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Mohamad T. ShahabView profile

Assistant Professor

• Control Theory
• Robotics
• Adaptive Control
• +4 more

Mohamad T. Shahab is an Assistant Professor in the Department of Electrical, Computer, and Biomedical Engineering at Toronto Metropolitan University (TMU), where he joined in 2025. His research focuses on control theory and autonomous systems, with particular expertise in adaptive control, robotics, and optimization. Dr. Shahab completed his PhD in Electrical and Computer Engineering at the University of Waterloo in 2020. He received his BSc in Control and Instrumentation Engineering (2007) and MSc in Systems Engineering (Automation and Control) (2015), both from King Fahd University of Petroleum and Minerals (KFUPM). Prior to joining TMU, he held postdoctoral appointments at KAUST working with Professor Eric Feron and worked as an R&D engineer with Baker Hughes. His research spans Control Theory, Robotics, Adaptive Control, Control & Optimization, and Autonomous Systems . Dr. Shahab's work particularly focuses on adaptive control strategies that yield linear-like behavior, robustness analysis of adaptive control systems, and applications to robotics and autonomous aerial vehicles. His recent work has centered on modular tetrahedral-shaped aerial robots and their control under uncertainty. Dr. Shahab has published numerous papers in top-tier journals including IEEE Transactions on Automatic Control, Automatica, and International Journal of Adaptive Control and Signal Processing, as well as in major conferences like IEEE ICRA and AIAA SciTech. His research shows a clear trajectory toward developing more robust and reliable adaptive control strategies for complex systems, particularly in robotics and autonomous vehicles. Dr. Shahab is actively recruiting graduate students (MASc, MEng, PhD) and undergraduate researchers interested in control theory, robotics, and autonomous systems. He encourages students with strong backgrounds in control, optimization, robotics, and machine learning to contact him.

• Algorithms
• Graph Theory
• Dynamic Algorithms
• +4 more

Kathrin Hanauer is an Assistant Professor at the University of Vienna, where she is affiliated with the Research Group Theory and Applications of Algorithms and the Research Network Data Science. She conducts research in the design, analysis, and experimental evaluation of fast algorithms, with a focus on Algorithm Engineering connecting theoretical foundations with practical implementations. Her research interests include: Algorithm Engineering for practical algorithm implementation Dynamic algorithms for efficiently handling changing data Graph algorithms and network analysis Reachability problems on directed graphs Ranking problems, particularly the NP-hard Feedback Arc Set problem Network analysis, motif search, and subgraph counting Dr. Hanauer's recent publications demonstrate a strong focus on dynamic graph algorithms, with significant contributions to reachability queries, subgraph counting, and datacenter network optimization. Her work spans both theoretical algorithm design and practical implementation, often with C++ software projects. Notable contributions include the O'Reach algorithm for faster reachability queries in large graphs and several dynamic algorithms for subgraph counting and network analysis. Her scientific contributions: O'Reach: A novel approach to reachability queries in large graphs that outperforms previous methods Dynamic algorithms for four-vertex subgraph counting with efficient update operations Work on demand-aware link scheduling for reconfigurable datacenters Interdisciplinary research on normative reasoning with Aristotelian diagrams Dr. Hanauer actively supervises student research, with numerous completed theses focusing on dynamic graph algorithms, geometric algorithms, and reachability problems. Her lab maintains several software projects related to graph algorithms, including a modular algorithms library for dynamic graphs written in C++ and specialized implementations for reachability queries and subgraph counting.

Patrick ForréView profile

Assistant Professor

• Causal Inference
• Machine Learning Theory
• Information Theory
• +4 more

Patrick Forré is an Assistant Professor and Lab Manager of the AI4Science Lab at the Informatics Institute, Faculty of Science, University of Amsterdam. His work bridges theoretical machine learning and scientific applications, fostering interdisciplinary collaboration across informatics, mathematics, ecology, chemistry, physics, biology, and astrophysics. His research centers on mathematical foundations of machine learning including causal inference, graphical models, information theory, conditional independence structures, and geometric deep learning. He specializes in applying these techniques to scientific data problems, particularly in electro-catalysis and nitrogen fixation, where machine learning enhances molecular simulations and quantum chemical modeling. His theoretical work addresses non-linear structural causal models with cycles and latent confounders. The AI4Science Lab under his management focuses on detecting hidden patterns in scientific data through projects like electrode-electrolyte interface modeling, nitrogen-fixing coordination complexes analysis, and classical DFT neural approximations. Located in LAB42 Building at Amsterdam Science Park, the lab connects diverse scientific disciplines through machine learning innovation while organizing colloquia, workshops, and PhD defenses.

