Dongsheng Chen is a researcher at the Technical University of Munich (TUM) within the Chair of Cartography and Visual Analytics under Prof. Dr.-Ing. Liqiu Meng. His work focuses on urban modeling, urban morphology, and explainable AI (XAI) for sustainable urban systems, applying GIS, remote sensing, and deep learning techniques. Research Interests: Urban mechanism, geo-knowledge graphs, sustainable development goals (SDGs), and integration of AI with urban studies. Key Projects: Modeling urban morphology for SDG 11 compliance, developing frameworks for carbon emission forecasting (CarbonVCA), and urban slum mapping via data fusion. Publications: Recent works include explainable graph neural networks for urban function analysis, vector landscape indices (VecLI), and causality-driven approaches to AI ethics and spatial modeling. Supervision: Advised Master's theses on slum mapping and participatory urban planning. Contact: Room 0507.01.767, dongsheng.chen@tum.de
Maja Čuletić Čondrić is a Senior Lecturer at the University of Slavonski Brod, where she contributes to various academic programs including Mechanical Engineering, Informatics, Economics, and Teacher Education. She teaches foundational and applied mathematics courses such as Calculus, Probability, Statistics, and Quantitative Methods. Her research interests lie in mathematical analysis, algebra, numerical mathematics, and geometry , with a strong emphasis on the practical application of mathematics in engineering and education. She actively publishes in applied mathematics and STEM education domains. The recent publications highlight trends in applying core mathematical concepts—such as vectors, exponential, and logarithmic functions—to mechanical engineering contexts, suggesting a focus on enhancing technical education through visual and applied methods. No scientific awards or honors are mentioned in the provided text. She mentors students in their final and diploma theses, as evidenced by her supervision records in institutional repositories. While no specific grants are listed, her involvement in academic projects and publications indicates active research engagement. She is affiliated with scholarly databases including Google Scholar, CRORIS, Web of Science, and Scopus. There is no mention of specific labs or research teams, but her collaborative publications suggest teamwork in interdisciplinary academic settings.
Salvatore Orlando is a Full Professor and Director of the Department of Environmental Sciences, Computer Science and Statistics at Ca' Foscari University of Venice. He holds academic roles including membership in the University Scientific Instrumentation Service Center (CSA) Management Committee and the Academic Senate. His research focuses on machine learning, information retrieval, and data mining, with notable contributions to learning-to-rank algorithms, decision tree ensembles, and adversarial machine learning. Key areas include efficient algorithms for large-scale systems, model interpretability, and fairness in AI. Orlando’s work bridges theoretical advancements with practical applications in database technology and cybersecurity. His recent publications emphasize optimizing ranking models, enhancing algorithm resilience, and addressing ethical considerations in AI systems. He is actively involved in organizing international conferences and serves on program committees, further contributing to the academic community.
Christopher Birkbeck is a Lecturer in Pure Mathematics at the School of Engineering, Mathematics and Physics, University of East Anglia. He is a member of the Algebra, Number Theory, Logic, and Representations (ANTLR) research group. His research focuses on formalization of mathematics, number theory, and arithmetic geometry, with notable contributions to Fermat's Last Theorem formalization and overconvergent Hilbert modular forms. He is currently leading the 'Scalable theorem proving via mathematical databases' project funded by Renaissance Philanthropy (2025–2027). His educational and professional background includes expertise in algebraic number theory and modular forms. Research interests span formal verification systems like Lean, p-adic geometry, and geometric representation theory. His work often intersects computational logic and theoretical mathematics, aiming to bridge formal proofs with advanced algebraic structures. Key research trends include advancing formalized mathematics through theorem provers, studying geometric properties of modular forms using perfectoid spaces, and exploring p-adic Hodge theory via Fargues-Fontaine curves. His projects emphasize computational methods to enhance mathematical rigor and scalability in theorem proving. He is actively accepting PhD students and has secured significant research funding for foundational mathematics projects. His collaborations span global institutions, reflecting his role in international mathematical communities.
