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Ivan MarinView profile

Professor

• Braid groups
• Reflection groups
• Lie algebras
• +6 more

Ivan Marin is a Professor of Mathematics at the University of Picardie-Jules Verne (also known as University of Amiens), currently on a CNRS delegation at the Institute of Mathematics of Jussieu-Paris Rive Gauche (UMR CNRS 7586). He works at LAMFA (Laboratoire Amiénois de Mathématique Fondamentale et Appliquée) where his office is C013. His research spans multiple areas of algebraic mathematics with a strong focus on structural properties of various algebraic systems. Marin's research interests primarily center around braid groups, reflection groups, and their representations. His work explores the deep connections between Lie algebras, Galois theory, and topological structures. He has made significant contributions to the understanding of Hecke algebras, monodromy representations, and the Broué-Malle-Rouquier conjecture. His approach often involves studying algebraic structures through their representations, examining how group-theoretic properties manifest in linear algebraic settings. Marin's work bridges pure algebra with topological and geometric perspectives, creating a rich interdisciplinary research program that has substantial implications for representation theory and algebraic topology. Analysis of his recent publications reveals a consistent focus on the structural properties of braid groups and their generalizations. His work shows increasing sophistication in handling complex reflection groups and their associated braid structures. Marin has been particularly active in proving the BMR conjecture for various cases and exploring cubic Hecke algebras. His research demonstrates a progression from foundational representation theory toward more specialized structures with applications in knot theory and topological quantum computing. Marin has established significant collaborations with mathematicians including Olivier Brunat, Marc Cabanes, Jean Michel, François Digne, Emmanuel Wagner, Filippo Callegaro, Götz Pfeiffer, Kay Magaard, Vincent Beck, Thomas Gobet, Anthony Henderson, and Juan González-Meneses. These collaborations span multiple institutions and have resulted in numerous joint publications addressing fundamental questions in algebra and topology. His academic journey includes completing his doctorate in 2001 on linear representations of infinitesimal braids, followed by his Habilitation à Diriger des Recherches (HDR) in 2009 focusing on linear representations of generalized braid groups associated with finite reflection groups. This trajectory demonstrates a sustained commitment to advancing our understanding of algebraic structures and their representations over more than two decades.

• Epidemiology
• Public Health
• Cardiovascular Disease
• +4 more

Emily Levitan is a Professor of Epidemiology at the University of Alabama at Birmingham (UAB), School of Public Health. She holds multiple appointments across UAB, including roles in the Graduate School, School of Medicine, and various research centers such as the Center for AIDS Research and the Center for Clinical and Translational Science. Her research focuses on cardiovascular disease prevention, long-term effects of HIV and COVID-19, and leveraging administrative data to improve public health outcomes. She leads projects like the REGARDS-MI study and the Deep South SARS-CoV-2 Recovery Cohort, contributing to the NIH RECOVER Initiative on long COVID. Dr. Levitan also serves as Vice Chairperson of the Epidemiology Department at UAB. Education: Doctor of Science in Epidemiology (Harvard University, 2006), Master of Science in Epidemiology (Harvard University, 2004), Bachelor of Science in Mathematics/Chemistry (Brown University, 2000). Research Interests: Cardiovascular epidemiology, HIV/AIDS, long-term sequelae of infectious diseases, health disparities, and administrative data utilization. Her recent work emphasizes the role of social determinants in health outcomes, such as comorbidity patterns in heart failure and the impact of adverse childhood experiences on asthma in sickle cell disease patients. Dr. Levitan has secured extensive funding from NIH, CDC, and other agencies, totaling over 76 grants, including studies on HIV testing strategies and cardiovascular biomarkers. Key collaborations involve multi-center initiatives like the RECOVER Cohort and the C4R study, which classify outcomes of acute and post-acute SARS-CoV-2 infections. Her research also addresses disparities in healthcare access and medication adherence among vulnerable populations.

