Paul Howard is a Professor of Mathematics at Oklahoma Christian University within the College of Engineering & Computer Science. He focuses on mathematics education and point set topology , emphasizing student engagement and mentorship. His affiliations include: Mathematics Association of America National Council of Teachers of Mathematics Outside academia, he is actively involved in the Edmond Church of Christ, teaching Bible classes and participating in marriage mentoring, alongside personal interests in baseball, chess, and outdoor activities.
Prof. Yonatan Loewenstein is a Professor in the Department of Neurobiology at the Hebrew University of Jerusalem. His research focuses on computational neuroscience and cognition, particularly the neural mechanisms underlying decision-making, reinforcement learning, and sensory processing. He leads an interdisciplinary laboratory exploring how learning principles govern behaviors in both biological and artificial systems. Key contributions include studies on somatosensory cortex organization, neuronal homeostasis, and human-machine synergy in decision-making. He co-authored the book Computational Models in Cognition , blending theoretical frameworks with empirical findings. Education details are not explicitly provided in the text, but his affiliations suggest advanced training in neurobiology and computational sciences. Research interests span decision-making biases, reinforcement learning dynamics, and the interplay between brain structure and function. His recent work addresses topics like idiosyncratic choice stability, value modulation in impulsivity, and abstract reasoning in neural networks. Publications highlight collaborations in fields ranging from cognitive dissonance to schizophrenia diagnostics. While no specific awards are listed, his involvement in high-impact journals and interdisciplinary projects underscores his academic contributions. The lab’s work is housed in the Goodman Faculty building, with active group members and experimental facilities.
Daisaku Yokoyama is an Assistant Professor at the Institute of Industrial Science, University of Tokyo, where he works in Department 3 of the Kitsuregawa-Toyoda Laboratory. His research focuses on parallel and distributed processing, combinatorial search, game tree search, and other search processes. He is also involved in the development of "Gekisashi," a computer shogi (Japanese chess) player. His academic background includes: March 1998: Graduated from the Department of Electronic and Information Engineering, Faculty of Engineering, The University of Tokyo March 2000: Completed Master's course in Information Engineering at the University of Tokyo 2002.3: Graduated from the Doctoral Program in Information Engineering, Graduate School of Engineering, The University of Tokyo September 2006: Obtained a PhD in Science from the Graduate School of Frontier Sciences, University of Tokyo Daisaku Yokoyama's research interests primarily center around parallel and distributed computing systems, with a particular focus on combinatorial search algorithms and game tree search techniques. His work bridges theoretical computer science with practical applications, especially in the domain of computer shogi where he has developed "Gekisashi." Beyond game AI, his research has expanded into big data analytics, particularly in transportation systems where he analyzes passenger flows in metro networks and driver behavior using vehicle recorder data. His work demonstrates a consistent thread of applying parallel processing techniques to solve computationally intensive problems across various domains. Yokoyama's publication record shows a clear evolution from foundational work in parallel combinatorial optimization (PopKern library) to more applied research in computer shogi and eventually to big data applications in transportation systems. His early work established frameworks for parallel search algorithms, while more recent publications demonstrate applications of these techniques to real-world problems involving massive datasets from metro systems and vehicle recorders. His research consistently emphasizes the importance of domain-specific knowledge in optimizing parallel algorithms. His notable scientific achievements include: DBSJ Best Paper Award 2014 for "Application and Evaluation of a Bayesian-Based Monte Carlo Tree Search Algorithm to Shogi" Game Programming Workshop Excellent Paper Award (awarded twice) Throughout his career, Yokoyama has been actively involved in academic service, serving on editorial boards, program committees, and as an organizer for numerous conferences and workshops related to programming, parallel computing, and game AI. His work on the Gekisashi shogi engine represents a long-term research project that has evolved from basic search algorithms to sophisticated AI systems, demonstrating both theoretical rigor and practical implementation skills. He is part of the Kitsuregawa-Toyoda Laboratory at the Institute of Industrial Science, University of Tokyo, which focuses on advanced computing systems, database technologies, and large-scale data processing. The laboratory provides a collaborative environment for research spanning theoretical computer science to real-world applications in transportation analytics and game AI.
Matteo Pellegrini is a researcher at the Catholic University of the Sacred Heart in Italy and collaborates with the University of Surrey through a fellowship focused on the computational analysis of language evolution. This project, hosted by Professor Erich Round from the School of Literature and Languages, investigates how Latin diversified into Romance languages like French, Italian, Spanish, and Romanian within 2000 years. Dr. Pellegrini’s work integrates open language data with advanced computational simulations to study paradigm evolution—such as why some languages have four verb forms while others have over a hundred—and historical transformations shaping Romance languages. His research bridges computational methods with linguistic theory to uncover evolutionary constraints and historical 'chess moves' in language development.
