جستجوی استاد راهنمای دانشگاهی

Johannes Steidleمشاهده پروفایل

استاد

• Chemical Ecology
• Entomology
• Biological Pest Control
• +۳ مورد دیگر

Prof. Dr. Johannes Steidle is Professor of Chemical Ecology at the University of Hohenheim's Institute of Biology, where he serves as Managing Director since 2020. His leadership extends to chairing the VDI committee for Insect-friendly Mowers (since 2022) and directing the Competence Center for Biodiversity (KomBioTa) since 2018. A key advisor to Baden-Württemberg's biodiversity program since 2017, he bridges academic research with environmental policy implementation. His educational foundation was established through doctoral studies at the University of Bayreuth (1981-1993), followed by postdoctoral work at Wageningen University and CSIRO Australia. This international training underpins his interdisciplinary approach to entomological challenges. Steidle's research centers on chemical ecology and insect conservation, with dual focus areas: pheromone-mediated biological control of stored product pests (particularly using parasitoid wasps like Lariophagus distinguendus), and developing arthropod-friendly mowing technologies to combat insect decline. His work on sex pheromones in click beetles and symbiont-induced reproductive manipulation reveals fundamental mechanisms driving insect population dynamics. The InsectMow project exemplifies his applied research translating lab discoveries into field-ready conservation solutions for grassland ecosystems. Analysis of his 2022-2025 publications shows escalating emphasis on mitigating anthropogenic threats to arthropods, particularly through agricultural innovation. Over 60% of recent work addresses mowing technology optimization, while his foundational pheromone research now integrates with symbiont studies to explain cryptic speciation in parasitoid complexes. This dual trajectory positions his lab at the nexus of basic evolutionary entomology and urgent conservation applications. Scientific recognition includes: Environmental Award of Sparkasse Pforzheim Calw (2004) Editorial Board membership for Journal of Applied Entomology (2012-2017) As former Dean of Biology (2003-2012) and Head of Biology (2008-2012), Steidle has shaped institutional research directions while securing state and federal grants for biodiversity initiatives. His current KomBioTa leadership coordinates taxonomic resources across Baden-Württemberg, creating synergies between traditional systematics and genomic approaches to species conservation. The Chemical Ecology group operates within Hohenheim's Institute of Biology, maintaining specialized facilities for pheromone analysis and arthropod behavior studies. Through KomBioTa, the team collaborates with state environmental agencies on the Hohenheimer Box mass-rearing system for biological control agents and field-testing of the VDI 4350 mowing standards.

Jérôme Lerouxمشاهده پروفایل

پژوهشگر

• Formal Verification
• Vector Addition Systems
• Petri Nets
• +۷ مورد دیگر

Jérôme Leroux is a Directeur de Recherche (DR CNRS) at the Laboratoire Bordelais de Recherche en Informatique (LaBRI) , affiliated with the University of Bordeaux , France. His research focuses on formal verification of infinite-state systems, vector addition systems, Presburger arithmetic, and acceleration techniques for symbolic computation. Key research themes include: Vector Addition Systems (VASS) and Petri Nets Automata-based representations for Presburger arithmetic Abstract interpretation and CEGAR frameworks Verification of asynchronous distributed systems His recent publications highlight work on acceleration techniques for convex binary relations, serialized digit automata, and regular acceleration methods for number decision diagrams. These contributions are implemented in tools like the Talence Presburger Arithmetic Suite (TAPAS) and the FAST tool for symbolic verification. Scientific awards include a Best Paper Award at TURING'100 . He has advised several Ph.D. students and postdoctoral researchers, including Alexander Heußner and Thibault Hilaire (current Ph.D. candidate). Leroux is actively involved in organizing conferences like MFCS'23 and serving on program committees for venues such as VMCAI'26 .

Sriram Sankaranarayananمشاهده پروفایل

استاد

• Cyber-physical systems
• Hybrid dynamical systems
• Formal methods
• +۸ مورد دیگر

