Hugo Duminil-Copin is a Full Professor at the University of Geneva (appointed 2013) and a permanent professor at the Institut des Hautes Études Scientifiques (IHES) in Bures-sur-Yvette (since 2016). He leads the Analysis, Mathematical Physics and Probability research group at the University of Geneva's Section of Mathematics. Born in 1985 in the Paris region, he graduated from the École Normale Supérieure (ENS) in Paris and the University of Paris-Saclay. His primary research explores probabilistic aspects of mathematical physics and combinatorics, with specific focus areas including: Statistical mechanics and phase transitions Percolation theory and random cluster models Lattice models and critical phenomena Combinatorial inequalities and probability theory His publications (2019-2024) predominantly address statistical mechanics, combinatorial optimization, and number theory, featuring rigorous analysis of lattice models, phase transitions, and mathematical physics frameworks. Recent works demonstrate advanced studies on the Blume-Capel model, Brunn-Minkowski inequalities, and self-avoiding walks. Awards include: Fields Medal (2022) for transformative contributions to statistical physics He supervises doctoral candidates and postdoctoral researchers, currently guiding PhD students Émile Averous , Aman Markar , and Tiancheng He , among others. His research group includes postdoctoral scholars Remy Mahfouf , Florian Schweiger , and Wildemann Peter .
Jeff Clark is a Professor in the Department of Mathematics and Statistics at Elon University , where he has taught since 1988. He earned his B.S., M.S., and Ph.D. in Mathematics from Yale University (1982-1987). His leadership roles include multiple terms as department chair, state director of the Southeastern Section of the Mathematical Association of America, and current Faculty Ombudsperson since 2019. Education: B.S., M.S., Ph.D. in Mathematics (Yale University) Current Roles: Professor, Faculty Ombudsperson Clark's research focuses on undergraduate research mentorship and mathematical education , particularly integrating technology (LaTeX, Python, Mathematica) and art into mathematics instruction. His publications span topics from term rewriting systems to pedagogical innovations, with recent projects exploring term rewriting and permutation group analysis . He has supervised numerous student research projects since 1998, covering cryptography, fractal geometry, game theory, and machine learning applications to Rubik's Cube. His awards include the Distinguished Service Award from the Southeastern Section of the MAA (2015) and Elon's Service-Leadership Award (2015). Scientific Awards: Distinguished Service Award, Southeastern MAA (2015) Elon Service-Leadership Award (2015) Clark actively presents at national conferences like MathFest and Joint Mathematics Meetings, with recent talks on topics including symmetry operations , derivative sign patterns , and Python in numerical analysis . He is fluent in LaTeX and Python, with a background in C, Perl, APL, and Fortran.
Zhenjiang Hu is a Chair Professor and Dean of the School of Computer Science at Peking University. He serves as Director of the Programming Languages Laboratory and has held significant academic positions including Professor at the National Institute of Informatics and University of Tokyo. BS and MS from Shanghai Jiaotong University (1988, 1991) PhD from University of Tokyo (1996) Lecturer/Assistant Professor at University of Tokyo (1997) Associate Professor at University of Tokyo (2000) Full Professor at National Institute of Informatics (2008) Full Professor at University of Tokyo (2018-2019) Professor Hu's research primarily focuses on programming languages and software engineering, with special emphasis on functional programming, bidirectional transformation, and software adaptation. His work explores transformational programming approaches for automatic program optimization, systematic parallelization of sequential programs, efficient manipulation of structured documents, and bidirectional model transformation for software development. His research has significantly advanced the field of bidirectional programming, developing foundational theories and practical applications that enable more reliable and maintainable software systems. His recent publications demonstrate a strong trajectory in bidirectional programming, program synthesis, and graph processing. The research shows increasing sophistication in handling program transformations, with growing emphasis on practical applications in software engineering contexts. His work increasingly integrates formal methods with practical programming language design, creating systems that maintain theoretical soundness while addressing real-world software development challenges. The research spans multiple venues including top conferences like PLDI, POPL, ICFP, and OOPSLA, reflecting its broad impact across programming language research. Fellow of JFES (Japan Federation of Engineering Society, 2016) ACM Distinguished Scientist (2016) Member of Academia Europaea (2019) IEEE Fellow (2020) Member of Engineering Academy of Japan (2020) Professor Hu actively mentors students and has welcomed excellent candidates to join his group through Peking University's International Elite PhD Program and Boya Postdoctoral Fellowship Program. He serves on numerous program committees for major conferences including PLDI, POPL, ICFP, and OOPSLA, and holds editorial positions for prestigious journals such as Journal of Functional Programming and Science of Computer Programming. His leadership extends to conference organization, having served as PC Chair for CNCC 2024 and General Co-Chair for SoICT 2019. As Director of the Programming Languages Laboratory at Peking University, Professor Hu leads a research team focused on advancing programming language theory and practice. His lab has developed influential frameworks like BiGUL for bidirectional programming and Fregel for graph processing. The laboratory maintains strong international collaborations and contributes to both theoretical foundations and practical implementations in programming languages and software engineering.