Constantinos PatsakisView profile

Associate Professor

• Cryptography
• Security
• Privacy
• +4 more

Constantinos Patsakis is an Associate Professor at the Department of Informatics, University of Piraeus, and an adjunct researcher at the Institute for the Management of Information Systems (IMIS) of Athena Research and Innovation Centre. He holds a Mathematics degree from the University of Athens, an M.Sc. in Information Security from Royal Holloway, University of London, and a PhD in Security from the University of Piraeus. Previously, he worked as a Researcher at the UNESCO Chair in Data Privacy at Rovira i Virgili University, a Research Fellow at Trinity College Dublin, and a Senior Researcher at the Luxembourg Institute of Science and Technology. His primary research areas include cryptography, security, privacy, data anonymization, malware analysis, and blockchain technology. With over 190 publications to his name, his work spans from theoretical security frameworks to practical applications in digital forensics and malware detection. He has contributed significantly to the development of datasets for security research, including the Malicious MS Office documents dataset, Social Live Streaming Service Grooming dataset, and the HYDRA dataset for Domain Generation Algorithm research. His recent publications (2024-2025) reveal a strong focus on emerging security challenges, with particular emphasis on LLM applications in security analysis, novel malware detection techniques, privacy-preserving protocols, and blockchain security. His work bridges academic research with practical security solutions, addressing both technical and human aspects of cybersecurity. As an editor for journals including Computers and Security, International Journal of Information Security, Scientific Reports, and Blockchain: Research and Applications, he plays a significant role in shaping the discourse in his field. He currently teaches undergraduate courses in Information & Code theory, Security Governance, Introduction to Computer Science, and Cryptography, as well as graduate courses in Digital Forensics & Malware Analysis, Cryptographic protocols, and Blockchain development. Principal Investigator for ALUNA (ISFP-2021-AG-CYBER) Coordinator for LAZARUS (HORIZON-CL3-2021-CS-01) Principal Investigator for Cut the Cord (ISFP-2020-AG-CYBER) Principal Investigator for HEROES (H2020-FCT-01-2020) His research group actively contributes to multiple EU-funded projects addressing critical cybersecurity challenges, with a particular focus on digital forensics, blockchain security, and privacy-preserving technologies. The team maintains strong collaborations with international institutions and industry partners, ensuring their research has real-world impact.

• Healthcare Applications
• ECG Signal Processing
• Sleep Apnea Detection
• +5 more

Nuno Gonçalo Coelho Costa Pombo is an active researcher and faculty member at the University of Beira Interior, Portugal, where he works in the School of Technology and Management within the Department of Computer Science. His academic career spans over a decade with consistent publication output from 2014 through 2025, demonstrating sustained research productivity and evolving expertise across multiple domains. Dr. Pombo's research interests center on the intersection of computer science and healthcare applications. His work prominently features ECG signal processing for medical diagnostics, with significant contributions to sleep apnea detection systems and clinical decision support systems . He has pioneered approaches in activities of daily living recognition using sensor data from mobile devices, which has important applications in elderly care and chronic disease management. His research methodology often combines machine learning techniques with Internet of Things (IoT) frameworks to create healthcare monitoring solutions that balance effectiveness with user privacy concerns. Recent work has expanded into educational technology applications, including augmented reality for programming instruction and futuristic thinking development in education. An analysis of Dr. Pombo's publication trends reveals a natural progression from foundational research in activity recognition to increasingly sophisticated healthcare applications. His early work focused on basic activity recognition frameworks, while more recent publications demonstrate advanced applications in specific medical conditions including diabetes management, mental health support, and cardiovascular diagnostics. The interdisciplinary nature of his research is evident in his extensive collaborations across engineering, medical, and social science domains, reflecting a holistic approach to healthcare technology development. Dr. Pombo maintains an active supervision role, with numerous publications featuring co-authors who appear to be students or early-career researchers. His work has been supported by research initiatives focused on healthcare technology innovation, though specific grant details are not evident from publication records. The consistent output across reputable venues including IEEE Access, Sensors, and international conferences demonstrates sustained research impact in his field.

