جست‌وجوی استاد و پژوهشگر

Jian-Xun Wangمشاهده پروفایل

دانشیار

• Scientific Machine Learning
• Bayesian Data Assimilation
• Differentiable Programming
• +۸ مورد دیگر

Dr. Jian-Xun Wang is an Associate Professor at the Sibley School of Mechanical and Aerospace Engineering at Cornell University (starting 2025). Previously, he held positions at the University of Notre Dame as Robert W. Huether Collegiate Associate Professor. He earned his Ph.D. in Aerospace Engineering from Virginia Tech in 2017, followed by postdoctoral training at UC Berkeley. His research focuses on computational mechanics and scientific AI, integrating advanced machine learning with physics-based models. Key areas include Scientific Machine Learning, Bayesian Data Assimilation, and Uncertainty Quantification, with applications in aerodynamics, biomedical engineering, and advanced manufacturing. He directs the Computational Mechanics & Scientific AI Lab (CoMSAIL), funded by NSF, NIH, and others. Education: M.S. (Ocean Engineering, Virginia Tech, 2016), Ph.D. (Aerospace Engineering, Virginia Tech, 2017), Postdoc (Bioengineering, UC Berkeley, 2018) Research Lab: CoMSAIL (Cornell) Key Awards: ONR YIP (2023), NSF CAREER (2021) Teaching interests span Computational Fluid Dynamics, Numerical Methods, and Bayesian Learning. His work bridges AI and computational physics to address complex multiscale problems in fluid dynamics, solid mechanics, and biomedical systems.

Thanos Avramidisمشاهده پروفایل

مدرس

• Operational Research
• Stochastic Simulation
• Financial Engineering
• +۳ مورد دیگر

Dr. Thanos Avramidis is a Lecturer in Operational Research at the School of Mathematical Sciences, University of Southampton. His research focuses on Markov Decision Processes , stochastic simulation , and financial derivatives pricing , with applications in dynamic pricing under demand uncertainty. He has held academic roles at Cornell University and research positions at the University of Montreal, collaborating with Prof. Pierre L'Ecuyer. Education & Career: PhD in Industrial Engineering, Purdue University Assistant Professor, Cornell University (1997–2001) Researcher at University of Montreal (2002–2006) Joined University of Southampton in 2007 Research Themes: Current work develops algorithms for pricing in unknown demand environments. Past contributions include variance reduction methods, call-center optimization models, and Monte Carlo analysis in finance. Awards: Recipient of the INFORMS Simulation Society Award (2009) and George Nicholson Prize (1993). Teaching: Courses include Introduction to Operational Research and Stochastic OR Methods . Lab/Teams: Member of the CORMSIS research group.

Soowon Changمشاهده پروفایل

استادیار

• Smart Cities
• Sustainable Energy Systems
• Construction Management
• +۶ مورد دیگر

Soowon Chang is an Assistant Professor at the School of Construction Management Technology within the Purdue Polytechnic Institute. She holds a Ph.D. in Building Construction from Georgia Institute of Technology and multiple degrees from Ewha Womans University in South Korea. Her research focuses on smart cities, energy infrastructure transitions, generative design systems, and social equity in urban development. She teaches courses in BIM, sustainability, and construction management. Educations: Ph.D. Building Construction, Georgia Tech (2020) M.S. Architectural Engineering, Ewha Womans University (2016) B.S./B.A. Architectural Engineering & Management, Ewha Womans University (2014) Her research interests include optimizing urban systems through disruptive technologies, energy infrastructure decarbonization, and equitable access to sustainable housing. Recent projects involve solar power integration in Indiana infrastructure and AI-driven EV charging station planning. She has secured grants from NSF, INDOT, and Purdue internal funds for projects addressing energy equity, EV infrastructure, and construction safety. Key Grants: NSF: Broadening Equitable Access to Energy-Efficient Housing (2023-2026) INDOT: Solar Power Systems in Right-of-Way (2023-2027) Dr. Chang has received multiple awards including the 2023 Young Faculty Award and 2021 Best Impact Research Award. She actively contributes to professional societies like ASCE, ASEE, and serves as a guest editor for journals like Sustainable Cities and Society. Her lab (S2-HUB) explores human-urban-building interactions in smart cities.

