Find Academic Supervisors

• High-Dimensional Statistics
• Portfolio Optimization
• Bayesian Analysis
• +3 more

Taras Bodnar is a Professor at the Department of Management and Engineering, Linköping University. His research focuses on high-dimensional statistical methodologies with applications in finance, portfolio optimization, and econometrics. He specializes in developing and analyzing advanced statistical models for asset allocation, risk management, and multivariate meta-analysis. Bodnar's work often involves Bayesian methods, shrinkage estimation techniques, and copula modeling to address challenges in financial data analysis. His recent contributions include the HDShOP package for portfolio selection and advancements in nonlinear shrinkage tests for large-dimensional covariance matrices. His research bridges theoretical statistics with practical financial applications, addressing issues such as dark uncertainty and efficient frontier estimation in high-dimensional settings. Key research interests include: High-Dimensional Portfolio Optimization Bayesian Analysis in Financial Contexts Covariance Matrix Estimation and Testing Uncertainty Quantification in Multivariate Analyses Statistical Software Development for Finance Recent publications (2024-2025) emphasize methodological innovations in portfolio selection, copula modeling, and robust statistical inference. His work has implications for both academic theory and practical investment strategies, particularly in managing large and complex financial datasets.

Klas MarkströmView profile

Professor

• Combinatorics
• Graph Theory
• Hypergraphs
• +3 more

Klas Markström is a Professor at the Department of Mathematics and Mathematical Statistics, Umeå University. He holds academic qualifications as a Docent and a Recognised University Teacher in Mathematics. His primary research interests include combinatorics, graph theory, hypergraphs, computational combinatorics, linear algebra, and mathematical physics. He utilizes Swedish supercomputing resources, including those at HPC2N, for his research. Markström has advised PhD students Lan Anh Pham (2019) and Joel Larsson (2018). His postdoctoral collaborators include Jakub Sliacan, Fei Song, and Victor Falgas-Ravry. His work spans theoretical contributions to combinatorics, computational methods, and applications in statistical mechanics and algorithm design. Notable research trends include explorations of Condorcet domains in social choice theory, extremal graph/hypergraph problems, and algorithmic solutions for SAT problems. His software contributions include the Umsat SAT-solver and GrafPack, a Mathematica package for graph theory. He has published extensively on topics such as Turán hypergraphs, covering designs, and computational techniques for graph enumeration. Collaborations involve interdisciplinary projects with physicists and computer scientists, reflecting his engagement with both pure and applied mathematics. His research often bridges combinatorial theory with computational experimentation, yielding impactful results in discrete mathematics and beyond.

• Quantum Photonics
• Single-Photon Detectors
• Quantum Dots
• +7 more

Val Zwiller is a Professor at the Royal Institute of Technology (KTH) in the Department of Quantum and Nanophysics, affiliated with the School of Engineering Sciences (SCI). His research focuses on quantum photonics, superconducting single-photon detectors, nanowire quantum dots, and integrated photonic systems. He teaches and coordinates courses such as Quantum Photonics (SK2900) and Quantum Technology (SK2903), and advises on degree projects in engineering and applied physics. Research Interests: Zwiller's work spans quantum entanglement generation, single-photon detection technologies, and applications in quantum communication and sensing. His recent studies include fractal superconducting nanowire detectors, telecom-band quantum dots, and quantum state tomography. His research bridges fundamental quantum physics with practical photonic device development. Course Responsibilities: Quantum Photonics (SK2900) - Examiner, Course Coordinator, Teacher Quantum Technology (SK2903) - Teacher, Assistant Optical Physics (SK2303) - Examiner Supervision of Degree Projects in Engineering Physics and Applied Physics Publications Trends: Zwiller's recent articles emphasize scalable quantum detection systems, nanowire-based quantum emitters, and advanced LIDAR technologies with single-photon sensitivity. His work frequently intersects quantum optics with material science and engineering. Awards/Grants: No specific awards or grants were explicitly mentioned in the provided texts. Lab/Teams: While not detailed in the input, his affiliation with the Department of Quantum and Nanophysics suggests involvement in KTH's quantum photonics and nanotechnology research groups.