Dr. Martin Možina is an Assistant Professor at the University of Ljubljana , affiliated with the Artificial Intelligence Laboratory since 2004. His work bridges classical machine learning with argumentation theory and focuses on interpretable AI methods. Research includes argument-based machine learning for knowledge-driven AI Developed nomogram visualization techniques for linear models Created automated chess tutors for move explanation Research projects span from 2004 to 2025, including: Current DRIFT project (2022-2025) on deep learning for power grid optimization Deep reinforcement learning applications in energy systems Argumentation for medical prognosis (lung cancer) and knowledge acquisition Publications show interdisciplinary work between: AI explainability (4/6 articles) Education technology (2/6) Healthcare AI applications (2/6) Graphical model interpretation (1/6) Multi-agent research (1/6) Teaching includes Decision Systems courses, with a focus on applied AI methods.
Qun Liu is a Structural Biologist at Brookhaven National Laboratory's Biology Department, where he joined as a Principal Investigator in 2015 with a joint appointment from NSLS-II. He also serves as an Adjunct Professor and faculty member in the Biochemistry and Structural Biology and Molecular and Cell Biology Programs at Stony Brook University. Ph.D. in biophysics from Cornell University Postdoctoral research at Cornell Synchrotron Light Source (CHESS) Former scientist at New York Structural Biology Center Worked with New York Consortium on Membrane Protein Structure Dr. Liu's research spans structural biology of membrane proteins, investigating metal transporters, lipid metabolizing enzymes, and protein-quality control mechanisms. His host-pathogen interaction studies focus on molecular mechanisms of pathogenicity, particularly relevant to emerging pathogens like SARS-CoV-2. In cellular structural biology, he leverages NSLS-II synchrotron X-rays and LBMS cryo-EM facilities to study cellular responses to environmental stimuli. His technological research develops experimental and computational methods including correlative X-ray imaging, cryoFIB-SEM/cryoET, and advanced computational data analysis techniques. Analysis of Dr. Liu's recent publications reveals a strong trend toward integrating AI and deep learning with structural biology. His work increasingly combines AlphaFold with traditional methods like X-ray crystallography and cryo-EM, while maintaining focus on metal transport mechanisms, host-pathogen interactions, and novel imaging techniques. The research demonstrates increasing sophistication in computational approaches to structural analysis while addressing fundamental biological questions. Brookhaven's Top 10 Discoveries of 2024 Scientists Find a New Way to Help Plants Fight Diseases Zinc Transporter Has Built-in Self-regulating Sensor Structure of 'Oil-Eating' Enzyme Opens Door to Bioengineered Catalysts As a Principal Investigator, Dr. Liu leads interdisciplinary research that bridges multiple Brookhaven facilities including NSLS-II, Laboratory for BioMolecular Structure (LBMS), Center for Functional Nanomaterials (CFN), and Computation and Data Sciences (CDS). His work exemplifies the integration of experimental and computational approaches to solve complex biological problems at multiple scales. Dr. Liu's laboratory focuses on integrative multiscale imaging approaches, developing technologies in correlative microscopy and computational data analysis. His team works at the intersection of biology, physics, and computational science to advance structural characterization capabilities, with particular emphasis on cellular and molecular structure-function relationships in health and disease contexts.
Hans Op de Beeck is a full professor at KU Leuven's Faculty of Psychology and Educational Sciences. He chairs the Brain and Cognition research unit within the Laboratory for Biological Psychology and is a member of the KU Leuven Brain Institute. His work bridges cognitive neuroscience, visual perception, and computational modeling approaches to understand human visual cognition. Professor Op de Beeck's research focuses on visual cognition, particularly object recognition, category selectivity, and the neural organization of the visual system. His work combines multiple methodologies including fMRI, computational modeling, and behavioral experiments to investigate how the brain processes visual information. His research spans basic visual neuroscience to applied domains like aesthetic appreciation and social scene processing. His recent publications demonstrate a strong trend toward integrating computational approaches with human neuroscience. The research spans visual category representation, social cognition, aesthetic processing, and cross-modal plasticity. A notable pattern is the increasing use of artificial neural networks to model and understand human visual processing, as well as the exploration of visual expertise in various domains including Braille reading and chess expertise. Professor Op de Beeck is actively involved in numerous research projects as both promotor and co-promotor, with ongoing work spanning from 2022 to 2030. His laboratory, the Brain and Cognition unit, investigates the fundamental principles of visual processing and their applications in understanding both typical and atypical cognitive functioning.
