Find Academic Supervisors

• Graph Analytics
• Graph Mining
• Graph Algorithms
• +3 more

Dr. Lijun Chang is an Associate Professor in the School of Computer Science at the University of Sydney. He holds an ARC Future Fellowship (2019–2022) and an ARC DECRA Fellowship (2015–2017). Previously, he was at the University of New South Wales. His research focuses on graph analytics, mining, algorithms, and network science. He teaches courses like INFO5011 (Competitive Programming), COMP5313 (Large Scale Networks), and COMP9120 (Database Management Systems), and coaches the USYD Programming Competition Teams. Education: B.Eng. in Computer Science & Technology from Renmin University of China; PhD from the Chinese University of Hong Kong. Research highlights include scalable graph processing systems (e.g., ScaleG), densest subgraph detection, and graph similarity search. He leads projects funded by ARC grants such as 'Advanced Search of Cohesive Subgraphs in Big Graphs' (2018) and 'Directionality-Aware Cohesive Subgraph Search' (2022). His work emphasizes efficient algorithms for large-scale networks and graph databases. Awards : ARC Future Fellow, ARC DECRA Fellow Students : Yu KONG, Rashmika MATHTHAKA GAMAGE, Mouyi XU Labs/Teams : Focuses on graph algorithms and systems research, contributing to open-source tools and large-scale network analysis.

Misa FaezipourView profile

Associate Professor

• System Dynamics Modeling
• Healthcare Systems Engineering
• Complex Systems Modeling
• +3 more

Dr. Misa Faezipour is an Associate Professor in the Department of Engineering Technology at Middle Tennessee State University (MTSU) and director of the Systems Engineering Lab (SEL). She holds a Ph.D. and M.Sc. in Industrial Engineering and Engineering Management from the University of Texas at Arlington and a B.Sc. in Software Engineering from Islamic Azad University North Tehran Branch. Her research focuses on system dynamics modeling, healthcare systems engineering, complex systems, and sustainability optimization. Dr. Faezipour has advised numerous graduate students in M.S. and Ph.D. programs, focusing on topics such as healthcare disparities, AI robustness, and policy impact analysis. She teaches courses including Engineering Economy, Operations Management, and Engineering Management Systems. Her work spans collaborations with institutions like NASA, the Systems Engineering Research Center, and healthcare organizations. Her recent publications emphasize AI-driven healthcare solutions, system dynamics applications in policy analysis, and optimizing healthcare delivery through data-driven models. She actively contributes to professional organizations such as INCOSE, IEEE, and the System Dynamics Society. Dr. Faezipour’s lab, SEL, develops innovative tools for healthcare informatics, including diagnostic recommenders and policy simulation frameworks. Her research bridges systems engineering with healthcare challenges, addressing issues like racial health inequities and pandemic response strategies.

• Social Network Analysis
• Network Science
• Data Science
• +7 more

Giacomo Fiumara is an Associate Professor at the University of Messina, Department of Mathematical and Computer Sciences, Physical Sciences and Earth Sciences. He holds academic rank since October 2021. Previously, he served as a Permanent Researcher (2008–2021) and secondary school teacher (1997–2008). He earned a Doctorate in Physics (1993) and a Degree in Physics (1989), both from the University of Messina. He is an associate member of the Accademia Peloritana dei Pericolanti and qualified as an associate professor in INF/01 and ING-INF/05 sectors. His research focuses on social network analysis, network science, data science, criminal networks, knowledge representation, bioinformatics, and computational modeling. He has supervised over 170 theses and advised PhD students in Mathematics and Computational Sciences. Key collaborations include work with Prof. Pasquale De Meo on criminal networks and complex systems, and international projects with institutions in the US, UK, China, and Australia. Teaching includes courses on Algorithms, Data Structures, Bioinformatics, and Machine Learning across Computer Science, Engineering, and Medical programs since 2000. He also contributed to international programs at Lviv Polytechnic, Birzeit University, Cluj-Napoca, and Murcia. His editorial roles include Associate Editor of IEEE Access and Academic Editor of Complexity. He holds a patent for predictive analysis of criminal organizations' social structures and has received FFABR research funding. Key awards include FFABR funding (2017) and recognition in the FFABR Unime 2020 II edition. He organized conferences like Crimenet 2014 and participated in high-profile events such as the 2022 Complex Networks conference in Palermo, presenting on quantum walks for criminal network analysis.

