Find Academic Supervisors

Vasileios MavroeidisView profile

Associate Professor

• Cybersecurity
• Security Automation and Orchestration (SOAR)
• Cyber Threat Intelligence (CTI)
• +6 more

Vasileios Mavroeidis is an Associate Professor in Digital Security at the Department of Informatics, University of Oslo (UiO). He specializes in security automation and orchestration (SOAR) and cyber threat intelligence (CTI) representation, reasoning, and sharing. He actively contributes to European cybersecurity initiatives, including Horizon Europe, Connecting Europe Facility, and the European Defense Fund, and serves as the primary representative of UiO at the OASIS standards development organization since 2017. Role : Associate Professor Department : Digital Security (SEC), University of Oslo Standardization Involvement : Chairman of OASIS Threat Actor Context (TAC), Leading Contributor to CACAO and OpenC2 Projects : Concordia, CyberHunt, JCOP (Joint Cyber Security Operations Platform), Oslo Analytics, P4C (Partnership for Cybersecurity) His research focuses on cyber threat intelligence (CTI), exploring its taxonomies, sharing standards (STIX, CACAO), and ontologies, with contributions to the European Union Agency for Cybersecurity (ENISA) Cybersecurity Playbooks task force. He analyzes quantum computing's impact on cryptography, develops automated threat detection systems using machine learning (e.g., recurrent neural networks for malware-generated domains), and investigates privacy issues under GDPR. Recent publications highlight his work on LLMs for code stylometry , neurosymbolic AI for cyber defense , and knowledge management systems for CACAO playbooks . His articles span 2017–2025, emphasizing formal verification, biometric data protection, and incident response automation. He collaborates with organizations like OASIS (Threat Actor Context, CACAO, OpenC2) and FIRST (Traffic Light Protocol), and participates in European research projects. His work includes standardization efforts in cybersecurity playbooks , MITRE ATT&CK representation, and quantum-resistant cryptography .

• Self-organized criticality
• Neuronal network dynamics
• Excitation/Inhibition balance
• +5 more

Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .

Mathias FunkView profile

Associate Professor

• Design Theory
• Systems Design
• Data-Driven Design
• +5 more

Mathias Funk is an Associate Professor in the Industrial Design department at Eindhoven University of Technology (TU/e), leading the Things Ecology lab. His research focuses on designing with data and systems behavior, particularly in IoT and medical technology contexts. He co-founded UXsuite GmbH and developed the OOCSI communication framework. With a PhD in Electrical Engineering from TU/e (2011), Funk has held academic roles since 2013, including postdoctoral positions in Electrical Engineering and Industrial Design. He teaches courses like 'Data-enabled design' and supervises projects on AI, data foundry, and OOCSI applications. His work bridges engineering and design, addressing sustainability and human values through systemic design approaches. Education: PhD in Electrical Engineering, TU/e (2011) Computer Science background from RWTH Aachen Postdoc at TU/e's Electrical Engineering and Industrial Design departments Research Interests: Data-driven design methodologies, IoT systems, medical technology innovation, sustainable design practices, and interactive systems for musical expression. Recent work emphasizes telemonitoring efficacy in heart failure care and federated learning applications. Awards: Best Paper Award (2014) Honorable Mention (2021) Grants/Projects: Leading initiatives like MEDICAID (medtech solutions), ACACIA (interpretable AI), and Data-Driven Servitizing in Professional Printing. Active in clinical workflow prototyping and cardiovascular disease detection projects. Lab Activities: Manages the Things Ecology lab, focusing on systemic design challenges in smart ecosystems. Collaborates internationally through spin-offs and visiting lectureships.

