Find Academic Supervisors

• Ultra-Wideband Technology
• Indoor Localization
• Ranging Systems
• +5 more

Laura Flueratoru is a researcher at the University of Tampere, Finland, specializing in ultra-wideband technology for indoor localization systems. Her work focuses on developing robust, energy-efficient positioning solutions with particular expertise in non-line-of-sight mitigation and cross-platform implementations. She has established a strong publication record since 2017, with significant output between 2020-2023, indicating active ongoing research in wireless communications and localization technologies. Her research interests center around ultra-wideband communications for precise positioning, with emphasis on energy efficiency for constrained devices, platform independence across different UWB hardware, and innovative solutions for non-line-of-sight conditions that commonly degrade localization accuracy. She has developed self-learning approaches for detecting and mitigating signal interference, contributing to more reliable indoor positioning systems applicable in various real-world scenarios. Analysis of her publication trends shows consistent focus on ultra-wideband technology since 2017, with increasing specialization in energy efficiency and robustness from 2020 onward. Her work spans theoretical algorithm development, practical implementation challenges, and creation of open datasets for community validation. The research bridges electrical engineering, computer science, and practical applications in indoor positioning and wireless communications. Laura frequently collaborates with Elena Simona Lohan and Dragos Niculescu, with whom she has co-authored multiple publications across journals like IEEE Internet of Things Journal and conference venues including IPIN and MobiCom. Her collaborative network extends to researchers across Europe, particularly in Finland, Romania, and Switzerland, reflecting the international nature of her research in wireless communications. She has contributed significantly to open science through publicly available datasets, including ultra-wideband ranging measurements across multiple hardware platforms and BLE signal strength data for contact tracing applications. Her work on the FlexTDOA framework represents a notable contribution to robust time-difference of arrival localization techniques using ultra-wideband devices.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +6 more

Matthew Watson is a researcher at Durham University , Department of Computer Science, UK. His work spans Computer Science , Artificial Intelligence , and Human-Computer Interaction , with a focus on autonomous systems , semantic search , and pedagogical tools . Research Trends : Recent publications include applications of machine learning in adaptive cruise control , UAV-based wildlife tracking , and deep learning frameworks like KerasCV/KerasNLP. Earlier work involved mathematical combinatorics and cognitive modeling in dialogue systems. Collaborations : Co-authored with Navid Mohajer, Darius Nahavandi, Ashok K. Krishnamurthy, and others in domains like robotics, bioinformatics, and software engineering. Publications include 13 peer-reviewed articles from 2008–2025, covering topics in control systems , knowledge graphs , and algorithm design .

• Hardware Security
• Machine Learning for Security
• VLSI Design
• +7 more

Sai Manoj Pudukotai Dinakarrao is a Professor in the Department of Computer Engineering at Rochester Institute of Technology's College of Engineering. He is an active researcher with numerous publications spanning hardware security, machine learning applications, VLSI design, and IoT security. His work focuses on developing innovative solutions for security challenges in hardware and embedded systems. Dr. Dinakarrao's research interests center around hardware security, machine learning for security applications, VLSI design methodologies, and IoT security frameworks. His work has pioneered approaches to hardware Trojan detection, processing-in-memory architectures, and energy-efficient security mechanisms for resource-constrained devices. He has made significant contributions to the understanding of hardware vulnerabilities and the development of robust countermeasures. His recent publications demonstrate a clear trend toward integrating machine learning techniques with hardware security solutions, particularly focusing on processing-in-memory architectures for efficient security implementations. His work spans both theoretical foundations and practical implementations, with applications in IoT security, malware detection, and hardware verification. The research shows increasing sophistication in addressing security challenges through novel hardware-software co-design approaches. Dr. Dinakarrao has mentored numerous graduate students who have co-authored publications with him, indicating an active research group focused on cutting-edge security and hardware design topics. His collaborations span multiple institutions and research groups, demonstrating his integration within the broader academic community.

