جست‌وجوی استاد و پژوهشگر

Daniel Cigandaمشاهده پروفایل

پژوهشگر

• Demographic Modeling
• Fertility Studies
• Agent-Based Modeling
• +۳ مورد دیگر

Daniel Ciganda is a Researcher at the Max Planck Institute for Demographic Research , leading the Laboratory of Fertility and Well-Being . His work integrates computational models with demographic research to analyze fertility patterns and their societal implications. Research Focus : Fertility dynamics, agent-based modeling, and population forecasting. Collaborations : A. Lorenti, N. Todd, M. Myrskylä, and others. Ciganda's research spans micro-level computational models to macro-level fertility projections. Recent work includes individual-level fertility timing simulations and sensitivity analyses of Schelling's segregation model. His publications emphasize social feedback mechanisms , educational gradients, and policy-relevant demographic trends. Key projects involve COMFERT (Computational Models for Fertility Forecasting) and extensions to agent-based segregation models. He contributes to open science via GitHub repositories like individual_fx for fertility modeling.

• Collective Intelligence
• Collaboration & Group Processes
• Network Analysis
• +۲ مورد دیگر

Nikolas Zöller is a research scientist at the Max Planck Institute for Human Development 's Adaptive Rationality department. His work focuses on collective intelligence, human-AI collaboration, and computational modeling of social systems. He combines methods from physics, computer science, and psychology to study decision-making processes in both natural and artificial collectives. Ph.D. candidate at Constructor University Bremen (2020-2024) Research Associate at Potsdam University of Applied Sciences (2015-2022) Master of Science in Physics from Freie Universität Berlin (2014) Bachelor of Science in Physics from Freie Universität Berlin (2010) His research spans: Collective intelligence frameworks Human-AI collaborative diagnostics Agent-based modeling of social networks Technological co-diffusion patterns Thermodynamic optimization in physical systems Recent publications highlight his work on: Diagnostic accuracy in human-AI collectives GitHub collaboration topology Affect control theory in group dynamics Co-diffusion of complementary technologies Scientific recognition includes: European Press Prize (2022) as part of a team Springer Best Masters Award (2014)

Shweta Suranمشاهده پروفایل

پژوهشگر ارشد

• Collective Intelligence
• Collective Behaviour
• Citizen Science
• +۱۰ مورد دیگر

Shweta Suran is a Postdoctoral Fellow at the Max Planck Institute for Human Development (Berlin, Germany) with affiliations as an External Researcher at Tallinn University of Technology (School of Information Technologies, Department of Software Science) and IT University of Copenhagen (Collective Intelligence Research Group). She previously held roles at TalTech, The Open University (UK), and Krishna Engineering College. Doctor of Philosophy (2022) in Computer Science, Tallinn University of Technology Research Master's Degree (2014) in Knowledge Engineering, SRM Institute of Science and Technology Her research focuses on Collective Intelligence, Collective Behaviour, Social Network Analysis, Data Mining, and Digital Image Processing. She explores how individuals interact with online content, the impact of conversational affordances on deliberation quality, and correlations between social media preferences, media choices, and socio-economic/political factors. Her recent publications include work on a digital collaborative platform for the silver economy (2023), frameworks for collective intelligence (2020), pathfinding in medical imaging (2014), and AI applications in spatial data infrastructure (2019-2021). She co-supervised Sijo Arakkal Peious (2019 Master's thesis). Marie Skłodowska-Curie Postdoctoral Fellowship (2024) Seal of Excellence from European Commission (2023) SAP Fellowship Award (2016) Gold Medal at SRM University (2014) She contributed to projects like the EU Green Deal-aligned Waterway Safety Management program (2020-2021) and cost-effective 3D spatial data infrastructure (2019-2021), with funding totaling €1,996,000. Her work bridges ICT, collective behavior, and societal challenges.

