Find Academic Supervisors

Sendil EthirajView profile

Professor

• Strategic Management
• Innovation
• Competitive Strategy
• +3 more

Sendil Ethiraj is a Professor of Strategy and Entrepreneurship at London Business School (LBS) and serves as Deputy Dean (Faculty). Originally from India, he earned his PhD in Management from The Wharton School at the University of Pennsylvania. Prior to joining LBS, he spent over a decade as faculty at the Ross School of Business, University of Michigan. His research focuses on strategic management, covering innovation processes, competitive strategy, organizational design, human capital management, and technology-driven industry transformation. He has extensively studied innovation in complex systems across sectors like oil and gas, mutual funds, and healthcare insurance. His work addresses organizational structures for capability development, employee goal alignment, and platform governance in digital ecosystems. At LBS, he contributes to academic leadership as a member of the Governing Body since 2022. His initiatives emphasize designing reward systems to attract global research faculty and advancing diversity, inclusion, and belonging through the LBS Advisory Board. He teaches core courses in LBS's Master's, Executive Education, and PhD programs. Key research areas: Innovation in unconventional industries Human capital dynamics Platform governance Strategic response to technological disruption Organizational design for complex systems Contact: sethiraj@london.edu

Alina EneView profile

Associate Professor

• Theoretical Computer Science
• Algorithms
• Optimization
• +3 more

Alina Ene is an Associate Professor in the Department of Computer Science at Boston University. Her research focuses on theoretical computer science, algorithms, optimization, and theoretical machine learning, with recent work emphasizing submodular optimization and convex/non-convex optimization techniques. Key research areas include Submodular optimization Convex and non-convex optimization Stochastic gradient methods Streaming and parallel algorithms Her recent publications analyze high-probability convergence in optimization, adaptive gradient methods, and submodular function applications in machine learning and network design. Current advisees include PhD students Fabian Spaeh and Ta Duy Nguyen. She has previously mentored students and postdocs such as Andrew Suh, Adrian Vladu, and Rafael da Ponte Barbosa. Alina has taught courses like CS 237 (Probability in Computing), CS 531 (Advanced Optimization Algorithms), and CS 591 E2 (Convex Optimization Algorithms) at Boston University, and earlier courses at the University of Warwick.

Olivia AdairView profile

Researcher

• Health Economics
• Colorectal Cancer Screening
• Cost-Effectiveness Analysis
• +3 more

Olivia Adair is a researcher in the School of Mathematics and Physics , focusing on health economics and colorectal cancer screening. Her work integrates microsimulation modeling, systematic reviews, and cost-effectiveness analysis to evaluate public health policies in Northern Ireland and internationally. Research Trends : Her recent publications emphasize comparative modeling methods, economic evaluations of screening programs, and systematic reviews of healthcare interventions. Key themes include cost-effectiveness , population-specific screening , and health policy validation . Scientific Awards : William and Betty MacQuitty Travel Scholarship (2023) Grants & Collaborations : She collaborates with experts like Dr. Felix Lamrock, Professor Mark Lawler, and Dr. Emma McFerran, focusing on regional cancer screening challenges. Her thesis project highlights interdisciplinary teamwork in healthcare modeling.

