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Zhengjia Nelson ChenView profile

Professor

• Biostatistics
• Epidemiology
• Clinical Trials
• +9 more

Zhengjia Nelson Chen is a Professor and Director of the Biostatistics Shared Resource Core at the University of Illinois Cancer Center. His research focuses on advancing clinical trials through novel Bayesian adaptive designs, developing statistical software for trial implementation, and collaborating with biomedical investigators. He has appointments in the Department of Epidemiology and Biostatistics. Education: Ph.D. in Biostatistics, University of Southern California (2008) M.S. in Biometry, University of Southern California (2001) M.S. in Biochemistry and Molecular Biology, Peking University (1998) B.S. in Microbiology, Peking University (1995) Dr. Chen’s research bridges theoretical statistical challenges with practical biomedical applications. His work on adaptive Phase I, II, and III clinical trial designs using Bayesian methodologies has significantly influenced cancer treatment protocols. He also develops user-friendly software tools like ID-NETS©TM to facilitate implementation of these designs in clinical practice. His recent publications highlight innovations in dose escalation strategies, toxicity control mechanisms, and proteomic analyses of cancer mutations. These works span disciplines including Biostatistics, Oncology, and Computational Methods, with subfields focusing on Bayesian optimization, trial duration minimization, and personalized medicine. Scientific Awards: 2017: ICSA Significant Contribution Award as Treasurer 2016: Winship Cancer Institute Clinical Translational Research Service Award 2013: Winship Cancer Institute High Quality Oncology Research Commitment Award As Director of the Biostatistics Shared Resource Core, Dr. Chen provides statistical expertise to multidisciplinary teams across institutions. He has served in leadership roles at the International Chinese Statistical Association and reviewed for clinical oncology study sections at the National Cancer Institute.

Chee Khian SimView profile

Senior Lecturer

• Mathematical Optimization
• Operational Research
• Supply Chain Management
• +5 more

Chee Khian Sim is a Senior Lecturer at the School of Mathematics & Physics, University of Portsmouth, and a member of the Centre for Operational Research & Logistics (CORL). He specializes in mathematical optimization and its applications in operational research, with expertise in optimization algorithms, supply chain management, and inventory systems. He holds a PhD from the National University of Singapore (2005), an MSc from the University of Washington (1997), and a BSc from the National University of Singapore (1992). His research focuses on first and second-order optimization methods, including interior point algorithms for conic optimization and subgradient methods for convex/nonconvex problems. He also investigates supply chain optimization, particularly inventory management strategies. He teaches modules such as Mathematical Models, Quantitative Supply Chain Management, and Logistics Modelling. Sim has contributed to over 20 research outputs, including articles on convergence analysis of splitting methods, inventory policies, and interior point algorithms. His work has been recognized with a Fellowship (2015). He actively reviews for journals like Optimization Methods and Software and participates in international conferences. He supervises PhD students in mathematical optimization and operational research.

Ayşen Dener AkkayaView profile

Professor

• Time series analysis
• Stochastic processes
• Risk and decision analysis
• +3 more

Prof. Ayşen Dener Akkaya is a Professor in the Department of Statistics at Middle East Technical University (METU), where she has held academic positions since 1985. She earned her Ph.D. in Statistics from METU in 1995, following her M.Sc. (1987) and B.Sc. (1985) in the same discipline. Her professional experience includes a Visiting Fellowship at Princeton University's Department of Civil Engineering and Operations Research (1997–1998). Her research focuses on time series analysis , stochastic processes , and risk analysis , with applications in earthquake engineering, seismic hazard modeling, and robust statistical methods. Key areas include autoregressive models, parameter estimation under non-normal conditions, and stochastic modeling of geotechnical systems. Her publications span journals like Journal of Computational and Applied Mathematics and Structural Safety , emphasizing robust statistical techniques and their engineering applications. She co-authored the influential textbook Robust Estimation and Hypothesis Testing (2004), which remains a key reference in statistical methodology.

Xingjie LiView profile

Associate Professor

• Numerical Analysis
• Partial Differential Equations
• Mathematical Physics
• +3 more

Xingjie (Helen) Li is an Associate Professor in the Mathematics & Statistics Department at the University of North Carolina at Charlotte. Her research focuses on numerical analysis, partial differential equations, and mathematical physics with an emphasis on nonlocal models, multiscale methods, and computational mechanics. She develops advanced numerical schemes for stochastic systems and explores applications in materials science, image processing, and epidemiological modeling. Her work bridges theoretical analysis and practical computation, addressing challenges in coarse-graining stochastic dynamics, atomistic-to-continuum coupling, and probabilistic collocation methods for heterogeneous materials. Recent studies include innovative approaches to nonlocal diffusion problems, peridynamic fracture modeling, and data-driven time-stepping algorithms for ergodic systems. She has also applied mathematical techniques to analyze real-world scenarios such as pandemic dynamics in Singapore and Japan. Key contributions include energy-preserving finite difference schemes, feature-oriented imaging compression frameworks, and inference-based adaptive methods. Her research portfolio reflects interdisciplinary collaboration spanning computational physics, applied mathematics, and data science.

