جست‌وجوی استاد و پژوهشگر

• Control Engineering
• Automatic Control
• Sliding Mode Control
• +۳ مورد دیگر

Staniša Perić is an Associate Professor at the Department of Automation, Faculty of Electronic Engineering, University of Niš, Serbia. Elected to this position in 2023, he specializes in control systems and signal processing with 21 impact-factor journal publications to his name. Education: PhD in Systems Control, Faculty of Electronic Engineering, University of Niš (2016) Diploma in Automation, Faculty of Electronic Engineering, University of Niš (2009) Research Focus: Dr. Perić's work bridges Control Engineering and Signal Processing , with groundbreaking contributions in Sliding Mode Control for Automotive Systems and Advanced FIR Filter Design . His research integrates Orthogonal Endocrine Neural Networks for real-time control applications, particularly in Anti-lock Braking Systems (ABS), while developing ultra-selective multiplierless filters for hardware-efficient signal processing. Publication Analysis: His 11 representative publications (2010-2017) reveal a clear trajectory: early work focused on orthogonal function applications in antenna systems and filter design, evolving into neural network-enhanced sliding mode control for automotive safety systems. The 2015-2017 period shows intensive development of biologically inspired neural networks for dynamic system modeling and time-series forecasting. Research Engagement: National Research Projects: 2 International Research Projects: 4 Collaborative Environment: As a core member of the Department of Automation, Dr. Perić contributes to research groups specializing in intelligent control systems and advanced signal processing. His work directly supports the faculty's strategic focus on automotive electronics and embedded control solutions, with experimental validation conducted through university-industry partnerships in automotive safety systems.

• Autism Spectrum Disorder
• Neurodevelopmental Disorders
• Clinical Neuroscience
• +۴ مورد دیگر

Dr. James C. McPartland is the Harris Professor in the Child Study Center at Yale School of Medicine. He is a licensed clinical psychologist and directs the Yale Developmental Disabilities Clinic and the McPartland Lab, which focuses on the clinical neuroscience of autism. His research explores brain mechanisms underlying social perception and communication in autism, aiming to develop biomarkers for clinical trials. McPartland leads the Autism Biomarkers Consortium for Clinical Trials (ABC-CT) and has contributed over 200 publications. He holds academic appointments in the Department of Psychology and directs undergraduate studies at the Child Study Center. Awards include the APA Sara S. Sparrow Early Career Research Award and the Klerman Prize for Clinical Research. His lab collaborates on studies involving electrophysiology, neuroimaging, and behavioral assessments to improve early detection and intervention strategies for neurodevelopmental disorders. Education & Training: BA, Harvard University, Psychology (1996) PhD, University of Washington, Child Clinical Psychology (2005) Fellowship, Yale Child Study Center (2005–2006) Research Interests: Autism spectrum disorder, neurodevelopmental disorders, clinical neuroscience, biomarkers, social perception, and translational research. Grants & Awards: NIMH, NICHD, NINDS grants Autism Science Foundation, Simons Foundation, and others Named top-funded autism researcher (2010–2019) Labs & Teams: McPartland Lab, Center for Brain and Mind Health, Autism Biomarkers Consortium for Clinical Trials (ABC-CT).

• Environmental AI
• Social-Ecological Systems
• Digital Technologies in Environmental Research
• +۷ مورد دیگر

Dr. Arash Akhshik is an environmental and AI researcher with a strong focus on social-ecological systems, digital technologies, and cultural tourism. He serves as the Principal Investigator for the National Science Centre (NCN) project titled Unlocking Urban Potential: Fostering Multispecies Justice in Informal Green Spaces , where he applies AI-assisted methods to study human interactions with riverscapes and urban green environments. His research integrates artificial intelligence, machine learning, and social science methodologies to address complex environmental challenges. Key areas of investigation include: Human-place attachment and environmental stewardship in cultural tourism Sentiment analysis of public behavior toward rivers during and after the pandemic Wildlife tourism as a pathway for megafauna conservation Community perspectives on ecosystem services in protected areas Applications of big data in land use and environmental policy The recent publications highlight a consistent trend in using innovative digital tools—particularly AI and big data—to understand human-environment dynamics across urban, rural, and protected landscapes. His interdisciplinary approach bridges environmental science with tourism studies and computational social science. Dr. Akhshik is actively involved in international and multidisciplinary collaborations, working closely with researchers such as Małgorzata Grodzińska-Jurczak, Joanna Tusznio, and Marianna Strzelecka. While no formal academic affiliation is specified in the text, his work is clearly embedded within the Polish academic research ecosystem, particularly in environmental policy and sustainability sciences. He has not been mentioned as advising students or receiving specific scientific awards in the provided text. There is also no information regarding teaching roles, grants beyond the NCN project, or educational background.

