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Martin HenzView profile

Associate Professor

• Programming Languages
• Software Engineering
• Artificial Intelligence
• +8 more

Martin Henz is an Associate Professor at the National University of Singapore , affiliated with the School of Computing and its Department of Computer Science . His academic journey includes an M.Sc. in Computer Science from Stony Brook University (1993) and a Dr.rer.nat. in Computer Science from Saarland University (1997). He has also worked as a Research Scientist at the German Research Centre for Artificial Intelligence. Research Focus : Scalable Experiential Learning, Systems for Teaching/Learning, AI in Education, Programming Languages, Algorithms, and Constraint Programming. Key Projects : Source Academy (immersive programming environment), Deep Teaching (LMS enhancements), and NUS Seafarers (maritime experiential learning). Publications span education technology, programming languages, and sustainable engineering, with recent works focusing on JavaScript-based pedagogy, automated question generation, and electric vehicle conversions. He supervised Rahul Singhal 's PhD, leading to the educational startup Cerebry, and co-founded Workforce Optimizer Pte Ltd with Alan Sevugan. Awards : NUS Annual Digital Education Award (2021) NUS Annual Teaching Excellence Award (2016/17) Fulbright Scholarship (1990) Startup @ Singapore Champion (2001)

Andreas MaierView profile

Researcher

• Drug Repurposing
• Network Medicine
• Bioinformatics
• +3 more

Andreas Maier is a Researcher at the University of Hamburg's Faculty of Mathematics, Informatics and Natural Sciences, affiliated with the Computational Systems Biology department. He began his PhD in May 2021 with Cosy.Bio (Center for Systems Biology) at UHH, focusing on drug repurposing projects such as REPO-TRIAL. Previously, he completed a Bioinformatics master's thesis at TUM (Technical University of Munich), developing a web application for analyzing molecular disease networks. His research interests emphasize network medicine, drug repurposing, and computational tools for biomedical discovery. He has contributed to platforms like NeDRex-Web, Drugst.One, and BioCypher, which democratize access to systems medicine workflows. His work bridges heterogeneous data integration, federated learning for rare diseases, and quantum computing applications in genetics. Maier's publications highlight innovations in knowledge graph-based drug discovery, privacy-preserving federated learning, and single-cell network analysis. He actively develops open-source bioinformatics tools to address challenges in disease module identification and patient stratification. His projects align with the REPO4EU consortium and other collaborative initiatives in translational bioinformatics.

Wei DingView profile

Professor

• Knowledge Discovery
• Data Mining
• Machine Learning
• +7 more

Wei Ding is a Professor in the Department of Computer Science at the University of Massachusetts Boston (UMass Boston). She earned her Ph.D. in Computer Science from the University of Houston in 2008. From 2019 to 2023, she served as a Program Director at the National Science Foundation's Division of Information and Intelligent Systems (IIS), overseeing programs in Information Integration, Smart Health, Deep Learning Foundations, and Scalable Systems. Her research integrates knowledge discovery, data mining, and machine learning with applications spanning health sciences, astronomy, geosciences, and environmental sciences. She employs advanced techniques like spatio-temporal modeling, deep neural networks, and semantic analysis to address complex real-world problems such as disease subtyping, physical activity prediction, and environmental forecasting. Her work emphasizes interdisciplinary collaboration and societal impact. Analysis of her recent publications reveals a focus on AI-driven healthcare solutions (e.g., neuroimaging biomarkers, disorder diagnosis), fundamental ML advancements (e.g., generalization, GAN stability), and cross-domain applications (e.g., climate forecasting, animal behavior analysis). Recurring themes include low-data learning, interpretability, and scalable algorithms. Awards & Honors: IEEE Fellow (2023) NSF Director's Award (2022) WISAY Distinguished Woman in Science Award, Yale University (2019) AI for Earth Award (2018) Best Paper Awards (ICTAI 2011, ICCI 2010) Advising & Grants: She mentors PhD and Master’s students in the Knowledge Discovery Lab (KDLab), with alumni at institutions like Facebook, Google, and McKinsey. Her research is funded by NSF, NIH, NASA, and DOE, including: NIH R01: Predicting youth physical activity (2016) NSF EAGER: Machine learning for cancer subtyping (2017) NIH R01: Accelerometer/gyroscope data for activity estimation (2022) Leadership: She directs the KDLab and co-founded the Women in Sciences Club (WINS). She serves as Associate Editor for ACM TKDD, TIST, and KAIS journals.

