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Carlos CardonhaView profile

Assistant Professor

• Discrete optimization
• Mixed-integer linear programming
• Combinatorial optimization
• +6 more

Carlos Cardonha is an Assistant Professor in the Department of Operations and Information Management at the University of Connecticut School of Business since 2019. He holds a Ph.D. in Mathematics from Technische Universität Berlin (2011) and degrees in Computer Science from the University of São Paulo (B.Sc. 2004, M.Sc. 2006). Previously, he worked as a Research Staff Member at IBM Research – Brazil (2012–2019). His research focuses on discrete optimization, approximation algorithms, and applications of machine learning and mathematical programming to operations research problems. He teaches courses such as OPIM 5641 (Business Decision Modeling) and OPIM 3511 (Business Data Analytics II), supported by DataCamp. Education: Ph.D. in Mathematics, Technische Universität Berlin, Germany (2011) M.Sc. in Computer Science, University of São Paulo, Brazil (2006) Bachelor in Computer Science, University of São Paulo, Brazil (2004) Research Interests: His work spans analytics, optimization, and theoretical computer science, with a focus on mixed-integer linear programming, combinatorial optimization, and algorithm design. He applies these techniques to real-world problems in scheduling, resource allocation, and machine learning model optimization. Grants & Advising: No specific grants or advisees are listed in the provided materials. Labs/Teams: Not explicitly mentioned in the text.

• High-Performance Computing (HPC)
• Parallel Programming
• Performance Modeling
• +6 more

Felix Wolf is a Full Professor at Technische Universität Darmstadt's Department of Computer Science since 2015 and leads the NHR4CES@TU Darmstadt HPC center. He served as Department Chair and Vice Department Chair at TU Darmstadt, with prior faculty roles at RWTH Aachen University (2006–2015) and adjunct positions at the University of Tennessee (2003–2008). His academic work spans performance analysis, parallel programming, and scalability modeling. Ph.D. in Computer Science, RWTH Aachen University (2003) M.Sc., RWTH Aachen University (1998) Research Interests : Wolf focuses on High-Performance Computing , including performance modeling , parallel programming tools , and I/O optimization . His work addresses GPU acceleration , machine learning integration into HPC systems, and structural plasticity simulation . Key projects involve tools like Scalasca, Score-P, and Extra-P for automated performance analysis. Article Trends : Recent publications emphasize HPC systems for AI-driven workflows , I/O contention reduction , and GPU port validation . His team explores empirical modeling for deep learning and scientific simulations , with applications in engineering and neuroscience .

• Cloud Computing
• High Performance Computer Architectures
• Parallel and Distributed Processing
• +3 more

Dr. Robert K. Chun is a Professor in the Department of Computer Science at San José State University (SJSU), part of the College of Engineering. He holds a BSEE (1979), MSCS (1981), and Ph.D. (1989) from UCLA. With 20+ years in industry, he joined SJSU as a full-time faculty in 2001. His research focuses on Cloud Computing, parallel processing, high-performance architectures, and AI. He received NASA Faculty Fellowships (2002-2003) and holds a U.S. patent for real-time expert systems integration. Education: Bachelor of Science, Electrical Engineering, UCLA (1979) Master of Science, Computer Science, UCLA (1981) Doctor of Philosophy, Computer Science, UCLA (1989) Research Interests: Cloud Computing architectures and scalability High-performance computing and parallel algorithms Software engineering for distributed systems CAD tools for VLSI design verification Artificial intelligence and neural networks Publications highlight advancements in parallel processing, distributed systems, and compiler optimization. Notable works include adaptive transactional memory algorithms, wireless cluster computing frameworks, and EJB performance measurement tools. His NASA-funded research at Ames Supercomputing Center explored parallel computing applications. Advising and Grants: Advised 16 Master’s theses, including topics like dynamic clusters and intelligent debuggers Contributed to industry-university partnerships through NASA collaborations Labs/Teams: Active in SJSU’s parallel processing and computer architecture research groups, contributing to course development in advanced parallel processing and operating systems.

