جست‌وجوی استاد و پژوهشگر

Qinghua Luمشاهده پروفایل

استاد

• Responsible AI
• Software Engineering for AI
• AI Safety
• +۵ مورد دیگر

Dr Qinghua Lu serves as Group Leader of the Software Systems Research Group at CSIRO's Data61 in Australia, where she leads pioneering research at the intersection of artificial intelligence and software engineering. With over 200 publications in premier international venues, she has established herself as a global authority in Responsible AI and Software Engineering for AI. Her research program focuses on two critical areas: Agent Engineering and Evaluation, and AI Safety/Responsible AI. She has developed architectural patterns for foundation model-based agents, reference architectures for trustworthy AI systems, and multi-layered safety frameworks including the influential Swiss Cheese Model for AI Safety. Her work bridges theoretical research with practical applications through tools like Prompt Sapper for AI chain development and SoapOperaTG for knowledge graph-based testing. Dr Lu actively shapes national and international AI policy as a contributor to Australia's AI Safety Standard, Frontier Model Forum, OECD.AI's trustworthy AI metrics, and the EU General-Purpose AI Code of Practice. She has received numerous accolades including the 2023 Asia-Pacific Women in AI Trailblazer Award and an ACM Distinguished Paper Award for her seminal work 'Towards a Roadmap on Software Engineering for Responsible AI'. As a prolific author, her book 'Responsible AI: Best Practices for Creating Trustworthy AI Systems' (2023) became Amazon's No. 3 AI book bestseller, recognized as the world's first responsible AI book for practitioners. Her follow-up work 'Engineering AI Systems: Architecture and DevOps Essentials' (2025) further establishes her leadership in AI system engineering. Dr Lu maintains active leadership in the academic community as Area Chair of Software Engineering for AI at ICSE'26, Program Chair for AIware'25 and CAIN'25, and associate editor for IEEE Transactions on AI. Her editorial service extends to multiple prestigious journals including Engineering Applications of Artificial Intelligence and IEEE Communications Surveys and Tutorials.

Annette G. Beck-Sickingerمشاهده پروفایل

استاد

• Protein Engineering
• Biomaterials
• Chemokine Biology
• +۴ مورد دیگر

Annette G. Beck-Sickinger is Professor of Bioorganic Chemistry and Biochemistry at the Institute of Biochemistry within Leipzig University's Faculty of Life Sciences. Her research focuses on chemical protein modification to characterize matrix-protein interactions, specifically developing strategies to improve implant biocompatibility through peptide-mediated immobilization of protein mediators and investigation of immune-modulatory chemokine interactions with extracellular matrix components. Her primary research areas include protein engineering, biomaterials science, and chemokine biology, with emphasis on glycosaminoglycan interactions (particularly for IL-8 and CXCL12/SDF-1), peptide-based surface coatings for controlled cytokine release, and light-activated chemokine systems. Current projects target wound healing enhancement via ECM-inspired biomaterials and reduction of implant-related inflammation through modified chemokine analogues. Analysis of her 2013-2021 publications reveals consistent innovation in biomaterial design for medical applications, characterized by interdisciplinary approaches combining synthetic chemistry, structural biology, and translational medicine. Key trends include enzyme-responsive release systems (MMP-9 mediated), photocontrol of protein activity, and mechanistic studies of chemokine-glycosaminoglycan binding to modulate immune responses. No scientific awards are mentioned in the available documentation. While specific advisees are not listed, her extensive publication record with multiple student co-authors (e.g., Bellmann-Sickert, Baumann, Panitz) indicates active supervision of graduate researchers. Her described projects on implant biocompatibility and wound healing suggest ongoing grant support, though specific funding sources are not detailed in the provided text. Her laboratory at Leipzig University's Institute of Biochemistry operates at the chemistry-biology interface, utilizing techniques including expressed protein ligation, NMR spectroscopy, and surface characterization. Collaborations with polymer scientists (Schnabelrauch, Hacker), biophysicists (Abel), and immunologists (Pisabarro) demonstrate a multidisciplinary research ecosystem focused on translating protein engineering into clinical solutions for regenerative medicine.

