جست‌وجوی استاد و پژوهشگر

Kevin Woodمشاهده پروفایل

دانشیار

• Biophysics
• Antibiotic Resistance
• Microbial Ecology
• +۳ مورد دیگر

Kevin Wood was an Associate Professor of Physics and Biophysics at the University of Michigan. He held a dual PhD in Theoretical Physics and Physical Chemistry from the University of California-San Diego (2007), followed by postdoctoral research in Systems Biology (2008-2013) and Chemistry (2007-2008) at Harvard University. His research focused on applying theoretical physics principles to study antibiotic resistance, microbial ecology, and cancer biology. Wood's work emphasized understanding population dynamics in bacterial colonies, drug resistance evolution, and optimizing treatment strategies through mathematical modeling. Education: B.S. Chemical Physics, Centre College (2001) M.S. Biology, University of California-San Diego (2003) Ph.D. Physics and Ph.D. Physical Chemistry, University of California-San Diego (2007) Postdoctoral Fellowships: Harvard University (2007-2013) Wood's research interests included spatial evolution of drug resistance, collateral sensitivity in bacteria, and critical phenomena in cancer cell populations. His lab developed novel frameworks for predicting antibiotic efficacy and designing treatment sequences to combat resistance. Despite his untimely passing, he left a legacy of impactful contributions to biophysics and mentorship of students. Advising and Grants: Wood mentored numerous students but specific grants were not detailed. His Wood Lab was located in the Randall Laboratory and Homer A. Neal Lab at the University of Michigan.

Ronald G. Larsonمشاهده پروفایل

استاد

• Complex Fluids
• Fluid Mechanics
• Polymer Physics
• +۶ مورد دیگر

Ronald G. Larson serves as the George Granger Brown Professor of Chemical Engineering and A. H. White Distinguished University Professor at the University of Michigan's College of Engineering, with additional appointments in Mechanical Engineering and Macromolecular Science & Engineering. His research leadership spans multiple departments within the Chemical Engineering Division, where he directs the Larson Lab focused on fundamental and applied soft matter physics. His research program investigates complex fluids through computational and theoretical frameworks, emphasizing polymer physics, rheology, and molecular simulations. Key thrusts include polymer melt processing, biomembrane dynamics, colloidal systems, and polyelectrolyte coacervation. The group employs advanced techniques like Brownian dynamics, coarse-grained modeling, and multiscale simulation to address challenges ranging from industrial polymer processing to biomedical applications. Recent publications (2023-2025) reveal strong momentum in rheological modeling of complex fluids, with particular emphasis on self-healing materials, wax deposition in pipelines, and crystallization mechanisms. The work bridges fundamental molecular insights with industrial applications, demonstrating consistent high-impact output across polymer science, soft matter physics, and chemical engineering domains. The Larson Lab operates as a collaborative hub within the Chemical Engineering Department, leveraging computational resources to advance understanding of fluid mechanics and material properties. Current projects integrate machine learning with traditional modeling approaches, reflecting the group's commitment to methodological innovation while maintaining strong connections to experimental validation and real-world engineering problems.

• Cryptography
• Information Security
• Applied Mathematics
• +۳ مورد دیگر

Carles Padro Laimon is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Mathematics and the School of Telecommunications Engineering. He is a leading researcher in cryptography and information security, focusing on secret sharing schemes, combinatorial structures, and cryptographic protocols. His work integrates discrete mathematics, coding theory, and algorithmic design to address security challenges in digital systems. Padro leads the MAK Research Group (Mathematics Applied to Cryptography) and the ISG-MAK Information Security Group. He has been involved in numerous competitive research projects, including initiatives on post-quantum cryptography and secure multi-user systems. His contributions span over 211 documented activities, including articles, theses, and conference participations. His research interests include the theoretical foundations of cryptography, with a focus on optimizing secret sharing schemes, analyzing matroid-based structures, and developing secure communication protocols. He has collaborated extensively with institutions like the UPC and European research networks, contributing to both academic and practical advancements in cybersecurity. Padro holds a PhD in Mathematics from UPC and has supervised doctoral theses and mentored researchers in his field. His work frequently appears in top journals like IEEE Transactions on Information Theory, Designs, Codes and Cryptography, and SIAM Journal on Discrete Mathematics.