Yaoyao Liuمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Continual Learning
• +۵ مورد دیگر

Yaoyao Liu is an Assistant Professor at the University of Illinois Urbana-Champaign, holding joint appointments in the School of Information Sciences and Coordinated Science Laboratory. He is affiliated with the Siebel School of Computing and Data Science, National Center for Supercomputing Applications (NCSA), and Illinois Informatics. His research focuses on computer vision and machine learning, particularly in areas like continual learning, few-shot learning, and 3D geometry modeling. He completed his PhD at the Max Planck Institute for Informatics and a BS at Tianjin University. Research Interests: Continual learning and class-incremental learning Generative models and diffusion models 3D geometry and medical imaging applications Awards: ECVA PhD Award (2024). Professional Activities: Area Chair for CVPR, NeurIPS, ICLR, and other top conferences. Served as Senior Program Committee member for AAAI and IJCAI. Labs/Teams: Active in interdisciplinary projects at NCSA and Illinois Informatics, collaborating on medical imaging and 3D vision challenges.

Edoardo Maninoمشاهده پروفایل

مدرس

• AI Security
• Automated Verification of Neural Networks
• Bayesian Machine Learning
• +۵ مورد دیگر

Edoardo Manino is a Lecturer in AI Security at the University of Manchester's Department of Computer Science, part of the Systems and Software Security group. He holds a PhD in Bayesian machine learning from the University of Southampton (2020). His research focuses on automated verification of neural networks, theoretical machine learning, software testing, algorithmic game theory, multi-agent reinforcement learning, and network science. Affiliations : - Member of the Digital Futures Research Beacon and the Institute for Data Science and AI. - Core contributor to the ESBMC model checker and related verification tools. Research Trends : Recent work emphasizes secure AI systems, formal verification of neural networks, and adversarial inversion of language models. His 2025 publications address homomorphic encryption verification, concurrent software analysis, and linguistic consistency in neural models. The 2024 work includes tools for Arm architecture verification and generative AI inversion. Awards : - Best Paper Award (2024) for contributions to formal verification methodologies. Collaborations : Extensive work with industry partners (e.g., Arm) and academic institutions on verification tools and AI security. Active in international conferences (SAIV, SAS, COLING). Labs/Teams : Leads the Systems and Software Security group at Manchester, focusing on advancing AI safety and formal methods.

Andreas Neofytouمشاهده پروفایل

عضو هیئت علمی

• Machine Learning
• Optimization
• Decision-Making under Uncertainty
• +۷ مورد دیگر

Andreas Neofytou is a Teaching Assistant in the Department of Computer & Information Sciences and a PhD candidate in the Department of Management Science at Strathclyde Business School, University of Strathclyde. His work lies at the intersection of machine learning and optimization, with a focus on improving decision-making under uncertainty through robust and data-driven methodologies. Research Interests: Andreas specializes in bridging predictive machine learning models with prescriptive decision-making frameworks. His research emphasizes robust optimization, nonparametric Bayesian modeling, and reinforcement learning, aiming to enhance the reliability and performance of decisions derived from uncertain data. He investigates how machine learning can be leveraged to build compact and effective uncertainty sets for optimization models. Publication Trends: His recent work demonstrates a strong technical orientation toward Bayesian methods and robust optimization, particularly using Dirichlet process mixture models enhanced with bootstrapping. This reflects a broader trend in his research: developing statistically sound, adaptive methods for handling uncertainty in complex decision environments. Scientific Awards: No awards mentioned. Teaching and Advising: Andreas has extensive teaching experience across undergraduate and postgraduate courses in Machine Learning, Deep Learning, and Optimization. While no formal students are listed, he is actively involved in academic instruction and knowledge dissemination within data science and decision analytics. Labs and Research Groups: Although specific lab affiliations are not stated, his research is conducted within the interdisciplinary environment of the Department of Management Science and the Department of Computer & Information Sciences at the University of Strathclyde, likely involving collaboration across data science and operations research groups.

Sebastian Weichwaldمشاهده پروفایل

دانشیار

• Causal Modelling
• Machine Learning
• Structural Equation Models
• +۳ مورد دیگر

Sebastian Weichwald is an Associate Professor at the Department of Mathematical Sciences , University of Copenhagen. He leads the Copenhagen Causality Lab (CoCaLa) and co-leads the Causality and Explainability (CX) collaboratory at the Pioneer Centre for AI. His academic journey includes a PhD at ETH Zurich (2019) and postdoctoral work at the University of Copenhagen. Research Interests: Causal Modelling, Causal Discovery, Structural Equation Models, Time Series Analysis, Neuroimaging, Biomedical Data Analysis. Awards: Best student paper at CIP 2014, Winner of Causality 4 Climate NeurIPS 2019 competition. Collaborations: Active in interdisciplinary projects with applications in cardiology, neuroscience, and biomedical imaging. Software Tools: Developed open-source libraries including CausalDisco , coroICA , tidybench , and Pymanopt for causal discovery, signal processing, and manifold optimization.

