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Ioannis S. TriantafyllouView profile

Associate Professor

• Statistics
• Applied Probability
• Nonparametric Statistical Process Control
• +3 more

Ioannis S. Triantafyllou is an Associate Professor at the Department of Statistics and Actuarial Science of the University of Piraeus, where he has been serving since 2022. Previously, he was an Associate Professor at the Department of Informatics with applications in Biomedicine at the University of Thessaly (2017-2021) and a Lecturer at the same department (2016-2017). He also serves as Collaborating Educational Staff at the School of Natural Sciences & Technology, Hellenic Open University since 2013. Education: Postdoctoral Research (2011) in Probability and Statistics at the University of Piraeus Doctoral Degree (2009) from the Department of Statistics & Actuarial Science, University of Piraeus Master's Degree in Applied Statistics (2005) from the University of Piraeus Bachelor's Degree (2002) from the Department of Mathematics, National & Kapodistrian University of Athens Dr. Triantafyllou's research focuses on Statistics and Applied Probability, with particular emphasis on Nonparametric Statistical Process Control, Nonparametric Statistical Inference, Statistical Reliability Theory, Order Statistics Theory, and Aging properties study. His work bridges theoretical statistics with practical applications in reliability engineering and quality control. He has developed innovative nonparametric methods for monitoring industrial processes without assuming specific distributional forms, which has significant implications for quality assurance in various industries. His recent publications demonstrate a strong focus on reliability structures, particularly consecutive-type systems, weighted components, and distribution-free control charts. The research shows a clear trend toward developing more sophisticated models that incorporate multiple failure modes, redundancy mechanisms, and nonparametric approaches that don't require distributional assumptions. His work increasingly intersects with applications in supply chain management, medical diagnostics, and engineering systems. Professional Recognition: Associate Editor at International Journal of Mathematical Engineering and Management Sciences Reviewer at Mathematical Reviews/MathSciNet Guest Editor for special issues in Mathematics, International Journal of Quality and Reliability Management, and Reliability: Theory and Applications Member of Scientific Committees for multiple Panhellenic Statistical Conferences Dr. Triantafyllou has supervised more than 60 undergraduate theses and over 35 master's theses throughout his career. His teaching spans 8 undergraduate and 23 graduate courses across multiple institutions, totaling more than 13 academic semesters of instruction. He has authored 18 teaching textbooks and served as a reviewer for 37 different international scientific journals, evaluating more than 70 papers. His research laboratory focuses on reliability engineering and statistical process control, with active collaborations across multiple institutions. Current projects involve developing advanced nonparametric control charts for industrial applications and studying complex reliability structures with multiple failure criteria.

Eric V. SludView profile

Professor

• Mathematical Statistics
• Probability
• Census Statistics
• +11 more

Eric V. Slud is a Professor in the Statistics Program within the Department of Mathematics at the University of Maryland, College Park. He has taught numerous graduate-level statistics courses including Mathematical Statistics, Categorical Data Analysis, and High-Dimensional Statistics since at least 2017. Dr. Slud's research spans several key areas of mathematical statistics and probability. His work focuses on: Census statistics, particularly demographic modeling of nonresponse to national surveys Small Area Estimation (SAE) with applications to the SAIPE program Survival data analysis including semiparametric inference and clinical trial design Meta-analysis in biostatistics Pharmaceutical statistical methods Large-scale data problems with emphasis on cross-classified data His publications demonstrate continued contributions across these domains, with particular emphasis on small area estimation methodologies, survival analysis techniques, and applications in survey statistics. Dr. Slud has developed innovative approaches to weighting adjustment, nonresponse correction, and estimation with complex survey data. Dr. Slud has advised several PhD students including Yang Cheng (2004), Sophie Tsou (2005), and others working on topics ranging from Factor Analysis to Survival Data. He has collaborated extensively with researchers at the Census Bureau and FDA. Dr. Slud maintains active research programs through multiple Research Interaction Teams (RITs) at the University of Maryland, focusing on advanced statistical methodologies and their applications to real-world problems in census methodology, healthcare, and other domains requiring sophisticated statistical analysis.

