Eric Masanet is a Professor and Mellichamp Chair in Sustainability Science for Emerging Technologies at the University of California, Santa Barbara (UCSB), holding a courtesy appointment in the Department of Mechanical Engineering. He leads the Bren School's Industrial Sustainability Analysis Laboratory, focusing on decarbonizing industrial and IT sectors while advancing equity and sustainability. His research spans energy system analysis, climate mitigation, and sustainable manufacturing. Education: Ph.D. in Mechanical Engineering from UC Berkeley, with M.S. and B.S. degrees from Northwestern University and the University of Wisconsin-Madison, respectively. Research interests include data center sustainability, industrial decarbonization, and the intersection of technology and climate policy. He has contributed to the IPCC's Sixth Assessment Report, advised the U.S. White House, and serves on the DOE's Industrial Technology Innovation Advisory Committee. Notable achievements include authoring the U.S. Data Center Energy Usage Report and leading the Resources, Conservation, and Recycling journal as former Editor-in-Chief. His work bridges academia with policy, influencing international energy and climate strategies. Advising and grants: Supervises PhD students (e.g., Jaxon Stuhr) and postdocs (Antoine Merlo, Jason Ye) in decarbonization research. Collaborates with Lawrence Berkeley National Laboratory and international organizations like the IEA. Labs/Teams: Directs the Industrial Sustainability Analysis Laboratory, advancing models for low-carbon industrial and IT systems.
Karl-Erik Årzén is Professor and Head of the Department of Control Engineering at Lund University's Faculty of Engineering. He is also Co-director of the Wallenberg AI, Autonomous Systems and Software Program (WASP) and a key member of ELLIIT, the excellence center in information technology. His roles include leadership in AI and digitalization profile areas at both LTH and Lund University. His research lies at the intersection of control engineering and computer science, with a focus on cyber-physical systems, real-time systems, embedded control, and resource management in cloud and edge computing environments. He has pioneered methods for predictable performance in cloud applications and dynamic resource allocation using control-theoretic approaches. The recent publications highlight a strong trend in control over the cloud and edge, real-time scheduling co-design, distributed camera systems, and reinforcement learning for auto-scaling. Key topics include model predictive control, LQG-based scheduling, bandwidth allocation, and robustness in cyber-physical systems. The work spans theoretical control design and practical implementation in distributed systems. His scientific awards include multiple Best Paper Awards from IEEE and ACM conferences in 2018, 2016, and 2004, recognizing excellence in autonomic computing, edge computing, and real-time systems. Best Paper Award, IEEE International Conference on Autonomic Computing, 2018 Best Paper Award, IEEE International Conference on Edge Computing (EDGE), July 2018 Best Paper Award - RTNS 2016 Best Paper Award - RTCSA 2004 Årzén has supervised over 20 PhD students, including Mikael Johansson, Anton Cervin, Yang Xu, and Per Skarin, and currently supervises Ahmed Al Bayati and Max Nyberg Carlsson. His grant portfolio includes major projects such as WASP, AORTA (VINNOVA), and ELLIIT's 'Robust and Secure Control over the Cloud'. He has also contributed to innovation through tools like TrueTime and Jitterbug. He leads the RobotLab LTH initiative and is involved in the Nordic University Hub on Industrial Internet of Things (HI2OT). His work bridges academia and industry, with collaborations on adaptive control, cloud-native systems, and autonomous robotics.
Robert G. Bland is a Professor at Cornell University's School of Operations Research and Information Engineering (ORIE). He joined Cornell in 1978 after roles at SUNY Binghamton and research fellowships in Belgium. He is affiliated with the Center for Applied Mathematics and specializes in linear programming, combinatorial optimization, and network flow theory. His research emphasizes algorithmic efficiency, duality theory, and applications in scheduling and resource allocation. Education: B.S. (1969), Cornell University M.S. (1972), Cornell University Ph.D. (1974), Cornell University Research Interests: Focuses on linear programming duality, combinatorial abstractions, computational methods for optimization, and applications in logistics, scheduling, and scientific computing. Notable work includes the development of new pivoting rules for the simplex method and empirical studies of network flow algorithms. Publications Insight: His work spans foundational LP theory, combinatorial optimization, and algorithmic analysis. Key themes include duality frameworks, Camion bases, and large-scale TSP applications in crystallography. Recent publications address abstract dualities and historical perspectives on pioneers like D. Ray Fulkerson. Awards: Recipient of Cornell's prestigious Merrill Outstanding Educator Award (3 times) and twice recognized as ORIE's best teacher. Member of the Mathematical Optimization Society and American Society for Engineering Education. Grants & Projects: Conducted service projects on vehicle routing and examination scheduling. Collaborated on computational studies of min cost flow algorithms and network flow performance. Labs/Teams: Active in ORIE's research groups, particularly those focused on optimization theory and computational methods.
