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Alexander SumichView profile

Professor

• Biopsychology
• Neuroimaging
• Neurobiology of mental health
• +6 more

Alexander Sumich is Professor of Mental Health and Biopsychology at Nottingham Trent University , School of Social Sciences. He co-directs the Centre for Public and Psychosocial Health and co-leads the Affect, Personality and Embodied Brain (APE) Research Group . He also serves as Adjunct Professor in Psychology at Auckland University of Technology, New Zealand , and holds editorial and leadership roles in organizations like the British Society for the Psychology of Individual Differences (Treasurer) and Personality and Individual Differences (Associate Editor). Education: MA in Psychology (First Class Honors), University of Auckland PhD in Psychology as Applied to Medicine, Institute of Psychiatry, London His research spans neuroimaging and electrophysiological methods to investigate the neurobiology of affect-driven behaviors (depression, aggression, hallucinations) and the immune system's role in mental health . He employs virtual reality exposure therapy (VRET) , spiking neural networks , and computational models to enhance mental health interventions. Key areas include gaming disorder , mindfulness effects , and gut-brain axis interactions . Notable research grants include: BIAL Foundation (2023-2024): €58,765 for trauma-paranormal-mental health links MBIE-Catalyst Programme (2020-2024): NZ$2,134,847 for computational neurogenetics UKRI KTP (2021-2024): £204,000 for therapeutic digital displays His external roles include: Treasurer, British Society for the Psychology of Individual Differences Associate Editor, Personality and Individual Differences Co-host, APE2020 Conference

• Reinforcement Learning
• Stochastic Dynamic Optimization
• Operations Research
• +7 more

Somayeh Moazeni is an Associate Professor at the School of Business, Stevens Institute of Technology. She holds a PhD in Computer Science from the University of Waterloo and has held academic appointments including Visiting Associate Professor at Northwestern University and Postdoctoral Research Associate at Princeton University. Her research focuses on Reinforcement Learning, Stochastic Dynamic Optimization, and applications in Energy Markets, Inventory Management, and Algorithmic Trading. She has authored over 30 peer-reviewed articles and serves as an associate editor for INFOR and PLOS One . Education: PhD (Computer Science, 2012), University of Waterloo; Postdoc (Operations Research, 2012-2014), Princeton University Industry Experience: Senior Risk Analyst at RBC (2011-2012), Risk Analyst at BMO (2010) Awards: IEEE Senior Member (2019), Anita Borg Institute GHC Faculty Scholar (2017), MITACS Poster Competition First Place (2009) Her research spans Bayesian Optimization , Resilient Network Design , and Energy Efficiency . Current funded projects include PSEG Foundation grants for energy resilience and NSF funding for distributed energy resource controls. She advises PhD students in Operations Research and Energy Systems and teaches graduate courses in Reinforcement Learning and Financial Engineering. Key Contributions: Developed stochastic optimization frameworks for energy storage, contact center reliability modeling, and risk-aware trading strategies. Her work on sequential learning for consumer-driven demand response programs has advanced smart grid applications.

Babak MehranView profile

Associate Professor

• Transportation Network Resilience
• Big Data Analytics in Transportation
• Autonomous and Connected Vehicles
• +4 more

Babak Mehran is an Associate Professor in the Department of Civil Engineering at the University of Manitoba (Price Faculty of Engineering). He holds a PhD in Civil Engineering from Nagoya University (2009). His research focuses on transportation network resilience, big data analytics, and AI-driven solutions for traffic management. He leads the Urban Mobility and Transportation Informatics Group (UMTIG), collaborating with government and industry on applied transportation research. Key research areas include autonomous vehicle integration, cold-region traffic vulnerability, and optimization of public transit systems. Dr. Mehran has advised graduate students on topics like traffic safety, sensor placement, and semi-flexible transit design. His work bridges theoretical models (e.g., reinforcement learning algorithms) with real-world applications such as winter road maintenance strategies and transit demand analysis. Recent publications emphasize AI-driven traffic prediction, climate adaptation for infrastructure resilience, and safety metrics for truck operations in harsh environments. He collaborates internationally on transportation policy and has contributed to methodologies for evaluating congestion relief strategies using travel time reliability analysis. Lab: Urban Mobility and Transportation Informatics Group (UMTIG) Key Partners: Government agencies, transportation industries, academic collaborators Current Focus: Autonomous shared mobility, cold-climate traffic systems, data fusion for traffic monitoring

