Find Academic Supervisors

• Algorithms
• Graph Algorithms
• Combinatorial Optimization
• +3 more

Jean R.S. Blair is a Professor of Computer Science at the United States Military Academy (USMA), where she serves as the EECS Distinguished Professor for Innovation. She has held leadership roles including Deputy to the Chief Academic Officer and directorship of academic programs. With sabbatical years at the University of Bergen, Norway, and prior faculty experience at the University of Tennessee, her career spans academia, research, and accreditation leadership. Education: Ph.D. and M.S. in Computer Science from the University of Pittsburgh; B.S. in Economics and Computer Science from Allegheny College. Research Interests: Jean’s research bridges algorithmic theory and educational innovation, focusing on: Design and Analysis of Graph Algorithms for Combinatorial Problems Curriculum Development in Emerging Computing Disciplines Data Education Frameworks Secure Computing Integration in Undergraduate Programs Recent Article Trends: Her publications reflect a dual emphasis on theoretical algorithm design (particularly chordal graphs) and transformative educational initiatives in cybersecurity and data science. Scientific Awards & Memberships: Recognized as an ABET Fellow and CSAB Fellow in 2023, she maintains leadership roles in: Institute of Electrical and Electronics Engineers (IEEE) - Senior Member Association for Computing Machinery (ACM) - Senior Member Society for Industrial and Applied Mathematics (SIAM) American Society of Engineering Education (ASEE) Advising & Grants: As a principal investigator, she has secured research funding from the National Science Foundation and Army Research Labs, driving projects in algorithmic theory and cybersecurity education.

Andrej RisteskiView profile

Associate Professor

• Machine Learning
• Statistics
• Theoretical Computer Science
• +5 more

Andrej Risteski is an Associate Professor at the Machine Learning Department of Carnegie Mellon University (CMU) since 2025. He previously held the Norbert Wiener Research Fellow position jointly between the Applied Math Department and IDSS at MIT (2017–2019) after completing his PhD in Computer Science at Princeton University (2012–2017) under Sanjeev Arora . His research focuses on the intersection of machine learning, statistics, and theoretical computer science , emphasizing generative models, representation learning, and out-of-distribution generalization with applications to natural language processing and scientific domains . His recent publications explore edge embeddings in Graph Neural Networks (GNNs) , score matching efficiency , and theoretical foundations of diffusion models . Key contributions include analyzing computational bottlene.com/activities/statistical-and-computational-challenges-in-probabilistic-scientific-machine-learning-sciml/">NSF CAREER Award , DOE Computational Science Graduate Fellowship for Stephen Huan, and co-organizing the COLT workshop on Theory of AI for Scientific Computing . He advises PhD students across Machine Learning, Computer Science, and Mathematics , including Bingbin Liu (Kempner Institute Fellow) and Elan Rosenfeld (Google Research Scientist). Teaching includes Probabilistic Graphical Models and Advanced Deep Learning at CMU, plus Applied Mathematics at MIT. His work is supported by NSF, DoD , and OpenAI Superalignment grants. Education : PhD in Computer Science (Princeton), BSc in Computer Science (Princeton) Current Positions : Associate Professor, CMU Machine Learning Department Former Positions : Norbert Wiener Fellow, MIT IDSS & Applied Mathematics Research Areas : Generative Models (GANs, Diffusion Models) Representation Learning Out-of-Distribution Generalization Neural Language Models AI for Scientific Applications Sampling and Optimization Algorithms Scientific Awards : NSF CAREER Award (2023) Google Research Award (2024) Amazon Research Award (2022) OpenAI Superalignment Grant (2023) Recent Talks (2023–2025): "Architectural Nuances and Benchmark Gaps in Scientific ML" (UC Berkeley, 2025) "The Statistical Cost of Score-Based Losses" (Simons Institute Boot Camp, 2024) "Neural Networks for PDEs" (ETH Zurich, 2024) "Discernible Patterns in Transformers" (Theory of Interpretable AI, 2024) He leads a research group producing work at the interface of computational complexity and graph learning , with empirical validation on topological bottlenecks and hub node dynamics . Current projects include ICML 2025 paper on edge embeddings in GNNs and COLT 2025 workshop on AI for Scientific Computing co-organized with MIT, Duke, and ETH Zurich collaborators.