• Machine Learning
• Big Data Analytics
• Computational Intelligence
• +4 more

Vahid Behbood is a Lecturer at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). He belongs to the Decision Systems and e-Service Intelligence Research Laboratory in the Centre for Quantum Computation and Intelligent Systems. Ph.D. in Software Engineering from UTS (2014) His research spans machine learning , big data analytics , computational intelligence , and transfer learning , with a focus on addressing data shortage challenges through fuzzy systems and domain adaptation. His work includes: Advancing fuzzy regression domain adaptation for cross-domain prediction Developing granular computing techniques for financial failure prediction Exploring SQL query error patterns in computer science education Designing IoT architectural frameworks via systematic reviews Publications demonstrate expertise in neural networks , fuzzy logic , Bayesian methods , and financial data analytics . He applies these techniques to banking ecosystems, real estate valuation, and educational data mining.

Feng LiuView profile

Assistant Professor

• Machine Learning
• Hypothesis Testing
• Trustworthy Machine Learning
• +3 more

Feng Liu is an Assistant Professor at the Decision Systems and e-Service Intelligence (DeSI) Lab within the Australian Artificial Intelligence Institute (AAII) at the University of Technology Sydney (UTS). He also serves as a Visiting Scientist at RIKEN-AIP, Japan. His academic journey includes a PhD in Computer Science from UTS (2020), an MSc in Probability and Statistics from Lanzhou University (2015), and a BSc in Mathematics from the same institution (2013). His educational background includes: Ph.D. (2020), Computer Science, University of Technology Sydney, Australia M.Sc. (2015), Probability and Statistics, Lanzhou University, China B.Sc. (2013), Mathematics, Lanzhou University, China Feng Liu's research centers on developing trustworthy intelligent systems through hypothesis testing and reliable knowledge transfer across domains. His work spans two-sample testing for distribution comparison, transfer learning for knowledge adaptation across domains, and defending against adversarial attacks to improve model robustness. His approach combines theoretical foundations with practical applications, particularly in domain adaptation with interval-valued data and secure multi-source learning. His recent publications demonstrate a strong focus on trustworthy machine learning, with significant contributions to interval-valued data processing, novel class discovery under unreliable sampling conditions, and privacy-preserving domain adaptation. His work bridges theoretical machine learning with practical applications in computer vision, bioinformatics, and recommender systems, showing a consistent pattern of addressing fundamental challenges in trustworthy AI. Among his notable recognitions are: Outstanding Reviewer Award of ICLR (2021) AAII Best Student Paper Award (2020) Best Student Paper Award from IEEE International Conference on Fuzzy Systems (2019) UTS-FEIT HDR Research Excellence Award (2019) Publons Peer Review Awards - Top 1% reviewers in Computer Science (2019, 2018) Dr. Liu has actively contributed to the academic community through supervision and service. He has helped supervise four students who collectively produced eight academic papers, three of which were published in CORE Tier A* venues. His service includes program committee roles for major conferences including NeurIPS, ICML, ICLR, and AAAI, as well as reviewing for prestigious journals like IEEE-TPAMI and IEEE-TNNLS. His research has been supported by various grants, including the Australian Laureate postdoctoral fellowship. As part of the AAII at UTS, Dr. Liu contributes to a vibrant research environment focused on advancing artificial intelligence through interdisciplinary collaboration. His work in the Decision Systems and e-Service Intelligence Lab addresses real-world challenges in trustworthy machine learning, with applications spanning healthcare, robotics, and secure information systems.

Rob DickersonView profile

Assistant Professor

• Programming Languages
• Formal Methods
• Relational Verification
• +3 more

Rob Dickerson is an Assistant Professor of Computer Science at Augustana, specializing in programming languages and formal methods with a focus on practical software engineering applications. His research bridges theoretical principles and real-world implementation challenges. His primary research interests include relational verification , e-graph applications , and modular program logic . He develops techniques for aligning programs, inferring library specifications through data-driven methods, and verifying relational properties across multiple executions. Current projects include KestRel for program alignment and RHLE for relational ∀∃ properties. Key contributions span publications at premier venues including OOPSLA, APLAS, and PLDI workshops. His work on Elrond received a Distinguished Artifact Award at OOPSLA 2021. Notable publications examine relational verification (2025), e-graph-based alignment (2025), and modular verification of relational properties (2022). Distinguished Artifact Award (Elrond, OOPSLA 2021) Dickerson maintains active industry connections through prior work at Square and participates in academic service through venues like the PurPL seminar at Purdue. Outside academia, he engages in épée fencing and amateur piano/violin performance.