Wei ShiView profile

Professor

• Distributed computing
• Cloud computing
• Data center networks
• +6 more

Professor Wei Shi is a faculty member at the School of Computer Science, Carleton University. His research focuses on distributed computing, cloud networks, algorithm design for sensor/actuator systems, big data analytics, and data privacy. Notable projects include federated learning optimizations, blockchain-enabled edge intelligence for IoT/vehicle networks, and AI-driven cybersecurity solutions. His work addresses challenges in dynamic resource allocation, anomaly detection, and privacy-preserving techniques. Research Interests: Distributed Computing: Optimizing federated learning and client selection algorithms for wireless networks. Blockchain & Edge Intelligence: Developing decentralized systems for IoT and vehicular networks using AI large models. Security & Privacy: Innovating methods to detect AI-generated content, combat cyber-physical attacks, and protect user data privacy. Publications: Recent works emphasize federated learning applications, blockchain integration with edge computing, and cybersecurity for vehicular/IoT ecosystems. Key themes include energy-efficient algorithms, dynamic resource allocation, and intrusion detection in constrained environments. Email: wei.shi@carleton.ca

Munir NazzalView profile

Professor

• Transportation Engineering
• Civil Engineering
• Pavement Engineering
• +4 more

Dr. Munir Nazzal is a Professor of Civil Engineering and Director of the Center for Smart, Sustainable & Resilient Infrastructure (CSSRI) at the University of Cincinnati. His research focuses on sustainable transportation infrastructure, pavement engineering, and innovative materials technology. He leads over $13M in grants from federal and state agencies, specializing in asphalt mixtures, pavement performance, and smart infrastructure. He serves as Principal Investigator (PI) on numerous projects, including optimizing maintenance equipment tracking, evaluating micro-surfacing bond strength, and developing AI-driven pothole detection systems. Nazzal is an editor for the ASCE Journal of Materials in Civil Engineering and Buildings Journal, and a licensed professional engineer in Ohio and Louisiana. His work integrates emerging technologies like IoT and autonomous vehicles for infrastructure monitoring. Key contributions include field validation of robotic crack sealing, analysis of aramid fibers in asphalt, and lifecycle cost studies of winter pothole patching methods. His research emphasizes cost-effective, environmentally friendly solutions for local and state transportation systems. Recent grants include a $600,000 federal project for district highway maintenance research, and a $1.2M study on robotic crack sealing methods. He collaborates with industry partners like Honda on vehicle-based roadway data pilots. Nazzal's lab explores nanoscale asphalt properties, thermal expansion effects, and recycled material utilization in pavements.

Daniel McKenzieView profile

Assistant Professor

• Derivative-free optimization
• Implicit neural networks
• Geometric methods in data science
• +3 more

Daniel McKenzie is an Assistant Professor in the Department of Applied Mathematics and Statistics at the Colorado School of Mines. His research focuses on derivative-free optimization, implicit neural networks, and geometric methods in data science. He holds a B.Sc.(hons) and M.Sc. in Mathematics from the University of Cape Town (2010, 2014) and a PhD in Mathematics from the University of Georgia (2019). B.Sc.(hons): Mathematics and Applied Mathematics, University of Cape Town, 2010 M.Sc.: Mathematics, University of Cape Town, 2014 PhD: Mathematics, University of Georgia, 2019 His research explores the intersection of optimization theory and machine learning, with applications in spatial data modeling, geometric data analysis, and high-dimensional clustering. Recent work emphasizes curvature-aware algorithms, comparison-based optimization, and implicit network architectures like LatticeVision. His methods address challenges in non-stationary spatial data and convex game equilibria prediction. Key contributions include Fermat distance metrics for clustering, Jacobian-Free Backpropagation (JFB) for implicit networks, and zeroth-order algorithms for black-box optimization. While no scientific awards are listed, his publications reflect a strong focus on advancing optimization techniques for modern data science problems. No specific grants or advising roles are detailed in the provided text. His work bridges computational mathematics and applied AI, with potential applications in robotics, spatial statistics, and algorithmic game theory.