Dr. Angela Escolme is a Senior Lecturer and Researcher in Geology and Geometallurgy at the University of Tasmania's School of Natural Sciences. Her research focuses on mineral deposits, particularly porphyry copper systems, and integrates field studies, microanalytical techniques, and hyperspectral data analysis. She leads the AMIRA P1202 project's Module 4, developing methodologies for characterizing porphyry copper deposits' transition zones. Education: PhD in Geology, University of Tasmania (2017) MSc in Earth Sciences (Hons), University of Manchester (2007) Research Interests: Dr. Escolme's work emphasizes mineralogical and geochemical characterization of ore deposits to improve geometallurgical modeling and environmental sustainability. Key areas include: Porphyry copper systems and their transition zones Hyperspectral imaging and machine learning for ore characterization Alteration overprints and mineral chemistry vectors Geometallurgical predictive modeling Teaching & Supervision: She coordinates the KEA711 Geometallurgy short course and has supervised multiple doctoral and masters students, including studies on the Valeriano Cu-Mo-Au Deposit and the Mankayan District gold system. Awards: Best student oral presentation, Society of Economic Geologists (2015) Grants & Projects: Leads or collaborates on AMIRA-funded projects P1202 and P1249, focusing on porphyry systems and complex orebody characterization. Recent funding includes $3.97 million for P1249 (2022–2026). Professional Activities: Active in industry partnerships and serves on the ARC TMVC Hub. Previously held postdoctoral roles and worked in exploration geology at a Western Australian gold mine.
Bin Ren is an Assistant Professor in the Department of Computer Science at the College of William & Mary, where he has been a faculty member since Fall 2016. He holds a Ph.D. in Computer Science and Engineering from The Ohio State University (2014) and was a postdoctoral research associate at Pacific Northwest National Laboratory from 2014 to 2016. Research Interests: His work centers on high-performance computing, compiler techniques, and machine learning systems, with a focus on enabling real-time and energy-efficient deep neural network execution on mobile and edge devices. He explores compiler optimizations, DNN pruning, neural architecture search, and GPU memory management to improve system performance and efficiency. Publication Trends: His recent publications (2023–2025) reveal a strong focus on compiler-aware deep learning systems, mobile and edge AI, and performance optimization across heterogeneous platforms. Key themes include DNN acceleration, memory efficiency, real-time inference, and hardware-software co-design. His work frequently appears in top-tier venues such as ASPLOS, SC, CVPR, and PLDI. Scientific Awards: NSF CAREER Award, 2021 Best Paper Award, SC 2020 Best Student Paper Nomination, SC 2020 Jeffress Trust Award, 2020 ISLPED Design Contest First Place, 2020 Student Cluster Reproducibility Challenge Paper, SC 2019 Best Paper Award, CGO 2013 SIGPLAN Research Highlights, 2013 Advising and Grants: Bin Ren has advised numerous Ph.D. and master’s students, many of whom have co-authored influential papers. His research has been supported by competitive grants, including the NSF CAREER Award. He actively mentors students in areas of parallel computing, compiler design, and machine learning systems. He has also received funding from the Jeffress Trust Awards and other sources to support interdisciplinary research. Professional Service: He has served in leadership roles such as Program Co-Chair for PPoPP'25 and HIPS'21, Track Co-Chair for ICPP'24 and HiPC'24, and Artifact Evaluation Co-Chair for PPoPP'24 and ALENEX'25. He is a frequent reviewer for top journals and conferences including TPDS, TACO, NeurIPS, and SC. Teaching: He teaches courses such as CS304 (Computer Organization) and CS642 (Compiler Techniques for High Performance Computing), contributing to both undergraduate and graduate education in systems and programming. Lab and Team: His research group focuses on system-software co-design for efficient AI deployment. Collaborators include researchers from institutions like Pacific Northwest National Laboratory and The Ohio State University. His team works on real-world applications in healthcare, autonomous systems, and scientific computing.
Anxiang Zeng is a researcher affiliated with the University of Kansas School of Business , Department of Management and Leadership. His work focuses on machine learning applications in e-commerce , particularly in search engines, recommender systems, and preference optimization.
Carlo Curino is a researcher at Microsoft Research , focusing on database systems, cloud computing, and machine learning integration. He has collaborated extensively with institutions including MIT, Microsoft, and the University of Wisconsin-Madison. His research spans Geo-distributed data analytics Automated configuration tuning Tensor-based database systems Data lake optimization Spark performance engineering Recent publications highlight his work on AI-driven systems like MotherNet and Rockhopper , alongside contributions to query processing over compressed data and log-structured tables. Collaborators include prominent figures such as Raghu Ramakrishnan and Jesús Camacho-Rodríguez . Key projects involve LST-Bench (cloud storage benchmarking), AutoComp (data compaction), and PyFroid (commodity workstation analytics). His work bridges database optimization with modern machine learning demands in enterprise environments.
