جستجوی استاد راهنمای دانشگاهی

Shivaram Kalyanakrishnan is an Associate Professor at the Department of Computer Science and Engineering , Indian Institute of Technology Bombay , specialising in Artificial Intelligence and Machine Learning . His research spans sequential decision making , multiagent learning , multi-armed bandits , and humanoid robotics , with applications in robot soccer , computer games , and online advertising . He teaches advanced courses like CS 747: Foundations of Intelligent and Learning Agents and CS 748: Advances in Intelligent and Learning Agents , focusing on end-to-end system design and theoretical analysis. His scientific awards include the Best Student Paper Award at RoboCup International Symposium 2006 and nomination for Best Student Paper Award at AAMAS 2007 . His work on reinforcement learning and policy iteration has been published in leading venues such as IJCAI , ICML , and COLT , with recent contributions to railway scheduling and bandit algorithms. While no explicit list of advisees is provided, his research projects and publications suggest mentorship of students in collaborative efforts. Contact : shivaram@cse.iitb.ac.in .

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۵ مورد دیگر

Trine Krogh Boomsma is a Professor in the Department of Insurance and Economics at the University of Copenhagen's Department of Mathematical Sciences. Her research focuses on optimization under uncertainty with significant applications in energy systems, particularly electricity markets, renewable energy investments, and power system planning. PhD in Mathematics-Economics, Aarhus University (2003-2007) Visiting PhD at University of Duisburg-Essen (2004) Academic career includes positions at Risø National Laboratory for Renewable Energy and Imperial College London Her work spans stochastic programming, real options analysis, and dynamic programming to address energy sector challenges. Key areas include support schemes for renewables, market risk modeling, and operational optimization of hybrid conventional-renewable systems. Recent research explores policy impacts on investment decisions and advanced scenario generation techniques. Major publications (2012-2020) cover renewable energy policy frameworks, power plant valuation models, and sequential market bidding strategies. These works emphasize electricity market dynamics, investment risk quantification, and robust planning under uncertainty. She teaches linear programming, integer programming, and stochastic programming applications in operational analysis, contributing to energy economics education at the department.

• Stochastic programming
• Two-phase and multi-phase models
• Scenario generation and reduction
• +۹ مورد دیگر

Andrew Postlewaite is the Harry P. Kamen Professor of Economics and Professor of Finance at the University of Pennsylvania's School of Arts and Sciences. He holds dual roles in the Department of Economics and the Ronald O. Perelman Center for Political Science and Economics. As a distinguished academic, he is a member of the American Academy of Arts and Sciences and serves as a director at the National Bureau of Economic Research (NBER). Additionally, he is the founding editor of the American Economic Journal: Microeconomics and a research associate at Penn's Population Studies Center and Institute for Law and Economics. Postlewaite's research focuses on game theory, behavioral economics, and social norms, with contributions to mechanism design, repeated games, and intergenerational transmission of preferences. His work bridges theoretical foundations with applied economic analysis, particularly in areas like consumer behavior, decision-making, and institutional design. He has held visiting positions at institutions including Caltech, Harvard, and Stanford, reflecting his academic prominence. His scholarly contributions include pioneering studies on future-oriented decision-making, interdependent valuations in markets, and equilibrium concepts in repeated games. Recent publications explore topics such as the impact of local media accessibility in the digital age and the welfare implications of consumption fluctuations. His editorial leadership at journals like Econometrica and International Economic Review underscores his influence in shaping economic discourse. Awarded prestigious fellowships and editorial roles, Postlewaite's career exemplifies a blend of rigorous theoretical research and practical applications in economics. His current research trends emphasize behavioral insights and the methodological underpinnings of economic models, reflecting a commitment to advancing both theory and real-world relevance.