Nils Morten KriegeView profile

Associate Professor

• Data Mining
• Machine Learning with Graphs
• Combinatorial Optimization
• +5 more

Dr. Nils Morten Kriege is an Associate Professor at the Faculty of Computer Science, University of Vienna, where he leads the Data Mining and Machine Learning research group. Previously, he served as Assistant Professor (2020-2023) at the same institution and held positions at TU Dortmund including Interim Professor (2019/2020) and Postdoctoral Researcher (2015-2020). His research focuses on graph-based machine learning methods with applications in cheminformatics and drug discovery. His educational background includes a Doctorate in Computer Science (2015) and Diploma in Computer Science (2009), both from TU Dortmund. He has also been a Visiting Researcher at the University of York, UK. Dr. Kriege's research centers on graph algorithms and machine learning with graphs, particularly focusing on graph neural networks, graph kernels, and their applications in cheminformatics and drug discovery. His work bridges theoretical computer science with practical applications, developing novel methods for graph similarity, graph classification, and network analysis. He has made significant contributions to understanding the expressivity and robustness of graph neural networks, as well as developing efficient algorithms for graph similarity search and molecular analysis. His recent publications (2023-2025) demonstrate a strong focus on graph neural networks, with particular attention to their expressivity, robustness against attacks, and practical applications in drug discovery. His work spans theoretical foundations (Weisfeiler-Leman hierarchy, graph isomorphism testing), practical implementations (efficient quantization, defense frameworks), and domain-specific applications (cheminformatics, drug discovery). Vienna Research Groups for Young Investigators (2019) - €1,466k funding for "Algorithmic Data Science for Computational Drug Discovery" Member of the Global Young Faculty V, Stiftung Mercator (2017) Dr. Kriege leads an independent research group funded through the Vienna Research Groups for Young Investigators program, focusing on computational drug discovery. He has served on program committees for major conferences including NeurIPS, ICML, IJCAI, AAAI, ICLR, and ICDM, and has reviewed for prestigious journals such as Transactions on Pattern Analysis and Machine Intelligence. His teaching portfolio includes courses on Data Mining, Graph Learning, and Introduction to Machine Learning. He leads the Machine Learning with Graphs work group within the Data Mining and Machine Learning Research Group at the University of Vienna, collaborating with researchers like Wilfried Gansterer and Petra Mutzel on graph-based methods for drug design and molecular analysis.

Zhou JianyingView profile

Professor

• Applied Cryptography
• Network Security
• Cyber-Physical System Security
• +2 more

Jianying Zhou is a Professor and Centre Director for iTrust at Singapore University of Technology and Design (SUTD), where he leads research in cyber security. He received his PhD in Information Security from Royal Holloway, University of London, following an MSc from the Chinese Academy of Sciences and a BSc from the University of Science and Technology of China. Education Background: PhD (Information Security) - Royal Holloway, University of London MSc (Computer Science) - Chinese Academy of Sciences BSc (Computer Science) - University of Science and Technology of China Professor Zhou's research focuses on applied cryptography and network security, cyber-physical system security, and mobile and wireless security. His work spans theoretical foundations to practical implementations, with significant contributions to secure communication protocols, authentication mechanisms, and security architectures for emerging technologies. He has been instrumental in developing security solutions for industrial control systems, smart grids, and mobile platforms, with numerous patents resulting from his research. An analysis of Professor Zhou's recent publications reveals a strong focus on cyber-physical system security, privacy-preserving technologies, and blockchain security. His work demonstrates a consistent trajectory from foundational cryptographic research toward practical security implementations in real-world systems. Recent publications show increasing emphasis on securing industrial automation systems, developing privacy-enhancing technologies for cloud environments, and addressing emerging threats in 5G networks and blockchain systems. Scientific Awards: ESORICS Outstanding Contribution Award (2020) Professor Zhou has supervised numerous PhD students, including Chuadhry Mujeeb Ahmed, Hamid Ghaeini, John Castellanos, Flavio Toffalini, Andrei Bytes, Bowen Liu, and Teik Guan Tan, among others. His research has been supported by significant grants that have enabled the development of iTrust's world-class CPS testbeds, including MariOT, which offers testbed as a service with remote access. He has served as Principal Investigator on multiple major research projects focusing on cyber security for critical infrastructure. As Centre Director for iTrust at SUTD, Professor Zhou oversees a comprehensive research ecosystem dedicated to cyber security, with particular emphasis on securing cyber-physical systems. iTrust hosts world-class CPS testbeds that enable realistic security testing and validation. Under his leadership, the center has established itself as a leading research institution in Asia for applied cyber security research, with strong industry partnerships and significant contributions to security standards.

