Find Academic Supervisors

Reihaneh BidarView profile

Lecturer

• Artificial Intelligence
• Digital Transformation
• Work Redesign
• +5 more

Dr. Reihaneh Bidar is a Lecturer in Business Information Systems at the University of Queensland (UQ) Business School, part of the Faculty of Business, Economics, and Law. She is also an Associate Investigator at the Automated Decision-Making and Society (ADM+S) Centre. She holds a PhD from Queensland University of Technology’s School of Information Systems. Her research focuses on how organizations manage AI, automation, and digital integration, particularly the role of human-AI hybrids in work redesign. She emphasizes mitigating negative consequences of emerging technologies while enhancing organizational readiness for AI adoption. Her teaching spans undergraduate and postgraduate Information Systems programs, including courses on Business Analytics, Enterprise Architecture, IoT Systems, and Mobile App Development. She has developed and coordinated courses that bridge theory and practice for both bachelor’s and master’s students. Dr. Bidar’s work addresses challenges in digital collaboration and value co-creation in online networks. Notable contributions include a thought-leadership report with SAP Institute for Digital Government, which classified tasks based on risk levels to redesign public sector work. These approaches were adopted by the NSW Department of Planning, Housing, and Infrastructure for invoice processing. Her research interests further explore digital collaboration dynamics, crowdsourcing challenges, and service innovation. She collaborates with industry to translate academic insights into actionable strategies for inclusive AI and sustainable digital practices.

Amanda ProrokView profile

Professor

• Multi-Agent Systems
• Robotics
• Collective Intelligence
• +6 more

Professor Amanda Prorok leads the Prorok Lab at the University of Cambridge's Department of Computer Science and Technology, focusing on multi-agent and multi-robot systems. Her work integrates machine learning, planning, and control to coordinate intelligent agents in shared environments, with applications in transport, environmental monitoring, and search-and-rescue. She is a Fellow of Pembroke College and holds editorial roles at IEEE Robotics and Automation Letters and Autonomous Robots. Education: Ph.D., EPFL (Switzerland); Postdoctoral Research, University of Pennsylvania (USA). Research Interests: The lab pioneers methods like differentiable communication between learning agents and develops decentralized algorithms for navigation, coverage, and coordination. Key themes include neural diversity in collective learning, resilient swarm systems, and environment-aware control. Notable Achievements: ERC Starting Grant, Amazon Research Award, EPSRC New Investigator Award ABB Prize for Best Thesis in Computer Science (EPFL) Teaching: Leads Computing for Collective Intelligence (MPhil/Part III) modules. Lab & Infrastructure: The Prorok Lab operates the Cambridge RoboMaster platform and develops testbeds for connected vehicles and robot swarms. Recent work emphasizes scalable reinforcement learning and graph neural networks for decentralized decision-making.

• Quantum Physics
• Quantum Computing
• Quantum Error Correction
• +3 more

Dr. Dominic Williamson is a theoretical quantum physicist and DECRA Research Fellow at the School of Physics, University of Sydney. He specializes in quantum phases of matter and their applications to quantum error correction and computing. His work bridges condensed matter theory and quantum information science, focusing on fracton topological phases and fault-tolerant quantum architectures. Education: PhD in Physics from the University of Vienna (2017); Postdoctoral research at Yale University, Stanford University, and IBM Quantum. Current roles include faculty membership at the University of Sydney’s Quantum Science Group and prior industry experience at IBM and PsiQuantum. Research interests: Topological phases of matter, quantum error correction codes (e.g., fracton codes, QLDPC systems), fault-tolerant quantum computing architectures, and non-Abelian anyon systems. Recent breakthroughs include low-overhead quantum architectures and novel approaches to parallelized logical measurements. Grants: 2022 ARC Discovery Early Career Researcher Award for topological phases in quantum computation. Collaborations include projects on gauging logical operators and quantum code surgery. Professional activities: Editor for Quantum , frequent speaker at international conferences, and mentor for students at all levels (undergraduate to postdoctoral). Active in open-source research and public engagement through platforms like arXiv and Google Scholar.

