Find Academic Supervisors

Alexander IhlerView profile

Professor

• Artificial Intelligence
• Machine Learning
• Graphical Models
• +5 more

Alexander Ihler is a Professor in the Department of Information and Computer Science at the University of California, Irvine. His work bridges artificial intelligence and machine learning with a focus on statistical methods for data analysis and approximate inference in graphical models. Applications span sensor networks, computer vision, and computational biology. His research emphasizes graphical models for structuring probability distributions and enabling efficient reasoning. Key contributions include analyzing convergence in belief propagation, extending variational techniques to continuous systems, and developing bounds for marginal MAP and decision-making problems. He also explores adaptive inference algorithms for dynamic problems, such as tracking changes in sensor data or biological systems. Recent publications highlight advancements in AI for protein design and astrophysics UAV mobility modeling deep learning integration with classical algorithms dynamic mode decomposition for forecasting Scientific accolades include the NSF CAREER award and first-place UAI 2014 Approximate Inference Challenge in five categories. He has advised numerous PhD and MS students, with graduates in fields like robotics, computational biology, and statistical learning. His group collaborates with institutions like Microsoft Research, DARPA, and the NIH, focusing on scalable methods for real-world systems. Additional personal details include a PhD from MIT under Alan Willsky and a prior role as co-program chair of the 2016 Uncertainty in Artificial Intelligence (UAI) conference.

• Study of Risks in operations
• Methods of Financial Engineering
• Interior Point Methods for Linear and Convex Programming
• +5 more

Romesh Saigal is a Professor at the Department of Industrial and Operations Engineering in the College of Engineering at the University of Michigan , where he has been employed since 1986. His career spans multiple prestigious institutions, including Northwestern University, the University of California, Berkeley, and Bell Telephone Labs. Education: B.Tech (Hons), I.I.T. Kharagpur, India, 1961 M.Tech, I.I.T. Kharagpur, India, 1963 Ph.D., University of California, Berkeley, 1968 Romesh Saigal’s research focuses on stochastic programming , financial engineering , interior point methods , Kalman filtering , continuous optimization , and game theory applications . His work bridges theoretical advancements with practical applications in transportation systems and risk analysis. His recent publications highlight the application of stochastic modeling to traffic flow prediction, dynamic pricing in toll lanes, and combinatorial auction mechanisms for traffic allocation. These works demonstrate a strong emphasis on integrating optimization algorithms and data-driven approaches in operations research. Romesh Saigal has supervised several PhD students, including Li Yang , Jie Ning , Hao Zhou , and Katherina Best , whose dissertations explore topics like risk management , traffic pricing , and higher education value analysis . He is also known for authoring the book Linear Programming: A Modern Integrated Analysis and co-editing the Handbook on Semidefinite Programming . Additional contributions include foundational work on interior point methods , accessible via collaborations at Argonne National Lab. His research continues to influence both academic and industrial practices in optimization and transportation systems.

Amy OusterhoutView profile

Assistant Professor

• Operating Systems
• Computer Networks
• High-Performance Computing
• +3 more

Amy Ousterhout is an Assistant Professor in Computer Science and Engineering at the University of California, San Diego . Prior to UCSD, she was a postdoctoral researcher at UC Berkeley's NetSys Lab and earned her Ph.D. from MIT CSAIL in 2019, where she was advised by Hari Balakrishnan and Adam Belay. She received her B.S.E. in Computer Science from Princeton University . Her research focuses on designing efficient, high-performance systems for datacenter applications , including CPU scheduling (Shenango, Caladan, Scheduling Policies), memory management (Cornflakes, Eden, Fastswap), and leveraging new hardware features (xUI, Aspen). She has taught graduate and undergraduate courses such as CSE 221 (Graduate Operating Systems) , CSE 291 (Operating Systems in Datacenters) , and CSE 120 (Operating Systems Principles) . Her publications span conferences like NSDI, ASPLOS, SOSP, HotOS, CIDR, EuroSys, HotCloud, and SIGCOMM , with notable awards including the Best Paper Award at NSDI 2025 . Her work explores microsecond-scale task scheduling , zero-copy serialization , far memory efficiency , and hardware-aware software design . Scientific Awards : Best Paper Award, NSDI 2025 Advising : She advises students such as Liam Arzola, Ani Canumalla, and Linsong Guo , with a focus on systems research and datacenter optimization.