Professor Zhu Qi is a faculty member in the Department of Electrical and Computer Engineering at Northwestern University's McCormick School of Engineering, with courtesy appointment in Computer Science. He leads the IDEAS Lab (Design Automation of Intelligent Systems Lab) where his research focuses on design automation for intelligent cyber-physical systems and Internet-of-Things applications. His research interests include safe and robust machine learning for embodied AI systems, cyber-physical security, energy-efficient CPS, and system-on-chip design. Professor Zhu's work particularly addresses safety, robustness, security, adaptability, resiliency, and energy challenges in the design and operation of embodied AI systems. His applications span connected and autonomous vehicles, robotics, advanced manufacturing, wearable computing, smart buildings and infrastructures, and IoT. Professor Zhu's recent publications reveal a strong focus on safety verification of neural network controlled systems, robust reinforcement learning methods, and applications of large language models in autonomous systems. His work often combines formal verification techniques with machine learning approaches to provide safety guarantees for AI-enabled cyber-physical systems. DATE 2022 Best Paper Award AutoSec 2021 Best Short Paper Award ACM TODAES 2016 Best Paper Award IEEE TCCPS Early-Career Award (2017) Humboldt Research Fellowship for Experienced Researchers (2017) NAE US Frontiers of Engineering participant (2020) Professor Zhu has secured multiple research grants from NSF (including FM, DESC, and Fuse grants), DOE, ONR, and industry partners including GM and Toyota. His advising includes PhD students Shuyue Lan and Hengyi Liang, and postdoc Chao Huang who became a Lecturer at University of Liverpool. He leads the IDEAS Lab which focuses on cross-layer design, verification, and adaptation of learning-enabled cyber-physical systems.
Dr. Frank Krueger is a full-time Professor at the School of Systems Biology , George Mason University, with dual affiliations in the Neuroscience Program and the Institute for Biohealth Innovation . A transdisciplinary researcher, he integrates social psychology, experimental economics, and social neuroscience to investigate the psychological and neurobiological mechanisms of social cognition and prosocial behaviors. Education: PhD in Cognitive Psychology (2001), Habilitation in Psychology, and Master's in Physics, all from German universities. His research leverages neuroimaging (fMRI, DTI, VBM), computational modeling, and behavioral experiments to study trust, reciprocity, empathy, and social bonding across diverse populations, including brain-injured patients and older adults. Recent work explores human-robot interaction, mental healthcare interventions, and the role of quantum mechanics in neurobiological systems. Current research trends focus on social trust dynamics , neural correlates of prosocial behavior , and interdisciplinary applications of AI and robotics . As Chief of the Social Cognition and Interaction: Functional Imaging (SCI:FI) lab, he leads studies on brain connectivity and psychopathic traits, with applications in PTSD treatment and neurofeedback therapies. Dr. Krueger has no listed scientific awards or advisees but maintains active leadership roles in multiple research centers, including the Center for Adaptive Systems of Brain-Body Interactions . His lab employs advanced machine learning and graph transformation techniques to decode complex social behaviors from neuroimaging data.