Dr. Nour Moustafa is an Associate Professor and ARC DECRA Fellow at the School of Systems & Computing (SysCom) , University of New South Wales (UNSW) Canberra , Australia. He leads the Intelligent Security Group and focuses on developing AI/ML-driven cybersecurity frameworks for smart systems. Educated at Helwan University (BSc/MSc in Information Systems) and UNSW (PhD in Cybersecurity). Research Interests include intrusion detection, threat intelligence, privacy preservation, digital forensics, and cyber resilience, with methodologies spanning statistical analysis , machine learning , and deep learning applied to IoT , Edge/Cloud , and Industrial IoT environments. His work emphasizes federated learning for privacy preservation, blockchain for secure AI, and digital twins for network self-healing. Notable contributions include the TON-IoT , Bot-IoT , and UNSW-NB15 datasets for cybersecurity evaluation. Scientific Awards : 2020 Spitfire Memorial Defence Fellowship ACM Distinguished Speaker IEEE Senior Member He has served as guest associate editor for IEEE Transactions journals and held leadership roles in conferences like IEEE TrustCom . His research bridges academia and industry, with over 75 publications in top-tier venues.
Rute C. Sofia is Industrial IoT Head at fortiss – the Bavarian research institute for software-intensive systems – and Invited Associate Professor at Universidade Lusófona de Humanidades e Tecnologias (ULHT). She is also an Associate Researcher at ISTAR, Instituto Universitário de Lisboa (Iscte-IUL). Previously she co-founded and served as Scientific Director of COPELABS/ULHT (2013-2017) and was Senior Researcher there from 2010-2019. Education Ph.D. in Computer Science, University of Lisbon, 2004 Visiting Scholar, Northwestern University (ICAIR) & University of Pennsylvania, 2000–2003 M.Sc. in Computer Science, University of Lisbon, 1999 B.Eng. in Computer Engineering, University of Coimbra, 1995 Research Interests Her work spans network architectures and protocols , Internet of Things (IoT) , edge and in-network computing , deterministic wired/wireless industrial networks , and network mining . A current focus is on resilient, AI-driven orchestration across the IoT–Edge–Cloud continuum for 6G and Industrial IoT systems. Scientific Awards & Recognition ACM Europe Councilor (2021–2025) ACM Senior Member & IEEE Senior Member IEEE ComSoc N2Women Awards co-chair (2020–2021) Highly Cited Paper Award, Applied Sciences MDPI (2023) Labs, Teams & Grants She currently leads the Industrial IoT competence field at fortiss, coordinating projects such as SemComIIoT (semantic communications for IIoT) and the open-source ns-3 DetNetWiFi framework. Earlier she co-founded the Portuguese startup Senception Lda (2013-2019) and the research unit COPELABS , driving EU H2020 initiatives like UMOBILE and shaping national strategies for cyber-physical systems.
Luís B. Elvas is an Assistant Professor at ISCTE-University Institute of Lisbon's Department of Social and Business Sciences (SINTRA) and a Research Assistant at ISTAR-Iscte Research Center. He holds qualifications including a Technical Specialization in TensorFlow for AI (Coursera, 2021) and certifications in IoT/Blockchain from ISCTE and cybersecurity from Palo Alto Networks. His research spans artificial intelligence, healthcare informatics, smart cities, and blockchain, with applied work in medical imaging, data sharing, and urban analytics. Research interests include: Healthcare AI : Developing deep learning models for cardiac diagnostics, medical imaging analysis, and blockchain-based health data systems Smart Cities : Implementing IoT solutions for urban mobility optimization, disaster management, and sustainable transportation Data Science : Creating predictive analytics frameworks for clinical decision support and urban planning His publications demonstrate a strong focus on AI-driven healthcare solutions (67% of recent works) and smart city technologies (33%), with emerging interests in blockchain and NLP. Research consistently targets real-world applications in clinical settings and urban environments. Awards: Award for best internship, Order of Engineers (2022) Distinction for best internship, Order of Engineers (2021) He leads/contributes to multiple EU research consortia including AMR-EDUCare (antimicrobial resistance education), NEEM (e-health in Nepal), and Blockchain.PT. Coordinates the IEEE Computational Intelligence Society Student Branch Chapter at ISCTE and developed the ManagiDiTH master's program in digital health transformation.