Pierre NyquistView profile

Associate Professor

• Probability Theory
• Mathematical Statistics
• Applied Mathematics
• +8 more

Pierre Nyquist is an Associate Professor and docent in the Department of Mathematical Sciences at Chalmers University of Technology and Gothenburg University. His research is sponsored by the Swedish Research Council, the Swedish e-science Research Center (SeRC), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP). He is also an elected member of the Young Academy of Sweden for the period 2024-2029 and has served as a scientific ambassador for EURANDOM since November 2021. Dr. Nyquist's research interests lie at the intersection of probability theory, mathematical statistics, and applied mathematics. His main expertise is in probability theory, with a focus on large deviations theory and stochastic numerical methods. He has a general interest in all aspects of probability theory and much of what is categorized as applied mathematics, particularly questions related to partial differential equations, optimization, and stochastic optimal control. Recently, he has become increasingly interested in the mathematical foundations of complex data analysis and modeling, and the interplay with ideas from physics. His current research interests include large deviations, gradient flows and their generalizations, stochastic numerical methods, statistical learning theory, stochastic processes, and random dynamical systems. Pierre Nyquist has received research funding from several prestigious sources including the Swedish Research Council, the Swedish e-science Research Center (SeRC), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP). His publications demonstrate a consistent focus on theoretical aspects of probability with applications to computational methods and data analysis, showing increasing integration with machine learning techniques in recent years. elected member of the Young Academy of Sweden (2024-2029) scientific ambassador for EURANDOM (since November 2021) Dr. Nyquist is actively involved in mentoring the next generation of researchers. He currently supervises several PhD students including Cinja Arndt (starting Aug. 2025), Niki Wilhemlson (started Aug. 2024), and Viktor Nilsson (started Aug. 2020). He has previously supervised successful PhD students such as Federica Milinanni (Aug. 2020-May 2025) and Carl Ringqvist (Aug 2015-June 2021). He regularly teaches graduate-level courses including "Modern methods of statistical learning" and has supervised numerous MSc theses on topics ranging from deep learning for time-series radar signals to neural network embedding in insurance pricing. His research group is active in both theoretical developments and practical applications, with current projects spanning from mathematical foundations of probability to applications in machine learning and data science. Dr. Nyquist maintains strong international collaborations, as evidenced by his frequent travel for conferences and research visits to institutions such as Brown University and TU Delft.

Christoph KesslerView profile

Professor

• Parallel Programming
• Compiler Technology
• Optimization Algorithms
• +3 more

Christoph Kessler is a Professor and Head of the Software and Systems (SAS) division at the Department of Computer and Information Science (IDA), Linköping University, Sweden. He leads the Programming Environment Laboratory’s research group focusing on compiler technology, parallel computing, and heterogeneous systems. His work includes the development of tools like OPTIMIST, PARAMAT, and SkePU, and he has contributed over 100 publications in journals and conferences. He holds a PhD from the University of Saarbrücken and a Habilitation from the University of Trier. Research interests span parallel programming, compiler optimization, and energy-efficient scheduling for heterogeneous systems. He has secured a 30M SEK grant from SSF for the ASTECC project, advancing adaptive software for edge-cloud computing. Notable contributions include frameworks for GPU-based systems and methodologies for optimizing resource allocation on many-core architectures. His team’s work emphasizes practical applications in high-performance computing, including tools for course management (StASy) and energy-aware scheduling algorithms. The SAS division, under his leadership, focuses on software engineering and computer systems research with strong industry collaboration.