Anne Ljungarsمشاهده پروفایل

پژوهشگر

• Antibody Technologies
• Antivenom Development
• Single-Domain Antibodies
• +۵ مورد دیگر

Anne Ljungars is a Senior Researcher at the Department of Biotechnology and Biomedicine, Technical University of Denmark , specializing in Antibody Technologies and Biologics Engineering . Her work focuses on advancing antivenom development, single-domain antibody engineering, and proteomics innovation. Expertise: Antivenom development, Phage display, Protein engineering, Toxin detection Key Projects: Supervising multiple PhD students in projects like AI-driven protein binder design and nanobody diagnostics Research trends are concentrated in snakebite envenoming , nanobody-based diagnostics , and computational proteomics . She contributes to UN Sustainable Development Goals via global health initiatives targeting neglected tropical diseases. Collaborations span immunology , toxicology , and biotechnology . Her recent publications emphasize single-domain antibodies , antibody-dependent enhancement , and high-throughput sequencing analysis .

• Computational chemistry
• Molecular modeling
• Drug discovery
• +۵ مورد دیگر

Marcus Weber serves as Head of the Computational Molecular Design research group within the Modeling and Simulation of Complex Processes department at the Zuse Institute Berlin (ZIB), which operates in close affiliation with Freie Universität Berlin. His interdisciplinary work spans computational mathematics, molecular modeling, drug discovery, and unexpected connections to Egyptology, demonstrating the broad applicability of mathematical approaches across diverse scientific domains. Dr. Weber's research interests focus on the mathematical foundations of molecular simulation and drug design: Developing advanced Markov state models for complex molecular systems Creating computational methods for efficient drug discovery Applying machine learning techniques to molecular dynamics Modeling pH-dependent receptor-ligand interactions Exploring metastable dynamics in biological systems Bridging mathematical approaches with Egyptological research His recent publications reveal a sophisticated integration of computational mathematics with practical pharmaceutical applications, particularly in opioid receptor research. The work demonstrates how mathematical modeling can identify pH-dependent drug candidates that maintain efficacy while reducing side effects. His research group has developed innovative algorithms like ISOKANN for learning Koopman eigenfunctions and has made significant contributions to understanding molecular transition rates and metastable dynamics. Dr. Weber leads multiple significant research projects including 'Drug Candidates as Pareto Optima in Chemical Space,' 'HPC and ML for Drug Discovery,' and interdisciplinary collaborations connecting mathematical approaches with Egyptology. His work on 'Mathematics and Egyptology' and 'Ancient Egyptian' demonstrates the unexpected breadth of mathematical applications. His research has direct implications for developing safer opioid medications and understanding molecular behavior in complex environments.

• Metabolomics
• Systems Biology
• Alzheimer's Disease Research
• +۶ مورد دیگر

Jan Krumsiek is an Assistant Professor at Weill Cornell Medicine's Graduate School of Medical Sciences, affiliated with both the Physiology, Biophysics & Systems Biology and Computational Biology programs. His research sits at the intersection of metabolism, computational biology, and disease mechanisms. Dr. Krumsiek's research focuses on how metabolism shapes health and disease across multiple conditions including neurodegeneration, cancer, and cardiometabolic disorders. His lab integrates metabolomics, proteomics, transcriptomics, and genomics with computational analysis to reveal actionable biomarkers and elucidate molecular mechanisms. Key ongoing projects investigate tau-related metabolic disruptions in Alzheimer's disease, the impact of dietary interventions on brain health, tumor escape pathways in resistant lymphomas via spatial metabolomics, and microbial biosynthetic clusters as therapeutic opportunities. The lab also utilizes large-scale NMR-based approaches to refine cardiometabolic risk models and investigate metabolic signatures tied to clinical outcomes in conditions like COVID-19. Analysis of Dr. Krumsiek's recent publications (2023-2025) reveals a strong focus on Alzheimer's disease metabolism, with numerous studies examining how dietary interventions, particularly modified Mediterranean ketogenic diets, affect metabolic signatures in neurodegenerative conditions. His work also spans cancer metabolism, space medicine omics, and multi-omics integration methodologies. A notable trend is the application of computational frameworks to analyze complex metabolic data across different disease states and interventions. Dr. Krumsiek leads the Krumsiek Lab, which employs a multidisciplinary approach combining wet-lab metabolomics techniques with advanced computational analysis. The lab's work spans from basic metabolic pathway analysis to clinical applications, with particular emphasis on translating metabolic findings into potential therapeutic interventions.