Monika PfallerView profile

Lecturer

• Mathematics Education
• Primary Education
• Curriculum Development
• +3 more

Monika Pfaller is a Lecturer at the University of Regensburg's Chair of Mathematics Education. She has extensive experience in primary school mathematics instruction, holding teaching qualifications in mathematics, German, biology, and art education. Since 2007, she has combined her academic role with practical teaching at the Johann-Michael-Sailer-Schule in Barbing, where she has served as a primary school teacher since 2004. Additionally, she works as a SINUS consultant and QuaMath program multiplier, focusing on enhancing mathematics education in Bavarian primary schools. Primary School Teaching Qualification University of Regensburg Lecturer (Mathematics Education) QuaMath Program Multiplier SINUS Consultant for Teacher Training Pfaller's research and professional work centers on mathematics education at the primary level. Her contributions include developing pedagogical strategies for geometry instruction, creating educational materials like the TRIDOMUS geometric construction system, and designing curriculum frameworks for grades 2-4. She emphasizes hands-on learning, problem-solving approaches, and contextual mathematics applications to foster deeper student understanding. Her publications demonstrate consistent engagement with practical mathematics instruction. Key themes include productive practice methods in geometry, solution strategies for arithmetic problems, and approaches to teaching large numbers through real-world contexts. Many of her works focus on lesson planning frameworks and teaching aids that align with competency-based educational standards. Co-author of TRIDOMUS geometry teaching materials Contributor to mathematics curriculum development Focus on solution-oriented teaching strategies Advocate for manipulative-based learning

Susanne WenzelView profile

Researcher

• Pattern recognition
• Image interpretation
• Machine Learning
• +6 more

Dr. Susanne Wenzel is a postdoctoral researcher and teaching assistant at the Photogrammetry group (IGG - Institute of Geodesy and Geoinformation) at the University of Bonn, and a scientific coordinator at Forschungszentrum Jülich since February 2018. Her academic journey began with studies in Geodesy at the Technical University of Berlin and University of Bonn, following professional training as a surveying technician at the Berlin Senate of Urban Development. Her research focuses on pattern recognition and image interpretation, particularly applying machine learning and deep learning techniques to photogrammetry and remote sensing problems. Wenzel's work prominently features Markov Marked Point Processes and the analysis of symmetries and repeated structures in images, with applications ranging from facade interpretation to oceanographic analysis. Her interdisciplinary approach bridges computer vision, geospatial analysis, and machine learning. The analysis of her 15 most recent publications reveals a strong trend toward applying advanced machine learning techniques, particularly deep learning and self-taught learning approaches, to photogrammetric and remote sensing problems. Her research spans multiple domains including urban modeling (facade interpretation), environmental monitoring (ocean eddies, sea level anomalies), and forensic applications (latent trace detection), demonstrating remarkable versatility while maintaining a core focus on image interpretation methodologies. Faculty Teaching Award 2014 Faculty Award for the best student in 2007 in Geodesy and Geoinformation Turbo-Preis 2007 of Society for Geodesy, Geoinformation and Land Management (DVW) Dr. Wenzel has supervised numerous Master's and Bachelor's students on diverse topics including neural network applications for ocean eddy tracking, hyperspectral imaging for latent trace detection, and deep learning for remote sensing image classification. Her teaching portfolio includes Photogrammetrie I and II courses since 2009, and she managed the development of the Geodetic Engineering Master's program at IGG from 2015-2018. Her research has been supported through positions at both the University of Bonn and Forschungszentrum Jülich, where she contributes to interdisciplinary projects bridging geospatial analysis and machine learning.

Jemil Avers ButtView profile

Lecturer

• Geomatic Engineering
• Remote Sensing
• Structural Monitoring
• +3 more

Dr. Jemil Avers Butt is a Lecturer at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering (D-BAUG), specializing in Geomatics and Geodetic Engineering. His research focuses on advanced geospatial technologies, including terrestrial radar interferometry, LiDAR sensing, and MIMO-SAR for structural and environmental monitoring. He develops novel methods for improving sensor accuracy, data fusion, and deformation analysis in civil engineering applications. His work addresses challenges in monitoring infrastructure such as bridges, wind turbines, and dams, leveraging machine learning and probabilistic modeling to enhance data interpretation. Recent projects include RGB-guided 3D displacement estimation, self-calibration of laser scanners, and phase ambiguity resolution in electronic distance measurements. He teaches courses like 'Computational Methods for Geospatial Analysis' and 'Project Parameter Estimation,' reflecting his expertise in both theoretical and applied aspects of geomatic engineering.