• Quantitative Finance
• Credit Risk
• Financial Engineering
• +3 more

Roles and Affiliations: Alexander Herbertsson is a Senior Lecturer in Statistics and Quantitative Finance at the University of Gothenburg, affiliated with the CFF-Centre for Finance. He is based in the Department of Economics with Statistics, located at Vasagatan 1, Gothenburg. His work focuses on financial risk management, credit risk modeling, and quantitative finance methodologies. Education: Ph.D. in Economics: Quantitative Finance (2007), University of Gothenburg Licentiate of Engineering in Industrial Mathematics (2005), Chalmers University of Technology M.Sc. in Engineering Physics (Applied Mathematics specialty) (2001), Chalmers University of Technology Research Interests: Herbertsson's research emphasizes applied financial mathematics and statistical methods in finance. Key areas include credit risk modeling (default contagion, systemic risks), financial engineering, and the development of dynamic models for portfolio credit risk. His work often integrates Markov chain models, phase-type distributions, and stochastic processes to analyze dependency structures and pricing of credit derivatives. Recent studies explore saddlepoint approximations for portfolio risk analysis and risk management under exogenous shocks. Teaching: He teaches advanced courses in credit risk modeling, quantitative finance, and applied probability theory, emphasizing practical applications in risk management and financial markets. Grants and Labs: While specific grants are not detailed in the text, his affiliation with the CFF-Centre for Finance suggests involvement in collaborative financial research projects. His work often addresses real-world applications of theoretical models in systemic risk and portfolio hedging.

Zhenxia LiuView profile

Associate Professor

• Mathematical Statistics
• Probability Theory
• Large Deviations
• +3 more

Zhenxia Liu is an Associate Professor in the Department of Mathematics at Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA). Her work contributes to theoretical and applied probability, with a focus on stochastic processes and statistical modeling. Her research interests lie at the intersection of mathematical statistics and probability theory. Key areas include large deviations , longest runs in Markov chains , and Monte Carlo methods . These topics are central to understanding rare events, sequential dependencies, and numerical estimation techniques in complex systems. The recent publications demonstrate a consistent focus on probabilistic analysis of dependent structures, particularly through Markov models. Her work combines theoretical rigor with applications in computational statistics, showing trends toward improving bounds and simulation efficiency in stochastic modeling. Mathematical Statistics Probability Theory Large Deviations Markov Chains Monte Carlo Methods Computational Mathematics Zhenxia Liu has actively contributed to high-quality journals such as Statistics and Probability Letters , Results in Applied Mathematics , and Probability and Mathematical Statistics . While no formal advising or grant information is available in the provided text, her collaborative publications suggest engagement in research networks within applied mathematics. She is part of the research environment in Applied Mathematics at Linköping University, which focuses on computational mathematics, optimization, and mathematical modeling across science and engineering disciplines.

Hui HanView profile

Senior Lecturer

• Edge AI
• TinyML
• Sustainable Supply Chain Management
• +7 more

Dr. Hui Han is a Senior Lecturer at Luleå University of Technology (LTU) in Sweden, working within the Machine Learning Group with a Wallenberg AI, Autonomous Systems and Software Program (WASP) professorship. She is affiliated with the Department of Computer Science, Electrical and Space Engineering, specifically within the Embedded Intelligent Systems LAB. Her office is located in Luleå at room A3424. Dr. Han earned her Ph.D. in Industrial Engineering from the Technical University of Madrid (UPM), Spain. Following her doctoral studies, she completed postdoctoral research at the Data Science department of Fraunhofer IESE in Germany and later served as a postdoctoral fellow in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU), Norway. Dr. Han's research focuses on data science with specific expertise in Edge AI and TinyML (Tiny Machine Learning). She leads research efforts in developing sustainable modeling for Sustainable Supply Chain Management, particularly in reverse logistics and circular economy. Her work also encompasses social commerce and Industry 4.0 applications. As the first author, she has published 17 papers including 10 journal papers, 4 conference papers, and 3 magazine papers, accumulating over 1,000 citations according to Google Scholar. Her publications appear in high-impact journals such as Technological Forecasting & Social Change (IF 13.3), Expert Systems with Applications (IF 8.5), and Education and Information Technologies (IF 5.5). Dr. Han's teaching portfolio includes Management Information Systems, Project in Industrial AI, and TinyML. She has created an introductory TinyML course available on her YouTube channel. Her research spans multiple interdisciplinary areas including Industry 4.0, IoT, Cloud Computing, Big Data Analytics, Cyber-Physical Systems, Multicriteria decision support systems (particularly Fuzzy TOPSIS), Complex Relationships Analysis (PLS-SEM), Data Science, TinyML, Edge AI, Random Forest techniques, Information Systems, Social commerce, E-commerce, Sustainable Supply Chain Management, Reverse logistics, Closed-loop Supply Chains, Circle Economy, and Healthcare applications. Her Embedded ML for Health Project demonstrates practical applications of TinyML in healthcare, using Arduino Nano 33 BLE Sense and OV7675 Camera for melanoma detection through image processing and machine learning algorithms. This project showcases her ability to apply theoretical research to real-world healthcare challenges.