Tore PedersenView profile

Visiting Professor

• Psychology of Judgment and Decision-Making
• Intelligence and National Security
• Human-Technology Interaction
• +2 more

Tore Pedersen is Visiting Professor of Intelligence Studies at King's College London's Department of War Studies within the Faculty of Social Science and Public Policy. He concurrently serves as Chief Research Scientist at the Norwegian Intelligence School and Professor of Psychological Science at Oslo New University College. His former leadership roles include Dean of Higher Education at the Norwegian Defence Intelligence School and Director of Research at multiple national institutes. Education includes: BSc in Social Sciences, University of Oslo MSc in Innovation Management, Karlstad University MSc in Cognitive Psychology, Karlstad University MA in Literary Nonfiction, University of South-Eastern Norway PhD in Cognitive Psychology, Karlstad University Research focuses on judgment and decision-making across individual, institutional, and technological contexts, with emphasis on intelligence analysis and cognitive bias mitigation. His interdisciplinary approach bridges psychological science with security studies. Recent publications explore AI bias transference, behavioral computing, and privacy frameworks. Works consistently integrate experimental methods with security applications, demonstrating strong cross-disciplinary trends. Awards include: Elected Fellow of the Royal Society of Arts International engagements include advisory roles for the ERASMUS-funded IMSISS Program and editorial positions in psychological science journals.

• Urology
• Oncology
• Bladder Cancer
• +5 more

Dr. Siamak Daneshmand is a Professor of Urology and Medicine (Oncology) at the Keck School of Medicine of USC, serving as Director of Urologic Oncology and Urologic Oncology Fellowship Director. His expertise includes bladder, testicular, and kidney cancers, with a focus on perioperative outcomes, molecular markers, and surgical techniques like nerve-sparing RPLND. He leads clinical trials (e.g., SEMS, SWOG) and chairs the SUO-CTC Bladder Section. Research interests span bladder cancer pathways, biomarkers (e.g., miRNA, ctDNA), and functional outcomes post-cystectomy. He chairs AUA guidelines for bladder and testicular cancers and is an SWOG local bladder committee member. Key articles focus on testicular cancer survivorship, novel therapies (TAR-200), bladder preservation strategies, and genomic classifiers. Over 400 peer-reviewed publications highlight his contributions to urologic oncology. Scientific awards include 13 years as 'America’s Top Cancer Doctor' and Alpha Omega Alpha membership. He has led over a dozen clinical trials and serves on multiple steering committees.

• Machine Learning
• Bioinformatics
• Computational Biology
• +6 more

Zafer Aydın is an Associate Professor at the Department of Computer Engineering, Graduate School of Engineering and Science, Abdullah Gul University. He holds a PhD in Electrical and Computer Engineering from Georgia Institute of Technology and has held academic positions at Bahcesehir University and Abdullah Gul University. Research Interests: Machine learning applications in bioinformatics, biomedical engineering, health informatics, and industrial problem-solving (network intrusion detection, traffic flow prediction, sentiment analysis). Broad research areas: Protein structure prediction, respiratory virus modeling, medical imaging analysis, network security Recent projects focus on deep learning, ensemble methods, and feature engineering for biological and industrial datasets Scientific Achievements: First place in multiple DREAM Challenge leaderboards (COVID-19, Parkinson's disease) Second place in Respiratory Viral DREAM Challenge T0 phase He has mentored 4 PhD and 6 Master's students, and participated in 12 funded research projects including TUBITAK grants. His lab develops AI solutions for biomedical and industrial applications while maintaining active collaborations with international researchers.