Knut ReinertView profile

Professor

• Algorithmic Bioinformatics
• Genomics
• Proteomics
• +3 more

Prof. Knut Reinert is a Professor of Algorithmic Bioinformatics at the Freie Universität Berlin (FU) and a Max Planck Fellow at the Max Planck Institute for Molecular Genetics (MPI). He leads the Algorithmic Bioinformatics (ABI) group at FU and the Efficient Algorithms for Omics Data group at MPI. His research focuses on developing mathematical models and algorithms for analyzing biomedical mass data, including sequence analysis, proteomics, and omics data. Reinert is a key contributor to the SeqAn C++ library, a widely used bioinformatics software tool, and has led initiatives such as the Intel® Parallel Computing Center to accelerate NGS data analysis. Education: Diploma in Computer Science (1994, Max Planck Institute for Computer Science & Universität des Saarlandes), PhD (1999, Dr. Ing., Universität des Saarlandes). Postdoctoral research at Celera Genomics (1999–2002). Research interests include algorithm engineering for genome comparison, high-throughput sequencing data analysis, and bioinformatics software development. His work emphasizes scalable solutions for omics data interpretation, with applications in metagenomics, proteomics, and computational biology. Key achievements include leadership in de.NBI (German Network for Bioinformatics Infrastructure), the development of tools like SeqAn, Lambda, Fiona, and Ganon, and collaborations with institutions like the Max Planck Society and international partners. Awards include Max Planck Fellow status and Intel® PCC leadership. Grants and collaborations: German-Turkish research initiatives, DFG-funded RNA research projects, and leadership in EU-funded projects like Predict-IV. His lab actively engages in teaching at undergraduate and graduate levels, emphasizing innovative methods like e-learning and inverted classrooms.

Daniel DietschView profile

Researcher

• Static Analysis
• Software Model Checking
• Formal Methods
• +3 more

Dr. Daniel Dietsch is a researcher at the Department of Computer Science, Faculty of Engineering, University of Freiburg. He specializes in Static Analysis , Software Model Checking , and Formal Methods , with focus areas including Requirements Engineering, Modeling Processes, and Software Verification. Research Trends : His recent work centers on automated verification tools for software systems, addressing bitwise operations, race conditions, and hybrid models. Key contributions include the Ultimate framework series (Automizer, Kojak, Taipan, GemCutter, Referee) for SV-COMP competitions. Scientific Output : Over 140 publications since 2011, with significant activity in 2024 (53 entries) and 2023 (39 entries). Tools developed by his team are available via GitHub and web interface . Teaching Roles : Regularly instructs courses like Automata Theory , Program Verification , and Cyber-Physical Systems since 2011, including both theoretical and practical components. Collaborations : Works closely with Matthias Heizmann, Frank Schüssele, Dominik Klumpp, and Andreas Podelski on verification technologies.

Tobias KögelView profile

Researcher

• Radar Localization
• Radar Signal Processing
• Software Defined Radio
• +5 more

Tobias Kögel is a doctoral candidate and researcher at the Institute of Microwaves and Photonics (LHFT) within the Department of Electrical-Electronic-Communication Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). His work focuses on advanced radar systems, signal processing, and RF technologies. Kögel holds an M.Sc. in Electrical Engineering and Information Technology (EEI) and has been actively engaged in research since 2020. His research interests span radar localization, radar signal processing in FPGAs, software-defined radio, switched injection-locked oscillators (SILOs), and UHF-RFID systems. He seeks students for projects involving hardware development, FPGA programming (VHDL/Verilog), and Python-based simulation of radar algorithms using Kalman filters. Kögel has contributed to over a dozen peer-reviewed publications since 2019, addressing topics like bistatic radar networks, time-frequency synchronization, and hybrid RFID-odometry localization. His work frequently appears in IEEE journals and conferences such as IEEE Radar Conference and International Microwave Symposium. He advises students on hardware design, FPGA implementation, and algorithm development. His lab (LHFT) focuses on microwave photonics, radar systems, and RFIC design.

• Computer Science
• Machine Learning
• Software Engineering
• +3 more

David Wilson is an academic affiliated with the University of South Wales, Severn School of Radiology, and the Severn Deanery. He has extensive research interests in Computer Science, Machine Learning, Medical Imaging, and Human-Computer Interaction. Research Interests: His work spans fields like Software Engineering, Artificial Intelligence, and Data Science, with a focus on applications in healthcare and inclusive technology. Publications: He has contributed to recent research on generative AI for medical imaging, accessible making, and deep learning applications in biomedical contexts. Collaborations: Notable co-authors include Sharon Zhang, Naira Hovakimyan, and Lisa Moseley, reflecting interdisciplinary research in healthcare technology and machine learning.

