parameterized algorithms and approximation techniques for NP-hard problems. Education: Ph.D., The Institute of Mathematical Sciences, Chennai (Advisor: Prof. C.R. Subramanian) Postdoctoral research under Prof. Sundar Vishwanathan at IIT Bombay Aravind's research explores structural questions in graph theory, social network modeling, and complexity bounds. His work on parameterized algorithms addresses tractable instances via input parameters, while his studies in graph coloring and forbidden subgraphs contribute to theoretical understanding. Recent publications focus on matching cut problems, happy coloring, and determinantal complexity. His 15 most recent publications (2010-2024) span computational complexity, graph algorithms, coloring problems, and determinantal complexity. These works emphasize parameterized approaches, structural graph theory, and algorithmic bounds for intractable problems. Scientific Awards: Aravind has mentored numerous PhD and MTech students, including Roopam Saxena, Anjeneya Swami Kare, and R.B. Sandeep, now holding academic positions at prestigious institutions. He has taught courses like Algorithms, Probability in Computing, and Cryptology, often collaborating with co-instructors such as Dr. Rakesh Venkat. His office is located in Room CS-408, Indian Institute of Technology Hyderabad.

• Applied mathematics
• Numerical and computational mathematics
• Advection-diffusion-reaction models
• +7 more

Dr. Elliot Carr is a Senior Lecturer in the School of Mathematical Sciences at Queensland University of Technology (QUT), Faculty of Science. He holds a PhD in Mathematics from QUT and has been a faculty member since 2015, progressing from Lecturer to his current rank. His research and teaching focus on applied and computational mathematics, with strong interdisciplinary applications. Education: PhD in Mathematics, Queensland University of Technology, 2009–2012 Bachelor of Applied Science (Honours) in Mathematics, QUT, 2008 Bachelor of Mathematics, QUT, 2005–2007 Elliot Carr's research lies at the intersection of applied mathematics and real-world physical systems. His work centers on developing and analyzing mathematical models of advection, diffusion, and reaction processes, particularly in heterogeneous media. He employs both deterministic (PDE-based) and stochastic (random walk) frameworks, contributing to analytical solutions, multiscale modeling, surrogate models, and numerical methods such as finite volume and Newton-Krylov techniques. His research has been applied to diverse fields including groundwater contamination, drug delivery, heat transfer, and tumor spheroid modeling. The latest publications reflect a consistent focus on transport phenomena in complex geometries and heterogeneous environments. Key themes include dual-grid mapping for contaminant transport, analytical modeling of drug release from spherical capsules, thermal diffusivity in shell geometries, and stochastic models of biological systems. His methodological contributions span analytical, numerical, and statistical approaches, demonstrating versatility across applied mathematics. Scientific Awards and Recognitions: JH Michell Medal, ANZIAM (2022) ARC DECRA Fellowship (2015) QUT Outstanding Doctoral Thesis Award (2012) University Medal, QUT (2008) Dean’s Award for top graduate in both Honours and Bachelor programs Keynote and plenary speaker at major conferences including ANZIAM and Forum “Math-for-Industry” Dr. Carr actively supervises PhD and Masters students, with completed and ongoing projects on diffusive transport, tumor modeling, and sports analytics. He has secured competitive research funding, including an ARC Discovery Project on multiscale modeling. His teaching includes computational mathematics, linear algebra, and differential equations, with a focus on MATLAB-based implementation. He is a member of the Australian Mathematical Society (AustMS) and ANZIAM. Research Labs and Teams: While not explicitly tied to a named lab, Carr is part of the broader Applied Modelling and Computation research environment at QUT. He collaborates extensively with researchers such as Ian Turner, Matthew Simpson, and Chris Drovandi, contributing to interdisciplinary teams in mathematical biology, environmental modeling, and statistical computation.