Zihao QuView profile

Assistant Professor

• Revenue Management
• Emerging Technologies
• Cloud Computing
• +3 more

Zihao Qu is an Assistant Professor in the Department of Operations & Information Management at the Isenberg School of Management, University of Massachusetts Amherst. He holds a Ph.D. from The University of Texas at Dallas (2024) and a BBA from The Chinese University of Hong Kong, Shenzhen (2018). His research focuses on revenue management, emerging technologies, and operations optimization with applications in healthcare and cloud computing. Ph.D., The University of Texas at Dallas (2024) BBA, The Chinese University of Hong Kong, Shenzhen (2018) His work addresses challenges in real-time decision-making systems, cloud cost optimization, and spatial matching algorithms. Recent publications include advancements in postacute healthcare service algorithms and cloud resource provisioning models. He teaches courses on supply chain management and emerging technologies. His research demonstrates expertise in combining theoretical operations research with practical industry applications, particularly in healthcare and technology sectors. No specific awards or grants are listed in the provided materials.

• Computer Networks
• Evolutionary Algorithms
• Fractal Patterns
• +3 more

Habiba Akter is a Lecturer in Networks and Digital Systems at Queen Mary University of London (QMUL), affiliated with the School of Electronic Engineering and Computer Science . She holds an MEng in Electronic Engineering and Computing (2016) and a PhD in Electronic Engineering (2021), both from QMUL. Her education prior to university was completed in Bangladesh. Her research focuses on Computer Networks , Evolutionary Algorithms , Fractal Patterns , and AI-Based Face Detection . She explores applications such as optimizing network routing, generating fractal patterns using genetic algorithms, and analyzing network tunneling protocols for enhanced security and efficiency. In teaching, she instructs undergraduate courses including Internet Protocols and Wireless Networks , integrating practical network design principles with theoretical foundations. No scientific awards or funded grants are explicitly listed. Her work includes collaborations on network topology analysis (e.g., the Fortaleza case study) and multi-constrained path optimization in tunnelled networks. No lab affiliations or team collaborations are mentioned in the provided information.

Ryan HenryView profile

Assistant Professor

• Applied Cryptography
• Secure Systems
• Privacy-Preserving Technologies
• +3 more

Ryan Henry is an Assistant Professor in the Department of Computer Science at the University of Calgary. His research focuses on applied cryptography, emphasizing the development of secure systems that prioritize user privacy. His work spans designing privacy-enhancing technologies, implementing cryptographic protocols, and analyzing number-theoretic attacks on cryptographic assumptions. He also explores theoretical aspects of cryptographic efficiency and practical deployment challenges. While specific educational background details are not provided in the text, his research contributions highlight expertise in cryptography, secure systems, and privacy-preserving technologies. His work has addressed topics such as Private Information Retrieval (PIR), secure messaging, and blockchain privacy. Key research interests include: Secure Multiparty Computation Privacy-Preserving Data Access Efficient Cryptographic Protocols Zero-Knowledge Proofs IoT Security Cryptocurrency and CBDC Design His recent publications emphasize advancements in distributed systems security, privacy in recommendation systems, and cryptographic efficiency. Notable contributions include the Grotto and Duoram frameworks for secure computation, and proposals for Canadian CBDC frameworks. Despite extensive research output, no scientific awards or grants are explicitly mentioned in the provided text. Collaborations and lab affiliations are not detailed, though his work suggests involvement in interdisciplinary projects on privacy and security technologies.

Liz RatnamView profile

Associate Professor

• Control of Power Systems
• Optimization of Energy Systems
• Secure and Resilient Power Grids
• +3 more

Associate Professor Liz Ratnam is a leading academic in the Department of Electrical and Computer Systems Engineering at Monash University, serving as Deputy Postgraduate Director of Education ECSE. Her expertise spans power systems control, energy optimization, and resilient grid design. She holds a BEng (Hons I) and PhD in Electrical Engineering from the University of Newcastle (2006 and 2016), with postdoctoral research at UC San Diego and Berkeley. She previously served as Senior Lecturer and Sub-Dean for Educational Programs at ANU's College of Engineering & Computer Science, supported by a Future Engineering Research Leader (FERL) Fellowship. Her current research focuses on advancing transactive energy markets, EV coordination in distribution networks, and secure grid operations. She leads major grants including the National Facility for Electricity Grid Security (ARC LIEF 2023) and projects on EV fast-charging infrastructure. Education Background: - BEng (Hons I) in Electrical Engineering, University of Newcastle (2006) - PhD in Electrical Engineering, University of Newcastle (2016) Research Interests: Resilient, carbon-neutral power grid design Control and optimization of energy systems Synchrophasor-based estimation for grid stability Transactive multi-agent systems for energy markets Electric vehicle integration and coordination in unbalanced grids Grants & Projects: ARC Linkage Infrastructure Grant LE23010058 (2023): National Facility for Electricity Grid Security ARC Discovery Project DP22010135 (2022): Neural Architecture Search for Deep Learning ARC Linkage Project LP21200473 (2022): Building Australia's EV Fast-Charging Infrastructure Awards & Memberships: Fellow of Engineers Australia Senior Member of IEEE FERL Fellowship (ANU)