Ton DiekerView profile

Associate Professor

• Stochastic Processes
• Operations Research
• Queueing Theory
• +3 more

Ton Dieker is an Associate Professor in the Department of Industrial Engineering and Operations Research at Columbia University's School of Engineering and Applied Science. He is a DSI Member and affiliated with the Center for Financial and Business Analytics. His research focuses on stochastic models, simulation techniques, and high-dimensional stochastic analysis. Dieker holds a Master’s in Operations Research from Vrije Universiteit Amsterdam (2002) and a PhD in Mathematics from the University of Amsterdam (2006). Dieker’s research explores stochastic processes, queueing theory, and rare-event simulation. He has contributed to methodologies like QPLEX for stochastic systems and advanced techniques in sequential analysis and exact simulation. His work bridges theoretical foundations with computational applications, addressing challenges in large-scale networks and high-dimensional problems. Education: PhD in Mathematics, University of Amsterdam, 2006 Master’s in Operations Research, Vrije Universiteit Amsterdam, 2002 Dieker’s articles emphasize computational modeling, stochastic calculus, and optimization, reflecting his focus on bridging theory and practice. His work often addresses efficiency in simulation, exactness in algorithms, and scalability in complex systems. Awards: Goldstine Fellowship (IBM Research) NSF CAREER Award Erlang Prize (INFORMS) Fouts Family Early Career Professorship (Georgia Tech) He serves on editorial boards for Operations Research and Mathematics of Operations Research . His research also addresses capacity management in stochastic networks and applications in cloud computing and commodity sourcing.

• Construction Safety and Risk Management
• Project Risk Assessment
• Railway and Highway Safety
• +3 more

Professor Min An is a Professor of Construction and Risk Management at the University of Salford, leading the Infrastructure Research Group within the School of Science, Engineering & Environment. He holds an honorary professorship at two overseas universities (China and Portugal) and serves on the editorial boards of 12 international journals. With over 40 years of experience, his career spans academic roles at Heriot-Watt University, Coventry University, and the University of Birmingham, alongside industry roles as a civil engineer and researcher. His research focuses on safety and risk management in construction, transportation systems, and energy sectors, with over 200 publications. Key areas include railway and highway safety, offshore oil & gas risk assessment, and nuclear reliability management. He has secured funding from EPSRC, EU, DfT, and industry partners, leading 20+ projects. Notable achievements include developing methodologies for infrastructure safety and maintaining collaborations with 30+ industrial partners. Professor An has supervised 30 PhD students and over 280 postgraduate projects, contributing to industry workshops and best practices. Awards include multiple science technology prizes and conference best paper/keynote recognitions. His teaching spans risk management, construction safety, and project management across MSc programs.

Giovanni De MicheliView profile

Professor

• Computer Architecture
• Electrical Engineering
• Integrated Systems
• +5 more

Giovanni De Micheli is a Professor of Electrical Engineering and Computer Science at EPF Lausanne, Switzerland. He also serves as Director of the Integrated Systems Centre and the Institute of Electrical Engineering at EPFL, and chairs the Scientific Committee of CSEM in Neuchatel. Previously, he held academic roles at Stanford University for 18 years, including Full Professor, Associate Professor, and Assistant Professor in the Department of Electrical Engineering. His research spans synthesis of digital circuits, hardware/software co-design, low-power design, and Networks on Chip (NoC) technology. 2003: IEEE Emanuel Piore Award 2000: Golden Jubilee Medal of the IEEE CAS Society 2000: ACM Fellow 1994: IEEE Fellow 1990: IEEE/CS Distinguished Service Award 1988: NSF Presidential Young Investigator Award His seminal contributions include pioneering C-based synthesis and Boolean matching algorithms for digital circuits, foundational work in dynamic power management using stochastic control, and the development of Network-on-Chip (NoC) technology. His publications, such as "Networks on Chips: A New SoC Paradigm" and "Dynamic Power Management for Portable Systems" , have shaped modern SoC design practices. With over 400 technical articles, 9 books, and an H-index of 56, his work remains highly influential.

