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Ruhul AminView profile

Assistant Professor

• Artificial Intelligence
• Bioinformatics
• Data Science
• +5 more

Ruhul Amin is an Assistant Professor at Fordham University's Department of Computer and Information Science, where he explores the intersection of Artificial Intelligence , Data Science , and Public Health . His work spans multiple domains including Bioinformatics , Natural Language Processing , and Computational Social Science . PhD in Computer Science (Stony Brook University, 2019) MS in Computer Science (Stony Brook University, 2015) BSc in Computer Science (Shahjalal University, 2007) His research focuses on developing deep learning algorithms for genome annotation, anomaly detection in high-cardinality spaces, and assistive technologies for visually impaired users. Notably, he designed the Mongol Dip bilingual screen reading software and the Pipilika Bengali text search engine. Recent publications (2025-2024) demonstrate his expanding interests in large language model alignment , sentiment analysis across languages, and distributed reinforcement learning for cybersecurity applications. His work frequently appears at IEEE conferences and in journals like PLOS One. Scientific Recognition BASIS National ICT Award (Bangladesh) Best Poster Awards at IEEE R10 HTC and CEWIT conferences Bangladesh Prime Minister’s Award (2012) mBillionth South Asia Award (2010) He maintains active collaborations with research teams at University of Toronto , University of British Columbia , and Stony Brook University . Contact: moamin@cs.stonybrook.edu

Keith S. JonesView profile

Professor

• Human-Computer Interaction
• Cybersecurity
• Human-Robot Interaction
• +3 more

Keith S. Jones is a Professor and Department Chair at Texas Tech University in the Department of Psychological Sciences . As a human factors psychologist, his research bridges human-robot interaction and cybersecurity , focusing on how humans perceive robotic capabilities and how social engineering attacks exploit human vulnerabilities. Education: Ph.D. in Human Factors Psychology (2000) from the University of Cincinnati. His work in human-robot interaction investigates how people perceive others' physical affordances, informing robot design for elder care and navigation. In cybersecurity, he analyzes phishing/vishing attacks, user susceptibility, and warning message effectiveness. Funding sources include the National Science Foundation , Office of Naval Research , and Microsoft . Former students hold roles at institutions like Amazon, Apple, and Sandia National Laboratories. Recent research trends highlight the use of transformer models (e.g., BERT) for phishing detection, affordance perception in assistive robotics, and persuasion principles in social engineering. Publications span journals like Ecological Psychology and International Journal of Social Robotics . Teaching includes graduate courses in Human Factors Methods and Human-Computer Interaction.

Michel BesserveView profile

Researcher

• Causal Machine Learning
• Complex Systems
• Neuroscience
• +4 more

Michel Besserve is a Senior Research Scientist in the Empirical Inference department at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. His research bridges machine learning theory with applications in neuroscience and complex systems analysis. He leads a research group focused on developing causal machine learning tools to uncover the internal structure and transformations of complex artificial, physical, and socioeconomic systems. Dr. Besserve's primary research interests center on causal machine learning and its applications to understanding complex systems. His work investigates how causality can provide principled ways to study and improve AI algorithms, particularly focusing on the identifiability of causal models and the principle of Independence of Causal Mechanisms (ICM). He develops theoretical frameworks and practical tools for causal inference in complex equilibrium systems, neural circuits, and socioeconomic contexts. His research has significant implications for building trustworthy and interpretable AI systems that can reliably handle real-world complexity. Analysis of Dr. Besserve's recent publications reveals a strong focus on causal representation learning, with significant contributions to independent mechanism analysis and the identifiability of nonlinear generative models. His work spans both theoretical foundations and practical applications, connecting machine learning with neuroscience to understand brain function through causal inference. The interdisciplinary nature of his research is evident in publications spanning top machine learning conferences (NeurIPS, ICML, ICLR) and leading neuroscience journals (Nature, PLOS Biology). Dr. Besserve has established productive collaborations across multiple institutions, particularly with researchers at the Max Planck Institute and ETH Zurich. His work demonstrates how integrating causal principles with machine learning can address fundamental challenges in AI robustness and interpretability, with applications ranging from brain network analysis to economic modeling. His research group focuses on developing the Causal Computational Model (CCM) framework, which aims to create digital representations of real-world systems that integrate data, domain knowledge, and interpretable causal structure. This work has potential applications in climate modeling, industrial digital twins, and economic simulation.