Huanjing WangView profile

Professor

• Machine Learning
• Big Data Analytics
• Feature Selection
• +2 more

Huanjing Wang is a Professor of Computer Science in the Ogden College of Science and Engineering at Western Kentucky University, where she also serves as CS Undergraduate Program Coordinator. She holds a Ph.D. in Computer Science from The University of Alabama (2005). Her research specializes in machine learning and big data analytics, with focus areas in feature selection techniques, fraud detection systems, and software defect prediction. She develops methods for handling high-dimensional data in applications ranging from healthcare fraud to software quality assurance. Recent publications (2019-2023) demonstrate sustained focus on fraud detection applications using explainable AI and ensemble feature selection methods. Her work provides comparative frameworks for evaluating feature selection stability and performance across diverse datasets. She serves as Associate Editor for the Journal of Big Data and has chaired multiple IEEE conferences including ICMLA 2019. Teaching responsibilities include Algorithms, Database Systems, Machine Learning, and Data Mining courses at undergraduate and graduate levels. Departmental service includes chairing the Undergraduate Curriculum Committee since 2015 and leadership in accreditation activities.

Reshma KarView profile

Assistant Professor

• Health Informatics
• Machine Learning
• Data Analysis
• +3 more

Dr. Reshma Kar is an Assistant Professor at the Data Science Institute, Saint Peter’s University. She holds a Ph.D. in Engineering from Jadavpur University, India, where she previously served as a project fellow, senior project fellow, and guest faculty. Her research focuses on health informatics and machine learning, with applications in healthcare analytics and predictive modeling. Dr. Kar teaches graduate-level courses including Data Analysis and Decision Modeling, Python in Data Science, and Machine Learning. Her professional achievements include a Regional i-Corps Grant from the NSF for diabetes prediction research (2024) and multiple Faculty Research Associate Awards at Saint Peter’s University. Notable research contributions include developing interpretable machine learning algorithms for ICU length-of-stay prediction and analyzing brain signals for motor activity detection. She has also contributed to natural language processing studies, such as classifying social media data related to the pandemic. Dr. Kar has over 20 peer-reviewed publications and actively mentors students in research projects, emphasizing programming and data science methodologies. Her work bridges computational techniques with real-world healthcare challenges, reflecting her commitment to advancing both academic and applied data science.

Davide BalzarottiView profile

Professor

• Reverse Engineering
• Forensic Memory Analysis
• Advanced Malware Analysis
• +3 more

Davide Balzarotti is a Professor and Head of the Digital Security Department at EURECOM. His expertise spans reverse engineering, forensic memory analysis, advanced malware analysis, and web security. He holds a PhD in Computer Engineering from the Polytechnic Institute of Milan (2006), followed by a postdoctoral fellowship at the University of California, Santa Barbara (2006–2008), and an HDR (Habilitation à Diriger des Recherches) from EURECOM in 2015. His research focuses on understanding malware behavior, improving forensic techniques, and enhancing web security through machine learning and static analysis. Notable contributions include frameworks like ModSec-Learn for boosting intrusion detection and PhishEye for monitoring phishing kits. He has received multiple awards, including the Best Paper Award at SAC 2017 and the ACM Europe Student Best Paper Award at CCS 2016. His work bridges academic research and practical cybersecurity challenges, addressing topics such as adversarial attacks, embedded system security, and evasion techniques in malware. His projects include evaluations of voting machine security (Project Everest) and collaborations to improve vulnerability detection in software. Education: PhD in Computer Engineering, Polytechnic Institute of Milan (2006) Postdoctoral Research at UCSB (2006–2008) HDR, EURECOM (2015) Awards: Best Paper Award (2017) ACM Europe Student Best Paper Award (2016) Best Student Paper Award (2013) Distinguished Paper Award (2011) Key Research Themes: Memory forensics Malware analysis Fuzzing and vulnerability discovery Web application security

