جست‌وجوی استاد و پژوهشگر

• Differential Transform Method
• Boundary Value Problems
• Approximate Analytical Solution
• +۳ مورد دیگر

Muhammed Kurulay is a Professor in the Department of Mathematics Engineering at Istanbul Technical University with an active research profile showing 41 documented publications and an h-index of 12 with 512 citations according to Scopus data. His primary research fingerprint reveals deep expertise in: Differential Transform Method (100% relevance) Boundary Value Problems (100%) Approximate Analytical Solutions (89%) Homotopy Analysis Method (74%) Fractional Partial Differential Equations (67%) Professor Kurulay's recent work (2023-2025) demonstrates remarkable interdisciplinary reach, bridging pure mathematics with practical applications across diverse fields. His publication pattern shows consistent scholarly output with notable spikes in 2012 (10 publications) and recent years, indicating sustained research productivity throughout his career. His scientific impact spans multiple domains: His plant disease detection research has achieved 30 citations and 141 reader captures, indicating significant agricultural technology impact Quantum error correction work has garnered 9 citations with notable interdisciplinary interest Virus spread modeling publications show medical relevance with news mentions Professor Kurulay maintains an extensive international collaboration network with co-authors from Turkey, Algeria, Saudi Arabia, and other regions, reflecting his global research engagement across computer science, mathematics, and life science applications.

Frank Haußerمشاهده پروفایل

استاد

• Numerical Mathematics
• Scientific Computing
• Mathematical Modeling
• +۴ مورد دیگر

Prof. Dr. Frank Haußer is a Professor at Beuth University of Applied Sciences Berlin in the Department II Mathematics - Physics - Chemistry. He serves as the academic advisor for the Applied Mathematics program and teaches courses including Numerical Mathematics, Mathematical Methods of Digital Image Processing, and Computational Engineering. His consultation hours are held during semesters and by appointment, with availability both in-person and online. Haußer's research focuses on: Numerical mathematics and scientific computing Mathematical modeling with applications in MATLAB/Octave Machine learning for medical imaging and insect monitoring Digital image processing techniques for biomedical applications Partial differential equations and computational engineering methods He has authored a textbook on mathematical modeling with MATLAB/Octave and leads interdisciplinary projects at the intersection of mathematics and technology. Analysis of his 15 most recent publications shows strong emphasis on: Medical imaging algorithms (especially retinal OCT analysis) Nanostructure dynamics and material science Computational physics and finite element methods Machine learning applications in biology and medicine Advanced mathematical modeling techniques His work consistently bridges theoretical mathematics with practical engineering applications. Haußer actively supervises student research, including: Machine learning for insect classification and localization Medical image processing algorithms Computational methods in engineering Finite element analysis applications Optimization and simulation techniques He has guided over 30 bachelor's and master's theses since 2009. His research projects include: KInsekt (2020-2023): AI-based insect monitoring funded by BMUV BeCRF (2015-2017): Medical image quality validation funded by BMWi QM ROCT (2013-2015): Automated OCT quality management funded by IFAF These interdisciplinary collaborations involve institutions across Germany.

Karol Koziolمشاهده پروفایل

استادیار

• Representation Theory
• Langlands Program
• p-adic Reductive Groups
• +۴ مورد دیگر

Karol Koziol is an Assistant Professor in the Mathematics Department at Baruch College, City University of New York (CUNY), where his office is located in the Newman Vertical Campus. His research focuses on various aspects of the (local) Langlands Program, specifically representation theory of p-adic reductive groups, associated Hecke algebras, and Galois representations. His work is partially supported by NSF grant DMS-2310225. Dr. Koziol's academic journey includes: Ph.D. from Columbia University under Rachel Ollivier Undergraduate degree from NYU NSF Postdoctoral Fellowship at University of Toronto with Florian Herzig EPDI Postdoctoral Fellowship at Institut des Hautes Études Scientifiques and Max Planck Institute Postdoctoral positions at University of Michigan and University of Alberta His research bridges algebra, number theory, and representation theory with significant contributions to understanding modular representations of p-adic groups. Dr. Koziol's work examines supersingular representations, Hecke module structures, and cohomological aspects of representation theory in characteristic p, advancing knowledge in the modular local Langlands correspondence. Analysis of his publications reveals consistent focus on representation theory of p-adic groups across multiple dimensions: structural properties of Hecke algebras, cohomological behavior of representations, classification problems for specific group types, and connections to broader Langlands program objectives. His collaborative work with leading mathematicians demonstrates strong integration within the international research community. As an educator, Dr. Koziol teaches Elementary Probability (Math 3120) and Graph Theory (Math 4140) at Baruch College. He has mentored multiple undergraduate researchers through REU programs on topics including modular representations of division algebras and quaternion algebras, often collaborating with other prominent mathematicians like Tasho Kaletha and Charlotte Chan. Dr. Koziol actively contributes to the mathematical community through organizational roles including: Co-organizing the Baruch Distinguished Lecture Series Co-founding the CARTOON (Cross Atlantic Representation Theory and Other topics ONline) conference Participating in POINT: New Developments in Number Theory Contributing to MANTIS 2021 and other specialized workshops Preparing materials for the 2025 Arizona Winter School