• Relational Machine Learning
• Deep Learning
• Complex Network Science
• +4 more

Dr. Rebekka Burkholz is a tenured faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany, leading the Relational Machine Learning Group . Her research bridges machine learning and complex network science to develop robust, data-efficient models with applications in molecular biology. Previously, she held positions at Harvard T.H. Chan School of Public Health and ETH Zurich. PhD in Systems Design (2016) from ETH Risk Center Mathematics and Physics BSc/MSc from TU Darmstadt Her work focuses on sparse training methods and theoretical deep learning , addressing challenges like computational efficiency and adversarial robustness. Recent publications explore: Sparse training via implicit sparsification GNN optimization through rescaling and rewiring Integration of domain knowledge in biomedical modeling Theoretical guarantees for batch normalization and lottery tickets Scientific awards include: Zurich Dissertation Prize (2016) CSF Best Contribution Award (2016) She actively advises PhD students and collaborates with interdisciplinary teams in biostatistics and systems biology.

• Biophysics
• Gene Regulatory Networks
• Immune System Dynamics
• +6 more

Aleksandra Walczak is a Professor of Biophysics at the École normale supérieure , leading research at the Laboratoire de Physique Théorique (LPENS). Her work bridges statistical physics and biology, focusing on understanding complex living systems through gene regulatory networks, immune system dynamics, and population genetics. Research Interests : Non-equilibrium biological systems, T-cell/B-cell receptor repertoires, stochastic molecular systems, and evolutionary dynamics. Funding & Collaborations : Supported by the Fondation Bettencourt Schueller, she co-organizes interdisciplinary workshops like the Paris Workshop on Immunology and contributes to GDRI Evolution, Regulation and Signaling network. Recent Publications explore gene regulatory principles, immune repertoire modeling, viral-immune coevolution, and collective behavior. She has secured competitive grants and mentors students in theoretical biophysics. Awards : Habilitation à diriger des recherches (2012), Princeton Center for Theoretical Physics Postdoctoral Fellowship (2007-2010). Students & Positions : Actively supervises PhD/Master projects; offers internships in biophysics and quantum engineering.

• Nanophotonics
• Quantum Technologies
• Computational Optics
• +5 more

Dr. Sven Burger is a leading Researcher at the Zuse Institute Berlin (ZIB) within the Modeling and Simulation of Complex Processes department. His work focuses on Nanophotonics , Quantum Technologies , and Optical Resonance Computation , particularly in photonic crystals, plasmonic systems, and quantum light sources. Key projects: NanoLab GRIPS 2024 , MATH+ TES QT , MATH+ PaA-1 (perovskite solar cells), Colour Impression of Solar Cells Collaborations: MATH+ , BIFOLD , Research Campus MODAL His research spans Bayesian optimization for quantum systems, quasinormal mode expansions , chiral plasmonics , and terawatt-scale photovoltaics . Recent work emphasizes RPExpand software for resonance analysis and AAA algorithm applications in photonic design. He contributes to quantum key distribution via plug&play single-photon sources, hot carrier dynamics in plasmonic nanocrystals, and high-efficiency light extraction for deep-UV LEDs. His computational methods address non-Hermitian systems , exceptional points , and self-interference nanoparticle tracking .

• Urban Mobility
• Traffic Simulation
• Smart City Technologies
• +2 more

Maria Paz Linares Herreros is a Senior Lecturer at the Universitat Politècnica de Catalunya (UPC) in the Department of Statistics and Operations Research within the Faculty of Mathematics and Statistics (FME). She is affiliated with the IMP - Information Modeling and Processing research group and inLab FIB. Her educational background includes a Licenciada en Matemáticas, a Doctorate from UPC, and a Master's in Logistics, Transportation, and Mobility. Licenciada en Matemáticas Doctorat from Universitat Politècnica de Catalunya Máster en Logística, Transporte y Movilidad Her research focuses on urban mobility , traffic simulation , and smart city technologies . She develops data-driven models for transportation systems and parking management, integrating deep learning techniques for real-time predictions. Her work addresses environmental impact assessment of traffic policies and inclusive mobility solutions . Recent publications analyze urban mobility trends through macroscopic and microscopic traffic models , deep learning applications for parking predictions, and dynamic ride-sharing systems . She explores IoT interoperability and data integration in transportation. Scientific awards include the IV International Award on Transport Infrastructure Management Research (2014). She participates in competitive R+D+I projects like ALGORAE and Virtual Mobility Lab, focusing on transportation innovation and smart city policy evaluation . She contributes to multimodal transport simulation frameworks such as CitScale and Barcelona Virtual Mobility Lab, which evaluate emerging mobility concepts and city policies using integrated modeling approaches .