• Automated Reasoning
• Machine Learning
• Knot Theory
• +3 more

Dr Alexei Lisitsa is a Senior Lecturer at the University of Liverpool specializing in theoretical computer science and mathematical structures. His work bridges automated reasoning, machine learning, and topological mathematics with applications in cybersecurity and network analysis. Active since at least 2008, he maintains a robust research profile with recent publications through 2025. His research focuses on computationally intensive problems in algebraic topology and formal methods. Key contributions include developing machine learning techniques for braid invariants, automating group theory proofs, and creating knot diagram analysis frameworks. This work consistently integrates experimental mathematics with computational verification, demonstrating innovative cross-disciplinary approaches to complex topological challenges. Analysis of his 2023-2025 publications reveals accelerating integration of neural networks with symbolic reasoning systems. His team pioneers applications of deep learning to traditionally intractable problems in braid theory and group presentations, while maintaining strong foundations in formal verification methods for temporal logic systems. Dr Lisitsa actively secures competitive research funding: Flexible Querying of Encrypted Graph Databases (Innovate UK, 2020) KTP with ValueChain Enterprise Systems (Innovate UK & VALUECHAIN, 2018-2022) Future AI and Robotics for Hub Space (FAIR-SPACE) (EPSRC, 2017-2021) Machine Learning for recognising tangled 3D objects (Leverhulme Trust, 2020-2023) Engineering Autonomous Space Software (EPSRC, 2008-2012) Discrete analogues of dynamical systems (NATO, 2008-2010) He supervises postgraduate research in network security and movement pattern analysis, with thesis topics including temporal logic-based intrusion detection systems and large-scale network prediction methodologies.

Jerry LiView profile

Associate Professor

• learning theory
• quantum information theory
• large foundation models
• +4 more

Jerry Li is an associate professor at the University of Washington's Paul G. Allen School of Computer Science & Engineering. Previously, he was a principal research scientist at Microsoft Research Redmond and was the VMware Research Fellow at the Simons Institute in Fall 2018. Li completed his Ph.D. at MIT under the supervision of Ankur Moitra and his master's degree at MIT under Nir Shavit. As an undergraduate, he also attended the University of Washington, where he worked on complexity of branching programs and hardness of learning problems in database theory and AI. His primary research interests focus on learning theory broadly defined, with specific expertise in quantum information theory, large foundation models, and high-dimensional statistics. He has a particular interest in applying analysis and analytic techniques to theoretical computer science problems. His recent work spans quantum computing, robust machine learning, and theoretical foundations of deep learning, showing a clear trend toward bridging quantum information theory with statistical learning theory. Li has made significant contributions to the fields of robust statistics, quantum computing, and theoretical machine learning, with numerous publications in top-tier conferences and journals including FOCS, STOC, NeurIPS, ICML, and Science. His work often bridges theoretical guarantees with practical applications in machine learning systems, particularly in the areas of robustness and quantum advantage. George M. Sprowls Award for outstanding Ph.D. theses in EECS at MIT Best Artifact Award at PPoPP 2015 for "The SprayList: A Scalable Relaxed Priority Queue" Communications of the ACM Research Highlights for "Robust Estimators in High Dimensions without the Computational Intractability" Invited to special issues of SIAM Journal on Computing for FOCS 2023 and STOC 2022 Spotlight Presentations at NeurIPS 2019 Notable top 5% paper at ICLR 2023 Li advises several Ph.D. students including Ziyun Chen (co-advised with Shayan Oveis Gharan) and numerous research interns. He has served on program committees for major conferences including STOC, SODA, and ITCS, and is co-organizing the FOCS 2024 Workshop on Recent Advances in Quantum Learning. His teaching includes courses such as CSE 422: Toolkit for Modern Algorithms and CSE 599-M: Robustness in Machine Learning, for which he has created publicly available video lectures.

• Theory of Computation
• Discrete Algorithms
• Theoretical Computer Science
• +5 more

Michael Saks is a Distinguished Professor of Mathematics at Rutgers, The State University of New Jersey . He is affiliated with both the Department of Mathematics and the Computer Science Department as a graduate faculty member. His research focuses on theory of computation and discrete algorithms , with applications in computational complexity, combinatorics, and algorithmic analysis. His recent publications include work on edit distance approximations , randomized algorithms , discrepancy of random matrices , and Boolean function analysis , reflecting a strong emphasis on theoretical foundations. He has contributed to journals such as Combinatorica , Journal of Graph Theory , and Discrete Applied Mathematics , and served on editorial boards and conference program committees, including the 2014 IEEE Conference on Computational Complexity . Michael Saks maintains active research and teaching pages , including guidance for graduate applicants and links to seminars like the Discrete Mathematics/Theory of Computing Seminar . He also participates in initiatives such as the Center for Computational Intractability and DIMACS .