Stephen LeeView profile

Assistant Professor

• Computer Systems
• Cyber-Physical Systems
• Data Mining and Data Science
• +3 more

Stephen Lee is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh, affiliated with Pitt Cyber. His research focuses on distributed systems, cyber-physical systems, and sustainability, emphasizing energy efficiency and cost optimization. Dr. Lee holds a PhD from the University of Massachusetts Amherst, a Master’s from Chennai Mathematical Institute, and a Bachelor’s from St. Stephen’s College, Delhi. He actively seeks students for his research group. Education: PhD, Computer Science, University of Massachusetts Amherst Master’s, Chennai Mathematical Institute Bachelor’s, St. Stephen’s College, Delhi Research Interests: Dr. Lee’s work integrates distributed systems, machine learning, and optimization to enhance sustainability. Key areas include IoT-enabled energy systems, emission-aware computing, and privacy-preserving frameworks. He leads projects like GreenWhisk (serverless emission reduction) and Sat2map (3D building modeling from satellite imagery). Recent Achievements: Best Paper Award in IEEE TPS 2024 DOE-funded Cyber Energy Center (2024) MCSI Seed Grant for Pitt building sustainability (2024) NSF Grant on sustainable distributed infrastructures (2023) Grants & Advising: Secured over $2M in grants, including NSF and DOE funding. Advises on energy-efficient systems and IoT security. Teaches CS 2510 (Operating Systems) and CS 1699 (Systems & Sustainability). Labs & Teams: Directs the Sustainable Systems Research Group, focusing on decarbonizing IT and optimizing renewable energy systems. Collaborates with industry partners on smart grid solutions and edge-cloud systems.

Chao TianView profile

Associate Professor

• Information Theory
• Distributed Data Storage
• Coding Theory
• +6 more

Dr. Chao Tian is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Cornell University (2005) and a B.E. from Tsinghua University (2000). His research focuses on information theory, distributed storage systems, coding theory, and machine learning applications, including large language models (LLMs) and privacy-preserving techniques like steganography and private information retrieval (PIR). He has received notable awards, including the 2017 IEEE Jack Wolf ISIT Best Student Paper Award and the 2014 IEEE ComSoc DSTC Best Paper Award. Dr. Tian’s work bridges theoretical foundations and practical applications, such as optimizing storage codes for distributed systems, developing secure PIR protocols, and exploring LLMs in energy and steganographic contexts. His group has pioneered methods for variable-order Markov chain modeling with transformers and designed quantization techniques for perceptual quality. Recent activities include a faculty development leave at MIT/Harvard and delivering distinguished lectures globally on AI and information theory. He advises students on cutting-edge topics like LLM-based steganography and diffusion models. His publications span journals like IEEE Transactions on Information Theory and conferences such as NeurIPS and ISIT, with a focus on coding theory, privacy, and machine learning. He has also contributed to open-source tools like the CAI toolbox for investigating information-theoretic limits. Current projects include optimizing cryptocurrency mining in energy markets and advancing federated learning with adversarial robustness.

Soledad VillarView profile

Assistant Professor

• Optimization for Data Science
• Machine Learning
• Representation Learning
• +5 more

Soledad Villar is an Assistant Professor in the Department of Applied Mathematics and Statistics and a member of the Mathematical Institute for Data Science at Johns Hopkins University. She also contributes to the Data Science and AI Institute . Her research focuses on computational methods for extracting information from data, emphasizing optimization for data science, machine learning, equivariant representation learning, and graph neural networks. Dr. Villar holds a PhD in Mathematics from the University of Texas at Austin and has been a research fellow at New York University and the Simons Institute at UC Berkeley. Her work bridges theoretical foundations with practical applications in fields like scientific computing and political analysis. Awards include the National Science Foundation CAREER Award (2024). Her research has addressed topics such as gerrymandering detection, fluid dynamics modeling, and graph representation learning. She collaborates on interdisciplinary projects and organizes academic events like the One World MINDS Seminar and the Cibercoloquio Latinoamericano de Matemáticas . Her research interests span computational methods, equivariant machine learning frameworks, and graph neural networks, with applications in physics, engineering, and data-driven decision-making. She actively engages in advancing machine learning techniques for scientific and engineering challenges.