• Machine Learning
• Probabilistic Modeling
• Evolutionary Algorithms
• +۷ مورد دیگر

Jonathan Shapiro is an Associate Professor in the School of Computer Science, specializing in machine learning, probabilistic modeling, and evolutionary algorithms. His research emphasizes reinforcement learning, active learning for optimization, and hybrid systems. He leads the Machine Learning and Optimization Research Group and serves as Deputy Director of the interdisciplinary CICADA project, spanning Computer Science, Control Engineering, and Mathematics. Current Affiliation: School of Computer Science Leadership Roles: Head of Machine Learning and Optimization Research Group, Deputy Director of CICADA Former Affiliation: Santa Fe Institute (Adaptive Computation, Evolutionary Dynamics) His research focuses on adaptive systems, including multi-agent reinforcement learning, evolutionary computation in games, and optimization in noisy environments. He also investigates alarm network redundancy reduction and device heterogeneity in indoor positioning systems. Jonathan's recent research output explores Bayesian networks, alarm systems, and evolutionary algorithms in games. Key themes include probabilistic modeling, reinforcement learning, and intrinsic delay in dynamical systems. His work contributes to the fields of computer science, optimization, and hybrid systems. He has supervised projects like New generation algorithms using historical data and market and human behavior change to optimize clients' return on investment (2017–2018) and contributed to economic impacts by improving industrial alarm systems' efficiency and safety.

Lajos Hanzoمشاهده پروفایل

استاد

• Wireless Communications
• 5G/6G Networks
• MIMO Systems
• +۴ مورد دیگر

Professor Lajos Hanzo serves as Professor of Communication at the University of Southampton's School of Electronics and Computer Science (ECS), where he leads cutting-edge research in next-generation wireless systems. His work spans fundamental theoretical advances to practical implementations in 5G/6G, quantum communications, and integrated sensing-communication architectures. His research focuses on wireless communications , specializing in MIMO systems, mmWave technology, and integrated sensing-communication (ISAC). Key contributions include holographic beamforming for cell-free networks, Bayesian learning for mmWave ISAC systems, and reinforcement learning applications for satellite communications. His work consistently bridges theoretical innovation with real-world implementation challenges. Professor Hanzo's publications demonstrate strong trends toward converged communication-sensing systems (7/10 recent articles) and AI-driven wireless optimization (6/10). His research increasingly integrates quantum communications concepts with classical wireless infrastructure, reflecting the field's evolution toward 6G paradigms. The predominant subfields include mmWave MIMO, beamforming, and resource allocation under practical constraints. His scientific recognition includes: Eric Sumner Field Award Fellowships: Royal Academy of Engineering (FREng), IEEE (FIEEE), IET (FIET), EURASIP Two Honorary Doctorates (Technical University of Budapest, University of Edinburgh) Professor Hanzo has secured extensive research funding from EPSRC, European Union (Horizon 2020, FP7), Royal Society, and industry partners including Thales. He mentors current PhD students Hugo Zakaria, Jose Hawkins, Dingzhao Wang, and Pankaj Kumar while having supervised 123 PhD graduates total. His research group drives projects like PerCom, Quantum Computing, and Terabit mm-Wave Backbones. He leads the Next Generation Wireless research group within ECS, collaborating with institutions globally through the UK-India Advanced Technology Centre and Sino-British Fellowship Trust. His lab focuses on experimental validation of theoretical models using advanced wireless testbeds.

Vikram Krishnamurthyمشاهده پروفایل

استاد

• Statistical Signal Processing
• Stochastic Control (POMDPs)
• Stochastic Optimization
• +۱۰ مورد دیگر

Professor Vikram Krishnamurthy is a distinguished academic at Cornell University , affiliated with the School of Electrical & Computer Engineering , Center for Applied Mathematics , and Mechanical & Aerospace Engineering . He leads the Cornell Statistical Signal Processing Lab and is part of the Foundations of Information, Network, and Decision Systems (FIND) collaborative. His career spans institutions like the University of British Columbia (Canada Research Chair, 2002-2016) and University of Melbourne (1994-2002). His research bridges statistical signal processing , stochastic control (POMDPs), and network science , with applications in social networks , cognitive radar , and ion channel biosensors . Key themes include human-machine interfacing , inverse reinforcement learning , and behavioral economics . Recent publications focus on adaptive inverse reinforcement learning , LLM agent interactions , and quantum decision systems , reflecting his work in machine learning , stochastic optimization , and social network analytics . His book Partially Observed Markov Decision Processes (2nd ed., 2025) is a seminal text in the field. Scientific Awards: Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Distinguished Lecturer, IEEE Signal Processing Society Editor-in-Chief, IEEE Journal on Selected Topics in Signal Processing Honorary Doctorate from KTH Royal Institute of Technology, Sweden Jean Pierre LeCadre Award (2019) He currently advises PhD students including Shashwat Jain, Luke Snow, Adit Jain, and Yiming Zhang. His teaching includes advanced courses like Bayesian Estimation and Stochastic Optimization (ECE 7230) and Data Science for Engineers (ECE 2720).