• Wireless Communications
• Information Theory
• Communication Networks
• +18 more

Cenk Gursoy is a Professor in the Department of Electrical Engineering and Computer Science at the College of Engineering and Computer Science, Syracuse University. He previously served as a faculty member at the University of Nebraska-Lincoln from 2004 to 2011. He holds a Ph.D. in Electrical Engineering from Princeton University and a B.S. from Bogazici University, Turkey. His research spans wireless communications, information theory, signal processing, and networking. Key areas include 5G/6G technologies, millimeter-wave communications, UAV-assisted networks, energy efficiency, intelligent reflecting surfaces, and machine learning for networking. He has made significant contributions to finite blocklength communications, QoS provisioning, and secure wireless transmissions. The recent publications highlight a strong trend in integrating machine learning—particularly deep reinforcement learning—into wireless resource allocation, network slicing, UAV trajectory planning, and anomaly detection. His work increasingly bridges theoretical information-theoretic models with practical implementations in emerging wireless systems such as RIS-aided networks, NOMA, and edge computing. NSF CAREER Award, 2006 2020 IEEE Region 1 Technological Innovation (Academic) Award IEEE Green Communications & Computing Technical Committee Best Journal Paper Award EURASIP Journal of Wireless Communications and Networking Best Paper Award IEEE PIMRC Best Paper Award Maude Hammond Fling Faculty Research Fellowship All-University Doctoral Prize (awarded twice to his students) Dr. Gursoy has advised over 20 Ph.D. and M.S. students, many of whom now hold positions at leading universities and tech companies. His research has been funded by multiple National Science Foundation grants as Principal or Co-Principal Investigator, focusing on fundamental limits of wireless systems, millimeter-wave networking, and green cloud platforms. He serves as an Area Editor for IEEE Transactions on Vehicular Technology and as an Editor for several other IEEE Transactions journals. He leads the Wireless Communications & Networking Lab and the Smart Vision Systems Lab at Syracuse University, fostering interdisciplinary research in smart networks, IoT, and intelligent sensing systems.

Tingting LiView profile

Lecturer

• AI for Cyber Security
• Automated Cyber Defence
• Diversification and Deception Strategies
• +8 more

Dr. Tingting Li is a Lecturer (Assistant Professor) in Cyber Security at Cardiff University's School of Computing and Informatics and a member of the Centre for Cyber Security Research. She also holds an Honorary Research Fellow position at Imperial College London, reflecting her ongoing research collaboration. Her work bridges artificial intelligence and cybersecurity, with a focus on protecting critical systems such as cyber-physical systems (CPS), industrial control systems (ICS/SCADA), and autonomous vehicles. BEng (Hons) in Information Security, Xidian University, China MSc in Computing, Imperial College London PhD in Artificial Intelligence, University of Bath Dr. Li’s research interests lie at the intersection of AI and cybersecurity, particularly in automated cyber defense , diversification/deception strategies , and symbolic AI for knowledge representation . She investigates how AI can enhance the resilience of critical infrastructures through intelligent, adaptive defense mechanisms. Her recent work explores quantum-inspired reinforcement learning and machine learning models for proactive threat mitigation in complex systems. Her recent publications demonstrate a strong trend in applying advanced AI techniques—especially deep reinforcement learning, LSTM networks, and Bayesian models—to cybersecurity challenges in industrial and autonomous systems. These works span domains like IoT malware suppression, network diversity for ICS resilience, and automated compliance checking, reflecting a multidisciplinary approach combining security, AI, and systems engineering. Dr. Li has secured significant research funding, including a grant from the Alan Turing Institute (2024–2025) on AI safety in autonomous cyber defense, a RITICS/NCSC-funded project on diversity-based cybersecurity (2021–2022), and an EPSRC-funded project on metric-driven cybersecurity frameworks for critical national infrastructure (2021–2023). She actively supervises PhD students in areas including cybersecurity for autonomous vehicles, moving target defense, and cyber-physical system security. Her team includes Iryna Bernyk, Sanyam Vyas, Victoria Marcinkiewicz, Ellis Doran, Stephen Morris, and Sam Braithwaite. She also leads teaching modules on databases (CM6125/CM6625) and cybersecurity (CM6224/CM6724). Dr. Li’s research is conducted within the Centre for Cyber Security Research at Cardiff University, where she collaborates with experts in AI, security, and critical infrastructure protection. Her work is highly interdisciplinary, involving partnerships with institutions like Imperial College London and funding bodies such as EPSRC, NCSC, and the Alan Turing Institute.