• Virtual Reality
• Human-Computer Interaction
• Affective Computing
• +4 more

Matias Volonte is a researcher affiliated with Northeastern University , Boston, MA, USA, focusing on virtual reality, human-computer interaction, and affective computing. His work investigates visual attention, emotion contagion, and behavioral modeling in VR environments. Key research areas: Virtual Human behavior, gaze analysis, user perception, and AI-driven interactions Collaborates with experts like Andrew T. Duchowski, Sabarish V. Babu, and Deyrel Diaz Research Trends : Recent publications (2025-2018) emphasize VR-based studies on embodied agents, including path modeling of attention , demographic bias evaluation , and emotional contagion in digital crowds . His work spans healthcare applications, educational AI, and cognitive modeling in immersive environments. Notable Contributions : Co-developed the HeadBox toolkit for facial blendshape animation in VR and explored trust dynamics with AI instructors. He also contributes to standardization of gaze analysis and multimodal interaction frameworks.

Erika Roldan RoaView profile

Visiting Assistant Professor

• Stochastic Topology
• First Passage Percolation
• Extremal Topological Combinatorics
• +3 more

Erika Roldán Roa is a mathematician with academic affiliations at Ohio State University (Visiting Assistant Professor, 2019-2020) and Technische Universität München (Marie Skłodowska-Curie Fellow, 2020-2022). She holds a Ph.D. in Probability and Statistics from CIMAT (2018) and has conducted postdoctoral research at institutions like EPFL and Ohio State. Her work bridges stochastic topology, combinatorics, and educational technology, focusing on topics like cubical sliding puzzles, Eden growth models, and extremal topological problems in polyominoes. Education: Ph.D. in Probability and Statistics, CIMAT (2018) M.Sc. in Mathematics, CIMAT (2014) B.Sc. in Mathematics, Universidad de Guanajuato (2010) Research Interests: Her research spans stochastic topology, extremal combinatorics, and applications of discrete geometry to puzzles and games. She explores configuration spaces, topological properties of random structures, and innovative educational approaches like the Music Math Project, which integrates music and mathematics. Awards: Includes the Marie Skłodowska-Curie Fellowship (2020-2022), Arana-Ordaz Prize (2019), and Sofia Kovalevskaia Prize (2019). She has also received grants from CONACYT and CIMAT. Grants & Outreach: Developed workshops in marginalized communities using music and math, and contributed to software tools like the Eden Model analysis package. Active in science popularization through articles and outreach programs. Labs/Teams: Collaborated on projects like the Eden Model (topology analysis software) and extremal animal configurations, often involving interdisciplinary teams in mathematics and computer science.

Marion WieseView profile

Researcher

• Technical Debt Management
• Software Architecture
• Agile Software Development
• +3 more

Marion Wiese is a Researcher at the University of Hamburg's Department of Informatics, part of the Faculty of Computer Science. She is a PhD student focusing on establishing sustainable Technical Debt Management (TDM) processes. Her work bridges academic research and industry practice, emphasizing awareness and process design for TDM in agile environments. Prior to academia, she spent over two decades at Gruner+Jahr GmbH as a Software Architect and Senior Developer, leading projects in system integration, BI applications, and ITIL implementation. Research Interests: Technical Debt Management, Software Architecture, Agile Development, Quality Assurance. Grants: Her doctoral project is funded by the German Federal Ministry of Education and Research (BMBF) under the Software Campus initiative. Awards: Recognized as 'Best Reviewer' in 2024. Teaching: Involved in courses like Software Development II and Agile Project Management. Publications: Over 9 peer-reviewed articles on TDM, architecture smells, and software decision-making. Roles: Program Chair for conferences like TechDebt, EASE, and ECSA.

Martin BrieschView profile

Researcher

• Program Synthesis
• Genetic Programming
• Machine Learning
• +3 more

Martin Briesch is a Research and Teaching Assistant at the Chair of Information Systems and Business Administration under Prof. Dr. Franz Rothlauf at Johannes Gutenberg University Mainz since March 2020. He holds a Master of Science in Management (2020) and a Bachelor of Science in Economics (2017), both from JGU Mainz. Roles: Research and teaching responsibilities, course coordination, and thesis supervision. Education: 2017–2020: M.Sc. Management, JGU Mainz 2014–2017: B.Sc. Economics, JGU Mainz Research Interests: Focuses on Program Synthesis , Genetic Programming , Machine Learning , Deep Learning , and Large Language Models . His work explores optimization techniques, evolutionary algorithms, and AI applications in problem-solving and decision support. Teaching: Leads courses such as "Introduction to Software Engineering" and "Introduction to Artificial Intelligence" since 2020. Supervises bachelor's and master's theses in information systems and business administration. Publications: Active researcher with contributions to genetic programming, program synthesis, and AI ethics. His work appears in venues like MKWI and Google Scholar.