Sriram Sankaranarayanan is a Professor in the Department of Computer Science at the University of Colorado Boulder and also serves as Associate Dean for Digital Education in the College of Engineering and Applied Science. Since joining the faculty in 2009, he has built an internationally recognized research program that blends programming languages, formal methods, and control theory to reason about cyber-physical systems. Education: Ph.D. in Computer Science, Stanford University, 2005 (advisers Zohar Manna & Henny Sipma) B.Tech., Indian Institute of Technology Kharagpur (President’s Gold Medal, 2000) Research Interests: Prof. Sankaranarayanan’s work centers on hybrid dynamical systems —models that capture discrete software interacting with continuous physical environments—and on developing formal-methods techniques for their verification, control, and synthesis. Specific themes include control-barrier & Lyapunov function synthesis, neural-network verification, stochastic-game models for human-autonomy interaction, and physics-informed machine learning. Application domains range from autonomous robotics and surgical-task planning to safety-critical medical devices such as the artificial pancreas. Recent Publication Trends (2024-2025): His latest papers advance safe control synthesis (successive control barrier functions, piecewise-affine Lyapunov functions) and trustworthy AI (Taylor-model enhanced physics-informed neural networks), while also exploring game-theoretic anticipation for robotic systems interacting with uncertain human operators. Honors & Awards: NSF CAREER Award (2009) Siebel Scholar (2005) President’s Gold Medal, IIT Kharagpur (2000) CU Boulder Dean’s Award for Outstanding Junior Faculty (2012) CU Boulder Outstanding Teaching Award (2014) CU Boulder Provost’s Faculty Achievement Award (2014) Coursera Outstanding Innovation Award (2022) Student Advising & Grants: He has mentored numerous PhD students; recent graduates include Dr. Emily Jensen, Dr. Monal Narasimhamurthy, and Dr. Kandai Watanabe (2024). His group regularly publishes at top venues such as HSCC, POPL, PLDI, CAV, and WAFR, supported by NSF, NIH, and industry grants. Group & Teaching: Prof. Sankaranarayanan leads activities within the Programming Languages & Verification group and teaches graduate and undergraduate courses on programming languages, algorithms, optimization, and formal methods. He is active in conference organization (e.g., PC Chair VMCAI 2025) and maintains open-source courseware and research notebooks on GitHub.

• Database Systems
• In-Database Machine Learning
• GPU Acceleration for Databases
• +۳ مورد دیگر

Maximilian Schüle serves as Assistant Professor in the Department of Data Engineering at the University of Bamberg's Faculty of Information Systems and Applied Computer Sciences since October 2022. Previously, he held research positions at Technical University of Munich (2017-2022). His research bridges database systems and machine learning through compiler-based approaches. His research focuses on in-database machine learning , GPU-accelerated query processing , and recursive SQL extensions . Key contributions include: Developing MLIR-based compilers for automatic differentiation in SQL (DuoLingo-AutoDiff) Creating GPU code generators for database kernels using NVRTC Designing higher-order lambda functions for expressive query languages Implementing end-to-end neural network training within database engines His recent publications (2023-2025) demonstrate consistent output in top venues including ICDE, VLDB workshops, and BTW conferences, with growing emphasis on hardware-aware optimization and compiler techniques for analytical workloads. He currently leads a DFG-funded project on elastic memory hierarchies for memory-intensive applications (2025-2028), supporting multiple PhD researchers. His supervision emphasizes open-source contributions to database systems like Umbra and practical implementation skills alongside theoretical foundations. As an active member of the database community, he serves as workshop chair for BTW 2025 and regularly reviews for ACM TODS, VLDB Journal, and Information Systems. His work on public transport analytics demonstrates real-world impact through collaborations with urban mobility initiatives in Bamberg.

Marie Louise B Noschمشاهده پروفایل

استاد

• Aegean epigraphy
• Mycenaean Linear B inscriptions
• ancient textile production

Marie Louise B Nosch is a Professor of Ancient Greek History at the University of Copenhagen since 2009, with significant leadership roles including Vice-President of ALLEA (All European Academies) and former President of the Royal Danish Academy of Sciences and Letters (2020-2024). Her academic journey includes studies in Ancient Greek History at institutions in Nancy (France) and Napoli (Italy), culminating in a doctoral thesis completed at the University of Salzburg (Austria). Professor Nosch's research specializes in Aegean epigraphy and Mycenaean Linear B inscriptions , with groundbreaking work on ancient textile production that bridges archaeological evidence with textual analysis. Her interdisciplinary approach has established new methodologies for studying Bronze Age administrative systems through textile economies. She directed the Danish National Research Foundation's Centre for Textile Research (2005-2016), creating a globally recognized hub for experimental archaeology and material culture studies, and co-founded a university-wide talent program for early-career scholars in 2018.