Alfredo BensoView profile

Professor

• Bioinformatics
• Systems Biology
• Artificial Intelligence
• +16 more

Alfredo Benso is a Full Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where he is also a member of the Interdepartmental Center PolitoBIOMed Lab - Biomedical Engineering Lab. He has been continuously involved in the Doctoral College for Computer and Systems Engineering since 2008, guiding PhD education across numerous cycles. His academic roles span teaching and research leadership in bioinformatics, systems biology, and computer engineering. Research interests: Bioinformatics and computational biology Systems biology and gene regulatory networks Artificial intelligence and machine learning in biomedicine Molecular modeling and multiscale simulation Biological database systems and data integration Health informatics and public health modeling His recent publications reflect a strong trend in applying AI and machine learning to biological and medical challenges, including protein function prediction, Alzheimer’s and Multiple Sclerosis modeling, food fraud detection, and viral genome analysis. These works span journals and conferences in bioinformatics, computational biology, and biomedical engineering, demonstrating interdisciplinary innovation. Scientific Awards: BIOINFORMATICS 2014 BEST PAPER AWARD (INSTICC, United Kingdom) Advising and Grants: He supervises PhD students, including Sofia Ostellino, and leads major research initiatives such as the BIGMECH (2023–2025) and FISHUB (2016–2018) projects. He has served as Scientific Director for collaborative agreements with public administrations and as Principal Investigator on EU, national (PRIN), and regional research grants focused on bioinformatics, space systems, and reliable digital technologies. Labs and Teams: He is a key member of the SBG - System Biology Group (DAUIN) and contributes to PolitoBIOMed Lab, fostering collaborative research in biomedical engineering and computational biology.

Xubo YueView profile

Assistant Professor

• Federated Data Analytics
• Bayesian Optimization
• Continuous Optimization
• +5 more

Xubo Yue is an Assistant Professor in the Department of Mechanical and Industrial Engineering at Northeastern University. His research focuses on federated data analytics, Bayesian optimization, continuous optimization, Gaussian processes, and deep learning. He holds a PhD in Industrial & Operations Engineering from the University of Michigan, Ann Arbor (2023). His work bridges theoretical advancements with practical applications in advanced manufacturing, predictive maintenance, and sustainable materials discovery. Key affiliations include the Institute of Industrial and Systems Engineers (IISE), INFORMS, and the American Statistical Association (ASA). Recent research emphasizes scalable federated learning frameworks for distributed systems, causal inference in sensor networks, and sharpness-aware optimization techniques to enhance generalization. His methodologies are applied to interdisciplinary domains such as materials science, IoT systems, and renewable energy simulations. Research trends reveal a focus on: Federated learning architectures for privacy-preserving analytics Bayesian optimization for high-dimensional design spaces Integration of causal reasoning with machine learning systems Autonomous experimentation for accelerated materials discovery No scientific awards are explicitly listed in the provided information. His academic advising and grant activities are not detailed in the current data.

Anahita KhojandiView profile

Associate Professor

• Decision Making Under Uncertainty
• Reinforcement Learning
• Markov Decision Processes
• +9 more

Anahita Khojandi is an Associate Professor in the Department of Industrial and Systems Engineering at the University of Tennessee, Knoxville, where she also holds the Heath Endowed Faculty Fellow in Business & Engineering title. She is also a Joint Associate Professor at the Bredesen Center for Interdisciplinary Research and Graduate Education and served as the Director of the Reliability and Maintainability Engineering Program from 2021 to 2024. Her interdisciplinary research spans healthcare, transportation, environmental engineering, and advanced manufacturing. Education: PhD in Industrial Engineering, University of Pittsburgh MS in Industrial Engineering, University of Pittsburgh BS in Industrial Engineering, Sharif University of Technology, Tehran, Iran Her research focuses on decision-making under uncertainty, reinforcement learning, Markov decision processes, and predictive analytics. These methodologies are applied to critical domains such as sepsis prediction, Parkinson’s disease treatment using wearable sensors, intelligent transportation systems, nuclear safety, and green infrastructure resilience. She integrates machine learning with stochastic modeling to improve real-time decision-making in complex systems. The recent publications highlight a strong trend toward AI-driven solutions in healthcare and infrastructure, with emphasis on explainability, anomaly detection, and real-time adaptation. Her work frequently appears in high-impact journals such as Management Science , IEEE Transactions , and Nuclear Technology , reflecting both methodological rigor and practical relevance. Scientific Awards and Honors: AAAS Rapid Response AI Fellow (2024) NIH AIM-AHEAD Fellowship in Leadership (2023) First Place, Harvey J. Greenberg Research Award (INFORMS, 2022) Best Paper Award, OMEGA Journal (2020, 2021) INFORMS Senior Member (2024) Teaching Award, IISE DAIS Division (2024) Khojandi has advised numerous graduate students and postdoctoral researchers, many of whom are co-authors on her publications. Her research has been supported by the National Science Foundation (NSF), National Institutes of Health (NIH), Department of Energy (DOE), and other federal and institutional grants. She actively contributes to professional service, including leadership roles in INFORMS, where she served as Chair of the Diversity, Equity, and Inclusion Committee and currently serves as Vice President of Membership and Professional Recognition. She leads research initiatives at the intersection of industrial engineering and data science, often in collaboration with the Health Innovation Technology & Simulation (HITS) Lab and the Center for Transportation Research. Her lab focuses on developing AI-enabled decision support tools for clinical and operational environments, with future work targeting autonomous systems, personalized medicine, and climate-resilient infrastructure.