Barbara Fantechi is a Full Professor of Geometry at SISSA (Scuola Internazionale Superiore di Studi Avanzati) in Italy since 2002. She previously held academic positions at the University of Udine (1999–2002) and the University of Trento (1990–1999). Her educational background includes a PhD in Mathematics (1990) and an undergraduate degree (1988) from the Università di Pisa and Scuola Normale Superiore, where her thesis focused on secant varieties and their projective applications. Fantechi specializes in Algebraic Geometry, particularly moduli spaces, algebraic stacks, and obstruction theories. Her work intersects with enumerative geometry, symplectic structures, and cohomological methods. Notable contributions include foundational research on virtual fundamental classes and orbifold cohomology. Her publications, including the influential 1997 article on The intrinsic normal cone and 2005 monograph Fundamental algebraic geometry , reflect trends in algebraic stacks, Hilbert schemes, and Gromov-Witten theory. Premio Romagnosi for young researchers (1999) Friedrich Hirzebruch Visiting Professor (2012) Chancellor Professor at MSRI/UC Berkeley (2018) Premio Tartufari per la matematica (2018) Fantechi has advised numerous PhD students in algebraic geometry, with a record of her students maintained through mathematical genealogy databases. Her research has been supported by visiting positions at institutions like the Max-Planck-Institut and the Mittag-Leffler Institute.
Adam Martin is a Research Fellow at Macquarie Medical School's Dementia Research Centre, leading the Peptides and Proteins Group. He previously held academic roles at the University of New South Wales, including Lecturer (2018) and Research Fellow (2016–2018), following postdoctoral appointments at UNSW (2013–2016) and the University of Nottingham (2011–2013). PhD in Supramolecular Chemistry from the University of Western Australia (2011) Martin's research focuses on structure-property relationships in self-assembling peptides, enabling tunable hydrogel properties for biomedical applications. His work bridges materials science and neuroscience, particularly in designing biocompatible scaffolds for culturing primary neurons to study Alzheimer's Disease and other neurodegenerative conditions. Recent publications highlight trends in hydrogel innovation, including antibacterial properties, theranostic nanosystems, and synthetic vaccine platforms. His studies span biomedical engineering, ecology, and nanotechnology, reflecting interdisciplinary collaborations. Scientific Awards Young Investigator Highlights Award (2018) RACI NSW Nyholm Youth Lecturer (2019) Martin's projects include 3D neuronal culture scaffolds, ECM-ligating hydrogels, and PROTAC-based therapies for ALS/FTD. He collaborates internationally, with contributions to root trait databases and plant ecology research. His current affiliation with Macquarie University involves leading the Peptides and Proteins Group, emphasizing translational tissue engineering and neurodegeneration modeling.
Daniele Micciancio is a Professor in the Computer Science & Engineering Department at the University of California, San Diego, where he has been faculty since 1999. He is a member of both the Cryptography and Security group and the Theory of Computation group within the Jacobs School of Engineering. His academic journey began with a PhD in computer science from the Massachusetts Institute of Technology in 1998. PhD in Computer Science, Massachusetts Institute of Technology (1998) Micciancio's research focuses on the intersection of theoretical computer science and cryptography, with particular emphasis on lattice-based cryptographic systems. His work spans lattice algorithms, complexity of lattice problems, symbolic analysis of cryptographic protocols, and various cryptographic primitives including zero-knowledge proofs. His research has significantly advanced the field of post-quantum cryptography, particularly in developing cryptographic systems based on the hardness of lattice problems that could withstand attacks from quantum computers. His recent publications demonstrate a continued focus on homomorphic encryption, lattice-based cryptography, and secure computation protocols. The trend shows increasing practical applications of his theoretical work, with publications addressing real-world implementation challenges in privacy-preserving computation, medical data analysis, and genomic research. Matchey Award (FOCS 1998) Sprowls Award (MIT EECS, 1999) CAREER Award (NSF, 2001) Hellman Fellowship (2001) Sloan Fellowship (2003) 20-years Test of Time Awards (FOCS 2022, FOCS 2024) Fellow of the IACR (1999) Professor Micciancio has advised numerous graduate students who have gone on to successful careers in academia and industry, including prominent researchers in lattice-based cryptography. His professional activities include serving on editorial boards for prestigious journals including SIAM Journal on Computing, Journal of Cryptology, and Information and Computation. He has also been heavily involved in conference organization, serving as program chair for TCC 2010, CRYPTO 2019, and CRYPTO 2020, and as general chair for TCC 2014. As a member of both the Cryptography and Security group and the Theory of Computation group at UCSD, Micciancio contributes to a vibrant research environment focused on foundational aspects of computer security and theoretical computer science. His work continues to influence both theoretical developments and practical implementations of cryptographic systems, particularly as the field prepares for the post-quantum era.
Tadej Škvorc is an Assistant Professor at the Department of Intelligent Systems, Faculty of Computer and Information Science, University of Ljubljana. He is a member of the Laboratory for Machine Learning and Language Technologies and actively engages in research projects related to media applications. Current affiliation: Department of Intelligent Systems, Faculty of Computer and Information Science, University of Ljubljana His research focuses on machine learning and language technologies, particularly vector embeddings for media applications. He contributes to projects such as ARRS (L2-50070) from 2023 to 2026. Research areas: Machine learning, NLP, media technologies As of the latest information, no scientific awards have been specified in his public profile. He is involved in teaching and laboratory activities, including classes on Intelligent Systems Programming and Databases. His office is located in Room R2.26 at the Laboratory for Machine Learning and Language Technologies.
