جست‌وجوی استاد و پژوهشگر

Nicola McConkeyمشاهده پروفایل

مدرس

• Neutrino Physics
• Quantum Technology
• Detector Development
• +۲ مورد دیگر

Nicola McConkey is an Ernest Rutherford Fellow and Lecturer in Particle Physics at the School of Physical and Chemical Sciences, Queen Mary University of London. She joined the Particle Physics Research Centre in 2024 and leads experimental work in neutrino interactions and detector development. Her affiliations include the Centre for Fundamental Physics and Centre for Experimental and Applied Physics. McConkey is an active member of international collaborations including SBND, DUNE, and MicroBooNE, where she contributed to the assembly of SBND and pioneered high-statistics measurements of electron-neutrino interactions using liquid argon detectors. Her research focuses on three primary domains: neutrino physics (particularly neutrino-argon scattering cross-sections), quantum technology applications for neutrino mass measurement, and liquid argon time projection chamber (LArTPC) detector development. McConkey's investigations aim to advance fundamental particle physics through precision measurements and technological innovation, with emphasis on improving detection capabilities for next-generation neutrino experiments. Publications predominantly explore neutrino interaction dynamics, cross-section measurements, and detector performance optimizations across MicroBooNE, SBND, and DUNE collaborations. Research trends demonstrate consistent focus on refining LArTPC technologies, developing machine learning applications for particle reconstruction, and probing beyond-Standard-Model physics through neutrino interactions. Scientific Awards: Ernest Rutherford Fellowship (2022) McConkey advises two PhD students (Oscar Chow, Yoshita Dabburi) and leads significant research funding including: STFC Grant: 'Piecing together the neutrino mass puzzle' (£431,666; 2024-2027) STFC Outreach Grant: 'Quantum Technologies for Neutrino Mass' (£99,999; 2024-2025) She coordinates research within the Particle Physics Research Centre laboratory and collaborates extensively within the SBND, DUNE, and MicroBooNE international teams, alongside leading the Quantum Technologies for Neutrino Mass collaboration developing novel measurement techniques.

• Quarks
• Hadrons
• Nuclei
• +۳ مورد دیگر

Thomas Cohen is a Professor and Associate Chair in the Department of Physics at the University of Maryland. He holds a B.A. from Harvard College (1980) and a Ph.D. from the University of Pennsylvania (1985). A Fellow of the American Physical Society, he was also an NSF Presidential Young Investigator from 1990 to 1995. His teaching accolades include the Celebrating Teaching Award, Dean's Award for Excellence in Teaching, and Distinguished Scholar-Teacher Award. Cohen's research focuses on quarks, hadrons, and nuclei, with affiliations to the Maryland Center for Fundamental Physics. His work explores QCD dynamics, exotic hadrons, and quantum algorithms for particle physics. Recent contributions include studies on gauge invariance, heavy-ion collisions, and adiabatic quantum computing. His research spans theoretical particle physics, nuclear physics, and computational methods, with notable publications on QCD phase diagrams, tetraquark states, and adiabatic state preparation. Cohen actively collaborates on projects involving quantum simulations and high-energy physics phenomena. His awards reflect both scholarly and pedagogical excellence.

Jedidiah Crandallمشاهده پروفایل

دانشیار

• Internet Security
• Censorship Resistance
• Network Security
• +۳ مورد دیگر

Jedidiah Crandall is an Associate Professor at Arizona State University's School of Computing and Augmented Intelligence, with an affiliation to the Biodesign Center for Biocomputing, Security and Society. His research focuses on Internet censorship, network security, and privacy-preserving technologies. Crandall collaborates with journalists and activists to expose surveillance mechanisms, particularly in politically sensitive regions like Russia and China. His work includes analyzing VPN vulnerabilities , decentralized censorship systems , and cross-border data flows . He teaches advanced courses in computer network security and advises graduate students on thesis/dissertation research. Research trends in his publications emphasize measuring state-level information control , attack vectors in modern networks , and secure communication technologies . Notable work includes TSPU: Russia's censorship infrastructure and Hidden Links: Analyzing Secret Families of VPN Apps . Crandall's teaching spans courses like Advanced Computer Network Security and Applied Cryptography , reflecting his commitment to preparing the next generation of security professionals. His Censored Planet project tracks global Internet censorship patterns through large-scale measurements.