Cresantus BiambaView profile

Senior Lecturer

• Educational Science
• Teacher Education
• Education for Sustainable Development
• +3 more

Cresantus Biamba is a Senior Lecturer at the University of Gävle, specializing in Educational Science. His research bridges education theory with technological advancements, focusing on teacher training, sustainability in education, and inclusive pedagogy. Researcher at University of Gävle (Education, Educational Science) Research interests include: Education for Sustainable Development (ESD) in global contexts Teacher education reform and policy analysis Inclusive classroom practices in the Global South Technological integration in educational systems Curriculum development for post-pandemic resilience Publication trends reveal interdisciplinary work combining AI, cloud computing, and IoT applications with educational challenges, particularly in African institutions. His articles address security optimization, healthcare technology, and sustainability frameworks. Academic activities involve collaborations with researchers in cybersecurity, AI, and energy systems, though specific grants or mentoring roles are not explicitly documented here.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +8 more

Jarosław Wąs is a Professor and Head of the Department of Applied Informatics at the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology in Kraków, Poland. His academic leadership extends to governance roles including the Senate, Faculty College, and Disciplinary Council for Technical Information Technology and Telecommunications. His research spans Artificial Intelligence, Machine Learning, and Data Mining with core expertise in Rough Sets theory. He develops computational models for crowd simulation and pedestrian dynamics critical for evacuation planning, and applies deep learning to renewable energy forecasting and health trajectory prediction. His work bridges theoretical computer science with practical applications in energy systems, healthcare analytics, and cosmic physics. Analysis of his 2023-2025 publications reveals interdisciplinary convergence: Rough Sets combined with cellular automata for crowd modeling, transformer networks for electronic medical records, and hybrid deep learning approaches for renewable energy forecasting. Key trends include zero-shot learning in healthcare, anomaly detection in cosmic data, and data-driven evacuation simulation with social group dynamics. No scientific awards are mentioned in available sources. Information regarding student advising and research grants is not provided in the source material. No laboratory or research team affiliations are specified in the available documentation.

Diogo Ribeiro FerreiraView profile

Associate Professor

• Data Science
• Applied Machine Learning
• Deep Learning
• +6 more

Diogo Ribeiro Ferreira is an Associate Professor of Information Systems at Instituto Superior Técnico (IST) , part of the University of Lisbon . He is also a researcher at the Institute of Plasmas and Nuclear Fusion (IPFN) . His career spans teaching, research, and collaboration with institutions like JET (Joint European Torus) in nuclear fusion. His research focuses on Data Science , Applied Machine Learning , Deep Learning , and Process Mining , with applications in Nuclear Fusion (plasma diagnostics, disruption prediction) and Information Systems (workflow management, healthcare process analysis). He has taught courses like Data Administration in Information Systems and Data Analysis and Integration since 2003. His work bridges Computer Science and Physics , particularly in using Graph Neural Networks for plasma modeling and GPU Acceleration for high-performance computing. He has supervised over 20 PhD and MSc students, including André C. Santos and Fernando Szimanski, and co-authored 15+ recent publications in journals like Journal of Plasma Physics and Fusion Engineering and Design . Scientific Awards: 2012 ICGA Journal Award Best Paper Award at 9th IEEE International EDOC Conference 2005 Collaborations: Affiliated with JET Contributors and IPFN, focusing on real-time tomography and fusion reactor optimization.