Patrik Hilber is a Professor at KTH Royal Institute of Technology, working in the Division of Electromagnetic Engineering and Fusion Science within the School of Electrical Engineering and Computer Science (EECS). He serves as Deputy Director of First and Second Cycle Education at EECS and heads the QED AM research group. He is also a board member of YH-electrical engineering. Research Interests: His research focuses on reliability engineering, asset management, maintenance optimization, and smart grid technologies in electric power systems. Key areas include transmission and distribution systems, dynamic line and transformer rating, wind power integration, multiobjective optimization, condition monitoring, and data quality in power systems. He applies advanced modeling and data-driven approaches to improve power system planning, operation, and resilience. The recent trends in his publications (2020–2025) highlight a strong emphasis on dynamic rating technologies (DLR and DTR), data quality and machine learning applications in outage analysis, reliability-centered planning for wind farms and distribution systems, and the integration of renewable energy and electric vehicles. His work bridges theoretical modeling with practical utility applications. Teaching and Academic Leadership: He is examiner and course responsible for several degree projects in electrical engineering, power systems, and energy innovation. He also teaches courses on reliability evaluation, asset management, and innovation in electric power engineering. Publications and Books: He has authored a book titled Reliability Analysis and Asset Management Applied to Power Distribution (2014) and a book chapter on cable segment replacement optimization. His scholarly output includes numerous peer-reviewed articles in leading journals such as IEEE Transactions on Power Systems , Reliability Engineering & System Safety , and Applied Energy . Education: He holds a Ph.D. (2008), a Licentiate degree (2005), and an M.Sc. (2000), all from KTH. He became a Docent (Associate Professor) in 2014.
Michael Pradel is a full professor at the University of Stuttgart, specializing in software engineering, programming languages, and machine learning. He will join CISPA as a faculty member from September 2025 while retaining his Stuttgart position. His research focuses on: Neuro-symbolic software analysis Web application analysis Dynamic analysis and test generation Quantum software testing Machine learning for code Recent publications address: LLM-based program repair (RepairAgent, Treefix) WebAssembly analysis (Wasm-R3, LintQ) Python security and analysis (DyLin, DyPyBench) Quantum program analysis (LintQ) Scientific awards: Ernst-Denert Software Engineering Award Emmy Noether grant (1.3M Euro) ERC Starting Grant (1.5M Euro) 3x ACM SIGSOFT Distinguished Paper Award at FSE ACM Distinguished Member Best Paper/Distinguished Paper Awards at ISSTA, ASE, ASPLOS, MSR Key contributions include: DeepBugs for name-based bug detection Getafix for automated bug fixing LintQ for quantum program analysis DyLin for Python dynamic analysis Neuro-symbolic developer tools
Yuan Zhong is an Associate Professor of Operations Management at the University of Chicago Booth School of Business . He previously held positions as an Assistant Professor at Columbia University’s Department of Industrial Engineering and Operations Research and was a Postdoctoral Scholar at UC Berkeley’s Computer Science Department. Education: PhD in Operations Research, MIT (2012) MA in Mathematics, Caltech (2008) BA in Mathematics, University of Cambridge (2006) His research focuses on applied probability and stochastic system design , with applications in cloud computing , supply chain management , and e-commerce logistics . Recent work explores multi-period production systems and dynamic resource allocation in data centers and healthcare operations . Recent publications analyze cloud value chains , sparse graph design for delivery networks, and process flexibility in manufacturing. He has contributed to journals like Operations Research , Annals of Applied Probability , and Stochastic Systems . Scientific Awards: 2012 Kenneth C. Sevcik Outstanding Student Paper Award Best Student Paper Award at ACM Sigmetrics (2012) He teaches courses in business process fundamentals and queueing theory , with a future schedule including Operations Management: Business Process Fundamentals (2025–2026). No explicit student advising list was provided.
