Find Academic Supervisors

Rui Soares BarbosaView profile

Researcher

• Quantum Foundations
• Quantum Computer Science
• Mathematics of Quantum Theory
• +6 more

Rui Soares Barbosa is a Staff Researcher at the Quantum and Linear-Optical Computation group at the International Iberian Nanotechnology Laboratory (INL). He holds a BSc in Computer Science from Universidade do Minho (2009), an MSc in Mathematics and Foundations of Computer Science from the University of Oxford (2010), and a DPhil in Computer Science from Oxford (2015) with a thesis on Contextuality in quantum mechanics and beyond. Prior to joining INL, he held post-doctoral positions at Oxford (2015–2019) and the University of Edinburgh (2019–2020), and a Research Fellowship at the Simons Institute for the Theory of Computing, UC Berkeley (2017). Barbosa's research lies at the intersection of Computer Science, Physics, and Mathematics, focusing on quantum foundations, quantum computer science, and the mathematics of quantum theory. His work emphasizes logical, structural, and compositional aspects, particularly investigating non-locality and contextuality - phenomena that distinguish quantum theory from classical physics and have been linked to quantum informatic advantage. He employs mathematical tools from category theory, logic, probability, algebraic topology, and operator algebras to achieve a structural understanding of quantum systems' non-classical features. His recent publications demonstrate a strong focus on contextuality as a quantum resource, exploring its connections to causality, computational advantage, and logical structures. The research spans theoretical foundations to practical quantum computing applications, with particular attention to mathematical frameworks like sheaf theory that elegantly express quantum contextuality. His work often involves collaborations with leading researchers in quantum foundations and theoretical computer science. Barbosa actively advises multiple PhD and MSc students including Angelos Bampounis, Rafael Wagner, Nico Witrock, and others. His research group at INL, the Quantum and Linear-Optical Computation group, conducts cutting-edge research at the intersection of quantum theory and computer science, regularly presenting findings at major conferences like the International Conference on Quantum Physics and Logic (QPL).

Bambang TrigunarsyahView profile

Associate Professor

• Project Management
• Infrastructure Project Delivery
• Post-Disaster Reconstruction
• +4 more

Associate Professor Bambang Trigunarsyah holds a position at RMIT University since 2018, affiliated with the School of Property, Construction and Project Management (PCPM). He earned his BSc in Civil Engineering from Colorado School of Mines, a Master's in Civil Engineering (Construction Management) from the University of Indonesia, and a PhD in Engineering Project Management from the University of Melbourne. His career spans industry roles, including project engineering in Indonesia's oil and gas sector, academic leadership at the University of Indonesia, and international teaching/research appointments at Queensland University of Technology (QUT) and King Fahd University of Petroleum and Minerals in Saudi Arabia. Research interests focus on post-disaster reconstruction, infrastructure project governance, constructability of infrastructure, and project management in developing countries. Notable projects include risk management frameworks using blockchain, circular economy in Saudi construction, and BIPV energy solutions. He actively supervises HDR students in areas like public-sector project success determinants and thermal environment studies. Bambang serves as Higher Degree Research Delegate Authority (HDR DA) for the School of PCPM and teaches postgraduate project management programs. Professional memberships include AIPM, PMI, and ASCE. His industry advisory roles include Indonesia's National Construction Service Development Board (2002-2007) and Australia-Indonesia Centre's Infrastructure Cluster (2016-2018). Recent research emphasizes BIM applications in infrastructure preservation, risk assessment methodologies, and climate-resilient construction. His work bridges theory and practice, addressing challenges in emerging economies and complex project environments.