Oliver Hohlfeldمشاهده پروفایل

استاد

• Network Security
• Network Measurements
• Internet Architecture
• +۳ مورد دیگر

Oliver Hohlfeld is a Professor at the University of Kassel where he leads the Distributed Systems Group . Prior academic appointments include professorships at Brandenburg University of Technology (heading the Computer Networks group) and RWTH Aachen University , with earlier work at TU Berlin/Deutsche Telekom Innovation Laboratories . He has served as visiting scholar at University of Wisconsin-Madison (Paul Barford's group) and holds a Dr. rer. nat. in Computer Science (2013) from TU Berlin under advisor Anja Feldmann. B.Sc. and M.Sc. in Computer Science from Darmstadt University of Applied Sciences, Institute Eurecom, and Darmstadt University of Technology Former Fraunhofer IGD researcher (2004-2006) working on telemedical network architectures His research interests focus on data-driven analysis of internet performance and security through empirical network measurement, psychological user studies, and machine learning. Key areas include DDoS defense mechanisms, TLS deployment analysis, social media interaction patterns, caching strategies, and internet protocol evaluation (HTTP/2, QUIC, BGP). Recent work examines cross-border information control circumvention through unconventional user reviews. Oliver has served on technical program committees for 25+ conferences including SIGCOMM'24 , NSDI'25 , and IMC'24 . His scientific leadership includes: 2022 IETF/IRTF Applied Networking Research Prize 2020 ACM Senior Member status 2018-2021 conference co-chair roles He leads major research projects : AIDOS: AI-based DDoS Mitigation at DE-CIX Internet Exchange (BMBF funded) DFG SFB 1053 MAKI: Multi-Mechanism Internet Adaptation (2017-2020) EU Horizon 2020 SSICLOPS: Secure Cloud Operations Internet Observatory Initiative

• Software Engineering
• Requirements Engineering
• Formal Methods
• +۳ مورد دیگر

Steve Riddle is a Research Professor with extensive contributions to software engineering, systems of systems (SoS) modeling, and requirements engineering. His work spans formal methods, traceability frameworks, and safety-critical system design across institutions and projects. Active in software traceability and requirements engineering Key contributor to SoS architectural modeling Collaborator in formal methods education initiatives His research focuses on traceability in software systems , contract-based analysis of SoS , and protective wrapping for off-the-shelf components . He explores how project metrics predict change-proneness and develops frameworks for competency-based computing education. Recent publications analyze team-based capstone projects in computing education and instructor perspectives on software engineering pedagogy. His work bridges theoretical approaches (like VDM++) with industry-aligned problem-solving .

• Wireless Networks
• 5G/6G Networks
• Performance Modeling and Optimization
• +۵ مورد دیگر

Fidan Mehmeti is a Senior Researcher and Lecturer at the Chair of Communication Networks at the Technical University of Munich (TUM), led by Prof. Wolfgang Kellerer. His expertise spans wireless networks, 5G/6G systems, edge computing, and network security. He holds a PhD from EURECOM/Telecom ParisTech and has held postdoctoral positions at institutions like the University of Waterloo and Penn State University. Education PhD, Institute EURECOM/Telecom ParisTech, 2015 MSc, University of Prishtina (Kosovo), 2009 BSc, University of Prishtina, 2006 Research Interests Fidan's work focuses on resource allocation, mobility management, edge computing, and AI-driven network optimization. He explores challenges like latency guarantees in 6G multi-domain networks and energy-efficient edge orchestration. His research bridges theoretical analysis (e.g., queueing theory) with practical applications in healthcare and autonomous systems. Recent Contributions Recent articles address mMTC traffic management, latency-critical 6G systems, and medical edge computing. He co-authored chapters on 6G architecture and healthcare applications in the upcoming '6G-life' book. Awards & Recognition Best Student Paper Award at IEEE/IFIP NOMS 2025 IEEE Senior Member (2023) Recipient of Best Paper Awards at CNSM 2024 and CCNC 2023 Grants & Projects He leads projects like '6G-ANNA' (BMBF-funded) and '6G-Life', addressing security, sustainability, and healthcare integration. Collaborations with industry partners like Nokia highlight applied research. Teaching Teaches courses on network modeling, optimization, and data networking at TUM. Recent offerings include 'Analysis, Modeling, and Simulation of Communication Networks' and 'Communication Networks Modeling and Optimization'.