Manfred Eppeمشاهده پروفایل

پژوهشگر

• Reinforcement Learning
• Cognitive Robotics
• Machine Learning
• +۵ مورد دیگر

Manfred Eppe serves as Chief Engineer at the Institute for Data Science Foundations, Hamburg University of Technology, where he leads the cognitive robotics laboratory and contributes to teaching the machine learning lecture. His research centers on computational cognitive models of representation learning and reinforcement learning for simulated and physical robotic agents, with applications spanning neuro-semantic systems and cognitive robotics. His educational background includes a Ph.D. in Computer Science from the University of Bremen (2014), followed by postdoctoral research at the University of Hamburg (2016-2021), UC Berkeley (2015), and IIIA-CSIC in Barcelona (2015) within the COINVENT project. These positions focused on data-driven neuro-semantic systems, human-robot interaction using neurocognitive methods, and AI-based concept representation. Eppe's research integrates reinforcement learning with cognitive modeling to develop agents capable of representation learning, concept blending, and commonsense reasoning. His work bridges symbolic AI and neural approaches, emphasizing neurocognitively inspired methods for natural language understanding and robotic applications in physical environments. He has secured over €1.5 million in research funding as principal investigator for DFG projects MoReSpace and LeCAREbot, alongside prior grants from Volkswagen Stiftung and DAAD. His teaching portfolio includes Data Science at West Coast University of Applied Sciences and the "From Data to Knowledge" course at Universität Hamburg, with thesis supervision limited to reinforcement learning topics using the Scilab-RL virtual robotics platform. Eppe directs the cognitive robotics laboratory, which develops the Scilab-RL framework for simulating robotic agents and testing learning algorithms. The lab emphasizes open collaboration and practical applications of computational cognitive models in real-world robotic systems.

Kexin Peiمشاهده پروفایل

استادیار

• Security
• Software Engineering
• Machine Learning

Kexin Pei is a Neubauer Family Assistant Professor at the Department of Computer Science, University of Chicago. She received her PhD from the Department of Computer Science at Columbia University. Her academic career spans research and teaching in the fields of Security, Software Engineering, and Machine Learning. Her educational background includes: PhD in Computer Science from Columbia University Dr. Pei's research interests focus on developing data-driven program analysis to improve the security and reliability of both traditional and AI-based software systems. She is particularly interested in creating machine learning models that can reason about program structure and behavior to precisely and efficiently analyze, detect, and fix software bugs and vulnerabilities. Her work bridges the gap between theoretical computer science and practical security applications, with significant contributions to binary analysis, program understanding, and AI security. Her research output shows a strong trend toward integrating machine learning with traditional program analysis techniques. Many of her recent publications explore how neural networks and language models can enhance software security and reliability, particularly in binary analysis, vulnerability detection, and code understanding. Her work often involves developing novel frameworks that combine execution traces, code structure, and semantic understanding to create more robust analysis tools. Dr. Pei has received several prestigious awards for her research: Best Paper Award at MASEC@NeurIPS 2023 Best Paper Award Runner-Up in CSAW 2018 Top-10 Finalist of Applied Research Competition ACM SigMobile Research Highlight MLSec @NIPS'17 CACM research highlight Distinguished Artifact Award at FSE 2016 Dr. Pei actively mentors students and has advised several PhD and Master's students. Her group includes PhD students Jie Zhu, Weichen Li, and Jun Yang (with Shan Lu), as well as MS students Sam Huang and Yiming Cheng (with Junchen Jiang). She has also worked with numerous student collaborators and visiting students from institutions including MIT, Georgia Tech, and UChicago. Her research has been supported by grants from various sources, including collaborations with Google DeepMind and Microsoft Research where she completed internships. Dr. Pei is involved in several research labs and teams, including collaborations with the CUMLSec group (as seen in repositories like trex and XDA on GitHub). Her work often involves interdisciplinary teams combining expertise in security, machine learning, and software engineering to tackle complex problems in program analysis.