Aaron Melmanمشاهده پروفایل

دانشیار

• Applied Mathematics
• Linear Algebra
• Numerical Analysis
• +۳ مورد دیگر

Aaron Melman is an Associate Professor and Chair of the Department of Applied Mathematics at the School of Engineering, Santa Clara University. He holds a Ph.D. in Applied Mathematics from the California Institute of Technology (Caltech) and an M.Sc. from the Technion (Israel Institute of Technology). His research focuses on applied mathematics, with primary interests in linear algebra, numerical analysis, matrix theory, polynomial equations, and computational algorithms for root-finding and eigenvalue estimation. His work frequently explores theoretical bounds and efficient numerical techniques for solving complex mathematical problems. Melman's extensive publication record includes over 70 articles, with recent works concentrated on matrix polynomials, eigenvalue localization, and polynomial zero exclusion theorems. His research demonstrates a consistent emphasis on refining classical mathematical theorems and developing practical computational frameworks.

Anna Krauseمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Time Series Analysis
• +۷ مورد دیگر

Dr.-Ing. Anna Krause is a researcher at the Chair of Data Science (Informatik X) within the Faculty of Mathematics and Computer Science at the University of Würzburg. She leads the Deep Learning for Dynamical Systems Group and has been actively involved in teaching at the university since 2019, including courses on Machine Learning for Time Series Analysis and Data Mining. Doctoral degree in Electrical Engineering (2019), University of Hannover Diploma in Electrical Engineering (2009), Technical University Dresden Her research focuses on Environmental Sensing and Time Series Analysis , particularly on enhancing physics-based models using machine learning techniques for meteorological applications and sparse sensor networks. She has made significant contributions to explainable AI, climate modeling, and fraud detection systems. Anna's recent publications demonstrate expertise in climate modeling (ConvMOS, ICLR 2024-2025), physics-informed neural networks (TaylorPDENet, ECMLPKDD 2023), and fraud detection (MIDAS workshops, ECMLPKDD 2020-2023). She actively contributes to conferences as organizer and PC member, including ECMLPKDD and ICLR workshops. Scientific Awards Best ML Innovation Award (2020) for Deep Learning in Climate Modeling Best Student Paper Award (2020) for Multi-Task Land Use Regression Best Paper Award (2020) for Financial Fraud Detection with INALU The DynaBench dataset introduced in 2023 provides benchmark tools for learning dynamical systems from low-resolution data. Her work combines theoretical advancements with practical implementations, including edge computing applications for beekeeping monitoring systems.

Christoph Böhmمشاهده پروفایل

استاد

• Differential Geometry
• Geometric Evolution Equations
• Ricci Flow
• +۸ مورد دیگر