Guido PerboliView profile

Professor

• Operations Research
• Logistics
• Last-mile Delivery
• +9 more

Guido Perboli is a Full Professor in the Department of Management and Production Engineering (DIGEP) at the Polytechnic University of Turin, where he also serves as Logistics Coordinator and Project Coordinator for activities supporting relationships with government bodies. He is a member of the Interdepartmental Center CARS@PoliTO (Center for Automotive Research and Sustainable Mobility) and serves as Director of the ICT for City Logistics and Enterprises (ICElab@Polito) research center, which he founded in 2016. His research interests span a broad range of topics including Operations Research, Logistics, Last-mile Delivery, Sustainable Logistics, Combinatorial Optimization, Stochastic Programming, Business Development, and Lean Business methodologies. His work particularly focuses on City Logistics, Green Logistics, and the application of emerging technologies like Blockchain and AI in supply chain management. He has developed GUEST, a Lean Business methodology for innovation processes from early idea definition to implementation. Professor Perboli's recent publications demonstrate a strong focus on urban logistics, last-mile delivery optimization, blockchain applications in supply chains, and the integration of AI techniques in transportation systems. His work shows an increasing trend toward interdisciplinary research that combines optimization methods with emerging technologies to address sustainable urban mobility challenges. Professional Recognition: CASE Best Paper award from IEEE Conference on Automation Science and Engineering (2011) Effective member of INFORMS (2019-present) Effective member of EURO (1995-present) Effective member of AIRO (1995-present) Associate Editor for Journal of Applied Research and Technology (2020-present) Associate Editor for Sustainability (2018-present) Professor Perboli actively advises PhD students and has supervised numerous research projects, including EU-funded initiatives like SINFONICA, HESTER, and 5G-LOGINNOV. He serves as Scientific Director for multiple commercial research projects focused on blockchain, IoT, and AI applications in logistics. Beyond academia, he is Chief Scientific Officer of Arisk S.p.A., a fintech company specializing in business crisis prediction using AI and machine learning. His research group, ICElab@Polito, focuses on two main areas supporting urban growth: logistics and enterprises. The center collaborates with numerous companies including Amazon, DHL, and FCA, addressing real-world challenges in urban logistics and supply chain management through innovative research approaches.

Frédéric Robert-NicoudView profile

Professor

• Economic Geography
• Urban Economics
• International Trade
• +1 more

Frédéric Robert-Nicoud is Full Professor of Political Economy at the Geneva School of Economics and Management (GSEM) , University of Geneva. He has held academic positions at HEC Lausanne and the London School of Economics (LSE), with visiting roles at CEMFI, CERAS, CORE, Princeton, and other institutions. His research focuses on Economic Geography, Urban Economics, and International Trade , with significant contributions to agglomeration theory, trade liberalization, and spatial sorting. Education: PhD in Economics, London School of Economics (2002) Master's in Econometrics and Mathematical Economics (Summa Cum Laude, 1999) Master's in International Relations (HEID, 1996) Bachelor's in Economics (University of Geneva, 1994) His research interests explore the interplay between trade liberalization, urban inequality, tech clusters, and labor market dynamics. Recent work analyzes climate change's geographic implications and structural transformation in cities. He has developed frameworks for understanding highway impacts on spatial sorting, agglomeration effects, and trade's role in unemployment. Key scientific awards include: CEPR Fellow Affiliated with Spatial Economic Research Centre (SERC) Former Editor of Journal of Economic Geography (6 years) Former Associate Editor of Review of International Economics (4 years) Member of Conseil scientifique des économistes His grants and fellowships span NBER, CEPR, and SERC affiliations. He has presented at major conferences including the Urban Economics Association Annual Meetings, European Economic Association Congress, and World Trade Institute seminars.

• Control Technology
• Distributed Optimization
• Machine Learning
• +4 more

Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.

