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Tulga ErsalView profile

Associate Professor

• System Dynamics and Control
• Autonomous and Semi-Autonomous Vehicles
• Human-Autonomy Interaction
• +6 more

Tulga Ersal is an Associate Professor in the Department of Mechanical Engineering at the University of Michigan's College of Engineering. His work focuses on system dynamics and control, with applications in autonomous vehicles, energy systems, and human-autonomy interaction. He leads research at the 2019 Auto Lab in Ann Arbor, Michigan, and mentors PhD and Master’s students in fields like robotics, powertrain optimization, and biofeedback systems. B.S.E. from Istanbul Technical University (2001) M.S. and Ph.D. from University of Michigan (2003, 2007) Dr. Ersal’s research spans three core areas: Vehicle Systems: Autonomous/semi-autonomous navigation, trajectory planning on deformable terrains, and powertrain management Energy Systems: Battery modeling, fuel cells, microgrid optimization using extremum seeking Human Factors: Driver distraction modeling, cognitive workload estimation via eye tracking, and haptic shared control frameworks His publications often integrate control theory with real-world applications, emphasizing computational efficiency and robustness. Recent work includes terrain-adaptive trajectory planning (2025), Bayesian workload estimation (2024), and 3D terrain navigation algorithms (2023). He advocates for model fidelity and practical implementation in hardware-in-the-loop simulations. Scientific awards include: Kenneth M. Reese Outstanding Research Scientist Award (2018-19) UMOR Research Faculty Recognition Award (2016) Dr. Ersal's advising philosophy prioritizes individualized guidance, weekly one-on-one meetings, and collaborative group discussions. His students frequently present at conferences like the American Control Conference and ASME Dynamic Systems and Control Conference. Funding for student travel comes through Rackham Travel Grants, conference support programs, and research projects.

• Tensor Decompositions
• Quantum Computing
• Fredholm Integral Equations
• +7 more

Patrick Gelß is a Postdoctoral Researcher at Zuse Institute Berlin, working in the AI in Society, Science, and Technology department. He leads the iol.QUANT research group focused on quantum-inspired and tensor-based methods, with affiliations to Freie Universität Berlin and collaborative projects like MATH+ Cluster of Excellence. Diploma in Mathematics/Physics (2013, Freie Universität Berlin) PhD in Mathematics (2017, summa cum laude, Freie Universität Berlin) Postdoctoral roles at Zuse Institute Berlin (2020-present) and CRC 1114 (2017-2022) His research bridges tensor decompositions, quantum computing, and dynamical systems, with notable contributions to: Graph Isomorphism: Continuous optimization approaches via doubly stochastic matrices and Frank-Wolfe algorithms Quantum Simulation: Tensor network representations for quantum circuits and the open-source WaveTrain package KvN Mechanics: Mathematical analysis of existence/uniqueness solutions for bounded domains Fredholm Networks: Novel training paradigms for neural networks using integral equations Scientific achievements include: Developing Scikit-TT for tensor-train computations Organizing major workshops (QML@SC2024, TMQS 2024) Leading the Thematic Einstein Semester 2024 on Mathematics for Quantum Technologies His work spans chemical kinetics, quantum dynamics, and quantum machine learning, with applications to CO oxidation models, exciton-phonon systems, and mutational hierarchies in medicine.