Arman Khoshghalbمشاهده پروفایل

مدرس ارشد

• Numerical Modelling
• Multi-phase Porous Media
• Unsaturated Soils
• +۳ مورد دیگر

Dr. Arman Khoshghalb is a Senior Lecturer in Geotechnical Engineering at the School of Civil and Environmental Engineering, UNSW Sydney, where he has been a faculty member since 2012. His academic credentials include a PhD in Geotechnical Engineering from UNSW (2012), an MSc from Sharif University of Technology (2005), and a BSc in Civil Engineering from the same institution (2003). His research focuses on numerical modeling of multi-phase porous media , with emphasis on unsaturated soils, large deformation analysis, and dynamic soil behavior. Key areas include meshfree computational methods, soil-structure interaction, bio-cementation, and thermo-hydro-mechanical processes in geotechnical systems. His work bridges theoretical advancements with practical applications in slope stability, foundation engineering, and sustainable ground improvement. Dr. Khoshghalb's publications predominantly explore geomechanical modeling, experimental soil mechanics, and computational techniques. Recent trends highlight innovations in bio-cemented soils, thermal properties of unsaturated soils, and adaptive numerical methods for complex geotechnical simulations. Awards & Honors: IACMAG Excellent Paper Award (2017) UNSW Research Excellence Award (2012) Advising & Grants: He has supervised 7+ PhD students on topics ranging from weak rock mechanics to computational geomechanics. Funded projects include: ARC Discovery Project (2019–2021): "Non-isothermal dynamic strain localisation in unsaturated porous media" ($298,257) ARC Linkage Infrastructure Grant (2015): "Earthquake shaking table for soil-structure interactions" ($320,000) ARC Linkage Project (2014–2017): "Constitutive modelling of weak rocks" ($314,280) He leads research within UNSW's geotechnical engineering group, collaborating on large-scale experimental testing and computational frameworks for infrastructure resilience.

Aidong Zhangمشاهده پروفایل

استاد

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

Aidong Zhang is the Thomas M. Linville Professor of Computer Science at the University of Virginia, with joint appointments in Biomedical Engineering and the School of Data Science. Her research focuses on machine learning, interpretable AI, federated learning, and generative AI applications in healthcare and bioinformatics. She holds a Ph.D. in Computer Science from Purdue University. Dr. Zhang has been honored with prestigious awards including the ACM Fellow (2017), IEEE Fellow (2009), and the 2025 Distinguished Researcher Award from UVA. Her work bridges computational methods with biomedical challenges, emphasizing fairness, robustness, and explainability in AI systems. Key research areas include federated learning frameworks, concept-based models, and large language models for scientific hypothesis generation. Dr. Zhang leads a lab offering PhD positions in machine learning, bioinformatics, and health informatics. Notable grants include NSF projects on explainable AI platforms and hardware-software co-design for extreme-scale machine learning. Education: Ph.D., Computer Science, Purdue University Affiliations: School of Engineering and Applied Science, School of Data Science Grants: NSF-funded projects on federated learning, multimodal analysis, and biomedical AI Labs/Teams: Zhang's Research Group focusing on interpretable machine learning and healthcare applications

Chris Volinskyمشاهده پروفایل

استاد بالینی

• Data Science
• Machine Learning
• Recommender Systems
• +۱۲ مورد دیگر

Chris Volinsky is a Clinical Professor of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University, joining in September 2023. His work bridges industry-scale data science and academic research, focusing on practical applications of machine learning and statistical modeling in business contexts. Education: PhD in Statistics, University of Washington BA in Statistics and Mathematics, University of Buffalo His research interests lie at the intersection of data science and business operations, with a focus on recommender systems , personalization , social network analysis , and mitigating bias in machine learning models . He also emphasizes data visualization and the ethical implications of data usage, particularly in balancing innovation with privacy concerns and regulatory compliance. While no specific publications are listed in the provided text, his career has been defined by high-impact, real-world applications of data science, particularly in telecommunications and entertainment industries. Scientific Awards: $1M Netflix Prize (2009) as member of BellKor's Pragmatic Chaos team Volinsky has extensive experience in advising and leading data science teams. He led a team of 40 data scientists at AT&T, where he oversaw projects with significant business impact, including fraud detection, customer complaint prediction, and computer vision applications. Although formal student advising is not detailed, his leadership roles imply substantial mentorship and team development. He has not disclosed specific grants, but his work at AT&T and NYU suggests engagement with large-scale, industry-funded research initiatives. He was instrumental in pioneering work on large-scale recommender systems and continues to contribute to the evolution of data-driven decision-making in enterprise settings.