• Federated Learning
• Edge Computing
• Mobile Computing
• +۴ مورد دیگر

Zimu Zhou is an Assistant Professor at Tsinghua University's School of Software, Department of Computer Science and Technology, specializing in federated learning, edge computing, and mobile systems. With an extensive publication record spanning from 2018 to 2025, Dr. Zhou has established themselves as a leading researcher in distributed machine learning systems. Dr. Zhou's research focuses on federated learning systems , mobile AI optimization , and spatial data processing . Their work addresses critical challenges in distributed machine learning including data heterogeneity, communication efficiency, privacy preservation, and practical deployment constraints. Recent research has expanded into federated large language models and advanced personalization techniques for mobile environments. Analysis of Dr. Zhou's publication trends reveals a strong emphasis on practical deployment of federated learning systems, with increasing focus on real-world applications in mobility, urban computing, and AIoT. Their work bridges theoretical advances with practical implementations, as evidenced by multiple publications in top-tier systems and AI conferences. Best Paper Award, MobiCom 2024 ACM SIGSPATIAL Best Paper Honorable Mention, 2022 Dr. Zhou has supervised several PhD students who have become active contributors in the field, including Xiaochen Li, Sicong Liu, and Yexuan Shi. Their research has been supported by multiple grants focused on edge intelligence and privacy-preserving distributed learning. Current projects include federated reasoning with large language models and resource-efficient AIoT systems. Dr. Zhou leads the Distributed Intelligence Lab at Tsinghua University, which focuses on building practical frameworks for decentralized machine learning across mobile and edge environments. The lab collaborates with industry partners to deploy federated learning solutions in real-world settings.

Frank Noéمشاهده پروفایل

دانشیار

• Machine Learning
• Computational Biology
• Molecular Dynamics
• +۳ مورد دیگر

Frank Noé is a W2 Professor (Associate Professor equivalent) of Mathematical Modeling in the Life Sciences at the Free University of Berlin, with promotion to W3 pending. He concurrently serves as an Adjunct Professor in the Department of Chemistry at Rice University. His research develops computational frameworks for molecular kinetics, integrating machine learning with biophysical simulation to decode complex biomolecular processes. Academic qualifications: Dr. rer. nat. in Informatics and Biophysics, Universität Heidelberg (2006, summa cum laude) Master of Science in Computing, Cork Institute of Technology, Ireland (2000-2002) Diplom Ingenieur (BA) in Electrical Engineering, Berufsakademie Stuttgart (1996-1999) Noé pioneers the application of deep learning to molecular simulation, notably through Boltzmann generators and VAMPnets. His work bridges statistical mechanics, biophysics, and data science to model protein dynamics, ligand binding, and cellular signal transmission at unprecedented timescales. This interdisciplinary approach has established new paradigms for analyzing molecular kinetics. Recent publications demonstrate a decisive shift toward AI-driven methodologies, where neural networks extract kinetic features from molecular trajectories. This enables atomistic exploration of processes previously inaccessible to simulation, such as millisecond-scale protein folding and membrane receptor dynamics. Major honors: Simons Fellow at IPAM (2019) ACS Early-Career Award in Theoretical Chemistry (2019) ERC Consolidator Grant (2017) ERC Starting Grant (2012) Eliteprogramm für Postdoktoranden fellowship (2007) Research leadership: Principal Investigator, DFG Collaborative Research Center 958 "Scaffolding of Membranes" (2011-present) Principal Investigator, DFG Collaborative Research Center 740 "From Molecules to Modules" (2009-present) Faculty member, International Max-Planck Graduate School for Computational Biology Organizer, International Workshop on Molecular Kinetics He directs theoretical research within the SFB/TRR 186 consortium on molecular switches, developing open-source software for kinetic analysis while mentoring the next generation of computational biophysicists through DFG-funded graduate programs.