• Operations Research
• Queueing Theory
• Traffic Management
• +3 more

Professor Neil Walton is a Professor and Deputy Head of the Department of Operations Management, Strategy, Innovation, Information Systems, and Entrepreneurship at the Business School. His research primarily focuses on operations research, queueing theory, stochastic processes, and their applications in transportation systems, networking, and optimization. He has contributed significantly to understanding scheduling algorithms, traffic signal control, and decentralized systems. His work bridges theoretical foundations with practical applications in urban mobility and resource allocation. Research interests include advanced scheduling techniques for quantum switches, stochastic approximation methods, and optimization frameworks for vehicular networks. He has explored topics such as regret analysis in multi-arm bandits and the convergence of stochastic processes. His studies often address real-world challenges like traffic congestion and healthcare resource management through rigorous mathematical modeling. Publications highlight a consistent focus on optimizing systems under uncertainty, with notable contributions to network stability, adaptive control mechanisms, and reinforcement learning applications. His work on platooning in connected vehicle networks and decentralized signal control systems underscores a commitment to cutting-edge solutions for modern transportation challenges. Professor Walton has advised students such as Yao Shao, contributing to the training of future researchers in operations management and systems optimization.

Dianne P. O'LearyView profile

Professor

• Scientific Computing
• Numerical Analysis
• Computational Science
• +6 more

Dianne P. O'Leary is a Professor affiliated with the Department of Computer Science at the University of Maryland. Her work focuses on scientific computing, numerical analysis, and computational science, with a particular emphasis on matrix factorizations, differential equations, and Monte Carlo methods. She authored the textbook *Scientific Computing with Case Studies* (SIAM Press, 2009), which integrates theoretical concepts with practical case studies across physics, engineering, epidemiology, and biology. Her research explores topics such as image deblurring, blind deconvolution, elastoplastic torsion, and iterative methods for solving large linear systems. The book includes nineteen case studies that illustrate algorithm design, mathematical modeling, and software development principles. Notably, the text emphasizes stability, error analysis, and MATLAB implementation grounded in machine arithmetic and memory management. While no explicit scientific awards or student advisees are mentioned in the provided texts, her contributions to computational education and algorithm development are central to her work. Supplementary materials include solution manuals, MATLAB code repositories, and detailed notes on topics like sensitivity analysis, computer memory management, and finite element methods. Her case studies often bridge theory and application, such as modeling epidemics, robot control systems, and structural engineering problems. The book’s structure reflects a commitment to hands-on learning, with units dedicated to dense matrix computations, optimization, Monte Carlo simulations, and sparse matrix techniques for partial differential equations.

• Ore Characterisation
• Geometallurgy
• Comminution
• +5 more

Dr. Louisa O'Connor is an Adjunct Senior Research Fellow at the University of Sydney's Rio Tinto Centre for Mining Automation and an Adjunct Associate Professor at the University of Queensland's WH Bryan Centre and the Australian National University's Department of Research Physics. She holds a prominent role as Principal Steel Decarbonisation at Rio Tinto's Centre of Excellence Orebody Knowledge. Her academic journey includes roles as Associate Dean for Teaching & Learning at the Western Australian School of Mines (WASM) and Senior Lecturer at Curtin University. Education: BSc (Hons) Applied Geology (University of Leicester), MSc Mining Geology (Camborne School of Mines), PhD Mining Engineering (University of Nottingham), and certifications in business and leadership. Research focuses on ore characterisation, geometallurgy, AI-driven digitization, and critical metals (precious metals and REEs). She pioneered methodologies in microwave comminution and geometallurgical sampling protocols, with notable contributions to gold and copper ore processing optimization. Teaching includes coordinating units like MINE1002 and MINE4000/4001, and managing the WASM PhD/MPhil Forum. She serves as a reviewer for Minerals Engineering and International Journal of Mineral Processing . Awards include Fellow of the Australasian Institute of Mining and Metallurgy (FAusIMM). Her work spans industry-academia collaborations, particularly in mine automation and decarbonization strategies for the steel industry.