Ghaith HiaryView profile

Professor

• Computational Number Theory
• Analytic Number Theory
• Random Matrix Theory
• +3 more

Ghaith Hiary is a Professor and Vice Chair for Graduate Recruitment in the Department of Mathematics at The Ohio State University. He holds a PhD from the University of Minnesota (2008). His research focuses on computational and analytic number theory, with emphasis on the Riemann zeta function, L-functions, random matrix theory, and asymptotic analysis. Hiary's work bridges theoretical mathematics and high-performance computation. He develops efficient algorithms (e.g., amortized-complexity methods) to evaluate zeta functions at extreme heights (e.g., near t=10 28 ), implemented in C++/Mathematica. His research uses random matrix models to study zeta-function moments and employs techniques like van der Corput estimates and hybrid methods for explicit bounds. His publications demonstrate consistent focus on zeta-function computations, factorization algorithms, and arithmetic biases. Recent work (2022-2025) explores invariant measures, Lehman's method generalizations, and sign changes in random multiplicative functions, maintaining strong ties to analytic and probabilistic number theory. Hiary shares code and datasets via GitHub, including implementations of T 1/3 algorithms for zeta computations. He collaborates with institutions like the University of Waterloo and maintains numerical databases of zeta zeros.

Aaron SmithView profile

Associate Professor

• Applied Probability
• Computational Statistics
• Monte Carlo Methods
• +3 more

Aaron Smith is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa, affiliated with the Faculty of Science. He holds a PhD from Stanford University. His research focuses on applied probability, computational statistics, Monte Carlo methods, and Markov chains, with an emphasis on advancing theoretical understanding and practical applications of these methodologies. Dr. Smith's work includes contributions to community detection algorithms, Markov chain mixing times, and synthetic health data generation. His recent publications explore topics such as nonstandard Dirichlet form representations, perturbation analysis of MCMC algorithms, and sparse Bayesian multidimensional scaling. He advises students in applied probability and has supervised postdoctoral researchers in related fields. His research interests span a wide range of topics, including stochastic processes, statistical inference, and algorithm design. He is particularly known for his analysis of convergence rates in Markov chains and the development of efficient sampling techniques for complex models. His interdisciplinary work bridges theoretical mathematics and practical computational challenges in data science and healthcare. Dr. Smith collaborates on projects involving synthetic data frameworks for privacy-preserving applications and has contributed to foundational work on mixing times and perturbation effects in stochastic systems.

David SchusterView profile

Associate Professor

• Condensed Matter Physics
• Quantum Computing
• Superconducting Circuits
• +3 more

David Schuster is an Associate Professor of Physics at the University of Chicago. His primary research focuses on experimental condensed matter physics, with a particular emphasis on circuit quantum electrodynamics (cQED), superconducting qubits, and quantum information science. He leads the Schuster Lab, which explores quantum systems, hybrid quantum technologies, and topological materials. Education: Ph.D. in Physics from Yale University (2007), advised by Robert Schoelkopf. His doctoral work pioneered advancements in circuit QED, demonstrating strong coupling between superconducting qubits and microwave resonators. Research Interests: The lab investigates superconducting quantum circuits, topological photonics, quantum sensors for dark matter, and scalable quantum computing architectures. Projects include developing fluxonium qubits, autonomous error correction, and hybrid systems involving trapped electrons on helium. Key Contributions: Published in Nature , Science , and Physical Review Letters on topics like topological circuits, photon blockade, and dark matter detection using superconducting cavities. Collaborates with groups at Stanford, Purdue, and other institutions on quantum technologies. Students and Collaborators: Advises numerous graduate and undergraduate students, including prominent alumni who have transitioned to postdocs and industry roles. Lab members present at major conferences like the APS March Meeting. Labs: Schuster Lab at the University of Chicago, with access to state-of-the-art facilities like the Pritzker NanoFabrication Facility. Collaborates with the Awschalom, Cleland, and Houck groups on hybrid quantum systems and materials science.

Diana AllenView profile

Professor

• Hydrogeology
• Groundwater resource evaluation
• Hydrogeological modeling
• +12 more

