Andreas Johnsson is an Adjunct Senior Lecturer at the Department of Information Technology , Uppsala University, Sweden. His research spans Machine Learning , Network Performance , and IoT Security in the context of 5G/6G Networks and Edge Computing . Research interests include federated learning, transfer learning, and network optimization techniques. His recent work (2024-2021) focuses on self-regulated learning models for 6G, multi-objective neural architecture search, IoT intrusion detection generalizability, and delay prediction in heterogeneous networks. He has co-authored over 15 publications in high-impact venues like IEEE Transactions on Machine Learning in Communications and Networking and IEEE NOMS . Andreas actively collaborates with researchers such as Jalil Taghia, Farnaz Moradi, and Hannes Larsson. His contributions extend to change detection algorithms, policy adaptation frameworks, and feature selection methodologies in dynamic network environments. No formal scientific awards or student advisement details are currently documented.
Roberto Tron is an Assistant Professor in the Mechanical Engineering and Systems Engineering departments at the Boston University College of Engineering , with his office located at 110 Cummington Mall. His research integrates control theory, robotics, and computer vision to solve complex multi-agent coordination problems. His primary research interests focus on Riemannian geometry applications , distributed multi-agent systems , and safety-critical control . Key methodologies include Control Barrier Functions (CBFs), Riemannian optimization, and distributed consensus algorithms, with applications spanning autonomous aerial vehicles, robotic manipulation, and multi-robot security systems. Analysis of his recent publications reveals a strong emphasis on safety verification and real-time optimization for autonomous systems. His work consistently bridges theoretical foundations in nonlinear control with practical implementations in robotics, particularly addressing challenges in limited sensor fields of view, distributed task allocation, and noise-robust navigation. The research shows increasing integration of formal methods like Signal Temporal Logic with learning-based approaches. Tron received his Ph.D. from The John Hopkins University and previously conducted post-doctoral research at the GRASP Lab, University of Pennsylvania. His work demonstrates significant contributions to provably safe autonomous systems through frameworks like the Control Barrier Function Toolbox.
Sofia Kantorovich is a Professor at the University of Vienna, serving as Deputy Head of both the Research Platform MMM (Mathematics-Magnetism-Materials) and Computational and Soft Matter Physics group. Her departmental affiliation is with Computational and Soft Matter Physics at Kolingasse 14-16, 1090 Wien (Room 03.22). She actively teaches undergraduate and graduate courses including Linear Algebra for Computational Science, Analysis for Computational Science, and seminars on soft matter physics through the 2025 academic year. Her research centers on computational modeling of magnetic soft matter systems, with emphasis on ferrofluids, magnetic nanoparticles, nanogels, and supracolloidal polymers. She investigates how external magnetic fields influence structural properties, self-assembly dynamics, rheological behavior, and transport phenomena in complex magnetic fluids. Key applications include drug delivery systems, responsive materials design, and magnetic composites engineering, employing molecular dynamics simulations and theoretical analysis to uncover fundamental mechanisms. Analysis of her 2023-2025 publications reveals consistent focus on field-responsive dynamics in magnetic colloidal systems. Her work frequently examines coarsening phenomena in ferrogranulate networks, morphology-dependent responses in magnetic nanogels, and filamentous structure behavior under applied fields. These studies demonstrate how particle shape, concentration gradients, and interaction potentials govern macroscopic properties in magnetic soft matter. No scientific awards are documented in the available sources. Information regarding graduate student advising and grant funding details is not provided in the current materials. Professor Kantorovich leads research within the Computational and Soft Matter Physics group and co-directs the interdisciplinary MMM platform, which integrates mathematical modeling, magnetic theory, and materials science to advance understanding of complex magnetic systems and their technological applications.