Christoph Böhm is a Professor at the Mathematical Institute, Department of Mathematics and Computer Science, University of Münster, Germany. He is a principal investigator in the Collaborative Research Center CRC 1442 "Geometry: Deformations and Rigidity" and a key member of the Excellence Cluster Mathematics Münster. His research lies at the intersection of differential geometry, geometric analysis, and dynamical systems, with a focus on Ricci flow, Einstein metrics, and geometric evolution equations. Research Interests: His work primarily explores Differential Geometry with deep focus on Ricci Flow , Homogeneous and Cohomogeneity-One Spaces , Einstein Manifolds , and Curvature Analysis . He investigates the long-term behavior of geometric flows, rigidity phenomena, and the interplay between symmetry and curvature. His projects such as "Geometric evolution equations" (CRC 1442 - B02) and "Harmonic maps and symmetry" (CRC 1442 - B04) reflect his interest in geometric PDEs under symmetry assumptions. He also contributes to understanding singular spaces and irregular gradient flows in geometric contexts. The recent publications highlight a consistent trend in homogeneous Ricci flows , non-compact Einstein manifolds , and scalar curvature behavior . These works demonstrate a strong emphasis on classification, existence, and optimal estimates in geometric evolution, often leveraging group actions and symmetry. The subfields span geometric PDEs, Lie group geometry, and variational methods. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: While no formal students are listed, Böhm has extensive collaborative research with mathematicians such as Ramiro Lafuente, Burkhard Wilking, Timothy Buttsworth, and Brian Clarke. He is actively involved in major funded projects including CRC 1442 (B02, B04), EXC 2044 (B1, C4), and a project on dynamical systems and irregular gradient flows. These grants support his research in geometric analysis and its connections to topology and mathematical physics. Labs and Teams: Böhm is part of the research group in differential geometry at the University of Münster, collaborating closely with Prof. Burkhard Wilking. He contributes to the broader Mathematics Münster cluster, which fosters interdisciplinary research in geometry, analysis, and topology. His work is integrated into a vibrant research environment focused on "Groups and actions", "Curvature, shape, and global analysis", and "Singularities and PDEs".

Nicolas Garcia Trillosمشاهده پروفایل

دانشیار

• Applied Analysis
• Computational Probability
• Statistics
• +۵ مورد دیگر

Nicolas Garcia Trillos is an Associate Professor in the Department of Statistics at the University of Wisconsin-Madison, with research spanning applied analysis, computational probability, statistics, and machine learning. His work focuses on the intersection of calculus of variations, optimal transport, and partial differential equations in learning systems. His educational background includes: Bachelor's degree in Mathematics from Universidad de Los Andes, Bogotá, Colombia (2010) Ph.D. in Mathematics from Carnegie Mellon University (2015) Prager Assistant Professor (postdoctoral position) at Brown University (2015-2018) Garcia Trillos' research centers on the mathematical foundations of machine learning , developing tools in optimal transport and calculus of variations to analyze graph-based learning and continuum models. Key contributions address adversarial robustness , federated learning , and spectral clustering , providing theoretical guarantees for large-scale problems through PDE frameworks. Recent publications (2022-2025) demonstrate a cohesive focus on geometric approaches to adversarial robustness , using optimal transport to derive classification bounds and study solution existence. He has advanced federated learning via consensus-based bi-level optimization (CB2O) and developed Fermat distances for metric approximation, bridging pure mathematics with statistical learning theory. No scientific awards were mentioned in the provided text. There is no information available regarding student advising, research grants, or laboratory affiliations in the source material.

Maciej Zworskiمشاهده پروفایل

استاد

• Partial Differential Equ
• Mathematical Physics
• Quantum Mechanics
• +۷ مورد دیگر

Maciej Zworski is a Professor in the Department of Mathematics at the University of California, Berkeley , affiliated with the College of Letters and Science. His research focuses on mathematical aspects of quantum mechanics , particularly scattering theory, microlocal analysis, and spectral theory. He has contributed extensively to understanding quantum decay rates, fractal Weyl laws, and nonlinear soliton dynamics. Education: Not explicitly mentioned in the text. Research Interests: Scattering theory and microlocal analysis in quantum systems Partial differential equations in mathematical physics Chaotic dynamics in quantum and classical systems Applications to graphene and black hole physics Numerical analysis of resonance phenomena Recent Publications (2024–2025) emphasize semiclassical limits, quantum chaos in graphene models, and resonance dynamics in black holes. His work bridges analytical techniques with experimental physics, as seen in microwave system studies. Advising: Mentored 28+ PhD students and postdocs, including prominent names like Semyon Dyatlov, Jeff Galkowski, and Jian Wang. Labs/Teams: Co-organizes the Bay Area Microlocal Analysis Seminar and collaborates internationally on quantum scattering projects.