Harald Baayen is a Professor at the University of Tübingen within its Faculty of Humanities, Department of Linguistics . His work spans computational linguistics, psycholinguistics, and phonetics, with a focus on discriminative learning models for lexical processing. After studying under Geert Booij in Amsterdam (PhD 1989), he held positions at the Max Planck Institute for Psycholinguistics (1990-1998), Radboud University (Associate Professor via Dutch Science Foundation award), and the University of Alberta (Full Professor 2007-2011). His 2011 Alexander von Humboldt Award and 2025 Honorary Doctorate from Tartu reflect his scholarly impact. Education: PhD in General Linguistics (Amsterdam, 1989) Awards: Alexander von Humboldt (2011), Dutch Science Foundation Career Advancement (1998) Teaching: Courses in Linguistics for Cognitive Science and Statistical Methods since 2011 at Tübingen Research Synthesis Baayen's research bridges computational modeling and empirical phonetics . Notable projects include: Linear Discriminative Learning (2019): A unified framework for morphological processing without morphemes Articulatory Phonetics : Using EMA and ultrasound to study dialect variation and articulation practice effects Statistical Methods : Pioneering generalized additive models and quantile regression in psycholinguistic analysis His 2025 publications explore Mandarin-English semantic differences , Ukrainian grammatical number , and deep learning alternatives to discriminative frameworks. His work consistently challenges traditional morphological theories by integrating discriminative learning with real-world phonetic data .
Rong Chen is an Associate Professor in the Department of Diagnostic Radiology and Nuclear Medicine at the University of Maryland School of Medicine. He serves as Associate Vice Chair of AI and leads the Biomedical Data Mining Laboratory, focusing on integrating machine learning, computational neuroscience, and neuroimaging to decode brain-behavior relationships. His work spans clinical and translational research for disorders like Alzheimer’s, Parkinson’s, autism, and HIV, and he develops open-source software (GAMMA suite, Advanced Connectivity Analysis) for neuroimaging data analysis. Education: BS in Biomedical Engineering, Southeast University, China (1996) MS in Electrical Engineering, The Graduate School of Chinese Academy of Sciences (1999) PhD in Electrical and Computer Engineering, Washington State University (2003) Postdoctoral Researcher in Radiology, University of Pennsylvania (2005) MTR in Translational Research, University of Pennsylvania (2012) Research Interests: Computational modeling of neural activity and behavior Development of machine learning frameworks for neuroimaging Brain-inspired AI and therapeutic concepts Longitudinal analysis of brain disorders Distributed data mining for heterogeneous databases Software tools for biomarker detection and functional connectivity Scientific Contributions: 20+ years of advanced modeling and algorithm development Two open-source neuroimaging software packages (GAMMA suite, ACA) NIH and BRAIN initiative-funded research Editorial roles in journals like Frontiers in Computational Neuroscience Honors: Senior Member of IEEE Labs & Collaborations: Dr. Chen collaborates with institutions like NIH and Oracle, and his lab has developed tools used in studies on sickle cell disease, autism, and traumatic brain injury.
İLKAY SİBEL KERVANCI serves as an Assistant Professor in the Department of Computer Engineering at Gaziantep University's Faculty of Engineering. Her academic career spans teaching and research in artificial intelligence, machine learning, and data mining with practical applications across finance and bioinformatics sectors. PhD in Computer Engineering, Çukurova University (2023) MSc in Informatics, Kahramanmaraş Sütçü İmam University (2017) BSc in Computer Engineering, Kocaeli University (2004) Her research centers on machine learning applications for cryptocurrency price forecasting using LSTM and GRU networks, neutrosophic logic implementations in regression problems, and drug-target interaction prediction. Recent work demonstrates expertise in hyperparameter optimization, time series analysis, and handling imbalanced datasets through hybrid feature reduction techniques. She actively contributes to advancing neural network architectures for financial and biomedical challenges. Publication trends reveal consistent focus on Bitcoin price prediction (6 publications since 2017), expanding into drug-target interaction modeling and neutrosophic applications. Her work bridges theoretical machine learning with industrial applications in cement manufacturing, stock markets, and pharmaceutical research through recurrent neural networks and optimization frameworks. Dr. Kervanci teaches graduate courses including Introduction to Artificial Intelligence Methods and Introduction to Data Mining Methods, alongside undergraduate courses such as Artificial Intelligence in Engineering and Discrete Mathematics, demonstrating commitment to computational education across academic levels.