Ricardo Dahis is an Assistant Professor in the Department of Economics at Monash University's Faculty of Business and Economics. His research spans the intersection of Politics, Environment, and Development Economics, with particular focus on Brazilian political economy, public finance, and institutional analysis. Dahis has established himself as a prominent researcher with publications in top economics journals including the Review of Economics and Statistics and the Journal of Public Economics. His research interests encompass political economy, development economics, environmental policy, public finance, and Brazilian institutional analysis. Dahis frequently examines how political institutions shape economic outcomes, with particular attention to corruption, electoral behavior, deforestation in the Amazon, and the impact of technological change on social outcomes. His methodological approach combines rigorous econometric analysis with innovative data collection techniques, often leveraging administrative data from Brazilian government sources. Dahis's publication record shows a consistent focus on Brazilian political economy with increasing attention to environmental issues and technological impacts. His recent work demonstrates sophisticated use of natural experiments and quasi-experimental designs to address causal questions in political economy. The articles reveal a researcher deeply engaged with both theoretical questions in economics and practical policy implications, particularly for developing countries. Dahis is actively involved in academic mentorship, offering supervision hours for students and postdocs. His teaching materials suggest engagement with both undergraduate and graduate instruction in economics. Co-founder of Data Basis (Base dos Dados), a nonprofit startup making high-quality data universally accessible in Brazil Affiliated with SoDa Labs and CDES research groups Active participant in the academic community with presence on Google Scholar, GitHub, and ORCID Dahis maintains an open door policy for academic discussions and offers specialized consultations for Monash University students through scheduled appointments. His work with Data Basis demonstrates a commitment to building research infrastructure that benefits academics, journalists, policymakers, and developers across Brazil.
Professor Ai-Chun Pang is affiliated with the National Taiwan University , serving in both the Department of Computer Science and Information Engineering and the Graduate Institute of Networking and Multimedia . He held leadership roles including Associate Dean (2018-2022) and Director (2013-2016) within the College of Electrical Engineering and Computer Science. His research spans Fog/Edge Computing , Wireless Networking , Mobile Computing , and AIoT Systems , with recent advancements in federated learning security, energy-efficient network design, and 5G/6G optimization. Collaborative work includes applications in vehicular networks, industrial control systems, and non-terrestrial connectivity. Key publication themes: Edge Intelligence and Privacy (2024) Federated Learning for Heterogeneous Devices (2023-2024) 5G Backhaul Optimization (2017-2021) Wireless Energy Transfer (2022) Awarded IEEE Fellow 2021 for contributions to mobile edge networks, he has received multiple IEEE Vehicular Technology Society awards, the CES 2019 Innovation Award , and teaching accolades including National Taiwan University Distinguished Teaching Award (2010) . His lab has produced 16 PhD students now in academia and industry. As Editor-in-Chief of IEEE Wireless Communications Letters and active in conference organization, he shapes global research directions. Current projects focus on GenAI for Networking and Non-Terrestrial Networks , with recent 2024 admissions for new students.
Lingjia Liu is a Professor and Bradley Senior Faculty Fellow at Virginia Tech's Bradley Department of Electrical and Computer Engineering. Her research focuses on enabling technologies for 5G/6G networks, including massive MIMO systems, dynamic spectrum access, and AI-driven communication networks. She holds a Ph.D. from Texas A&M University (2008). Research Interests : 5G/6G Network Architectures (3D MIMO, cloud-RAN, ultra-low latency) AI in Communications (Reservoir Computing, federated learning) IoT & Cyber-Physical Systems (energy harvesting, privacy protection) Non-Terrestrial Networks (satellite-based connectivity) Recent work emphasizes generative AI for network simulation, explainable AI in communication systems, and secure dynamic spectrum sharing. Her research spans theoretical foundations (e.g., OTFS modulation analysis) and practical implementations (e.g., FPGA-based reservoir computing). Awards : Bradley Senior Faculty Fellow (Virginia Tech). Her contributions bridge communication theory and AI, addressing 6G challenges through innovative algorithmic and architectural solutions. Current projects explore agentic protocol learning, federated multi-agent RL for spectrum access, and resilient ML under adversarial conditions.
Shivakant Mishra is a Professor in the Department of Computer Science at the University of Colorado, Boulder, and currently on leave as a Program Director at the NSF's CSR (Computer Systems Research) program. He holds roles as Site Co-Director of the NSF IUCRC Pervasive Personalized Intelligence Center and co-founded the Colorado Research Center for Democracy and Technology. Affiliations: Department of Computer Science, College of Engineering and Applied Sciences Professional Roles: NSF Program Director, Center Leadership Education: Ph.D. in Computer Science (University of Arizona), M.S. (Southern Illinois University), B.Tech. (IIT Bombay). Research focuses on distributed systems, edge computing, socio-technical systems for environmental justice, CyberSafety, and technology's role in democracy. Key projects include the C70 community impact study and smart agriculture systems. His work integrates technology with societal challenges, such as mitigating highway construction impacts and combating cyberbullying. Teaching includes courses on operating systems, distributed systems, and special topics like democracy through technology. Advised Ph.D. students Fei Hu and Jinpeng Miao. Professional activities include organizing conferences (e.g., DSN 2017, CyberSafety workshops) and serving on NSF panels.