Cezary KaliszykView profile

Professor

• Automated Reasoning
• Formal Methods
• Formalized Mathematics
• +7 more

Cezary Kaliszyk is a Professor in Theoretical Computer Science at the University of Melbourne, previously affiliated with the University of Innsbruck. He is actively involved in research and leadership in formal methods, automated reasoning, and machine learning for theorem proving. Research Interests: Automated Reasoning and Interactive Theorem Proving Formalized Mathematics and Proof Automation Machine Learning for Logic and Theorem Proving Integration of AI with Proof Assistants (Coq, Isabelle) Dependent Type Theory and Higher-Order Logic His recent publications (2023–2025) span topics in dependently-typed logic, learning for proof guidance, formalization of surreal numbers, and blockchain-based formal methods. The works consistently bridge formal logic with machine learning, emphasizing automation, explainability, and cross-system integration. Scientific Leadership and Projects: Principal Investigator, ERC project FormalWeb3 Lead Developer, CoqHammer , Tactician , ProofWeb WG5 Leader, COST Action EuroProofNet (until 2024) Contributor to HOL(y)Hammer , Isabelle Enigma He supervises multiple PhD students and has mentored several graduates in formal methods and AI. He teaches courses in theoretical computer science, logic, and machine learning. There are no listed awards in the provided data, but his extensive publication record and project leadership indicate significant recognition in the field. Labs and Research Groups: He leads a research group focused on formal methods and learning-based reasoning, collaborating internationally on projects involving proof automation, formal libraries, and semantic technologies.

Austin ZeidermanView profile

Associate Professor

• Urban Studies
• Environmental Studies
• Security Studies
• +5 more

Austin Zeiderman is an Associate Professor in the Department of Geography and Environment at the London School of Economics (LSE), with interdisciplinary expertise in urbanization, environmental governance, and political dynamics in Latin America and the Caribbean. His research integrates anthropology, geography, sociology, and history, focusing on Colombia, particularly the Magdalena River and Bogotá’s urban periphery. Zeiderman holds a PhD in Anthropology from Stanford University, an MA in Environmental Science from Yale, and a BA in Economics from Colgate University. He has led projects like LSE’s Urban Uncertainty initiative and remains a Research Associate at LSE Cities. His work explores how security, risk, and environmental challenges shape marginalized communities, with notable monographs including *Endangered City* (2016) and *Artery* (2025). Research interests include racial ecologies, colonial capitalism, urban futures, and the interplay of race, environment, and security. He has been recognized with fellowships from the Fulbright Program, Wenner-Gren Foundation, and others. His recent scholarship critiques infrastructure projects like Colombia’s Magdalena River logistics corridor, linking environmental crises to systemic racial inequalities. Publications span journals like *Antipode* and *Public Culture*, and he engages public discourse through media like the Guardian and podcasts. His work bridges academic rigor with societal impact, addressing urgent global issues such as climate adaptation and post-conflict urban resilience.

• Cybersecurity
• Maritime Sector
• Human Error Probability
• +3 more

Gizem Kayişoğlu is an Assistant Professor in the Department of Maritime Transportation and Management Engineering at Istanbul Technical University. Her research focuses on cybersecurity challenges in maritime systems, human error probability, and risk assessment methodologies. She actively explores vulnerabilities in shipboard systems such as ECDIS, VDR, and radar while emphasizing regulatory compliance and maritime safety standards. Her work integrates frameworks like CORAS, Fuzzy FUCOM, and SLIM to address cybersecurity dynamics and human factors in maritime operations. Key collaborations involve analyzing ransomware threats, AIS system vulnerabilities, and port infrastructure protection through interdisciplinary approaches. Her articles highlight trends in maritime cybersecurity, including ransomware mitigation, cyber hygiene practices, and critical infrastructure protection. She has contributed to developing risk assessment tools and checklists tailored for maritime environments, emphasizing both technological and human aspects of safety. No scientific awards have been explicitly mentioned. She has not listed formal advisees, and her professional activities focus on advancing maritime cybersecurity through academic and applied research.