Gheorghe Paunمشاهده پروفایل

پژوهشگر

• Automata and Language Theory
• Natural Computing
• DNA Computing
• +۳ مورد دیگر

Gheorghe Paun is a Senior Researcher at the Institute of Mathematics of the Romanian Academy (1994–present) and a Visiting Researcher at the University of Sevilla (2009–present). He earned his Ph.D. in Mathematics from the University of Bucharest (1977), supervised by Solomon Marcus. His research spans Automata and Language Theory (descriptional complexity, regulated rewriting), Natural Computing (DNA computing, membrane computing), and he is the initiator of membrane computing . Education : M.Sc. in Mathematics (1974), University of Bucharest (specialization: computer science) Ph.D. in Mathematics (1977), University of Bucharest His work includes foundational contributions to DNA computing (book DNA Computing. New Computing Paradigms , 1998) and membrane computing (book Membrane Computing. An Introduction , 2002). He has collaborated with over 100 researchers globally and advised multiple Ph.D. students, including Victor Mitrana and Gianina Georgescu. His scientific awards include the Gheorghe Lazar award (1981), Corresponding Member of the Romanian Academy (1997), Doctor Honoris Causa from Silesian University of Opava (2007), and recognition as an ISI Highly Cited Scientist (2007). He also holds honorary citizenships in Curtea de Arges (1998), Arges County (2007), and Cicanesti village (2009). Paun has served on editorial boards for journals like Theoretical Computer Science. Natural Computing Series and Natural Computing , and organized major workshops in membrane computing. His academic contributions include over 150 publications and editorial roles in 20+ journals.

Eric Thunمشاهده پروفایل

دانشیار

• Political Economy of China
• Emerging Markets Strategy
• Global Value Chains
• +۵ مورد دیگر

Eric Thun is a Tutorial Fellow in Management at Brasenose College and the Peter Moores Associate Professor in Chinese Business Studies at the University of Oxford’s Saïd Business School. He has taught at Princeton University and joined Oxford in 2005, focusing on undergraduate management education and executive programs. Thun holds a BA from Princeton University (1990) and a PhD from Harvard University (1999). He served as a postdoctoral fellow at MIT’s Industrial Performance Center before returning to Princeton as an Assistant Professor in the Woodrow Wilson School and Department of Politics. His research interests center on China’s political economy, particularly how industrial structures and policies influence firm capabilities and competition. Thun has extensively studied China’s automotive industry, multinational corporations, and the integration of China into global production networks. More broadly, he explores strategies of indigenous and multinational firms in emerging markets and the impact of digital transformation on global value chains. Thun’s recent articles (2021–2025) highlight themes of modular ecosystems, state-market dynamics, and challenges in reshoring supply chains. Earlier works (2019–2000) emphasize innovation, policy impacts on GVCs, and sustainability in global trade. No scientific awards are explicitly listed. While he teaches and advises across multiple programs, no formal advisees or grants are detailed in the provided texts. His affiliations include Brasenose College and Saïd Business School, with no lab or team affiliations mentioned.