Marion K. Matters-Kammerer is a Full Professor of Electrical Engineering at Eindhoven University of Technology, leading research in terahertz (THz) and millimeter-wave systems. She holds positions in the Center for Wireless Technology, THz Electronics and Integration Lab, and RF Sensing & Communication Lab. Her expertise includes integrated circuits, antenna design, and power amplifier systems. She has led EU projects like 3DmicroTune and ULTRA, and co-authored over 70 journal/conference papers with 13 US patents. Education: MSc in Physics from École Normale Supérieure (Paris) and TU Berlin (1999), PhD in Physics from RWTH Aachen (2007). Past roles include Senior Scientist at Philips Research (1999–2011) and Guest Professor at RWTH Aachen (2009–2010). Research focuses on THz spectroscopy, mm-wave integrated circuits, and energy-efficient wireless systems. Key projects involve THz biosensing, 60 GHz sensor networks, and co-integration of photonics and electronics. Her work addresses UN SDGs like affordable and clean energy, and industry-academia collaboration via NXP Smart Mobility projects. Recent articles highlight advancements in mm-wave power amplifiers, waveguide integration, and radar signal processing. Grants include €2.5M for TeraIBs (2025–2028) and €1.8M for Future Wireless Interfaces (2024–2029). Labs include THz Electronics Lab and RF Sensing Team, advancing sensor and communication technologies.
**Daniel Romero** is a **Professor** in the **Department of Information and Communication Technology** at the **University of Agder**, Norway. His research focuses on UAV communications, time-series analysis using machine learning and network science, and decentralized processing for sensor networks. He holds a Ph.D. in Signal Theory and Communications from the University of Vigo (2015), an M.Sc. in Signal Theory (2011), and a Telecommunication Engineering degree (2009). **Education**: Ph.D. in Signal Theory and Communications, University of Vigo (2015) M.Sc. in Signal Theory and Communications, University of Vigo (2011) Telecommunication Engineering, University of Vigo (2009) **Research Interests**: His work spans UAV communication systems (focusing on low-latency, high-reliability networks), time-series analysis for complex systems (using ML and network science), and decentralized computation in sensor networks to improve robustness and hardware efficiency. Recent projects include radio map estimation for mmWave beam alignment, spoofing detection via graph neural networks, and aerial base station placement optimization. **Publications**: Over 30+ peer-reviewed articles in top venues like IEEE Transactions on Wireless Communications and ICC. Recent trends emphasize radio map estimation (2023–2024), UAV-enabled spectrum surveying (2022), and robust D2D communications (2022). **Advising & Grants**: Teaches PhD courses (Statistical Signal Processing, Advanced Optimization) and leads the **Advanced Signal Processing Lab (ASL)**. Collaborates with the **CIEM (Center for Integrated Emergency Management)** on crisis-related communication systems. **Labs/Teams**: Directs the Advanced Signal Processing Lab (ASL.uia.no) and contributes to CIEM, applying ML and signal processing to emergency management challenges.
Marco Nie is a Professor and Chair of the Department of Civil and Environmental Engineering at Northwestern University, where he has been a faculty member since 2006. His research focuses on Transportation Systems Analysis, emphasizing interdisciplinary approaches that integrate optimization, network science, traffic flow theory, economics, and statistics to address complex interactions between human activities, infrastructure, and urban networks. He teaches three courses: an undergraduate/graduate introduction to transportation engineering, and two graduate courses on analytical and computational tools for surface transportation systems design. Education: Marco Nie earned a BS in Structural Engineering from Tsinghua University (Beijing), followed by graduate studies in Transportation at the National University of Singapore (NUS), and a PhD in Transportation from the University of California, Davis. Research interests include improving transportation efficiency, sustainability, and equity through policy and technology. Recent work explores autonomous vehicles, modular transit systems, congestion pricing, and data-driven solutions for EV charging and ride-hail platforms. He has expressed challenges in securing research funding, noting its critical role despite inherent flaws in evaluating research impact through monetary metrics. Scientific Awards: He received the 2021 Transportation Science Meritorious Service Awards . Marco also serves on editorial boards, including Service Science (2023), and actively publishes on topics like urban mobility, freight logistics, and policy analysis. Advising & Grants: While no specific students or grants are listed, he highlights the importance of funding mechanisms for research. His work often involves collaborations with sponsors and stakeholders to address real-world transportation challenges. Labs/Teams: Affiliated with the Center for Science and Protection of Engineered Environments and engages in interdisciplinary research groups focusing on sustainable and equitable urban systems.