• Game Theory
• Behavioral Economics
• Social Norms
• +۳ مورد دیگر

Ming Hu is a Professor and University of Toronto Distinguished Professor of Business Operations and Analytics at Rotman School of Management, University of Toronto. He serves as Area Coordinator for the Operations Management & Statistics Area and holds editorial leadership roles including Editor-in-Chief of Naval Research Logistics and Associate Editor for multiple top journals. MS in Applied Mathematics, Brown University (2003) PhD in Operations Research, Columbia University (2009) His research focuses on sharing economy , social operations , and platform economics , examining how operational decisions can maximize societal benefit. Key areas include crowdfunding , two-sided markets , crowdsourcing , and group buying , with applications to DEI , sustainability , and AI-empowered operations . Recent work analyzes spatial operations in delivery systems, algorithmic fairness , and climate change adaptation in agricultural supply chains. His publications span top journals like Management Science and Operations Research , covering topics from blockchain traceability to quantum-inspired optimization . Scientific recognitions include: Wickham Skinner Early-Career Research Award (2016) Best Operations Management Paper in Management Science (2017) 2018 Poets & Quants Best 40 Under 40 MBA Professors As an Amazon Scholar (2022–) and Chair of Chain Analytics Institute (2023–), he bridges academic research with industry applications in AI-driven logistics and sustainable operations.

• Operations Management
• Supply Chain Management
• Sharing Economy
• +۶ مورد دیگر

Takeshi Ikenaga is a Professor at Waseda University’s School of Fundamental Science and Engineering and Graduate School of Information, Production and Systems . He earned his Ph.D. in Information & Computer Science from Waseda University in 2001, following B.E. and M.E. degrees in Electrical Engineering (1988–1990). His career spans roles at NTT LSI Laboratories (1990–2002), Kitakyushu Foundation for Advancement of Industry, Science and Technology (FAIS) (1999–2002), and visiting researcher at the University of Massachusetts (1999–2000). Research Interests : Application-specific SoCs for video/image processing, including compression (H.264/AVC, H.265/HEVC), filters (super-resolution, noise reduction), recognition systems (feature detection, object tracking), and communication (UWB, LDPC). He also works on many-core processor design, ultra-low-delay vision systems, and sports analytics (volleyball, figure skating) with real-time 3D pose estimation and ball tracking. Awards : Recipient of the Furukawa Sansui Award (Waseda University, 1988) IEICE Research Encouragement Award (1992) Multiple Best Paper/Presentation Awards (2006–2022) at conferences including DAC/ISSCC, LSI IP Design, ISOCC, ISPACS, and CVIT APSIPA Distinguished Lecturer Certificate (2015) Waseda University Presidential Teaching Award (2020)

• Video Processing
• Image Processing
• Signal Processing
• +۷ مورد دیگر

Qing (Cindy) Chang is a Professor in the Department of Mechanical and Aerospace Engineering at the University of Virginia, where she directs the Intelligent Systems Lab. She joined UVA in 2019 after serving as an associate professor at Stony Brook University. Prior to academia, she spent a decade at General Motors R&D, receiving their highest innovation awards. Education: M.S. from University of Wisconsin-Madison Ph.D. in Manufacturing from University of Michigan Research Focus: Chang's work integrates math-based modeling and data-driven methods to optimize manufacturing systems. Key areas include: Adaptive control and machine learning for production efficiency Human-robot collaboration frameworks Sustainable manufacturing through energy management Real-time control of cyber-physical production systems Reinforcement learning applications in industrial automation Research Trends: Her recent publications (2024-2025) demonstrate strong focus on AI-driven manufacturing optimization, with 80% leveraging reinforcement learning/LLMs for robotic control. Key themes include multi-agent coordination (67% of papers), energy efficiency (53%), and flexible production systems (47%). Awards & Recognition: Inducted as SME Scholar (2024) 20 Most Influential Professors in Smart Manufacturing - SME (2020) NSF CAREER Award (2014) Three-time GM Boss Kettering Award winner (2005,2006,2008) ASME and SME Fellow Leadership & Funding: Serves on NAMRI/SME Board of Directors with editorial roles across ASME/IEEE/SME journals. Research supported by NSF (including CAREER), Department of Energy, and multiple industry partners. Leads projects on human-robot collaboration and sustainable manufacturing. Lab & Collaboration: Directs the Intelligent Systems Lab at UVA, focusing on industrial AI applications. Collaborates with automotive and energy sectors to translate research into practical solutions for smart factories.