• Robotics
• Computer Vision
• Sensor Systems
• +۳ مورد دیگر

Dr. Olga Vysotska is a Researcher affiliated with the Professorship for Robotic Systems at ETH Zurich's Department of Mechanical and Process Engineering. Her work focuses on advancing robotic systems through research in sensor-based navigation, SLAM (Simultaneous Localization and Mapping), and autonomous systems. She holds a doctoral degree and is based in Zurich, Switzerland. Her email is olga.vysotska@inf.ethz.ch. Research Interests: Olga's research spans robotics, computer vision, and autonomous navigation. She specializes in LiDAR-based place recognition, SLAM algorithms, and cross-modal localization using 3D scene graphs. Her work addresses challenges such as environmental changes, sensor fusion, and data association in dynamic environments like agriculture and underground exploration. Key themes include robust localization, loop closure detection, and adaptive algorithms for real-world robotic applications. Publications Overview: Olga's recent work emphasizes diffusion-based LiDAR place recognition (2025), 4D spatial-temporal mapping for agricultural robots (2023), and SceneGraphLoc for cross-modal localization (2024). Her research trends highlight innovation in sensor integration, algorithmic robustness, and practical applications in challenging environments. Earlier contributions include exploration of catacombs with mobile robots (2013) and SLAM enhancements using public map data (2017). Grants & Advising: While specific grants or student advisement details are not listed, her active publication record suggests involvement in funded research projects. Her work often collaborates with industry and academic partners to advance robotic autonomy in complex scenarios. Labs/Teams: As part of the Robotic Systems Professorship, she likely contributes to ETH Zurich's robotics labs focused on SLAM, sensor systems, and autonomous navigation. Her projects may intersect with the Department's broader initiatives in mechanical and process engineering.

Yuanzhu Chenمشاهده پروفایل

استاد

• Computer Networking
• Mobile Computing
• Complex Networks
• +۵ مورد دیگر

Yuanzhu Chen is a Professor in the School of Computing at Queen’s University, affiliated with the Faculty of Arts and Science. He previously served as Professor and Department Head at Memorial University of Newfoundland (2005–2021). His research focuses on computer networking, mobile computing, complex networks, and applied machine learning, emphasizing wireless innovation beyond traditional wired systems. He holds a PhD from Simon Fraser University (2004) and a B.Sc. from Peking University (1999). Education: PhD in Computing Science (Simon Fraser University, 2004); B.Sc. in Computer Science (Peking University, 1999). Earlier roles include Post-doctoral Researcher at Simon Fraser University (2004–2005) and leadership positions at Memorial University, including Department Head (2019–2021). Research Interests: Network Coding and Opportunistic Routing Mobile and Wireless Network Protocols Complex Network Analysis Machine Learning Applications Indoor Positioning Systems Social Network Dynamics Selected Awards: Recipient of Queen’s University President's Award for Distinguished Teaching. Lab Affiliation: Director of the Wireless Networking and Mobile Computing Lab (WineMocol). Active in collaborative projects involving smartphone sensors, community-based environmental monitoring, and stock market prediction using web data.

Baoyu Zhouمشاهده پروفایل

استادیار

• Large-scale Continuous Optimization
• Constrained Stochastic Optimization
• Nonsmooth Optimization
• +۳ مورد دیگر

Baoyu Zhou is an Assistant Professor of Industrial Engineering at Arizona State University (ASU), School of Computing and Augmented Intelligence. He holds a PhD in Industrial and Systems Engineering from Lehigh University (2018–2022), an M.S. in Industrial Engineering from Lehigh University (2016–2018), and a B.E. in Mechanical Engineering from Shanghai Jiao Tong University (2012–2016). His research focuses on developing efficient algorithms for large-scale, stochastic, and constrained optimization problems, with contributions to sequential quadratic programming, nonsmooth optimization, and derivative-free methods. Before joining ASU, Zhou was a postdoctoral researcher at the University of Michigan (Department of Industrial and Operations Engineering) and the University of Chicago (Booth School of Business). He has received the Van Hoesen Family Best Publication Award and the Elizabeth V. Stout Dissertation Award. His work bridges optimization theory and practical applications, emphasizing scalability and robustness in complex systems. Zhou teaches courses such as IEE 470: Stochastic Operations Research at ASU and has guest-lectured at the University of Michigan. He actively contributes to the academic community through organizing conference sessions, reviewing for top journals, and participating in workshops at NeurIPS and SIAM. His group currently advises three PhD students focusing on optimization algorithms and their applications. Key research areas include large-scale continuous optimization, constrained stochastic optimization, and derivative-free methods. His publications span journals like SIAM Journal on Optimization and INFORMS Journal on Optimization, addressing challenges in nonlinear systems, variance reduction, and algorithmic convergence.