Thomas E. DoyleView profile

Associate Professor

• Biomedical Engineering
• Human-Computer Interfacing (HCI)
• Machine Learning
• +6 more

Dr. Thomas E. Doyle is an Associate Professor at the McMaster School of Biomedical Engineering and the Department of Electrical & Computer Engineering at McMaster University. His research focuses on biomedical signal processing, human-computer interfacing (HCI), and machine learning applications for healthcare augmentation, rehabilitation, and enhancement. He holds a Ph.D. from Western Ontario, Canada, and teaches courses like COMPENG 2DI4 (Logic Design). His work bridges cybernetics and clinical applications, emphasizing AI-driven solutions for medical diagnostics, patient monitoring, and space exploration. Education: B.E.Sc, B.Sc, M.E.Sc, Ph.D. from Western Ontario, Canada Recent Projects: Developed AI systems for remote healthcare diagnostics (2023) Collaborated with NASA on medical emergency simulators for deep space missions (2017–2023) Led ventilator development efforts for local hospitals during the pandemic (2020) His research interests span machine learning for mental health diagnostics, trust quantification in medical AI, and extended reality (XR) for medical training. He emphasizes interdisciplinary approaches, integrating computational methods with healthcare challenges. Recent publications highlight applications in pediatric emergency care, chronic pain management, and reliable medical device design. Dr. Doyle actively engages in educational initiatives, including first-year engineering pedagogy and experiential learning programs. He has received funding for projects such as the Educating the Engineer of 2025 (EtE-25) awards and contributes to initiatives like the Digital & Smart Systems and Health & Bio-innovation research clusters at McMaster.

Min ChiView profile

Professor

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

Dr. Min Chi is a Professor in the Department of Computer Science at North Carolina State University, where she joined in 2013 as a Chancellor's Faculty Excellence Program cluster hire in the Digital Transformation of Education. Her academic journey includes a Ph.D. and M.S. in Intelligent Systems from the University of Pittsburgh and a B.E. in Information Science and Technology from Xi'an Jiaotong University, China. She completed postdoctoral fellowships at Carnegie Mellon University's Machine Learning Department and Stanford University's Human Sciences and Technologies Advanced Research Institute. Dr. Chi's research focuses on the development and empirical evaluation of cutting-edge Artificial Intelligence, Deep Learning, and Reinforcement Learning frameworks tailored for addressing human-centric challenges. Her work spans multiple domains including advanced learning technologies, AI and intelligent agents, data sciences and analytics, and human-computer interaction. She has made significant contributions to intelligent tutoring systems, healthcare applications, nuclear power systems, and humanitarian efforts such as food distribution and disaster relief. Her publication record demonstrates a strong focus on applying AI techniques to real-world educational challenges, with recent work examining metacognitive knowledge transfer, reinforcement learning for pedagogical policy induction, and deep learning approaches for proactive help in educational settings. Her research also extends to healthcare applications, food distribution systems, and other socially impactful domains. 10 Best Paper, Best Student Paper, and Outstanding Paper Awards Prestigious Alcoa Foundation Engineering Research Achievement Award NSF CAREER Award Dr. Chi leads multiple significant research projects funded by the National Science Foundation, National Institutes of Health, and the Department of Energy, with a total funding exceeding $7 million. Her work bridges theoretical advances in AI with practical applications that address critical societal challenges in education, healthcare, and humanitarian operations.

