جست‌وجوی استاد و پژوهشگر

Nadine von Frankenberg und Ludwigsdorffمشاهده پروفایل

پژوهشگر

• Ubiquitous computing
• Smart environments
• Building control systems
• +۴ مورد دیگر

Dr Nadine von Frankenberg und Ludwigsdorff is a Postdoctoral Researcher at the Technical University of Munich (TUM) , affiliated with the Institute for Informatics I1 . She conducts research and teaches in the areas of ubiquitous computing, smart environments, and occupant-centric building control systems. Her office is located in Garching b. München, and she can be reached at nadine.frankenberg@tum.de . Education & Dissertation: In 2022 she defended her doctoral dissertation “Rationale Management for Event-Based Arbitration of Thermal Quality in Shared Spaces” at TUM, laying the groundwork for her subsequent work in human-in-the-loop building controls. Research Interests: Ubiquitous and pervasive computing Smart environments and IoT Building control systems with human-in-the-loop Occupant-centric & personalized indoor environmental quality Digital twins, machine learning, and occupant health Her work unites computer science, HCI, and building technology to create adaptive systems that balance energy efficiency with individual comfort and well-being. Publication Profile: Across 2016-2022 she authored or co-authored more than a dozen peer-reviewed papers in top venues such as BuildSys , CHI , Ubicomp , ICSE , and SIGCSE . A clear trend emerges: early work (2016-2017) focused on visualization and assessment tools for smart buildings, followed by 2018-2020 contributions on sensor-based thermal comfort inference, machine-learning control algorithms, and cloud architectures for multi-device studies. Most recently, her 2021-2022 publications center on conflict resolution and rationale management for shared thermal spaces, culminating in her 2022 dissertation. Scientific Service & Awards: Program Committee member: ICSE 2021-2023, SIGCSE 2017-2023, HICSS-56 2023, CSEE&T 2020-2022, SEUH 2020 Publicity Chair: CSEE&T 2020, SEUH 2020 Publication Co-Chair: BuildSys 2020 Teaching & Student Supervision: She has been a recurring instructor and lab supervisor for Introduction to Software Engineering (EIST) , iPraktikum , and Interactive Learning seminars since 2018. Additionally, she has advised or co-advised more than 25 Bachelor’s, Master’s, and Guided Research theses on topics spanning thermal comfort systems, digital twins, mHealth applications, and IoT network simulation. Labs & Teams: She is a core member of the Research Group for Applied Software Engineering (ASE) at TUM, collaborating closely with Prof. Bernd Brügge’s chair and interdisciplinary partners in architecture, psychology, and health informatics.

• Global Optimization
• Augmented Reality
• Computational Biology
• +۳ مورد دیگر

Ignatov Andrey Dmitrievich is a lecturer at the Faculty of Computer Science , Department of Software Engineering at National Research University Higher School of Economics (HSE), since 2019. He holds a postgraduate degree in Informatics and Computer Engineering (2023) and Master's in Applied Mathematics and Computer Science from Lomonosov Moscow State University (2019). Lecturer in Software Engineering and Applied Mathematics programs Conducts research in combinatorial optimization and protein structure modeling Recipient of multiple teaching awards including Best Teacher (2023, 2025) His research focuses on global optimization , parallel computing , and computational biology with applications to cloud scheduling and protein modeling. Publications demonstrate expertise in heuristic algorithms , branch-and-bound optimization , and BOINC-based distributed computing . Recent teaching includes courses on Algorithms and Data Structures, Protein Structure Modeling, and Optimization Methods. 2023-2024, 2024-2025: Allowance for young teachers 2023: Gratitude from Faculty of Computer Science 2024: Category 'New Lecturers' 2025: Co-inventor of legal assistance software patent