Christiane Barzمشاهده پروفایل

استاد

• Operations Research
• Revenue Management
• Stochastic Dynamic Optimization
• +۳ مورد دیگر

Christiane Barz is a Professor of Mathematics at the University of Zurich's Institute for Business Administration since 2016. Previously, she held academic roles at the UCLA Anderson School of Management, the Chicago Booth School of Business, and the Technical University (TU) Berlin. Her research focuses on stochastic dynamic systems, Markov decision processes, and their applications in revenue management. She emphasizes making mathematical tools accessible and practical for real-world problem-solving, particularly in optimizing decision-making under uncertainty. Education includes a degree in industrial engineering and a doctorate from the University of Karlsruhe (TH), Germany. Her career path includes postdoctoral research at the University of Chicago's Booth School of Business and roles as an Assistant Professor at UCLA. She combines academic excellence with balancing family life, advocating for gender equity in STEM fields. Her research explores risk-sensitive decision-making frameworks, dynamic pricing models for transportation and healthcare, and optimizing resource allocation in complex systems. Recent work includes applications in FlixBus, air cargo networks, and improving patient admission scheduling in hospitals. Barz's teaching philosophy prioritizes demystifying mathematics for students, encouraging critical engagement rather than fear of complexity. She collaborates with industry partners to apply operations research methods to real-world challenges, emphasizing both theoretical rigor and practical relevance.

Mehdi Farahaniمشاهده پروفایل

استادیار

• Operations Management
• Socially-Responsible Operations Management
• Service Operations Management
• +۵ مورد دیگر

Mehdi Farahani is an Assistant Professor at the C.T. Bauer College of Business, University of Houston, in the Department of Decision & Information Sciences. He holds a Ph.D. in Operations Management from the Jindal School of Management, The University of Texas at Dallas, and has previously served as an Assistant Professor at the University of Miami and as a Postdoctoral Associate at MIT's Center for Transportation & Logistics. His research focuses on Operations Management , with specialized interests in Socially-Responsible Operations , Service Operations , and Supply Chain Contracting . His work integrates sustainability, equity, and efficiency in operational decision-making, particularly in humanitarian and environmental contexts. The recent publications highlight a strong trend in sustainable and resilient operations, with applications in agriculture, infrastructure, cloud computing, and urban services. His research often involves modeling complex trade-offs under uncertainty and designing contracts or policies to improve system performance. Scientific Awards and Recognitions: Honorable Mention: POMS Humanitarian Operations and Crisis Management Best Paper Award Selected for presentation at the SIG Meeting on Service Operations MSOM 2021 Conference Featured in INFORMS Analytics Collections (formerly Editor's Cut) Mehdi Farahani advises on operations and supply chain research and contributes to graduate education through courses such as SCM 6301 (Supply Chain Management in Executive MBA) and SCM 7330 (Demand and Supply Integration). While specific grant details are not mentioned, his publication record in premier journals indicates strong research support and scholarly impact. He collaborates with leading researchers including M. Dawande, G. Janakiraman, and H. Gurnani. He is actively contributing to the advancement of operations management through high-impact research and academic engagement, with no indication of affiliation with a formal lab or research team beyond his published collaborations.

Dahlia Malkhiمشاهده پروفایل

استاد

• Distributed Systems
• Blockchain Technology
• Financial Technology
• +۳ مورد دیگر