Ashutosh Trivediمشاهده پروفایل

دانشیار

• Formal Methods
• Reinforcement Learning
• Software Engineering
• +۴ مورد دیگر

Ashutosh Trivedi is an Associate Professor of Computer Science at the University of Colorado Boulder, specializing in formal methods for safety-critical learning-enabled systems. His research bridges computer science, control theory, and machine learning with a focus on trustworthy AI systems that operate safely, fairly, and responsibly. His research interests encompass Formal Methods , Reinforcement Learning , Software Fairness , and Software Accountability . Trivedi develops mathematical approaches to bring precision to AI system design, using formal languages, automata, and logic to transform vague natural-language instructions into clear specifications. His work emphasizes principled interaction with AI, creating explainable and reliable systems through techniques like reinforcement learning algorithms for cardiac pacemaker design, SAT solvers for grounding LLM outputs, and formal logic for capturing legal obligations. His recent publications reveal a strong trend toward integrating formal verification with machine learning systems, particularly focusing on fairness testing, safety constraints in reinforcement learning, and neurosymbolic approaches. The research spans applications from legal-critical software to medical devices, with consistent emphasis on accountability and ethical considerations in AI systems. 2022 NSF CAREER award Liverpool Fellowship Distinguished Paper Award at CAV for Regular Reinforcement Learning Senior Member of the ACM Royal Society Wolfson Visiting Fellowship NeuS 2025 Disruptive Idea Award Trivedi actively mentors PhD students, having supervised multiple successful thesis defenses including Shadi Tasdighi Kalat on multi-agent games, Mateo Perez on formal languages for reinforcement learning, John Komp on pacemaker therapy, Vishnu Murali on functional proofs, and Taylor Dohmen on sequential optimization. His group, the Programming Languages and Verification (CUPLV) at CU Boulder, focuses on making AI systems more trustworthy through mathematical precision and formal verification techniques. Currently on sabbatical at the University of Liverpool, Trivedi continues to bridge the gap between machine learning and responsible system design, helping AI systems earn the trust we increasingly place in them for critical applications.

Lingming Zhangمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages
• Machine Learning
• +۵ مورد دیگر

Lingming Zhang is an Associate Professor in the Department of Computer Science at the Grainger College of Engineering, University of Illinois Urbana-Champaign. He maintains an active research program focused on the intersection of Software Engineering, Programming Languages, and Machine Learning, with particular emphasis on LLM-based software testing, repair, and synthesis. His research has resulted in over 100 publications with an h-index exceeding 50. His work has practical impact, having helped detect over 1,000 bugs and vulnerabilities in open-source projects from Apache and GitHub, as well as software systems from major technology companies including eBay, Google, Meta/Facebook, Microsoft, NVIDIA, OctoML, Oracle, and Yahoo!. Zhang's research interests span several key areas in software engineering and programming languages: LLM-based software testing, debugging, and repair Code generation and understanding with large language models Automated program repair Software testing and fuzzing techniques Deep learning for software engineering His recent publications demonstrate a strong focus on leveraging large language models for software engineering tasks, with notable contributions including Agentless, TitanFuzz, AlphaRepair, and ChatRepair. His group has also released influential open-source code language models including StarCoder2 and Magicoder, which have garnered over 1 million downloads worldwide. Zhang has received numerous prestigious awards and recognitions for his work: ACM Distinguished Member ACM SIGSOFT Early Career Researcher Award NSF CAREER Award UIUC Dean's Award for Excellence in Research Multiple ACM SIGSOFT Distinguished Paper Awards He has successfully mentored numerous PhD and MS students, many of whom have gone on to positions at top technology companies and academic institutions. His research is supported by grants from major technology companies including Alibaba, Amazon, Google, Kwai Inc, Meta/Facebook, NVIDIA, and Samsung. Currently, he serves as program co-chair for ASE 2025 and LLM4Code 2025, demonstrating his leadership in the software engineering research community.