Professor Minh N. Do is the Thomas and Margaret Huang Endowed Professor in Signal Processing & Data Science at the University of Illinois at Urbana-Champaign (UIUC), with primary appointment in the Department of Electrical and Computer Engineering. He holds multiple affiliate appointments across campus including with the Coordinated Science Laboratory, Beckman Institute for Advanced Science and Technology, Department of Bioengineering, Department of Computer Science, Institute for Genomic Biology, College of Medicine, and School of Computing and Data Science. Additionally, he serves as Director of the joint VinUni-Illinois Smart Health Center and holds an Honorary Vice-Provost position at VinUniversity. Professor Do received his B.Eng. in Computer Engineering (First Class Honors) from the University of Canberra, Australia in 1997, followed by his Dr.Sci. in Communication Systems from the Swiss Federal Institute of Technology Lausanne (EPFL) in 2001. His educational journey was marked by exceptional achievement, earning the University Medal from the University of Canberra and a Silver Medal from the 32nd International Mathematical Olympiad. Professor Do's research focuses on developing new multidimensional signal processing tools with applications across several domains. His primary research interests include smart health, data science, computational imaging, and signal processing. His work spans biomedical imaging, machine learning, computer vision, and robotics, with particular emphasis on geometric image representations, integrating image formation and processing, and image processing from multiple sensors. His research bridges theoretical investigations with practical applications, creating impactful solutions in healthcare, diagnostics, and AI systems. His recent publications demonstrate a consistent trajectory toward multimodal AI systems, robust learning frameworks, and healthcare applications. Professor Do's work increasingly integrates signal processing with deep learning approaches to address challenges in medical imaging, cross-modal transfer, and real-world deployment of AI systems. His research shows strong emphasis on practical applications with societal impact, particularly in healthcare diagnostics and smart health technologies. Professor Do's scientific achievements have been recognized with numerous prestigious awards: Member of the National Academy of Artificial Intelligence (2025) Fellow of Asia-Pacific Artificial Intelligence Association (2023) Thomas and Margaret Huang Endowed Professor, UIUC (2020-present) Fellow of IEEE (2014) Young Author Best Paper Award, IEEE Signal Processing Society (2008) CAREER award from the National Science Foundation (2003) Best Doctoral Thesis Award from EPFL (2001) As an educator, Professor Do has taught numerous courses spanning digital signal processing, probability, data science, and image processing. His teaching excellence has been recognized with multiple "Teachers Ranked as Excellent" awards at UIUC. He also maintains active industry connections through tech-transfer efforts, having co-founded Personify and served as Chief Scientist of Misfit. His leadership extends to administrative roles, having served as Vice-Provost for VinUniversity during 2020-2021. Professor Do leads research initiatives at the intersection of signal processing and healthcare applications, with particular focus on the Smart Health Center collaboration between UIUC and VinUniversity. His lab develops innovative solutions for medical diagnostics, point-of-care testing, and neurological assessment using advanced signal processing and AI techniques.
Lewis J. Lehe is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign (UIUC), specializing in Transportation Systems. He holds a Ph.D. from UC Berkeley (2016) and prior degrees from UC Berkeley, University of Leeds, and University of Pittsburgh. His research focuses on transportation pricing, urban traffic dynamics, and the economic implications of transportation policies. Lehe's work includes groundbreaking studies on congestion pricing, parking policy impacts, and the integration of AI tools (e.g., GTFS Segments, TransitGPT) for transportation data analysis. He has been recognized with the Gordon F. Newell Award (2016) and Ove Arup Transport Prize (2012), and leads the Urban Traffic & Economics Lab (UTEL) at UIUC. Notable research contributions include modeling hyperdemand in traffic systems, analyzing bus stop spacing, and evaluating taxation strategies for ride-hailing services. His recent articles explore transit pricing determinants, parking lot segmentation via NIR datasets, and equilibrium tolling paradoxes. Lehe advises graduate students like Ayush Pandey and Shirin Qiam, advancing topics such as bus route optimization and GTFS data benchmarking.
