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Mehrisadat Makki AlamdariView profile

Senior Lecturer

• Structural Health Monitoring
• Structural Dynamics
• Signal Processing
• +3 more

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 WilliamsView profile

Assistant Professor

• Adaptive Experimentation
• Machine Learning
• Human-Computer Interaction
• +6 more

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 PineauView profile

Professor

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +7 more

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 TraganitisView profile

Assistant Professor

• Statistical Signal Processing
• Machine Learning
• Crowdsourcing
• +4 more

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 SoleymaniView profile

Research Fellow

• Mobile Robotics
• Natural Sciences and Engineering
• Artificial Intelligence
• +9 more

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.

• Wireless Communications
• Networking
• Signal Processing
• +3 more

Sneha Kasera is a Researcher at the University of Utah's Department of Computer Science, affiliated with the University of Utah. Her work focuses on wireless communications, networking, and signal processing, with an emphasis on spectrum management, machine learning applications in networks, and security. She has contributed extensively to projects like the POWDER platform for wireless experimentation and spectrum analysis. Her research interests include developing advanced techniques for spectrum monitoring, dynamic radio resource allocation, and leveraging machine learning for network optimization. Key projects include Radio Dynamic Zones (RDZ) for efficient spectrum utilization and Bayesian learning methods for heatmap construction in outdoor wireless environments. Her recent work explores multi-agent reinforcement learning for mmWave power allocation, adversarial attacks on localization systems, and privacy-preserving exposure notification via hash collision techniques. She has also pioneered methods for crowdsourced spectrum monitoring and calibration-free full-duplex systems. Collaborations include contributions to the POWDER platform, which enables scalable wireless research. Her technical expertise spans hardware-software integration, network security, and algorithm design for 5G/6G systems.

Malik HassanalyView profile

Researcher

• Statistical Methods
• Machine Learning
• Computational Fluid Dynamics
• +5 more

Malik Hassanaly is a Researcher III in Computational Science at the National Renewable Energy Laboratory (NREL). His work integrates statistical methods, machine learning, and computational fluid dynamics to enhance numerical model accuracy and efficiency. Education: PhD in Aerospace Engineering, University of Michigan MSc in Aerospace Engineering, University of Texas at Austin MSc in General Engineering, École centrale de Lille His research spans turbulent combustion, multi-fidelity modeling, surface chemistry, and uncertainty quantification. Recent work focuses on Bayesian calibration, dynamical system security, and exascale ODE solvers for energy applications. Scientific Awards: Richard and Eleanor Towner Prize for Distinguished Academic Achievement (2019) Malik is affiliated with professional societies including The Combustion Institute, SIAM, and AIChE, and contributes to anomaly detection frameworks and renewable energy manufacturing processes.

Juan Guzman-InigoView profile

Lecturer

• Aeroacoustics
• Fluid Mechanics
• Noise Control
• +3 more

Dr. Juan Guzman-Inigo is a Lecturer in the Department of Engineering at City St George's, University of London . He previously held Research Associate positions at Imperial College London and University of Cambridge . His research focuses on aeroacoustics and fluid mechanics , particularly the reduction of noise in aerospace systems (e.g., aircraft, wind turbines, compressors) and the application of reinforcement learning to flow-induced noise control. Education : PhD from Ecole Polytechnique Paris/ONERA (2015), MRes from National Higher French Institute of Aeronautics and Space (2011), and MEng from Universidad de Sevilla (2011). Research : Explores physical mechanisms of sound generation and develop novel damping techniques using numerical simulations (e.g., linearised Navier-Stokes equations ), experimental validation, and Bayesian optimization for geometrical design. Grants & Awards : ERC Consolidator Grant AFIRMATIVE (2018–2023). Professional Memberships : UKAN+. Languages : English, French, and Spanish (proficient in reading, writing, speaking, and peer review).