Peng Diمشاهده پروفایل

دانشیار مدعو

• Program Analysis
• Compiler
• Parallel Computing
• +۵ مورد دیگر

Peng Di serves as an Adjunct Associate Professor at the University of New South Wales, Sydney while working as a Senior Staff Engineer and leading the Intelligent Platform Engineering team at Ant Group in Hangzhou, China. They also serve as an Industrial PhD Mentor at Zhejiang University. PhD from UNSW under supervision of Scientia Professor Jingling Xue Former Research Associate at Compiler Research Group Former Technical Expert of AI compiler at Huawei Peng Di's research spans the intersection of programming languages, artificial intelligence, and software engineering. Their work focuses on leveraging large language models for software development, program analysis techniques, compiler technologies, and security applications. They have pioneered approaches to integrate code large language models with traditional program analysis, creating hybrid systems that enhance software development efficiency and security. Their research in parallel computing and optimizations for heterogeneous systems has resulted in practical industrial applications, particularly in the domain of AI compilers and microservices architecture. Recent publications demonstrate a clear trend toward integrating graph-based representations with large language models for code understanding, advancing static analysis techniques using datalog and neural networks, and applying these innovations to practical industrial settings. Their work bridges theoretical program analysis with real-world software engineering challenges, particularly in microservices architectures and security-critical applications. T-Star Award (Ant Group's highest technological award, 2024) ACM SIGSOFT Distinguished Paper Award (2025) Distinguished Dissertation Award by the CCF Systems Software (2024) distinguished paper award of ASE'24 (2024) Peng Di leads the Intelligent Platform Engineering team at Ant Group, which has developed several significant open-source projects including CodeFuse (a pretrained code large language model), Ling-Coder-Lite (a MoE code LLM), and OpenDeRisk (an AI-native risk intelligence system). Their team collaborates with academic institutions including UNSW, East China Normal University, ETH Zurich, Chinese Academy of Sciences, and Nanjing University on cutting-edge research. Peng Di actively mentors PhD students as an Industrial PhD Mentor at Zhejiang University and recruits technical experts and interns for research in machine learning, program analysis, and AI software engineering. The Ant Intelligent Platform Engineering team, led by Peng Di, is committed to building a safe and trusted framework for intelligent, automated software engineering. They manage the CodeFuse AI Coding and DeRisk AIOps communities, which encompass pre-trained Code LLMs, agents, frameworks, toolchains, and databases. Their team has also contributed to agent development platforms Tbox and WeaveFox, with the latter being an AI frontend development platform launched by Ant Group.

Amin Milani Fardمشاهده پروفایل

دانشیار

• Software Engineering
• Cybersecurity
• Software Testing
• +۵ مورد دیگر

Amin Milani Fard is an Associate Professor of Computer Science at New York Institute of Technology - Vancouver Campus, and a visiting faculty member in Management Information Systems at Simon Fraser University's Beedie School of Business in Vancouver, Canada. Previously, he served as an Assistant Professor at NYIT Vancouver from 2018 to 2023. Dr. Milani Fard received his Ph.D. in Computer Software Engineering from the University of British Columbia (2012-2017), his M.Sc. in Computer Science from Simon Fraser University (2009-2010), and his B.Sc. in Computer Software Engineering from Ferdowsi University of Mashhad. His academic journey began with notable achievements including the 1st Rank Khwarizmi Award (awarded by Iran's President Mohammad Khatami) and an Exceptional Talents Admission Award. His research spans multiple disciplines, primarily focusing on Security, Privacy, and Assurance of Software and Information , with significant contributions to software testing, blockchain security, financial market prediction, and machine learning applications. His work on JavaScript security code smells, Ethereum smart contract vulnerabilities, and financial market prediction using multimodal data has been widely recognized. His publications demonstrate a consistent pattern of high-quality research with numerous papers in top conferences including ASE, ICST, and IEEE journals. Dr. Milani Fard's research portfolio shows a clear evolution from foundational work in software testing and JavaScript analysis toward more complex applications in financial technology and AI-driven security solutions. His most recent work focuses on integrating LLM technologies with security applications and advancing financial prediction models using sophisticated time series analysis. Scientific Awards and Recognition: Most Influential Paper Award at IEEE SCAM 2023 Presidential Excellence Award Finalist for Teaching at NYIT (2023) Best Paper Award at ECIR 2019 Multiple research grants including NSERC Alexander Graham Bell Canada Graduate Scholarship IEEE Best Paper Award Nominee at ICST 2017 As an active researcher, Dr. Milani Fard serves on program committees for major conferences including ASE and has contributed significantly to the software engineering community through his publications and research leadership. His work bridges theoretical computer science with practical applications in cybersecurity and financial technology, demonstrating both academic rigor and real-world impact.