Ruediger EhlersView profile

Professor

• Formal Methods
• Reactive Synthesis
• Automated Reasoning
• +4 more

Ruediger Ehlers is a Professor for Embedded Systems at Clausthal University of Technology, focusing on formal methods , automated reasoning , and cyber-physical systems . His research aims to bridge formal methods and artificial intelligence to enhance the efficiency and reliability of computational systems. University: Clausthal University of Technology Institute: Institute for Software and Systems Engineering Research Interests: His work centers on making correct-by-construction design processes more efficient, particularly through reactive synthesis , verification of finite- and infinite-state systems , and runtime monitoring . He has developed tools like slugs and planet for synthesis and neural network verification. Publications Trends: Recent articles emphasize temporal logic , reinforcement learning under constraints , and algorithm engineering for practical applications in manufacturing and quantum computing. Scientific Awards: Volkswagenstiftung Momentum endeavor (2023) Grants and Projects: He leads the EU/H2020 Project SAFE-10-T (2021) and secured funding from the German Science Foundation (2016) for synthesizing GUI code. His Open-MPW-6 program (2023) involves hardware implementation of runtime monitoring.

Salar MohtajView profile

Researcher

• Transfer Learning for Natural Language Processing
• Text complexity/readability prediction
• Evaluation of Large Language Models
• +1 more

Dr. Salar Mohtaj is a senior researcher at the German Research Center for Artificial Intelligence (DFKI) and affiliated with TU Berlin's Quality and Usability Lab. He holds a PhD from TU Berlin (2024), specializing in transfer learning for harmful content detection. His work focuses on NLP applications in fake news/hate speech detection, LLM evaluation, and text complexity analysis. Education: Bachelor's: Information Technology, Shahrood University of Technology (Iran) Master's: Information Technology, Amirkabir University (Iran) PhD: TU Berlin (2024), supervised by Prof. Sebastian Möller Research Interests: Transfer learning for NLP, LLM evaluation, text complexity prediction, and detection of harmful content. He leads projects on multimodal fake news detection and semantic text similarity models. Recent Contributions: Developed the Hamtajoo plagiarism detection system, contributed to the KIALOPRIME dataset, and led EU-funded projects on AI ethics and disinformation. His work bridges NLP techniques with societal challenges like misinformation. Awards & Grants: Received Software Campus funding (2019-2024) for projects on sentiment analysis in fake news. Collaborates with Holtzbrinck Publishing Group and Deutsche Telekom. Labs & Affiliations: Speech & Language Technology group at DFKI and Quality and Usability Lab at TU Berlin. Teaches Natural Language Processing courses at TU Berlin and KI Campus.

Daniel LohmannView profile

Professor

• Operating system construction
• Embedded systems
• System software for dependable real-time systems
• +6 more

Daniel Lohmann is a Full Professor and Fachgebietsleiter (Head of Department) at the Department of Operating Systems and Middleware within the College of Engineering and Computer Science at Leibniz Universität Hannover . His research focuses on operating system construction , embedded systems , and dependable real-time systems , with a strong emphasis on generative approaches , software product lines , and hardware-RTOS co-design .

Rainer DyckerhoffView profile

Professor

• Multivariate Statistics
• Data Depth
• Central Regions
• +3 more

Rainer Dyckerhoff is an apl. Professor at the Department of Economic and Social Statistics within the WiSo Faculty (Faculty of Management, Economics and Social Sciences) at the University of Cologne. He holds a PhD in Economics (Dr. rer. pol.) from Helmut Schmidt University Hamburg (1994) and a Diploma in Mathematics from Technical University Darmstadt (1988). His research focuses on multivariate statistics, data depth, computational statistics, and decision theory. He has held academic positions at the University of Cologne since 1995, including roles as Scientific Assistant (1995–2001), Academic Council member (2001–2006), and Akademischer Oberrat (since 2006). His work emphasizes methodological advancements in statistical depth functions, central regions, and computational algorithms for multivariate data analysis. Notable contributions include exact computation of halfspace depth and depth-based nonparametric tests. His publications span top journals like Computational Statistics and Data Analysis and Annals of the Institute of Statistical Mathematics . No scientific awards are explicitly mentioned in the provided text. Rainer Dyckerhoff has been affiliated with the Department of Economics at Helmut Schmidt University Hamburg during his early career (1989–1995). His current role combines research leadership and academic service in statistical methodology.