Chao Peng is a Principal Research Scientist at ByteDance where he leads the Software Engineering Lab, focusing on AI agents for software engineering. He holds a part-time position as a Postgraduate Student Mentor at Fudan University's School of Computer Science. His research bridges industry and academia, with significant contributions to software testing, program repair, and LLM applications in software development. Education: PhD in Informatics, 2021, University of Edinburgh, UK MSc in High Performance Computing and Data Science, 2017, University of Edinburgh, UK BEng in Computer Science and Technology, 2016, Xuzhou University of Technology, China Dr. Peng's research interests center on the intersection of artificial intelligence and software engineering. He explores how large language models can transform traditional software development practices, particularly in code generation, testing, and bug fixing. His work on LLM4Code has led to innovative frameworks like CodeVisionary for evaluating code generation capabilities and Trae Agent for software engineering tasks with test-time scaling. He investigates the synergy between machine learning techniques and compiler optimizations to enhance software reliability and developer productivity. His recent publications reveal a strong focus on practical evaluation frameworks for LLMs in real-world software engineering contexts. Rather than theoretical benchmarks, his work emphasizes real-world applicability, as seen in RepoMasterEval which evaluates code completion in actual repository settings. He examines multi-faceted challenges including code generation, bug reproduction, issue resolution, and repository-level question answering, consistently addressing the gap between laboratory evaluations and practical development environments. Scientific Awards: Distinguished Reviewer for FSE'25 Invited to program committees for FSE'26, SANER 2026, ASE 2025, and others School of Informatics Scholarship (fully-funded PhD) Multiple national scholarships during undergraduate studies Honours Spot Bonus at ByteDance Dr. Peng actively mentors postgraduate students at Fudan University while leading research initiatives at ByteDance that foster university collaborations. His laboratory work translates academic research into practical tools for software development, with several frameworks deployed in industrial settings. He serves on multiple conference program committees, contributing to the advancement of software engineering research through rigorous peer review and community building. His Software Engineering Lab at ByteDance operates at the forefront of AI-assisted development, exploring how agent-based systems can automate complex software engineering tasks. The team's work on frameworks like AEGIS for bug reproduction and DialogAgent for code question answering demonstrates their commitment to solving practical challenges faced by developers in real-world settings.
Professor Yuan Miao is a distinguished academic at Victoria University (VU), serving as Professor in the College of Arts, Business, Law, Education & IT and Head of the Information Technology Program. With a PhD from Tsinghua University's Automation Department, his academic journey spans prestigious institutions including the University of Melbourne and Nanyang Technological University in Singapore before settling at VU where he has been Professor since January 2010, following his Associate Professorship from August 2004 to December 2009. Education: BSc, Shandong University, China MEng, Tsinghua University, China PhD, Tsinghua University, Automation Department, China Professor Miao's research centers on Large Language Models (LLMs) and Generative AI, where he has identified critical barriers in practical applications including limited memory length in systems like ChatGPT and Gemini, contradictory explanations, lack of local knowledge integration, and significant errors in text-data hybrid reasoning (up to 38%). His innovative solutions involve cognitive map graphs and rational intelligence models to create customized AI systems. His work spans diverse application areas including human knowledge modeling, multimodal interaction, healthcare analytics (particularly dementia detection), cybersecurity, and robotics powered by rational intelligence. Analysis of Professor Miao's recent publications reveals a strong focus on integrating LLMs with specialized knowledge domains across healthcare, cybersecurity, and social media analysis. His research consistently addresses practical limitations of current AI systems while developing novel frameworks for more reliable and context-aware applications. The interdisciplinary nature of his work is evident in publications spanning medical informatics, cybersecurity analytics, and educational technology. Scientific Recognition: Two articles in fuzzy cognitive map modeling ranked among top 10 most cited works since 2000 (Google Scholar 2000-2016) Development of adversarial dataset based on SQuAD 2.0 that reduced BERT and ELECTRA accuracy from ~90% to ORCID identifier 0000-0002-6712-3465 with 138 peer-reviewed publications Professor Miao actively supervises PhD and Master's students across diverse research topics including access control systems, healthcare analytics, cybersecurity, and social behavior analysis. His research has secured substantial funding from both industry giants (Microsoft, Amazon, Oracle, Google) and government bodies (Australia Research Council, Data61, Singapore's NRF), with recent projects including Digital Transformation for Construction Industry ($1.258 million), Western Health SharePoint Development ($68,000), and Big Data Analysis for Domestic Violence Research (US$100,000). His current grant portfolio demonstrates strong industry-academia collaboration addressing real-world challenges. Professor Miao leads research teams focused on rational intelligence systems that overcome current LLM limitations, with particular emphasis on creating practical AI solutions for healthcare, cybersecurity, and smart city applications. His work with Maribyrnong City Council on the Smart City at Footscray Park project ($850,000) exemplifies his commitment to applying advanced AI research to community-level challenges.