• Sports Technology
• Sustainability Science
• Systems Modeling
• +۳ مورد دیگر

Fredrik Ståhl serves as a Senior Lecturer at Mid Sweden University within the Faculty of Science, Technology and Media, specifically affiliated with the Department of Engineering, Mathematics and Subject Didactics. He holds a Doctor of Philosophy degree and maintains active research roles through the Sports Tech Research Centre, with physical presence in Östersund (Room Q281A). His educational foundation includes doctoral research completed at Umeå University, culminating in the 2000 dissertation At the edge of space and time - exploring the b-boundary in general relativity . This early work established his expertise in mathematical relativity before his research trajectory evolved toward applied interdisciplinary domains. Ståhl's research demonstrates a profound transition from theoretical physics to practical applications. Initial investigations centered on spacetime singularities and boundary constructions in general relativity, while recent work focuses on sports technology optimization and sustainability systems. His current projects—including Augmented Sports and the Mid Sweden Craft Brewing Lab —leverage mathematical modeling for athletic performance enhancement and regional sustainability frameworks, reflecting an integrative approach bridging engineering, environmental science, and human movement analysis. Analysis of his publication history reveals three distinct research phases: foundational work in mathematical relativity (1997-2002), transitional biomechanics studies (2008), and current sustainability/sports technology applications (2013-2015). This evolution showcases increasing interdisciplinary collaboration, particularly with sustainability researchers like Skytt and Nielsen, while maintaining rigorous mathematical methodology throughout his career. As an active member of the Sports Tech Research Centre, Ståhl contributes to projects examining human performance through computational modeling. His recent conference participations at the International Society for the Systems Sciences and Global Cleaner Production forums indicate strong engagement with global sustainability challenges, particularly through system modeling approaches for Anthropocene governance and long-term decision support tools.

Iair Arcaviمشاهده پروفایل

دانشیار

• Observational Astronomy
• Astrophysics
• Supernovae
• +۷ مورد دیگر

Iair Arcavi is an Associate Professor at Tel Aviv University's School of Physics and Astronomy, specializing in observational astronomy and astrophysics. He directs the Wise Observatory and focuses on transient cosmic phenomena like supernovae, kilonovae, and tidal disruption events. His work bridges theoretical modeling with multiwavelength observations to study extreme astrophysical environments. Education: BSc (Physics & Mathematics), Tel Aviv University (2007) MSc (Physics), Weizmann Institute (2011) PhD (Astrophysics), Weizmann Institute (2013) Research Interests: Arcavi investigates core-collapse supernovae to determine progenitor properties and explosion mechanisms. He pioneered rapid follow-up techniques for kilonovae linked to gravitational-wave events like GW170817. His team also explores tidal disruption events (TDEs), revealing UV-dominated emission and He II signatures in post-starburst galaxies. This work informs galaxy evolution, black hole physics, and transient classification. Scientific Awards: Alon Fellowship (2018) Einstein Postdoctoral Fellowship (2016) Harvey L. Karp Discovery Award (2015) Wolf Foundation Scholarship (2012) Weizmann Institute Dean’s Prize (2011) Collaborations & Outreach: Arcavi co-leads international transient follow-up programs (e.g., Las Cumbres Observatory) and founded Astronomy on Tap chapters in Santa Barbara and Tel Aviv. His outreach emphasizes public engagement through informal talks and interactive events.

• Physics-based statistical signal processing
• Subsurface sensing
• Auditory prostheses
• +۶ مورد دیگر