Damien RafteryView profile

Lecturer

• Higher Education Pedagogy
• Generative AI in Education
• Academic Integrity
• +5 more

Damien Raftery is a Lecturer in the Department of Business at South East Technological University (SETU Carlow), where he has served since 1995. He holds roles as eLearning Development Officer in the Teaching and Learning Centre (since 2008) and External Examiner. His academic leadership includes Programme Directorships, Academic Council membership (2013-2015), and chairing the Teaching, Learning & Support Services Committee. Education: BSc (Hons) and MSc in Mathematical Science from University College Dublin (1994-1995), followed by an MA in Management in Education (Waterford Institute of Technology, 2003) and an MA in Educational Studies (University of Sheffield, 2017). Research focuses on higher education pedagogy, with emphasis on generative AI's impact on teaching/assessment, blended/flipped learning methodologies, and quantitative literacy. He actively contributes to educational technology through roles in the Irish Learning Technology Association (founding treasurer, conference organizer) and co-developed the StatsSkills web app for statistics education. His recent work addresses AI-driven challenges in assessment, VLE optimization, and digital pedagogy innovations. Collaborative efforts include organizing the 2024 Digital Education Conference and publishing on AI ethics in academic settings.

Benjamin TurnbullView profile

Associate Professor

• Cyber Security
• Simulation
• Digital Forensics
• +6 more

Associate Professor Benjamin Turnbull is Deputy Head of School at UNSW Canberra's School of Systems & Computing. His research focuses on cybersecurity, simulation, and emerging technologies, aiming to develop decision support systems for cyber resilience. He leads large-scale research projects funded by organizations like CSIRO, Australian Defence Force, and the US Naval Research Laboratory, totaling over $3 million AUD in grants. Teaching contributions include co-creating the Bachelor of Cyber Security program and directing multiple postgraduate cybersecurity offerings. His educational design emphasizes industry-relevant courses, from undergraduate to executive programs like the UNSW Online Cyber Security Masters. He has extensive experience in curriculum design, content development, and online education. Research interests span cyber situation awareness, secure simulation platforms, and automated learning from big datasets. Notable projects include the UNSW-MG24 microgrid cybersecurity dataset and frameworks like OQFL (quantum-based federated learning) for intelligent transportation systems. His work bridges theory and practice, with outputs including software tools and policy recommendations for cyber resilience in critical infrastructure. Key collaborations include the Cyber Cooperative Research Centre and DST Group. He has authored over 100 peer-reviewed publications, focusing on IoT security, blockchain-enabled learning, and cyber-physical-social systems resilience. His grants emphasize applied research with direct societal impact, such as mitigating supply chain cyber risks and enhancing microgrid cybersecurity.

Alina EneView profile

Associate Professor

• Algorithms
• Combinatorial Optimization
• Submodularity
• +3 more

Alina Ene is an Associate Professor in the Department of Computer Science at Boston University, within the Faculty of Arts & Sciences. She holds a BSE in Computer Science (High Honors) from Princeton University (2008) and a PhD from the University of Illinois at Urbana-Champaign (2013), advised by Chandra Chekuri. Her research focuses on algorithms, combinatorial optimization (submodularity, graph theory), and their applications to machine learning. She previously served as Assistant Professor at the University of Warwick, Faculty Fellow at the Alan Turing Institute, and postdoctoral researcher at Princeton's Center for Computational Intractability. Key research themes include distributed submodular maximization, streaming algorithms, optimization theory, and machine learning. Her work bridges theoretical guarantees with practical applications in data science and distributed systems. Notable contributions include frameworks for submodular maximization under constraints, randomized coordinate descent methods, and routing algorithms with balance considerations. Publications emphasize algorithm design for submodular functions, stochastic optimization, and graph problems. Recent work explores high-probability convergence in stochastic gradient methods, clipped gradient techniques, and online ad allocation strategies. Her 2016 FOCS paper on distributed submodular maximization remains influential in large-scale optimization. Professional service includes editorial work for conferences like FOCS/STOC/SODA and grants from NSF's CAREER program. No explicit scientific awards are listed, though her fellowships and postdoctoral appointments reflect scholarly recognition.