Diana Allen is a Professor in the Department of Earth Sciences at Simon Fraser University, where she leads the Groundwater Resources Research Group (GRRG). Her research focuses on hydrogeology with particular emphasis on groundwater resource evaluation and hydrogeological modeling, with current research on climate change impacts on groundwater systems, groundwater resources in mountainous and coastal regions, and low temperature geothermal systems. Education: B.Sc. Honours, Carleton University, 1986 M.Sc., Carleton University, 1988 Ph.D., Carleton University, 1996 Dr. Allen's research spans several key areas of hydrogeology and water resources. Her work on climate change impacts examines how extreme climate events affect groundwater systems, particularly in mountainous regions. She has developed methodologies for reconstructing historical groundwater levels using tree ring widths to understand long-term drought patterns. Her research on coastal aquifers assesses vulnerability to salinization, while her work in Northeast British Columbia evaluates water security risks associated with shale gas development. She also investigates aquifer-stream connectivity and the impacts of pumping on streamflow. Dr. Allen's publication record shows a strong focus on applied hydrogeology with significant contributions to understanding groundwater responses to climate extremes, drought assessment methodologies, and coastal aquifer vulnerability. Her research combines field studies with numerical modeling across diverse environments from mountainous regions to coastal deltas. Dr. Allen has successfully secured funding from major agencies including NSERC, the Canadian Mountain Network, the Pacific Institute for Climate Solutions, and various BC government ministries. Her research has practical applications for water resource management under climate change. As a dedicated educator and mentor, Dr. Allen supervises numerous graduate students working on cutting-edge hydrogeological research projects across British Columbia and beyond. Her students pursue careers in hydrogeology and environmental geoscience, applying GIS methods and computational hydrogeology to analyze field data.

Brendan T. O'ConnorView profile

Associate Professor

• Natural Language Processing
• Computational Social Science
• Statistical Text Analysis
• +2 more

Brendan T. O'Connor is an Associate Professor at the College of Information and Computer Sciences, University of Massachusetts Amherst, where he directs the SLANG Lab and serves as Associate Director of the Computational Social Science Institute. His research bridges statistical machine learning and natural language processing with social science applications, particularly using text data from news and social media to understand societal patterns. His work focuses on developing text analysis methods to answer social science questions in domains like political science and sociolinguistics. Current collaborative projects include combating misinformation, analyzing bias in news coverage, and developing tools for clinical discourse assessment using large language models. O'Connor's publications demonstrate a consistent focus on computational social science, with recent work exploring multilingual analysis, legal discourse patterns, and sociolinguistic variation. His methodological contributions span coreference resolution, event extraction, and argument mining. At UMass, he contributes to multiple research centers including the Computational Social Science Institute, UMass NLP group, and Centers for Data Science and Intelligent Information Retrieval. He teaches graduate seminars in natural language processing and maintains active collaborations across disciplines.

• Respiratory Drug Delivery
• Pulmonary Drug Delivery
• Inhalation Aerosols
• +5 more

Professor Hak-Kim Chan of the Sydney Pharmacy School at the University of Sydney is a world-renowned expert in respiratory drug delivery, particularly pulmonary aerosols and inhalation therapies. With over 480 publications and 17,580+ Google Scholar citations, he has pioneered advancements in powder formulation , in silico modeling , and clinical applications of inhalation technologies. His work includes the development of FDA-approved diagnostics like Aridol™ (inhaled mannitol for asthma) and Bronchitol™ (for cystic fibrosis), and groundbreaking research on inhaled bacteriophage therapy to combat antibiotic-resistant respiratory infections. Education: BPharm (University Medal, 1983), PhD (1988), DSc (2009) from University of Sydney Professional Experience: Postdoc at University of Minnesota (1988–89), Scientist at Genentech Inc. (1992–95) Leadership: Executive Editor of Advanced Drug Delivery Reviews , Fellow of AAPS and RACI His research spans in vitro production methods, computational modeling of inhaler design, and in vivo imaging of aerosol deposition. Current projects focus on nanomedicine , phage therapy , and combating superbugs via inhalation routes. He has secured significant recognition for his work, including NHMRC case studies highlighting public health impacts. Professor Chan has supervised numerous researchers, including PhD student Grace YAU studying pulmonary probiotic delivery . His team's 10 patents (7 as first inventor) reflect practical innovations in dry powder inhalers , antimicrobial formulations , and drug stabilization technologies.