Maliheh Izadiمشاهده پروفایل

استادیار

• Software Engineering
• Machine Learning
• Large Language Models for Code
• +۷ مورد دیگر

Maliheh Izadi is a tenure-track assistant professor in the Faculty of Electrical Engineering, Mathematics, and Computer Science at Delft University of Technology (TU Delft), Netherlands. She leads the AISE (AI-enabled Software Engineering) research lab and serves as the scientific manager for the TU Delft/JetBrains Collaboration (AI4SE). She is also a member of the Software Engineering Research Group (SERG) at TU Delft and actively supervises PhD, MSc, and BSc students. Dr. Izadi's research focuses on enhancing software development tools through building smarter software and tailoring machine learning and NLP techniques to source code. Her primary interests include building and tailoring large language models (LLMs) and autonomous agents to source code, with specific focus areas including evaluation, benchmarking, model memorization, IDE integration, in-IDE Human-AI interaction, and extending models' capabilities to low-resource programming languages. Her work bridges the gap between deep learning and source code analysis, with applications in code understanding, generation, documentation, and developer productivity enhancement. Her recent publications reveal a strong trend toward evaluating and improving LLMs for code, with emphasis on safety, usability, and efficiency. She has made significant contributions to benchmark development, harmfulness assessment of LLMs in programming contexts, and improving the integration of AI tools within development environments. Her research consistently addresses real-world industrial challenges while advancing theoretical understanding of model behavior. Google Research Scholar Award (2025) for proposal on Tackling LLM Hallucinations Amazon Research Award (2024) for proposal on Addressing Memorization in Code LLMs ACM SIGSOFT Distinguished Paper Award (2025) for How Much Do Code Language Models Remember? ACM SIGSOFT Distinguished Paper Award (2024) for A Transformer-Based Approach for Smart Invocation of Automatic Code Completion Best Tool Award at SaTML'22 competition for STACC Best Tool Award at NLBSE'22 competition for Catiss Dr. Izadi actively collaborates with industry partners, particularly JetBrains Research, where she leads the AI4SE ICAI lab. She has supervised multiple PhD and master's students and has served on program committees for major software engineering conferences including ASE, ICSE, FSE, and MSR. Her research has been published in top venues such as IEEE/ACM ICSE, FSE, ASE, TOSEM, EMSE, MSR, ICSME, SANER, and JSS. She is also organizing the First International workshop on Autonomous Agents in Software Engineering (AgenticSE) co-located with ASE'25.

Tianyi Zhangمشاهده پروفایل

استادیار

• Software Engineering
• Human-Computer Interaction
• Artificial Intelligence
• +۵ مورد دیگر

Tianyi Zhang is a Tenure-Track Assistant Professor of Computer Science and Societal Impact Fellow at Purdue University's College of Science, where he leads the Human-Centered Software Systems Lab. His research focuses on building interactive intelligent systems that synergize human expertise with machine intelligence to improve programming productivity and software robustness. Dr. Zhang's research interests span Software Engineering, Human-Computer Interaction, and Artificial Intelligence. His work centers on developing systems that augment human intelligence with data-driven insights and augment machine intelligence with human guidance, primarily for programming domains including software developers, novice programmers, and computer end-users. His research on code mining and visualization helps programmers make more informed decisions through GitHub and Stack Overflow analysis, while his work on program synthesis assists novices with enriched feedback loops and interpretability. His recent publications (2024-2025) demonstrate a strong focus on leveraging large language models for code generation, program repair, and data wrangling, with particular emphasis on interactive systems that incorporate human feedback. This research direction shows consistent growth in understanding the intersection between human cognition and AI capabilities in programming contexts. Awards and Recognition: NSF Career Award for research on safe and reliable LLM-based code generation Amazon Research Award for human-in-the-loop deep learning optimization Best Paper Honorable Mention Award from SIGCHI for visualizing examples of deep neural networks Best Paper Honorable Mention Award from VAHC for interactive cohort analysis Dr. Zhang actively serves the research community as Program Committee member for major conferences including ICSE, ASE, FSE, CHI, and UIST. His service includes chairing workshops and student research competitions, demonstrating his commitment to mentoring the next generation of researchers. His lab develops systems that address real-world challenges in programming productivity and software safety, with applications spanning from autonomous driving systems testing to data science workflows.