Brian M DealView profile

Professor

• Sustainable Planning
• Decision Support Systems
• Urban Land Use Modeling
• +2 more

Brian M Deal serves as a Professor of Landscape Architecture within the School of Architecture at the University of Illinois at Urbana-Champaign, holding additional appointments in Urban and Regional Planning, the European Union Center, the Center for Latin American and Caribbean Studies, and the National Center for Supercomputing Applications (NCSA). His expertise bridges sustainable planning theory, energy systems, and spatial modeling to develop practical decision-support tools for community development and climate resilience. His educational foundation includes a PhD in Regional Planning (2002), Master of Architecture (1997), and BS in Architectural Studies (1983), all earned at the University of Illinois at Urbana-Champaign. Prior academic experience encompasses a decade of professional architecture practice and senior research at the Army Construction Engineering Research Laboratory (CERL), where he specialized in sustainable military facility design using spatial simulation. Deal's research centers on sustainable planning systems and climate adaptation, with current projects examining urbanization impacts on Korean rural amenities, advancing the University of Illinois' climate action plan (iCAP), and developing next-generation 'sentient' planning support systems. His work integrates land-use modeling, energy systems analysis, and decision-support technologies to address complex urban environmental challenges through interdisciplinary collaboration. Recent publications reveal a pronounced shift toward data-driven sustainability solutions, featuring AI applications for carbon-neutral planning, multi-scaled green infrastructure optimization, and socio-ecological modeling. Key themes include urban carbon sequestration, post-pandemic park dynamics, and climate-resilient coastal design, demonstrating consistent innovation in translating theoretical frameworks into actionable planning tools for real-world implementation. Professor Deal's scientific awards and honors were not detailed in the provided text. As faculty mentor to the Student Sustainability Committee and chair of campus sustainability planning efforts, Deal actively guides student development and institutional policy. His leadership of the LEAM Laboratory and SEDAC involves managing research grants focused on urban resilience, energy systems, and climate adaptation, fostering partnerships with government agencies and community organizations to deploy planning tools that directly impact community decision-making processes. Deal directs the Land Use Evolution and Impact Assessment Modeling (LEAM) Laboratory and Smart Energy Design Assistance Center (SEDAC), leading interdisciplinary teams in developing spatial simulation models and decision-support systems. His operational leadership extends to authoring the university's climate action plan and chairing campus sustainability committees, positioning him at the nexus of academic research, institutional policy, and community engagement for sustainable urban futures.

• Optimization Algorithms
• Smart Grids
• Renewable Energy Systems
• +3 more

Silvia Jiménez Fernández is an Associate Professor in the Department of Signal Theory and Communications at Universidad Autónoma de Madrid. Her research focuses on optimization algorithms, smart grids, renewable energy systems, telemedicine, and machine learning applications. She holds a Ph.D. from Universidad Politécnica de Madrid (2009), supervised by Dr. Francisco del Pozo Guerrero and Dr. Paula de Toledo Heras. Her work integrates interdisciplinary approaches, such as combining evolutionary algorithms with engineering challenges in energy systems and healthcare. Key contributions include advancements in coral reefs optimization algorithms for energy management, machine learning for battery health estimation, and telemedicine systems for chronic disease monitoring. Recent research trends emphasize hybrid learning models in education, multi-objective optimization in renewable energy systems, and risk analysis in smart grids with electric vehicles. She is affiliated with the GHEODE Research Group (Modern Heuristics and Network Design).

Ameya JagtapView profile

Assistant Professor

• Scientific Machine Learning
• Deep Learning
• Computational Physics
• +8 more

Ameya Jagtap is an Assistant Professor (Tenure-Track) in the Department of Aerospace Engineering at Worcester Polytechnic Institute (WPI), USA. Prior to this, he served as an Assistant Professor of Applied Mathematics (Research) at Brown University from 2021 to 2024. He holds a Ph.D. and M.E. in Aerospace Engineering from the Indian Institute of Science (IISc), and completed postdoctoral research at TIFR-CAM (India) and Brown University's Division of Applied Mathematics. His research bridges mechanical/aerospace engineering, applied mathematics, and computation, focusing on scientific machine learning algorithms that integrate data and physics. Key areas include physics-driven deep learning, uncertainty quantification, multi-scale simulations, and novel neural network architectures like quantum and graph networks. He serves on editorial boards for Neural Networks , Neurocomputing , and others. His work emphasizes interpretable neural operators for PDE solutions, domain decomposition methods, and adaptive activation functions to enhance PINN convergence. Notable contributions include XPINNs (extended physics-informed neural networks) and causal sweeping frameworks for PDEs. His research has been widely cited, particularly for PINN applications in supersonic flows and high-dimensional PDEs. Jagtap has delivered invited talks at institutions like Los Alamos National Laboratory, Tsinghua University, and the Alan Turing Institute. He is also recognized as a Top 2% World Scientist by Stanford University.