• Social Media Analysis
• Sentiment Analysis
• Computational Journalism
• +6 more

Fabrício Benevenuto is an Associate Professor in the Computer Science Department at Federal University of Minas Gerais (UFMG), where he conducts interdisciplinary research at the intersection of social media analysis, data science, and computational journalism. His work spans complex networks, machine learning, and natural language processing with strong societal impact. His research focuses on social media dynamics, particularly in Brazilian contexts, with major contributions to hate speech detection, fake news analysis, and political discourse monitoring. He leads large-scale projects against misinformation, including development of systems like WhatsApp Monitor, Media Bias Monitor, and Purple Feed. His work combines technical innovation with real-world applications for election transparency and public discourse integrity. Benevenuto's recent publications demonstrate strong trends in multilingual NLP for social media analysis, with emphasis on Brazilian Portuguese contexts. His team produces both theoretical contributions and practical systems addressing hate speech, misinformation, and media bias. Notable methodological approaches include combining network analysis with linguistic features, developing culturally-aware detection systems, and creating large annotated datasets for understudied languages. CAPES award for best Brazilian computer science thesis (2010) Humboldt Foundation scholarship recipient (2017-2018) Member of TikTok Safety Advisory Council WWW'20 Best Paper Nominee & CNIL-INRIA Privacy Protection Prize winner Multiple best paper awards at CEAS, WBC, and ICWSM conferences Test-of-Time Award at ICWSM'20 Benevenuto actively mentors PhD and MSc students, with numerous advisees securing academic positions at Brazilian universities and research roles at institutions like Max Planck Institute. His projects often receive funding supporting interdisciplinary collaborations across computer science and social sciences. Current work includes large-scale analysis of Telegram political groups, real-time election monitoring systems, and developing culturally-aware NLP tools for Portuguese. He leads research teams working on social media analysis systems with societal impact, particularly focused on Brazilian digital ecosystems. Projects involve cross-institutional collaborations with researchers from MPI-SWS, Max Planck Institute, and various Brazilian universities, emphasizing practical applications for public discourse integrity.

Valentina BoevaView profile

Assistant Professor

• Machine Learning
• Bioinformatics
• Genomics
• +3 more

Prof. Valentina Boeva is an Assistant Professor at the Department of Computer Science, ETH Zürich, specializing in biomedical informatics. Her research focuses on integrating machine learning and computational methods to address challenges in genomics, oncology, and precision medicine. She holds a position in the Professur für Biomedizininformatik (Biomedical Informatics) and is based at CAB G32.2, Universitätstrasse 6, Zürich, Switzerland. Her work emphasizes applications such as cancer biomarker discovery, tumor heterogeneity analysis, and epigenetic profiling. She teaches courses including Machine Learning Seminar, Data Science Lab, and Machine Learning for Genomics. Her research group develops computational tools like CDState and UniversalEPI to decode complex biological systems. She actively publishes in top-tier journals, with recent work on exosome-driven diagnostics and chromatin interaction modeling. Her scientific contributions span methodologies for single-cell data analysis, survival modeling, and drug response prediction. She collaborates across disciplines to bridge computational science with clinical applications in cancer research.

• AI Safety
• Multi-Agent Systems
• Formal Verification
• +5 more

Dr. Muhammad Najib is an Assistant Professor (Lecturer in the UK system) at Heriot-Watt University's School of Mathematical and Computer Sciences in Edinburgh. He is also an Associate Member of the University of Oxford's Department of Computer Science. His research focuses on ensuring AI safety through formal verification methods, particularly in multi-agent systems. Najib holds a DPhil/PhD from the University of Oxford, supervised by Julian Gutierrez and Mike Wooldridge, and has industry experience at Samsung Electronics. Education: BSc from Sepuluh Nopember Institute of Technology, MSc from the University of Liverpool, DPhil/PhD from the University of Oxford. He previously worked as a postdoctoral researcher at TU Kaiserslautern under Anthony Lin. Research Interests: Logic and game theory in AI foundations, equilibrium verification in multi-agent systems, temporal logics, and formal verification techniques. He developed the EVE tool for rational verification. Najib actively supervises PhD students and collaborates on projects like the UKRI AI CDT-D2AIR. Key Contributions: Published 17+ research outputs since 2018. Areas include equilibrium design, probabilistic multi-agent systems, and computational complexity analysis. His work bridges formal methods with AI safety, emphasizing automated synthesis and model checking.