Sean WelleckView profile

Assistant Professor

• Large Language Models
• Mathematical Reasoning
• Code Generation
• +4 more

Sean Welleck is an Assistant Professor at Carnegie Mellon University's School of Computer Science, specifically within the Language Technologies Institute (LTI). He leads the L3 Lab and serves as an advisor for the AI for Math Fund. His academic journey includes a PhD from New York University under Kyunghyun Cho and postdoctoral positions at the Allen Institute for Artificial Intelligence and the University of Washington with Yejin Choi. Dr. Welleck's educational background shows a strong foundation in computer science. He earned his PhD in Computer Science from New York University, where he worked under the mentorship of Kyunghyun Cho and Zheng Zhang. Prior to this, he completed his MSE and BSE in Computer Science from the University of Pennsylvania, demonstrating a long-standing commitment to the field. Dr. Welleck's research focuses on bridging informal and formal reasoning with AI, with particular emphasis on developing learning, inference, and evaluation algorithms for large language models. His work spans multiple cutting-edge areas including mathematical reasoning , code generation , inference algorithms , and AI reasoning agents . A significant portion of his recent work involves combining AI with formal methods for mathematics, where he has developed frameworks like Llemma (an open-source language model for mathematical reasoning) and meta-generation (for inference-time algorithms). His research is characterized by a strong theoretical foundation coupled with practical applications that push the boundaries of what AI systems can achieve in formal reasoning domains. Analysis of Dr. Welleck's recent publications reveals a clear research trajectory focused on enhancing language models' capabilities in formal reasoning and mathematical problem-solving. His work demonstrates an evolution from foundational research in neural text generation to increasingly sophisticated approaches that integrate formal methods with deep learning. Key trends include the development of inference-time algorithms that improve model performance without additional training, frameworks for mathematical reasoning that connect informal and formal proofs, and novel evaluation methodologies for language models. His publications consistently appear in top-tier conferences including NeurIPS, ICLR, ICML, and ACL, reflecting the high impact of his contributions to the field. Dr. Welleck's scientific achievements have been recognized with several prestigious awards: NAACL 2025 Best Paper Award ICLR 2025 Oral Presentation (Top 2%) ICLR 2025 Spotlight Presentation (Top 5%) NeurIPS 2021 Outstanding Paper Award (Top 0.1%) for MAUVE NVIDIA AI Labs Pioneering Research Award (2017 and 2018) As an educator and mentor, Dr. Welleck actively guides the next generation of AI researchers. He currently advises multiple PhD students including Pranjal Aggarwal, Weihua Du, Andre He, and Seungone Kim (some co-advised with other faculty), along with MS students Riyaz Ahuja, Jiewen Hu, Qinyue Tan, and Thomas Zhu, and undergraduate Tate Rowney. At CMU, he teaches advanced courses such as Neural Code Generation and Advanced NLP, and has previously taught at New York University and the University of Washington. His commitment to education extends to creating resources like the Thesis Review Podcast and developing tutorials on neural theorem proving that have been presented at major conferences. Dr. Welleck leads the L3 Lab at CMU, which focuses on the intersection of language, learning, and logic. The lab brings together students and researchers to tackle challenging problems in AI reasoning, with particular emphasis on mathematical reasoning and code generation. Recent initiatives include the development of Llemma, an open-source language model specialized for mathematical reasoning, and work on inference-time algorithms that enable language models to improve their performance through additional computation during inference rather than through additional training.

Ulrike BerendtView profile

Professor

• Family Sociology
• Housing Policy
• Urban Sociology
• +7 more

Prof. Dr. Ulrike Berendt is a University Professor for Practice-Oriented Social Sciences, specializing in Family and Housing, at the University of Duisburg-Essen's Faculty of Social Sciences. She has held this position since the Summer Semester of 2002, following her appointment at Gerhard-Mercator-University Duisburg (which later became the University of Duisburg-Essen). From SS 2008 to WS 2009/10, she served as Vice Dean for Studies and Teaching at the Department of Social Sciences. Her educational background includes studying social sciences, journalism and communication studies, and law at Ruhr University Bochum from 1989-1995, where she graduated with a degree in applied social research with a specialization in sociology. She received her doctorate in Social Sciences (Dr. rer. soc) from Ruhr University Bochum in 1998, supported by a doctoral scholarship from 1995-1997. Berendt's research focuses on family sociology and social change, urban and housing sociology, policy analysis, and governance. Her work examines generational relations, gender equality policy in Europe, welfare state models, urban development, migration in cities, and civil society. She analyzes policy fields, theoretical foundations, policy processes, actor networks, governance theories, and decision-making mechanisms. Her research consistently bridges academic theory with practical social policy applications. Her publication history reveals a strong emphasis on practical social science applications, particularly in housing policy, social welfare, and urban development. The most recent works show increasing focus on gender-sensitive housing solutions, neighborhood stabilization through cross-sectoral cooperation, and adapting urban environments to demographic changes like aging populations. Her research demonstrates consistent engagement with practical policy challenges faced by municipalities and social service providers. Berendt has held significant administrative roles including Chair of the Examination Board for BA Political Science, Equal Opportunities Officer for the Department of Social Sciences, and membership on the Faculty Council, Tuition Fees Steering Group, and Senate Commission for Teaching, Studies and Continuing Education. She has led numerous research projects funded by government ministries, particularly the Ministry for Health, Social Affairs, Women and Family of North Rhine-Westphalia. Her team includes scientific assistants Martin Krybus and Till Behmenburg, along with secretariat support from Stefanie Schuylen-Matuschek. Her work bridges academic research with practical social policy implementation, focusing on real-world social challenges in urban environments.