Moniek de MaatView profile

Professor

• Biochemistry of Haemostasis
• Thrombosis
• Fibrinogen Variants
• +7 more

Prof. Moniek de Maat is a leading researcher in the Biochemistry of Haemostasis and Thrombosis at the Erasmus University Medical Center (Rotterdam, Netherlands) and a Visiting Professor at the University Hospital of South West Denmark. Her work focuses on fibrinogen variants , fibrin network architecture , and their roles in thrombosis , stroke outcomes , and coagulation disorders . Education: PhD in Biochemistry (1995, Erasmus University), Chemistry (Biochemistry) at Utrecht University (1987-1991). Career: Senior researcher at TNO (1991-1999, 1999-2003), Associate Professor (2003-2018), and Professor (2018-present) at Erasmus MC, Head of Hemostasis Lab (2008-2023). Research Focus: Her work explores how genetic and protein variation in fibrinogen affects clot stability , fibrinolysis , and thrombosis risk . She investigates biomarkers for acute ischemic stroke and NETosis (neutrophil extracellular traps) in cardiovascular and infectious diseases like COVID-19 . Her team studies fibrin network properties under flow and in biofilms , aiming to optimize patient treatment. Publication Trends: Recent articles (2023-2025) highlight her expertise in fibrinogen variants , NETs , and stroke biomarkers , with collaborations in diabetes , COVID-19 coagulopathy , and biofilm-related thrombosis . Key journals include J Thromb Haemost , Acta Biomater , and TH Open . Scientific Awards: Outstanding Investigator Award, International Fibrinogen Research Society (2010) Advising: She mentors PhD students (Aarazo Barakzie, Hande Eyisoylu) and postdocs (Charlene Jacqmarcq), with alumni (Tirsa Koopman). Her lab collaborates with departments in epidemiology , cardiology , pediatric hematology , microbiology , and neurology across institutions like TU Delft and MUMC+ .

Sebastian PeitzView profile

Professor

• Multiobjective Optimization
• Optimal Control
• Machine Learning
• +3 more

Sebastian Peitz is Professor (previously Assistant Professor) at Paderborn University's Department of Computer Science, leading the Data Science for Engineering group. He obtained his PhD in Multiobjective Optimization from Paderborn University and MSc in Mechanical Engineering from RWTH Aachen. His research develops computational methods for multiobjective optimization, optimal control, and machine learning with applications in fluid dynamics, autonomous systems, and industrial processes. He leads the BMBF-funded Multicriteria Machine Learning group. Research trends show consistent focus on Koopman operator theory, reinforcement learning applications in control systems, and physics-informed machine learning across publications. Achievements include the 2019 PRECEDE Best Paper Award and leadership in international optimization conferences.

Antonis PapachristodoulouView profile

Professor

• Control Engineering
• Nonlinear Systems
• Synthetic Biology
• +5 more

Professor Antonis Papachristodoulou is a faculty member at the University of Oxford , serving as the Professor of Control Engineering and an Official Fellow at Kellogg College . He previously held roles as a Tutorial Fellow at Worcester College (2010-2024), EPSRC Fellow (2015-2021), and Director of the EPSRC & BBSRC Centre for Doctoral Training in Synthetic Biology (2014-2023). His academic journey includes an MA/MEng in Electrical and Information Sciences from the University of Cambridge (2000) and a PhD in Control and Dynamical Systems at the California Institute of Technology (2005) with a minor in Aeronautics. His research spans Control Engineering , Systems Biology , and Synthetic Biology , focusing on robust analysis of nonlinear networked systems, Sum of Squares optimization, and applications in biological systems, fluid mechanics, and smart grids. His recent publications highlight advancements in Distributed Control , Neural ODE-based Controllers , and Safe Reinforcement Learning . Scientific Awards: European Control Award (2015) O. Hugo Schuck Best Paper Award IEEE Fellow He leads the SYSOS (System of Systems) Group , collaborating with Oxford Biochemistry, Engineering Science, and international institutions. His grants include the EPSRC Programme Grant EEBio and co-I roles in bioengineering and autonomous systems initiatives.

Felix DietrichView profile

Professor

• Physics-Enhanced Machine Learning
• Scientific Computing
• Harmonic AI
• +7 more