Boyu ZhangView profile

Assistant Professor

• Medical Image Analysis
• Machine Learning
• Deep Learning
• +4 more

Boyu Zhang is an Assistant Professor in the Department of Computer Science at the University of Idaho, part of the College of Engineering. He holds a Ph.D. in Computer Science & Technology from Harbin Institute of Technology (2016), an M.S. from the same institution (2009), and a B.S. from Jilin University (2005). His research focuses on medical image analysis, deep learning, and AI applications in healthcare. Key areas include breast cancer detection via ultrasound imaging, explainable AI, graph neural networks for multi-omics data integration, and materials science predictions using machine learning. His work emphasizes interpretability in AI systems, such as the Bi-RADS-Net series for breast cancer diagnosis and the development of sharpness-aware optimizers for medical imaging tasks. He also explores multi-task learning frameworks and novel neural network architectures like SepNet for directional data analysis. His contributions span medical imaging benchmarks (e.g., BUSIS dataset) and computational methods for materials property prediction.

Liping LiuView profile

Associate Professor

• Machine Learning
• Data Science
• Deep Learning
• +3 more

Liping Liu is an Associate Professor in the Department of Computer Science at Tufts University's School of Engineering. He holds a Ph.D. from Oregon State University and has held postdoctoral positions at Columbia University and Tufts. His research focuses on machine learning, generative models, graph learning, and their applications in biochemical data analysis and fluid dynamics simulation. His work on graph generative methods earned the NSF CAREER Award. Education: Ph.D. (Oregon State University, 2016), M.Sc. (Nanjing University, 2009), B.S. (Hebei University of Technology, 2006). Research Interests: Machine Learning, Deep Learning, Generative Models, Time Series, Graph Learning. Dr. Liu's research emphasizes probabilistic modeling and neural networks, addressing challenges in graph generation, data-driven physics simulation, and biochemical analysis. His recent work includes advancements in graph-based recommendation systems, turbulence modeling, and enzymatic reaction prediction. His publications span top AI conferences like NeurIPS, ICML, and ICLR. He has secured grants totaling over $9 million, including the NSF CAREER Award and NIH funding for metabolomics and enzymatic promiscuity studies. His teaching includes courses on generative models, deep learning, and machine learning for graph analytics. Awards: NSF CAREER Award (2023), NIH grants, DARPA ACT-NOW project (2019). Service: NSF panelist, program committee member for AAAI, NeurIPS, and IJCAI.

Babak HeydariView profile

Associate Professor

• Socio-technical Systems
• Complex Networks
• Resilience Engineering
• +3 more

Babak Heydari is Associate Professor in Mechanical and Industrial Engineering at Northeastern University, with joint appointments in the College of Engineering and School of Public Policy. He directs the Multi-AGent Intelligent Complex Systems (MAGICS) Lab and serves as Co-Director of the Engineering Management program. Education: PhD in EECS from UC Berkeley, with graduate certificate in Management of Technology. Research interests: Focuses on socio-technical systems, complex networks, and resilience engineering. Develops computational frameworks for understanding platform ecosystems, sharing economy models, and network resilience using AI and game theory approaches. Publications demonstrate consistent innovation in network modeling, with recent applications to AI governance and resilient infrastructure. Work combines computational methods with policy insights. National Science Foundation CAREER Award Wiley Systems Engineering Journal Best Paper Award Best Paper Award, Journal of Systems Engineering (2016) Leadership includes chairing the Executive Committee of the Council of Engineering Systems Universities and serving on advisory boards for international resilience initiatives.