Flavio EspositoView profile

Associate Professor

• Cyber-physical Systems
• Networked Systems
• Network Virtualization
• +5 more

Flavio Esposito is an Associate Professor in the Computer Science Department at Saint Louis University's School of Engineering. He also serves as a Research Institute Fellow and CS Graduate Coordinator. His office is located in ISE 234D at 3450 Lindell Blvd, St. Louis, MO. Dr. Esposito's research focuses on cyber-physical systems and networked systems, including network virtualization, network management, Software-Defined Networks (SDN), network architectures, and wireless networks. He has a strong interest in interdisciplinary applications of these technologies to medicine and agriculture. His work bridges theoretical networking concepts with practical implementations. His publications span key areas in networking research, with recent work focusing on congestion control algorithms, virtual network embedding, recursive network architectures, and edge computing applications. The research trends show a progression from foundational networking protocols toward more sophisticated applications integrating machine learning, edge computing, and cyber-physical systems, with increasing emphasis on real-world applications in diverse domains. Outstanding Graduate Mentoring Faculty Award from the School of Engineering (2021) Finalist for the Undergraduate Mentoring Award in the College of Arts and Sciences Multiple NSF research awards including US Ignite, ICE-T, CNS Core, CC* Integration, CPS:TTP, and ModernCARE projects COMCAST Innovation Fund Award (January 2020) International Center for Responsible Gaming (ICRG) Award ($150K) Dr. Esposito actively mentors PhD and MS students, with numerous current and past students who have gone on to positions at major tech companies, universities, and research institutions. He has been a Principal Investigator on multiple significant research grants totaling millions of dollars. He co-founded Spaghetti Code Labs with former PhD student Alessandro Sangiorgi, whose cybersecurity educational app WeeNet has achieved 5.7M+ downloads. He leads several research labs and teams focused on cyber-physical systems, with current openings for PhD students, visiting researchers, and postdocs working on networks, learning, edge computing, and applications to medicine and agriculture. His teams have developed numerous software systems including Software Mutant, Neighborhood Method Prototype, VINEA, ProtoRINA, and BUtorrent.

John E. TaylorView profile

Professor

• Smart City Digital Twins
• Urban Infrastructure Resilience
• Network Dynamics
• +5 more

John E. Taylor is the Frederick Law Olmsted Professor and Associate Chair for Faculty Development and Research Innovation at the Georgia Institute of Technology's School of Civil and Environmental Engineering within the College of Engineering. His research focuses on the intersection of human and engineered networks, with particular emphasis on creating resilient infrastructure systems that serve society's needs while creating more livable communities. Taylor's research interests span multiple domains including Smart City Digital Twins , Urban Infrastructure Resilience , Network Dynamics , and Building-Occupant Interaction . His work examines how human behavior, infrastructure systems, and environmental factors interact during normal operations and extreme events. He has developed innovative approaches to understanding urban systems through the lens of network theory and computational modeling. His publication record demonstrates consistent contributions to the fields of urban analytics and infrastructure resilience, with a recent focus on digital twin technologies for urban systems. Taylor's work shows a clear trajectory toward increasingly sophisticated integration of AI, network science, and civil infrastructure engineering to address complex urban challenges. His research has particular relevance for cities facing climate change impacts and seeking to build more equitable and resilient communities. Taylor leads the Network Dynamics Lab at Georgia Tech, where he mentors PhD students and postdoctoral researchers. His lab has produced significant work on human-infrastructure interaction, particularly during disasters and extreme events. The lab's research combines computational modeling, data analytics, and field studies to understand and improve urban systems. His work has been applied to real-world challenges including river emergency response systems, urban heat exposure forecasting, and disaster response optimization. Taylor has collaborated with city officials and agencies to implement systems that have demonstrable community benefits, such as the AI-enabled camera system for drowning prevention on the Chattahoochee River and crime reduction systems using mobile cameras guided by AI algorithms.