Stefan BalkeView profile

Researcher

• Music Information Retrieval
• Deep Learning
• Jazz Music Analysis
• +3 more

Stefan Balke is a Researcher at the AudioLabs Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), focusing on wind music research and Music Information Retrieval (MIR). Previously, he held a PostDoc position at the Institute of Computational Perception, JKU Linz (2018–2019) and later worked as a Data Scientist and Team Lead in industry. He temporarily served as a professor at Hochschule Weserbergland in 2023/24. Education: PhD (Dr.-Ing.) in MIR from FAU (2018), Electrical Engineering studies at Leibniz Universität Hannover (2008–2013). Research Interests: Music Information Retrieval, Deep Learning, Jazz and Wind Music Analysis, Dataset Development. His work includes creating datasets like ChoraleBricks (wind music) and JSD (jazz structure analysis), and tools like trackswitch.js for audio visualization. Grants & Projects: Secured 35k€ funding for his orchestra through the 'Engagiertes Land' program (2024). Collaborated on exhibits such as 'The Listening Machine' (Ars Electronica) and 'Con Espressione!' (mathematics of music exhibition). Contributions: Active on GitHub with repositories for datasets (choralebricks, jsd) and tools. Co-developed web-based audio tools and contributed to open-source projects like Sonic Visualiser and librosa.

Rik SenguptaView profile

Part-Time Lecturer

• Graph Theory
• Algorithms
• Fair Allocation
• +3 more

Rik Sengupta is a Part-Time Lecturer at the University of Massachusetts Amherst, located in the Lederle Graduate Research Center. His research focuses on graph theory, algorithms, combinatorics, and fair allocation mechanisms. He has contributed to theoretical computer science, discrete mathematics, and algorithmic economics. His work spans dynamic graph streaming algorithms, fair division under structured constraints, and optimization problems with fairness metrics. He also explores graph reconstruction techniques, quantifier complexity in boolean functions, and Ramsey theory applications. Recent publications highlight trends in graphical allocation models, probabilistic graph analysis, and algorithmic solutions for resource distribution challenges. Notable areas include EFX allocations over graphs, time-aware fairness in knapsack problems, and approximation algorithms for housing markets. Rik holds no explicitly listed academic awards or grants. His advising activities are not detailed in the provided texts. He is affiliated with computational theory research groups but no specific labs or teams are mentioned.

• Combinatorial Optimization
• Dynamic Programming
• Medical Decision Making
• +1 more

Dr. Michal Mankowski is a Research Fellow in Econometrics at Erasmus University, developing computational models for medical decision-making systems. His methodological research creates dynamic programming solutions for complex combinatorial optimization challenges. Applied work includes designing kidney exchange matching algorithms that improve transplant outcomes. Recent publications establish evidence-based reporting standards for European organ exchange programs and advance multi-criteria optimization theory.

Joan FeigenbaumView profile

Professor

• Computer Science
• Cybersecurity
• Privacy-Preserving Technologies
• +5 more

Joan Feigenbaum is the Grace Murray Hopper Professor of Computer Science at Yale University, with a joint appointment in Economics. She holds a B.A. in Mathematics from Harvard University and a Ph.D. in Computer Science from Stanford University. Her career includes significant contributions to AT&T's Information Sciences Research Center, where she pioneered research in algorithmics for massive datasets. Her research focuses on cybersecurity, privacy-preserving technologies, computational complexity, and the intersection of economics and computation. She has developed influential algorithms for network data analysis and incentive-compatible distributed systems. Feigenbaum has also led efforts in accountable computing, privacy frameworks, and blockchain governance. Key Contributions: Trust management, privacy-preserving protocols (e.g., PriFi), and formal models of accountability. Service: Editor-in-Chief of the Journal of Cryptology (1997–2002), Program Chair for Crypto 1991 and Complexity 1998. Feigenbaum's awards include Fellowships from the ACM and AAAS, and recognition as a 'Woman of Innovation' by the Connecticut Technology Council. She leads Yale's multidisciplinary blockchain research center funded by a $5.75M grant. Grants & Labs: Collaborative research on legally accountable cryptographic systems (LAChS), privacy-preserving data sharing frameworks (PRShare), and blockchain governance models.