• Stochastic processes and control
• Stochastic orderings
• Finance and Insurance mathematics
• +3 more

Prof. Dr. Nicole Bäuerle is a distinguished Professor at the Institute of Stochastics within the Department of Mathematics at Karlsruhe Institute of Technology (KIT). She maintains an active research program and teaching schedule, with courses planned through Summer Semester 2025 including Markov Decision Processes and Stochastic Seminars. Her research focuses on stochastic processes and control, stochastic orderings, finance and insurance mathematics, and stochastic networks. Bäuerle has made significant contributions to Markov Decision Processes with applications to finance, risk-sensitive optimization, and portfolio management. Her work bridges theoretical mathematics with practical applications in financial markets and insurance. Prof. Bäuerle has advised numerous PhD students since 2003, with her most recent doctoral student Tamara Göll completing in 2023. Several of her PhD students have received prestigious awards including the GAUSS Award and SCOR Award. She has also supervised many Master's theses across various topics in financial mathematics and stochastic processes since 2003. Her editorial service is extensive, having served as Editor for Applied Probability Journals since 2012, Statistics & Risk Modeling since 2011, and previously for SIAM Journal on Control and Optimization (2019-2024), TOP (2012-2020), Mathematical Methods of Operations Research (2007-2020), and Stochastic Models (2000-2012). Prof. Bäuerle holds significant leadership positions including Member of the Board of Trustees of the Fraunhofer Institute for Industrial Mathematics ITWM since 2017 and Member of the Bachelier Finance Society Council since 2021. She previously served on the Executive Board of the Deutsche Gesellschaft für Finanz- und Versicherungsmathematik (2003-2011) and the Fachgruppe Stochastik (2010-2014).

Jeff OrchardView profile

Associate Professor

• Complex Systems
• Neuroscience
• Evolutionary Theory
• +5 more

Jeff Orchard is an Associate Professor at the Cheriton School of Computer Science , University of Waterloo , with cross-appointments to the Department of Applied Mathematics and Department of Education . He directs the Neurocognitive Computing Lab and is a core member of the Centre for Theoretical Neuroscience . In 2022, he completed a 6-month sabbatical at the International Centre for Neuromorphic Systems (Western Sydney University). Education: PhD in Computing Science, Simon Fraser University (2003) MSc in Applied Mathematics, University of British Columbia (1996) BMath in Applied Mathematics, University of Waterloo (1994) Dr. Orchard's research focuses on understanding the brain through computational and mathematical modeling, particularly through neural networks , predictive coding , and vector symbolic architectures . His work bridges neuroscience , machine learning , and neuromorphic engineering , seeking mechanistic rules governing cognition and behavior. Earlier research involved medical image processing , including MRI motion compensation , image denoising , and retinal vessel segmentation . Recent publications (2022-2025) highlight his exploration of biologically-plausible learning algorithms , neuromorphic computing , and AI alignment through active inference models. His work on predictive coding networks and hyperdimensional computing demonstrates interdisciplinary innovation across computer science , mathematics , and cognitive neuroscience .

• Artificial Intelligence
• Neuro-Symbolic Integration
• Knowledge Graphs
• +4 more

Luciano Serafini is a researcher at Fondazione Bruno Kessler in Trento, Italy, specializing in Artificial Intelligence with a focus on Neuro-Symbolic Integration and Knowledge Graphs . His work bridges Machine Learning and Symbolic Reasoning , emphasizing Planning , Relational Learning , and Visual-Textual Grounding . Key Research Areas : Neuro-symbolic systems, logic-based knowledge representation, planning under uncertainty, and computer vision. Recent Publications highlight trends in Embodied AI for open-world tasks, Weighted Model Counting , and Graph Generative Models . His contributions include Logic Tensor Networks for integrating deep learning with formal logic and methods to mitigate Data Sparsity through knowledge transfer. Collaborations span institutions like the University of Trento and research teams in Computer Vision and Reasoning , with applications in Social Navigation and Event Recognition .