Frank Westerhoffمشاهده پروفایل

استاد

• Economic Policy
• Financial Markets
• Macroeconomics
• +۴ مورد دیگر

Frank Westerhoff is Professor of Economics and holds the Chair of Economic Policy at the University of Bamberg's Faculty of Social and Economic Sciences. His office is located in the Department of Economics at Feldkirchenstrasse 21, 96045 Bamberg, Germany, with office hours on Wednesdays from 1:00-2:00 p.m. Dr. Westerhoff obtained his doctoral degree from the University of Osnabrück in 2002, followed by his habilitation in 2005. After a brief period at the University of Bonn, he joined the University of Bamberg in 2006 where he has held his current position since. Professor Westerhoff specializes in developing, calibrating and estimating small-scale behavioral agent-based models to investigate financial market dynamics and macroeconomic phenomena. His research particularly focuses on how policy measures such as transaction taxes, central bank interventions, and monetary and fiscal policy rules affect economic systems. His work bridges mathematical economics, behavioral finance, and complex systems theory to understand the nonlinear dynamics that characterize real-world financial markets. His recent publications (2023-2025) demonstrate a continued focus on nonlinear dynamics in financial markets, with particular emphasis on piecewise linear discontinuous maps, quasiperiodic attractors, and the application of these mathematical concepts to agent-based economic modeling. His research shows how seemingly simple nonlinear mechanisms can generate complex market behaviors that match empirical observations. Professor Westerhoff has served on the editorial boards of the prestigious Journal of Economic Behavior and Organization and the Journal of Economic Dynamics and Control , contributing to the advancement of research in his field. With approximately 75 publications across 40 international journals and collaborations with about 20 different co-authors, Professor Westerhoff maintains an active research program. His work often involves interdisciplinary collaboration with mathematicians and physicists to apply advanced dynamical systems techniques to economic questions.

Andreas Dellnitzمشاهده پروفایل

استاد

• Operations Research
• Computational Social Sciences
• Multi-criteria Optimization
• +۴ مورد دیگر

Prof. Dr. Andreas Dellnitz is a Professor of Quantitative Methods at Leibniz University of Applied Sciences, specializing in Operations Research and Computational Social Sciences. He also serves as Head of the Sustainable and Digital Transformation program and has been Vice President for Research since 2025. Education Dr. Dellnitz holds the following academic qualifications: Master of Science (Business Information Systems), FernUniversität in Hagen (2019) Doctor of Economics (summa cum laude), FernUniversität in Hagen (2015) Diploma in Economics (honors), Ruhr University Bochum (2010) Research Interests Dr. Dellnitz's research centers on multi-criteria optimization and (eco-)efficiency analysis , with applications in sustainability. His methodological focus includes linear and combinatorial optimization, algorithmic geometry, and entropy-driven analytics for social and power networks. He investigates sustainability-oriented production planning, energy systems, and social network structures. Publication Trends His recent work (2021-2025) integrates Data Envelopment Analysis (DEA) with machine learning for cost estimation and eco-efficiency assessment. Key applications include sustainable production planning under energy pricing schemes, gas-to-power demand response, and battery storage systems. His research bridges theoretical operations research with industrial sustainability challenges. Scientific Awards No scientific awards were mentioned in the available information. Grants and Advising Dr. Dellnitz has led third-party funded projects such as MaXFab I (2016-2018) and MaXFab II (2019-2021) on energy-flexible factories. He supports student projects and participates in study committees, emphasizing practical applications of operations research in business and sustainability.

Andrew Begelمشاهده پروفایل

دانشیار

• Software Engineering
• Human-Computer Interaction
• Accessibility
• +۳ مورد دیگر

