جست‌وجوی استاد و پژوهشگر

Richard Townsendمشاهده پروفایل

عضو هیئت علمی

• Functional languages
• Compilers for embedded systems
• Program analysis and optimization
• +۵ مورد دیگر

Richard Townsend serves as an Assistant Teaching Professor in the Department of Computer Science within the School of Engineering at Tufts University. He joined the institution in September 2019 after completing his Ph.D. at Columbia University, making a transition from research to teaching. Townsend is also the Founding Director of the Emerging Scholars Program in the Computer Science Department at Tufts University. His educational background includes a Doctor of Philosophy (2019), Master of Philosophy (2016), and Master of Science (2015) from Columbia University, along with a Bachelor of Arts in Computer Science from Oberlin College (2013). Townsend's research focuses on the application of functional programming languages to hardware design, specifically developing techniques to translate recursive algorithms with irregular memory access patterns into efficient hardware implementations. His work centers around an optimizing Haskell-to-SystemVerilog compiler project, exploring the semantic connections between functional languages and hardware specifications. He specializes in compilers for embedded systems, program analysis and optimization, and embedded domain-specific languages. His publication record demonstrates consistent contributions to the intersection of functional programming and hardware design, with multiple papers on compositional dataflow circuits, hardware synthesis from functional languages, and resource allocation for hardware implementations. His work shows a clear trajectory from theoretical foundations to practical implementations in hardware compilation. Senior Survey 2022 Significant Impact and Best Course (2022) Significant Impact Award (2021) Significant Impact Award (2020) Townsend actively mentors junior faculty through the Teaching Track Faculty Mentoring program and serves on various committees including the Teaching Track Search Committee and Computer Science Curriculum Committee. He teaches a range of courses including Programming Languages, Introduction to Computer Science, and specialized topics like Algorithmic Music Composition. His teaching approach incorporates evidence-based STEM teaching practices, as evidenced by his participation in multiple teaching development programs.

Giovanni Bacciمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Markov Chains
• +۳ مورد دیگر

Giovanni Bacci is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark. His primary affiliation is within The Technical Faculty of IT and Design, specifically the Distributed, Embedded and Intelligent Systems group, focusing on Artificial Intelligence and Machine Learning. He holds an ORCID identifier (0000-0001-8529-0681) and his contact information includes giovbacci@cs.aau.dk and a personal website. His research interests center on probabilistic systems, formal methods, and stochastic models, with a focus on Markov chains, Markov decision processes, and bisimilarity distances. He has contributed to the development of tools like the BisimDist library and Jajapy, a learning library for stochastic models. Bacci has received notable awards, including the Best Paper Award at QEST 2018 and Teacher of the Year 2022/2023 for the Faculty of IT and Design. His work spans theoretical foundations and practical applications, such as optimizing membrane desalination processes via reinforcement learning (2023-2024 project). Key publications include studies on dissimilarity in linear dynamical systems, parameter estimation in continuous-time Markov chains, and active learning of MDPs using Baum-Welch algorithms. His research often bridges theoretical computer science and real-world engineering challenges.

• Inorganic Chemistry
• Materials Science
• Chemical Synthesis
• +۳ مورد دیگر

Dr. Michael Warskulat is a Lecturer at the Institute of Inorganic Chemistry, affiliated with the Faculty of Natural Sciences at Leibniz University Hannover. His professional role involves teaching and research within inorganic chemistry disciplines. No detailed educational background or formal qualifications are explicitly listed in the provided texts. His research areas are inferred from his departmental affiliation to include inorganic chemistry, materials science, and related synthetic methodologies. No specific scientific awards, grants, or publications are documented in the source material. His involvement in academic advising or student supervision also remains unreported. Contact information includes a phone number (+49 2233 964-603), fax (+49 2233 964-80603), and an email address (michael.warskulatorioncarbons.com). The profile was last updated on 22.05.25 by Hannah Pieper.