Wei PangView profile

Professor

• Machine Learning
• Artificial Intelligence
• Data Mining
• +7 more

Wei Pang is a Professor of Computer Science and Bicentennial Research Leader at the School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh. He leads the BCML Lab and is affiliated with the Edinburgh Centre for Robotics and National Robotarium. His expertise spans bio-inspired computing, machine learning, and AI applications in healthcare, robotics, and sustainability. Pang holds a PhD in Computing Science from the University of Aberdeen, with prior roles including Senior Lecturer at the University of Aberdeen and research fellowships in systems biology. Affiliations: Heriot-Watt University, Edinburgh Centre for Robotics, National Robotarium Education: PhD in Computing Science (2009), MEng (by research), BSc (Jilin University, China) Research Interests: Bio-inspired computing (e.g., artificial immune systems, swarm intelligence), machine learning (deep learning, explainable AI), healthcare applications (medical imaging, disease detection), and interdisciplinary projects in robotics and environmental science. His work addresses challenges in robust AI, fairness, and accountable machine learning. Recent Projects: EPSRC-funded RAIns and MI projects, CRUK-funded Endo.AI, and PRIME project on minority ethnic communities' digital experiences. His research has secured over £10M in grants, including £3.5M institutional funding. Awards: Scottish Crucible Award (2015), ADMA Best Paper Runner-Up (2016), EPSRC PRIME Award (2024) Grants/Advising: Supervised 12 PhD completions; contributed to £10M+ external funding. Labs/Teams: BCML Lab (focusing on bio-inspired AI), collaborations with Oxford, Cambridge, and industrial partners like Weather2 and Data2Text.

Olga VitekView profile

Professor

• Statistical Science
• Machine Learning
• Mass Spectrometry
• +5 more

Olga Vitek is a Professor at Northeastern University's Khoury College of Computer Sciences, with affiliated faculty status in the Department of Chemistry and Chemical Biology. Her research bridges statistical science and machine learning with mass spectrometry-based proteomics and systems biology, focusing on developing open-source software tools like MSstats and Cardinal for quantitative proteomic analyses and imaging. Education: PhD in Statistics (Purdue University), Postdoc at the Ruedi Aebersold Lab (Institute for Systems Biology) Leadership: Director of the Barnett Institute for Chemical and Biological Analysis Her work emphasizes: Statistical experimental design Signal detection in complex mass spectrometry data Causal inference in biomolecular networks Reproducible computational infrastructure Recent publications highlight advancements in quantitative proteomics , mass spectrometry imaging , and causal modeling , with applications spanning cancer research, immunology, and clinical diagnostics. Notable trends include deep learning integration for image analysis and open-source tool development for scalable, transparent workflows. Scientific accolades: Elected Fellow of the American Statistical Association 2021 Gilbert S. Omenn Computational Proteomics Award NSF CAREER award Chan-Zuckerberg Essential Open-source Software award Senior Member, International Society for Computational Biology

Randall P. EllisView profile

Professor

• Health Economics
• Industrial Organization
• Econometrics
• +3 more

Randall P. Ellis is a Professor of Economics at Boston University, specializing in Health Economics, Industrial Organization, and Econometrics. His research focuses on healthcare payment systems, insurance design, predictive modeling, and international health economics. He holds a PhD from MIT and has contributed extensively to understanding managed care systems, risk adjustment frameworks, and healthcare policy evaluation. Education: PhD in Economics from Massachusetts Institute of Technology. Research Interests: Ellis’s work spans health care payment reforms, risk selection mechanisms, and the application of machine learning to health economics. He examines issues such as Medicaid managed care, obesity treatment economics, and the impact of anti-corruption programs on healthcare systems. His research emphasizes improving equity and efficiency in healthcare markets, particularly through innovative payment systems and risk adjustment models. Key Contributions: Ellis has developed frameworks for disease surveillance and risk adjustment (e.g., Diagnostic Items Classification System), evaluated payment system performance, and analyzed factors influencing healthcare utilization and costs. His work bridges theoretical economic models with practical policy applications, informing both academic and policy audiences. Grants & Collaborations: While specific grants are not detailed here, his research frequently involves collaborations with institutions like Harvard and MIT through the Health Economics Seminar. His work often addresses global health challenges, including pandemic financing and healthcare corruption in developing regions. Labs/Teams: Ellis is affiliated with Boston University’s economics department and collaborates with interdisciplinary teams on projects related to health policy and econometric modeling.

