Find Academic Supervisors

• Quantum Optics
• Terahertz Technology
• Photonics
• +4 more

Lukas Seitner is a researcher at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Electrical Engineering. He operates within the Associate Professorship of Computational Photonics led by Prof. Christian Jirauschek, focusing on advanced modeling of quantum cascade devices and terahertz photonics systems. His research spans quantum cascade lasers (QCLs), terahertz frequency combs, optical solitons, and computational photonics. Seitner has developed sophisticated simulation frameworks including Maxwell-Bloch and density matrix approaches to study nonlinear dynamics in optoelectronic devices. Key contributions involve passive mode-locking mechanisms in THz QCLs, graphene-integrated saturable absorbers for pulse generation, and backscattering effects in ring-cavity soliton formation. His work bridges theoretical modeling with practical device engineering for next-generation terahertz sources. As an educator, Seitner serves as assistant lecturer for multiple courses including Computational Photonics Laboratory (5 PR), Partial Differential Equations for Electrical Engineering (4 VI), and Simulation of Quantum Devices (4 VI). He actively participates in doctoral candidate seminars and specialized courses on quantum engineering, demonstrating strong commitment to academic training in photonics and quantum device physics. His teaching integrates cutting-edge research concepts into practical computational exercises. Seitner maintains active collaboration within the EU Project QOMBS and contributes to TUM's Computational Photonics group research infrastructure. His technical expertise encompasses numerical methods for partial differential equations, semiconductor device simulation, and nonlinear optical modeling. Current projects focus on optimizing THz comb sources for spectroscopic applications and extending quantum walk models for novel frequency comb generation mechanisms.

• Adaptive nonparametric statistics
• High-dimensional data analysis
• Financial time series modeling
• +5 more

Prof. Vladimir Spokoiny is a leading figure in stochastic algorithms and nonparametric statistics at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) and Humboldt University of Berlin . His work bridges mathematical statistics with practical applications in finance, medicine, and machine learning. Born in 1959 in Moscow, USSR PhD from Lomonosov Moscow State University (1988) Habilitation from Humboldt University (1996) Head of WIAS research group since 2000 Professor at Humboldt University since 2002 Spokoiny's research focuses on adaptive nonparametric methods, high-dimensional data analysis, and statistical finance. His innovations in local homogeneity testing and propagation-separation methods have advanced volatility modeling, image analysis, and manifold learning. He employs Bayesian optimization frameworks and stochastic control techniques for financial instrument pricing. Recent scientific contributions include generalized bootstrap procedures for Bures-Wasserstein barycenters (2024), dimension-free Laplace approximation bounds (2023), and structure-adaptive manifold estimation (2022). His 19+ PhD students and editorial roles in top journals like The Annals of Statistics demonstrate sustained academic impact. International Statistical Institute member American Statistical Association fellow Institute of Mathematical Statistics member Bernoulli Society member

• Algebraic Geometry
• Hyperkähler Manifolds
• Rozansky-Witten Invariants
• +3 more

Prof. Dr. Marc Nieper-Wißkirchen is a University Professor (W3) for Algebra and Number Theory at the Institute of Mathematics, University of Augsburg, since 2008. He has held visiting positions at the Max Planck Institute for Mathematics in Bonn and the University of Cambridge, and served as a Junior Professor at Johannes Gutenberg University Mainz (2004–2008). Since October 2021, he has been Vice Dean of the Faculty of Mathematics, Natural Sciences and Technology (MNTF). Education: Diploma in Mathematics (2000) from the University of Cologne, preceded by preliminary diplomas in physics (1997) and mathematics (1997). His doctoral studies (2000–2002) under Prof. Daniel Huybrechts focused on characteristic classes and Rozansky-Witten invariants of hyperkähler manifolds. His research spans algebraic geometry, differential geometry, and mathematical physics, with a focus on hyperkähler manifolds, Hilbert schemes of points, irregular singular connections, and Stokes phenomena. He has contributed to the understanding of Rozansky-Witten invariants, Chern numbers, and topological aspects of differential equations. Recent publications include works on Betti structures of hypergeometric equations (2022) and Stokes matrices for confluent hypergeometric equations (2022), reflecting his ongoing exploration of differential equations' geometric underpinnings. Earlier works address Hochschild homology, equivariant cohomology, and operads in algebraic contexts. His computational contributions include software demonstrations for elliptic curve arithmetic, Collatz sequences, and Google Matrix simulations. Technical expertise includes programming languages (TeX, PHP, HTML) and systems (Unix/Linux, Zope/Plone).

