جست‌وجوی استاد و پژوهشگر

Martin Müllerمشاهده پروفایل

استاد

• Artificial Intelligence
• Game Theory
• Monte Carlo Tree Search
• +۷ مورد دیگر

Martin Müller is a Professor in the Department of Computing Science at the University of Alberta, where he conducts research in artificial intelligence, game theory, and heuristic search. He holds the Canada CIFAR AI Chair at Amii and is an Amii Fellow, underscoring his leadership in AI. His research group focuses on Monte Carlo tree search, reinforcement learning, combinatorial game theory, and automated planning, with applications in games such as Go, Hex, and NoGo. His research interests span Monte Carlo and exact methods in game-tree search , exploration in heuristic search and machine learning , and algorithms in combinatorial game theory . He has developed open-source software like MCGS (Minimax-based Combinatorial Game Solver) and contributes to game-playing systems such as Fuego for Go. His work bridges theoretical foundations with practical implementations in AI-driven game solvers. Recent publications show a strong trend in reinforcement learning , particularly in deep Q-learning, policy gradient methods, and anomaly detection in deep RL. His team also explores combinatorial game solving , sparse reward environments , and imperfect information games . The research integrates machine learning with classical AI techniques, emphasizing empirical validation and algorithmic innovation. Canada CIFAR AI Chair Amii Fellow Best student paper award at IEEE Conference on Games 2024 Best paper award at IEEE COG 2021 Outstanding paper award at AAAI-18 Faculty of Science Dissertation Award (2016) Dissertation Award from the Canadian Artificial Intelligence Association (2013) Müller has supervised numerous PhD and MSc students, including Hongming Zhang, Henry Du, and Timo Bertram, many of whose theses focus on game AI, reinforcement learning, and combinatorial optimization. He is funded by NSERC, Mitacs, and Compute Canada. His group collaborates on projects involving neural networks for game playing, SAT solving, and planning algorithms. He is currently on sabbatical but remains academically active, teaching a graduate course on combinatorial games in 2025 and hosting visiting researchers. His lab is involved in the development of MCGS, a solver for sum games, and contributes to open-source AI software. The team publishes regularly in top venues such as NeurIPS, ICML, AAAI, and IEEE Transactions on Games. Future work includes advancing combinatorial game solvers, improving deep RL robustness, and exploring generalization in game representations.

Deepa Kundurمشاهده پروفایل

استاد

• Cybersecurity
• Signal Processing
• Complex Dynamical Networks
• +۴ مورد دیگر

Deepa Kundur is the Professor & Chair of The Edward S. Rogers Sr. Department of Electrical & Computer Engineering at the University of Toronto. She earned her BASc, MASc, and PhD in Electrical and Computer Engineering from the same institution in 1993, 1995, and 1999, respectively. Current roles: IEEE Spectrum Advisory Board Conference leadership: General Chair of 2018 GlobalSIP Symposium, TPC Co-Chair for IEEE SmartGridComm 2018, among others Her research focuses on cybersecurity , signal processing , and complex dynamical networks , particularly in smart grid applications. She has authored over 200 publications and pioneered techniques for detecting false data injection attacks, enhancing grid resilience, and integrating machine learning into power systems. Her recent work spans quantum learning for grid security , LLM-based mental health prediction , and resilient control systems . She has received 14 best paper recognitions, including IEEE SmartGridComm (2015) and IEEE INFOCOM Workshop (2008). Fellowships: IEEE Fellow (2015), Canadian Academy of Engineering Fellow (2016), Massey College Senior Fellow (2019) Teaching awards: Tenneco Meritorious Teaching Award (2005), Gordon Slemon Teaching of Design Award (2002) Early career honors: NSERC Scholarships (PGS A/B), Canada Scholarship She leads the Kundur Research Group , developing models for cyber-physical systems in smart grids and autonomous vehicle networks. Her team explores reinforcement learning for grid defense , transmissibility-based fault detection , and privacy-preserving smart grid analytics .