John StraubView profile

Professor

• Theoretical Chemistry
• Computational Biophysics
• Protein Dynamics
• +3 more

John Straub is a Professor of Chemistry at Boston University, affiliated with the Chemistry Department. His research focuses on theoretical and computational studies of protein dynamics, thermodynamics, and phase transitions in molecular systems. He leads efforts to develop advanced algorithms for simulating phase changes in complex systems, including work supported by a National Science Foundation (NSF) grant (CH-1114676) to improve computational methods for phase transition modeling. His group has pioneered generalized simulated tempering and replica exchange algorithms, enabling more accurate simulations of phenomena like vapor-liquid phase changes and peptide aggregation. Dr. Straub also engages in science outreach through collaborations with the Pinhead Institute, supporting K-12 education programs and student internships. His research spans diverse topics such as cholesterol interactions in lipid membranes, amyloid fibril formation mechanisms, and the structural basis of protein aggregation in neurodegenerative diseases. His computational methods have been applied to study membrane proteins, lipid rafts, and the role of environmental factors in protein behavior. Key contributions include modeling amyloid-β aggregation pathways and investigating the impact of membrane composition on protein stability.

Vahid YazdanpanahView profile

Assistant Professor

• Multiagent Systems
• Artificial Intelligence
• Responsibility of AI Systems
• +3 more

Vahid Yazdanpanah is an Assistant Professor (Lecturer) of Computer Science at the University of Southampton and a Visiting Lecturer at the University of Twente. He holds a PhD from the University of Twente (2019), an MSc in Artificial Intelligence from Utrecht University (2015), and an MBA from the University of Greenwich (2012). His research focuses on multiagent systems, AI responsibility, and circular economy applications. He leads the Agents, Interaction and Complexity (AIC) research group and is an RRI Champion for the UKRI CDT in AI for Sustainability (SustAI). Research interests include agent-based computing, formal logics for multiagent decision-making, and socio-technical systems. Notable awards include the Best Paper Award at PRIMA 2016 and recognition at AAMAS 2021. He teaches courses such as COMP2211 (Software Engineering) and supervises PhD students in AI and sustainability. Active projects include the ARGOS (AI Resilience Governance) and AutoTrust (Internet of Vehicles) initiatives. He has served on program committees for AAAI, IJCAI, and AAMAS, and reviews for journals like AI & Society and Annals of Operations Research. His work bridges technical AI advancements with ethical, legal, and societal challenges in responsible AI deployment.

Ghassan HamarnehView profile

Professor

• Medical Image Analysis
• Scientific computing
• Signal and image processing
• +5 more

Ghassan Hamarneh is a Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on medical image analysis, with expertise in super-resolution microscopy, explainable AI, and biomedical computing. He teaches courses in biomedical computing and scientific computing, emphasizing practical applications like signal processing and health informatics. Education: Ph.D. in Signal and Systems (Chalmers University, 2001), M.Sc. in Digital Communications (Chalmers, 1997), B.Sc. in Electrical Engineering (Jordan University, 1995). Research Interests include developing AI-driven tools for medical imaging, analyzing cellular structures using super-resolution techniques, and addressing ethical challenges in AI deployment. His work bridges computational methods with clinical applications, such as lesion segmentation, PET image analysis, and bias mitigation in medical algorithms. Recent publications highlight advancements in network analysis of molecular structures, debiasing AI models, and improving diagnostic accuracy through deep learning. His lab contributes to open-source software like SuperResNET and MCS-DETECT for super-resolution microscopy analysis. No scientific awards explicitly listed, but his extensive publication record reflects recognition in the field. Advising and grants information is not detailed in the provided texts. Active in teaching, including CMPT 340 (Biomedical Computing) and special research projects.