Arun Nagarajahمشاهده پروفایل

استاد

• Product Development Processes
• Data Management
• Digitalization in Engineering
• +۷ مورد دیگر

Prof. Dr.-Ing. Arun Nagarajah is Chair of Product Development Processes and Data Management at the University of Duisburg-Essen's Faculty of Engineering (IPE). He holds a doctorate from RWTH Aachen University (2010) and previously worked in the automotive industry and supply sector, leading product data management and digitalization initiatives. His research focuses on digitalization in engineering processes, product lifecycle management, machine learning applications, and knowledge graph-based standards interpretation. He leads projects like the UMEK initiative on multidisciplinary engineering processes in power plant construction and has contributed to over 50 peer-reviewed publications since 2011. Education: Mechanical Engineering & Quality Management, University of Wuppertal Doctorate in Mechanical Engineering, RWTH Aachen University (2010) Research Interests: Digital twins, sustainable product design, machine learning in engineering, requirements mining from standards, and modular product development. His work bridges theoretical frameworks (e.g., knowledge graphs) with practical applications in automotive, manufacturing, and energy sectors. Grants & Projects: Led the BMWI-funded UMEK project (2015-2018), focusing on multidisciplinary engineering processes in power plants. Collaborates on initiatives like SMART Standards for machine-actionable engineering guidelines. Labs/Teams: Heads the Product Engineering Processes (PEP) research group, specializing in data-driven product development methodologies and digital transformation in industry.

Ali Ebnenasirمشاهده پروفایل

دانشیار

• Software Engineering
• Formal Methods
• Parallel and Distributed Computing
• +۳ مورد دیگر

Ali Ebnenasir is an Associate Professor of Computer Science at Michigan Technological University, specializing in software engineering, formal methods, and fault-tolerant distributed systems. He holds a B.E. (1994) from the University of Isfahan, M.E. (1998) from Iran University of Science and Technology, and Ph.D. (2005) from Michigan State University. His doctoral work was nominated for the ACM Doctoral Dissertation Award. He worked as a postdoctoral researcher at Michigan State University’s Software Engineering and Network Systems Lab (2005–2006). Research focuses on formal verification of distributed systems, quantum computing algorithms, and fault-tolerant embedded systems. Notable areas include automated analysis of fault tolerance, self-stabilizing protocols, and specification languages for safety-critical systems. He has published extensively in venues like IEEE TDSC, ACM TOSEM, and ICDCS. Educations: B.E. in Computer Engineering, University of Isfahan, Iran (1994) M.E. in Software Engineering, Iran University of Science and Technology (1998) Ph.D. in Computer Science, Michigan State University (2005) His work bridges theoretical foundations with practical implementations, addressing challenges in distributed computing and quantum algorithm optimization. Awards include an IEEE Travel Grant and Michigan State University fellowships. Current research explores quantum circuit distribution and formal methods for legal system modeling. Grants/Awards: AF: Small: Framework for Algorithmic Design of Self-Stabilizing Protocols (2011) Teaching emphasizes software engineering principles and parallel computing. Active in advising graduate students on distributed systems and formal verification methodologies. His lab focuses on GPU-based algorithm acceleration and fault-tolerant system design.

Marco Montaliمشاهده پروفایل

استاد

• Artificial Intelligence
• Process Science
• Business Process Management
• +۵ مورد دیگر

Marco Montali is a Full Professor in Computer Engineering at the Faculty of Engineering, Free University of Bozen-Bolzano. His research focuses on artificial intelligence, process science, and data-aware systems, emphasizing the integration of model-driven and data-driven techniques. He holds a BEng, MEng, and PhD from the University of Bologna. His work bridges AI, formal methods, and process mining, with over 250 publications and significant contributions to declarative process modeling and runtime verification. Education: BEng cum laude in Computer Science Engineering, University of Bologna (2003) MEng cum laude in Computer Science Engineering, University of Bologna (2005) PhD in Electronics, Computer Science and Telecommunications Engineering, University of Bologna (2009) Research Interests: AI and formal methods for process modeling Data-aware dynamic systems and process mining Runtime verification and declarative constraints Object-centric processes and multi-process systems Awards: 2015 Marco Somalvico Award (Best under-35 AI researcher in Italy) 10 Best Paper Awards and 2 Test-of-Time Awards Top 2% Most Cited Scientists (2023) Grants/Projects: Lead the PINPOINT PRIN project and contributed to EU projects like ACSI and Optique. Active in third-party collaborations, including Ontopic s.r.l., a semantic technologies startup. Labs/Teams: Director of the Artificial Process Intelligence (API) research group and co-founder of Ontopic s.r.l.