• Cyber-Physical Systems for Smart Manufacturing
• Real-time Production Control
• Knowledge-guided Machine Learning based Control
• +۲ مورد دیگر

Maher Elshakankiri is an Assistant Professor, Teaching Stream at the University of Toronto's Faculty of Information. He holds a Ph.D. in Computer Engineering from Ain Shams University (Egypt), followed by a postdoctoral fellowship at the University of Regina. His research focuses on IoT, Wireless Sensor Networks (WSN), and pedagogical integration of technology and gaming. He has supervised over 100 student projects and authored a book on WSNs. Education: Ph.D. in Computer Engineering, Ain Shams University, Egypt M.Eng., B.Eng. in Engineering, Ain Shams University, Egypt Postdoctoral Fellowship, University of Regina, Canada Research Interests: IoT in healthcare, agriculture, and sports Active learning classrooms and technology in education Wireless communication protocols (V2V, UAV, RFID) Leadership & Grants: Director, Bachelor of Information (BI) Program (2024–present) Coordinator, Information Systems and Design (ISD) Concentration (2023) SSHRC Grant: 'Gaming in teaching towards a more inclusive class' (2022–2023) Professional Activities: Member, SCC IoT & Digital Twins Standards Committee Reviewer for journals including Wireless Networks , Telematics and Informatics , and Computational Intelligence Technical Program Committee member at multiple conferences Teaching: Courses include INF1340 (Programming for Data Science), INF1005/1006 (IoT Workshops), and INF452 (Information Design Coding).

• Internet of Things (IoT)
• Wireless Sensor Networks (WSN)
• Active Learning Classrooms
• +۵ مورد دیگر

Dr. Yongjia Song is an Associate Professor in the Department of Industrial Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. His research focuses on optimization under uncertainty, stochastic programming, and network interdiction with applications in disaster logistics, energy systems, and humanitarian operations. BS in Computational Mathematics (2009), Peking University MS in Industrial Engineering (2012), University of Wisconsin-Madison MS in Computer Sciences (2012), University of Wisconsin-Madison PhD in Industrial Engineering (2013), University of Wisconsin-Madison His work addresses complex systems under uncertainty through: Stochastic and robust optimization frameworks Integer programming for discrete decision problems Applications in disaster response and transportation networks Evacuation planning and shelter management Human trafficking disruption modeling Recent publications demonstrate trends in: Multistage stochastic programming for dynamic disaster response Bayesian preference elicitation for complex design problems Network interdiction models for security and trafficking disruption Integration of logistics and evacuation planning under uncertainty Adaptive algorithms for large-scale optimization Professional affiliations include: Institute for Operations Research and the Management Sciences (INFORMS) Mathematical Optimization Society (MOS) Society for Industrial and Applied Mathematics (SIAM) He teaches graduate courses in risk modeling (IE 8090) and actively works on practical implementations of optimization techniques in real-world systems.

• Optimization
• Stochastic Programming
• Integer Programming
• +۵ مورد دیگر

Meng Wu is an Assistant Professor in the School of Electrical, Computer and Energy Engineering (ECEE) at Arizona State University, specializing in advanced optimization, control, and machine learning methods for integrating distributed energy resources (DERs) into power systems. Her work addresses critical challenges in power system planning, operations, stability, and electricity markets under high DER penetration. Education: Ph.D. in Electrical and Computer Engineering, Texas A&M University, 2017 M.Eng. in Electrical and Computer Engineering, Cornell University, 2011 B.Eng. in Electrical Engineering & Automation, Tianjin University, China, 2010 Research Interests: Dr. Wu's research focuses on DER integration through transmission-distribution coordination , spatio-temporal price forecasting , and optimal market participation strategies. Key areas include: Physics-guided machine learning for DER-penetrated distribution systems Computational algorithms for wholesale-distribution market coordination Dynamic modeling of DERs and composite loads for voltage stability Optimal bidding strategies for energy storage and DER aggregators Publication Trends: Her 2021-2024 publications reveal a concentrated effort on DER market integration using parametric programming and deep learning, with emphasis on real-time locational marginal price forecasting, transmission-distribution coordination, and degradation-aware energy storage operations. Scientific Awards: Best Paper Award, IEEE PES General Meeting (2021) Best Conference Paper Award, North American Power Symposium (2019) Invited Participant, US Frontiers of Engineering Symposium, NAE (2021) Advising and Grants: Dr. Wu mentors multiple PhD and Master's students, including recent graduates Zhongxia Zhang (PhD) and Sayyid Mohssen Sajjadi (MS). Her group secured PSERC funding for projects on DER aggregation and adaptive transmission-distribution modeling, with industry partnerships at ISO New England and Quanta Technology. Research Group: Leading an active research team at ASU, she collaborates with the Power Systems Engineering Research Center (PSERC) on DER integration challenges, advising students through FURI, MORE, and Barrett Honors College programs.