Angela VioliView profile

Professor

• Bio-Nanotechnology
• Biomedical Computation and Modeling
• Nanoparticle-Biological Interactions
• +4 more

Angela Violi is the Arthur F. Thunau Professor of Biomedical Engineering at the University of Michigan, with affiliations in Mechanical Engineering, Electrical Engineering and Computer Science, and Chemical Engineering. Her research focuses on computational methods to study nanoparticle interactions with biological systems, leveraging molecular simulations and machine learning. The Violi Lab develops interdisciplinary approaches to address challenges in nanoscience, including nanoparticle-membrane interactions, drug delivery, and environmental applications. Key research areas include bio-nanotechnology, biomembranes, and biomedical computation. Recent work emphasizes geometric deep learning for nonthermal plasma modeling and ensemble methods for feature selection. The lab's innovations aim to reduce computational costs while enhancing predictive accuracy in complex systems. Publications highlight advancements in nanoparticle growth prediction, machine learning-driven reaction network analysis, and domain-agnostic interaction modeling. Collaborative efforts span engineering, biology, and computer science, reflecting a commitment to translational research with societal impact. Angela Violi actively contributes to diversity initiatives in academia, as seen in her participation in DEI lectures and mentorship programs. Labs/Teams: The Violi Lab is a leader in nanobiotechnology, combining experimental and computational methods. Current projects focus on predictive models for nanoparticle behavior, sustainable materials design, and biomedical applications of AI.

Noomane Ben KhalifaView profile

Professor

• Sustainable Manufacturing
• Metal Forming Technologies
• Additive Manufacturing
• +4 more

Prof. Dr.-Ing. Noomane Ben Khalifa is a Professor of Manufacturing – Innovative Manufacturing and Head of the Institute for Production Technology and Systems (IPTS) at Leuphana University. He concurrently leads the Institute for Material and Process Design at Helmholtz Center Hereon. His work focuses on sustainable manufacturing processes, particularly extrusion, sheet metal forming, and additive manufacturing, emphasizing eco-friendly production across material value chains. Education: Awarded a Doctorate in Engineering from TU Dortmund University (2012) with distinction for research on helical profile extrusion. Holds a Mechanical Engineering degree from TU Dortmund (2005), specializing in production engineering and forming technology. Research Interests: Sustainable manufacturing processes; material recycling (e.g., aluminum chips); lightweight material development; composite extrusion; die design optimization; and process simulation. His work bridges advanced manufacturing with environmental impact reduction, leveraging AI and machine learning for material-property modeling. Key Article Trends: Recent studies emphasize machine learning applications in materials science, die design for magnesium alloys, and sustainable recycling processes. His work spans from fundamental material behavior analysis to industrial process optimization. 2025: Focus on AI-driven texture-property modeling in Mg alloys and dieless wire drawing improvements 2024: LCA of aluminum recycling and deep-drawing process reliability Awards: 2009 ISPF Award for extrusion research; 2014 ZukunftErfindenNRW for innovative extrusion press design; invited keynote speaker at ICIT & MPT 2014. Advising & Grants: Oversees OPTUM-MAGNA (magnesium nanocomposites), FERNAPRO (sustainable manufacturing tech), and projects like TrICo (innovation cooperation). Collaborates with industry on tool design and eco-processes. Labs & Teams: Leads IPTS and co-leads Helmholtz material design institute. Supervises interdisciplinary teams focusing on compound casting, incremental forming, and smart manufacturing systems.