Prof. Michael Zöllner serves as Professor of Interaction Design and Vice Dean of the Faculty of Interdisciplinary and Innovative Sciences at Hof University, where he leads the Research Group 'Interaction & Data Driven Design' at the Institute for Information Systems (iisys). Appointed in 2012, he previously directed Media Design and Communication Design programs until 2022 and held pro-rectorate roles across the university's faculties. His academic foundation includes design studies at Würzburg-Schweinfurt University of Applied Sciences and the imedia Academy (RISD) in Providence, Rhode Island. Professional experience spans interactive agencies like Pixelpark AG and a.f.i.m. GmbH serving clients including Adidas and Bertelsmann, followed by a decade at Fraunhofer IGD where he became Deputy Department Head for Virtual and Augmented Reality, leading EU projects iTACITUS, CHESS, and MotionBank. Research centers on experimental human-technology interfaces, exploring beyond visual modalities to incorporate haptic, gestural, auditory, and olfactory interactions. His work investigates emerging technologies through data-driven design frameworks, focusing on museum installations, industrial applications, and cultural heritage preservation via mobile AR and multi-touch systems. Current initiatives include M4SKI Skateboard Movement Analysis (2023-2024), Timetravel Fichtelgebirge (2023-2024), and urban data visualization projects. He emphasizes empowering students to treat technology as emancipatory design tools through hands-on collaboration with museums and industry partners. Teaching philosophy prioritizes experimental confidence in emerging technologies, implemented through seminars where students develop prototypes for real-world exhibitions and trade fairs using physical computing and machine learning frameworks.
Professor Peter Schilke is a distinguished astrophysicist at the University of Cologne's Institute of Physics I, where he has been faculty since March 2009. He serves as chair of the Bachelor of Science Examination Board and leads an active research group focused on high-mass star formation and astrochemistry. His work involves advanced observational techniques across multiple wavelengths and sophisticated modeling approaches. PhD from University of Bonn (1992) Research Fellow at Caltech (1992-1995) Postdoc at I. Physikalisches Institut (1995-1997) Scientist at Max-Planck-Institut for Radioastronomy (1997-2009) Professor Schilke's research focuses on the formation processes of high-mass stars, astrochemistry, and the evolution of molecular clouds. His work particularly emphasizes the modeling of observational data to understand physical and chemical processes in star-forming regions. He investigates how material is transported from large-scale molecular clouds to dense cores, how feedback mechanisms from newly-born stars affect this process, and how chemistry evolves from the diffuse interstellar medium to dense star-forming regions. His group has expanded studies to include clusters surrounding high-mass stars and extragalactic star formation in the Large Magellanic Cloud. Analysis of his recent publications reveals a strong focus on large-scale observational programs, particularly the ALMAGAL survey using ALMA to study high-mass protocluster formation across the Galaxy. His work combines observations from multiple facilities including ALMA, APEX, IRAM instruments, SOFIA, and SMA. The research spans both observational and computational approaches, with significant emphasis on developing software tools for data analysis and modeling. Albertus-Magnus teaching award in physics (2020) Professor Schilke actively mentors PhD students, with current advisees including Akash Gupta, Han-Tsung Lee, and Eleonore Rodrigues da Costa, among others. He has successfully supervised recent PhD graduates including Fanyi Meng, Mahya Sadaghiani, and Andreas Schwörer. His research is supported by multiple significant grants, including leadership of Project C3 in the SFB 956, involvement in projects A4 and A6, and participation in the ALMAGAL large program. He previously served as co-I of Herschel/HIFI and was involved in the HEXOS and CHESS key programs. His research group develops and maintains several important astronomical software packages including STATCONT for continuum level determination, MAGIX for model optimization, and XCLASS for spectral line analysis. The group actively participates in major observational campaigns using ALMA, planning for CCAT-prime, and analyzing data from previous missions like Herschel. They maintain strong collaborations with international partners through projects like the SFB 956 and various ALMA committees.
Hope McIlwain is a Professor of Mathematics and Co-Chair of the Mathematics and Statistics Department at Mercer University within the College of Liberal Arts and Sciences, holding a Ph.D. in Mathematics from Rice University and a B.S. in Mathematics from Furman University. Education: Ph.D. in Mathematics, Rice University B.S. in Mathematics, Furman University Research Interests: Specializing in graph theory and combinatorics, Dr. McIlwain develops analytics and ranking methodologies for sports prediction systems. Her work bridges theoretical mathematics with practical applications in epidemiology (e.g., SIR modeling for Humans vs. Zombies) and sports analytics, utilizing statistical frameworks to solve real-world problems in competitive environments. Publication Trends: Her recent articles reveal a consistent pattern of applying mathematical modeling to sports ranking systems (PGA Tour, chess, volleyball) and recreational game dynamics, demonstrating interdisciplinary expertise where combinatorial methods intersect with data-driven performance analysis across diverse competitive contexts. Advising: Dr. McIlwain has mentored six undergraduate researchers including Elijah Evers (NHL prediction), Jonathan Beall (Humans vs. Zombies modeling), Elizabeth Knapper (volleyball analytics), Tyler Allee (PGA Tour rankings), Alex White (population data), and Sophia Rivera (trade ranking systems), guiding projects that translate theoretical mathematics into tangible real-world applications.