Piotr SankowskiView profile

Professor

• Algorithms
• Machine Learning
• Graph Theory
• +5 more

Piotr Sankowski is a Professor at the Institute of Informatics, University of Warsaw, specializing in algorithms and theoretical computer science with applications in economics and machine learning. He is also CEO of MIM Solutions, deploying ML solutions, and heads the IDEAS NCBR center for AI and Digital Economy. His research focuses on algorithm design, computational complexity, and interdisciplinary applications in physics and economics. Education & Affiliations: Professor at University of Warsaw's Faculty of Mathematics, Informatics, and Mechanics (MIM) Head of IDEAS NCBR Center (Artificial Intelligence & Digital Economy) CEO of MIM Solutions (AI/ML consulting) Research Interests: Piotr's work bridges theoretical computer science and practical applications, with key areas: Algorithms for matching problems, graph theory, and optimization Machine learning explainability and feature importance Quantum tunneling phenomena in semiconductor nanostructures (physics) Algorithmic game theory and network design His ERC grants highlight contributions to algorithmic foundations and interdisciplinary research. Grants & Awards: ERC Starting (2010), Proof of Concept (2015), and Consolidator (2017) Grants First Pole to secure three ERC grants Fellow of the Institute of Informatics at University of Warsaw Publications: Over 80 papers spanning CS (algorithms, ML) and physics (solid-state theory). Recent work focuses on online algorithms, dynamic graph problems, and scalable ML systems. Labs & Projects: Leads the IGAFIT initiative promoting algorithms in Europe, and the EXALT project for explainable optimization tools. Active in the IDEAS NCBR center for AI innovation.

Kasimir ForthView profile

Researcher

• Circular Engineering
• Sustainable Building Design
• Building Information Modeling (BIM)
• +7 more

Kasimir Forth is a Researcher at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering, affiliated with the Professorship for Circular Engineering for Architecture. He is completing his PhD at Technical University of Munich (TUM) on BIM-based semantic enrichment using Large Language Models under Prof. André Borrmann. His research focuses on automating environmental assessments for sustainable buildings using digital methods like BIM and LLMs, particularly in early design stages. Currently, he contributes to the SWIRCULAR project, developing automated Digital Product Passports via BIM. Education: B.Sc. in Engineering Science (TUM) M.Sc. in Energy-Efficient and Sustainable Building (TUM, Distinction) Research Interests: BIM integration in sustainability analysis AI-driven semantic enrichment for environmental metrics Disassembly potential and circular economy applications Automated material passport generation Professional Experience: Former Managing Director of TUM’s Leonhard Obermeyer Center (Digital Methods for Built Environment) Consultant at Drees & Sommer (Energy Design/Sustainable Building) Key Projects: SWIRCULAR (automated Digital Product Passports), BIM4EarlyLCA (uncertain LCA visualization). Labs/Teams: Active in ETH Zurich’s Circular Engineering group and TUM’s Computational Modeling & Simulation lab.

• Computational Neuroscience
• Systems Neuroscience
• Neural Circuits Characterization
• +7 more

Matthew Smith is a Professor in Biomedical Engineering and Neuroscience Institute with a focus on computational and systems neuroscience. As Co-Director of the Center for the Neural Basis of Cognition, his research explores neural circuits, motor control, and spatial cognition. His work bridges experimental and computational methods, including studies on neural plasticity, electrophysiological recordings, and non-invasive neurostimulation techniques. Key contributions include advancing methods for estimating intracranial pressure and optimizing brain stimulation protocols. His interdisciplinary approach integrates machine learning, statistical analysis, and neuroimaging to understand complex brain functions. Research interests span neural coding mechanisms, visual perception dynamics, and the interplay between arousal systems and motor planning. Notable projects include investigations into V4 neuronal activity modulation by recent experience and the development of compact deep neural network models for visual cortex analysis. Smith’s lab employs advanced signal processing tools, such as SLEX analysis and latent dynamic factor modeling, to decode high-dimensional neural data. His recent publications highlight breakthroughs in understanding working memory robustness, transcranial ultrasound modulation specificity, and the spatial organization of prefrontal cortical networks. While no formal awards are listed, his extensive grant-funded research includes collaborations on cerebral autoregulation studies and neurovascular impedance modeling. Advising activities focus on training interdisciplinary PhD candidates in systems neuroscience and neural computation.

• AI Alignment
• Reinforcement Learning
• Optimal Transport
• +4 more

Francesco Quinzan is a Researcher at the University of Oxford's Department of Computer Science. His work focuses on advancing AI alignment, causal machine learning, and combinatorial optimization with applications in medical imaging, reinforcement learning, and fair algorithm design. He leads the ELSA project under Prof. Marta Kwiatkowska's supervision. Research interests include: Safe AI development through causal representation learning and doubly robust methods Optimization techniques for submodular functions and evolutionary algorithms Counterfactual analysis for bias detection in medical AI systems Reinforcement learning frameworks incorporating human feedback Recent publications (2020-2025) demonstrate contributions to: Causal feature selection and invariant predictors Scalable optimization methods for large-scale problems Robustness in multi-agent systems and diffusion-based models Algorithmic fairness in constrained feature selection Current projects involve: ELSA: Developing explainable and safe AI systems Optimal transport applications for domain correction Causal discovery from temporal data streams