James GrantView profile

Lecturer

• Bandit Learning
• Reinforcement Learning
• Online Sequential Decision Making
• +4 more

Dr. James Grant is a Lecturer in Statistics at Lancaster University's School of Mathematical Sciences since 2021, where he also serves as the EDI Lead for both the School and the ProbAI Research Hub. He is active in several roles at the Royal Statistical Society and serves as an Associate Editor for the ACM Transactions on Probabilistic Machine Learning. Grant completed his PhD in 2019 through the STOR-i Centre for Doctoral Training at Lancaster University under the supervision of Professors David Leslie, Kevin Glazebrook, and Roberto Szechtman. Prior to his PhD, he worked as a Machine Learning Research Student at Secondmind.ai and as a Research Associate within the STOR-i Centre for Doctoral Training. Dr. Grant's research focuses on online sequential decision making for problems with complex data structures, particularly in bandit learning and reinforcement learning. He specializes in multi-armed bandits, online optimization, and recommender systems where algorithms learn optimal decisions through iterative processes of decision-making, data observation, and model updating. His research emphasizes collaboration with industry, having worked on the Next-Generation Converged Digital Infrastructure project with BT. His publication record demonstrates strong expertise in bandit algorithms with applications spanning federated learning systems, anomaly detection in time series data, and phylogenetic tree analysis. Grant's work consistently bridges theoretical foundations with practical applications across telecommunications, surveillance systems, and decision-making frameworks. Notable Paper Award Winner at AISTATS 2020 for "On Thompson Sampling for Smoother-than-Lipschitz Bandits" Associate Editor for ACM Transactions on Probabilistic Machine Learning Active participant in Royal Statistical Society initiatives Dr. Grant currently supervises six PhD students working on diverse research topics including bandit learning, tropical statistics, anomaly detection, sequential simulation methods, safety in reinforcement learning, and Bayesian optimization. His former research associate, Changjiang He, has progressed to become a Lecturer in Computer Science at the University of Roehampton. As an EDI Lead, Grant actively promotes equality, diversity, and inclusion within the academic community while maintaining a robust research program that bridges theoretical statistics with real-world applications.

James A. WaltzView profile

Associate Professor

• Schizophrenia
• Reinforcement Learning
• Reward Processing
• +5 more

James A. Waltz, PhD , is Associate Professor in the Department of Psychiatry at the University of Maryland School of Medicine and Principal Investigator of the IDRIS lab at the Maryland Psychiatric Research Center (MPRC) in Catonsville, Maryland. His federally-funded program uses behavioral experiments, functional neuroimaging, and computational modeling to understand how disturbances in reinforcement learning and reward processing give rise to the motivational and cognitive deficits observed in schizophrenia. Education & Training BA, Psychology (Cognitive Science), Yale University (1994) MA, Psychology (Cognitive Neuroscience), University of California, Los Angeles (1995) PhD, Psychology (Cognitive Neuroscience), UCLA (1999) Post-doctoral Fellow, Neurophysiology Department, Max Planck Institute for Brain Research, Frankfurt, Germany (1999–2004) Post-doctoral Fellow, Maryland Psychiatric Research Center (2004–2006) Research Interests Dr. Waltz’s work centers on the neural and computational mechanisms of reinforcement learning and decision-making , with a translational focus on schizophrenia . His lab combines functional MRI, EEG, eye-tracking, and computational modeling to dissect how abnormalities in dopaminergic reward circuits—particularly fronto-striatal networks—contribute to anhedonia , avolition , and other negative symptoms of psychotic illness. Current projects examine (1) altered reward-prediction-error signaling in striatal and cortical regions; (2) effort-cost computations and motivational impairment; and (3) cognitive flexibility deficits and their relationship to delusions and hallucinations. Ongoing clinical protocols recruit first-degree relatives, patients with schizophrenia/schizoaffective disorder, and healthy volunteers to probe the impact of stress, genetics, and antipsychotic medications on reward processing and learning. Scientific Awards & Honors National Research Service Award, NIH (1998–1999) Joseph A. Gingerelli Award for Distinguished Dissertation, UCLA (1999) H. McKee Jarboe Award for Outstanding Research in Psychiatry, UMSOM (2009) Young Investigator Mentor, International Congress on Schizophrenia Research (2013) Funding & Collaborations Dr. Waltz’s research is supported by an NIH R01 grant (R01MH094460) and is conducted in close collaboration with the Gold CANS Lab at MPRC, the Neuroimaging Research Branch at NIDA (Dr. Elliot Stein), and the Section of Human Neurogenetics at NIAAA (Dr. David Goldman). The IDRIS lab is embedded within MPRC’s Center for Brain Imaging Research (CBIR) , which houses a 3-Tesla Siemens Trio MRI scanner, 64-channel EEG suites, and eye-tracking facilities. Advising & Team The laboratory trains a continuum of researchers, from undergraduate volunteers through post-doctoral fellows. Current trainees include Adam J. Culbreth, PhD (Assistant Professor), Jacob Nudelman, BS (Senior Clinical Research Assistant), Elie Holzel, BA, and Safa Riaz, BA. Lab alumni have progressed to faculty positions at Charité Medical University, Maastricht University, KU Leuven, and Northwestern University.