Sanjay JainView profile

Professor

• Inductive Inference
• Recursion Theory
• Complexity Theory
• +3 more

Sanjay Jain is a Provost's Chair Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). His research focuses on theoretical computer science with particular emphasis on inductive inference, recursion theory, complexity theory, and computational learning theory. Education: B.Tech. in Computer Science from Indian Institute of Technology Kharagpur, India (1986) M.S. in Computer Science from University of Rochester, USA (1988) Ph.D. in Computer Science from University of Rochester, USA (1990) Professor Jain's research spans multiple areas of theoretical computer science. His primary contributions are in computational learning theory, where he has made significant advances in understanding the intrinsic complexity of language identification and the limits of inductive inference. His work on recursion theory explores fundamental questions about computability and complexity, while his research in complexity theory addresses structural aspects of computational problems. A notable achievement was his work on "Deciding Parity Games in Quasipolynomial Time," which won the prestigious STOC 2017 best paper award and later the EATCS-IPEC Nerode Prize. Professor Jain's publication record shows a consistent focus on theoretical foundations of computer science, particularly in learning theory and computational complexity. His recent work has expanded into automatic structures, semiautomatic models, and connections between computational learning and algebraic structures. There is a clear progression from foundational work on language identification to more complex models involving automatic functions, transducers, and connections to mathematical logic. Scientific Awards: STOC 2017 Best Paper Award for "Deciding Parity Games in Quasipolynomial Time" EATCS-IPEC Nerode Prize (2021) Professor Jain has served on the editorial board of Information and Computation and has been actively involved in the academic community through program committee memberships for major conferences including COLT, ALT, LATA, TAMC, and PRICAI. He has held leadership roles as program co-chair for ALT 2000 and ALT 2013, and conference chair for ALT 2005. His work has been supported by various research grants, though specific details are not provided in the available materials. Professor Jain leads research in theoretical computer science at NUS, where he has built a strong research group focused on computational learning theory and related areas. His work often involves collaborations with researchers from around the world, particularly with Frank Stephan, with whom he has co-authored numerous papers. His research group has made significant contributions to understanding the fundamental limits and possibilities of computational learning models.

• Computational Design
• Architectural Education
• Artificial Intelligence in Architecture
• +9 more

Assoc. Prof. Dr. Sema Alaçam Doğan has been affiliated with Istanbul Technical University since 2014, serving as an Associate Professor in the Department of Architecture . She has held administrative roles including Deputy Head of Department and Erasmus Coordinator. Education : PhD in Informatics in Architectural Design (2008-2014), MS in Informatics in Architectural Design (2005-2008), and BS in Architecture (1999-2005) from Istanbul Technical University. Her research explores Computational Design , Artificial Intelligence in Architecture , and Sustainable Material Innovation . She investigates digital tools for heritage preservation, daylight optimization in BIM, and cognitive development in architecture students. Recent publications analyze AI-assisted design literacy , machine learning for Sinan mosques , and environmental comfort in Harran houses . Her work integrates algorithmic frameworks with sustainable practices. Scientific awards include multiple ITU Publication and Performance Awards (2021-2024), FABFEST Prizes , and the 2024 Artemis Educator Award from NASA. Active projects like "Physical Computation in Architectural Drawing" and "Robotic Fabrication with Recycled Wind Turbine Blades" demonstrate her leadership in computational and sustainable research.

Minwoo JungView profile

Assistant Professor

• Queer Theory
• Gender Studies
• Sociology of Neoliberalism
• +3 more

Minwoo Jung is an Assistant Professor at Loyola University Chicago, jointly appointed in the Department of Sociology and Women's Studies and Gender Studies (WSGS). His research interrogates the intersections of gender, sexuality, neoliberalism, and globalization through decolonial and queer theoretical frameworks. Research interests include: Queer theory and its epistemological limits Decolonial approaches to globalization and rights Gender and sexuality in East and Southeast Asian contexts Neoliberal political economy and cultural critique Urban inequality in South Korea Transnational LGBTQ+ movements His recent scholarship examines contested cosmopolitanisms in queer activism, flexible masculinities in global Asia, and rights projects as relational sociological phenomena. Current work analyzes sovereignty imaginaries in Taiwan's marriage equality debates and neoliberal violence in global media like Squid Game . Scientific achievements include: Mary Griffin Essay Award Laura Bassi Scholarship Active in teaching and mentorship, Jung contributes to undergraduate and graduate programs in WSGS while maintaining a robust publication record.