Ahmed M. A. SayedView profile

Senior Lecturer

• Distributed Systems
• Machine Learning Systems
• Federated Learning
• +7 more

Dr. Ahmed M. A. Sayed is a Senior Lecturer (equivalent to Associate Professor) and Director of the MSc Big Data Science Programme at Queen Mary University of London's School of Electronic Engineering and Computer Science. He leads the SAYED Systems Group and focuses on distributed systems, federated learning, edge computing, and network optimization. His research bridges system design and machine learning, emphasizing scalability and efficiency. Education: PhD in Computer Science (HKUST, 2017), M.Sc. and B.Sc. (Assiut University, 2012 and 2007). Prior roles include Research Scientist at KAUST and Senior Researcher at Huawei's Future Network Lab. Research Interests: Systems for ML, federated learning, edge/Cloud computing, network congestion control, and IoT. He has secured £730K+ in grants, including a UKRI-EPSRC grant for the KUber project (2024–2027). Awards: 2024 Best Student Paper (IJCAI FL Workshop), Hong Kong PhD Fellowship (2013–2017), and numerous travel grants. Actively supervises PhD/MSc students and postdocs. Grants & Leadership: PI of UKRI-EPSRC KUber project, Co-I in HKRGC and KAUST grants. Organizes workshops at venues like MobiSys and serves on TPC for ICML, EuroSys, and NeurIPS. Labs: Leads SAYED Systems Group, affiliated with Networks Group and DT4SGD Lab at Queen Mary.

Nicolò Cesa-BianchiView profile

Professor

• Machine Learning
• Online Learning
• Multi-Armed Bandits
• +4 more

Nicolò Cesa-Bianchi is a Professor of Computer Science at the University of Milan, Department of Computer Science (Dipartimento di Informatica), and affiliated with the DEIB Department at Politecnico di Milano. His research focuses on foundational aspects of machine learning, particularly online learning, multi-armed bandits, reinforcement learning, and graph analytics. He is an ELLIS Fellow and a corresponding member of the Accademia Nazionale dei Lincei. Research interests include the design and analysis of algorithms for prediction, clustering, and online decision-making, with applications to digital markets, social networks, and bioinformatics. Notable contributions span cooperative online learning, multitask learning, and bandit algorithms. He co-authored the influential book Prediction, Learning, and Games (2006). Professional roles include Board member of ELLIS, co-director of the Milan ELLIS unit, and involvement in EU initiatives like ELSA (Secure & Safe AI) and ELIAS (AI for Sustainability). He teaches graduate courses on statistical methods, machine learning, and reinforcement learning, with a focus on theoretical foundations. Key awards: ELLIS Fellowship (2020), Corresponding Member of the Accademia Nazionale dei Lincei (Italian National Academy of Sciences). His work bridges theory and practice, addressing challenges in adaptive systems, market design, and algorithmic fairness. Current projects explore distributed learning, regret minimization in adversarial environments, and interpretable models.

Pedro U. LimaView profile

Professor

• Robotics
• Multi-Robot Systems
• Autonomous Systems
• +4 more

Pedro U. Lima is a Full Professor at Instituto Superior Técnico (IST), University of Lisbon, and President of the Institute for Systems and Robotics (ISR/IST), coordinating the Intelligent Robots and Systems group (IRSg). He holds a Ph.D. in Electrical Engineering from Rensselaer Polytechnic Institute (1994). His research focuses on intelligent control systems, multi-robot coordination, and human-robot interaction, with contributions to robotics competitions like RoboCup and initiatives like the European Robotics League (E.R.L.). Lima has served as Vice President of the RoboCup Federation, Trustee of IEEE Portugal RAS Chapter, and Portuguese National Delegate to the European Space Agency’s Human Spaceflight Program Board. He led the Cátedra de Excelencia sabbatical at UC3M (2010), contributing decision-theoretic approaches to human-robot interaction. His work integrates robotics with societal impact, including outreach through the Pavilhão do Conhecimento and media appearances. He coordinates large projects like euROBIN (EU Horizon Europe) and CRAI (Portugal’s PRR), advancing robotics in domains like autonomous delivery and social robotics.