Fatemeh Hendijani Fardمشاهده پروفایل

استادیار

• Code Intelligence
• Transfer Learning
• AI4SE
• +۵ مورد دیگر

Fatemeh Hendijani Fard is an Assistant Professor in the Department of Computer Science at the University of British Columbia's Okanagan campus. She serves as a graduate student supervisor and teaches courses in Computer Science and Data Science. Dr. Fard is a member of the CITECH program and MMRI, part of the Killam family of scholars, and an active member of both IEEE and ACM. Her research focuses on the intersection of Natural Language Processing and Software Engineering, with particular emphasis on developing code intelligence models for low-resource programming languages like R. She conducts empirical studies and develops techniques to improve the computational efficiency of code-language models while making them accessible to communities with restricted GPU access. Her work strongly advocates for Diversity and Inclusion in STEM, particularly for underrepresented females. Analysis of Dr. Fard's recent publications reveals a strong research trajectory in adapting Large Language Models for code intelligence with a focus on efficiency and accessibility. Her work spans multiple dimensions including code summarization, method name prediction, code search, code clone detection, and program repair, with special attention to low-resource programming languages. A notable trend is her exploration of adapter-based approaches for knowledge transfer that reduce computational requirements while maintaining performance. Izaak Walton Killam Memorial Scholarship Alberta Innovates Technology Futures (AITF) NSERC Discovery NSERC CREATE Mitacs Accelerate UBC Start-up Fund Dr. Fard has secured significant research funding including NSERC Discovery, NSERC CREATE, Mitacs Accelerate, and UBC Start-up funds to support her work on code intelligence for low-resource programming languages. She actively serves as a graduate student supervisor, guiding research in areas related to code representation learning and mining software repositories. Her service to the academic community is extensive, having served on program committees for major conferences including FSE, MSR, ASE, SANER, and ICSME across multiple years. Dr. Fard leads research initiatives focused on making code intelligence accessible to communities working with understudied programming languages. Her team conducts empirical studies and develops new techniques specifically designed for low-resource languages, with particular attention to the R programming language. This work addresses diversity and inclusion in AI tools by ensuring developers with limited computational resources can benefit from advances in neural networks and automated tools.

Thomas Schallerمشاهده پروفایل

استاد

• Business Process Management
• Workflow Management
• IT Security
• +۳ مورد دیگر

Prof. Dr. Thomas Schaller serves as Professor at Hof University of Applied Sciences' Faculty of Informatics, where he has coordinated the joint Administrative Informatics program with the University of Applied Sciences for Public Administration and Law since his 2002 appointment. His office resides in Building C, Room C118 at Alfons-Goppel-Platz 1, Hof, maintaining Monday office hours from 13:15-14:00. He completed both undergraduate studies and doctoral research in Business Informatics at Bamberg University, specializing in system development, office automation, and manufacturing systems with his dissertation focusing on workflow management systems. Prior academic service included industry roles at Bertelsmann AG and Loewe AG. Research centers on business process automation across economic and administrative domains, with core expertise in workflow management, IT security, and enterprise architecture. His work bridges theoretical frameworks like Subject-oriented BPM (S-BPM) with practical implementations in public administration digitization and Industry 4.0 contexts. Key contributions address role-based access control, cross-organizational process modeling, and paperless government systems. Recent publications (2012-2022) reveal evolving specialization from foundational workflow systems toward smart factory security and cloud-based resource management, consistently emphasizing public sector applications. The 15 most recent works demonstrate methodological progression from organizational modeling to context-sensitive cross-institutional dependencies, with increasing focus on cybersecurity challenges in industrial IoT environments. As program coordinator for Administrative Informatics, he oversees curriculum development integrating business process management with public administration requirements. His industry background informs practical teaching approaches in electronically supported business processes, maintaining strong connections between academic research and real-world governmental IT implementations.