Oscar Carl Olof DahlstenView profile

Associate Professor

• Quantum Information Science
• Information Thermodynamics
• Foundations of Quantum Theory
• +3 more

Oscar Carl Olof Dahlsten is an Associate Professor in the Department of Physics at City University of Hong Kong. He works in the field of quantum information science with research spanning information thermodynamics, foundations of quantum theory, and quantum computation and machine learning. His academic journey includes training at Imperial College and previous positions at ETH Zurich, NUS Singapore, Oxford University, and SUSTech before joining CityUHK. Dahlsten's research interests focus on the intersection of quantum mechanics and information theory. His work explores how quantum systems process information, the thermodynamic implications of quantum operations, and the application of quantum principles to computational problems. Key areas include quantum causal inference, quantum energy harvesting, black hole information theory, and quantum machine learning algorithms. His fingerprint analysis shows strong contributions to Quantum Theory (100%), Statistical Mechanics (55%), Quantum Dot physics (55%), and Free Energy concepts (40%). Recent publications demonstrate a strong trend toward experimental validation of quantum information concepts, particularly in quantum causal inference and quantum thermodynamics. His work bridges theoretical foundations with practical applications, especially in energy harvesting and quantum computing. The integration of quantum principles with thermodynamic laws appears as a consistent theme across his recent publications. Dahlsten currently serves as Principal Investigator for the GRF project 'Exploiting Quantum Systems for More Efficient Extraction of Energy From Random Sources' starting September 1, 2025. He actively supervises PhD students in quantum information science and is accepting new PhD candidates. His research group focuses on cutting-edge problems at the intersection of quantum information, thermodynamics, and computation.

Feng ChenView profile

Associate Professor

• AI Safety
• Ethical AI
• Machine Learning
• +7 more

Feng Chen is an Associate Professor in the Department of Computer Science at The University of Texas at Dallas (UT Dallas), part of the Erik Jonsson School of Engineering & Computer Science. He directs the AI Safety Laboratory and holds tenure. His research focuses on AI safety, ethical machine learning, uncertainty quantification, and fair algorithms. He earned a Ph.D. from Virginia Tech (2012), M.S. from Beijing University of Aeronautics & Astronautics (2004), and B.S. from Hunan University (2001). His research spans AI safety frameworks, resilient AI systems, fairness in healthcare and finance, cybersecurity, and environmental AI applications. Key contributions include uncertainty-aware deep learning, causal representation learning, and adversarial vulnerability analysis. He has authored over 150 peer-reviewed publications, including top venues like NeurIPS, ICML, KDD, and ICDM. Grants: $4.2M+ in funding from NSF, Army Research Office, IARPA, and industry partners. Teaching: Courses in data mining, anomaly detection, and artificial intelligence since 2014. Notable achievements include an NSF CAREER Award (2018) and leadership in projects like EMBERS (social media event prediction). His work bridges theory and practice, addressing societal challenges through ethical AI principles. Labs/Teams: AI Safety Laboratory (UT Dallas), collaborative projects with Virginia Tech, Carnegie Mellon, and industry partners on software vulnerability analysis and causal AI. Awards: Multiple best paper nominations, including 2nd Place in NAE Security Category (2009) and UT Dallas Outstanding Research (2021-2022).

Jianxi GaoView profile

Associate Professor

• Network Science
• Artificial Intelligence
• Control Theory
• +3 more

Jianxi Gao is an Associate Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). His research focuses on network science, particularly network resilience, robustness, and control, integrating network theory, control theory, statistical physics, and operations research. He also explores the intersection of network science and AI, including applications of AI to network analysis and vice versa. His work aims to understand, predict, and control the resilience of complex systems against cascading failures. Key research areas include network resilience in transportation systems, quantum networks, and biological systems, with applications to pandemic response and infrastructure optimization. Gao's contributions span theoretical frameworks and computational tools, such as the NuRsE MATLAB package for network resilience analysis. His GitHub repositories (e.g., NuRsE and NON) showcase his open-source contributions to network science and computational methods. His recent publications address topics like AI-driven network analysis, quantum network percolation, and pandemic-induced healthcare system stress. He actively collaborates on interdisciplinary projects, emphasizing real-world applications of network science principles.