Ahmad HemmatiView profile

Associate Professor

• Maritime Logistics
• Inventory Routing Problems
• Combinatorial Optimization
• +3 more

Dr. Ahmad Hemmati is an Associate Professor at the Department of Informatics, University of Bergen. His research focuses on optimization models and algorithms for logistics, transportation, and maritime systems. He holds a PhD from the Norwegian University of Science and Technology (NTNU) in 2015. His work addresses challenges in inventory routing, drone delivery systems, and combinatorial optimization using methods like reinforcement learning and metaheuristics. Key contributions include models for maritime cargo optimization, synchronized truck-drone delivery, and feeder network design. He has collaborated internationally with institutions like MIT and the University of Antwerp. His findings are published in journals such as European Journal of Operational Research and Computers & Operations Research , with a focus on practical solutions for industrial logistics problems. Education: PhD in Operations Research (NTNU, 2015). Research Interests: Maritime Logistics, Inventory Routing Problems, Combinatorial Optimization, Drone Delivery Systems, and Algorithm Design. His work bridges theoretical models with real-world applications in transportation networks and sustainability. Publications Trends: Recent articles emphasize deep reinforcement learning applications and maritime environmental monitoring. Earlier work includes foundational contributions to tramp shipping and VMI service routing. His methodologies often blend heuristic algorithms with mathematical programming. Grants/Awards: No specific awards mentioned, but his work has been funded by the Research Council of Norway and industry collaborations.

Emanuele CrisostomiView profile

Associate Professor

• Smart Grids
• Electric Vehicles
• Control Systems
• +2 more

Emanuele Crisostomi is Associate Professor of Electrical Systems at the University of Pisa's Department of Energy, Systems, Territory and Constructions Engineering. He holds PhD and graduate degrees in Automation Engineering from the University of Pisa, with research visits to University of Cambridge, University of Leicester, and Hamilton Institute. His research bridges control theory, energy systems, and smart mobility. His research integrates: Smart grid control and renewable energy integration Electric vehicle charging infrastructure and routing algorithms Urban traffic modeling and optimization Nonlinear system dynamics and Lyapunov stabilization Machine learning applications in energy systems Crisostomi's publications demonstrate consistent innovation in electric vehicle-grid integration, with recent focus on predictive charging control, highway EV circulation patterns, and hydropower monitoring. His work shows increasing application of deep reinforcement learning for multi-regional power systems and hybrid network models for infrastructure monitoring. He leads research on IoT-enabled energy efficiency in buildings and contributes to epidemic modeling through network science. Current projects examine battery sizing optimization, charging station scheduling under uncertainty, and synergetic building management systems integrating environmental monitoring with energy conservation.

Cristiano PolitowskiView profile

Assistant Professor

• Software Engineering
• Video Game Development
• Artificial Intelligence for Software Engineering (AI4SE)
• +3 more

Cristiano Politowski is an Assistant Professor in the Department of Computer Science at Ontario Tech University's Faculty of Science. His research focuses on software engineering for video games, automated testing, AI4SE, and empirical studies. He holds a Ph.D. in Software Engineering from Concordia University (2022), an M.Sc. in Computer Science from Universidade Federal de Santa Maria (2017), and a B.Sc. in Computer Science from Universidade Regional do Estado do Rio Grande do Sul (2014). Prof. Politowski has extensive postdoctoral experience at Université de Montréal (GEODES group) and École de Technologie Supérieure (ÉTS), focusing on game engine architecture, requirements specification, and certifiable RTOS systems. He has organized workshops like ASE4Games 2021/2022 and FaSE4Games 2024, emphasizing game software engineering. His research interests span video game development challenges, software testing methodologies, and leveraging AI for software engineering. Notable projects include analyzing game engine subsystem coupling, assessing video game balance with autonomous agents, and curating datasets like PlayMyData. Prof. Politowski has secured $20K from MITACS for a 5G infrastructure project during the pandemic and received the Concordia Accelerator Award (2021). His work has been published in venues like MSR, ICSE, and IEEE Transactions.