Leonard HarrisView profile

Assistant Professor

• Cancer systems biology
• Computational modeling
• Multiscale modeling
• +7 more

Leonard Harris is an Assistant Professor in the Department of Biomedical Engineering within the College of Engineering at the University of Arkansas. His research focuses on cancer systems biology, with expertise in computational modeling and simulation of complex intracellular signaling pathways and cell-cell interactions in tumors. Working closely with experimental collaborators, his lab develops comprehensive, mechanistic models of molecular pathways underlying non-genetic mechanisms of drug resistance in cancer cells. Education: Postdoctoral Research Fellow, Biochemistry, Vanderbilt University School of Medicine Postdoctoral Associate, Computational & Systems Biology, University of Pittsburgh School of Medicine Ph.D., Chemical and Biomolecular Engineering, Cornell University B.S., Chemical Engineering, University of Colorado, Boulder Dr. Harris's research centers on cancer systems biology with a focus on phenotypic plasticity and non-genetic heterogeneity in anticancer drug response. His lab develops multiscale models of intracellular signaling pathways and cell-cell interactions to understand tumor heterogeneity. A key aspect of his work examines intrinsic stochasticity in cell fate decision making and its role in therapeutic resistance. His computational approaches aim to create in silico platforms for virtual anticancer drug screens to identify novel molecular targets and improve cancer treatment outcomes. Analysis of Dr. Harris's recent publications reveals a consistent focus on computational approaches to understand tumor heterogeneity and drug resistance mechanisms. His work integrates mathematical modeling with experimental data to dissect genetic, epigenetic, and stochastic sources of variability in cancer cell populations. His research spans multiple cancer types, with particular emphasis on melanoma and lung cancer, examining how non-genetic mechanisms contribute to therapeutic resistance. The publications demonstrate progression from foundational computational methods to increasingly sophisticated models of tumor dynamics and drug response. Scientific Awards: NIH/NCI Transition Career Development Award to Promote Diversity (K22) National Library of Medicine Biomedical Informatics Postdoctoral Fellowship Semiconductor Research Corporation Graduate Fellowship Dr. Harris serves as principal investigator for multiple research grants focused on cancer systems biology and computational oncology. His lab receives funding from the National Institutes of Health, including the NCI K22 award supporting his transition to independence. His research integrates experimental and computational approaches through collaborations with wet-lab researchers at the University of Arkansas and other institutions. Current projects examine tumor-induced bone disease, phenotypic plasticity in small cell lung cancer, and mechanisms of drug tolerance in melanoma. Dr. Harris leads a computational cancer biology lab that develops and applies advanced modeling techniques to understand tumor heterogeneity and drug resistance. His team works at the intersection of computational biology, systems pharmacology, and cancer biology, creating models that bridge molecular, cellular, and population scales. The lab collaborates extensively with experimental groups to validate model predictions and generate new hypotheses about cancer progression and therapeutic response.

• Computational Neuroscience
• Neurophysics
• Dynamical Systems
• +4 more

Prof. Dr. Klaus R. Pawelzik is a Professor at the University of Bremen's Institute for Theoretical Physics, where he leads the Theoretical Bio- and Neurophysics research group. His research bridges theoretical physics and neuroscience, with laboratories located in the Cognium building on the university campus. His primary research interests include: Computational models of neural dynamics and information processing Neurophysics and mechanisms of cortical computation Brain-computer interfaces and neuroprosthetic systems Dynamical systems approaches to causality and network analysis Biologically plausible learning algorithms for spiking neural networks Attention mechanisms and sensory processing in primate brains Recent publications (2015-2020) demonstrate strong focus on attention mechanisms in visual processing, causal inference methods for dynamical systems, hardware implementations for neuroprosthetics, and biologically inspired learning algorithms. The work consistently integrates mathematical rigor with experimental neuroscience, featuring collaborations with neurophysiology labs and engineering groups. Prof. Pawelzik leads an active research team developing both theoretical frameworks and experimental platforms for neuroscience research. The lab specializes in open-source neurotechnology solutions, including wireless implantable devices for electrocorticography and FPGA-based processing systems for real-time neural signal analysis.