Professor Dahlia Malkhi is a leading academic and researcher in distributed systems and blockchain technology. She currently holds a faculty position at the University of California, Santa Barbara (UCSB), where she heads the Foundations of Financial Technology (FfTech) research lab. Her work focuses on reliability, security, and consensus mechanisms in distributed systems, with a recent emphasis on blockchain innovations like HotStuff, which underpins Diem, Aptos, and other blockchains. She has held influential roles at industry leaders such as Chainlink Labs, Diem Association, VMware, and Microsoft Research. Education: Ph.D. in Computer Science from The Hebrew University of Jerusalem. Past roles include CTO of Diem Association (2019–2022), Principal Researcher at VMware (2014–2019), and Partner Principal Researcher at Microsoft Research (2004–2014). Research Interests: Blockchain consensus algorithms (e.g., HotStuff, Flexible Paxos), Byzantine Fault Tolerance (BFT), secure multi-party computation (FairPlay), and distributed database systems (CorfuDB). Her work bridges academic theory with industrial applications, emphasizing practical scalability and security. Awards: ACM Fellow (2011), IEEE TCDP Outstanding Technical Achievement Award (2021), IBM Faculty Award (2003/2004). She has also held leadership roles in conferences like Usenix ATC and program chairs for multiple distributed systems events. Advising & Grants: Advises projects at Space Computer, Lyquor Labs, and Chainlink Labs. Her research labs and collaborations include work on BBCA-Chain, Lumiere, and BFTBrain, advancing consensus mechanisms in decentralized systems. Labs/Teams: Leads UCSB’s FfTech lab, co-founded VMware Research, and contributed to foundational blockchain projects like DiemBFT and Espresso Systems. Her work impacts technologies such as NSX-T control planes and distributed financial infrastructure.

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +۴ مورد دیگر

Yong-Bin Kang is a Senior Data Science Research Fellow at the ARC Centre of Excellence for Automated Decision Making and Society (ADM+S) at Swinburne University of Technology, affiliated with the School of Social Sciences, Media, Film and Education. He holds a PhD in AI from Monash University and leads numerous transdisciplinary research projects applying artificial intelligence to address complex societal challenges. Education: PhD in Faculty of IT, Monash University, Australia Dr. Kang's research focuses on Responsible AI and Society, with specific interests in developing Societal-AI platforms that integrate social data with ethical principles. His work spans healthcare, humanitech, education, financial planning, environmental health, and justice domains. He investigates how AI can enhance decision-making processes while promoting societal well-being, with particular attention to ethical implementation and human-centered approaches. His expertise encompasses AI, natural language processing, machine learning, and decision-making optimization. Analysis of Dr. Kang's recent publications reveals a strong trajectory toward socially responsible AI applications across diverse domains. His work consistently bridges technical AI capabilities with social implications, particularly focusing on ethical frameworks, community-centered design, and addressing societal inequalities through technology. The publications demonstrate increasing collaboration across disciplines including criminology, environmental science, mental health, and education. Dr. Kang is actively involved in significant research funding initiatives, with multiple ongoing projects that address critical societal challenges through AI. His supervision availability includes Doctorate (PhD) candidates, indicating his commitment to mentoring the next generation of researchers in AI and data science fields. Current Flagship Areas: Digital Capability Innovative Society Manufacturing Futures Sustainable Development Goals: Good Health and Well Being (SDG 3) Industry, Innovation and Infrastructure (SDG 9) Affordable and Clean Energy (SDG 7)

Nikolaos Tziavelisمشاهده پروفایل

استادیار

• Database Systems
• Database Theory
• Query Processing and Optimization
• +۳ مورد دیگر

Nikolaos Tziavelis is an Assistant Professor in the Department of Computer Science and Engineering at Basking Engineering, University of California, Santa Cruz. His research bridges theoretical and practical aspects of database systems, focusing on improving real-world data processing through novel algorithmic solutions. Education: Ph.D. from Northeastern University (advised by Mirek Riedewald and Wolfgang Gatterbauer) Diploma from National Technical University of Athens, Greece Research Interests: Data Management Database Theory Query Processing and Optimization Algorithms for Big Data Integration of Machine Learning with Database Systems Publication Trends: His work emphasizes ranked enumeration, join algorithms, and query optimization, with applications in responsive database systems and machine learning integration. Key themes include theoretical foundations, practical system improvements, and algorithmic efficiency for complex data processing tasks. Scientific Awards: 2022 Google PhD Fellowship PODS 2021 Best of Recognition 2023 VLDB PhD Workshop Best Paper Award 2024 Khoury Research Award from Northeastern University Service: He has served on program committees for major conferences including SIGMOD, VLDB, PODS, EDBT, ICDE, and Northeast Database Day.