• Bayesian Statistics
• Machine Learning
• Medical Imaging
• +5 more

Dr. Yanan Fan is a Senior Principal Research Scientist at CSIRO's Data61 and an Adjunct Professor of Statistics at the University of New South Wales (UNSW). His research focuses on Bayesian models, computational methods for real-world problems, and interdisciplinary applications in fields like medical imaging, cosmology, and climate science. He holds a PhD in Statistics from the University of Bristol and has over 20 years of academic experience at UNSW's School of Mathematics and Statistics. Education: PhD in Statistics, University of Bristol, UK Undergraduate Degree in Mathematics, University of Melbourne Research Interests: Fan develops Bayesian semiparametric models, approximate Bayesian computation (ABC), and scalable computational methods for medical imaging (e.g., PET), cosmology, and climate modeling. He also investigates gender bias in educational evaluations and leads initiatives like the Data4Good stream of UDASH. His work emphasizes practical problem-solving through advanced statistical techniques. Leadership & Contributions: As Team Leader of Bayesian Computational Methods and Applications at Data61, he drives innovation in statistical methodologies. He has served on the Scientific Committee of MATRIX research institute and as an Associate Editor for major statistical journals. His projects include probabilistic climate projections and bias analysis in student evaluations. Labs & Groups: Active member of the StatML Group and leader of the Bayesian Computational Methods team, focusing on integrating machine learning and statistical computing.

Muhammad Shahid JavaidView profile

Research Fellow

• Neuroscience
• Stem cell research
• Precision Medicine
• +7 more

Dr. Muhammad Shahid Javaid is a postdoctoral research fellow in the Department of Neuroscience at the School of Translational Medicine, Monash University. He completed his PhD from Monash University and has presented his research on national and international platforms. His research focuses on: Development of precision medicines using human stem cell-based in vitro epilepsy models Biological reprogramming of patients' blood cells into stem cells Converting stem cells into brain cells, neurons, and 3D brain organoids Creating personalized 'disease-in-a-dish' models for patients with medically intractable epilepsy Bioinformatics-based computer-aided medicine design His recent publications demonstrate expertise in integrating in vitro human iPSCs-derived neuron and in vivo animal approaches for anti-seizure compound screening, developing human in vitro models of epilepsy using stem cells, conducting molecular docking studies for redox balance regulation in epilepsy, analyzing sex differences in adverse drug reactions from antiseizure medications, and performing in silico analysis of stem cell differentiation mechanisms. His awards include: Gold Medal (2016) Monash Graduate Scholarship (MGS) (2018) Monash International Tuition Scholarship (MITS) (2018) Dr. Javaid has been actively involved in research projects related to patient-specific disease models for Homer1 gene mutations, pre-clinical evaluation of adenosine A1 receptor modulators for drug-resistant epilepsy, intracerebral delivery of Neuropeptide Y for epilepsy treatment, and drug screening using patient-specific iPSCs-derived neurons. He also served as an Authorised Officer and Public Health Officer at the Department of Health (Victoria) from February 2021 to June 2022, demonstrating his commitment to public health applications of his research.

Mingjun ZhongView profile

Lecturer

• Machine Learning
• Computational Statistics
• Non-intrusive Load Monitoring
• +12 more

Dr. Mingjun Zhong is a Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen. His research focuses on machine learning and computational statistics with applications in healthcare, energy systems, and medical imaging. He is actively engaged in teaching and academic service, including editorial roles in prominent journals. Position: Lecturer Institution: University of Aberdeen School: School of Natural and Computing Sciences Email: mingjun.zhong@abdn.ac.uk Dr. Zhong's research interests center on probabilistic and statistical machine learning methodologies applied to real-world data. He works on healthcare data analysis, non-intrusive load monitoring (NILM), spectroscopy, EEG/fMRI, and energy disaggregation. His methodological expertise includes variational inference, Markov chain Monte Carlo, Bayesian matrix factorization, and deep learning. He has developed lightweight and efficient neural network models for applications in medical imaging and smart grids. The most recent publications reflect a strong trend in applying advanced machine learning techniques—particularly deep learning, self-supervised learning, and capsule networks—to diverse domains such as medical diagnostics, energy disaggregation, and clinical decision support. There is a clear emphasis on developing efficient, interpretable, and robust models for real-world deployment, often addressing challenges like class imbalance, domain adaptation, and data scarcity. Scientific recognition includes: Fellow of the Higher Education Academy (FHEA) Associate Editor, Neural Processing Letters Review Editor, Frontiers in Applied Mathematics and Statistics Regular reviewer for top-tier journals and conferences in AI and machine learning Grant reviewer for multiple funding bodies Dr. Zhong advises a number of students, as evidenced by co-authorships on numerous publications. His research is supported by academic collaborations and likely external grants, though specific funding details are not mentioned. He teaches courses in Robotics, Machine Learning, and Knowledge Representation and Reasoning, contributing significantly to the curriculum in computing sciences. He is involved in interdisciplinary research, particularly through projects like ARCHERY (Artificial intelligence to Revolutionise the patient Care pathway in Hip and knEe aRthroplastY), which integrates AI into orthopaedic care. His work bridges computer science, statistics, and domain-specific applications, demonstrating a strong commitment to impactful, application-driven research.