• Photonic Interconnects
• Distributed Machine Learning
• Cloud Computing
• +۳ مورد دیگر

Rachee Singh is an Assistant Professor of Computer Science at Cornell University, leading the sysphotonics research group. She concurrently serves as an Amazon Scholar within the SageMaker Hyperpod teams, specializing in large-scale machine learning infrastructure development for cloud environments. Her research focuses on photonic interconnect systems for server-scale, rack-scale, and long-haul communication networks, targeting performance optimization for distributed machine learning and planet-scale cloud workloads. Key specialties include optical network design, fault-tolerant WAN architectures, and energy-efficient datacenter interconnects, with strong emphasis on practical deployment in real-world systems. Her group bridges theoretical networking principles with applied AI infrastructure challenges. Recent publications demonstrate concentrated innovation in photonic network optimization for ML workloads, particularly in wavelength management, collective communication algorithms, and chip-to-chip photonic fabrics. This work spans optical physics, distributed systems, and machine learning, revealing a trajectory toward sustainable, high-performance AI infrastructure. Scientific recognition includes: Amazon Research Award (2023) Cisco Research Award Dr. Singh actively mentors graduate researchers including Jonathan Aimuyo, Byungsoo Oh, and Arjun Devraj, whose co-authored publications form the core of her group's output. Research funding is secured through competitive grants from the NSF (including a $1M award for chip-to-chip photonic fabrics), SRC/DARPA JUMP 2.0 program, Cisco, and Cornell's Atkinson Center for Sustainability. The sysphotonics group operates as Cornell's hub for photonic network systems research, developing programmable integrated photonics solutions and collaborating with Amazon on SageMaker Hyperpod for next-generation ML infrastructure.

Joss Wrightمشاهده پروفایل

دانشیار

• Internet Censorship
• Privacy-Enhancing Technologies
• Cybercrime
• +۴ مورد دیگر

Joss Wright is an Associate Professor and Senior Research Fellow at the Oxford Internet Institute , University of Oxford. He co-directs the Oxford EPSRC Cybersecurity Doctoral Training Centre and the Oxford Martin Programme on the Wildlife Trade, focusing on computational approaches to social science questions about information control and privacy. Education : PhD in Computer Science from the University of York (research on anonymous communication systems), postdoctoral work at the University of Siegen (cloud computing security). His research spans internet censorship , privacy-enhancing technologies , and cyber-enabled crime (notably the online illegal wildlife trade ). He bridges technical analyses of security systems with their social and political implications, advising the European Commission and UK Parliamentary Science Committee on digital policy. Recent work includes machine learning applications to detect patent filing trends related to wildlife trade and analyzing Chinese smart city surveillance for human rights risks. He has contributed to media outlets like the Guardian and New Scientist. Notable projects include the Oxford Martin Programme on Wildlife Trade and studies on discriminatory effects of internet filtering . He supervises students like William Lugoloobi (DPhil in Social Data Science) and former advisee Samantha Bradshaw (now Assistant Professor at American University).

• Neuromorphic Computing
• Memristor-Based Architectures
• Energy-Efficient AI Hardware
• +۷ مورد دیگر

Said Hamdioui serves as a full Professor in the Department of Computer Engineering within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. His research focuses on cutting-edge hardware architectures for neuromorphic computing and energy-efficient AI acceleration, with particular emphasis on memristor-based systems, emerging memory technologies, and fault-tolerant designs for edge applications. His research interests span Neuromorphic Computing , Memristor-Based Architectures , and Energy-Efficient AI Hardware , addressing critical challenges in hardware security, computation-in-memory, and reliable edge AI deployment. Recent work demonstrates significant advancements in RRAM/FeFET testing methodologies, spiking neural network implementations, and spin wave computing alternatives to traditional CMOS. His publications reveal strong trends toward real-world deployment of brain-inspired hardware with practical constraints like power efficiency, testability, and security. Award highlights include: DATE'20 Best Paper Award DFT'21 Outstanding Student Paper ETS 2021 Best Paper Award LATS 2018 & 2022 Best Paper Awards Professor Hamdioui actively contributes to the research community through editorial roles at IEEE Transactions on VLSI Systems , IEEE Design & Test , and Journal of Electronic Testing from 2017-2018. His leadership in multi-partner projects like CONVOLVE and NEUROKIT2E demonstrates strong industry-academia collaboration for edge AI solutions. Current work shows increasing focus on practical deployment challenges including in-field fault monitoring, security vulnerabilities in neuromorphic systems, and realistic brain simulation frameworks.