• Mechanism Design
• Dynamic Pricing
• Robust Mechanism Design
• +۵ مورد دیگر

Dirk Bergemann is the Douglass and Marion Campbell Professor of Economics at Yale University, with additional appointments in Finance and Computer Science. He serves as Contributing Faculty at Yale-NUS College in Singapore and has been affiliated with the Cowles Foundation for over twenty years. His research bridges economic theory with digital market applications. Education PhD in Economics, University of Pennsylvania, 1994 Bergemann specializes in dynamic mechanism design, robust pricing strategies, information structures, and market design. His work integrates game theory with practical challenges in cloud computing, digital advertising, and data-driven markets. He develops theoretical frameworks for optimal pricing under uncertainty and examines how information shapes strategic interactions in multi-product environments. His recent publications (2023-2025) demonstrate a sharp focus on the economics of digital technologies, particularly cloud services, large language models, and digital advertising auctions. Key themes include robust mechanism design against informational uncertainty, strategic topology of information flows, and data-driven approaches to preference elicitation. This body of work consistently connects foundational microeconomic theory with emerging challenges in computational economics. Awards and Honors Douglass and Marion Campbell Professor of Economics Research Fellow, CEPR Research Fellow, CESifo Research Fellow, European Economic Association Research Fellow, Society for Advancement of Economic Theory Research Fellow, Econometric Society Member of the Council of the Econometric Society (2021) Co-editor, American Economic Review: Insights As a senior Yale professor, Bergemann mentors doctoral students in economic theory and mechanism design. His research program is supported by substantial grants enabling cutting-edge work at the intersection of economics and computer science, though specific grant details are not publicly itemized. He actively contributes to the academic community through editorial leadership and professional society service.

Nasibeh Esmaeiliمشاهده پروفایل

استادیار

• Demography
• Agent-Based Modeling
• Mathematical Modeling
• +۱ مورد دیگر

Nasibeh Esmaeili is an Assistant Professor in the Department of Demography at the University of Tehran, Iran. She holds a Ph.D. in Demography and maintains active research in computational demographic modeling approaches. Her research focuses on agent-based modeling , intelligent and mathematical modeling approaches , and neural-network-wavelet-based forecasting in demography. Her work bridges computational methods with demographic analysis to address complex population dynamics, particularly in the Iranian context. Her most recent publication (2024) demonstrates her expertise in applying multi-agent-based modeling to examine how family policies and economic conditions impact low fertility rates in Tehran, Iran. This research represents the intersection of computational social science and demographic analysis. Dr. Esmaeili maintains professional contact through the University of Tehran and continues to contribute to demographic research methodology.

• Mathematical modeling
• Cell motility
• Agent-based modeling
• +۶ مورد دیگر

Professor Franziska Matthäus is a theoretical biologist at Goethe University Frankfurt and a Fellow at the Frankfurt Institute for Advanced Studies (FIAS). Her research focuses on mathematical modeling of spatiotemporal processes in biological systems, particularly cell motility and pattern formation. She leads an active research group within the Life- and Neurosciences department, collaborating extensively with experimental partners to develop models that bridge microscale cellular processes with population-level behavior. Dr. Matthäus earned her doctorate summa cum laude from the University of Warsaw in 2005 after studying biophysics at Humboldt University of Berlin. She held postdoctoral positions at the University of Heidelberg, where she became head of the research group "Complex Biological Processes" in 2011. In 2016, she accepted a junior professorship at the University of Würzburg before joining Goethe University Frankfurt and FIAS, where she currently holds a Giersch endowed professorship. Her research employs agent-based models, partial differential equations, and image analysis techniques to study cell motility, pattern formation, and tissue dynamics. Her group develops mathematical models describing individual cells and large cell populations, incorporating internal signaling processes and interactions with chemical and mechanical environments. Current projects investigate embryo development, cancer cell migration, organoid dynamics, and pattern formation in biological systems. They use advanced computational methods including GPU implementations for efficient simulations and have developed specialized tools like a Julia package for 3D particle image velocimetry. Professor Matthäus has organized significant academic initiatives including "The Beauty of Theoretical Biology" project with Springer Publishing, which collects scientific images from theoretical biology research for publication and exhibition. Her publication record shows consistent output across mathematical biology, with recent work focusing on vertex models, pattern formation mechanisms, and advanced image analysis techniques for studying cellular dynamics. Giersch endowed professorship at Goethe University She actively supervises PhD students including Camile Kunz, Zoë Lange, Tim Liebisch, Marc Pereyra, and Rutian Zhou, guiding them in research projects that combine mathematical modeling with biological applications. Her teaching includes courses in computational biology, bioinformatics, and theoretical biology for both bachelor's and master's students at Goethe University. She offers specialized modules in the Physical Biology of Cells and Cell Interactions (PBioC) program, emphasizing hands-on research experience with data analysis, modeling, and simulation techniques. Professor Matthäus maintains strong international collaborations and has contributed to numerous interdisciplinary research projects, particularly those bridging mathematics, physics, and biology. Her work has applications in understanding metastasis formation, parasite-host interactions, and embryonic development processes.