Samuel Lederمشاهده پروفایل

پژوهشگر

• Robotics
• Architectural Design
• Computational Design
• +۵ مورد دیگر

Samuel Leder is a doctoral researcher at the Institute of Computational Design and Construction (ICD) under the Cluster of Excellence IntCDC at the University of Stuttgart. His work focuses on the integration of robotics and architectural design, particularly in developing distributed robotic systems for timber construction. He has been actively involved in research projects such as RP 19-1 – Robotic Kinematic System for Parallel Construction and RP 19-2 – Co-Design for Distributed Cooperative Multi-Robot Systems . Additionally, he serves on the Equal Opportunity Commission at ICD. Bachelor of Design in Architecture (summa cum laude), Washington University in St. Louis Bachelor of Applied Science in Systems Science and Engineering (magna cum laude), Washington University in St. Louis MSc in Architecture via the Integrative Technologies and Architectural Design Research (ITECH) program, University of Stuttgart Samuel’s research explores the synergies between agent-based modeling , robotic systems , and architectural design . His work aims to create minimal robotic machines capable of constructing complex spatial assemblies, particularly with timber structures . He investigates the co-design of robots and the structures they build, emphasizing modular systems and kinematic behaviors . Recent publications highlight advancements in digital twins , adaptive assembly , and human-robot collaboration for timber construction. The 15 most recent articles reveal trends in collective robotic construction , agent-based modeling , and material-robot interaction . These works emphasize timber fabrication , modular systems , and interactive simulation for large-scale construction tasks. Key sub-fields include adaptive assembly , cyber-physical systems , kinematic control , and human-guided robotics . Scientific Awards: German Academic Exchange Service (DAAD) Award for Outstanding Achievement Deutschlandstipendium Samuel’s research is conducted within the ICD at University of Stuttgart , where he collaborates on the Wood Building Systems for Distributed Robotics associated project. His work bridges architecture , robotics , and computational design , aiming to redefine on-site construction methodologies through innovative robotic systems.

Olga Zinovievaمشاهده پروفایل

مدرس

• Additive manufacturing
• Metals and alloys
• Computational mechanics
• +۱۲ مورد دیگر

Dr. Olga Zinovieva is a Lecturer in Mechanical Engineering and Program Coordinator at UNSW Canberra's School of Engineering and Technology. Her research focuses on computational modeling in metal additive manufacturing, particularly on processing-microstructure-property relationships. She has held research positions at the University of Bremen, Russian Academy of Sciences, and Tomsk Polytechnic University, and visiting roles in Australia, Germany, Brazil, and France. Research Interests: Modeling for additive manufacturing Multiscale methods Computational materials science Computational mechanics Microstructure evolution in 3D printing Mechanical behavior under dynamic loading Recent research trends from her publications emphasize predictive modeling of mechanical properties in additively manufactured metals, microstructure simulation, and digital solutions for advanced manufacturing. Her work integrates ICME approaches and high-performance computing to optimize alloy performance and process parameters. Scientific Awards and Grants: ARC Discovery Early Career Researcher Award (2025–2028) NSW DIN Pilot Project (2024–2025) CSIRO ON Prime Performance Bonus (2024) UNSW Start-up Grant (2022–2024) DFG-RFBR Project (2017–2022) Multiple travel and research grants from RFBR, University of Bremen, and Tomsk State University Supervision and Grants: Dr. Zinovieva actively supervises PhD and undergraduate research students in projects related to additive manufacturing modeling. She has secured over 20 grants as a Chief Investigator, including leadership in international collaborations between Germany and Russia. She mentors students through UNSW’s HDR programs and industry-linked research initiatives. Labs and Teams: She leads computational research in metal additive manufacturing at UNSW Canberra, utilizing high-performance computing resources. She collaborates with international teams at the University of Bremen and participates in editorial and advisory roles for journals such as Metals and Journal of Materials Informatics .