Seokgi LeeView profile

Assistant Professor

• Distributed Control Systems
• Predictive Analytics
• Artificial Neural Networks
• +6 more

Dr. Seokgi Lee is an Assistant Professor in the Department of Industrial and Systems Engineering at Youngstown State University (YSU), part of the Rayen School of Engineering. Previously, he served as an Assistant Professor in Industrial Engineering at the University of Miami. He holds a Ph.D. in Industrial and Manufacturing Engineering from Pennsylvania State University and B.S./M.S. degrees in Industrial Engineering from Hanyang University, South Korea. His research focuses on distributed control systems, predictive analytics, and simulation-optimization for service engineering processes in supply chains, transportation, and healthcare. Notable expertise includes energy-aware manufacturing systems, prevention service analytics, and applications of machine learning in logistics and healthcare. Dr. Lee’s educational background includes teaching roles at YSU, the University of Miami, and Penn State, covering courses such as Decision Analysis for Engineering, System Analysis and Design, Operations Research, and Production Systems. He has contributed to institutional service through committees like the New Faculty Search in ISEN (YSU) and PhD recruitment in Industrial Engineering (University of Miami). His research trends emphasize interdisciplinary solutions, blending control theory with machine learning to address energy efficiency, healthcare optimization, and sustainable logistics. Recent publications highlight work on dynamic scheduling algorithms, opioid prescribing disparities, and green transportation systems. While no specific awards are listed, his active engagement in academic service underscores his commitment to institutional advancement. Teaching and advising form a core part of his role, with a focus on graduate and undergraduate engineering curricula. His work integrates real-world applications, such as optimizing infusion center operations and modeling energy costs in manufacturing, demonstrating a strong practical orientation.

Akshitha SriramanView profile

Assistant Professor

• Computer Architecture
• Hardware-Software Co-design
• Data Center Computing
• +4 more

Akshitha Sriraman is an Assistant Professor in the Department of Electrical and Computer Engineering and Computer Science at Carnegie Mellon University. She holds a PhD in Computer Science from the University of Michigan (2021) and an M.S. in Embedded Systems from the University of Pennsylvania (2015). Her research focuses on bridging computer architecture and systems software to design efficient, sustainable, and equitable data center systems, emphasizing hardware-software co-design. Her work has been deployed in real hyperscale data centers, influencing Intel’s Alder Lake CPU architectures and Infrastructure Processing Unit designs. Recipient of the NSF CAREER Award (2024), Intel Rising Star Award (2023), and multiple dissertation prizes. Advances include reducing global carbon emissions via server design and introducing equity as a first-order design concern in web systems. Advises PhD and Master’s students on topics like sustainable computing, equitable systems, and hardware optimization. Key contributions span energy-efficient data center architectures, bias-free scheduling systems, and cloud GPU power management. She leads a research group prioritizing socially responsible computing and has received grants from NSF, AWS, and industry partnerships.

Alan HuView profile

Professor

• Formal Verification
• Algorithms
• Computer Architecture
• +3 more

Alan Hu is a Professor in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. His research focuses on formal verification, algorithms, computer architecture, and electronic design automation. He teaches courses such as Intermediate Algorithm Design and Analysis (CPSC 320) and Introduction to Formal Verification and Analysis (CPSC 513). Dr. Hu has received notable awards, including the IEEE Council on Electronic Design Automation Outstanding Service Award and the IBM Faculty Award. His work emphasizes scalable verification techniques, SAT-based algorithms, and optimization in cloud computing and hardware systems. His research spans formal methods for hardware/software systems, including verification of embedded software, cache coherence, and network function virtualization. Notable contributions include advancements in SAT modulo theories, data race detection in heterogeneous systems, and cloud resource allocation frameworks like Cospot. Dr. Hu has been actively involved in teaching and curriculum development, consistently offering courses on algorithms, formal verification, and software design since 2000. His publications reflect a blend of theoretical foundations and practical applications in electronic design, cloud infrastructure, and verification tools. His scientific achievements include innovations in post-silicon validation, emulation-based coverage reduction, and formal analysis for debug trace optimization. He also contributes to the academic community through conference organization and editorial roles in formal verification and computer-aided design.