Christian Rossowمشاهده پروفایل

استاد

• Network Security
• System Security
• Software Security
• +۹ مورد دیگر

Prof. Dr. Christian Rossow is a faculty member at CISPA – Helmholtz Center for Information Security in Dortmund, Germany. He also serves as a professor at Saarland University and an honorary professor at TU Dortmund. His research focuses on network security , system/software security , and cyber attacks and countermeasures . His recent publications include work on CSS exploitation for browser fingerprinting, Spectre mitigations on ARM, DDoS attack analysis, and hardware-assisted security extensions. Christian Rossow regularly serves as a PC member and track chair for top cybersecurity conferences like ACM CCS and USENIX Security . He advises multiple PhD students and leads a research group exploring foundational and practical security solutions.

• Mathematical Optimization
• Algorithmic Intelligence
• Energy Systems Modeling
• +۵ مورد دیگر

Thorsten Koch serves as Head of the Department of Applied Algorithmic Intelligence Methods within the Division of Mathematical Algorithmic Intelligence at Zuse Institute Berlin (ZIB). His research spans mathematical optimization, energy systems modeling, quantum computing applications, and scientometrics. Koch leads significant research projects including FAN (focusing on AI in scholarly communication), UNSEEN (energy scenarios), HPO-NAVI (research software visibility), and Multi-Energy Models for European Energy System Planning. Koch's research interests center on developing advanced optimization algorithms for complex systems, particularly in energy networks and scientific data analysis. His work bridges theoretical mathematics with practical applications in gas network optimization, wind farm design, portfolio management, and quantum computing. He has pioneered methods for large-scale mixed-integer programming, scenario generation, and the integration of machine learning with traditional optimization techniques. His recent publications demonstrate growing emphasis on quantum optimization, scientometrics, and the application of AI to scientific communication infrastructure. His publication trends reveal a strategic expansion from traditional mathematical optimization into quantum computing applications and scientific data infrastructure. Recent work shows increasing collaboration across disciplines - connecting energy systems analysis with financial modeling, integrating machine learning with optimization solvers, and applying computational methods to scientometrics. The 15 most recent articles highlight three major thrusts: quantum optimization (33%), energy systems modeling (27%), and scientific data infrastructure (40%), reflecting his leadership in both theoretical algorithm development and practical implementation for societal challenges. Koch actively contributes to research infrastructure through leadership roles in projects like KOBV (Berlin-Brandenburg Cooperative Library Network), HDC (Humanities Data Centre), and CIB (future library networks). His work on the DeepGreen initiative focuses on establishing legally secure workflows for implementing open-access components in scientific publication licensing agreements, demonstrating his commitment to open science principles and research data management.

Todd D. Murpheyمشاهده پروفایل

استاد

• Computational Dynamics and Control
• Human-Swarm Interaction
• Algorithmic Matter
• +۱۰ مورد دیگر