Sophie Nowickiمشاهده پروفایل

استاد

• Glaciology
• Ice-sheet modeling
• Climate modeling
• +۳ مورد دیگر

Sophie Nowicki is an Empire Innovation Professor at the University at Buffalo's RENEW Institute, Department of Earth Sciences within the College of Arts and Sciences. She serves as Director of the Center for Geological and Climate Hazards. Her research focuses on ice sheet and sea level dynamics, using a combination of applied mathematics, remote sensing observations, and numerical modeling to understand how ice sheets interact with the global climate system. Empire Innovation Professor, University at Buffalo Director, Center for Geological and Climate Hazards Member of UB RENEW Institute Department of Earth Sciences, College of Arts and Sciences Dr. Nowicki's research interests center on glaciology, ice-sheet modeling, climate modeling, and sea level change. She studies how ice sheets interact with the global climate system and affect sea level change using a spectrum of models from idealized to large-scale continental ice sheet models. Her work is integral to climate models that provide forcing for ice sheet models, particularly through the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). She teaches courses including Introduction to Computational Earth Science, Environmental Remote Sensing, and various graduate research courses. Her recent publications reveal a strong focus on Antarctic and Greenland ice sheet modeling, sea level rise projections, and the development of advanced modeling frameworks. The research spans from fundamental glaciological processes to large-scale climate impacts, with a particular emphasis on quantifying uncertainties in ice sheet contributions to sea level rise. Her work frequently appears in top journals including Nature, The Cryosphere, and Geophysical Research Letters, and she has made significant contributions to the IPCC Sixth Assessment Report. Empire Innovation Professor recognition Lead contributor to IPCC AR6 Working Group I Principal Investigator for ISMIP6 (Ice Sheet Model Intercomparison Project) Dr. Nowicki actively mentors graduate students and postdoctoral researchers, with current advisees working on various aspects of ice sheet dynamics and sea level change. Her research is supported by multiple grants from NASA, NSF, and other agencies focused on improving our understanding and projections of ice sheet behavior in a warming climate. She leads the development of critical tools like the Cryosphere Model Comparison Tool (CmCt) and has been instrumental in establishing community standards for ice sheet modeling. She is involved with several research groups and initiatives including the Ice Sheet & Sea Level Lab at UB, which focuses on understanding how ice sheets will evolve in a warming world and what this means for future sea levels. Her team combines observational data with sophisticated modeling approaches to address key questions about ice-ocean and ice-atmosphere interactions that drive ice sheet changes.

• Databases
• Transaction Management
• Data Management in the Cloud
• +۱۲ مورد دیگر

Heiko Schuldt is a Full Professor of Computer Science at the University of Basel and leads the Databases and Information Systems (DBIS) group. His research spans databases, transaction management, cloud data systems, digital libraries, and multimedia retrieval, with a focus on distributed systems, data streams, and service-oriented architectures. He studied at the University of Karlsruhe (KIT) and received his PhD from ETH Zurich in 2001. From 2003-2006, he served as an associate professor at UMIT, Austria. Education: University of Karlsruhe (Computer Science), ETH Zurich (PhD, 2001) Research Interests: His work integrates databases, cloud computing, and multimedia retrieval, emphasizing scalable systems for lifelog data, sports analysis, and VR/AR environments. Projects include vitrivr, Polypheny-DB, and StreamTeam. Recent Publications: Trends focus on VR/AR multimedia retrieval, cross-modal analysis, and polystore systems. Key contributions include open-source frameworks for video/image retrieval and immersive analytics. Advising: Supervised over 50 theses in areas like mixed reality, polystore optimization, and gesture-based interfaces.

Mauro Werderمشاهده پروفایل

مدرس

• Glaciology
• Hydrology
• Numerical Modeling
• +۷ مورد دیگر

Dr. Mauro Werder is a Lecturer at the Department of Civil, Environmental and Geomatic Engineering at ETH Zurich. His work focuses on glaciology, subglacial hydrology, and numerical modeling, combining computational methods with field measurements. He has developed widely used models such as GlaDS (Glacier Drainage System) and BITE (Bayesian Ice Thickness Estimation), and contributed to projects like SHMIP and 4D-Antarctica. Current Projects: Gladder (2025-2028), DIWING (2023-2026), LEAD (2020-2026), 4D-Antarctica (2019-2022), CORDS (2023-2024) Education: PhD in Glaciology (2009, Swiss National Science Foundation funded) His research spans subglacial drainage systems, sediment transport (SUGSET model), Bayesian inversion techniques, and field experiments involving artificial lakes and R-channels. He actively teaches courses on GPU-based PDE solving, applied glaciology, and reproducible scientific computing. Scientific Awards: Swiss National Science Foundation (SNF) Fellowship for Prospective Researchers (2010-2011) European Union (FP7) Marie Curie International Outgoing Fellowship (2011-2014) He collaborates with institutions like the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), and contributes to software development through packages like BITEmodel.jl and Parameters.jl. His fieldwork includes experiments on Greenland's Jakobshavn Isbræ and Switzerland's Plaine Morte glacier.