Qing WangView profile

Associate Professor

• Graph Machine Learning
• Data Management
• Data Analytics
• +3 more

Dr. Qing Wang is an Associate Professor at the Australian National University (ANU) since 2012, leading the Graph Research Lab and the Database Group within the School of Computing. Her research focuses on graph machine learning, data management, and algorithms for dynamic networks. With over a decade of industry experience in data analysis and management, she holds a PhD in Computer Science (Summa Cum Laude) from Christian-Albrechts-University Kiel, Germany. Education: PhD (Dr.rer.nat.) in Computer Science, Christian-Albrechts-University Kiel, Germany (2010) Master of Information Systems (First Class Honours), Massey University, New Zealand (2009) Master of Economics, Jinan University, China Bachelor of Engineering, South China University of Technology, China Research Interests: Dr. Wang explores cutting-edge techniques in graph machine learning, scalable algorithms for large-scale data, and knowledge tracing. Her work bridges theoretical foundations with practical applications, such as improving graph neural networks' expressivity and optimizing distance queries on dynamic road networks. She actively contributes to the development of tools for data integration and privacy-preserving analytics. Grants & Awards: ARC Discovery Project (2021–2023): 'Deep Learning for Graph Isomorphism' ARC Discovery Project (2016–2018): 'Creating the Social Genome' ANU Dean’s Award for Teaching Excellence (2015) Fellow of the Higher Education Academy (2015) Labs & Teams: She leads the Graph Research Lab , focusing on advancing graph algorithms and their applications in real-world systems. Her team collaborates on projects involving dynamic data integration, privacy-preserving techniques, and educational data analytics.

Ismail AkturkView profile

Adjunct Assistant Professor

• Neuromorphic Computing
• Hardware Security
• Heterogeneous Systems
• +5 more

Ismail Akturk serves as an Adjunct Assistant Professor in the Electrical Engineering and Computer Science (EECS) department with a courtesy appointment. His research bridges hardware architecture, neuromorphic computing, and security, focusing on energy-efficient systems and novel computational paradigms. His research interests center on neuromorphic engineering with significant contributions to Intel's Loihi architecture, implementing bio-realistic neural models and exploring scaling limits. He investigates hardware security through microarchitectural vulnerability assessments and develops frameworks for secure edge computing. His work in heterogeneous systems includes optimizing parallel programming models across GPUs and CPUs while pioneering techniques like weight update skipping and value recomputation to accelerate deep learning. Recent publications demonstrate a strong trajectory toward secure, energy-efficient neuromorphic systems with practical applications in edge environments. Dr. Akturk's publication record reveals a strategic evolution from distributed storage systems (2009-2012) toward cutting-edge neuromorphic and security research. His 2023-2025 work shows increasing focus on secure miniservers, RTL code generation via LLMs, and bio-realistic neural implementations – indicating convergence of AI, hardware security, and neuromorphic computing. The consistent emphasis on energy efficiency across publications suggests this remains a core research thread. His scientific contributions include novel frameworks like ACR (Amnesic Checkpointing and Recovery) and Holistic Hardware Security Assessment, though formal awards aren't documented in available sources. His methodology consistently combines theoretical modeling with hardware implementation, particularly evident in Loihi processor optimizations. As an adjunct faculty member, Dr. Akturk contributes to EECS education while maintaining active research output. His current trajectory suggests growing involvement in secure edge computing systems and neuromorphic AI acceleration, with potential implications for low-power IoT security and real-time neural processing applications.

Aida KhajaviradView profile

Assistant Professor

• Mixed Integer Nonlinear Optimization
• Nonconvex Optimization
• Algorithm Development
• +3 more

Aida Khajavirad is an Assistant Professor in the Department of Industrial and Systems Engineering at Lehigh University. Her research focuses on advancing Mixed-Integer Nonlinear Optimization (MINLP) at theoretical, algorithmic, and software levels, integrating convex analysis, integer programming, and constraint programming. She has recently expanded her work to optimization algorithms for data science and machine learning applications. Education: Ph.D. in Mechanical Engineering from Carnegie Mellon University (2012). Research Interests : Mixed Integer Nonlinear Optimization Nonconvex Optimization Algorithm Development Convex Analysis Combinatorial Optimization Her work emphasizes bridging theoretical foundations with practical algorithm design for real-world challenges. Awards : 2023 INFORMS Computing Society Prize This prestigious award recognizes her contributions to optimization theory and applications. Grants & Funding : Air Force Office of Scientific Research ($1M award, 2023) This funding supports her research on advanced optimization algorithms. Labs and Affiliations: Based in the Harold S. Mohler Laboratory, she leads interdisciplinary efforts in optimization and data science.