Dr. Todd D. Murphey is a Professor of Mechanical Engineering at Northwestern University's Robert R. McCormick School of Engineering and Applied Science. He serves as Director of Transformative Research and Director of the Master of Science in Robotics Program at Northwestern, leading initiatives in computational dynamics, control systems, and robotics. His work bridges engineering, neuroscience, and biomedical applications, with a focus on developing systems that interact effectively with humans and their environments. Dr. Murphey received his Ph.D. in Control and Dynamical Systems from the California Institute of Technology in 2002, with a thesis titled "Control of Multiple Model Systems." Prior to that, he earned a B.S. in Mathematics, summa cum laude, from the University of Arizona in 1997. Dr. Murphey's research centers on computational methods in dynamics and control, with applications spanning neuroscience, health science, robotics, and automation. His work in the Interactive & Emergent Autonomy Lab focuses on computational models of embedded control, biomechanical simulation, dynamic exploration, and hybrid control. The group develops mathematical approaches that lead to orders of magnitude improvement in computational efficiency for real-time implementation. Key application areas include assistive exoskeleton control, stabilization of energy networks, bio-inspired active sensing, entertainment robots, robotic exploration, and software-enabled stroke rehabilitation. Analysis of Dr. Murphey's recent publications reveals a strong emphasis on human-swarm interaction, algorithmic matter, and control of cyber-physical systems in uncertain environments. His work increasingly integrates information theory with physical systems, exploring how both autonomous and biological systems interact with environments to learn and improve behaviors. Recent trends show growing applications in rehabilitation technology, with particular focus on human-machine interaction in biomedical devices and embodied intelligence. Dr. Murphey has received numerous honors and awards for his contributions to robotics and engineering: Named Director of Transformative Research at Northwestern University (2025) Appointed IEEE Robotics and Automation Society Vice President of Publication Activities (2022) Co-recipient of Best Paper Award for IEEE Transactions on Robotics (2020) Appointed to Air Force Scientific Advisory Board (2019) Recipient of ABB Best Student Paper Award for CPL-SLAM research (2019) Cole-Higgins Award from Northwestern Engineering (2015) Dr. Murphey has supervised numerous graduate students including Taosha Fan, Giorgos Mamakoukas, and Ian Abraham, with research spanning robotic exploration using electrosense and mechanical contact, human-in-the-loop control, and shared control for rehabilitation devices. His lab has secured significant funding from the National Science Foundation, DARPA, and industry partners including Siemens and Ekso Bionics, supporting research in algorithmic matter, emergent behavior, and human-swarm collaboration. The Interactive & Emergent Autonomy Lab, led by Dr. Murphey, investigates how both autonomous systems and biological systems interact with their environments to learn and improve behaviors. Current projects include active learning and data-driven control, active perception in human-swarm collaboration, algorithmic matter and emergent computation, control for nonlinear and hybrid systems, cyber physical systems in uncertain environments, harmonious navigation in human crowds, information maximizing clinical diagnostics, reactive learning in underwater exploration, robot-assisted rehabilitation, and software-enabled biomedical devices. The lab collaborates with researchers across Northwestern and institutions including Georgia Tech, MIT, and industry partners.

• Artificial Intelligence
• Machine Learning
• Ethical AI
• +۷ مورد دیگر

Adrian Weller is a Director of Research in Machine Learning at the University of Cambridge and Head of Safe and Ethical AI at The Alan Turing Institute, where he also serves as a Turing Fellow. He additionally directs the Trust and Society programme at the Leverhulme Centre for the Future of Intelligence. His career includes senior roles in finance and advisory positions for governmental AI ethics bodies. His research integrates technical and societal dimensions of artificial intelligence, with major foci including: Foundational ML : Statistical methods, high-dimensional inference, causality, and Monte Carlo techniques Trustworthy AI : Fairness, privacy, bias mitigation, and algorithmic transparency Applied Domains : Computer vision, reinforcement learning, and bio-applications of ML Socio-technical Systems : Policy frameworks, human perceptions of algorithms, and ethical deployment Notable recognition includes an MBE (2022) for pioneering contributions to digital innovation. His work actively informs UK and EU AI policy discussions.

Marios C. Angelidesمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Creative Computing
• +۵ مورد دیگر

Professor Marios C. Angelides is a full-time faculty member at Brunel University London , serving as Professor of Computing and Divisional Lead within the College of Engineering, Design and Physical Sciences . He leads the Creative Computing Research Group under the Institute of Digital Futures and contributes to the Digital Media department at Brunel Design School. BSc (First Class Honours) and PhD in Computing from the London School of Economics (LSE) Chartered Engineer (CEng) and Chartered Fellow of the British Computer Society (FBCS CITP) His research focuses on Creative Computing , specifically applying Machine Learning , Serious Gaming , and Cognitive Modeling to develop Smart IoT Applications . His work spans autonomous drone fleets for environmental monitoring, cybersecurity middleware for Android systems, wearable technology for lifestyle recommendations, and historical analysis of Alan Turing’s legacy in modern AI. Recent publications highlight trends in deploying Machine Learning for: IoT systems optimization Autonomous aerial/underwater vehicle coordination Deepfake detection using Turing’s Imitation Game Energy allocation in CubeSats via gaming mechanics Scientific recognition includes being Deputy Editor of The Computer Journal and runner-up for the 2016 Oxford University Press Wilkes Award . He has supervised PhD students in topics like Smart Android Middleware for Cybersecurity and Wearable Recommendation Systems , with active involvement in editorial boards and international conferences.