Leslie M. Collins is a distinguished Professor of Electrical and Computer Engineering, Professor of Biomedical Engineering, and Professor in the Department of Head and Neck Surgery & Communication Sciences at Duke University's Pratt School of Engineering. She is also a Faculty Network Member of the Duke Institute for Brain Sciences, demonstrating her interdisciplinary reach across engineering, medicine, and neuroscience. Dr. Collins earned her BSEE from the University of Kentucky, followed by MSEE and PhD degrees from the University of Michigan, Ann Arbor. After working as a Senior Engineer at Westinghouse Research and Development Center from 1986 to 1990, she joined Duke University in 1995 as an Assistant Professor, was promoted to Associate Professor in 2002, and to full Professor in 2007. Her research spans physics-based statistical signal processing, subsurface sensing, auditory prostheses, and pattern recognition. Dr. Collins has made significant contributions to landmine detection systems, having been part of the team that transitioned MURI-developed algorithms to the Army HSTAMIDS and GSTAMIDS systems. Her work on cochlear implants and brain-computer interfaces represents groundbreaking advances in auditory prosthetics and neural engineering. She has secured substantial research funding from agencies including ARO, NVESD, SERDP, ESTCP, NSF, and NIH. Analysis of her recent publications reveals a strong trajectory toward integrating deep learning with traditional signal processing approaches across multiple domains. Her work bridges military applications (landmine detection), medical technology (cochlear implants, heart monitoring), and infrastructure analysis (power grid mapping), demonstrating remarkable versatility in applying core signal processing principles to diverse real-world problems. DoD UXO Cleanup Project of the Year (2000) Member of Tau Beta Pi honor society Member of Sigma Xi honor society Member of Eta Kappa Nu honor society Dr. Collins has been a dedicated mentor, graduating 15 PhD students as of 2015. Her research has been supported by numerous grants from major federal agencies including ARO, NVESD, SERDP, ESTCP, NSF, and NIH, reflecting the significance and impact of her work across defense, environmental, and medical applications. She has played a key role in translating research into practical systems, particularly in landmine detection where her work has directly impacted military operations. Her research group operates at the intersection of multiple laboratories and teams, including collaborations with the Duke Institute for Brain Sciences and various medical departments at Duke. This interdisciplinary approach enables her to tackle complex problems that span traditional academic boundaries, from developing algorithms for landmine detection to creating improved speech processing for cochlear implant users.

Sven Buchholzمشاهده پروفایل

استاد

• Applied Computer Science
• Data Management
• Data Mining
• +۶ مورد دیگر

Prof. Dr.-Ing. Sven Buchholz serves as a Professor in the Department of Computer Science and Media at Brandenburg University of Technology in Brandenburg an der Havel, Germany. His office is located in Building C, Room C.2.18 at Magdeburger Straße 50, with contact information including telephone +49 3381 355-482 and email sven.buchholz@th-brandenburg.de. Specializing in Applied Computer Science , Prof. Buchholz focuses on data management and data mining with a distinctive research trajectory in geometric algebra applications. His scholarly work demonstrates expertise across multiple domains including neural network architectures using Clifford algebra, solving complex partial differential equations, protein structure prediction, and computer vision systems. The evolution of his research shows progression from foundational theoretical work on Clifford neurons in the early 2000s to increasingly interdisciplinary applications in recent years. Analysis of his publication history reveals a significant research focus spanning over 15 years, with a notable resurgence of activity in 2024. His recent work demonstrates sophisticated applications of geometric algebra to solve challenging problems across physics (Maxwell's equations, Navier-Stokes equations), molecular biology (protein structure prediction), and robotics (camera pose estimation). This interdisciplinary approach connects mathematical theory with practical implementations in scientific computing and artificial intelligence. Prof. Buchholz maintains an active research profile with multiple high-impact publications in 2024, indicating ongoing contributions to the advancement of geometric algebra applications in computational science. His work bridges theoretical mathematics with practical machine learning implementations, contributing to both academic knowledge and potential real-world applications in scientific computing and data analysis.

• Dynamical Systems
• Geometric Group Theory
• Rational Billiards
• +۵ مورد دیگر

Alex Eskin is the Arthur Holly Compton Distinguished Service Professor in the Department of Mathematics at the University of Chicago. He has been a faculty member at the University of Chicago since 1999 and is a leading researcher in dynamical systems, geometric group theory, and the theory of moduli spaces. His educational background includes: Undergraduate studies at UCLA PhD from Princeton University (1993) under Peter Sarnak Eskin's research focuses on rational billiards, geometric group theory, and the dynamics of the SL(2,R) action on moduli spaces of translation surfaces. His work bridges several areas of mathematics including ergodic theory, geometry, and number theory. He is particularly known for his breakthrough results on the classification of invariant and stationary measures for the SL(2,R) action on moduli spaces, which led to his Breakthrough Prize in 2020. Eskin's recent publications demonstrate a continued focus on measure rigidity, Lyapunov exponents, and the geometry of moduli spaces. His work often involves deep collaborations with other leading mathematicians such as Maryam Mirzakhani (until her passing), Amir Mohammadi, and others. The research spans from foundational theoretical work to applications in counting problems and effective estimates. His major scientific achievements include: Clay Research Award (2007) for work on quasi-isometric rigidity of solvable groups Fellow of the American Mathematical Society (2012) Election to the National Academy of Sciences (2015) Breakthrough Prize in Mathematics (2020) for classification of P-invariant and stationary measures for the moduli of translation surfaces Eskin has advised several PhD students including Moon Duchin and Simion Filip. His research has been supported by major grants from the National Science Foundation and the Simons Foundation. He has been an influential figure in the field, giving invited talks at the International Congress of Mathematicians in 1998 and 2010. His work has opened new directions in the study of dynamics on moduli spaces and has deep connections to other areas of mathematics. While not explicitly mentioned in the provided texts, Eskin is likely involved in research groups or seminars at the University of Chicago related to geometry, topology, and dynamical systems. His extensive collaboration network suggests active participation in the broader mathematical community through workshops, conferences, and collaborative research projects.