Haluk TopcuogluView profile

Professor

• Multicore Architectures
• Parallel Algorithm Design
• Fault Tolerant Computing
• +5 more

Haluk Topcuoglu is a Full Professor in the Department of Computer Engineering at Marmara University's Faculty of Engineering. He holds a PhD from Syracuse University (1999) and has been at Marmara University since 1999, progressing through roles as Assistant, Associate, and Full Professor. His research focuses on multicore architectures, parallel algorithms, fault tolerance, dynamic optimization, and hybrid evolutionary algorithms. He has led multiple funded projects, including TUBITAK initiatives on reliability optimization and sensor placement. Education: PhD in Computer Science (Syracuse University, 1999), MSc and BSc in Computer Engineering (Boğaziçi University, 1993 and 1991). Research Interests: Task scheduling for multicore systems, reliability-aware computing, dynamic optimization, and applications of evolutionary algorithms in cloud/fog computing. His work emphasizes balancing performance, energy efficiency, and fault tolerance in parallel systems. Awards include the 2008 IBM Faculty Award, 2010 IEEE ISDA Best Paper Award, and Marmara University's 2012 Publication Impact Award. He has supervised 4 PhD and 19 MSc theses, with ongoing research in machine learning-assisted metaheuristics and dynamic monitor selection. Editorial roles include Cluster Computing Journal (SCI-Expanded) and Journal of Aeronautics and Space Technologies. He teaches courses on evolutionary computing, multicore computing, and parallel processing. Current projects include cross-layer reliability frameworks and scheduling algorithms for fog computing, reflecting his focus on industry-relevant computational challenges.

• Computer Networking
• Network Protocols
• Data Center Architecture
• +3 more

Peter Willis is a Visiting Lecturer in the School of Information at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology. He teaches courses such as Principles of Computing (ISCH-110), Computer Systems Concepts (NSSA-102), Introduction to Routing and Switching (NSSA-241), and Networking Essentials for Developers (NSSA-290). His teaching focuses on foundational computing concepts, networking technologies, and developer-oriented network principles. His research interests revolve around computer networking , with a particular emphasis on data center network protocols , resilient network design , and routing algorithm optimization . He explores topics like meshed tree protocols for loop avoidance, folded-Clos topology routing, and failover mechanisms in switched networks. Recent publications (2018–2024) highlight trends in evaluating meshed tree protocols for resilience, analyzing network performance in data centers, and optimizing routing strategies. His work often combines theoretical protocol design with practical implementation testing on platforms like the GENI testbed. Willis has not been listed as having formal advisees or receiving scientific awards in the provided materials. His academic contributions are primarily through teaching and protocol-focused research in networking domains.

Dario PaccagnanView profile

Senior Lecturer

• Game Theory
• Optimization
• Control Systems
• +7 more

Dario Paccagnan is a Senior Lecturer (US Associate Professor) in the Department of Computing at Imperial College London, affiliated with the Computational Optimization Group and the Artificial Intelligence Network. He holds a PhD from ETH Zurich and dual M.Sc. degrees from the University of Padova and Technical University of Denmark. His research focuses on multi-agent systems, game theory, optimization, control systems, and their applications in intelligent mobility and electricity markets. Education: B.Sc. in Engineering, University of Padova (2011) M.Sc. in Engineering, University of Padova and Technical University of Denmark (2014) PhD in Optimization and Control, ETH Zurich (2018) His research interests include modeling, analysis, and control of self-interested decision-making in multi-agent systems. He explores societal impacts through theoretical models and real-world applications like traffic flow prediction (e.g., WardropNet ) and energy markets. His work bridges control theory, machine learning, and algorithmic game theory. Recent publications highlight advancements in Gaussian Process compression ( Pick-to-Learn ), fair interventions in congestion games, and optimal tax mechanisms. His contributions emphasize generalization bounds, algorithmic efficiency, and distributed resource allocation. Awards: ETH Medal for Outstanding PhD Dissertation (2019) EECI Best PhD Award Finalist (2019) Best Student Paper Award at AISTATS 2025 He advises students on projects spanning multi-agent learning, congestion games, and robotic surveillance. Collaborations include MIT, UC Berkeley, and NVIDIA. He organizes seminars like Frontiers in Economics and Computation - UK and workshops on autonomous cities. Professional roles include Board Member at Aleph Digital Industry and editorial contributions to journals like IEEE TAC. He is actively involved in Imperial’s research initiatives, including the MIT-Imperial Seed Fund for collaborative projects.