• Saddlepoint-Based Bootstrap
• Time Series Modeling
• Longitudinal Modeling
• +3 more

Dr. A. Alexandre Trindade is a Professor in the Department of Mathematics & Statistics at Texas Tech University. His research focuses on developing novel statistical methodologies for complex data structures, with applications spanning finance, physics, and environmental science. He teaches graduate courses in Time Series Analysis and Nonparametric Statistics, and serves as advisor for the undergraduate Actuarial Science Minor program. His primary research areas include: Saddlepoint-Based Bootstrap (SPBB) : Methods for approximate inference in complex models with intractable distributions Time Series & Volatility Modeling : Multivariate autoregressive models, GARCH variants, and asymmetric distributions Spatial & Longitudinal Analysis : State-space models for missing data and spatial regression techniques Tail Risk Quantification : Nonparametric estimation of systemic risk measures like CoVaR Nonparametric Inference : Density estimation and signal detection for high-energy physics His publications demonstrate consistent innovation in statistical theory, particularly in developing resampling-based inference and extending time series methodologies. Collaborative projects include interdisciplinary work with petroleum engineering, nuclear science, and finance. Dr. Trindade maintains active research partnerships with national laboratories and industry, including reliability studies for Boeing and medical device research. He has developed specialized software for multivariate time series analysis and maintains public repositories for statistical computing resources.

Agha Iqbal AliView profile

Professor

• Data Analytics
• Supply Chain Management
• Operations Research
• +4 more

Agha Iqbal Ali is the Karen & Chuck Peters Family Endowed Professor of Management Science and Operations Management at the Isenberg School of Management, University of Massachusetts Amherst. He holds a PhD from Southern Methodist University (1980) and prior master's degrees in Mathematics and Statistics. His academic leadership includes serving as Chairman of the Operations & Information Management Department (2013–2018) and the Finance & Operations Management Department (2006–2012). His research focuses on Data Analytics , Supply Chain Management , and Mathematical Programming Models , with notable contributions to disaster relief logistics, FIFA World Cup capacity planning, and banking deregulation analysis. Teaching interests include Operations Management, Optimization, and Algorithm Design. Dr. Ali has been honored with the prestigious Goodeve Medal (2018) from the Operational Research Society, and multiple teaching awards including the College Outstanding Teacher Award (1999–2000). His work bridges theoretical optimization with real-world applications in manufacturing, military personnel planning, and international cooperatives.

Stéphan ThomasséView profile

Professor

• Graph Theory
• Combinatorics
• Algorithms
• +3 more

Stéphan Thomassé is a Professor in the Computer Science Department at Ecole Normale Supérieure de Lyon, where he is affiliated with the Parallel Computing Laboratory (LIP) and leads the Computational Models, Complexity, Combinatorics (MC2) research team (2023-2025). He was a Member of the University Institute of France from 2016-2021 and has held significant administrative roles including Head of M2 Fundamental Computer Science (2013-2015) and Deputy Director of the Computer Science Department (2013-2015). Thomassé's research focuses on graph theory, combinatorics, algorithms, and theoretical computer science. His work spans structural graph theory , parameterized complexity , graph coloring , and combinatorial optimization . He is particularly known for his groundbreaking work on twin-width, a graph parameter that has revolutionized understanding of sparse and dense graph classes and their algorithmic properties. His research connects combinatorial structures with computational complexity, often bridging theoretical insights with practical algorithmic applications. His recent publications demonstrate a strong focus on structural graph theory, with the twin-width series representing a major contribution to the field. These papers have established connections between graph parameters, model theory, and algorithm design, particularly for problems that were previously thought to be intractable on dense graph classes. Thomassé's work on graph coloring, particularly regarding dense triangle-free graphs and the Erdős-Hajnal conjecture, has resolved long-standing open problems. Member of University Institute of France (2016-2021) ANR projects: GRAAL, AGAPE, STINT, COMPA, Digraphs, TWIN-WIDTH, GODASse As an advisor and educator, Thomassé has taught numerous advanced courses including Algorithmics, Optimization and Approximation, Tree Decompositions and FPT Algorithms, and Graph Decompositions. His teaching reflects his research expertise, emphasizing both theoretical foundations and practical applications of discrete mathematics. His administrative leadership in the MC2 team and the Computer Science Department demonstrates his commitment to advancing research and education in theoretical computer science. Thomassé maintains active collaborations with researchers worldwide, as evidenced by his extensive publication record with co-authors from various institutions. His work continues to shape the landscape of structural graph theory and parameterized algorithms, with recent results opening new avenues for research in combinatorics and theoretical computer science.