Marie-Christine ROUSSETView profile

Professor

• Knowledge Representation
• Information Integration
• Ontology-based data access
• +7 more

Marie-Christine ROUSSET is a Professor of Computer Science at the University of Grenoble Alpes (UGA) in France, where she is a member of the LIG (Laboratoire d'Informatique de Grenoble) in the SLIDE group. Previously affiliated with Paris-Saclay (LRI), she has established herself as a leading researcher in Knowledge Representation and Information Integration. She holds the distinguished position of Senior member of the Institut Universitaire de France (IUF) (2011-2016, renewed for 2016-2021) and serves as co-responsible for the chair Explainable and Responsible AI within MIAI Grenoble Alpes. Her research focuses on ontology-based data access, logic-based mediation between distributed data sources, query rewriting using views, data linkage, and distributed reasoning for the Semantic Web. She skillfully combines artificial intelligence and database techniques to address complex information integration challenges, with applications spanning biomedical informatics, educational technology, and trustworthy AI. Her work demonstrates consistent innovation from foundational research to practical implementations, as evidenced by her co-authorship of the book 'Web Data Management' published by Cambridge University Press. Professor ROUSSET's recent publications (2019-2022) reveal a growing emphasis on data privacy, RDF graph anonymization, and interactive ontology engineering, while maintaining her strong contributions to semantic web technologies and knowledge representation. Her research shows increasing attention to trustworthy AI concerns, aligning with her leadership roles in relevant projects. Scientific Recognition Senior member of Institut Universitaire de France (IUF) (2011-2016, renewed for 2016-2021) Junior member of Institut Universitaire de France (IUF) from 1997 to 2002 Chevalier de l'Ordre National du Merite (July 11, 2011) EurAI Fellow (nominated ECCAI Fellow in 2005) Best Paper Award at AAAI'96 for 'Verification of Knowledge Bases based on Containment Checking' Professor ROUSSET maintains an active role in the scientific community through editorial work and organizational leadership. She serves on the Editorial Board of Communications of the ACM (CACM) and has held significant roles including PC chair of EGC 2019, Workshops co-Chair of WWW 2018, and Area Chair of IJCAI 2017. Her consistent service on program committees of major international conferences demonstrates her standing in the field. Her laboratory, the SLIDE group within LIG, focuses on semantic web technologies, knowledge representation, and data integration. The group maintains strong connections with the international research community and participates in collaborative projects addressing cutting-edge challenges in artificial intelligence and data management, with particular emphasis on trustworthy and explainable AI systems.