Yishay MansourView profile

Professor

• Machine Learning
• Reinforcement Learning
• Algorithmic Game Theory
• +4 more

Yishay Mansour is a Professor of Computer Science at Tel Aviv University's School of Computer Science, where he has been a faculty member since 1992. He previously served as the first head of the Blavatnik School of Computer Science (2000-2002) and currently directs the Israeli Center of Research Excellence in Algorithms. He maintains a part-time research position at Google Research in Tel Aviv and has held previous positions at Microsoft Research in Israel. Professor Mansour received his PhD from MIT in 1990, followed by a postdoctoral fellowship at Harvard and a position as Research Staff Member at IBM T.J. Watson Research Center. His research spans several key areas including machine learning (particularly reinforcement learning and multi-armed bandits), algorithmic game theory, communication networks, and theoretical computer science. He has published over 100 journal papers and 200 conference proceedings, with recent work focusing on regret minimization, differential privacy, and reinforcement learning theory. His recent publications demonstrate a strong focus on theoretical foundations of machine learning, with particular emphasis on bandit algorithms, reinforcement learning, and game theory. The trends in his work show increasing integration of societal challenges like fairness and privacy into learning algorithms, alongside continued theoretical advances in optimization and learning guarantees. ACM Fellow (2014) ELLIS Fellow (2020) Asia-Pacific Artificial Intelligence Association Fellow (2022) AI Industry Alliance Fellow (2023) ERC Advanced Grant (2020) Multiple best paper awards including at COLT (2007), EC (2007), and EC (2025) Professor Mansour has supervised over a dozen doctoral students and numerous master's students, with his advisees working on topics ranging from reinforcement learning algorithms to algorithmic game theory and online learning. He has served on numerous program committees including as Program Chair for STOC (2016) and COLT (1998), and is currently an associate editor for several distinguished journals. His research has been supported by multiple grants from the Israel Science Foundation, ERC Advanced Grant, and other funding bodies. He leads the COLT-MDP lab which focuses on advancing the theory of reinforcement learning through three pillars: compact representation, efficient computation, and addressing societal challenges including fairness and privacy in learning algorithms.

• Probability and Statistics
• Point Processes
• Reinforcement Learning
• +7 more

Didier Gemmerlé is a CNRS Research Engineer affiliated with the Institute Élie Cartan de Lorraine (IECL) at the University of Lorraine's Faculty of Science and Technology. His work bridges Probability and Statistics with computational methods, focusing on spatial point processes, Bayesian inference, and machine learning applications in astrophysics and paleobiology. Research Areas: Point Processes, Reinforcement Learning, Causality, Neural Networks, Galaxy Distribution Modeling Responsibilities: Head of IT Infrastructure, CNRS/University signature delegations, Mathrice network member His recent publications emphasize computational frameworks for spatial statistics and cosmology. He actively contributes to open-source tools like DRlib (C++) and Treex (Python). Mentoring across M2 IMSD projects and co-supervising doctoral students (e.g., Nathan Gillot) highlights his advisory role. Current involvements include the European Twinning EXCOSM project for cosmological research.