Rahmatullah Roche serves as an Assistant Professor in the Department of Computer Science at Columbus State University's TSYS School of Computer Science, joining the tenure-track faculty in Fall 2024. His academic credentials include: Ph.D. in Computer Science and Applications from Virginia Tech (2024) M.S. in Computer Science and Software Engineering from Auburn University (2021) B.S. in Computer Science and Engineering from Bangladesh University of Engineering and Technology (2016) Dr. Roche's research spans Computational Biology , Applied Machine Learning , Data Science , and Human-Computer Interaction , with primary focus on macromolecular predictive modeling for intra- and inter-molecular interactions using cutting-edge AI techniques. His work bridges computer science and biology to solve complex structural bioinformatics challenges. Analysis of his 14 publications (2020-2025) reveals a consistent trajectory in applying deep learning—particularly transformer networks, equivariant graph neural networks, and biological language models—to protein-RNA complex prediction, protein-nucleic acid binding, and protein-protein interaction site identification, published in high-impact journals including Cell Systems and Nucleic Acids Research. Key scientific recognitions include: Pratt Fellowship Award (2023-2024) Turner College Research Award (2025) Professional Development Award for Core Course redesign (2025) Core Course Design Institute Professional Development Award (2024) 3rd place Flash Talk award at VT GPSS Symposium (2024) Best Poster Award at ACM-BCB 2020 YSEA Finalist Travel Award at MCBIOS 2024 Dr. Roche actively recruits undergraduate and graduate students for his research lab while securing resources through NSF ACCESS grants. His teaching portfolio includes Computer Science I, Computer Organization, and Graphical User Interface Development, complemented by service as Discipline Coordinator for Web Development and reviewer for top bioinformatics journals. His research group collaborates with Virginia Tech on advancing biomolecular modeling through interdisciplinary AI applications.
Lorraine Li is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh, affiliated with the Interdisciplinary Science Program (ISP) and the School of Computing and Information. She holds a PhD from the University of Massachusetts Amherst (2022) and conducted postdoctoral research at AI2's Mosaic team. Her work focuses on NLP, machine learning, and socially responsible AI systems. Education: PhD in Computer Science (UMass Amherst, 2022) Research explores evaluation frameworks for commonsense knowledge, model interpretability, and ethical AI applications in domains like education and law. Key interests include probabilistic models, long-tail reasoning, and geographic robustness in LLMs. Recent publications address confirmation bias in reasoning chains (ACL 2025), geographically diverse prompting (CVPR 2024), and uncommon scenario reasoning (NAACL 2024). She co-organized the AAAI 2024 Make symposium and serves on committees for ACL, EMNLP, and NAACL. Grants: Pitt Cyber funding (2024) Lab: Pitt NLP Seminar group
Associate Professor Fatemeh Vafaee is a leading researcher at the University of New South Wales (UNSW) , holding appointments as Associate Professor in the School of Biotechnology and Biomolecular Sciences (BABS) and Deputy Director (Science) of the UNSW AI Institute . She previously served as Deputy Director of the UNSW Data Science Hub (uDASH) and has held academic positions at the University of Toronto and the University of Sydney. PhD in Artificial Intelligence from University of Illinois at Chicago Postdoctoral Fellowships at University of Toronto and University of Sydney Founded the AI-Enhanced Biomedicine Laboratory in 2017 Her research focuses on deploying advanced AI techniques to address biomedical challenges through: Biomarker Discovery for cancer and neurodegenerative diseases Single-Cell Multi-Omics data integration and analysis Computational Drug Repositioning and network pharmacology Multi-Omics Data Fusion and temporal network modeling Recent publications demonstrate expertise in liquid biopsy development , single-cell imaging , and AI-driven cancer diagnostics . Her methodological contributions include novel deep learning architectures for omics data analysis and graph neural networks for drug synergy prediction. Scientific accolades include: Winner, Women in AI Asia-Pacific Health Award (2023) Runner-Up, WAI-APAC Innovator of the Year (2023) Top 10 Women in AI in Asia-Pacific (2023) Australian Bioinformatics and Computational Biology Society Research Excellence Award (2023) She supervises PhD candidates across computational biomedicine and AI in healthcare , with significant grant achievements exceeding $17M in competitive funding, including schemes from ARC Discovery , NHMRC , and Medical Research Future Fund .
Bharat Biswal is a Distinguished Professor in the Department of Bio-Medical Engineering at New Jersey Institute of Technology (NJIT), serving as Director of the Center for Brain Imaging. His primary affiliation is with the College of Engineering, where he leads neuroimaging research initiatives. Biswal’s work focuses on functional magnetic resonance imaging (fMRI), brain connectivity analysis, and translational applications in neuropsychiatric disorders. Research Interests: Functional and Resting-State fMRI Methodology Neurovascular Coupling Mechanisms Connectomics and Network Neuroscience Clinical Applications in ADHD, OCD, and Neurodegenerative Diseases Post-COVID Neuroimaging White Matter Function Grant Activity: NIH-funded projects on laminar-specific connectivity (2022–2025) National Science Foundation MRI infrastructure grants (2019–2021) Longitudinal HIV brain studies (2015–2018) Recent Articles Highlight: Biswal’s 2025 work advances understanding of cocaine use disorder neurobiology, obsessive-compulsive disorder network dysregulation, and standardized PET nomenclature. His lab also innovates in AI-driven defect detection and transcriptomic-neuroimaging integrations. Awards: Recipient of NJIT’s 2024 Excellence in Research Award for pioneering contributions to resting-state fMRI and brain connectivity research. Labs/Teams: Leads the Center for Brain Imaging at NJIT, collaborating internationally on neuroimaging standards and translational neuroscience projects.