Mahsa GhasemiView profile

Assistant Professor

• Reinforcement Learning
• Control Theory
• Active Perception
• +3 more

Mahsa Ghasemi is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, leading the AKADEMI Group. Her research focuses on theoretical advancements in trustworthy sequential decision-making for autonomous systems, emphasizing human-aware collaboration and adaptation to dynamic environments. She is affiliated with the Institute for Control, Optimization and Networks (ICON). Education: PhD in Electrical and Computer Engineering from The University of Texas at Austin (2021), MSE in Mechanical Engineering (2017), and BSc in Mechanical Engineering from Sharif University of Technology (2014). Research Interests: Reinforcement learning, control theory, active perception, multi-agent systems, robotics, and online learning. Applications span disaster response, healthcare, and autonomous systems design. Key methodological directions include compositional learning, human-AI collaboration, and adaptive decision-making under uncertainty. Teaching: Courses include Reinforcement Learning Theory (ECE 59500), Introduction to Reinforcement Learning (ECE 49595), and Python for Data Science (ECE 20875). Awards: Finalist for Student Best Paper Award at the 2018 American Control Conference (ACC). Students: Current advisees include Maheed H. Ahmed, Jayanth Bhargav, and Somtochukwu Oguchienti. Past members include Lai Wei and Juan Sebastian Mateo Ruiz Bulla. Service: Editorial roles at ICRA, ICCPS, and IFAC workshops. Reviewer for top conferences (NeurIPS, ICML) and journals (Automatica, IEEE TAC). Labs/Teams: Leads the AKADEMI Group, focusing on algorithmic and theoretical research in autonomous decision-making systems.

• Mathematical Statistics
• Applied Statistics
• Probability Theory
• +6 more

Holger Dette is a Professor and Chair Holder of Stochastics (specializing in Statistics) at the Faculty of Mathematics, Ruhr University Bochum. He leads the prominent Group Dette within the Institute of Statistics, overseeing a team of researchers, doctoral students, and administrative staff including Birgit Tormöhlen as team assistant. His research group is deeply integrated within the university's mathematical ecosystem, collaborating with other research groups across algebra, analysis, numerics, and topology. Dette's research spans mathematical statistics with strong applications in real-world problems. His primary interests include optimal experimental design, time series analysis, functional data, change point problems, nonparametric regression, biostatistics, special functions, goodness-of-fit tests, and random matrices . His work bridges theoretical statistics with practical applications, particularly evident in his collaborations with pharmaceutical giants Novartis and Bayer AG in biostatistics, as well as Quasol, a spin-off company from his statistics institute. His recent publications (2024-2025) reveal a research program increasingly focused on high-dimensional and functional data analysis, privacy-preserving statistics, and novel methodological approaches to longstanding statistical problems. Dette's work shows strong interdisciplinary connections, particularly with biomechanics (analyzing joint angles during fatigue phases) and data science (addressing challenges in the era of big data). His research group is actively involved in multiple DFG-funded projects including the newly established 'Small Data' collaborative research center (Sonderforschungsbereich 1597) and the Spatio-temporal Statistics for the Transition of Energy and Transport (Transregio 391). Dette has received significant recognition including the prestigious Humboldt Research Award . His paper 'With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors' achieved second place at the CSAW'24 Applied Research Competition MENA. His research group has also secured multiple significant funding awards from the German Research Foundation (DFG). As an advisor, Dette supervises numerous doctoral and master's students including Pascal Quanz, Marius Kroll, and Carina Graw. His group offers statistical consulting services for scientists and students across bachelor's, master's, and doctoral phases. The group maintains strong industrial partnerships, particularly in biostatistics applications, demonstrating Dette's commitment to translating theoretical statistics into practical solutions for real-world challenges.