• Clinical Psychology
• Psychobiology
• Neuroimaging
• +۳ مورد دیگر

Samuel Suarez Suarez is a Researcher affiliated with the University of Santiago de Compostela, holding a position in the Department of Clinical Psychology and Psychobiology within the Faculty of Psychology. He is part of the Institute of Psychology (IPsiUS). He obtained his Doctorate from the same institution in 2020 with a thesis investigating the impact of intensive alcohol consumption on the adolescent brain using functional neuroimaging techniques, supervised by Dr. Sonia Doallo Pesado and Dr. Fernando Cadaveira Mahía. His research focuses on the neurobiological and psychological effects of alcohol use, particularly in adolescents and emerging adults. Key topics include binge drinking's influence on brain structure and function, neuroimaging analysis of addictive behaviors, and longitudinal studies tracking developmental outcomes. His work also explores interventions like physical exercise for substance use disorders and the interplay between mood, craving, and attentional biases in addictive behaviors. His publications consistently highlight structural and functional brain changes linked to alcohol consumption, with a strong emphasis on longitudinal methodologies and translational neuroscience. No formal advising relationships or grants are explicitly mentioned in the provided text. His research is conducted within the interdisciplinary framework of the Institute of Psychology (IPsiUS), though specific lab affiliations are not detailed here.

Shan Linمشاهده پروفایل

دانشیار

• Cyber physical systems
• Wireless networks
• Networked information systems
• +۷ مورد دیگر

Shan Lin is an Associate Professor in the Department of Electrical and Computer Engineering at Stony Brook University, affiliated with the College of Engineering and Applied Sciences. His research focuses on cyber-physical systems, wireless networks, and sensing technologies, addressing challenges in resilient control, smart mobility, and healthcare applications. Lin's work spans theoretical foundations (e.g., formal methods, game theory) and practical implementations (e.g., wearable sensors, smart city systems). He holds a PhD in Electrical Engineering and has contributed to projects like the CyberCardia initiative modeling implantable cardiac devices. His research integrates machine learning, optimization, and embedded systems to solve real-world problems in energy management, healthcare monitoring, and urban infrastructure resilience. Lin leads interdisciplinary teams developing solutions for electric vehicle coordination, real-time emotion recognition, and resilient cyber-physical systems. His publications emphasize safety-critical systems, adaptive control, and human-in-the-loop systems, with applications ranging from smart grids to biomedical devices.

Geoff Hamiltonمشاهده پروفایل

دانشیار

• Program Transformation
• Automated Theorem Proving
• Formal Verification
• +۳ مورد دیگر

Dr. Geoff Hamilton is Associate Professor at Dublin City University's School of Computing. His research develops program transformation hierarchies for applications in theorem proving, verification, optimization, and energy-efficient computing. The distillation algorithm—a key contribution—achieves orders-of-magnitude efficiency gains over standard techniques. Work spans automated verification tools, termination provers, security protocol analyzers, and complexity bounds calculators. Recent publications focus on side-channel attacks, concurrency optimization, and tight complexity bounds for loop programs. Teaching includes comparative programming languages, cryptography, and formal methods. Research consistently advances transformation-based approaches for parallelization, verification, and resource analysis. Current directions include smart contract verification and energy-aware program optimization methodologies.