Ulrik Bo Rufus EnstadView profile

Research Fellow

• Operator Alge
• Harmonic Analysis
• C*-algebras
• +3 more

Ulrik Bo Rufus Enstad is a Postdoctoral Fellow at the University of Oslo, Faculty of Mathematics and Natural Sciences, Department of Mathematics. He began his postdoc in August 2024, after leading a three-year researcher project with international mobility funded by the Research Council of Norway, including stays at Stockholm University and the University of Potsdam. He earned his PhD at the University of Oslo in 2020 under Professor Nadia Larsen and Professor Franz Luef. Education: PhD in Mathematics, University of Oslo (2020) His research focuses on operator algebras and harmonic analysis, particularly sampling problems approached via C*-algebras and von Neumann algebras of associated groups and groupoids. Recent work includes criteria for Gabor frames over rational lattices, density theorems for projective representations, and applications of nilpotent group algebras to projection comparison. Publications span 2019-2025, concentrating on time-frequency analysis, sampling theory, and structural properties of operator algebras. Collaborators include Jordy Timo van Velthoven, Sven Raum, Eduard Vilalta, and others. Teaching roles include MAT1120 Linear Algebra (Fall 2024), advanced functional analysis, and calculus courses since 2016. Affiliated with the Operator Algebras research group and C*-seminar at the University of Oslo.

George HadjinicolaView profile

Professor

• Operations Management
• Supply Chain Management
• International Manufacturing
• +5 more

Dr. George Hadjinicola is a Professor of Operations Management and Chairperson of the Department of Business and Public Administration at the University of Cyprus. He holds a Ph.D. in Business Administration from the University of Southern California (1993), alongside multiple master's degrees in Statistics, Computer Engineering, and a B.S. in Electrical Engineering (Cum Laude, 1988), all from USC. His roles include Co-Director of the MBA Program at the University of Cyprus (2005–2016), Academic Coordinator for MBA programs at the Open University of Cyprus (2014–2016), and Chairman of the Cyprus Cooperative Bank (2017–2019). His research focuses on the intersection of marketing and production, international operations, serial production systems, and product positioning. He has published in journals such as Journal of Operations Management , European Journal of Operational Research , and Production and Operations Management . Teaching experience spans institutions like ALBA Graduate Business School, SKEMA Business School, and universities in multiple countries. He has also delivered executive training to organizations like Carrefour and Siemens. Key research interests include optimizing production systems, cost analysis in manufacturing, and strategic decision-making in supply chains. His work frequently integrates statistical and mathematical modeling, such as Markovian frameworks and self-organizing maps, to address real-world operational challenges.

Philip G ZhaoView profile

Senior Lecturer

• Artificial Intelligence
• Robotics
• Cyber-Physical Systems
• +5 more

Dr. Philip G Zhao is a Senior Lecturer in Robotics and AI at the University of Manchester's Department of Computer Science. Prior to this role (since 2024), he held positions at the University of Glasgow from 2018 to 2023. He leads the Machine Intelligence Alliance across multiple universities and is a Senior Member of IEEE and IET member. His research focuses on AI-driven cross-system design and optimization for robotics, Cyber-Physical Systems (CPS), IoT, communication networks, and computer vision. His work emphasizes practical applications such as edge intelligence, teleoperation systems, and medical robotics. Dr. Zhao has secured £3M in total research funding, including £800k as Principal Investigator, and holds two U.S. patents. Notable achievements include three Best Paper Awards and over 2,800 Google Scholar citations. His recent projects involve edge-enabled industrial CPS co-design, metaverse teleoperation frameworks, and dynamic human-robot interaction studies. He actively supervises PhD students and promotes collaborative robotics in healthcare and education. Labs/Teams: Machine Intelligence Alliance (MIA), leading interdisciplinary research across multiple universities.

Yoshua BengioView profile

Professor

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +5 more

Yoshua Bengio is a Full Professor at Université de Montréal's Department of Computer Science and Operations Research, and a leading global expert in deep learning. He is the founder and scientific advisor of Mila Quebec AI Institute, and holds a Canada CIFAR AI Chair. His work has revolutionized AI, earning him the 2018 Turing Award ("Nobel of computing") and multiple accolades including the Killam Prize and Officer of the Order of Canada. Education: PhD in Computer Science (McGill, 1991), postdocs at MIT (1991-1992) and AT&T Bell Labs (1992-1993). Research focuses on deep learning theory, generative models, and AI ethics. He co-leads the CIFAR Learning in Machines & Brains program and advises IVADO. Concerned about AI's societal impact, he co-authored the Montreal Declaration for Responsible AI and chairs global AI safety initiatives. Recent research spans AI safety frameworks, causal machine learning, and climate change applications. Over 200 students supervised, including PhDs and postdocs. Key awards include Royal Society Fellowships, IEEE Pioneer Award, and UN Scientific Advisory Board membership.