Zhiying QianView profile

Assistant Professor

• Applied Linguistics
• Psycholinguistics
• Neurolinguistics
• +3 more

Zhiying Qian is an Assistant Professor in the Department of Modern Languages and Linguistics at Florida State University (FSU). She holds a Ph.D. in East Asian Languages and Cultures with a certificate in Second Language Acquisition and Teacher Education from the University of Illinois at Urbana-Champaign (2015). Her research focuses on psycholinguistics, second language acquisition, and language program management, employing neuroscientific methods like EEG and eye-tracking to study cognitive processes in language comprehension. She leads the Chinese Language Teachers Association’s Research Special Interest Group, fostering collaboration among over 200 researchers in Chinese language education. **Education**: - Ph.D. in East Asian Languages and Cultures (UIUC, 2015) - Certificate in Second Language Acquisition and Teacher Education (UIUC, 2015) - Advanced Japanese studies at the Inter-University Center for Japanese Language Studies (Yokohama, Japan) **Research Interests**: - Applied linguistics and neurolinguistics - Sentence processing and classifier systems in Chinese/Japanese/English - Second language acquisition mechanisms - Technology-enhanced language pedagogy - Blended learning and teacher training **Grants and Funding**: - Seed Grant ($100k, FSU, 2024–2026) - Collaboration with Dr. Zilong Xie on neural tracking of linguistic units - Multiple institutional grants (e.g., $20k First-Year Assistant Professor Grant, 2019) **Lab & Outreach**: Directs the Language Processing and Eye-Tracking Lab, offering research opportunities in cognitive linguistics and second language acquisition. She serves as Review Editor for the Journal of Chinese as a Second Language (2024–2027) and actively promotes collaborative research through her SIG leadership.

Kristian NybomView profile

Senior Lecturer

• Edge Computing
• Edge Analytics
• Systematic Mapping Study
• +7 more

Kristian Nybom is a Senior Lecturer in the Faculty of Natural Sciences and Engineering at Åbo Akademi University, Finland, affiliated with the Department of Information Technology. He is actively contributing to research in computer science, with a strong focus on edge computing, IoT systems, and software engineering practices. Research Interests: His research spans several key areas in modern computing, particularly Edge Computing and Edge Analytics , where he has conducted multiple systematic mapping studies to synthesize research trends and challenges. He is also deeply involved in API documentation generation , exploring tools and methodologies to improve developer experience and software maintainability. Additional interests include IoT applications in maritime environments , distributed sensor systems , and data engineering . The analysis of his recent publications reveals a consistent methodological focus on systematic mapping studies , which are used to survey and classify research in emerging domains. These works often sit at the intersection of software engineering and distributed systems, emphasizing practical tools and real-world applications, particularly in edge and maritime IoT contexts. Projects: He is currently active in multiple research initiatives, including the EU-funded AIDOart project and national projects like ADMO: 5G-Advanced for Digitalization of Maritime Operations and Cyber security training cooperation network . Earlier projects include N4S: Need for Speed and Enabling Next Generation Networks for broadcast Services . Collaboration and Funding: His work is supported by prominent funding bodies such as the European Commission and Business Finland. He frequently collaborates with researchers like Ivan Porres, Joakim Björkqvist, and Dragos Truscan. While no formal advisees are listed, his role as a senior lecturer suggests involvement in student supervision and curriculum development. Laboratories and Research Groups: Though not explicitly named, his affiliation with the Turku Center for Computer Science (TUCS) and participation in large-scale projects indicate integration within active research teams focused on software systems, edge computing, and digitalization.