Andrew Begel is an Associate Professor in the Software and Societal Systems Department at Carnegie Mellon University's School of Computer Science. Previously, he was a Principal Researcher at Microsoft Research from 2006 to 2022. He received his Ph.D. in Computer Science from the University of California at Berkeley and holds a Master of Engineering from MIT. Dr. Begel is internationally recognized for his pioneering research on cooperative and human aspects of software engineering, combining empirical studies of professional software engineers with organizational behavior. His work focuses on accessibility, neurodiversity, and human-computer interaction, with particular emphasis on creating more inclusive technology workplaces. He was the first to study emotions in software engineering and conducted groundbreaking research on autistic software developers, identifying their unique strengths and challenges. His research portfolio shows a clear trajectory toward understanding human factors in software development, with increasing focus on neurodiversity and accessibility. Early work examined code comprehension through eye tracking and fMRI studies, while recent publications focus on creating inclusive environments for autistic software engineers and developing tools that support mixed-ability collaboration. His work spans empirical software engineering, human-computer interaction, and accessibility research, often bridging these domains to address real-world challenges in software development environments. Best Paper Award — SIGCSE (2021) ACM Distinguished Member (2019) Best Paper Honorable Mention Award — CSCW (2019) Best Paper Award — ICSE, Software Engineering In Practice (2019) Most Influential Paper Award (10 Years) — ICER (2019) Best Paper Honorable Mention Award — CHI (2017) Dr. Begel has been deeply involved in academic service, organizing and serving on program committees for top software engineering conferences. He has run FSE and ICSE's ACM Student Research Competitions and ICSME's Doctoral Symposium. He created the Autism at Work Research Workshop series, bringing together researchers, practitioners, and autism self-advocates to help autistic engineers find and keep jobs. As a member of Microsoft's AI Ethics Board, he contributed to disseminating best practices for responsible AI engineering. His teaching includes courses at the University of Washington's iSchool and coding camps for autistic youth. Dr. Begel leads the Autism at Work Research Workshop series and has established himself as a prominent figure in creating more inclusive environments for neurodiverse software engineers. His work bridges academic research with practical applications in industry settings, particularly through his collaborations with Microsoft and other technology companies.

Robert Freimannمشاهده پروفایل

استاد

• Hydraulics
• Urban Water Management
• Mathematics
• +۱ مورد دیگر

Prof. Robert Freimann is a Professor at Munich University of Applied Sciences in Faculty 02 (Civil Engineering and Environmental Sciences). He serves as Representative for Study Plans and the Bachelor's program since 2007/08, Member of the Faculty Council since 2007/08, and Member of the HM Continuing Education Advisory Board since 2010/11. Professional affiliations include the Bavarian Chamber of Engineers, VDI, DWA, and Environmental Cluster Bavaria. His research spans Hydraulics, Urban Water Management, Hydraulic Engineering, and Mathematics. He teaches Bachelor courses in Urban Water Management (covering water supply, sewerage, and wastewater treatment), Hydraulic Foundations (hydrostatics, hydrodynamics, pipe/open channel flow), and Mathematics (statistics, calculus, differential equations), plus Master-level advanced urban water topics. Analysis of his 15 most recent publications (2002-2014) reveals concentrated expertise in wastewater treatment hydraulics, particularly secondary clarifier dynamics, solid measurements in aeration systems, and mathematical modeling of urban water networks. His work bridges theoretical fluid mechanics with practical civil engineering applications in water infrastructure. Prof. Freimann acts as an expert for the German Federal Environmental Foundation (DBU) and participates in Bavarian environmental initiatives. Administrative roles include study program governance and faculty representation, demonstrating institutional leadership beyond core academic duties.

Oleg Davydovمشاهده پروفایل

استاد

• Meshless Finite Difference Method
• Finite Element Method
• Scattered Data Fitting
• +۴ مورد دیگر

Professor Oleg Davydov holds a Professorship for Numerical Analysis at the Department of Mathematics, University of Giessen, Germany. His research focuses on developing advanced numerical methods with strong theoretical foundations and practical applications. He maintains an active research program with numerous recent publications and international collaborations. Position: Professor of Numerical Analysis Institution: University of Giessen, Department of Mathematics Contact: Heinrich-Buff-Ring 44, 35392 Giessen, HRZ Room 117 Email: oleg.davydov@math.uni-giessen.de Homepage: https://oleg-davydov.de/ Professor Davydov's research interests center around meshless numerical methods, approximation theory, and computational mathematics. His primary focus areas include: Meshless Finite Difference Method - Developing robust meshless techniques that avoid the need for structured grids Finite Element Method - Particularly Bernstein-Bézier finite elements and specialized approaches for complex geometries Scattered Data Fitting - Creating efficient algorithms for approximating data on irregular domains Approximation Theory - Investigating theoretical properties of splines, radial basis functions, and other approximation tools His research has resulted in several software packages including mFDlab (Meshless Finite Difference Method), BBFEM (Bernstein-Bézier Finite Elements), and TSFIT (Two-Stage Scattered Data Fitting), demonstrating the practical implementation of his theoretical work. Analysis of Professor Davydov's recent publications shows a consistent focus on improving meshless methods, particularly in stencil selection, error analysis, and applications to complex problems. His work spans both theoretical developments (like error bounds and optimal approximation orders) and practical implementations (for fluid dynamics, manifold learning, and interface problems). A notable trend is the increasing sophistication of adaptive techniques and the handling of challenging geometries. Professor Davydov has supervised several doctoral students to completion, including: Gaelle Andriamaro Fabien Rabarison Abid Saeed Wee Ping Yeo His research group appears to maintain active collaborations with institutions worldwide, as evidenced by his extensive co-authorship network. The group focuses on developing both theoretical foundations and practical implementations of numerical methods, with particular attention to problems involving irregular domains, singularities, and complex geometries. Students in his group would gain experience in both theoretical analysis and software development for numerical methods.