Bimlesh WadhwaView profile

Associate Professor

• Software Engineering
• Human-Computer Interaction (HCI)
• Computing Education
• +4 more

Dr. Bimlesh Wadhwa is an Associate Professor in the Department of Computer Science at the National University of Singapore (NUS) and serves as Assistant Dean for Student Life at Tembusu College. She holds dual affiliations as a Residential Fellow at Tembusu College and an active member of the School of Computing's academic community. Her work bridges software engineering, human-computer interaction (HCI), and computing education. Dr. Wadhwa's research focuses on gender issues in HCI, software design quality, and digital well-being. She has pioneered initiatives like the Code for Community program to bring computing education to underserved groups. Her pedagogical contributions include developing courses on object-oriented design, software engineering principles, and interaction design. She actively engages with professional communities through roles such as Faculty Representative at the Singapore Computer Society Student Chapter and advisor to the Computing Club at NUS. Her recent work addresses pressing societal challenges like technology-facilitated sexual violence and integrates AI into educational contexts. She has organized major competitions including CodeXtreme and the Singapore Science and Engineering Fair, fostering innovation among students. Her interdisciplinary collaborations span healthcare applications, smart city technologies, and educational equity initiatives. Dr. Wadhwa’s scholarly contributions include over 80 publications in top venues such as FSE, CHI, and IEEE conferences. She has co-edited book chapters for Springer and Wiley on HCI and informatics, reflecting her commitment to advancing both technical and societal dimensions of computing. In addition to her academic roles, she leads MakerLab initiatives and contributes to national educational policies on computational thinking. Her work emphasizes bridging academia-industry gaps through projects like the GEAR mobile game-assisted rehabilitation system and cloud service brokerage frameworks.

Abigail JacobsView profile

Assistant Professor

• Artificial Intelligence
• Computational Social Science
• Data Science
• +6 more

Abigail Jacobs is an Assistant Professor at the University of Michigan , jointly appointed in the School of Information and the College of Literature, Science, and the Arts . She is also affiliated with the Center for Ethics, Society, and Computing (ESC) and the Michigan Institute for Data Science (MIDAS) . Education: PhD in Computer Science, University of Colorado Boulder (2015-2019) BA in Mathematical Methods in the Social Sciences and Mathematics, Northwestern University (2011-2015) Research Interests: Dr. Jacobs examines measurement and validity in machine learning , focusing on hidden assumptions in AI systems, governance structures in sociotechnical systems, and inequality in algorithmic design. Her work bridges AI, data science, and social science methodologies, emphasizing interdisciplinary collaboration. Recent Publications (2024-2025) explore generative AI evaluation, algorithmic transparency in government (e.g., US Census Bureau), motion capture data ethics, and sociotechnical frameworks for AI governance. Earlier works (2023) address fairness in ranking systems and racial categorization in algorithmic bias studies. Scientific Awards: Microsoft Research AI & Society Fellowship (2024) NSF Graduate Research Fellowship (during PhD) Advising & Grants: She co-advises Ph.D. students Amina Abdu and Meera Desai . Her research includes a Notre Dame-IBM Tech Ethics Lab grant on AI audits and collaborations with institutions like UC Berkeley, Microsoft Research, and the National Academies .

Debdeep PatiView profile

Professor

• Machine Learning
• Bayesian Statistics
• High Dimensional Data Analysis
• +3 more

Debdeep Pati is a Professor in the Department of Statistics at the University of Wisconsin-Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on Bayesian methods, high-dimensional data analysis, machine learning, and computational statistics, with applications in health data and network analysis. He has contributed to approximate Bayesian computation, graphical models, and fair algorithms. Key research interests include Bayes theory in high dimensions, hierarchical modeling, efficient Bayesian computation, and real-time tracking algorithms. His work bridges theoretical advancements with practical applications in areas like electronic health records and nuclear physics constraints. Recent work emphasizes Wasserstein-guided nonparametric Bayes, fair clustering algorithms, and variational inference in singular models. He has developed software for covariate-dependent Gaussian graphical modeling, published in ACM Transactions on Mathematical Software . Grants: NSF proposal on Wasserstein-guided nonparametric Bayes, NIH R01/R21 grants on periodontal disease and diabetes comorbidity. Advising: No named advisees listed but actively supervising research in Bayesian computation and high-dimensional statistics. Awards: 2024 JASA reproducibility award for 'Covariate-Assisted Bayesian Graph Learning.' He is an Associate Editor for Journal of Computational and Graphical Statistics and has organized workshops at Banff International Research Station (BIRS) and the Institute for Mathematics and its Applications (IMSI).