• Architecture
• Urban Design
• Sustainable Design
• +۱ مورد دیگر

Bryant Pitak is an Adjunct Professor at the Illinois Institute of Technology College of Architecture. He is affiliated with the university's main campus in Chicago, IL, and contributes to both academic and administrative activities within the College of Architecture. While specific research interests are not explicitly detailed, his role in architectural education suggests a focus on pedagogical approaches in design studios and professional practice within architecture. His courses include advanced architecture studios (ARCH 417, 418, 419, and 545), emphasizing synthesis and advanced design methodologies. No scientific awards, grants, or formal advisees are listed in the provided information. His professional contributions are centered on teaching and studio instruction within the College of Architecture.

• Crystallography
• Crystal Growth
• Materials Science
• +۳ مورد دیگر

Juan Manuel García Ruiz is an Ikerbasque Research Professor affiliated with the Donostia International Physics Center (DIPC) in San Sebastián, Spain. His expertise lies in directing research initiatives and managing scientific teams within DIPC's interdisciplinary framework. Education: No formal educational details are provided in the text. Research Interests: His work centers on Crystallography and Crystal Growth , with a focus on understanding mineral formation mechanisms, materials synthesis, and structural characterization of novel compounds. He bridges theoretical physics principles with applied materials science, contributing to advancements in both fundamental and applied research. Advising & Grants: While no specific grants or advisee names are mentioned, his role as a permanent researcher at DIPC implies involvement in mentoring early-stage and post-doctoral researchers. He has undertaken 3 research stays since 2023, though locations/durations are unspecified. Labs & Teams: He collaborates within DIPC's technical and research support groups, contributing to the center's mission in advancing physics and materials science through cutting-edge projects.

Pierre-Alain Reynierمشاهده پروفایل

استاد

• Formal Methods
• Verification
• Model-Checking
• +۷ مورد دیگر

Pierre-Alain Reynier is a Professor at Aix-Marseille University, affiliated with the Computer Science department within the Faculty of Science. He is a researcher at the Laboratoire d'Informatique et Systèmes (LIS), a member of the MoVe team, and co-head of the Pôle Calcul division at LIS and the Verification working group of the GDR IM (CNRS). His research focuses on formal methods, verification, automata theory, timed systems, and robustness analysis. He has led or contributed to multiple national and international research projects, including ANR TickTac, ANR DELTA, and FP7 Cassting. Reynier has supervised numerous PhD and Master’s students, contributing to advancements in areas like weighted timed games and data word transducers. His work bridges theoretical computer science with practical applications, emphasizing robust controller synthesis and formal verification tools. He has published extensively in top-tier conferences and journals, with a strong emphasis on algorithmic solutions for complex system analysis. Education and Academic Contributions: While specific educational milestones are not detailed, his career includes doctoral supervision, postdoctoral mentorship, and academic leadership roles. His research spans theoretical foundations and practical implementations, such as tools for Petri net analysis and timed automata verification. Research Interests: His primary areas include formal methods, model-checking, automata theory, transducers, concurrency, and the robustness of timed systems. He explores decidability questions, controller synthesis, and applications of these theories to real-time embedded systems. Advising and Grants: He has supervised over a dozen PhD and Master’s students, many of whom have gone on to academic and industry roles. His projects often involve collaborations with institutions like INRIA, CNRS, and international partners, addressing challenges in distributed and timed systems. Labs and Teams: As part of the MoVe team at LIS, he contributes to collaborative projects on verification and synthesis. His involvement in the Pôle Calcul division highlights his role in advancing computational methods for complex system analysis.