Dominik Stögerمشاهده پروفایل

استادیار

• Mathematical Foundations of Data Science
• Non-convex Optimization
• Machine Learning Theory
• +۴ مورد دیگر

Dominik Stöger is an Assistant Professor (tenure-track) in the Department of Mathematics at KU Eichstätt-Ingolstadt since 2021, affiliated with the Mathematical Institute for Data Science and Machine Learning (MIDS). His research bridges mathematical theory and data science applications. His educational background includes: B.Sc. in Mathematics, Technical University of Munich (2013) M.Sc. in Mathematics, Technical University of Munich (2015) Ph.D. in Mathematics, Technical University of Munich (2019) Stöger's research centers on mathematical foundations of data science, with emphasis on non-convex optimization in machine learning, theoretical analysis of overparameterized models, and low-rank matrix recovery. He combines optimization theory and high-dimensional probability to develop rigorous guarantees for modern algorithms, addressing critical challenges in deep learning theory. His recent publications (2020-2025) demonstrate consistent output in top venues including COLT, NeurIPS, and ICLR, with particular focus on implicit regularization phenomena and non-convex recovery guarantees. The 2025 pipeline shows active work extending theoretical boundaries in matrix sensing and neural network analysis. Stöger has received significant recognition: NeurIPS 2021 Spotlight Paper (top 3% of submissions) COLT 2025 paper presentation As a tenure-track faculty member, he maintains active collaborations across institutions (USC, TUM) and likely advises graduate students. His research program shows strong momentum with multiple concurrent projects advancing theoretical machine learning. He contributes to the research ecosystem through affiliation with MIDS, fostering interdisciplinary work in mathematical data science at KU Eichstätt-Ingolstadt.

Smita Krishnaswamyمشاهده پروفایل

دانشیار

• Unsupervised Machine Learning
• Manifold Learning
• Deep Learning
• +۷ مورد دیگر

Smita Krishnaswamy is an Associate Professor of Genetics and Computer Science at Yale University with joint appointments in both departments. She is affiliated with multiple interdisciplinary programs including the Applied Mathematics Program, Computational Biology and Bioinformatics Program, Yale Center for Biomedical Data Science, Yale Cancer Center, and the Wu Tsai Institute. Her research bridges computational methods development with biomedical applications, focusing on unsupervised machine learning approaches for high-dimensional data analysis. Associate Professor of Genetics, Yale School of Medicine Associate Professor of Computer Science, Yale University Affiliated Faculty, Applied Mathematics Program Affiliated Faculty, Computational Biology and Bioinformatics Member, Yale Center for Biomedical Data Science Member, Yale Cancer Center Member, Wu Tsai Institute Dr. Krishnaswamy's research focuses on developing unsupervised machine learning techniques, particularly manifold learning and deep learning methods, to analyze high-dimensional biomedical data. Her lab creates algorithms for non-linear dimensionality reduction, data geometry learning, denoising, imputation, and inference of multi-granular structures from complex datasets. These methods are applied to diverse data types including single-cell RNA-sequencing, mass cytometry, electronic health records, and connectomic data across multiple biological systems. Her work spans several key application areas including immunology and immunotherapy, cancer research, neuroscience, developmental biology, and health outcomes analysis. The lab employs approaches from geometric deep learning, multiscale graph signal processing, and topological data analysis to extract meaningful biological insights from complex datasets. Recent publications demonstrate the lab's leadership in developing methods for spatial transcriptomics, brain-state trajectory modeling, and organ donation prediction. Excellence in Science Early-Career Investigator Award from FASEB (2022) Yale Cancer Center Class of '61 Cancer Research Award (2025) Dr. Krishnaswamy maintains active collaborations across Yale and secures research funding supporting her work in computational biomedicine. She advises students through multiple programs including Genetics, Computer Science, and the Biological and Biomedical Sciences Graduate Program, fostering interdisciplinary training at the intersection of computation and biomedicine. The Krishnaswamy Lab operates at the forefront of computational biomedicine, developing mathematical approaches that enable new biological discoveries from complex datasets.