Battista BiggioView profile

Professor

• Machine Learning Security
• Adversarial Machine Learning
• Cybersecurity
• +4 more

Battista Biggio is a Full Professor at the University of Cagliari, Italy, affiliated with the Department of Electrical and Electronic Engineering under the Faculty of Engineering and Architecture. His research focuses on machine learning security, adversarial attacks, and cybersecurity. He co-founded the cybersecurity firm Pluribus One and has pioneered foundational work in poisoning attacks and adversarial robustness. Education: MSc (2006), PhD (2010). He holds editorial roles as Associate Editor-in-Chief for Elsevier's Pattern Recognition Journal and serves on IEEE TNNLS and IEEE CIM editorial boards. His awards include the 2022 ICML Test of Time Award and the 2021 Pattern Recognition Medal. He chairs IAPR TC1 and organizes conferences like S+SSPR and AISec. Research interests span adversarial machine learning, malware detection, and secure AI systems. He leads initiatives such as the sAIfer Lab and co-develops the SecML-Torch library. Teaching includes courses on Machine Learning Security and Industrial Software Development. Notable contributions include seminal papers like 'Poisoning Attacks against Support Vector Machines' and 'Wild Patterns.' He manages over 10 research projects and advises on AI security for industrial applications. His work bridges academic research with practical cybersecurity solutions.

Ana DamjanovicView profile

Research Professor

• Ion Channels
• Protein Electrostatics
• Membrane Electrostatics
• +3 more

Ana Damjanovic is an Assistant Research Professor in the Thomas C. Jenkins Department of Biophysics at Johns Hopkins University (JHU), affiliated with the Zanvyl Krieger School of Arts & Sciences. Her research focuses on ion channels, protein and membrane electrostatics, and computational biophysics. She holds a Ph.D. in Physics from the University of Illinois at Urbana-Champaign, where she studied quantum physics of photosynthetic light harvesting under Prof. Klaus Schulten. Subsequent postdoctoral research included work on photosynthesis with Prof. Graham Fleming at UC Berkeley, and molecular dynamics studies of protein ionization at JHU. Her current lab investigates ion channel mechanisms, protonation dynamics, and electrostatic effects in biological systems using advanced computational tools. Group members include graduate student Nauman Sultan (co-supervised with NIH's Bernard Brooks) and undergraduates Marianne Ri and Vivek Booshan. Past advisees include Ada Chen (now a NIH postdoc) and Maggie Li. Key research contributions include developing pH replica exchange methods, protein pKa prediction using machine learning, and structural-functional studies of voltage-gated sodium channels. Her work has been published in high-impact journals like Proceedings of the National Academy of Sciences and Biophysical Journal . Lab affiliations include the Computational Biophysics Group at JHU, with access to cutting-edge simulation techniques and experimental validation platforms. Ongoing projects explore ion channel selectivity, membrane protein dynamics, and computational modeling of protonation-dependent phenomena.

Amin HammadView profile

Professor

• BIM and Digital Twins
• Construction Automation & Robotics
• Sustainable Infrastructure Management
• +4 more

Amin Hammad is a Professor at the Concordia Institute for Information Systems Engineering, with an additional appointment as Affiliate Professor in Building, Civil, and Environmental Engineering at Concordia University. His research focuses on advancing construction technology through digital transformation, automation, and AI integration. He leads work in BIM applications, 4D simulation, robotic systems, and sustainable infrastructure management. His interdisciplinary approach bridges civil engineering with computer science and data analytics. Key research areas include: Automation and robotics in construction (Construction 4.0) BIM and digital twin lifecycle management AI-driven defect detection and inspection systems Occupational safety through exoskeleton performance evaluation Multi-purpose utility tunnel optimization Energy-efficient building systems Recent work emphasizes applying machine learning to construction equipment activity recognition, UAV path optimization for infrastructure inspection, and ontology development for integrated systems. His research addresses industry challenges in productivity, safety, and sustainability through data-driven solutions.