Alexander MigdalView profile

Research Professor

• Turbulence
• Statistical Field Theory
• Quantum Field Theory
• +4 more

Alexander Migdal serves as a Research Professor of Physics within the Faculty of Arts and Science at New York University, where his work bridges theoretical physics and fluid dynamics through innovative applications of statistical field theory to turbulence. His research establishes deep connections between classical turbulence phenomena and quantum field theory frameworks, challenging conventional paradigms in fluid mechanics. Migdal earned his Ph.D. from the Landau Institute, a foundation that shaped his approach to complex theoretical problems. His educational background in rigorous theoretical physics provides the basis for his unconventional methodologies in turbulence research. His primary research interests focus on turbulence as statistical field theory, with significant contributions to duality methods that map classical fluid dynamics to quantum mechanical systems. Migdal has pioneered exact solutions for decaying turbulence and passive scalar transport, revealing profound links between turbulence statistics and quantum chromodynamics. His work demonstrates how vortex dynamics, confinement phenomena, and string theory concepts provide new mathematical tools for solving intractable problems in fluid mechanics, particularly through the lens of Navier-Stokes equation analysis and energy cascade mechanisms. Analysis of Migdal's 2023-2025 publications reveals a concentrated research program on dual theories of decaying turbulence, featuring systematic development of exact solutions through fermionic representations, matrix ensembles, and dimensional reduction techniques. His work consistently applies quantum field theory methods to classical turbulence problems, establishing connections to Yang-Mills theory and magnetohydrodynamics while demonstrating the absence of finite-time singularities in Navier-Stokes dynamics. The recurring themes across his publications include duality transformations, statistical mechanics approaches to vortex sheets, and the application of large N expansions to fluid systems. No scientific awards were documented in the provided source material. The available information does not specify graduate students under Migdal's supervision or details of research grants supporting his work, though his extensive publication record suggests sustained research activity. His theoretical focus appears to operate without dependency on specific laboratory facilities or large experimental teams. Migdal maintains an active research program without indication of institutional laboratory affiliations; his work appears predominantly theoretical and computational, leveraging mathematical physics techniques rather than experimental apparatus. His recent publications suggest ongoing collaboration with computational physicists through numerical simulation components of dual turbulence theories.

Massimiliano TamborrinoView profile

Associate Professor

• Stochastic Processes
• Approximate Bayesian Computation (ABC)
• Parallel-in-Time Numerical Methods
• +4 more

Massimiliano Tamborrino is an Associate Professor in the Department of Statistics at the University of Warwick since August 2024. He holds a WIHEA Fellowship (2023-2026) and has organized the One World ABC Seminar (2020–present), focusing on approximate Bayesian computation (ABC) and simulation-based inference. He co-organizes the BioInference conference series, which explores mathematical modeling in biological systems. His research integrates stochastic processes, numerical methods, and statistical inference, with applications in neuroscience, biology, and parallel-in-time algorithms. Education: PhD in Probability Theory and Statistics from the University of Copenhagen (2013), supervised by Prof. Susanne Ditlevsen. Previously held roles include University Research Assistant at JKU Linz (2014–2019) and Postdoc at the University of Copenhagen (2012–2014). Research interests include stochastic processes (diffusions, point processes), parallel-in-time numerical schemes (PinT), and ABC methods. His work bridges stochastic numerics with computational statistics, particularly in neuroscience and biological systems. He has developed R packages for exact simulation of non-Gaussian processes (e.g., shot noise, OU processes). Awards: WIHEA Fellow (2023–2026). Active in grants, including leadership of the EPSRC-funded project on AI-informed decision-making using Decision Field Theory. Teaching responsibilities include ST232/ST233: Introduction to Mathematical Statistics for undergraduate students.

• Artificial Intelligence
• Computational Complexity
• Knowledge Compilation
• +2 more

Alexis de Colnet is a PostDoc Researcher at the Vienna University of Technology , affiliated with the Faculty of Informatics and the Algorithms and Complexity department. Their work focuses on overcoming intractability in knowledge compilation, computational complexity, and model counting. Research Interests: Knowledge Compilation Computational Complexity Artificial Intelligence Model Counting Answer Set Programming Theoretical Computer Science Recent Publications explore trends in proof systems, compilation efficiency, and translations between machine learning models for explainability. These works are deeply rooted in theoretical computer science and AI, addressing challenges in knowledge representation and computational hardness. Projects: Overcoming Intractability in the Knowledge Compilation Map (2022–2025) QBFPC (2022–2025) Funded by the Austrian Science Fund (FWF).