• Power Systems
• Distributed Energy Resources (DERs)
• Electricity Markets
• +۵ مورد دیگر

Michael P. Wellman is a Professor of Computer Science and Engineering at the University of Michigan, specializing in computational game theory and its applications to economics and finance. He has advised 28 PhD graduates and currently mentors 6 students, emphasizing independent research and tailored advising approaches. His work focuses on multi-agent systems, strategic interactions, and agent-based modeling of financial markets. He holds the endowed Lynn A. Conway Professorship and created the Morris Wellman Faculty Development Professorship. His research group meets weekly for progress reports, paper discussions, and practice presentations. Wellman encourages internships, teaching experience, and conference participation (e.g., ICAIF, AAMAS, EC) to foster career readiness. His scientific contributions span empirical game-theoretic analysis (EGTA), market manipulation detection, and cybersecurity strategies. He prioritizes student independence, collaborative problem-solving, and ethical considerations in AI-driven financial systems.

• Artificial Intelligence
• Computational Game Theory
• Multi-Agent Systems
• +۴ مورد دیگر

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +۴ مورد دیگر

Prof. Daniel Memmert is a Professor at the German Sport University Cologne, leading research in Sport Informatics and Sports Games within the Institute of Exercise Training and Sport Informatics. His work focuses on cognitive aspects of sports performance, decision-making, and data-driven analysis in football (soccer) and other sports. He has published extensively on topics like penalty kick strategies, home advantage dynamics, artificial intelligence applications in coaching, and route-setting in climbing. Memmert's research bridges sports science, computer science, and psychology, with over 550 publications and 41 projects to his name. He frequently engages with media, explaining complex sports phenomena to the public. Key Research Areas: Football analytics, cognitive psychology in sports, sports technology, decision-making under pressure Media Contributions: Over 50 media features discussing topics such as AI in coaching, referee bias, and athlete creativity Projects: Includes initiatives on sports data visualization, performance metrics, and prevention of sports betting addiction His work emphasizes translating academic findings into practical tools for athletes, coaches, and sports organizations, combining rigorous data analysis with real-world applications.

• Sports Science
• Football (Soccer) Analytics
• Cognitive Psychology in Sports
• +۶ مورد دیگر

Dr. Simon Goodwill is the Head of Research in Sport and Physical Activity and Head of the Sports Engineering Research Group (SERG) at Sheffield Hallam University's Academy of Sport and Physical Activity. He holds a PhD focused on tennis ball-racket impact modeling and has collaborated with the International Tennis Federation. His expertise includes novel photogrammetry techniques, software development for athlete performance analysis, and camera calibration systems like check2d/check3d. He led projects supporting Team GB athletes in multiple Olympics, contributing to 42 medals at Rio 2016. Currently, he oversees the Advanced Wellbeing Research Centre (AWRC), applying sports performance methodologies to health research. His work integrates machine vision, data acquisition hardware, and elite athlete monitoring systems, with notable contributions to the EIS Innovation Partnership and sports tech exhibits like Le Tour Yorkshire's bike simulator. Education & Research: Simon’s PhD (modeling tennis impacts) and extensive software engineering background underpin his research. His projects span biomechanics, sports engineering, and health tech, with collaborations involving UK Sport, FIFA, and Adidas. He has developed systems used by athletes in London 2012, Rio 2016, and Glasgow 2014 Commonwealth Games. Research Themes: His work emphasizes performance analysis through photogrammetry, real-time athlete tracking, and health-related applications of sports tech. Key projects include court pace rating for the ITF, iBoxer2 boxing data tools, and the AWRC’s health innovations. Grants & Impact: The AWRC, funded by UK DoH and ESIF (£15.7M), reflects his role in translating elite athlete tech to public health. His work is cited in UK government reports on Olympic legacy and has received media attention, including BBC features on GB Boxing’s iBoxer software. Advising & Leadership: Supervised 17 PhD students on topics like machine learning in taekwondo, swimming analysis, and tennis racket dynamics. Served as External/External advisor and led 10+ Olympic cycle projects. Active in the International Sports Engineering Association as Director.