Prof. Dr. Felix Dietrich holds the Professorship for Physics-Enhanced Machine Learning at the Technical University of Munich (TUM), within the TUM School of CIT and Department of Computer Science. He is actively engaged in teaching courses including Scientific Computing and Machine Learning for the Winter semester 2025/26 and supervises Master's theses in Machine Learning in Crowd Modeling & Simulation. His research focuses on the intersection of scientific computing and machine learning, particularly through his 'Harmonic AI' framework that bridges linear operator theory with deep learning. Key research areas include physics-enhanced machine learning, Koopman operator theory, scientific machine learning, and numerical algorithms for complex systems. His work applies to diverse domains such as crowd dynamics, quantum systems, molecular dynamics, and civil engineering applications like structural defect detection. Prof. Dietrich leads several major research initiatives including the Emmy Noether group 'Harmonic Artificial Intelligence based on Linear Operators', the Scientific Machine Learning Focus Group with the IAS, GNI projects DeepMonitor and FORWARD, and AutoMD-AI for molecular dynamics simulations. His recent publications demonstrate strong activity in connecting mathematical rigor with machine learning applications. His scientific contributions span developing data-driven algorithms that combine the reliability of traditional scientific computing with the flexibility of AI approaches. This work has resulted in numerous publications in top-tier journals including SIAM Journal on Scientific Computing, Chaos, Nature Communications, and PNAS Nexus. Emmy Noether group on Harmonic AI Scientific Machine Learning Focus Group with IAS GNI project DeepMonitor for structural defect detection GNI project FORWARD for pedestrian flow prediction AutoMD-AI for molecular dynamics surrogate modeling Prof. Dietrich actively mentors PhD students and postdoctoral researchers, with several advisees including Chinmay Datar, Erik Lien Bolager, and Vladyslav Fediukov appearing as co-authors on recent publications. His research group (SCML) develops open-source software for physics-enhanced machine learning applications.

Xiaofeng ShaoView profile

Adjunct Professor

• Statistics
• Data Science
• Time Series Analysis
• +5 more

Xiaofeng Shao is an Adjunct Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign, affiliated with the College of Liberal Arts & Sciences. His research focuses on statistical methodology for time series, spatial data, and high-dimensional data, with applications in econometrics, environmental science, and neuroscience. Shao earned his PhD in Statistics from the University of Chicago in 2006. He has held grants as PI or co-PI from NSF-CMG, NSF-DMS, the Research Board at UIUC, and industry partners like ABInBev and Jump. His work has been recognized with awards such as the Tjalling C. Koopmans Econometric Theory Prize (2009) and the Centennial Scholar designation (2013–2016). His research spans long memory processes, functional data analysis, resampling methods, and applications in atmospheric science, economics, and neuroscience. Notable contributions include self-normalization techniques for high-dimensional time series and spatial bootstrap methods. He has advised numerous students and collaborates across disciplines, maintaining an active publication record in top journals like the Journal of the American Statistical Association and Biometrika.

Zhongwei ShenView profile

Professor

• Analysis
• Partial Differential Equations
• Homogenization Theory
• +3 more

Zhongwei Shen is a Professor of Mathematics at the University of Kentucky, Department of Mathematics, within the College of Arts & Sciences. He joined UK in 1995 and was promoted to full professor in 2003, serving as Department Chair from 2007 to 2011. His research focuses on analysis and partial differential equations, with expertise in homogenization theory, elliptic systems, and spectral analysis. Education: Ph.D., Mathematics, University of Chicago, 1989 M.S., Mathematics, Chinese Academy of Sciences, 1985 B.S., Mathematics, Peking University, 1982 His research interests span mathematical analysis of PDEs, including boundary value problems in nonsmooth domains, homogenization in perforated media, and spectral properties of magnetic operators. Recent work emphasizes fluid dynamics, stochastic dynamical systems, and data-driven modeling frameworks for complex systems. Publications reflect deep engagement with elliptic systems, Stokes operators, and asymptotic analysis in diverse domains. Awards and grants details are pending explicit mentions in source texts.

Penelope LewisView profile

Professor

• Sleep and memory consolidation
• Neuroplasticity
• Emotional memory processing
• +5 more

Professor Penelope Lewis is a leading neuroscientist at Cardiff University's School of Psychology, where she holds the rank of Professor. Her research focuses on the intersection of sleep, memory consolidation, and brain plasticity, with a particular emphasis on 'Sleep Engineering'—methods to manipulate sleep for cognitive and health benefits. Education: BA – Cornell University DPhil – University of Oxford Research Interests: Professor Lewis investigates how memories are consolidated during sleep and wakefulness, emphasizing: Procedural skill reinforcement Emotional memory processing Episodic to semantic memory transitions Sleep engineering techniques Her work explores how targeted memory reactivation, auditory stimulation, and environmental manipulations during sleep can enhance memory, reduce emotional distress, and combat age-related cognitive decline. Scientific Contributions: Her recent publications span high-impact journals such as Neuropsychologia , Communications Biology , Journal of Neuroscience , and Sleep , focusing on REM and NREM sleep mechanisms, memory reactivation, and neuroplasticity. Students and Supervision: She currently supervises several PhD students including: Mahmoud Eid Abdelhafez Abdellahi Imogen Birch Holly Kings Anne Koopman Martyna Rakowska Jules Schneider Collaborations and Funding: Her research is supported by major funding bodies including the BBSRC, MRC, EPSRC, Wellcome Trust, and DARPA. She collaborates internationally with institutions in Germany, the UK, and beyond.