Andreas Zieglerمشاهده پروفایل

استاد

• System Software
• Linux Configuration
• Binary Tailoring
• +۶ مورد دیگر

Andreas Ziegler is a Professor at the Department of Computer Science (INF) of Friedrich-Alexander University Erlangen-Nürnberg. His research focuses on system software optimization , particularly in Linux kernel configurability and binary tailoring . He leads the Chair of Computer Science 4 (System Software) and develops open-source tools for minimizing software stacks while preserving functionality. University: Friedrich-Alexander University Erlangen-Nürnberg School: College of Engineering Department: Department of Computer Science Rank: Professor Research Interests: Ziegler investigates methods to automate software stack tailoring. His work spans: Configuration Interface Utilization (e.g., Linux kernel modules) Binary-Level Code Removal (ELF file manipulation without source access) Maintenance Impact Quantification (AST hashing for change detection) Publication Trends: Recent works emphasize attack surface reduction and scalable configuration testing , while earlier studies focus on feature modeling and compilation redundancy . Tools developed include GitHub-hosted open-source solutions . Advising: Supervised multiple Master’s theses on topics like header analysis for dead code detection and dynamic variability management in Linux systems.

Sara Nazariمشاهده پروفایل

پژوهشگر

• Groundwater Modeling
• Integrated Water Resources Management
• Sustainability Analysis
• +۵ مورد دیگر

Sara Nazari is a Researcher at the Leibniz Center for Tropical Marine Research (ZMT) , affiliated with the Working Group: Submarine Groundwater Discharge . She integrates interdisciplinary methodologies to address climate change, land use, and socio-economic impacts on water resources. Education: MSc in Water Resources Management and Engineering, Isfahan University of Technology (2015–2018) Her research emphasizes: Groundwater modeling Integrated and adaptive water resources management Sustainability analysis Big-Data analysis Conflict resolution theories Her publications focus on global groundwater recharge trends, computational models, and conflict resolution frameworks. She developed open-source tools for hydrological assessments and contributed datasets on global recharge patterns. She is based at Fahrenheitstr. 6, Bremen, Germany, and can be reached at sara.nazari@leibniz-zmt.de .

• Self-Organizing Multi-Agent Systems
• Decentralized Coordination
• Distributed Systems
• +۳ مورد دیگر

Prof. Dr. Wolfgang Renz is a faculty member at the Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Hamburg. He works on decentralized coordination mechanisms in self-organizing multi-agent systems, with applications in demand-side management, logistics, and urban mobility. Current affiliation: University of Hamburg (Informatik) Research focus: Systemic modeling, emergent behavior analysis, and validation of adaptive systems His publications cover topics like ant-colony optimization for energy management, peer-to-peer coordination spaces, and mesoscopic stochastic models for validating agent dynamics. He has supervised several doctoral and diploma theses on related subjects.

Abhijeet Mishraمشاهده پروفایل

پژوهشگر

• Environmental Science
• Forestry
• Climate Change Mitigation
• +۳ مورد دیگر

Dr. Abhijeet Mishra is a climate impact researcher affiliated with the Potsdam Institute for Climate Impact Research (PIK) and currently serving as a contact at the International Food Policy Research Institute (IFPRI) since 2023. His work focuses on integrating forestry and land-use systems into global climate mitigation strategies through advanced modeling frameworks. Doctorate : Agricultural Sciences, Humboldt University, Berlin Master’s : Nutrition and Rural Development with a major in Rural Economics and Management, Ghent University Bachelor’s : Agricultural Marketing and Cooperation, University of Agricultural Sciences, Bangalore Mishra specializes in climate change mitigation via forest management and global land-use modeling . His research includes: Developing dynamic forestry sector models in MAgPIE (Land-Use Model) Optimizing timber plantations using the Faustmann model Assessing carbon savings from international agreements like COP26 His recent publications analyze: Timber cities and their role in reducing carbon emissions Global land conversion trade-offs under deforestation policies Municipal waste trends and their environmental impacts Scientific Awards : Erasmus Mundus Action 2 Scholarship (2014-2016) National Talent Scholarship, India (2009-2013) As a contributing developer to the MAgPIE open-source land-use model, Mishra actively participates in the Land Use Transition working group at PIK. His work bridges climate policy , agricultural economics , and global sustainability targets .