Mike Mingcheng WeiView profile

Associate Professor

• Supply Chain Management
• Revenue Management and Dynamic Pricing
• Consumer-Oriented Operations Models
• +2 more

Dr. Mike Mingcheng Wei is an Associate Professor of Operations Management at the School of Management, University at Buffalo. He specializes in optimizing business operations through advanced analytical methods, with a focus on supply chain management, revenue management, and machine learning applications. His research bridges theoretical models and real-world decision-making challenges in dynamic pricing, consumer behavior analysis, and transportation networks. Dr. Wei holds a PhD from Washington University in St. Louis, an MS from Pennsylvania State University, and a BBA from Fudan University in China. His academic journey reflects expertise in both quantitative methodologies and practical operational strategies. His research interests span multiple domains: (1) Supply Chain Innovation - developing responsive pricing strategies for fashion products and C2C marketplaces; (2) Machine Learning Applications - advancing algorithms for high-dimensional data analysis and online decision-making; (3) Transportation Networks - exploring congestion pricing mechanisms and traffic bottleneck solutions through financial derivatives. His work frequently integrates game theory, stochastic modeling, and big data analytics. Professional memberships include INFORMS, MSOM, POMS, and DSI, reflecting his engagement with leading operations research communities. His research has been published in top-tier journals like Management Science and Transportation Research Part B, as well as conferences such as ICML. Dr. Wei’s office is located at 326C Jacobs Management Center, Buffalo, NY, where he advises students on operations and strategy topics. Though no specific grants or labs are highlighted in the provided text, his work demonstrates a commitment to advancing analytical frameworks for contemporary business challenges.

• Computer Networking
• Internet Video Streaming
• Quality of Service
• +4 more

Hui Zhang is a Professor in the School of Computer Science at Carnegie Mellon University, where he has made significant contributions to networking research for over two decades. He received his PhD from the University of California, Berkeley in 1993 and has maintained a prominent research career focusing on internet protocols, video streaming, and network management. His research interests span computer networking, internet video streaming, quality of service, content delivery networks, network protocols, multimedia communication, and network management. Professor Zhang's work has been particularly influential in developing adaptive video streaming technologies and content delivery mechanisms that power much of today's internet video infrastructure. His recent publications demonstrate continued innovation in networked systems, with a focus on improving video quality of experience through machine learning techniques and advanced network control mechanisms. The research trends show an evolution from foundational network protocol design to more application-focused solutions for video delivery and user experience optimization. Professor Zhang has mentored numerous successful researchers who have gone on to make their own significant contributions to the field. His collaborative work spans multiple institutions including Carnegie Mellon University, University of California, and various industry research labs. His research has been supported by substantial grants from NSF, industry partnerships, and has resulted in technologies that have been widely adopted in commercial video streaming services. Professor Zhang maintains an active research laboratory focusing on next-generation networked multimedia systems.

Konstantinos P. TriantisView profile

Professor

• Management Systems Engineering
• Data Envelopment Analysis
• Critical Infrastructure Systems
• +7 more

Konstantinos P. Triantis is the John Lawrence Professor of Industrial and Systems Engineering at Virginia Tech's College of Engineering, based at the Northern Virginia Center. He serves as Director of the Industrial and Systems Engineering (ISE) program in the National Capital Region (NCR) and co-director of the System Performance Lab (SPL). His roles include leadership on the NCR College of Engineering executive committee and former directorship of the Systems Engineering Program. Education: Ph.D., Industrial Engineering and Operations Research, Columbia University (1984) M. Phil., Industrial Engineering and Operations Research, Columbia University (1980) M.S., Industrial and Management Engineering, Columbia University (1977) B.S., Industrial and Management Engineering, Columbia University (1976) Research Interests: Dr. Triantis focuses on Management Systems Engineering , Data Envelopment Analysis (DEA) , and Critical Infrastructure Resilience . His work bridges engineering with social sciences, emphasizing dynamic performance evaluation (DPEM), complex adaptive systems, and efficiency measurement in transportation, healthcare, and disaster response systems. Recent themes include automation reliance in safety-critical roles and policy evaluation for infrastructure sustainability. Grants & Funding: He has led over $6.6M in funded projects from NSF, NASA, DOD, and others. Notable areas include resilience of interdependent infrastructures and traffic congestion mitigation strategies. Awards: John L. Lawrence Professorship (2013) Ralph H. Bogle Professor Fellow (2008) Advising: Advised over 60 graduate students, with six current advisees. Pioneered TV/compressed video course delivery in the NCR. Labs & Outreach: Co-directs the System Performance Lab (SPL), focusing on interdisciplinary solutions for engineered systems. Active in NSF-funded initiatives like the Resilient Infrastructure Processes and Systems (RIPS) program.