Manuel SchallerView profile

Assistant Professor

• Numerical Mathematics
• Optimal Control
• Port-Hamiltonian Systems
• +5 more

Manuel Schaller is a Junior Professor of Numerical Mathematics at the Faculty of Mathematics, Chemnitz University of Technology. His research focuses on the theoretical and applied aspects of numerical methods for control systems, particularly in optimal control theory and data-driven approaches for dynamical systems. He maintains an active collaboration network with researchers across Germany, especially with Karl Worthmann and colleagues at TU Ilmenau and other institutions. Dr. Schaller's research interests center on the intersection of numerical mathematics, control theory, and data-driven methods. His work explores the theoretical foundations and practical applications of port-Hamiltonian systems, Koopman operator theory, and model predictive control. He investigates how these mathematical frameworks can be applied to solve complex control problems in engineering and medical applications, particularly focusing on energy efficiency, stability guarantees, and computational efficiency. His recent publications (2024-2025) demonstrate a strong trend toward integrating data-driven methods with classical control theory, particularly through Koopman operator approaches. These works address fundamental theoretical questions while maintaining strong connections to practical applications, especially in medical technology (retinal laser treatment) and energy systems. The research shows increasing sophistication in handling infinite-dimensional systems and providing rigorous error bounds for data-driven approximations. Scientific recognition includes: Publication award 2023 of TU Ilmenau, Category "Mathematics and Natural Sciences" Finalist for Best Student Paper Award of ECC 2022 While specific details about his advising activities are limited in the provided text, his extensive publication record with multiple co-authors suggests active mentorship of graduate students and junior researchers. His work on retinal laser treatment indicates collaboration with medical researchers, suggesting interdisciplinary grant funding. Dr. Schaller appears to be part of a vibrant research group focused on numerical methods for control systems at TU Chemnitz, with strong connections to other German institutions.

• Nonlinear Dynamics
• Optical Fiber Communication
• Signal Processing
• +4 more

Prof. Sander Wahls is a Professor at the Institute of Industrial Information Technology (IIIT) at Karlsruhe Institute of Technology (KIT). His research focuses on nonlinear dynamics, optical fiber communication, signal processing, and machine learning applications. He leads the KIT-IIIT team and has contributed significantly to advancing methodologies in nonlinear Fourier transforms, fiber parameter identification, and rogue wave analysis. His work bridges theoretical frameworks with practical implementations in optical systems and marine engineering. Key research interests include nonlinear wave phenomena, soliton-based signal processing, and data-driven approaches for communication systems. His publications highlight innovations in fiber nonlinearity monitoring, machine learning for optical channels, and experimental validations of novel signal recovery techniques. Prof. Wahls' recent work explores the implications of solitons in rogue wave occurrences, leveraging nonlinear Fourier transforms for spectral analysis of buoy measurements. He also investigates fiber parameter estimation using Koopman operators and develops algorithms for blind demultiplexing in optical communication. His research has been applied to real-world scenarios such as oceanographic wave analysis, high-speed optical transmission systems, and environmental monitoring. Collaborations span interdisciplinary areas like ocean engineering and computational mathematics.