Tudor DumitrașView profile

Associate Professor

• System Security
• Machine Learning
• Big Data
• +5 more

Tudor Dumitraș is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Maryland, with a joint appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and core faculty status in the Maryland Cybersecurity Center. His research focuses on data-driven security, empirical studies of adversary behavior, and the security of machine learning systems. Education Ph.D. and M.S. in Electrical and Computer Engineering from Carnegie Mellon University Diplôme d’Ingénieur in Computer Science from Ecole Polytechnique, Paris, France B.S. in Computer Science from "Politechnica" University, Bucharest, Romania Dumitraș's work bridges system security, big data analysis, and machine learning. He developed the Worldwide Intelligence Network Environment (WINE), an innovative platform for sharing cybersecurity field data between academia and industry. His research includes malware detection using machine learning, analysis of zero-day attacks, and mitigation of adversarial threats against AI models. Recent publications highlight his contributions to adversarial machine learning, vulnerability exploit prediction, and neural network security. Scientific awards include the NSA Best Scientific Cybersecurity Paper Honorable Mention (2013), A.G. Jordan Award from Carnegie Mellon (2011), ACM SIGPLAN John Vlissides Award (2009), and multiple conference best paper awards. He has secured research funding from Amazon Web Services and received scholarships from the French government. Dumitraș advises graduate students and contributes to professional communities through program committee service, including on USENIX Security'24. He leads the development of security datasets and tools like Exploitability.app, HardwareFail.ml, and FeatureSmith. His outreach includes TED-style talks explaining cybersecurity concepts to broad audiences.

Florian T. PokornyView profile

Associate Professor

• Robotics
• Machine Learning
• Artificial Intelligence
• +5 more

Florian T. Pokorny is an Associate Professor in Machine Learning at the Division of Robotics, Perception and Learning (RPL), Department of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology. He coordinates research projects such as the Horizon Europe-funded SoftEnable and the WASP-funded Intelligent Cloud Robotics for Real-Time Manipulation at Scale . His research focuses on data-driven methods for robotic manipulation, including transfer learning, cloud robotics, and caging-based manipulation of rigid and deformable objects. Education: PhD in Mathematics, University of Edinburgh (supervisor: Michael Singer) MSc in Advanced Study in Mathematics (Part III), University of Cambridge BSc in Mathematics, University of Edinburgh Research Interests: His work emphasizes scalable robotic manipulation, leveraging deep learning and geometric/topological methods. Key areas include training data requirements, transfer learning, and cloud robotics paradigms for manipulation at scale. Recent projects explore energy margin analysis, caging-guided morphology optimization, and robust policy learning. Publications: His recent work spans topics like caging-based manipulation, federated learning, and cloud robotics infrastructure. Notable contributions include CageCoOpt (manipulation robustness), CloudGripper (open-source testbed), and RealCraft (zero-shot video editing). Awards & Grants: Funded by WASP, Horizon Europe, and the Knut and Alice Wallenberg Foundation. His group hosts the CloudGripper platform and collaborates on benchmarks like DLO@Scale. Advising & Labs: Supervises multiple PhD students and research engineers. Alumni include Robert Gieselmann (Amazon Robotics), Yiannis Karayiannidis (Chalmers University), and Anastasiia Varava (Postdoc at KTH). Active in organizing workshops like RoDGE (IROS 2025) and ICRA 2025 robotics learning events.

Eric NalisnickView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Probabilistic Modeling
• +5 more

Eric Nalisnick is an Assistant Professor in the Department of Computer Science at Johns Hopkins University. He holds affiliations with the Institute for Assured Autonomy, Mathematical Institute for Data Science, and Data Science and AI Institute. His research focuses on developing safe and robust intelligent systems through probabilistic modeling and computational statistics, with applications in healthcare, online content moderation, and sign language processing. His work emphasizes human-centered design, exploring how to incorporate prior knowledge, detect system failures, and integrate human-machine decision-making. Research Trends: Recent publications highlight advancements in uncertainty quantification (e.g., Lightning UQ Box tool), early-exiting neural networks for risk control, and generative models for symmetry transformations. He also investigates calibration in multi-distribution learning and ethical considerations in hate speech detection systems. Advising & Contributions: Eric mentors a diverse cohort of PhD students and has developed influential open-source tools like Lightning UQ Box and Learning to Defer frameworks. His teaching includes courses on Deep Learning, Human-in-the-Loop Machine Learning, and Bayesian Methods.