Andrew J. Schuler is an Associate Professor in the Department of Civil Engineering at the University of New Mexico, where he has been since 2007. His research focuses on microbial processes in wastewater treatment and bioremediation, with particular emphasis on modeling distributed bacterial states, biofilm dynamics, and integrating molecular methods with environmental engineering. He teaches courses in water/wastewater treatment (CE 335) and biological wastewater treatment (CE 536). Ph.D., Civil and Environmental Engineering, University of California at Berkeley (1998) M.S., Civil and Environmental Engineering, UC Berkeley (1993) B.S., Civil Engineering, University of Colorado at Boulder (1987) Dr. Schuler's research addresses critical challenges in biological wastewater treatment , including: Microbial storage products and density effects on solids separation Agent-based modeling of bacterial state distributions Integrated fixed-film activated sludge (IFAS) systems Photolytic and microbial degradation of Superfund chemicals His recent publications explore biofilm surface chemistry , algae-based wastewater treatment , and computational modeling of microbial communities . Notable funded projects include NSF CAREER grants, NIEHS Superfund subprojects, and North Carolina Biotechnology Center collaborations. National Science Foundation CAREER Award (2004) Paul L. Busch Award (2008) AEESP/CH2M HILL Outstanding Doctoral Dissertation Award (1999) Japan Society for the Promotion of Science Postdoctoral Fellowship (1999) Dr. Schuler leads the Schuler Laboratory , which investigates microbial dynamics in wastewater treatment systems, develops the DisSimulator agent-based modeling tool, and provides practical solutions for activated sludge settling problems through density analysis. Current funding supports advanced research in biofilm optimization and sustainable water reuse technologies.
Dr. Kiran T. Thakur serves as the Herbert Irving Associate Professor of Neurology at Columbia University Irving Medical Center and practices as an inpatient neurologist at NewYork-Presbyterian/CUIMC. Her clinical expertise spans neuroinfectious diseases, neuroimmunology, emergency neurology, and global health, with active clinical and research engagements across eight countries in Africa, Southeast Asia, and the Caribbean. She holds leadership roles including Neurology Clerkship Director at Perdana University Graduate School of Medicine (Malaysia) and serves as a neurology consultant for the World Health Organization. Her educational background includes an MD from Tufts University School of Medicine (2008), internship at Johns Hopkins Hospital's Osler Medical Service, neurology residency at Harvard's Brigham & Women's Hospital (where she served as chief resident), and fellowship in neuroinfectious disease/neuroimmunology at Johns Hopkins Hospital. She is additionally pursuing a Master of Science in clinical trials at the London School of Tropical Medicine and Hygiene. Dr. Thakur's research program focuses on improving detection and management of neuroinfectious diseases in hospital settings, with particular emphasis on vulnerable populations including HIV-positive individuals and immigrants. Her work integrates clinical, translational, and implementation science approaches to address cerebral malaria, tuberculous meningitis, Zika-related complications, and SARS-CoV-2 neurological outcomes. Current projects investigate novel diagnostics for infectious meningoencephalitis, risk factors for neuroinvasive infections, and therapeutic strategies for viral encephalitis. Analysis of her 15 most recent publications reveals consistent focus on neuroinfectious disease epidemiology (particularly in resource-limited settings), diagnostic challenges in immunocompromised patients, and global health implementation strategies. Her work prominently features Zika virus complications, tuberculous meningitis management, and CNS infections in travelers, reflecting her dual expertise in tropical medicine and neuroimmunology. Scientific recognition includes: Lewis P. Rowland Teaching Award (Columbia University, 2016) CDC Nakona Citation Award (2014) Dr. Thakur directs the NIH NINDS K23-funded study on pathogen identification in neurological infections (2018-2023) and leads Columbia's post-doctoral neuroinfectious disease fellowship for physician-scientists from low/middle-income countries. Her research program receives support from the NIH, World Federation of Neurology, and American Academy of Neurology, with implementation studies conducted in collaboration with WHO, PAHO, and CDC. The Thakur Laboratory at Columbia's Neurological Institute coordinates international research networks across Malawi, Vietnam, Bangladesh, Uganda, and the Dominican Republic. Her team conducts clinical trials on antimicrobial dosing, develops diagnostic algorithms for resource-limited settings, and implements training programs to build neuroinfectious disease capacity in underserved regions, with current emphasis on SARS-CoV-2 neurological sequelae and tropical disease management.