Tor Skeie is a Professor at the University of Oslo's Department of Informatics, specializing in networks and distributed systems. His research focuses on high-performance networking, InfiniBand technologies, and adaptive routing systems. Current investigations include automated parameter tuning for reservoir simulations, adaptive routing in InfiniBand hardware, and modeling WiFi quality attenuation. His work develops efficient solutions for virtualized HPC environments and cloud computing infrastructures. Recent publications demonstrate innovations in network modeling, adaptive routing algorithms, and performance analysis of distributed systems. Research collaborations span European projects on high-performance networking infrastructures. Leads the Networks and Distributed Systems (ND) research group investigating fault-tolerant routing, network virtualization, and congestion control mechanisms.
R. Manmatha is an Adjunct Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst and a Principal Scientist at Amazon A9 since 2013. His academic journey includes a Ph.D. in Computer Science from University of Massachusetts Amherst (1997), an M.S. in Electrical Engineering from University of Hawaii (1986), and a B.Tech in Electrical Engineering from Indian Institute of Technology Kanpur (1983). Research Interests Manmatha's work spans Computer Vision , Information Retrieval , and Document Analysis . Key projects include: Developing Vision-Language Models for GUI grounding and OCR-free document understanding Creating Word Spotting techniques for historical manuscripts like George Washington's papers Advancing Image Retrieval through statistical and relevance models Building Meta Search systems using score distribution analysis Optimizing Diffusion Transformers for text-to-image generation Scientific Contributions His research has led to numerous publications in conferences like SIGIR , CVPR , and ICDAR , focusing on: Automatic Image Annotation using cross-media relevance models Scale Space Techniques for handwritten manuscript segmentation Alignment Methods for document-groundtruth generation Indian Language Document Search via locality-sensitive hashing Transformer-based architectures for multimodal and document tasks Advising & Collaborations Manmatha has mentored students including Jiwoon Jeon , Shaolei Feng , Toni Rath , Jamie Rothfeder , and Nitin Srimal . He co-founded Snaptell (acquired by Amazon) and contributed to Amazon's mobile search technology. Labs & Teams He leads the Multi-media Indexing and Retrieval (MIR) group at the Center for Intelligent Information Retrieval (CIIR) , focusing on non-textual information indexing through ASCII conversion and direct content analysis.
Prashant Shenoy is a Distinguished Professor and Associate Dean in the College of Information and Computer Sciences at the University of Massachusetts Amherst. He has been on the faculty since 1998 and heads the Laboratory for Advanced Systems Software while directing the Center for Smart and Connected Society. His research focuses on systems issues for distributed systems ranging from large server clusters to networks of small sensors. Shenoy received his PhD in Computer Science from the University of Texas at Austin in 1998, following an MS from the same institution in 1994. He earned his BTech in Computer Science and Engineering from the Indian Institute of Technology, Bombay in 1993. His academic progression at UMass Amherst has been from Assistant Professor (1998-2004) to Associate Professor (2004-2009) to Professor (2009-2020) to Distinguished Professor (2020-present). His primary research interests include distributed systems, networking, cloud and edge computing, mobile computing and Internet of Things, and energy and sustainability. Over the past decade, his work has increasingly focused on computational decarbonization, as evidenced by his recent $12 million NSF Expedition award in this area. His research group maintains several important resources including the UMass Trace Repository, UMass CS Weather Station, BenchLab, and Smart* Dataset. Shenoy's publications reflect a progression from foundational distributed systems work to increasingly sustainability-focused research. His recent work centers on carbon-aware computing, energy optimization, and computational decarbonization across various computing domains including cloud, edge, and IoT systems. ACM Fellow (2019) AAAS Fellow (2018) IEEE Fellow (2013) ACM Sigmetrics Test of Time Award (2016) NSF Career Award recipient Conti Research Fellowship recipient Lilly Foundation Teaching Fellow As an educator, Shenoy has consistently taught Distributed and Operating Systems (Compsci 677) and has mentored numerous PhD students who have received awards and gone on to successful careers. He serves as the founding Chair of the ACM Special Interest Group on Energy (SIGEnergy) and has organized numerous conferences including serving as PC chairs for the ACM Symposium on Edge Computing in 2025. His research has secured significant funding, including a recent $12 million NSF Expedition in Computational Decarbonization awarded in May 2024. Shenoy leads the Laboratory for Advanced Systems Software at UMass Amherst and directs the Center for Smart and Connected Society. He serves on editorial boards of several journals including ACM Transactions on IOT (TIOT), ACM Modeling and Performance Evaluation of Computing Systems (TOMPECS), and ACM Transactions on the Web (TWEB).