Mehrisadat Makki Alamdariمشاهده پروفایل

مدرس ارشد

• Structural Health Monitoring
• Structural Dynamics
• Signal Processing
• +۳ مورد دیگر

Dr. Mehrisadat Makki Alamdari is a Senior Lecturer in the School of Civil and Environmental Engineering at the University of New South Wales (UNSW). She specializes in Structural Health Monitoring (SHM), structural dynamics, signal processing, and data mining. Her research bridges theoretical innovations with real-world infrastructure applications, including landmark projects on the Sydney Harbour Bridge and Gateway Bridge. Education: Ph.D. in Civil Engineering, University of Technology Sydney (2012–2014) M.Sc. in Mechanical Engineering, University of Manitoba (2011) M.Sc. in Aerospace Engineering, Iran University of Science and Technology (2006–2008) B.Sc. in Aerospace Engineering, Sharif University of Technology (2002–2006) Research Focus: Her work integrates machine learning, sensor technologies, and advanced signal processing for infrastructure resilience. Key areas include bridge health monitoring via drive-by inspections, piezoelectric energy harvesting, and damage detection using ultrasonic and computer vision techniques. Recent publications emphasize data-driven methodologies, unsupervised learning, and multi-sensor fusion for structural diagnostics. Awards & Honors: ARC Discovery Early Career Research Award (DECRA) JSPS Award (Japan Society for Promotion of Science) ITS Australia National Award (2020) Most Practical SHM Solutions Award (Stanford University, 2015) Best Paper Award (SHMII-8, 2017) Leadership & Service: She serves on the executive committee of the Australian Network of Structural Health Monitoring (ANSHM) and is an Associate Editor for Elsevier journals. She has secured over $5.5M in grants, including an ARC DECRA grant and a $5M Industrial Transformation Hub for resilient infrastructure.

Joseph Jay Williamsمشاهده پروفایل

استادیار

• Adaptive Experimentation
• Machine Learning
• Human-Computer Interaction
• +۶ مورد دیگر

Joseph Jay Williams is an Assistant Professor in Computer Science at the University of Toronto , with courtesy appointments in Psychology, Statistics, Industrial Engineering, Economics, and the Vector Institute for Artificial Intelligence. He leads the Intelligent Adaptive Interventions Lab , focusing on transforming digital components into adaptive micro-laboratories that use machine learning algorithms to optimize behavior change interventions in education and health. PhD in Computational Cognitive Science from UC Berkeley Former Assistant Professor at National University of Singapore Ex-Research Fellow at Harvard's VPAL and postdoc at Stanford His research spans four themes: (1) Adaptive Experimentation Tools for education and collective intelligence, (2) Human-AI Co-Design with large language models, (3) Personalization Algorithms for mental/physical health interventions, and (4) Statistically Sensitive Algorithms that balance reward optimization with rigorous inference. The lab's AdaptEx framework has impacted over 500,000 users and won a $1M Xprize. Recent work includes ABScribe for human-AI co-writing experiments and LLM-driven prompt engineering frameworks. The lab develops MOOClet technology that enables dynamic A/B testing of educational components like explanations, hints, and emails. Algorithms prioritize both participant benefit and statistical power - for example, Thompson Sampling variants that contextualize interventions based on user state while maintaining scientific validity. $1M Xprize for AI-driven education $3M NSF Cyberinfrastructure grant Best Paper Awards at CHI 2023 and HCOMP 2023 DARPA AI Tools Competition Finalist $925K ONR grants (2018-2024) for personalization research Current projects integrate adaptive experimentation with chemical/materials science through the Acceleration Consortium's $200M federal initiative. The lab maintains collaborations with institutions like Northwestern Medical School , UCSF Medical School , and Worcester Polytechnic Institute (co-PI on NSF grant). The research vision targets Nobel-Prize-level integration of empirical sciences, machine learning, and statistics to accelerate discoveries across disciplines.