Jim DaiView profile

Professor

• Applied Probability
• Operations Research
• Health Systems
• +3 more

Jim Dai is the Leon C. Welch Professor of Engineering at Cornell University's School of Operations Research and Information Engineering (ORIE). He previously held the Chandler Family Chair at Georgia Institute of Technology (1990–2012) and serves as a Special Term Professor at Tsinghua University and a Visiting Professor at National University of Singapore. His research focuses on applied probability models for service systems, healthcare operations, and transportation networks. He holds a B.S. and M.S. from Nanjing University and a Ph.D. in Mathematics from Stanford University. Research interests include stochastic models for resource allocation in processing networks, healthcare systems, and ridesharing networks. His work emphasizes fluid and diffusion models, Stein’s method, and reinforcement learning. Awards include the Erlang Prize (1998), ACM SIGMETRICS Achievement Award (2018), and the John von Neumann Theory Prize (2024). He is Editor-in-Chief of Mathematics of Operations Research and a Fellow of INFORMS and the Institute of Mathematical Statistics. Teaching spans Markov chains, stochastic processes (both undergraduate and graduate levels), and optimization. Service roles include editorial positions at journals like Stochastic Systems and advisory roles at Singapore University of Technology and Design.

Agha Iqbal AliView profile

Professor

• Data Analytics
• Supply Chain Management
• Operations Research
• +4 more

Agha Iqbal Ali is the Karen & Chuck Peters Family Endowed Professor of Management Science and Operations Management at the Isenberg School of Management, University of Massachusetts Amherst. He holds a PhD from Southern Methodist University (1980) and prior master's degrees in Mathematics and Statistics. His academic leadership includes serving as Chairman of the Operations & Information Management Department (2013–2018) and the Finance & Operations Management Department (2006–2012). His research focuses on Data Analytics , Supply Chain Management , and Mathematical Programming Models , with notable contributions to disaster relief logistics, FIFA World Cup capacity planning, and banking deregulation analysis. Teaching interests include Operations Management, Optimization, and Algorithm Design. Dr. Ali has been honored with the prestigious Goodeve Medal (2018) from the Operational Research Society, and multiple teaching awards including the College Outstanding Teacher Award (1999–2000). His work bridges theoretical optimization with real-world applications in manufacturing, military personnel planning, and international cooperatives.

Panagiotis AndrianesisView profile

Academic

• Electricity Distribution Networks
• Energy Economics
• Power Systems Optimization
• +3 more

Panagiotis Andrianesis is a Research Associate Professor in Systems Engineering at Boston University's College of Engineering. He specializes in electricity distribution network economics and energy systems optimization. His research focuses on developing robust frameworks for future grid operations, including uncertainty management, renewable integration, and market design. He holds a PhD from the University of Thessaly, Greece, and is affiliated with the Center for Information and Systems Engineering (CISE) and the BU Institute for Sustainable Energy as a Senior Fellow. Education: PhD in Systems Engineering from the University of Thessaly, Greece. Research Interests: His work spans electricity market mechanisms, distributed energy resource coordination, and resilient energy forecasting. Key areas include optimizing power grid economics, designing pricing strategies for EV charging stations, and addressing non-convexities in energy markets. His contributions often blend optimization theory with practical grid applications. Scientific Awards: Senior Fellow at the BU Institute for Sustainable Energy, highlighting his leadership in sustainable energy research. Advising & Grants: While no specific grants or advisees are listed, his research collaborations with industry and academic partners suggest active involvement in funded projects. His work frequently addresses real-world grid challenges. Labs/Teams: Affiliated with the BU Institute for Sustainable Energy and the CISE, where he contributes to interdisciplinary energy systems research.