Moïse BlanchardView profile

Assistant Professor

• Machine Learning Theory
• Online Algorithms
• Optimization
• +4 more

Moïse Blanchard is an Assistant Professor and Tennenbaum Early Career Professor at the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, having joined in August 2025. Previously, he was a Postdoctoral Fellow at Columbia University Data Science Institute. His academic journey includes a Ph.D. in Operations Research from MIT (2024), and M.Sc. and B.Sc. degrees in Applied Mathematics from École Polytechnique. Blanchard's research focuses on the intersection of machine learning theory, statistics, and optimization. His work addresses fundamental questions in universal learning, online algorithms, and convex optimization under memory constraints. His research program explores learnability under minimal assumptions, query complexity/memory tradeoffs, and decision-making in adversarial environments. This work has significant implications for theoretical computer science, operations research, and statistical learning theory. His publications reveal a strong emphasis on foundational aspects of learning theory and optimization. Recent work demonstrates expertise in universal learning frameworks, memory-constrained optimization, and probabilistic analysis of combinatorial problems. His research often bridges theoretical computer science with practical optimization challenges, particularly in contexts where traditional i.i.d. assumptions don't hold. Columbia DSI postdoctoral fellowship, 2024 INFORMS Transportation Science & Logistics (TSL) best student paper award, 2023 Air Force Office of Scientific Research Grant (AFOSR), with Prof. Patrick Jaillet, 2023 COLT 2022 Best student paper runner-up Bronze medal, Alibaba Global Mathematics Competition, 2022 Blanchard has received significant research funding including an Air Force Office of Scientific Research Grant. His work has been recognized with multiple prestigious awards, including the INFORMS TSL best student paper award for his research on the k-Traveling Salesman Problem. His extensive publication record in top venues demonstrates a strong research trajectory with impactful contributions to theoretical machine learning and optimization.

Tsun-Ming TsengView profile

Professor

• Microfluidic Large-Scale Integration
• Optical Network-on-Chip
• Microfabrication
• +3 more

Tsun-Ming Tseng is a Professor and principal investigator at the Chair of Electronic Design Automation at the Technical University of Munich (TUM). He leads the Emerging Technology Group and oversees multiple DFG/BMBF-funded research projects in the areas of microfluidic large-scale integration, optical network-on-chip design, and novel microfabrication techniques. Dr. Tseng's research focuses on design automation for emerging technologies, with particular expertise in three main areas: microfluidic large-scale integration, optical network-on-chip systems, and novel microfabrication processes. His work bridges the gap between electronic design automation and cutting-edge applications in bioengineering, photonics, and advanced manufacturing. His research group develops sophisticated algorithms and tools for optimizing design, reliability, and performance in these emerging domains. Analysis of Dr. Tseng's recent publications reveals a strong focus on practical implementation challenges in emerging technologies. His work spans both theoretical algorithm development and practical system implementation, with particular emphasis on reliability, performance optimization, and manufacturing considerations. The research shows increasing integration between different technology domains, particularly the convergence of microfluidics, optical networking, and electronic design automation. Dr. Tseng has been awarded multiple significant research grants including: "DE-TW-CloudWRONoC" (BMBF-NSTC project, PI, 2025-2028, EUR 797.7K) "DE-TW-PI3D" (BMBF-NSTC project, PI, 2024-2027, EUR 391.6K) "Physical Design for Microfluidic Large-Scale Integration" (DFG research grant, PI, 2024-2026, EUR 331.9K) Multiple other DFG and industrial projects totaling over EUR 3 million in funding He has successfully supervised numerous doctoral researchers and postdoctoral fellows, with current group members including Jiahui Peng, Debraj Kundu, Liaoyuan Cheng, and several others. Dr. Tseng leads the Emerging Technology Group at TUM, which focuses on developing design automation methodologies for next-generation technologies. The group maintains strong collaborations with international institutions, including partnerships with researchers in Taiwan and Hong Kong. The team operates state-of-the-art facilities for research in microfluidics, optical networking, and advanced microfabrication techniques.