Andrea LodiView profile

Professor

• Mixed-Integer Programming
• Data Science
• Operations Research
• +4 more

Andrea Lodi serves as the Andrew H. and Ann R. Tisch Professor at the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion, and holds the Canada Excellence Research Chair in “Data Science for Real-time Decision Making” at Polytechnique Montréal. He earned his Ph.D. in system engineering from the University of Bologna in 2000 and was an IBM Goldstine Fellow (2005–2006). Previously, he was a full professor at the University of Bologna (2007–2015). His research focuses on mixed-integer programming, nonlinear optimization, and data science applications in decision-making systems. Notable contributions include advancements in cutting-plane algorithms, machine learning integration in optimization, and fair dynamic resource allocation. Awards include the IBM and Google Faculty Awards, and leadership roles in EU projects and IVADO (Montréal Institute for Data Valorization). Education: Ph.D. in System Engineering, University of Bologna (2000) Consulting: IBM CPLEX R&D team since 2006 Grants: Canadian Federal Government’s Apogée Programme (2024), EU projects His work bridges theoretical optimization and practical applications, including healthcare scheduling, transportation logistics, and algorithmic fairness. Recent projects include developing reinforcement learning frameworks for bike-sharing rebalancing and fairness-aware dynamic decision systems.

• Design of Experiments
• Factorial Designs
• Orthogonal Arrays
• +5 more

Evaggelaras Charalampos is an Associate Professor at the Department of Statistics and Insurance Science, University of Piraeus. He holds a PhD in Mathematics (Statistics) from the National Technical University of Athens (NTUA), an M.Sc. in Applied Mathematics from NTUA, and a Diploma in Mathematics from the University of Athens. He has been teaching since 2008, offering courses in Applied Statistics, Regression Analysis, and Statistical Packages across multiple institutions including the University of Central Greece and Open University of Greece. Research Interests: Focuses on experimental design methodologies, including Orthogonal Arrays, Fractional Factorial Designs, Supersaturated Designs, and Latin Hypercubes. His work emphasizes Off-line Quality Control, Algebraic Statistics, and efficient model identification using combinatorial methods. Has published 47 peer-reviewed journal articles and 2 book chapters, contributing to statistical design theory and applications. Authored two Greek-language textbooks on regression analysis for academic and practical use. Teaching roles span undergraduate and postgraduate programs in Applied Statistics, including specialized courses on Data Analysis, Experimental Design, and Quality Assurance. Actively involved in curriculum development and statistical package training.

Psarrakos GeorgiosView profile

Associate Professor

• Actuarial Science
• Ruin Theory
• Risk Management
• +4 more

Psarrakos Georgios is an Associate Professor at the Department of Statistics and Insurance Science, University of Piraeus. His academic career includes roles in teaching and research, with a focus on actuarial mathematics, risk theory, and stochastic processes. He holds a PhD in Actuarial Mathematics (2007), M.Sc. in Statistics and Operations Research (2002), and B.Sc. in Mathematics (1998), all from Greek universities. His research interests span ruin theory, heavy-tailed distributions, stochastic orders, and phase-type distributions. He teaches undergraduate courses like Ruin Theory and Actuarial Models for Life Contingencies, as well as postgraduate courses such as Stochastic Processes in Finance and Actuarial Science. He has been an invited speaker at institutions including the University of Liverpool and University of Salerno, and actively participates in international conferences on insurance mathematics and stochastic modeling. His publications focus on actuarial science and probability theory, addressing topics like defective renewal equations, covariance in risk models, and entropy-based measures. He serves as a referee for journals such as Insurance: Mathematics and Economics and the Journal of Applied Probability.

Benoit ValironView profile

Researcher

• Formal Methods
• Logic and Programming Languages
• Quantum Computing
• +4 more

Benoit Valiron is a Researcher affiliated with the Formal Methods Laboratory, focusing on quantum computing, formal methods, and programming languages. His work integrates theoretical foundations with practical applications in quantum software development and formal verification. Research interests include semantics of quantum programming languages, reversible computing, and categorical logic. He explores quantum algorithms' operational and denotational semantics while developing tools like Qbricks for formal verification of quantum systems. Recent publications emphasize quantum circuit optimization, concurrency models in quantum systems, and applications of ZX-calculus. His work bridges theoretical advancements with real-world quantum technologies, addressing challenges in NISQ-era hardware and algorithm design. Key contributions include foundational papers on quantum control structures, time synchronization protocols for real-time systems, and vectorial λ-calculus extensions. His research often involves collaborations with institutions advancing quantum software ecosystems and formal methods in safety-critical systems.