Paola FlocchiniView profile

Professor

• Distributed Computing
• Distributed Algorithms
• Sense of Direction
• +3 more

Paola Flocchini is a Professor and University Research Chair on Intruder Agents and the Decontamination of Communication Networks at the University of Ottawa's School of Electrical Engineering and Computer Science. She also serves as Associate Director of the OCICS Program. Her PhD is from an unspecified institution. Her research focuses on distributed computing, algorithms for autonomous robots, distributed systems, and cellular automata. She has contributed to advancing theoretical frameworks for mobile robotic networks, fault-tolerant systems, and dynamic network exploration. Research Interests: Distributed Computing Mobile Entities and Autonomous Robots Algorithm Design for Dynamic Networks Black Hole Detection and Network Decontamination Asynchronous Algorithms and Schedulers Her recent work emphasizes computational power of energy-constrained robots, pattern formation under sequential schedulers, and fault localization in dynamic topologies. She has published extensively in venues like ICALP, IWOCA, and DISC. Her research bridges theory and application, addressing challenges in distributed systems, robotics, and network security.

Anirudh SivaramanView profile

Assistant Professor

• Computer Networks
• Network Programmability
• Distributed Systems
• +3 more

Anirudh Sivaraman is an Assistant Professor of Computer Science at New York University's Courant Institute, specializing in computer networks and programmability. He holds a PhD from MIT (2017) and a BTech from IIT Madras (2010). His research focuses on enabling ubiquitous network programmability through hardware-software systems, including compilers, runtimes, and applications like distributed consensus and financial exchanges. Education: Ph.D. in Computer Science, MIT (2012–2017) S.M. in Computer Science, MIT (2010–2012) B.Tech in CSE, IIT Madras (2006–2010) Research Interests: Network programmability (e.g., P4, Barefoot Networks) High-speed packet processing and compiler optimization Distributed systems and consensus protocols Cybersecurity in programmable networks Cloud computing and microservice autoscaling Notable Articles Trends: Focus on programmable networks (P4, NIC design) Hardware-software co-design for performance Security and isolation in multi-tenant environments Compiler-based solutions for bug detection Awards: ACM SIGCOMM Doctoral Dissertation (2017), SIGCOMM Best Paper (2017), and multiple teaching awards. Advising & Grants: PI on grants totaling $900K+ (Google, Amazon, NSF). Advises 8+ PhD/Master’s students. Serves on committees for SIGCOMM, NSDI, and HotNets. Labs/Teams: Technical advisor at Clockwork. Co-organizes workshops on programmable networks and P4 standards.

Masoumeh EbrahimiView profile

Associate Professor

• Computer Architecture
• Machine Learning
• Embedded Systems
• +6 more

Masoumeh Ebrahimi is an Associate Professor at KTH Royal Institute of Technology, Division of Electronics and Embedded Systems, and holds an Adjunct Professor position at the University of Turku, Finland. She leads research in hardware acceleration, neural architecture search, and fault-tolerant systems. Her work bridges machine learning, embedded systems, and network-on-chip (NoC) design. Research Interests: Hardware-Accelerated Machine Learning 6G Network Architectures Fault-Tolerant Computing High-Performance GPU Systems Network-on-Chip (NoC) Design Federated Learning Key Projects: Co-supervisor of Hui Chen’s postdoc project Generalizing hardware acceleration for nonlinear functions . Active in Digital Futures, a cross-disciplinary center focusing on societal challenges using digital tech. Collaborates on edge computing, 6G networks, and resilient embedded systems. Labs & Teams: Core member of KTH’s Digital Futures initiative, advancing AI accelerators and next-gen communication systems. Engaged in EU-funded projects on NoC reliability and federated learning frameworks.