• Optical Networks-on-Chip
• Wavelength-Routed Optical Networks
• Electronic Design Automation
• +4 more

Zhidan Zheng is a researcher at the Technical University of Munich (TUM), working within the Chair of Electronic Design Automation led by Prof. Ulf Schlichtmann. His office is located in room 0509.05.911 at Arcisstr. 21, 80333 Munich, with direct contact available via email zhidan.zheng@tum.de and phone +49 (89) 289 - 23692. Zheng holds a Master of Science degree as indicated by his academic title M.Sc. and has been actively contributing to the field of optical interconnects and network-on-chip design. Zheng's research focuses on wavelength-routed optical networks-on-chip, with particular expertise in network topology optimization, fault tolerance mechanisms, waveguide routing algorithms, and bandwidth allocation strategies. His work addresses critical challenges in photonic integrated circuit design, including thermal variation effects, crosstalk mitigation, and lifetime extension for communication-intensive systems. Zheng has developed several innovative methodologies including ToPro+ for topology projection, LightR for fault-tolerant architectures, and WROXIM for network-level simulation. Analysis of Zheng's publication trends from 2021-2025 reveals a consistent focus on practical implementation challenges of optical networks-on-chip. His research has evolved from foundational topology design (Light, 2021) to increasingly sophisticated solutions addressing reliability (LightR, 2023) and comprehensive system integration (ToPro+, 2025). The work demonstrates strong collaboration with researchers including Mengchu Li, Tsun-Ming Tseng, and Ulf Schlichtmann across multiple high-impact venues including DAC, DATE, ICCAD, and ASP-DAC. Zheng actively contributes to the Electronic Design Automation research group at TUM, participating in projects related to analog EDA, emerging technologies, and optical networks. His research is situated within TUM's broader initiatives in photonic integration and high-performance computing architectures, working closely with Prof. Schlichtmann's team on funded projects in the optical NoC domain.

Michael C RodriguezView profile

Professor

• Psychometrics
• Educational Measurement
• Test Accessibility
• +7 more

Michael C Rodriguez is a Professor in the Quantitative Methods in Education department at the University of Minnesota's College of Education and Human Development. He serves as Dean in the Administration (CEHD) and is a leading expert in psychometrics and educational assessment. PhD in Measurement and Quantitative Methods, Michigan State University (1999) MA in Educational Psychology, University of Minnesota (1995) His research focuses on psychometric properties of tests , optimizing test accessibility for diverse learners, and advancing applied measurement through collaborations with schools. Key areas include constructed-response vs. multiple-choice item design, SDG-aligned educational practices, and the intersection of developmental assets with student engagement . Recent publications examine automated evaluation tools , bullying impacts on foster youth, and test validity frameworks . His work aligns with the UN Sustainable Development Goal 4 (Quality Education) . He collaborates on grants from the U.S. Department of Education and Institute of Education Sciences , including projects like the Individual Growth and Development Indicators for Hmong preschoolers and Computer Adaptive Storybook Assessment (CASA).

• Wireless Communications
• 5G/6G Networks
• Mobile Communications
• +6 more

Marina Petrova is a Professor at RWTH Aachen University, holding positions in both the Teaching and Research Area of Mobile Communications and Computing and the Chair and Institute for Networked Systems. She is also a member of the Steering Committee for the Mobility & Transport Engineering (MTE) profile area at the university. Her office is located at Kackertstraße 9, 52072 Aachen, Germany. Professor Petrova's research focuses on cutting-edge wireless communication technologies, with particular emphasis on next-generation mobile networks. Her work spans multiple dimensions of wireless systems including: 5G and 6G network architectures and protocols Cell-Free Massive MIMO systems Millimeter-wave communications Resource allocation and scheduling in wireless networks Wi-Fi sensing and coexistence analysis Integration of distributed learning services in wireless networks Beamforming and beam management techniques Ultra-Reliable Low-Latency Communications (URLLC) Her recent publications demonstrate a strong trend toward the integration of artificial intelligence and machine learning techniques in wireless network design and optimization. She has been particularly active in exploring the convergence of communication and sensing functionalities (ISAC - Integrated Sensing and Communication), which is considered a key enabler for future 6G networks. Professor Petrova's research also addresses practical implementation challenges in next-generation wireless systems, with several publications focusing on ns-3 implementations and experimental validations. Professor Petrova has received recognition for her contributions to the field through numerous publications in top-tier venues, though specific awards are not mentioned in the available information. Her work shows strong industry relevance with applications in smart industries, autonomous systems, and future communication networks.