• Human-Centered Artificial Intelligence
• Natural Language Processing
• Computer Vision
• +۵ مورد دیگر

Carlos Toxtli is an Assistant Professor at Clemson University where he leads the Human-AI Empowerment Lab. His research applies Human-Centered AI to create fair workplace tools, focusing on NLP, computer vision, and crowdsourcing ethics. He holds a PhD in Computer Science from Northeastern University and previously worked at Google, Microsoft Research, and Snap Inc. Education: Ph.D. Computer Science, Northeastern University M.S. Innovation & Technological Entrepreneurship, Monterrey Institute of Technology MBA, IEDE Business School B.S. Computer Science, University of the Valley of Mexico B.S. Computer Engineering, National Autonomous University of Mexico Research Focus: Dr. Toxtli develops AI systems promoting workplace fairness through ethical frameworks. His work spans: 1) Human-AI collaboration for task management, 2) Bias mitigation in gig economies, 3) Multimodal interfaces for accessibility, and 4) Culturally adaptive systems. His lab explores how AI can augment human capabilities while ensuring transparency. Publication Trends: Recent works demonstrate strong focus on LLM reliability (7 papers), human-AI teaming (4 studies), and ethical AI frameworks (3 publications). Over 60% of 2024-2025 output addresses real-world deployment challenges. Awards: UNESCO IRCAI Global Top 100 project Google Apps Developer Challenge 1st Place Facebook Developers World Hack 1st Prize Intel Innovation Latin App 1st Prize NSF STIR Labs Research Grant Leadership: Founded the Human-AI Empowerment Lab securing $1.2M in NSF/industry grants. Supervises 8 PhD students in human-centered computing projects. Previously co-founded 5 tech startups including ComproPago (acquired by Coca-Cola FEMSA).

• Statistical Machine Learning
• Bayesian Inference
• Variational Methods
• +۵ مورد دیگر

Michael C. Hughes ("Mike") is an Assistant Professor in the Department of Computer Science at Tufts University's School of Engineering, where he develops statistical machine learning methods for healthcare applications. His work focuses on building predictive models that extract actionable insights from complex clinical data, including electronic health records and medical imaging. PhD, Computer Science, Brown University (2016) MS, Computer Science, Brown University (2012) BS, Computer Science, Franklin W. Olin College of Engineering (2010) Research interests center on: Bayesian hierarchical models for documents, sequences, and medical images Optimization algorithms for approximate inference Model fairness and interpretability in clinical contexts Semi-supervised learning for medical diagnostics Recent publications demonstrate these capabilities through applications in cardiovascular disease diagnosis, opioid overdose forecasting, and ICU risk prediction. His lab emphasizes reproducibility through open datasets like TMED-2 and open-source tools like BNPy. Grants include NIH R01 funding for heart valve disease detection, NSF CAREER support for model interpretability, and NSF GCR funding for educational uncertainty research. Scientific awards include: NIH R01 Award (PI) for heart valve disease detection (2025) NSF CAREER Award (2024) NSF GCR Grant (2024) Best Poster Award at Time Series Workshop (ICML 2021) Top 10% Reviewer Awards at AISTATS (2023, 2022) Teaching activities include courses on Bayesian Deep Learning, Introduction to Machine Learning, and Statistical Pattern Recognition. He previously served as postdoctoral fellow at Harvard SEAS.