Andreas Seidel-Morgensternمشاهده پروفایل

استاد

• Chemical Engineering
• Process Engineering
• Reactor Design
• +۷ مورد دیگر

Andreas Seidel-Morgenstern is a full Professor and Chair holder for Chemical Process Engineering at the Faculty of Process and Systems Engineering, Otto von Guericke University Magdeburg since 1995. He serves as a Scientific Member and Director at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, leads the research group 'Physical-Chemical Fundamentals of Process Engineering', and has held administrative roles including Dean (2005-2006) and Managing Director (2007-2008, 2015-2016). Education: Diploma in Process Engineering (1982), Leuna-Merseburg University of Applied Sciences PhD in Physical Chemistry (1987), Academy of Sciences of the GDR His research specializes in reactor design, chromatographic reactors, membrane reactors, crystallization, and enantiomer separation. He focuses on mathematical modeling, process simulation, and optimization of chemical systems, with applications in adsorption, heterogeneous catalysis, and transport processes in porous media. Recent publications explore methanol synthesis optimization and multistage reactor control systems, reflecting interests in chemical reaction engineering and sustainable energy processes. His work emphasizes process intensification and modeling-driven optimization. Scientific Recognition: Humanity in Science Award (2015) Max-Buchner-Preis (1999) 4 Faculty Best Dissertation Awards (OvGU) Ehrendoktorates from Lappeenranta University of Technology (Finland, 2008) and Syddansk Universitet (Denmark, 2012) He leads international collaborations with institutions like TU Dortmund, ETH Zurich, University of Manchester, and Syncom (Netherlands), focusing on integrated chemical processes in multiphase systems.

JuHyun Leeمشاهده پروفایل

دانشیار

• Architectural design computing
• Design cognition
• Urban complexity
• +۸ مورد دیگر

JuHyun Lee is an Associate Professor of Architecture and Computational Design in the School of Built Environment at the Faculty of Arts, Design and Architecture (ADA), University of New South Wales (UNSW) Sydney, where they also hold the prestigious title of Scientia Academic. With a professional background in architecture and construction (1998-2002), they have held academic positions across Australia including a five-year post-doctoral fellowship at the University of Newcastle (2012-2017) and a senior research fellowship at the University of South Australia (2018), following earlier research and teaching roles in South Korea (2003-2011). Lee specializes in architectural design computing, design cognition, and urban complexity, integrating computational methods, cognitive science, and architectural theory to advance architectural intelligence and human-centered design. Their research spans architectural visualization, analysis and design methods, algorithm/protocol design, and data visualization with computational approaches. They have established a strong research program examining the intersection of language, culture, and design cognition, particularly focusing on cross-cultural design communication between Australia and Korea. Lee's recent publications demonstrate a clear trajectory toward increasingly sophisticated integration of computational methods with architectural design theory, particularly in the areas of shape grammar, space syntax, and machine learning applications. Their work shows consistent focus on practical applications of computational design methods to real-world architectural problems, with growing emphasis on cross-cultural collaboration and intelligent design systems. The research portfolio reveals a deepening engagement with AI and machine learning techniques applied to architectural design assessment and generation. Scientia Academic at UNSW Sydney Associate Fellow of the Higher Education Academy (AFHEA, 2020) As an educator, Lee develops cutting-edge courses in computational design and Building Information Modeling (BIM), integrating experiential learning and industry engagement. They have secured over $11 million in research funding, including multiple ARC Discovery Projects and an Australia-Korea Foundation grant. Lee co-directs the Advanced Architectural Analytics Laboratory (A 3 LAB), leading interdisciplinary research on design automation, spatial analysis, and machine learning applications in architecture, while also leading cross-cultural initiatives like the Australia-Korea Architects' Network (AKAN). Lee supervises multiple HDR students working on culturally sustainable urban design, socio-spatial patterns in public housing, and computational layout generation. Their research has significant implications for improving design communication across cultural boundaries and developing more coherent, clear, and accessible built environments through computational design approaches.