• Reinforcement Learning
• Bandit Algorithms
• Learning Theory
• +4 more

Claire Vernade is a Group Leader at the University of Tübingen within the Cluster of Excellence Machine Learning for Science. She holds an Emmy Noether award (2022) and an ERC Starting Grant (2024) for her projects FoLiReL and ConSequentIAL , focusing on theoretical reinforcement learning and non-stationary environments. Education: PhD from Telecom ParisTech (2017) Post-doctoral researcher at University of Magdeburg (2018) Her research bridges sequential decision making, bandit problems, and reinforcement learning theory. Recent work explores lifelong learning, distributional RL, and game-theoretic approaches to PCA, emphasizing mathematical rigor and algorithmic innovation. Recent publications highlight trends in non-stationary RL , continual learning , and bandit algorithms with complex feedback structures. Key subfields include meta-learning, adaptive control, and theoretical guarantees in dynamic programming. Scientific Awards: Emmy Noether Award (2022) ERC Starting Grant (2024) Outstanding Paper Award & Oral Presentation, ICLR (2021) She mentors PhD and master's students in theoretical machine learning, with current advisees including Nicolas Nguyen, Onno Eberhard, and Ziyad Sheebaelhamd. Her lab actively recruits candidates in bandit algorithms and RL theory through the IMPRS-IS and ELLIS doctoral programs. Claire co-leads diversity initiatives like Tübingen Women in Machine Learning and Women in Learning Theory, advocating for inclusivity in AI research. She has organized workshops at ICML and EWRL, and contributed to union activism in the tech industry.

Matthew K. TamView profile

Associate Professor

• continuous optimisation and its applications
• monotone operator theory
• variational and convex analysis
• +3 more

Matthew K. Tam is an Associate Professor at the School of Mathematics and Statistics, The University of Melbourne, specializing in Operations Research. He is also an investigator at the Melbourne Centre for Data Science and an associate investigator in the ARC Training Centre OPTIMA. PhD in Mathematics (2016) from University of Newcastle under Jonathan Borwein Postdoctoral research at University of Göttingen with RTG-2088 and Alexander von Humboldt Foundation Junior Professor at University of Göttingen (2017-2020) His research focuses on continuous optimization, monotone operator theory, and variational analysis, with applications in wavelet construction and inverse problems. Key trends include distributed algorithms, resolvent splitting, and convergence analysis for feasibility problems. Discovery Early Career Researcher Award (DECRA) Alexander von Humboldt Fellowship He collaborates with institutions like ANZIAM, Springer, and IEEE, with publications spanning mathematical optimization, harmonic analysis, and computational mathematics. His work emphasizes algorithmic design for complex data systems and real-world applications in imaging and industrial modeling.

Jalal EtesamiView profile

Assistant Professor

• Machine Learning
• Statistical Decision Making
• Causal Inference
• +4 more

Prof. Jalal Etesami is an Assistant Professor in the Department of Computer Science at Technical University of Munich (TUM), leading the Decision Sciences & Systems group. He holds a Ph.D. in Industrial and Systems Engineering from the University of Illinois at Urbana-Champaign and was a Postdoctoral Fellow at EPFL in Switzerland. His research focuses on machine learning, causal inference, multi-agent systems, and game theory, with applications to systemic risk modeling and market design. He teaches advanced courses such as Causal Inference in Time Series , Algorithmic Game Theory , and Optimization, Learning, and Market Design . Notable contributions include work on causal structure learning, stochastic optimization, and non-Gaussian causal models. Recent research explores causal effect identification under confounding, neural networks for market analysis, and optimal experiment design. Prof. Etesami’s work appears in top venues like NeurIPS, AAAI, and IEEE journals. He actively contributes to the academic community, organizing seminars and workshops on topics ranging from causal reasoning to computational social choice.