Forest Agostinelliمشاهده پروفایل

استادیار

• Deep Learning
• Reinforcement Learning
• Search Algorithms
• +۷ مورد دیگر

Forest Agostinelli is an Assistant Professor in the Department of Computer Science and Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina, where he is also affiliated with the AI Institute. His research focuses on designing AI algorithms for pathfinding problems, integrating deep learning, reinforcement learning, heuristic search, and formal logic. He holds a Ph.D. in Computer Science from the University of California, Irvine, an M.S. from the University of Michigan, and a B.S. in Electrical and Computer Engineering from The Ohio State University. Research Overview : Agostinelli’s work emphasizes solving pathfinding problems in domains like robotics, theorem proving, and molecular optimization. His group develops explainable AI methods to enable collaboration between humans and machines. Key projects include DeepCubeA (solving the Rubik’s Cube via deep reinforcement learning) and neural activation function research. Funding & Awards : He has secured grants from NSF, NASA EPSCoR, and South Carolina’s ASPIRE and MADE programs. Notable awards include the NSF Graduate Research Fellowship and the Graduate Education for Minority Students Fellowship. Teaching : He teaches courses in Artificial Intelligence (CSCE 580) and Deep Reinforcement Learning and Search (CSCE 790), mentoring over 15 students at undergraduate and graduate levels. Labs & Collaborations : Active in AI-driven education and interdisciplinary projects, his lab contributes to tools like ALLURE for children’s learning and Bioinformatics platforms like CircadiOmics.

Sadaf Salehkalaibarمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Generative models
• +۵ مورد دیگر

Sadaf Salehkalaibar is an Assistant Professor in the Department of Computer Science at the University of Manitoba, Winnipeg, Canada. She holds an office in the EITC building (E2-416) and has previously held academic positions at the University of Tehran, University of Toronto as a research associate, and visiting roles at McMaster University, Telecom Paristech, and National University of Singapore. Her research focuses on explainable artificial intelligence, generative models, and information theory with an emphasis on rate-distortion-perception tradeoffs in video and image processing. Her educational background includes teaching courses such as Signals and Systems, Digital Signal Processing, and Network Security at the University of Tehran. She currently teaches COMP4190 (Artificial Intelligence) at the University of Manitoba. Research interests revolve around developing efficient algorithms for AI systems, with key contributions in learned video compression, federated learning, and privacy-preserving techniques. Notable work includes the M22 algorithm for communication-efficient federated learning and the NSERC Discovery Grant-funded project on data-driven learning efficiency. Recent publications highlight advancements in perception loss functions, Gaussian vector source analysis, and secure distributed hypothesis testing. She actively serves on editorial boards (e.g., IEEE Transactions on Communications) and conferences (ISIT, ITW). Awards include the prestigious NSERC Discovery Grant (2025). Supervision highlights 13 MSc students at the University of Tehran, focusing on topics like privacy-preserving systems and distributed learning. Labs/teams: Leads research group at University of Manitoba focusing on AI and information theory applications in multimedia systems.

Ioannis Emirisمشاهده پروفایل

استاد

• Computational Geometry
• Algebraic Algorithms
• Robotics
• +۴ مورد دیگر

Ioannis Z. Emiris is a Professor in the Department of Informatics & Telecoms at the National & Kapodistrian University of Athens and concurrently serves as President and General Director of the ATHENA Research Center in Greece. He holds a BSc in Computer Science from Princeton University (1989) and a PhD in Computer Science from UC Berkeley (1994). His research spans computational geometry, algebraic algorithms, robotics, structural bioinformatics, and optimization. He is a leading expert in sparse elimination theory, geometric modeling, and algorithmic algebra. Affiliations: ATHENA Research Center, National & Kapodistrian University of Athens, INRIA Sophia Antipolis (France via joint AROMATH team). Education: BSc (Princeton), PhD (UC Berkeley). Research Interests Emiris's work focuses on geometric algorithms, algebraic systems, and their applications. His contributions include advancements in sparse elimination theory, computational geometry for high-dimensional data, and robotics. He has developed algorithms for polynomial system solving, Voronoi diagrams, and geometric predicates for ellipses. Articles Overview His recent work bridges theoretical advances with practical applications, such as deep learning for protein structure prediction (HydraProt) and geometric algorithms for high-dimensional data analysis. He explores intersections between algebraic geometry and computational methods, with applications ranging from robotics to bioinformatics. Scientific Awards Best Paper Award at ISSAC 2003 and 2010 MSCA Network GRAPES (2019-2023) Advising & Grants Emiris has supervised numerous students and researchers, contributing to interdisciplinary projects. He has secured grants for initiatives like the GRAPES network and has led teams in algorithm design and geometric software development. His work on MARS (Maple/Matlab/C Resultant-Based Solver) exemplifies his focus on practical algorithm implementation. Labs & Teams He directs the Lab of Geometric & Algebraic Algorithms and collaborates with the AROMATH team at INRIA. His research group develops open-source tools for computational geometry and algebraic computations.