Frank KoopmansView profile

Assistant Professor

• Molecular Neurobiology
• Proteomics
• Neuroscience
• +3 more

Frank Koopmans is an Assistant Professor at the Faculty of Science, Vrije Universiteit Amsterdam, with a joint appointment in the Molecular and Cellular Neurobiology department and Amsterdam Neuroscience's Cellular & Molecular Mechanisms division. His research focuses on synaptic proteomics, neurodegenerative diseases, and molecular mechanisms underlying Alzheimer's and schizophrenia. He employs advanced proteomic techniques like mass spectrometry and affinity purification to study synapse biology, mitochondrial dysfunction, and synaptic protein complexes. Key research areas include synaptic vesicle secretion, GABAA receptor proteomics, and the role of mitochondrial pathways in neurodegeneration. Recent work explores how early-life stress and amyloidosis share pathogenic pathways, as well as genetic overlaps between schizophrenia and developmental processes. Koopmans has contributed to studies on STXBP1-related disorders, synaptic proteome changes, and centenarian brain proteomics. His publications span 42 peer-reviewed articles and 7 datasets, emphasizing quantitative proteomics, synapse biology, and neurodegenerative disease modeling. Collaborations include work on iPSC-based disease modeling and genetic-environmental interactions in psychiatric disorders. His lab is part of Amsterdam Neuroscience, a multidisciplinary research institute focused on brain diseases.

• Biomechanical Engineering
• Orthopedic Surgery
• Medical Devices
• +3 more

Bart F.J.M. Koopman is a Professor at the Faculty of Engineering Technology and a member of the TechMed Centre at the University of Twente. His research focuses on biomechanical engineering, orthopedic surgery, and rehabilitation robotics. He has contributed extensively to musculoskeletal systems analysis and medical device development. Key research interests include wearable technologies for post-stroke rehabilitation, biomechanical modeling of joints, and improving surgical outcomes for total knee arthroplasty. His work addresses osteophyte effects and functional reconstruction in orthopedic procedures. Recent publications highlight advancements in sensor-driven digital twins for gait analysis and quantitative musculoskeletal assessment. His work has been recognized with the ISTA Award 2023. He has supervised 23 research works and contributed to organizing conferences like the IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics. His activities include examining doctoral candidates and collaborating internationally. He is affiliated with the TechMed Centre, a multidisciplinary research institute focused on biomedical engineering and healthcare solutions.

Jeffrey IchnowskiView profile

Assistant Professor

• Robotics
• Motion Planning
• Grasping Algorithms
• +4 more

Jeffrey Ichnowski is an Assistant Professor at Carnegie Mellon University's Robotics Institute. He holds a Ph.D. from the University of North Carolina at Chapel Hill and a B.A. from UC Berkeley. His research focuses on robot motion and grasp planning using cloud computing, optimization, and deep learning. He leads the Momentum Robotics Lab and has advised students including Hongyi Chen and Bardienus Duisterhof. Notable work includes developing FogROS2 for cloud robotics and achieving superhuman surgical peg transfer using deep learning. Awards include the 2022 Best Systems Paper for untangling long cables. His lab explores applications in soft robotics, hair care, and deformable object manipulation. Recent projects include MOE-Hair (finalist at HRI 2025) and dynamic pen-spinning by soft robots.

Slawomir SoleckiView profile

Professor

• Logic
• Set Theory
• Model Theory
• +4 more

Slawomir Solecki is a Professor in the Department of Mathematics at Cornell University within the College of Arts and Sciences, where he conducts research at the intersection of mathematical logic, combinatorics, and topological dynamics. His educational background includes: Ph.D. from California Institute of Technology (1995) Professor Solecki's research focuses on canonical topological spaces and dynamics of large groups lacking Haar measure, deeply rooted in mathematical logic (set theory and model theory). His work integrates combinatorics through Ramsey theory, probability theory via concentration of measure phenomena, and algebraic topology using fixed point theorems. This interdisciplinary approach reveals profound connections between seemingly distinct mathematical domains, particularly in analyzing measure-preserving transformations and structural properties of continua. His publication trajectory from 2019-2025 reveals consistent innovation across multiple fronts: extending Ramsey theory through category-theoretic frameworks, developing combinatorial models for topological spaces like the Menger curve, and exploring group representations without unitary structures. Key thematic threads include the interplay between descriptive set theory and topological dynamics, classification of submeasures, and dual Ramsey phenomena in hierarchical structures. His scientific recognition includes: Election as Fellow of the American Mathematical Society While specific student advising records aren't publicly detailed, his collaborative publications with researchers like A. Panagiotopoulos and F. M. Schneider demonstrate active mentorship within research teams. Grant funding specifics remain unpublicized, but his sustained output in top-tier journals indicates robust research support. His work occurs within Cornell's Mathematics Department framework without dedicated laboratory infrastructure, emphasizing theoretical collaboration over experimental facilities.