Clinton Fookesمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Human Behavior Analysis
• +۷ مورد دیگر

Professor Clinton Fookes is a faculty member at the Queensland University of Technology (QUT) within the School of Electrical Engineering & Robotics . His research focuses on leveraging computer vision and artificial intelligence to develop automated systems that understand, anticipate, and interact with human behaviors, with applications in medical diagnostics, autonomous vehicles, defense, and industrial efficiency . Research areas include AI adaptability, multimodal biosignal analysis, and human-machine interaction Collaborates with CSIRO Data61, Defence Science and Technology Group, Orica, Airbus, and Sentient Vision Systems Develops systems for human action detection, infrastructure monitoring, and stress response prediction His work addresses critical challenges in AI deployment, such as environmental adaptability and reducing diagnostic errors in medical and autonomous systems. Recent publications highlight trends in self-supervised learning, zero-shot knowledge transfer, multimodal integration , and 3D reconstruction for healthcare , while exploring ethical AI use in sectors like mining and defense . Professor Fookes emphasizes interdisciplinary collaboration, bridging engineering, medicine, and social sciences to advance AI systems capable of real-world impact. His research agenda includes improving AI memory capabilities and explainability for safer, more reliable automation.

Doreen Mollenhauerمشاهده پروفایل

استاد

• Polymer Science
• Energy Storage
• Energy Conversion
• +۳ مورد دیگر

Prof. Dr. Doreen Mollenhauer serves as a Professor at Friedrich Schiller University Jena through a joint appointment with the Helmholtz Institute for Polymers in Energy Applications (HIPOLE), a collaboration between the university and Helmholtz Center Berlin for Materials and Energy (HZB). She is based in the Institute of Technical Chemistry and Environmental Chemistry within the university's research ecosystem. Her research centers on computational simulation of polymers for next-generation energy storage and conversion technologies . This work integrates molecular modeling with materials engineering to develop advanced polymers for batteries, fuel cells, and solar energy systems. The research bridges theoretical chemistry and practical energy applications, addressing critical challenges in sustainable power solutions. Prof. Mollenhauer's working group operates from Jena's new CEEC research complex (Center for Energy and Environmental Chemistry), which features state-of-the-art laboratories for collaborative energy materials research. Her dual affiliation with HIPOLE creates unique opportunities for students to access both academic and large-scale national laboratory resources. Contact: doreen.mollenhauer@uni-jena.de | Phone: +49 3641 9-48986 | Location: Philosophenweg 7a, 07743 Jena

Christos Makrisمشاهده پروفایل

دانشیار

• Data Structures
• Information Retrieval
• Data Mining
• +۵ مورد دیگر

Christos Makris is an Associate Professor in the Department of Computer Engineering and Informatics at the University of Patras, Greece. His academic career spans over two decades with significant contributions to computer science, particularly in data structures, algorithms, and information systems. He maintains active research collaborations and supervises graduate students in his areas of expertise. Dr. Makris's research spans several key areas in computer science with a strong focus on efficient data organization and processing. His work encompasses Data Structures , Information Retrieval , Data Mining , String Management and Processing Algorithms , Computational Geometry , Internet Technologies , Bioinformatics , and Multimedia Databases . His interdisciplinary approach bridges theoretical computer science with practical applications across various domains including web technologies, bioinformatics, and emergency response systems. Analysis of Dr. Makris's publication record reveals a consistent research trajectory focused on efficient algorithms for information management. His work demonstrates evolution from foundational data structure research in the 1990s to more applied work in web technologies, social media analysis, and machine learning applications in recent years. A notable pattern is his ability to adapt core algorithmic techniques to emerging application domains while maintaining theoretical rigor. Dr. Makris maintains an impressive scholarly record with over 3,000 citations, an h-index of 29, and an i10-index of 71 according to Google Scholar metrics. These indicators reflect the significant impact of his research within the computer science community. As an active faculty member, Dr. Makris maintains regular office hours on Tuesdays from 18:00-20:00 and Thursdays from 12:00-14:00. He is accessible via email at makri@ceid.upatras.gr or makri@upatras.gr for academic inquiries and student supervision.

Pavel P. Kuksaمشاهده پروفایل

عضو هیئت علمی

• Bioinformatics
• Computer Science
• Functional Genomics
• +۵ مورد دیگر

Pavel P. Kuksa is a Research Assistant Professor in the Department of Pathology and Laboratory Medicine, specializing in bioinformatics, computer science, and functional genomics. His work focuses on high-throughput sequencing analysis, chromatin interaction data, and developing scalable software platforms for genomics research.