Alex Aikenمشاهده پروفایل

استاد

• Programming Languages
• Compiler Construction
• Parallel Computing
• +۲ مورد دیگر

Alex Aiken is the Alcatel-Lucent Professor of Computer Science at Stanford University and serves as the Computer Science Division Director at SLAC. He has been a faculty member at Stanford since 2003, after previously serving as a Professor in the EECS department at UC Berkeley (1993-2003) and as a Research Staff Member at IBM Almaden Research Center (1988-1993). Dr. Aiken received his Bachelors degree in Computer Science and Music from Bowling Green State University in 1983 and his Ph.D. from Cornell University in 1988. He previously served as chair of the Stanford Computer Science Department. Alex Aiken's research focuses primarily on programming languages, compilers, and parallel computing systems. He is renowned for his work on the Legion programming system for heterogeneous parallel machines, the FlexFlow system for deep learning parallelization, and the development of Moss, a widely-used plagiarism detection system. His research spans both theoretical foundations and practical implementations, with a strong emphasis on translating research into usable software tools. His work has significant applications in high-performance computing, software analysis, and compiler technology. His recent publications reflect a continued focus on parallel and distributed systems, with increasing attention to the intersection of programming systems and machine learning. The 15 most recent articles demonstrate his leadership in task-based programming models, verification techniques, and the application of AI to programming systems. His work spans from low-level GPU optimizations to high-level language design and verification. ACM Fellow Recipient of ACM SIGPLAN's Programming Languages Achievement Award Phi Beta Kappa's Teaching Award Professor Aiken has advised numerous Ph.D. students throughout his career, with eight current advisees including Benjamin Driscoll, Qiantan Hong, and Rupanshu Soi. His research group has received substantial funding for projects related to parallel programming systems, compiler technology, and software analysis tools. The group maintains several active open-source projects including Legion, FlexFlow, and Moss. Aiken leads the Legion research group, which focuses on developing programming models and runtime systems for heterogeneous and distributed parallel machines. The group has made significant contributions to the field of task-based parallelism and has developed several widely-used software systems that bridge the gap between theoretical research and practical implementation.

Hai Wangمشاهده پروفایل

استاد

• High-Speed Propulsion
• Combustion Chemistry
• Renewable Energy Conversion
• +۵ مورد دیگر

Hai Wang is a Professor of Mechanical Engineering at Stanford University with a distinguished career in combustion science, high-speed propulsion, and renewable energy conversion. His research spans combustion chemistry of conventional and renewable fuels, detonation dynamics, quantum-chemistry guided battery materials design, and transport theories in nanoparticle systems. Education : Ph.D. in Fuel Science from Pennsylvania State University (1992), M.S. in Chemical Engineering from Michigan Technological University (1986), B.Eng. in Polymer Materials from East China University of Science and Technology (1984). He has authored seminal works on soot formation in flames, catalytic oxidation of methane, and laminar flame speed modeling. His recent publications address interdisciplinary challenges such as eco-anxiety and advanced data plane verification. Notable administrative roles include co-founding Hestia Tec, LLC (2010-2014) and serving as President of the Combustion Institute (2024-present). Wang’s work has been recognized with the Humboldt Senior Research Award (2019), Mercator Fellow (2019), and multiple fellowships. He mentors doctoral and master’s students and leads research initiatives like the Combustion Energy Frontier Research Center (2010-2014).

Sunki Hongمشاهده پروفایل

استادیار

• Product Design
• Furniture Design
• Design Philosophy
• +۳ مورد دیگر

Sunki Hong serves as an Assistant Professor at Carnegie Mellon University's School of Design, specializing in product and furniture design through the integration of cultural, natural, and philosophical frameworks. Her work manifests in tangible expressions defined by balanced aesthetics and refined, understated color palettes. Education: Bachelor's degree in Industrial Design, Ewha Womans University (Korea) MFA in Industrial Design, Ewha Womans University (Korea) Master's degree in Furniture and Metal Design, Iowa State University Professor Hong's research explores the synthesis of abstract concepts into physical design solutions, emphasizing philosophical depth through minimalist visual language. Her methodology bridges theoretical inquiry with material execution, creating objects that communicate cultural narratives through restrained formalism and intentional materiality. Scientific Awards: European Product Design Awards Korean International Design Awards New York Product Design Awards While the source text confirms her faculty role and award-winning practice, specific details regarding student advising, research grants, laboratory facilities, or collaborative teams were not documented in the provided materials.