Iyad KanjView profile

Professor

• Parameterized Complexity
• Graph Theory
• Algorithms
• +5 more

Iyad Kanj is a Professor in the School of Computing at DePaul University , part of the College of Computing and Digital Media (CDM) . He joined DePaul in 2001 after earning his Ph.D. from Texas A&M University under Jianer Chen. His research focuses on theoretical computer science, particularly parameterized complexity, algorithm design, and applications in robotics and bioinformatics. Education: Ph.D. in Computer Science, Texas A&M University (Advisor: Jianer Chen) Research Interests: Kanj’s work spans parameterized algorithms, graph theory, computational geometry, and robotic motion planning. He leads projects in matrix completion for machine learning and robotic navigation, contributing to the RoME (Robotics and Medical Engineering) Lab at CDM. Recent projects include energy-efficient motion planning for continuum robots and clustering algorithms for incomplete data. Teaching: Kanj teaches courses in algorithms, discrete structures, and theoretical computing, including Design and Analysis of Algorithms , Theory of Computation , and Combinatorial Optimization . Collaborations & Service: He has served on program committees for major conferences like AAAI, SOCG, and IPEC , and is a guest editor for Algorithmica . His work is accessible via DBLP and Google Scholar . Labs & Projects: Active in the RoME Lab, focusing on bio-inspired robotics and medical engineering applications. Current students include Ph.D. candidates Jiahao Deng and Brandon Meng, and M.S. student Atul Ponda.

Debbie YusterView profile

Associate Professor

• Combinatorics
• Polyhedral Geometry
• Machine Learning
• +3 more

Debbie Yuster is an Associate Professor of Data Science and Mathematics at Ramapo College of New Jersey, joining in 2020. She holds a Ph.D. in Mathematics from Columbia University and a B.A. in Mathematics with a Computer Science concentration from Cornell University. Her research focuses on combinatorics, polyhedral geometry, and machine learning, with contributions to algebraic geometry and STEM education. Education: Ph.D., Mathematics, Columbia University B.A., Mathematics (Computer Science concentration), Cornell University Research Interests: Combinatorics: Cluster methods, enumerative problems Polyhedral Geometry: Hyperdeterminants, triangulations Machine Learning: Interdisciplinary applications STEM Education: K-12 collaboration and curriculum development Publications: Her work bridges pure mathematics and computational biology, with key contributions in tropical geometry and epistatic modeling. Recent focus includes applying combinatorial techniques to high-dimensional geometric problems. Awards/Grants: None listed in current materials. Advising/Teaching: Teaches courses in data science (Python, ethics), mathematics (calculus, linear algebra), and collaborates on interdisciplinary programs. No formal advisees listed in profile. Labs/Teams: Active in the School of Theoretical and Applied Science's research initiatives, including the STEM Center and bioinformatics programs.

Anna De Mier VinueView profile

Associate Professor

• Combinatorics
• Graph Theory
• Matroid Theory
• +3 more

Anna De Mier Vinue is an Associate Professor at the Department of Mathematics, Universitat Politècnica de Catalunya (UPC), affiliated with the Facultat d'Informàtica de Barcelona (FIB). She leads research in combinatorics, graph theory, and matroid theory as part of the UPC's GAPCOMB research group (Geometric, Algebraic and Probabilistic Combinatorics). Her work focuses on structural combinatorics, polynomial invariants, and discrete geometry. Education: Holds a PhD in Applied Mathematics from UPC. Research Interests: Combinatorial enumeration, graph polynomials (Tutte, U-polynomials), matroid representations, non-crossing structures, and algorithmic combinatorics. Her contributions bridge algebraic methods with geometric and probabilistic approaches. Publications: Over 77 works including articles in Discrete Mathematics, European Journal of Combinatorics, and SIAM Journal on Discrete Mathematics. Recent research explores marked graphs, Tutte polynomial evaluations, and lattice approximations in clutter theory. Active in international projects like COCOA and R+D+I initiatives. Grants & Collaborations: Co-leads projects on extremal combinatorics, geometric algorithms, and mathematical education. Collaborates with researchers from BarcelonaTech and international institutions. Involved in initiatives addressing gender equality in STEM education through statistical analysis. Labs/Teams: Core member of the UPC's GAPCOMB group, contributing to interdisciplinary projects at the Institut de Matemàtiques de la UPC-BarcelonaTech. Supervises research in combinatorial geometry and algebraic structures.