• Breast Cancer
• Surgical Oncology
• Cancer Epidemiology
• +3 more

Irma Fredriksson is an active researcher affiliated with the Department of Molecular Medicine and Surgery at Karolinska Institutet, Stockholm, Sweden, holding her current position from 2024 to 2027. She leads the Breast Surgery research group at MMK, focusing on patient-oriented clinical studies in breast cancer surgery, epidemiological analyses using Swedish national registries, and translational research bridging laboratory findings with clinical practice. Her academic credentials include: Doctor of Philosophy (PhD), Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet (2002) University Medical Degree, Karolinska Institutet (1991) Fredriksson's research centers on breast cancer epidemiology and surgical outcomes , with major themes including pregnancy-associated breast cancer, ductal carcinoma in situ progression, vacuum-assisted excision optimization, and biomarker-driven treatment efficacy. Her work consistently leverages Sweden's comprehensive cancer registries for large-scale population-based studies, emphasizing real-world data on age-specific mortality, surgical complications, and healthcare utilization patterns. Analysis of her 15 most recent publications (2024-2025) reveals three dominant trends: pregnancy-cancer interactions (6 studies on maternal/fetal outcomes), surgical technique refinement (3 trials on vacuum-excised lesions and axillary procedures), and biomarker epidemiology (4 studies on HER2/ER subtypes and immune infiltrates). She prioritizes registry-based designs and contributes to AI-driven screening protocols like ScreenTrustMRI. Scientific awards: No awards, fellowships, or medals were documented in the provided text Advising and grants: While no doctoral students or specific grant awards are listed, her leadership in multi-institutional trials (e.g., Swedish VAE, ScreenTrustMRI) indicates active collaboration on major funded projects. She co-developed Breast Cancer Data Base Sweden 2.0 (BCBaSe 2.0), suggesting involvement in infrastructure grants. Labs and teams: She directs the Breast Surgery research group at Karolinska University Hospital (Elite Hotell, Solna), working within the Department of Molecular Medicine and Surgery. The team specializes in registry epidemiology, surgical outcome analysis, and translational studies involving tissue biobanking, with ongoing projects on vacuum-assisted excision, pregnancy-related cancer, and biomarker validation.

• Control Systems
• Cyber-Physical Systems Security
• Power Systems Resilience
• +3 more

Kamil Hassan is a Doctoral student and Researcher at KTH Royal Institute of Technology, affiliated with the Division of Decision and Control Systems. His work focuses on securing cyber-physical infrastructure through advanced control methodologies. His research spans Control Systems , Cyber-Physical Security , and Power Systems Resilience , with emphasis on mitigating attacks in time-critical networks. Key contributions include finite-time control barrier functions for power inverters and randomized detector tuning for attack impact reduction. His methodologies integrate hardware-in-the-loop validation with theoretical guarantees. Recent publications (2021–2025) reveal a trajectory toward resilient control architectures for energy systems, blending multiagent consensus theory with security-aware design. Work on power inverter networks dominates his output, addressing grid stability under cyber threats through novel barrier function frameworks and simulation-validated approaches. Hassan serves as course assistant for Cyber-Physical Security in Time-Critical Systems (EL2850) and operates within KTH's Decision and Control Systems division, contributing to hardware-in-the-loop testing environments for critical infrastructure protection.