Maria Grazia ScutellaView profile

Professor

• Combinatorial Optimization
• Robust Optimization
• Network design and routing
• +5 more

Roles & Affiliations: Full Professor at the Department of Computer Science, University of Pisa. Served as Vice Director of the Department (2020–2024). Active in editorial roles for journals like Networks and Computers & Operations Research . Education: Laurea in Information Science (University of Pisa, 1985, cum laude), PhD in Computer Science (University of Pisa, 1990). Qualified as Full Professor in Operations Research (2013). Research Focus: Specializes in Combinatorial Optimization, Robust Optimization, Network Design, and Logistics. Key contributions include green wireless networks, home care optimization, and vehicle routing. Awarded the Best Paper in Omega (2018) and recognized for seminal work in Discrete Applied Mathematics (1998). Teaching: Teaches courses in Operations Research, Logistics, and Network Optimization at both undergraduate and graduate levels, including international programs. Grants & Projects: Led national and international projects, including PNRR initiatives on sustainable mobility and IREAD-4.0 for warehouse optimization. Collaborated with industries like Softec S.r.l. and Siemens. Professional Activities: Organized major conferences (e.g., INOC 2009). Served on editorial boards and scientific committees for AIRO, INOC, and other networks. Active in mentoring doctoral students and fostering academic-industrial partnerships.

• Machine Learning
• Stochastic Optimization
• Networked Systems
• +4 more

Juan Bazerque Giusto is a Visiting Assistant Professor at the Department of Electrical and Computer Engineering, University of Pittsburgh, within the Swanson School of Engineering. He holds a B.Sc. in Electrical Engineering from Universidad de la República (Uruguay), and M.Sc. and Ph.D. degrees from the University of Minnesota. His research focuses on machine learning, stochastic optimization, and networked systems, with emphasis on reinforcement learning, swarm robotics, and power systems optimization. Education: B.Sc., Electrical Engineering, Universidad de la República, 2003 M.Sc., Electrical and Computer Engineering, University of Minnesota, 2010 Ph.D., Electrical and Computer Engineering, University of Minnesota, 2013 His work bridges theoretical advancements in optimization and signal processing with practical applications in robotics, energy systems, and wireless networks. Notable contributions include multiagent systems for mobile infrastructure, safe reinforcement learning algorithms, and sparse kernel-based methods for signal recovery. Publications: Over 15 peer-reviewed articles in IEEE Transactions and top conferences, emphasizing interdisciplinary research in reinforcement learning, distributed optimization, and cognitive networks. Recent work explores networked robotics and energy-efficient datacenter management. Awards: University of Minnesota Master Thesis Award (2009-2010) Best Paper Award at ICCRON 2007 Professional Experience: Previously served as Assistant Professor at Universidad de la República (Uruguay) before relocating to the U.S. in 2022.

Kaisong HuangView profile

Researcher

• Database Engines
• Transaction Processing
• Data Storage Management
• +3 more

Kaisong Huang is a Researcher at the University of Calgary , where he leads the Data Systems and Infrastructure Lab (DSIL) . His work bridges database systems with modern hardware innovations, focusing on performance optimization and storage technologies. PhD, Computer Science, Simon Fraser University (Sep 2020 - May 2025) MMath, Computer Science, University of Waterloo (Sep 2018 - Aug 2020) BEng, Software Engineering, Nanjing University (Sep 2014 - Jun 2018) Huang's research spans database engines , transaction processing , data storage management , resource disaggregation , and AI/LLM-driven database systems . His work includes optimizing latency in database systems, leveraging non-volatile memory, and addressing cross-engine transaction consistency. Recent publications highlight trends in hardware-aware DBMS design, persistent memory indexing, and SSD performance trade-offs. Huang has received prestigious recognitions, including: ACM SIGMOD Best Paper Award (2025) ACM SIGMOD Student Travel Award (2025) ACM SIGMOD Research Highlight Award (2023) Simon Fraser University (SFU) Graduate Fellowship (2020-2024) SFU Computing Science Travel Awards (2022, 2025) SFU Professional Development Grant (2025) He actively contributes to academic service as: PC member for SIGMOD 2026, ICDE 2026, CIKM 2023-2025, VLDBJ 2025-2026 Reviewer for SIGMOD 2022 and the Journal of Systems Architecture Committee member for the SIGMOD Availability Committee