Christian PlesslView profile

Professor

• High-Performance Computing
• Reconfigurable Computing
• FPGA Acceleration
• +3 more

Prof. Dr. Christian Plessl is a W3 Professor of High-Performance Computing at the Institute of Computer Science, University of Paderborn. He leads the Paderborn Center for Parallel Computing (PC²), a national HPC center within the NHR alliance. His roles include Director of PC², Board Member of the NHR association, and member of the Sonderforschungsbereich 901. Education: PhD (Dr. sc. ETH) in Computer Engineering, ETH Zürich (2006) MSc in Electrical Engineering, ETH Zürich (2001) Postdoc at ETH Zürich (2007–2011) Research Interests: Architecture and tools for high-performance parallel and reconfigurable computing, FPGA acceleration, quantum chemistry, scientific computing, adaptive systems, and energy-efficient HPC solutions. Key projects include EKI-App (FPGA-based neural networks), FPGA4XPCS (X-ray spectroscopy), and HighPerMeshes (unstructured grid frameworks). Publications: Over 100 peer-reviewed works, focusing on FPGA acceleration, HPC frameworks, and quantum computing. Recent trends emphasize energy-efficient neural networks, FPGA-based quantum computing, and scalable HPC algorithms. Awards: Best Paper Awards at HEART 2023, ReConFig 2012/2014 Paderborn University Research Awards (2018, 2009) SEW-EURODRIVE Student Award (2001) Grants & Projects: Principal investigator in DFG, BMBF, and EU-funded projects. Collaborates with AMD/Xilinx, Intel/Altera, and Fujitsu. Leads initiatives like PerficienCC (custom computing) and HighPerMeshes. Labs/Teams: Directs the High-Performance Computing group at PC², focusing on FPGA supercomputing and HPC infrastructure. Active in the NHR alliance for national HPC coordination.

• Soil Biogeochemistry
• Carbon Cycling
• Mineral-Soil Interactions
• +3 more

Dr. Marion Schrumpf is a Group Leader in the Soil Biogeochemistry research group at the Max Planck Institute for Biogeochemistry, affiliated with the Department of Biogeochemical Processes. Her work focuses on soil carbon dynamics, mineral-organic matter interactions, and climate change impacts on soil systems. Research areas: Soil biogeochemistry, carbon cycling, mineral-soil interactions, nutrient stoichiometry, microbial ecology, and climate modeling. Email: mschrumpf@... Phone: +49 3641 57-6182 Office: B2.015 Her recent publications address themes like mineral control over soil carbon stabilization, drought effects on soil processes, microbial stoichiometric adaptation, and the Jena Soil Model's role in simulating carbon-nutrient interactions. She leads efforts to disentangle the complex relationships between land use, mineralogy, and soil organic matter turnover across diverse ecosystems.

Beate PaulusView profile

Professor

• Quantum Chemistry
• Theoretical Chemistry
• Computational Chemistry
• +8 more

Beate Paulus is a Professor for Theoretical Chemistry at the Freie Universität Berlin , affiliated with the Chemistry and Biochemistry college and the Chemistry department. Her research focuses on advanced quantum chemical methodologies and applications to 2D materials, spintronics, and catalysis. Current affiliation: Freie Universität Berlin Key research areas: Quantum Chemistry, Density Functional Theory, 2D Materials, Spintronics, Electrocatalysis Her work spans computational modeling of electronic structures, magnetic properties, and chemical reactions using Density Functional Theory (DFT) with specialized corrections. She investigates systems like MoS2 , graphene heterostructures , and transition metal complexes , aiming to understand and optimize properties for energy applications, biosensors, and nanoelectronics. Recent publications highlight her contributions to quantum mechanical fluorine tunnelling , spin-selective transport in doped nanoribbons , and surface functionalization strategies for 2D materials. Her group also explores mechanically interlocked molecules and redox-responsive polymers with potential biomedical applications. Beate Paulus leads the Paulus Group , which actively publishes in high-impact journals and collaborates on interdisciplinary projects involving experimental and theoretical approaches.