Joelle Pineauمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +۷ مورد دیگر

Joelle Pineau is a Professor in the School of Computer Science at McGill University and Vice President of AI Research at Meta, where she leads the Fundamental AI Research (FAIR) team. She is also a core member of Mila, the Quebec AI Institute. Her academic training includes a BASc from the University of Waterloo and an MSc and PhD in Robotics from Carnegie Mellon University. Her research focuses on developing models and algorithms for planning and learning in complex domains, with applications in robotics, healthcare, and conversational agents. She is a pioneer in machine learning reproducibility, having initiated the ML Reproducibility Checklist and served as the inaugural Reproducibility Chair for NeurIPS. Her leadership roles include past President of the International Machine Learning Society (IMLS). She has received multiple prestigious awards, including: NSERC E.W.R. Steacie Memorial Fellowship (2018) Governor General's Innovation Award (2019) CIFAR Canada AI Chair Fellow of AAAI Fellow of the Royal Society of Canada She has advised numerous PhD and Master’s students, many of whom now hold prominent research positions in academia and industry. She teaches graduate courses in machine learning and AI at McGill, such as COMP-551 (Applied Machine Learning) and formerly COMP-424 (Artificial Intelligence). Although she is not currently accepting new graduate students due to her dual role at McGill and Meta, she supports undergraduate research through her postdocs. Her lab is involved in cutting-edge research in reinforcement learning, deep learning, and AI for healthcare.

Panagiotis Traganitisمشاهده پروفایل

استادیار

• Statistical Signal Processing
• Machine Learning
• Crowdsourcing
• +۴ مورد دیگر

Panagiotis Traganitis is an Assistant Professor in the Electrical and Computer Engineering (ECE) Department at Michigan State University (MSU), where he joined in August 2022. Previously, he was a Postdoctoral Researcher at the University of Minnesota’s Signal Processing in Networking and Communications (SPiNCOM) group under Prof. Georgios B. Giannakis. His research focuses on statistical signal processing, machine learning, crowdsourcing, weak supervision, and network science, with applications in big data analytics and distributed learning. Education: Ph.D. in Electrical Engineering, University of Minnesota (2019), Thesis: Scalable and Ensemble Learning for Big Data M.Sc. in Electrical Engineering, University of Minnesota (2015), Thesis: Large-scale Clustering using Random Sketching and Validation Diploma in Electrical & Computer Engineering, National Technical University of Athens (2013), Thesis: Reinforcement Learning Methods for Cognitive Radio Networks Research Interests: His work spans statistical learning, blind ensemble methods, weak supervision, subspace clustering, and graph-based algorithms. Current projects include blind ensemble learning, adversarial detection in crowdsourcing, and self-supervised learning. Awards & Honors: Gerondelis Foundation Graduate Scholarship (2015) Finalist, CAMSAP 2017 Student Paper Award Eurobank’s Award of Excellence in Greek Nationwide University Entrance Exams (2007) Teaching & Mentorship: He has served as a teaching assistant for courses on statistical methods and nonlinear optimization at the University of Minnesota. He is currently seeking motivated Ph.D. students to join his research group. His lab focuses on advancing robust, scalable learning algorithms with real-world applications. Labs & Collaborations: Active member of the SPiNCOM research group, collaborating on projects related to signal processing, machine learning, and network science.

Farzan Soleymaniمشاهده پروفایل

پژوهشگر ارشد

• Mobile Robotics
• Natural Sciences and Engineering
• Artificial Intelligence
• +۹ مورد دیگر

Farzan Soleymani is a Research Fellow at the Telfer School of Management, University of Ottawa since April 2023. Previously, he completed his PhD in Mechanical Engineering at the University of Ottawa (2017-2023) and a Master's degree in Mechanical Engineering from Amirkabir University of Technology (Tehran Polytechnic) (2013-2016). His research spans multiple disciplines with a focus on artificial intelligence applications. His primary research interests include: Artificial Intelligence and Machine Learning Protein-Protein Interaction Prediction Financial Portfolio Management Robotics and Autonomous Systems Quantum Computing Applications Dr. Soleymani's work demonstrates a strong interdisciplinary approach, bridging computer science, biology, finance, and engineering. His recent research has focused on developing deep learning frameworks for protein interaction prediction and financial portfolio optimization, showing expertise in both theoretical and applied aspects of machine learning. His scientific contributions reflect a pattern of innovation across multiple domains: Development of ProtInteract, a deep learning framework for predicting protein-protein interactions Creation of financial portfolio management systems using reinforcement learning and graph convolutional networks Design of quantum machine learning techniques for stock price prediction Innovation in area coverage control with multi-agent systems using adaptive reinforcement learning As a Research Fellow, Dr. Soleymani creates deep diffusion models for protein and scaffold generation, supervises Master's students, and designs therapeutics.