Katharina JochemkoView profile

Associate Professor

• Geometric Combinatorics
• Discrete Geometry
• Algebraic Combinatorics
• +3 more

Katharina Jochemko is an Associate Professor in the Department of Mathematics at KTH Royal Institute of Technology. She holds a PhD (summa cum laude) from Freie Universität Berlin and a Diplom in Mathematics (with distinction) from LMU Munich. Her research focuses on geometric and algebraic combinatorics, discrete geometry, and the geometry of polynomials, with recent explorations into applications in artificial intelligence. She leads multiple research projects funded by prestigious organizations including the Wallenberg AI, Autonomous Systems and Software Program and the Swedish Research Council. Her academic contributions include groundbreaking work on Ehrhart polynomials, lattice polytopes, and syzygy bundles, as evidenced by her extensive publication record and the Best Paper Award 2018 from TU Wien. She actively supervises PhD students and postdocs, with current advisees including Jon Pål Hamre and Aryaman Jal. She serves as an associate editor for Beiträge zur Algebra und Geometrie and represents the Swedish Mathematical Society at institutional levels. Her teaching includes advanced courses in enumerative combinatorics and graph theory at KTH. She co-organizes international research initiatives like the Polytopes and Friends conference and the MaP-Trop seminar series on matroids and tropical geometry. Her research spans foundational work in geometric combinatorics with interdisciplinary applications.

Laurie BloomfieldView profile

Associate Professor

• Bioacoustics
• Vocal communication
• Species discrimination
• +3 more

Dr. Laurie Bloomfield is an Associate Professor in the Department of Psychology at Algoma University. Her research focuses on songbird vocal complexity, examining both production and perception mechanisms across species like black-capped, mountain, and boreal chickadees. Utilizing fieldwork, lab experiments, and neural analysis, she investigates acoustic communication, species recognition, and auditory processing. PhD: University of Alberta (2002-2007) MA: Queen’s University (2000-2002) BA: Laurentian University (1996-2000) Her work explores: Acoustic structure and function of chick-a-dee calls Cross-species vocal discrimination Neural correlates of auditory perception Environmental influences on vocal development Frequency-range discrimination and absolute pitch Comparative analysis of sympatric/allopatric populations Recent publications emphasize neural encoding, computational modeling, and ecological adaptations in vocal communication. She maintains a lab in CC 103 at Algoma University, with contact details provided.

Ali GolbafView profile

Lecturer

• Artificial Intelligence
• Clinical Management of Cancers
• Medical Imaging
• +6 more

Ali Golbaf is an Associate Lecturer at the School of Engineering, Computing and Mathematics within the Faculty of Science and Engineering at the University of Plymouth. He is currently pursuing a PhD in Computer Science, funded by UK Research and Innovation (UKRI). Prior to this, he earned a BSc in Mechanical Engineering from Shahrood University of Technology (2014) and an MSc in Mechanical Engineering from the University of Guilan (2017), both in Iran. His research focuses on leveraging Artificial Intelligence to develop non-invasive, objective biomarkers for clinical management of cancers, particularly brain tumors. His interdisciplinary work bridges mechanical engineering principles—such as finite element analysis, fluid-structure interaction, and scaling laws—with AI-driven healthcare solutions. Notable contributions include studies on turbulent boundary layers, dynamic systems scaling, and material compaction processes. Ali’s publications span topics ranging from AI applications in oncology to mechanical engineering fundamentals, reflecting his dual expertise. He has actively collaborated on experimental and numerical investigations across diverse fields, including impact mechanics, powder metallurgy, and thermal dynamics. His PhD research aims to integrate machine learning with clinical diagnostics, addressing unmet needs in precision oncology. While no scientific awards are explicitly listed, his work contributes to the UN Sustainable Development Goals (SDGs) related to health and innovation. Ali is funded by UKRI for his doctoral studies and currently has no listed advisees or grants.

Laszlo KozmaView profile

Assistant Professor

• Data Structures
• Algorithms
• Combinatorics
• +4 more

László Kozma is an Assistant Professor at Freie Universität Berlin in the Theoretical Computer Science department. He obtained his PhD at Saarland University under Raimund Seidel, followed by postdocs at Tel Aviv University and TU Eindhoven. His work focuses on data structures , combinatorics , and algorithmic adaptivity , with significant contributions to self-adjusting heaps, binary search trees, and geometric optimization. Recent research includes pattern-avoiding sequences and saddlepoint algorithms . His publications span exponential algorithms , TSP variants , and heap structures , with a recurring theme of connecting combinatorial geometry to algorithm design. Co-authors include leading researchers like Robert Tarjan, Uri Zwick, and Haim Kaplan. Key software implementations (e.g., smooth heap ) are publicly available. He has developed tools like Cuckoo Hashing Visualization and the historical WikipediaVision project, demonstrating practical engagement with algorithmic concepts. His mathematical genealogy traces back to classical researchers.