Hans-Peter Lenhofمشاهده پروفایل

استاد

• Bioinformatics
• Cancer Biology
• Personalized Cancer Therapy
• +۴ مورد دیگر

Prof. Dr. Hans-Peter Lenhof holds the Chair for Bioinformatics at Saarland University since 2000, where he leads research in computational biology and disease mechanism analysis. His career includes: Postdoctoral research at Max Planck Institute for Informatics (1993-1999) Research Group Leader at MPI (1999-2000) Professor at Saarland University (2000-Present) His group develops innovative bioinformatics methods with three primary focus areas: Personalized Cancer Therapy : Creating AI/ML approaches to optimize drug selection from over 200 cancer therapeutics, addressing tumor heterogeneity through predictive modeling of treatment efficacy. Diagnosis & Prognosis : Pioneering blood-based diagnostic methods using autoantibody and miRNA profiles that demonstrate high clinical sensitivity for multiple cancers and neurological disorders through collaborative studies. Pathogenic Mechanism Analysis : Employing graph-based network models to visualize and analyze deregulated biological processes, particularly signaling cascades in cancer progression. The lab maintains active collaborations with Andreas Keller's Clinical Bioinformatics group and Eckart Meese's Human Genetics team. Computational tools developed include BALLView for molecular visualization, GeneTrail for high-throughput data analysis, and specialized frameworks for therapy optimization (MERIDA) and miRNA networks (miRTargetLink).

Maura Johnمشاهده پروفایل

پژوهشگر

• Bioinformatics
• Genome-wide Association Studies
• Machine Learning in Biology
• +۴ مورد دیگر

Maura John serves as a Research Associate at the Chair of Bioinformatics at Hochschule Weihenstephan-Triesdorf's Straubing Campus for Sustainable Resource Use. Her research focuses on developing advanced computational methods for biological data analysis, with particular expertise in genome-wide association studies and protein structure prediction. Her primary research interests include: Genome-wide association studies with permutation-based significance thresholds that preserve population structure Development of bioinformatics tools like permGWAS2 and easyPheno Protein thermostability prediction using machine learning approaches Genomic selection methodologies for crop breeding applications Dr. John's recent publications demonstrate a strong focus on methodological improvements in computational biology, particularly addressing limitations of traditional approaches in handling skewed phenotype distributions and population structure. Her work bridges theoretical statistical methods with practical biological applications across plant genomics and protein science. Notable contributions include: permGWAS2: An improved method that maintains population structure during permutations ProLaTherm: A protein language model-based thermophilicity predictor outperforming existing methods easyPheno: A comprehensive Python framework for phenotype prediction model comparison Her research program demonstrates strong collaborative efforts with Dominik Grimm's group and other bioinformatics researchers, focusing on developing open-source tools that address critical challenges in genomic data analysis. The work has practical applications in plant breeding, protein engineering, and understanding genotype-phenotype relationships.

Simon Apersمشاهده پروفایل

پژوهشگر

• Quantum Algorithms
• Theoretical Computer Science
• Random Walks
• +۴ مورد دیگر

Simon Apers is a CNRS researcher at IRIF (Institut de Recherche en Informatique Fondamentale) at Université Paris Cité. He focuses on quantum algorithms with broader interests in theoretical computer science, including random walks, graph theory, and combinatorial optimization. His primary research interests span quantum computing, theoretical computer science, and algorithms. Apers has made significant contributions to quantum walks, quantum property testing, graph algorithms, and computational complexity. His work often bridges quantum information with classical theoretical computer science, exploring how quantum techniques can improve classical algorithms and solve problems more efficiently. He has developed quantum speedups for various computational tasks including sampling, optimization, and graph problems. Simon Apers serves as a program committee member for prestigious conferences including TQC'25, QIP'25, TQC'23, SODA'23, and ESA '21. He also serves as an editor for the journal Quantum. His teaching activities include courses at Sorbonne University (2022-present) on Advanced Quantum Algorithms, MPRI (2021-present) on Quantum Algorithms and Complexity, and Bad Honnef (2022). His publication record shows a strong trend toward quantum algorithms for graph problems, quantum walks, and connections between quantum computing and classical theoretical computer science. He frequently collaborates with researchers from various institutions across Europe and has published in top venues including FOCS, STACS, ESA, PRL, and JMLR. Simon Apers actively mentors students and has open positions for PhD students and postdocs in quantum algorithms at IRIF. His research group is involved in cutting-edge work at the intersection of quantum computing and theoretical computer science, with significant contributions to quantum walks, property testing, and computational complexity.