Maximilian Langeمشاهده پروفایل

پژوهشگر

• Remote Sensing
• Vegetation Dynamics
• Land-Use Monitoring
• +۸ مورد دیگر

Dr. Maximilian Lange is an Academic Staff researcher in the Department of Remote Sensing at the Helmholtz Centre for Environmental Research - UFZ in Leipzig, Germany. His work focuses on applying remote sensing, artificial intelligence, and modeling techniques to monitor vegetation dynamics, land-use, and land-use intensity. As part of the Remote Sensing Team led by Prof. Dr. Jian Peng, he contributes to several research programs under UFZ's 2021-2027 strategic framework. Dr. Lange's research interests center on the derivation of vegetation dynamics, land-use and land-use intensity with remote sensing, artificial intelligence and modeling techniques. He specializes in organizing big data from remote sensing datasets and developing software tools for environmental analysis. His work on phenological data analysis through R packages like phenex and phenmod has established him as a key contributor to environmental informatics. His research bridges the gap between satellite data acquisition and practical environmental monitoring applications, particularly in forest condition assessment and grassland management. Analysis of Dr. Lange's publication record reveals a strong focus on practical applications of remote sensing data for environmental monitoring. His work demonstrates expertise in time series analysis of satellite data, particularly using Sentinel-2 imagery, with applications spanning forest condition monitoring, grassland management, and phenological studies. Recent publications show an increasing integration of machine learning techniques with traditional remote sensing approaches, reflecting the evolving nature of environmental data science. Dr. Lange has been actively involved in several significant research projects including the Forest Condition Monitor (2021-2024), which provides national-scale depiction of forest condition using satellite data, and Project iForest (2024), which integrates citizen science data into tree species classification methods. His earlier work on validating Sentinel-2 products has contributed to the robust application of these data for environmental monitoring across Germany.

• Hydrological modeling
• Flood forecasting
• Drought monitoring
• +۴ مورد دیگر

Robert Schweppe is a Researcher at the Department of Computational Hydrosystems , Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. He has held this position since 2017, following prior roles as Project Engineer at HYDRON GmbH (2014-2017) and Research Assistant at UFZ (2013). Expertise in hydrological modeling using mHM, ECLand, LARSIM Developed pyflow configuration tools and MPR software for environmental data processing Key contributor to 4DHyro project integrating Earth Observation data with hydrological models His research interests span hydrological modeling , drought forecasting , machine learning applications , and climate change impacts on water systems. His 2022 publications on parameter regionalization and floodplain modeling demonstrate methodological innovations in environmental data analysis. Notable scientific recognitions include the Deutschlandstipendium scholarship (2012-2013) and Helmholtz Field Study Fellowship (2022). He collaborates extensively with European institutions on compound environmental risks and smart monitoring technologies .

Jonathan Tschanterمشاهده پروفایل

پژوهشگر

• Virtual Reality
• Augmented Reality
• Mixed Reality
• +۵ مورد دیگر

Jonathan Tschanter is a researcher at the University of Würzburg's Human-Computer Interaction department, specializing in virtual reality (VR) and extended reality (XR) systems. He contributes to the Reality Stack team's development of reusable XR software components and has worked on projects like BMBF-funded HiAvA (social VR accessibility) and ViLeArn (XR education). Educational Background: B.Sc. in Human-Computer Systems M.Sc. in Human-Computer Interaction (both from University of Würzburg) His research focuses on XR interaction design , including: Harassment dynamics in social VR environments Comparative analysis of handwriting input across VR/VST AR platforms Virtual-to-physical surface alignment techniques Modular pipeline architectures for real-time character animation Key publication trends highlight: Systematic studies of avatar self-similarity for harassment mitigation Empirical evaluations of XR display technologies and surface alignment Open-source software frameworks for character animation pipelines Scientific Recognition: IDEATExR Best Paper Award (2025) Currently teaching XR development courses, he previously contributed to: HiAvA project (2021-2024): Scalable hybrid avatar-agents technology ViLeArn project (2021-2023): Student worker role Ubiquitous Interaction group (2023): Research intern at University of Helsinki