Archie Singh KhumanView profile

Senior Lecturer

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

Dr. Archie Singh Khuman serves as Associate Head of Education and Senior Lecturer at De Montfort University's School of Computer Science and Informatics. An award-winning academic and proud DMU alumnus (BSc 2009, MSc 2011, PhD 2017), he specializes in Artificial Intelligence, Computational Intelligence, and Uncertainty Modelling with extensive leadership in curriculum development and Universal Design for Learning. His educational background demonstrates deep institutional commitment: PhD in Computer Science (2017, De Montfort University) MSc in Intelligent Systems (2011, De Montfort University) BSc (Hons) in Computer Science (2009, De Montfort University) Dr. Khuman's research pioneers hybrid uncertainty frameworks, notably the R-fuzzy grey analysis framework (RfGAf), bridging Grey Theory, Fuzzy Logic, and Rough Sets. His work focuses on quantifying perception-based uncertainty for applications in healthcare diagnostics, robotic swarm intelligence, and risk assessment systems, emphasizing real-world implementation through industry partnerships. His publication trajectory reveals increasing interdisciplinary convergence, particularly in healthcare AI applications since 2021. Recent outputs integrate Grey-Fuzzy methodologies for medical diagnostics (diabetes, heart disease), sentiment analysis of pandemic communications, and adaptive systems in automotive/industrial contexts, demonstrating strong translation from theoretical foundations to societal impact. Major recognitions include: Five consecutive Students' Choice Teaching Awards (2018-2024) De Montfort Teacher Fellowship (2022) Senior Fellow of the Higher Education Academy (2022) Leverhulme Trust-funded international Grey Systems network (£124,997) Best Paper Award at Grey Systems and Uncertainty Analysis (2016) As academic lead for the Data Analytics Project, he mentored students in developing machine learning solutions that generated £2.5m savings for retail partners. His grant portfolio includes Royal Society funding for IoT-based scheduling (£12,000) and leadership of the Race Equality Network. Current initiatives focus on transnational education partnerships and SI-PASS peer learning programs. Dr. Khuman co-leads DMU's Institute of Artificial Intelligence research group and serves as School Governor for Inglehurst Junior School, maintaining active collaborations with institutions in China, Canada, Romania, and Spain through the International Network on Grey Systems.

Marenglen BibaView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +6 more

Marenglen Biba is a Full Professor and Dean of the Faculty of Engineering and Architecture at the University of New York Tirana, where he also chairs the Department of Computer Science. His academic journey includes a Ph.D. in Computer Science from the University of Bari, Italy (2009), and a Laurea degree cum laude from the same institution (2004). His research spans: Core AI/ML : Machine learning algorithms, statistical relational learning, pattern recognition Language technologies : NLP for Albanian, stemming, morphological analysis Applied domains : Computational biology, social network mining, emotion detection Recent publications (2018–2021) focus on NLP for low-resource languages, deep learning surveys, and emotion-aware data mining. Award highlights include: ICT Academic of the Year 2012 (Albania) Best Paper Award at ILP 2008 Multiple University of Bari research grants He has led EU-funded projects like ADRIATinn (€4.6M budget) and maintains collaborations with the University of Washington, University of Bari, and Asian universities. Administrative leadership includes decade-long oversight of UNYT's Computer Science department and current deanship.

• Interpretable Machine Learning
• Explainable AI
• High-Stakes Decision Systems
• +5 more

Cynthia Diane Rudin is a Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & Bioinformatics at Duke University, where she directs the Interpretable Machine Learning Lab. Previously, she held faculty positions at MIT Sloan School of Management and research roles at Columbia University and NYU. PhD in Applied and Computational Mathematics, Princeton University BS in Mathematical Physics and Music Theory, University of Chicago Her research pioneers interpretable machine learning for high-stakes domains where transparency is non-negotiable. She challenges the accuracy-interpretability tradeoff myth, proving that transparent models can match black-box performance in healthcare, criminal justice, and power grid reliability applications. Her work has produced deployable systems like the 2HELPS2B ICU seizure predictor and NYPD's Patternizr crime detection tool. Rudin's publication record shows consistent focus on interpretable algorithms since 2007, with recent work exploring the Rashomon set of equally accurate models and causal inference frameworks. Her highly cited 2018 Nature Machine Intelligence paper fundamentally shifted industry practices toward transparent AI. Squirrel AI Award for AI Benefiting Humanity (2022) Guggenheim Fellowship (2022) Triple INFORMS Innovative Applications Award winner (2013, 2016, 2019) Fellow of AAAI, ASA, and IMS She actively mentors students through Duke's Data+ program and has advised teams winning international competitions. Her lab maintains strong industry partnerships focused on ethical AI deployment, with current grants from NSF, NIH, and DARPA supporting healthcare and criminal justice applications. Rudin serves on National Academies committees shaping federal AI policy and has testified before Congress on algorithmic transparency. The Interpretable Machine Learning Lab maintains an open-science ethos with all code publicly available. Current projects focus on sparse decision trees for medical diagnostics, causal inference frameworks for high-stakes decisions, and extending the Rashomon effect theory to new application domains.