Mohamed Faouzi AtigView profile

Professor

• Model Checking
• Program Verification
• Concurrency
• +7 more

Mohamed Faouzi Atig is a Professor in Computer Systems at the Department of Information Technology, Uppsala University. His career spans roles as Senior Lecturer (2018-2021), Associate Senior Lecturer (2014-2018), and Researcher (2012-2018) at the same institution. He obtained his Doctoral Degree in Computer Science from the University of Paris Diderot-Paris 7 (2010) and a Master in Engineering from Tunisia Polytechnic School (2005). Current Role: Professor in Computer Systems Institution: Uppsala University Research Focus: Model checking, verification of infinite-state systems, weak memory models, automata theory, string constraints, concurrent program analysis His research explores formal methods for concurrent programs, weak memory models (TSO/PSO/POWER), automata theory for verification, and SMT solvers for string constraints. Recent work integrates graph neural networks with word equation solving and advances stateless model checking techniques. Key article trends include Weak Memory Model Verification (TSO, PSO, POWER) Stateless Model Checking Algorithms String Constraint Solvers (TRAU, Norn) Timed Automata and Multi-Pushdown Systems Mohamed Faouzi Atig has led collaborations on fence insertion procedures, timed pushdown automata, and database-driven system verification. His contributions are recognized through publications in top-tier conferences and journals.

Andrés Alayón GlazunovView profile

Academic

• Antenna Systems
• Wireless Communications
• Electromagnetic Theory
• +5 more

Andrés Alayón Glazunov is an ELLIIT Senior Associate Professor at Linköping University's Department of Science and Technology (ITN), specializing in Physics, Electronics, and Mathematics. He holds a Docent (Habilitation) in Antenna Systems from Chalmers University and a PhD from Lund University. His career includes roles at Ericsson Research, Telia, and academic positions at Chalmers, KTH Royal Institute of Technology, and the University of Twente. His research focuses on antenna systems, millimeter-wave technologies, and over-the-air (OTA) characterization, with contributions to 3GPP standards and EU projects like is3DMIMO and WAVECOMBE. Education: Docent (2017): Chalmers University, Antenna Systems PhD (2009): Lund University, Radio Systems MSc (1994): St. Petersburg Polytechnic University, Physical Electronics Research Interests: His work spans MIMO systems, mmWave antennas, electromagnetic theory, OTA testing, and wireless channel modeling. Notable milestones include pioneering 3GPP standardized OTA techniques and developing hybrid multipath-LOS chambers. Publications: Over 175 papers, including foundational works on spherical vector wave expansions and massive MIMO channel measurements. Recent articles focus on GRIN lenses, Rician channel emulation, and automotive radar antenna design. Awards: Marie Curie Senior Research Fellowship (2009-2010). Grants & Collaborations: Led EU projects like is3DMIMO and contributed to ITU/3GPP standardization. Collaborates with industries like Volvo Cars and RISE. Teaching: Oversees the Master's Project Course CDIO at Linköping University.

Lambros RoumbanisView profile

Associate Professor

• Organization of expert judgments
• Evaluation technologies
• Selection mechanisms
• +12 more