Wesley Tanseyمشاهده پروفایل

استادیار

• Statistical Machine Learning
• Computational Oncology
• Bayesian Statistics
• +۳ مورد دیگر

Wesley Tansey serves as Assistant Professor in the Computational Oncology group within the Department of Epidemiology and Biostatistics at Memorial Sloan Kettering Cancer Center (MSKCC). His research bridges statistical machine learning with cancer biology, focusing on developing novel computational frameworks for oncology applications. Dr. Tansey's research program centers on Bayesian statistical methods for biological data analysis, with particular emphasis on spatial transcriptomics (evidenced by his BayesTME framework), drug response modeling , and multi-omics integration . His lab develops scalable algorithms for high-dimensional biological data, including UnitedMet for metabolite imputation and MultiTME for spatial profiling analysis. Current projects address combinatorial drug screening optimization, tumor microenvironment characterization, and predictive oncology platforms for rare cancers. His recent publications (2023-2025) demonstrate strong focus areas: Bayesian active learning for drug screening (6+ publications) Spatial biology methods (BayesTME, MultiTME) Metabolomics-transcriptomics integration (UnitedMet) Causal inference in biological systems Scientific recognition includes serving as Area Chair for AISTATS 2022 and frequent invited talks at major conferences including SIAM's Mathematics of Data Science meeting. Dr. Tansey actively mentors lab members including Sophie Jaro (Spotlight presenter at ICML Workshop), Haoran Zhang (contributed talk presenter), Christopher Tosh (Associate Research Scientist), and Jeff Quinn (Bioinformatics Software Engineer). His lab receives research funding supporting development of computational oncology platforms with clinical translation potential. The VIVO Lab maintains active GitHub repositories for core methodologies including BayesTME, reflecting strong software engineering practices in computational biology.

• Quantum Algorithms
• Quantum Complexity Theory
• Quantum Cryptography
• +۲ مورد دیگر

Nai-Hui Chia is an Assistant Professor in the Department of Computer Science at Rice University. His academic journey includes postdoctoral fellowships at the University of Maryland's Joint Center for Quantum Information and Computer Science (QuICS) and UT Austin, following a Ph.D. in Computer Science and Engineering at Penn State University under Dr. Sean Hallgren and undergraduate studies at National Taiwan University. Ph.D.: Computer Science and Engineering, Penn State University, 2018 Bachelor's: National Taiwan University Chia's research focuses on quantum algorithms, quantum complexity theory, and quantum cryptography. He investigates quantum computing's capabilities, limits, and its transformative potential for computer science, particularly in computational tasks with quantum advantages, quantum depth verification, and circuit complexity. His work bridges theoretical exploration with practical applications in quantum machine learning and cryptographic security. Recent publications highlight advancements in adversarially robust quantum state learning (FOCS 2025), quantum depth verification (COLT 2025, TQC 2024), and quantum-inspired classical algorithms for low-rank matrix problems. His research also addresses fundamental questions in quantum complexity theory, including black-box simulation barriers and impossibility results for quantum zero-knowledge protocols. Honors include the NSF CAREER Award (2024), DOE Quantum Testbed Pathfinder (2023), Google Research Scholar (2023), and an NSF grant (2022). He serves on program committees for TQC 2024, CCC'23, and Crypto 2022, and organizes quantum computing sessions at IOS 2024 and QuantIPS 2023. Chia advises students like Yu-Ching Shen and Chia-Ying Lin, and has mentored postdocs including Jianqiang Li and Daniel Liang. His teaching includes courses on cryptography and quantum computing at Rice and Indiana University, and guest lectures at UT Austin. Outside academia, he enjoys sports and history.