• Signal Processing
• Statistical Signal Processing
• System Identification
• +۵ مورد دیگر

Tomas McKelvey is a Full Professor and Deputy Head of the Department of Electrical Engineering at Chalmers University of Technology, where he leads the Signal Processing research group. He has been with Chalmers since 2000 and has held a full professor position in Signal Processing since 2006. Professor McKelvey received his Electrical Engineering education at Lund University between 1987 to 1991 and earned his PhD in Automatic Control at Linköping University in 1995. Between 1995 and 1999, he held research and teaching positions at Linköping University, where he became docent in 1999. He was a visiting researcher at the University of Newcastle, Australia between 1999 and 2000 before joining Chalmers University of Technology. Professor McKelvey's research spans multiple domains within signal processing and control systems. His primary interests include model-based signal processing, statistical signal processing, system identification, and automatic control. His work has significant applications across several fields including radar systems, biomedical engineering, power systems, and automotive propulsion. He has developed innovative methods for signal processing in challenging environments, particularly in radar and power systems applications, often bridging theoretical advances with practical implementation challenges. His recent publications demonstrate a strong trend toward integrating machine learning and deep learning approaches with traditional signal processing methods, particularly for applications in radar systems, power grid stability, and automotive control. His work shows increasing sophistication in handling non-stationary signals, developing efficient computational methods, and creating robust solutions for real-world engineering problems. Professor McKelvey has been actively involved in numerous research projects, as indicated by his extensive publication record spanning multiple disciplines. His leadership position as Deputy Head of Department reflects his significant standing within the academic community. His research group maintains strong connections with both academic and industrial partners, as evidenced by the applied nature of much of his work. The cross-disciplinary nature of his publications indicates collaborations across engineering fields and with medical researchers, particularly in the biomedical applications of microwave technology.

Yuqi Songمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Data Mining
• +۳ مورد دیگر

Yuqi Song is an Assistant Professor in the Department of Computer Science at the University of Southern Maine (USM), where she joined in August 2023 after completing her Ph.D. at the University of South Carolina. Her interdisciplinary research bridges machine learning with materials science, tourism, and recommender systems. Her educational background includes: Ph.D. in Computer Science, University of South Carolina (2023), supervised by Dr. Jianjun Hu M.S. and B.S. in Computer Science, Chongqing University, supervised by Dr. Ming Gao Dr. Song's research focuses on applying state-of-the-art deep learning techniques—including generative adversarial networks, graph neural networks, and transformer models—to solve real-world problems. She develops AI-driven solutions for materials discovery (predicting crystal structures and properties) and tourism applications (employee turnover prediction systems). Her work uniquely combines computational methods with domain-specific challenges, emphasizing practical implementation through user-friendly tools like her materials informatics web platform MaterialsAtlas.org. Analysis of her recent publications (2023-2025) reveals three dominant research thrusts: (1) materials informatics using transformer-based generative models for crystal structure prediction, (2) robust recommender systems security against data hybrid attacks, and (3) computer vision innovations in depth estimation and medical image analysis. Her work consistently leverages attention mechanisms and cross-disciplinary data integration. Dr. Song actively mentors graduate students, currently advising Reihaneh Maarefdoust (Complex Learning and Machine Learning) and Zahra JahediBashiz (NLP, Generative AI). She teaches core courses including Software Engineering (COS 430) and Artificial Intelligence (COS 470), emphasizing practical programming skills. Her lab seeks motivated students for projects in materials discovery and tourism analytics. She leads a research group focused on interdisciplinary AI applications, collaborating with materials scientists and hospitality industry partners to develop deployable solutions. Current initiatives include deep learning models for predicting piezoelectric properties and generative design of 2D materials, alongside tools for tourism workforce analytics.