Lambros Roumbanis is an Associate Professor of Sociology at Stockholm University, affiliated with the Department of Social Anthropology and the Stockholm Centre for Public Sector Research (SCORE). Based in Room E 2344 at Frescativägen 14A, he conducts research at the intersection of sociology, decision-making processes, and technology. Dr. Roumbanis specializes in the organization of expert judgments, evaluation technologies, and complex decision-making processes, with particular focus on how organizations distribute scarce resources through group deliberations, lotteries, and AI systems. His research examines algorithm-based recruitment systems, human-AI interaction in hiring processes, and academic peer review mechanisms for research funding. He also explores sociological concepts including relationality, chance, absence, 'the single individual', socio-materiality, and technology/techne. His scholarly work reveals consistent themes in the sociology of evaluation, showing how traditional peer review systems are being challenged by algorithmic approaches in personnel selection and research funding. His publications demonstrate growing interest in meta-algorithmic judgments and the complex interplay between human expertise and artificial intelligence in decision-making contexts across multiple sectors. His research suggests that chance and randomness can be productively incorporated into evaluation processes, as evidenced by his proposals for lottery systems in research funding allocation. Dr. Roumbanis has conducted extensive ethnographic research with the Swedish Research Council, examining status hierarchies, gender bias, and disrespect in review panel groups. His recent work advocates for innovative approaches to resource allocation, including arguments for pure lottery systems as alternatives to traditional grant competitions that consume significant researcher time and institutional resources.

• Randomized algorithms
• Machine learning
• Big Data
• +3 more

Devdatt Dubhashi is a Professor at the Data Science and AI 3 department at Chalmers University of Technology. His research spans multiple domains within computer science and data science, with a focus on theoretical foundations and practical applications of algorithms and machine learning. Professor Dubhashi's primary research interests include the design and analysis of randomized algorithms, machine learning for Big Data, and computational biology. His work demonstrates a strong interdisciplinary approach, connecting theoretical computer science with practical applications in diverse fields such as quantum computing, transportation modeling, genomics, and social sciences. His research often bridges the gap between theoretical foundations and real-world implementations, as evidenced by his numerous collaborations across different domains. Dubhashi's recent publications reveal a consistent research trajectory focusing on algorithmic foundations of machine learning, with increasing emphasis on interdisciplinary applications. His work shows significant contributions to bandit algorithms, kernel methods, graph-based learning, and the theoretical understanding of deep learning models. There's also a growing focus on societal implications of AI, as seen in his publications addressing responsible AI development and policy considerations. Among his notable scientific contributions are publications in prestigious venues including ACM, IEEE, and Nature journals, covering topics from fundamental algorithm design to applications in computational biology and social sciences. His work has been cited extensively across multiple disciplines, reflecting its broad impact. Professor Dubhashi actively supervises PhD students and collaborates with researchers across Chalmers and internationally. His research group appears to focus on the intersection of theoretical computer science and practical machine learning applications, with projects spanning quantum computing, transportation modeling, and biological applications.

• Statistics
• Machine Learning
• Artificial Intelligence
• +6 more

Erik Kristiansson is a Professor in the Department of Applied Mathematics and Statistics at Chalmers University of Technology's School of Engineering Sciences. His academic work bridges computational methods with biological applications, focusing on the analysis of complex biological datasets using advanced statistical and machine learning approaches. Professor Kristiansson's research centers on applying statistical methods, machine learning, and artificial intelligence to analyze complex datasets from molecular biology and medicine. His primary applications focus on infectious diseases, antibiotic resistance mechanisms, and environmental science challenges. His work demonstrates a strong interdisciplinary approach, connecting mathematical modeling with real-world biological problems, particularly in understanding the spread and evolution of antibiotic resistance through environmental pathways and clinical settings. His publication record shows a consistent focus on antibiotic resistance gene identification, environmental contamination with pharmaceuticals, and the development of computational methods for biological data analysis. Recent work demonstrates increasing application of advanced machine learning techniques, including transformer models, to predict chemical toxicity and antibiotic resistance patterns. His research often involves large-scale genomic and metagenomic data analysis to track resistance gene dissemination across different environments. Professor Kristiansson actively collaborates across disciplines, working with environmental scientists, microbiologists, and medical researchers to address complex problems at the intersection of computational science and biological systems. His work on optical DNA mapping for bacterial typing represents an innovative approach to pathogen identification directly from clinical samples.