Nikola Sakačمشاهده پروفایل

استاد

• Analytical Chemistry
• Chemical Sensors
• Food Safety
• +۵ مورد دیگر

Prof. Dr. Nikola Sakač is affiliated with the Faculty of Chemical Engineering and Technology at the University of Zagreb, Croatia. His research spans analytical chemistry, sensor development, food safety, and sustainable methodologies in science and education. He actively collaborates with researchers across Europe, particularly in Croatia, Italy, and Slovenia. His research interests focus on analytical and environmental chemistry , particularly in developing potentiometric sensors , microfluidic systems , and green analytical methods . His work addresses real-world challenges in food safety (e.g., mycotoxin detection), water quality monitoring , and promoting sustainability in higher education . Nikola Sakač employs advanced materials such as nanocomposites and ionic liquids in sensor design. The recent publications show a strong trend toward interdisciplinary research, combining chemistry, engineering, and environmental science. His work integrates nonlinear modeling, biosensing, and sustainable technologies, reflecting a commitment to innovation and environmental responsibility in scientific practice. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: While no students or grants are listed, his frequent co-authorship with early-career researchers and participation in multidisciplinary projects suggest active mentorship and potential grant involvement in EU or national research programs related to food safety and environmental monitoring. Labs and Teams: Nikola Sakač is likely part of research teams focused on analytical chemistry and sensor development at the University of Zagreb, possibly collaborating with the Department of Food Technology and environmental engineering groups. His work with microfluidics and biosensors indicates engagement with advanced instrumentation and sustainable lab practices.

Benny Avelinمشاهده پروفایل

مدرس ارشد

• Partial Differential Equations
• Analysis
• Nonlinear Analysis
• +۱۲ مورد دیگر

Benny Avelin is a Senior Lecturer in the Department of Mathematics at Uppsala University, Sweden, with affiliations in the research groups on Analysis and Partial Differential Equations, and Statistics, AI and Data Science. Research Interests: His work spans a broad spectrum from pure mathematical analysis to applied machine learning. He specializes in partial differential equations, particularly those of p-Laplace and degenerate parabolic types, as well as stochastic partial differential equations. More recently, his research has expanded into mathematical aspects of deep learning, including neural ODEs, deep limits of networks, and uncertainty quantification in AI models. Publication Trends: A close analysis of his recent publications reveals a strong trajectory bridging classical analysis with modern data science. While maintaining a deep engagement with nonlinear PDEs and potential theory, he increasingly contributes to the mathematical foundations of AI, especially in understanding the behavior of deep neural networks through continuous limits and stochastic modeling. His recent work also includes interdisciplinary applications in medical imaging and extreme value statistics. Scientific Contributions: Author of the doctoral thesis Boundary Behavior of p-Laplace Type Equations (2013). Active collaborator with leading researchers such as Kaj Nyström, Tuomo Kuusi, and Vesa Julin. Contributor to high-impact journals including Journal of the European Mathematical Society , Communications in Mathematical Physics , and Journal of Machine Learning Research . Advising and Grants: While specific information about students, advising, or grants is not available in the provided texts, his extensive publication record and collaborative research suggest active involvement in academic mentoring and likely participation in research funding projects. Labs and Teams: Benny Avelin is a member of the Analysis, PDEs and Applications research group at Uppsala University, which organizes the PDE & Applications seminar. His work appears to be highly collaborative, involving both theoretical and computational components, though no dedicated lab or team name beyond the research group is mentioned.

• Flight Mechanics
• Spacecraft Attitude Dynamics
• UAV Control
• +۵ مورد دیگر

Mauro Mancini is a Fixed-term Assistant Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) at Politecnico di Torino. He belongs to the College of Mechanical, Aerospace, and Automotive Engineering and specializes in Flight Mechanics (Scientific disciplinary sector IIND-01/C) within Area 0009 - Industrial and Information Engineering. Dr. Mancini teaches multiple courses across various degree programs including Aerospace Engineering and Mechatronic Engineering. His teaching portfolio includes Advanced Flight Dynamics: Modeling and Simulation, Control Systems Design for Spacecraft Attitude Dynamics at PhD level, and Fundamentals of Flight Mechanics at undergraduate level. He has also delivered lectures for the English-taught Program at Ho Chi Minh City University of Technology. His research focuses on aerospace control systems with particular emphasis on spacecraft attitude dynamics, UAV control, and autonomous systems. Dr. Mancini employs advanced control techniques including sliding mode control, artificial potential fields, and adaptive control strategies to address challenges in spacecraft maneuvering, orbital dynamics, and precision agriculture applications. His work demonstrates strong interdisciplinary connections between aerospace engineering, robotics, and agricultural technology. Analysis of Dr. Mancini's publication record reveals a consistent research trajectory in spacecraft attitude control and autonomous vehicle systems. His work shows expertise in sliding mode control techniques applied to spacecraft with actuator constraints, orbital simulation for target inspection maneuvers, and precision agriculture applications. A notable trend is the integration of artificial potential fields with advanced control methodologies to solve complex navigation and maneuvering problems in both space and terrestrial environments. Dr. Mancini actively supervises three PhD students: Pierantonio Bertuccio, Jean-Luc Sarvadon, and Riccardo Enrico (whose research focuses on robust control of UAV under uncertain conditions). He has secured commercial research contracts including a 2024 Flight Mechanics project as Scientific Manager and previous collaborations with Ho Chi Minh City University of Technology. He is a member of the Dynamics, Control and Flight Simulation research group at DIMEAS, where he contributes to advancing aerospace control methodologies and their practical applications in both academic and commercial contexts.