• Wireless Communication
• Wireless Sensor Networks
• Network Coding
• +3 more

Nadieh Moghadam is an Assistant Professor of Electrical Engineering at the University of San Diego (USD), affiliated with the College of Engineering. She holds a PhD from the University of Louisville (2017), an MS from Iran University of Science and Technology, and a BS from K.N. Toosi University of Technology. Her research focuses on wireless communication, network coding, sensor networks, and embedded systems. She has authored over 15 publications and served as a reviewer for IEEE Transactions journals since 2015. Education : PhD in Electrical Engineering, University of Louisville (2017) MS in Electrical Engineering, Iran University of Science and Technology BS in Electrical Engineering, K.N. Toosi University of Technology (Iran) Research Interests : Dr. Moghadam advances wireless communication systems through innovations in network coding, optimization algorithms, and embedded system design. Her work addresses challenges in multicast efficiency, queue stability, and energy conservation in IoT and sensor networks. She also explores blockchain applications in modern wireless standards like IEEE 802.11ax. Awards : Graduate Dean’s Citation Award (2017) University Fellowship (PhD period) Theobald Scholarship (PhD period) Industry Experience : She co-founded Ivani, a startup developing IoT wireless sensing systems using Bluetooth Low Energy (BLE) and Zigbee protocols. Her team designed low-power occupancy sensing solutions leveraging RF environments.

• Hardware-Software Systems
• Embedded Systems
• Intelligent Robotic Environments
• +5 more

Dr. Alexandru Iovanovici is an Associate Professor at the Department of Computer Science, Faculty of Automatic Control and Computers, Politehnica University of Timisoara. His research focuses on hardware-software systems, embedded systems, intelligent robotic environments, and complex networks analysis with applications in urban traffic optimization and sensor networks. He holds a Dr. Eng. degree and has received Merit Diplomas in 2021 and 2023. His academic work combines theoretical network science with practical engineering solutions. Notable projects include the INCEPTION initiative exploring IoT and complex networks for COPD management, and TACTICS framework for adaptive traffic control systems. His research bridges computer science, transportation engineering, and biomedical informatics. Education: Dr. Eng. in Computer Science (degree institution unspecified) Key Skills: Network motif analysis, metaheuristic optimization, real-time traffic modeling Research trends show strong emphasis on applying complex networks theory to urban systems: 60% of recent publications (2019-2022) address traffic infrastructure optimization, while 30% explore biomedical applications like drug interaction networks. Recent work demonstrates cross-disciplinary innovation, combining machine learning with embedded systems for wearable healthcare monitoring. Awards: 2023 Merit Diploma for academic contributions, 2021 Merit Diploma Events Organized: 2024 Workshop on Secure Real-Time Data Systems, 2025 IEEE Symposium on Applied Computational Intelligence Active in both academic and applied research, Dr. Iovanovici leads projects involving EU economic dynamics modeling and pandemic-era mobility studies. His lab develops high-availability monitoring systems for sleep disorders and maintains a spatial analysis framework for traffic data integration.

• Operations Research
• Optimization
• Game Theory
• +3 more

V.M. Timmermans is a Lecturer in Quantitative Economics at Maastricht University's School of Business and Economics, specializing in Operations Research. His work bridges optimization theory, game theory, and transportation systems. He holds a Doctoral Thesis from Maastricht University (2019) titled Uniqueness and computation of equilibria in resource allocation games . Research interests focus on equilibrium computation in resource allocation games, congestion game analysis, and optimization of transportation networks. His recent work explores collaborative strategies in inland waterway efficiency, emphasizing fuel optimization and skipper collaboration. Publications span journals like European Journal of Operational Research and conferences such as IPCO and MAPSP. His work integrates algorithmic design with real-world logistics challenges, demonstrating expertise in both theoretical and applied operations research.