Yuzhe Yang is an Assistant Professor of Computational Medicine and Computer Science at UCLA, with a visiting research scientist role at Google Health. He holds a PhD in Computer Science from MIT (2024), advised by Dina Katabi, and a B.S. with honors from Peking University. Research Focus: Machine learning for healthcare, medical AI fairness, and AI-driven biomedical discovery Key Contributions: Ten Notable Advances (Nature Medicine) and Ten Crucial Advances (The Lancet Neurology) His lab develops Trustworthy Learning Algorithms and Generalist Health Models that integrate Multimodal Data for personalized health coaching. Notable projects include AI-based Parkinson's Disease Biomarkers via nocturnal breathing and Foundation Models for Equitable Medicine . Recent publications at ICLR 2025 (wearable foundation models), Nature Medicine 2024 (medical AI fairness), and Science Advances 2025 (vision-language medical bias) highlight his interdisciplinary work. He serves on ML4H workshops and reviews for top conferences like NeurIPS and ICML. Awards include Forbes 30 Under 30 , Takeda Fellowship , and Baidu PhD Fellowship . Advising opportunities: Recruiting PhD students (CS/CompMed) and postdocs in AI for health. Lab: Health Intelligence Lab (HAIL)
Seokjun Youn is an Assistant Professor in the Department of Management Information Systems at the Eller College of Management, University of Arizona . His research focuses on improving healthcare delivery systems through technological innovations and operational strategies, with additional expertise in supply chain logistics and platform business models. Education: PhD in Operations and Supply Chain Management from Texas A&M University; MS and BS in Industrial Engineering from Texas A&M University and Seoul National University, respectively. Research Areas: Healthcare operational efficiency, patient flow, health IT, human-AI collaboration, and supply chain management. Scientific Awards: DSI Best Theory-Driven Empirical Research Paper Award (2024) CMIH Grant (2020, 2021, 2023, 2024, 2025) POMS Emerging Scholars Program (2021, 2023) Professional Service: Editorial board member for Production and Operations Management and Decision Sciences journals; track chair for conferences like CHITA and DSI. His work combines econometric methods and optimization techniques to address challenges in healthcare systems, with findings published in top journals including Manufacturing & Service Operations Management and Journal of Operations Management .
Vasil Georgiev Tsunizhev is a Professor of Computer Informatics at the Faculty of Mathematics and Informatics, Sofia University, with office hours Monday and Wednesday 13:00-14:00 in room FMI-110. Contact: v.georgiev@fmi.uni-sofia.bg, +359 2 8161-594. His research spans cloud computing, distributed systems, and grid technologies with emphasis on resource management, load balancing, and service modeling. Key contributions include numerical solutions for cloud servicing, distributed coordination mechanisms, and fault-tolerant information services. His work integrates open-source components and addresses scalability challenges in cloud environments. Analysis of his 2009-2015 publications reveals consistent focus on cloud infrastructure optimization, with recurring themes in resource frameworks, quality-of-service models, and distributed coordination. His research demonstrates strong technical depth in numerical modeling and system architecture while addressing real-world scalability and fault tolerance requirements. Professor Tsunizhev collaborates internationally through projects like CoreGRID (contributing to grid security white papers) and works with researchers including R. Zhelev and L. Kirchev. His laboratory work focuses on commodity grid platforms and lightweight resource management systems for distributed computing environments.
Dr. Frank Loh is a researcher at the Department of Computer Science III, University of Würzburg, specializing in energy efficiency, network performance, and Quality of Experience (QoE) in communication networks. His work focuses on optimizing LoRaWAN deployments, serverless computing, and edge-cloud environments, with an emphasis on reducing message collisions and improving resource utilization. He actively contributes to methodologies for gateway placement, traffic modeling, and energy consumption metrics. Research Areas Energy Efficiency in Communication Networks Quality of Service (QoS) and Quality of Experience (QoE) LoRaWAN Network Planning Edge and Serverless Computing Network Resource Analysis Recent Publications 2025: Energy modeling for 6G base stations 2025: Server cluster resilience via Markov models 2024: Serverless computing in edge-cloud environments 2024: LoRaWAN channel access optimization