Li TangView profile

Associate Professor

• Immunoengineering
• Cancer Immunotherapy
• Immune Metabolism
• +7 more

Li Tang is an Associate Professor with tenure at École polytechnique fédérale de Lausanne (EPFL), affiliated with the Institute of Bioengineering (IBI) and the Institute of Materials Science and Engineering (IMX) within the School of Engineering (STI). She leads the Laboratory of Biomaterials for Immunoengineering, focusing on developing innovative strategies at the intersection of immunology, materials science, and cancer therapy. Her work bridges fundamental research and clinical translation, with multiple ongoing clinical trials based on CAR-T cell therapies developed in her lab. B.S. in Chemistry, Peking University (2003–2007) Ph.D. in Materials Science and Engineering, University of Illinois at Urbana-Champaign (2007–2012) Postdoctoral Fellow, MIT (2013–2016) Her research lies at the forefront of immunoengineering, integrating chemical, metabolic, and mechanical approaches to modulate immune responses. Key areas include cancer immunotherapy, immune metabolism, mechano-immunology, and biomaterials. She investigates how physical and biochemical cues can reprogram T cells, overcome exhaustion, and enhance tumor targeting. Her work emphasizes multidimensional immunity-disease interactions, aiming to develop safer and more effective therapies for cancer and autoimmune diseases. The recent publications highlight a strong trend in engineering immune cells (especially CAR-T) for enhanced durability and function, using advanced biomaterials and metabolic reprogramming. There is a clear focus on overcoming challenges in solid tumors, modulating the tumor microenvironment, and translating findings into clinical applications. The use of nanoparticle delivery, single-cell analysis, and biomechanical cues are recurring themes across her work. Notable scientific awards include: Friedrich Miescher Award (2025) ERC Starting Grant (2018) MIT TR35 Innovators Under 35 (China Region, 2020) Nano Research Young Innovator Award (2018) Biomaterials Science Emerging Investigator (2019) Materials Horizons Emerging Investigator (2020) Li Tang actively mentors PhD students across multiple doctoral programs (EDBB, EDMS, EDMX) and has advised numerous graduates who have gone on to prestigious postdoctoral and faculty positions. She is involved in significant research grants, including an Innosuisse project with Novochizol SA, and her lab is supported by competitive funding. She teaches core courses such as Immunoengineering and Next-Generation Biomaterials, shaping the next generation of scientists. Her lab fosters interdisciplinary collaboration and innovation, with active projects in chemical, metabolic, and mechanical immunoengineering, as well as CAR-T cell development. She is the Principal Investigator of the Tang Lab, which includes postdoctoral fellows, PhD students, and technical staff. The lab is actively recruiting and has a strong publication and clinical translation record. Tang Lab is also involved in multiple MA/BA training projects and promotes student engagement in cutting-edge research. The lab’s discoveries are being translated into clinical trials, reflecting a strong commitment to translational science.

• Computational Logic
• Symbolic Machine Learning
• Neuro-symbolic AI
• +3 more

Professor Alessandra Russo leads the Structured and Probabilistic Knowledge Engineering (SPIKE) research group at Imperial College London's Department of Computing. With expertise spanning computational logic, symbolic machine learning, and neuro-symbolic AI, she develops foundational AI techniques applied to security, network management, healthcare, and adaptive systems. Professor Russo holds a PhD in Computing from Imperial College London and an MSc in Computer Science from Ionian University. Her research pioneers logic-based learning systems for intelligent adaptive technologies, with projects including declarative networking for security management, privacy-preserving federated learning, and hybrid neuro-symbolic approaches for robust reasoning. Her current work focuses on developing interpretable AI systems through neuro-symbolic integration, creating frameworks that combine neural networks with symbolic reasoning for explainable decision-making. Recent publications explore rule learning from knowledge graphs, transformer-based world models, and formal methods for representation learning. Professor Russo teaches courses on Logic-Based Learning and AI Applications, and has received the Google PhD Fellowship for her research contributions. She mentors numerous PhD students in areas spanning theoretical foundations and practical applications of computational logic and machine learning.