• Macroeconomics
• Macro-Finance
• Econometrics
• +7 more

Prof. Dr. Alexander Meyer-Gohde is a Professor of Financial Markets and Macroeconomics at Goethe University Frankfurt’s Faculty of Economics and Business, and a key figure at the Institute for Monetary and Financial Stability (IMFS). His research spans macroeconomic theory, macro-finance, numerical methods, and econometrics, focusing on DSGE models, nonlinear dynamics, and the impact of risk and uncertainty on monetary policy. Education : PhD in Economics (Technische Universität Berlin), MA in Economics and Management (Humboldt-Universität zu Berlin), BA in Language, Literature & Culture (Colorado State University). Research Interests : Macroeconomics, macro-finance, numerical methods, recursive preferences, stochastic volatility, and model uncertainty. Grants : DFG Individual Research Grant (2021-2024) and MatlabMakro DigiTeLL Grant (2022-2023). Publications : Focus on DSGE model solution methods, numerical stability, term premia, and nonlinear dynamics in macroeconomics. Students : Supervises job market candidates Johanna Saecker and Mary Tzaawa-Krenzler. Leadership : Chair of Financial Markets and Macroeconomics at Goethe University (2018–present) and coimplementation of the IMFS “Project Monetary and Financial Stability”.

• GPU Computing
• Parallel Computing
• Domain-Specific Languages
• +5 more

Prof. Dr.-Ing. Richard Membarth is a Research Professor for System-on-a-Chip and AI at the Edge Computing at Technische Hochschule Ingolstadt (THI). He is affiliated with the Hardware-Software Co-Design group and holds a secondary position at the German Research Center for Artificial Intelligence (DFKI) Saarbrücken. Co-creator of DSL frameworks like AnyDSL and Hipacc Key contributor to MetaDL (AI metaprogramming) and PRIME (predictive rendering) His research bridges GPU computing , domain-specific languages , and compiler technology , with recent work on Vulkan SPIR-V compilation and device-driven SpMV algorithms . Notable awards include the HiPEAC Paper Award (2018) and multiple Best Paper Awards for his compiler frameworks.

Georg GarnweitnerView profile

Professor

• Nanomaterials Synthesis
• Non-aqueous Nanoparticle Formation
• Solid-State Battery Materials
• +5 more

Prof. Dr. Georg Garnweitner is a full Professor of Nanomaterials at the Institute for Particle Technology , Faculty of Mechanical Engineering, Technische Universität Braunschweig. He has served as Dean of Studies since 2023, DFG Liaison Lecturer since 2021, and head of the Laboratory for Emerging Nanometrology (LENA) board since 2013. University Professorship in Nanomaterials (2013–present) Junior Professorship in Nanoparticles/Nanocomposites (2007–2013) Research at Max Planck Institute (2005–2006) His research focuses on nanomaterial synthesis , non-aqueous nanoparticle formation , and energy storage materials , particularly for lithium-sulfur batteries. He also explores drug delivery systems using silica aerogels and optofluidic particle analysis . His work combines materials science , surface chemistry , and advanced characterization techniques . Recent publications highlight breakthroughs in solid-state electrolyte design (2023), solvent-free drug loading (2022), and nanoparticle migration dynamics (2020). He contributes to crystal engineering (2021) and population balance modeling (2017) for nanoparticle formation.

Ferdinand EversView profile

Professor

• Condensed Matter Physics
• Quantum Mechanics
• Molecular Electronics
• +7 more

Prof. Dr. Ferdinand Evers is a Chair of Computational Condensed Matter Theory at the Institute of Theoretical Physics , University of Regensburg. His research spans quantum transport , spintronics , molecular electronics , and many-body localization , with a focus on ab initio and DFT-based modeling of nanostructures and low-dimensional systems . Key Research Areas: Quantum transport in molecular junctions Spin-orbit coupling and chiral effects Multifractality at quantum phase transitions Electronic structure of topological materials Ultrafast laser-driven electron dynamics Anderson localization and disorder Recent Article Trends (2021–2024): High-harmonic generation in topological insulators Spin-selective transport in chiral systems Mechanical torque in molecular rotors Self-consistent GW methods for molecular electronics Quantum interference in graphene nanoribbons Teaching: Lecturer for Theoretical Physics I-IV , Advanced Quantum Mechanics , and Scientific Perspectives courses at the University of Regensburg Focus on statistical mechanics , quantum transport , and computational nanoscience