Dietmar Weinmannمشاهده پروفایل

پژوهشگر

• Theoretical Solid State Physics
• Quantum Effects in Electronic Properties
• Mesoscopic Physics
• +۱ مورد دیگر

Dietmar Weinmann is a Senior Researcher at the CNRS (Centre National de la Recherche Scientifique) affiliated with the IPCMS (Institut de Physique et Chimie des Matériaux de Strasbourg) and the University of Strasbourg. His work focuses on theoretical solid-state physics, particularly quantum effects in electronic properties, mesoscopic physics, and quantum transport phenomena. His research explores non-local heating in quantum thermoelectrics, scanning gate microscopy applications in graphene and semiconductor heterostructures, power dissipation asymmetry in quantum point contacts, and orbital magnetization mechanisms in mesoscopic systems. Key themes include electron correlations, spin-orbit interactions, and disorder effects in nanoscale devices. Scientific awards include the Marie Curie Fellowship during his postdoctoral work at SPEC Saclay. He teaches an elective course on Electronics for Quantum Science and Technology at the University of Strasbourg. As a member of the Mesoscopic Quantum Physics team, his research combines theoretical modeling with experimental collaborations on quantum transport imaging and inverse problem solving via machine learning.

• Astrophysics
• Dark Matter
• Galactic Dynamics
• +۳ مورد دیگر

Sarah Pearson is an Assistant Professor at the Niels Bohr Institute (University of Copenhagen). Her research focuses on astrophysics, particularly dark matter, stellar streams, galaxy dynamics, and cosmology. She works within the DARK research group and contributes to observational and computational studies of galactic structures. Affiliation : Niels Bohr Institute, University of Copenhagen Contact : sarah.pearson@nbi.ku.dk | +4535323870 Research Interests Dark Matter Halo Structure Globular Cluster Dynamics Galactic Archaeology Extragalactic Stellar Streams Galaxy Formation and Evolution Observational Cosmology Recent Publication Trends (2022-2025): Sarah's work emphasizes using stellar streams to probe dark matter distribution, with applications to upcoming telescopes like the Nancy Grace Roman Space Telescope. Her research spans simulations of galaxy mergers, observational studies of tidal features, and methodological advancements in stream detection.

François-Xavier Coudertمشاهده پروفایل

استاد

• Metal-Organic Frameworks (MOFs)
• Nanoporous Materials
• Machine Learning in Materials Science
• +۵ مورد دیگر

François-Xavier Coudert is a Senior Researcher at CNRS and Professor at PSL University. His work focuses on computational and experimental studies of metal-organic frameworks (MOFs) , nanoporous materials , and machine learning applications in material discovery. He leads an active research group and collaborates internationally, including with Shinshu University on graphene oxide nanosheets. Academic Roles: Professor (PSL University), Senior Researcher (CNRS) Key Research Areas: MOFs, Adsorption/Diffusion in Nanopores, Elastic Properties, Topology Analysis His team develops open-source software like CrystalNets for topology identification and ELATE for elastic tensor analysis. Recent publications highlight AI-driven MOF discovery, hybrid glasses, and diffusion coefficient prediction. He was recognized as a pioneer in hybrid glass research via the 2025 Dalton Horizon Prize . Former students include PhD graduates Nicolas Castel , Emmanuel Ren , and Lionel Zoubritzky .

Stefan Seipelمشاهده پروفایل

استاد

• Geospatial Information Science
• Computer Science
• Interactive Visualization
• +۶ مورد دیگر

Stefan Seipel is a Professor at the University of Gävle's Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Computer Science division. He leads research in geospatial visualization and digital image processing with human-centered focus. Key collaborations with Lantmäteriet , KTH , and Ghent University 's Cartography & GIS group Research initiatives include Smart Build Environment projects and Urban 3D Visualization for smart city planning Research Focus: Interactive 3D visualizations for real estate formation Visual anomaly detection in digital maps Algorithm development for urban environment modeling Solar energy potential assessment using geospatial data Technical Contributions: Developed methods for 3D property visualization, augmented reality applications in facility management, and precision tools for thermographic building diagnostics. His work integrates geospatial analysis with cognitive modeling for decision support systems.