Mr. Yi Wang is a researcher at the Department of Mechanical Engineering, Imperial College London, specializing in Multi-scale Modeling of Powder Forging and related fields. His work bridges mechanical engineering, energy systems, and artificial intelligence. University: Imperial College London Department: Department of Mechanical Engineering Research Interests include electric vehicle-grid integration, microgrid resilience, reinforcement learning applications in energy systems, and multi-energy network optimization. His publications emphasize AI-driven strategies for low-carbon transitions and grid stability. Email: yi.wang12@imperial.ac.uk
Bikramjit Das is an Associate Professor and Associate Head of Pillar (Graduate Programme) at Singapore University of Technology and Design (SUTD). He holds a PhD in Operations Research from Cornell University and prior to SUTD, was a postdoctoral researcher at ETH Zurich’s RiskLab. His research focuses on extreme events analysis using applied probability, optimization, and statistical learning, with applications in finance, telecommunications, federated learning, and climate modeling. He teaches courses in Probability, Stochastic Modeling, and Analytics, and directs the Master of Science in Technology and Design (Data Science) program. Education: PhD in Operations Research (Cornell University), B.Stat & M.Stat (Indian Statistical Institute). Research emphasizes heavy-tailed distributions, risk contagion, and network modeling. Key areas include risk analysis in financial networks, robust optimization under uncertainty, and extreme value theory. His work bridges theoretical probability and real-world applications in data science and public policy. Notable contributions include studies on asymptotic independence in high dimensions, robust newsvendor models, and inference techniques for heavy-tailed data. His articles explore topics ranging from federated learning under noise to climate modeling and congestion phenomena in sparse networks. Collaborations include visiting positions at MIT and the Karlsruhe Institute of Technology. Active in academic leadership, he has contributed to technical reports on healthcare provider choice analysis and probabilistic flood risk assessments for nuclear power plants.
Nicholas Marshall is an Assistant Professor in the Department of Mathematics at Oregon State University. His work bridges analysis, geometry, and probability with strong applications in data science. Current faculty: Oregon State University Postdoctoral training: Princeton University (NSF Fellowship) Doctoral training: Yale University Undergraduate education: Clarkson University His research focuses on: Interplay between geometric structures and probabilistic models Development of numerical methods for high-dimensional data Applications in cryo-electron microscopy and hyperdimensional computing Analysis of stochastic algorithms and convergence properties Recent publications indicate significant contributions to: Harmonic expansion techniques Equivariant function learning Momentum-accelerated optimization methods Binary hyperdimensional geometry NSF Postdoctoral Fellowship (Princeton) He actively mentors graduate and undergraduate students, including Wyatt Whiting, Peter Cowal, Heather Fogarty, and Seth Alderman. Teaching appointments include advanced courses in probability theory, numerical linear algebra, and mathematics of data science.
Julien Perret is a senior researcher at the National Institute of Geographic and Forest Information (IGN) in France, affiliated with the LASTIG laboratory and STRUDEL research team. His work spans geographical information science, urban dynamics, and historical cartography. Current Affiliation: National Institute of Geographic and Forest Information (IGN) Research Team: LASTIG, STRUDEL Academic Rank: Senior Researcher (Directeur de Recherche) Education Habilitation (HDR) in Geographical Information Science, Université Paris-Est (2016) PhD in Computer Science, Université Rennes 1 (2006) Engineering Degree in Computer Science, INSA Rennes (2002) Research Interests Perret's research focuses on urban dynamics through computational approaches, including agent-based modeling and 3D urban simulation . He investigates historical cartographic data like the Napoleonic land registry and Cassini Carte de France to understand long-term urban evolution. His work integrates geospatial data with epidemiological modeling using digital twins, particularly for pandemic simulations. Scientific Contributions Key publications include: 2025: Building change models for urban densification studies 2024: Historical map vectorization benchmarks 2017: Scalable point cloud management systems 2015: 3D analysis of urban regulation impact 2005: Procedural geometry modeling with FL-systems Advising and Collaborations Perret has supervised multiple PhD students and collaborated on projects like SoDUCo (1789-1950 Paris urban dynamics) and iSpace&Time (4D GIS for city modeling). He contributes to open-source GIS tools like GeOxygene .