Cyrille ArthoView profile

Associate Professor

• Software Engineering
• Formal Methods
• Cybersecurity
• +5 more

Cyrille Artho is an Associate Professor in the Division of Theoretical Computer Science at KTH Royal Institute of Technology, actively contributing to research in formal methods, software testing, and cybersecurity. His work spans model checking, smart contract security, and concurrent systems verification, with significant contributions to tools like Java Pathfinder and Modbat. PhD from ETH Zurich (2005) with dissertation on multi-threading fault detection Teaches Software Safety and Security, Software Engineering Fundamentals, and supervises degree projects His research focuses on developing formal techniques for safety-critical systems, particularly in blockchain security and distributed applications. Recent work emphasizes smart contract verification, anomaly detection in microservices, and trusted execution environments for secure cloud analytics. The 15 most recent publications reveal a strong trend toward blockchain security (6 articles), formal verification of distributed systems (5), and novel testing methodologies (4), with increasing integration of machine learning for vulnerability detection. As chair of the FTSCS workshop series and contributor to major conferences like ASE and ICST, Artho has significantly shaped the formal methods community. His leadership in organizing workshops demonstrates commitment to advancing safety-critical systems research. Principal investigator for C3.ai DTI Cyber Safety Cage for Networks project Develops Modbat framework for model-based API testing Active in Digital Futures research initiative at KTH

Liangming PanView profile

Assistant Professor

• Natural Language Processing
• Large Language Models
• Machine Learning
• +3 more

Liangming Pan is an Assistant Professor at the University of Arizona's College of Information Science. His research focuses on building trustworthy large language models (LLMs) with an emphasis on logical reasoning, truthfulness, and safety. He holds a PhD in Computer Science from the National University of Singapore (2022), a Master's from Tsinghua University, and a Bachelor's from Beihang University. Education : PhD in Computer Science, National University of Singapore (2022) Master of Engineering in Computer Science, Tsinghua University (2017) Bachelor of Engineering in Computer Science, Beihang University (2014) Research Interests : Dr. Pan's work centers on enhancing LLMs' reliability through: Logical reasoning mechanisms to ensure faithful deductions Truthfulness verification to combat misinformation Safety protocols to mitigate societal harm Key Contributions : Developed TART, an open-source framework for explainable table-based reasoning Created benchmarks like SCITAB and FactCheck-Bench for evaluating LLMs Advanced techniques for knowledge editing and causal reasoning Awards : Best Paper Runner-Up at NeurIPS Table Representation Workshop (2024) Area Chair Award for Question Answering (IJCNLP-AACL 2023) Service & Outreach : He serves as an Area Chair for EMNLP (2024), COLING (2025), and ACL (2024). He has delivered invited talks at Tsinghua University, Peking University, and other institutions.

Jiang HuView profile

Professor

• VLSI Design Automation
• Hardware Security
• Machine Learning Applications
• +3 more

Jiang Hu is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, holding the Eric D. Rubin '06 Endowed Professorship. He also serves as Co-Director of Graduate Programs and is affiliated with the Computer Science & Engineering department. His research focuses on VLSI design automation, machine learning applications, and hardware security. He has held roles as editor for IEEE Transactions on CAD and ACM Transactions on Design Automation, and chaired the 2012 ACM International Symposium on Physical Design. Education: B.S. in Optical Engineering (Zhejiang University, 1990), M.S. in Physics (1997), and Ph.D. in Electrical Engineering (University of Minnesota, 2001). He worked at IBM Microelectronics before joining Texas A&M in 2002. Research interests include energy-efficient VLSI circuits, on-chip communication fabrics, analog layout automation, and AI-driven EDA. Recent work emphasizes machine learning for design closure, privacy-preserving frameworks, and systolic array-based architectures. Awards: IEEE Fellow (2016) Humboldt Research Fellowship (2012) Multiple best paper awards at DAC, ICCAD, and ASPDAC Advising and grants: Leads initiatives like the SLICE project, NSF workshops on ML-EDA infrastructure, and serves as Editor-in-Chief of ACM TODAES since 2024. His work bridges academic research and industry applications in EDA and semiconductor design. Labs/Teams: Active contributor to open-source tools like ALIGN for analog layout generation and collaborations on machine learning for EDA commons.