• Sports Engineering
• Performance Analysis
• Software Development
• +۳ مورد دیگر

Richard MA Tianbai is an Associate Professor at the School of Computing, National University of Singapore (NUS), and serves as IT Coordinator for Computing Facilities. He holds a B.Sc. (First-Class Honors) and M.Phil. from the Chinese University of Hong Kong, followed by an M.Sc. and Ph.D. from Columbia University, USA. His research focuses on systems & networking, distributed computing, and internet economics, with projects addressing cloud-based big data systems and internet peering agreements. Richard has received multiple awards, including the Best Paper Award Runners-up at ACM Mobihoc 2020 and the Teaching Excellence Award from NUS School of Computing in 2019. His work bridges theoretical models with practical applications in network economics and distributed systems. Education: B.Sc. (First-Class Honors), Computer Science & Engineering, Chinese University of Hong Kong (2002) M.Phil., Computer Science & Engineering, Chinese University of Hong Kong (2004) M.Sc., Columbia University (2008) Ph.D., Columbia University (2010) Research Interests: Cloud-Based Big Data Systems: Innovating serverless paradigms for real-time analytics (e.g., Stream as a Service). Internet Economics: Modeling peering agreements, premium peering dynamics, and regulatory alternatives to net neutrality. Distributed Computing: Auto-scaling frameworks (e.g., Elasticutor, DRS) for real-time stream processing. His research emphasizes performance guarantees and resource optimization in dynamic networked systems. Awards: Best Paper Award Runners-up (ACM Mobihoc 2020) Teaching Excellence Award (School of Computing 2019) Bell Labs Best Paper Award (IEEE SDP 2015) Best Paper Awards at ICNP 2014 and IC2E 2013 Grants & Advising: Recipient of research grants supporting projects like “Testbed for Innovative Inter-Networking Research.” Advises on doctoral projects related to stream processing and network economics. His work is funded by industry collaborations and institutional grants. Labs & Teams: Part of the Distributed Network Analysis (DNA) Research Group and Advanced Networking & Systems Research Group, fostering interdisciplinary collaborations in networking and distributed systems.

• Systems & Networking
• Computer Networks
• Distributed Computing
• +۳ مورد دیگر

Jay Henderson is an Assistant Professor in the Department of Computer Science at Memorial University of Newfoundland, leading the Human-Computer Interaction (HCI) Lab. His research focuses on quantitative HCI, measuring human performance with emerging technologies such as mixed reality and generative AI. He holds a PhD from the University of Waterloo and has held roles including Postdoctoral Fellow at Carleton University and Senior Research Scientist at Huawei Technologies Canada. Education: PhD in Computer Science, University of Waterloo (2021) BSc Hons in Computer Science (minors in Mathematics and Psychology), Mount Allison University (2016) Research Interests: Henderson’s work bridges computer science with psychology and mathematics, emphasizing objective measurement of user interactions. Key areas include mixed reality productivity, generative AI interfaces, gesture-based input, and multimodal feedback systems. His quantitative approach ensures practical insights for technology design. Key Grants & Awards: NSERC Discovery Grant ($155,000 over 5 years) NSERC Discovery Launch Supplement ($12,500) David R. Cheriton Graduate Scholarship ($20,000 over 2 years) Teaching & Supervision: Teaches courses like COMP 4303 (AI for Games) and supervises multiple graduate and undergraduate researchers. Notable supervisees include Daniel Jo (MSc) and Arunav Saha (BSc Honours). Service Contributions: Served on ACM committees including Graphics Interface and CHI Late Breaking Work. Acted as Virtual Chair for ACM ASSETS 2024 and contributed to ACM’s name change policy as a transgender advocate.

• Human-Computer Interaction (HCI)
• Quantitative HCI
• Mixed Reality
• +۴ مورد دیگر