Francis YanView profile

Assistant Professor

• Networked Systems
• Computer Networks
• Machine Learning for Systems
• +3 more

Francis Yan is an Assistant Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Siebel School of Computing and Data Science and the Electrical & Computer Engineering department. He leads the Illinois Networked Systems and AI (NSAI) Lab, focusing on integrating practical machine learning into networked systems. Previously, he was a Senior Researcher at Microsoft Research Redmond, working on systems and networking research. His work emphasizes deployable ML solutions, validated through large-scale platforms like Puffer and Pantheon. Education: Ph.D. in Computer Science (Stanford University, 2020); B.S. in Computer Science (Tsinghua University, 2015) and B.A. in Economics (Tsinghua University, 2015). Research Interests: Networked systems, machine learning for systems and networking, intelligent video systems, and cloud resource management. His lab develops frameworks like DeDe, Mowgli, and Autothrottle to enhance system performance and robustness. Awards: USENIX NSDI Outstanding Paper Award (2024), Applied Networking Research Prize (2021), and USENIX ATC Best Paper Award (2018). He serves on program committees for NSDI, SIGCOMM, and ACM workshops. Grants & Service: Co-chair of SIGCOMM 2024 Artifact Evaluation. Active in open-source projects and research platforms supporting reproducible networking experiments. Labs/Teams: Directs the NSAI Lab, collaborating on projects such as Puffer (400k-user video service), Teal (WAN optimization), and Genet (curriculum learning for networking).

Kristjon CikoView profile

Research Fellow

• Network Architecture
• Distributed Systems
• Recursive InterNetwork Architecture (RINA)
• +3 more

Kristjon Ciko is a Research Fellow at the University of Oslo's Faculty of Mathematics and Natural Sciences, affiliated with the Department of Informatics under the Networks and Distributed Systems group. His work focuses on advancing network architectures and distributed systems. Research Interests: Ciko's research spans network architecture, particularly Recursive InterNetwork Architecture (RINA), congestion control mechanisms, and energy-efficient computing. His studies address challenges in next-generation network design, throughput optimization, and sustainable computing systems. Publications: Recent work includes innovations in energy efficiency for throughput computing, software-based server energy management, and congestion control solutions for datacenter networks. His research bridges theoretical advancements with practical implementations.

Michael CaramanisView profile

Professor

• Power Systems
• Smart Grid
• Energy Markets
• +4 more

Michael Caramanis is a Professor of Mechanical Engineering and Systems Engineering at Boston University's College of Engineering. He holds affiliations with the Energy, Sustainability & Climate initiative. His research focuses on power markets, microgrid cyber capabilities, and stochastic production systems. Notably, he served on the Greek National Energy Council (1976-79), MIT Energy Laboratory (1979-82), and chaired Greece's Energy Regulatory Authority (2005-recently). His work emphasizes smart grid development, decision support algorithms, and integrating sustainable energy technologies like wind, storage, and EVs. Education: PhD in Engineering/Decision and Control from Harvard University. Additional affiliations include Mechanical Engineering and Systems Engineering departments at BU. Research Interests: Caramanis explores cyber-physical-energy systems, real-time market mechanisms, and load management. His recent work addresses distribution network retail markets, topology control algorithms (TCA), and the economic dispatch of distributed energy resources. Key areas include locational marginal pricing, demand response strategies, and grid resilience. Publications: Over 100 peer-reviewed articles and co-authorship of Spot Pricing of Electricity (1988). Recent trends focus on smart grid algorithms, market design innovations, and integrating renewable energy into power systems. His research bridges optimization theory with practical grid challenges. Grants & Projects: Funded by NSF, EPRI, NYSERDA, and industry. Projects include topology control for congestion mitigation, data center participation in markets, and distributed energy resource coordination. His work has been applied in PJM grid simulations and real-world distribution networks. Labs & Teams: Leads research teams advancing smart grid controls and market mechanisms. Collaborates on projects like the AitF: Collaborative Research initiative and ETEM-SG: Smart Energy Systems . Active in developing policy frameworks for competitive electricity markets.