Işıl Dilligمشاهده پروفایل

دانشیار

• Program analysis
• Program synthesis
• Program verification
• +۴ مورد دیگر

Işıl Dillig is an Associate Professor of Computer Science at the University of Texas at Austin, where she leads the UToPiA research group. Her academic career spans over a decade of significant contributions to programming languages research, particularly in program analysis, verification, and synthesis. Dr. Dillig received all her academic degrees (BS, MS, and PhD) from Stanford University before joining the faculty at UT Austin. Her educational background established the foundation for her innovative research approach that bridges theoretical computer science with practical applications. Her research focuses on developing techniques to make software systems more reliable, secure, and easier to build through advanced program analysis, verification, and synthesis methods. She has pioneered approaches that combine symbolic reasoning with machine learning to tackle complex software engineering challenges across multiple domains including security, databases, and programming language theory. Her work demonstrates exceptional depth in creating practical tools that address real-world software development problems while maintaining strong theoretical foundations. Analysis of Dr. Dillig's publication record reveals a consistent trajectory of innovation in program synthesis, with recent work expanding into neurosymbolic approaches that bridge neural networks with formal methods. Her research shows strong connections between theoretical foundations and practical applications, particularly in security-critical systems, database technologies, and blockchain applications. The evolution of her work demonstrates increasing sophistication in handling complex program structures while maintaining practical usability. Dr. Dillig has received prestigious recognition for her research contributions: Sloan Fellowship NSF CAREER award As a dedicated educator and research leader, Dr. Dillig has served in significant roles including Program Chair for PLDI 2022 and Steering Committee member for PLDI. She has mentored numerous students through her UToPiA research group, guiding research in program synthesis, verification, and analysis. Her work has been supported by substantial research grants that have enabled innovative projects at the intersection of programming languages and security. Dr. Dillig leads the UToPiA (UT Austin Programming, Languages, and Analysis) research group, which focuses on developing novel techniques for program analysis, verification, and synthesis. The group maintains strong collaborations with industry partners and academic institutions worldwide, translating theoretical advances into practical tools that address real software engineering challenges.

• network security
• network measurement and analysis
• cloud computing
• +۴ مورد دیگر

Dr. Kuai Xu serves as a Professor of Computer Science at Arizona State University's School of Mathematical and Natural Sciences within the New College of Interdisciplinary Arts and Sciences. He maintains affiliations with the Center for Cybersecurity and Trusted Foundations, focusing on securing critical network infrastructures. His academic credentials include: Ph.D. in Computer Science, University of Minnesota (2006) M.S. in Computer Science, Peking University, China (2001) B.S. in Computer Science, Peking University, China (1998) Dr. Xu's research centers on network security , network measurement , and IoT systems , with significant contributions to smart home security, network behavior analysis, and social media epidemiology. His work bridges theoretical modeling and practical implementations for real-world network challenges. Recent publications (2022-2025) reveal a concentrated focus on IoT security frameworks, machine learning-driven activity inference from device traffic, and pandemic surveillance through social media analysis. Key themes include DNS security hardening, attack graph modeling, and privacy-preserving spatial analytics in smart environments. Through courses like ACO 331 (Network Forensics Analysis) and ACO 361 (Secure Coding Concepts), Dr. Xu mentors students in cybersecurity fundamentals while supervising research via ACO 399 and individualized instruction (ACO 499). His teaching integrates cutting-edge research into classroom applications. As an active affiliate of ASU's Center for Cybersecurity and Trusted Foundations, Dr. Xu contributes to interdisciplinary initiatives addressing emerging threats in home networks, IoT ecosystems, and social media platforms.

Sinho Chewiمشاهده پروفایل

استادیار

• Optimal Transport
• Log-concave Sampling
• Machine Learning Theory
• +۳ مورد دیگر

Sinho Chewi is an Assistant Professor of Statistics and Data Science at Yale University, where he conducts research at the intersection of mathematics, statistics, and machine learning. His work focuses on theoretical aspects of computational statistics, particularly leveraging optimal transport theory for solving complex problems in sampling and inference. Chewi earned his B.S. in Engineering Mathematics and Statistics from the University of California, Berkeley in 2018, followed by a PhD in Mathematics and Statistics from the Massachusetts Institute of Technology in 2023 under the supervision of Philippe Rigollet. Prior to joining Yale, he was a postdoctoral researcher at the Institute for Advanced Study during the 2023-2024 academic year. His research interests span optimal transport theory , log-concave sampling , variational inference , and theoretical foundations of machine learning . Chewi is currently authoring a comprehensive book on the complexity of log-concave sampling, building on his extensive publication record. His work bridges theoretical mathematics with practical applications in statistical computing and artificial intelligence. Analysis of Chewi's publication record reveals a strong focus on developing rigorous mathematical frameworks for sampling algorithms, with particular emphasis on complexity analysis, convergence guarantees, and connections between functional inequalities and optimization. His recent work extends into diffusion models, neural network theory, and parallel sampling algorithms, demonstrating both depth in core statistical theory and breadth across machine learning applications. ICLR 2023 (Notable Top 5%) ALT 2023 (Best Student Paper) NeurIPS 2021 (Spotlight) ICLR 2025 DeLTA Workshop (Best Short Paper Award) Chewi has received significant research support through his postdoctoral position at the Institute for Advanced Study and collaborations with leading researchers at institutions including MIT, NYU, and Microsoft Research. His teaching portfolio includes advanced courses in optimization techniques and sampling methods, reflecting his expertise in theoretical foundations of machine learning algorithms.