Yufeng ZhangView profile

Professor

• Operations Management
• Supply Chain Management
• Sustainability
• +12 more

Professor Yufeng Zhang is a Chair in Operations Management and Head of the Department of Management at Birmingham Business School, University of Birmingham. He is also a Senior Fellow of the Higher Education Academy and a Fellow of the Alan Turing Institute. His academic journey began at Shanghai Jiao Tong University, continued at the Technical University of Berlin, and culminated with a PhD from the University of Cambridge, where he also worked as a Research Associate before joining Birmingham in 2009. PhD, Engineering – University of Cambridge MSc/Dipl-Ing, Global Production Engineering – Technical University of Berlin BEng, Mechanical Engineering & Automation – Shanghai Jiao Tong University BEng, International Finance – Shanghai Jiao Tong University PG Cert in Learning & Teaching in Higher Education Yufeng Zhang’s research focuses on sustainability, digitalisation, engineering innovation, and global network operations. His work integrates engineering and management perspectives, employing qualitative, quantitative, and simulation methods. He has led numerous projects funded by UK, EU, and international bodies, and has published over 80 refereed papers in top-tier journals such as Journal of Operations Management , International Journal of Production Economics , and Journal of Cleaner Production . His recent publications highlight trends in green operations, sustainable supply chains, digital transformation, and network capabilities. The research spans subfields including environmental performance, IoT integration, supply chain flexibility, and servitisation, reflecting a strong interdisciplinary focus on modern operational challenges. His scientific awards include the University’s Excellence in Teaching and Learning Award (2013), Literati Nominations for Highly Recommended Papers (2008, 2013), and recognition as a Senior Fellow of the Higher Education Academy and Turing Fellow. Yufeng has supervised over 20 PhD students and has held key roles such as Deputy Lead for REF2021, Director of Business Programmes with Engineering, Sciences, Law and Languages (2010–2017), and Coordinator of Europe China High Value Engineering Networks (EC-HVEN). He is an External Examiner for multiple universities and a Visiting Professor at Zhejiang University, demonstrating extensive academic leadership and collaboration. He leads and contributes to research labs and networks focused on engineering innovation and sustainable operations, including collaborations with industry and international institutions. His future work continues to explore the intersection of digital technologies, sustainability, and global operations, aiming to shape resilient and responsible business practices.

Funlade SunmolaView profile

Senior Lecturer

• Applied Artificial Intelligence
• Sustainable and Smart Industries
• Manufacturing Engineering
• +5 more

Funlade Sunmola is a Principal Lecturer in Manufacturing and Industrial Engineering at the University of Hertfordshire , affiliated with the School of Engineering and Computer Science and the Department of Engineering and Technology. He holds a PhD in Computer Science (Artificial Intelligence and Robotics) from the University of Birmingham and has nearly 40 years of professional experience across civil engineering, manufacturing, healthcare, and academia. Education: BEng (Hons) in Civil Engineering, Ahmadu Bello University MSc in Industrial Engineering, University of Ibadan MA in Accounting and Finance, Birmingham City University MPhil in Manufacturing Engineering, University of Birmingham PhD in Computer Science, University of Birmingham Research Interests: Focuses on Applied Artificial Intelligence , Sustainable and Smart Industries , and Industry 4.0 . Key areas include supply chain visibility, blockchain integration, machine learning applications in manufacturing, and virtual engineering. Leads the Duncan Calder Virtual Engineering Lab and oversees MSc Online Engineering Programmes. Grants & Projects: PI of LINK: Digital Direct Connection for Salvage Construction Materials (Circular Economy) PI of N-BICC: Cassava Innovation Deployment Co-I in Solar Cool System (So-Cool) for Smallholder Farmers Labs/Teams: Heads the Duncan Calder Virtual Engineering Lab , focusing on immersive technologies and virtual product design.