Ruth CharneyView profile

Professor

• Geometric Group Theory
• CAT(0) Spaces
• Hyperbolic Groups
• +4 more

Ruth Charney holds the Theodore and Evelyn Berenson Professorship in Mathematics at Brandeis University. Her research specializes in geometric group theory, particularly CAT(0) spaces, hyperbolic groups, Artin groups, and automorphism groups. She earned her Ph.D. from Princeton University (1977) after undergraduate studies at Brandeis. She previously held positions at Ohio State University, Yale University, and UC Berkeley. Her foundational work explores geometric structures of groups, boundaries of metric spaces, and random group properties. Recent publications focus on outer space constructions for Artin groups, Morse boundary classifications, and acylindrical hyperbolicity in infinite-type groups. Honors include presidencies of the American Mathematical Society (2021-2023) and Association for Women in Mathematics (2013-2015), Polya Lectureship, and multiple fellowships. She has supervised 17 Ph.D. students and secured continuous NSF funding since 2011 for projects on automorphism groups and geometric structures. She organized major conferences including Artin groups workshops at MSRI and Oberwolfach, and serves on advisory boards for CRM Montreal, MSRI, and multiple NSF panels. Her outreach includes lectures at international conferences and programs advancing women in mathematics.

Octavia A. DobreView profile

Professor

• Wireless Communications
• Optical Communications
• Underwater Communications
• +4 more

Octavia A. Dobre is a full Professor and Canada Research Chair Tier-1 in Ubiquitous Connectivity at Memorial University's Faculty of Engineering and Applied Science. Her research focuses on wireless, optical, and underwater communications, integrated sensing and communication systems, and AI-driven network innovations. She leads over 500+ journal/conference publications and holds positions like VP Publications of IEEE Communications Society. Education: Dipl.-Ing. and PhD from Politehnica University of Bucharest Roles: Editor-in-Chief of IEEE Open Journal of Communications Society, former Editor of IEEE Communications Letters Her work spans IEEE standards development, conference leadership (e.g., General Chair, Technical Chair), and industry partnerships with entities like Huawei and DRDC. Recognized globally with awards like IEEE Fellow (2020), Fellow of the Canadian Academy of Engineering (2021), and Top 2% Global Scientist (Stanford, 2020-2024). Her lab explores cutting-edge topics including quantum networks, reconfigurable surfaces, and 6G innovations.

David MouldView profile

Professor

• Computer Graphics
• Computer Games
• Non-Photorealistic Rendering
• +4 more

David Mould is a Professor in the School of Computer Science at Carleton University. His research focuses on computer graphics, procedural modeling of natural phenomena, non-photorealistic rendering, and computer games. He holds a PhD from the University of Toronto (2002), MSc from the University of Saskatchewan (1996), and BSc from the University of British Columbia (1994). PhD: University of Toronto (2002) MSc: University of Saskatchewan (1996) BSc: University of British Columbia (1994) Research interests include procedural modeling of trees, lightning, and terrain; image stylization techniques such as stained glass transformation and wax crayon simulation; and nonlinear storytelling in games. His work emphasizes algorithmic innovation and perceptual quality in graphics. Recent publications span topics like texture synthesis, real-time video stylization, and fluid animation techniques. He leads the Graphics, Imaging, and Games (GIGL) research group at Carleton. Teaching responsibilities include courses in game development (COMP 1501–4501), technical writing (COMP 3301), and graduate courses on game design and image processing.

• Applied Mathematics
• Mathematical Biology
• Epidemiology
• +4 more

Paul Tupper is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. He holds a Ph.D. in Scientific Computing from Stanford University (2002). His research focuses on applied mathematics with emphasis on mathematical modeling in epidemiology, speech perception, neural networks, and computational linguistics. He teaches advanced courses in probability, numerical linear algebra, and calculus for social sciences. His work bridges theoretical mathematics and real-world applications, particularly in understanding complex systems like disease transmission dynamics and cognitive processes. Recent studies include modeling the transition of pandemics to endemic states, genomic analysis of viral spread, and audio-visual perception mechanisms in speech. He actively contributes to public health policy discussions through epidemic modeling research. Professor Tupper's research has been published in high-impact journals and conferences, with notable contributions to diversity metrics in biology and geometry, stochastic differential equations, and connectionist models of linguistic phenomena. His courses reflect interdisciplinary interests, integrating mathematical rigor with practical computational methods.

Shiva JahangiriView profile

Assistant Professor

• Big Data Management Systems
• Databases for AI/ML
• Knowledge Graphs
• +6 more

Shiva Jahangiri is an Assistant Professor in the Department of Computer Science and Engineering at Santa Clara University's School of Engineering. His research focuses on Big Data Management Systems, Databases for AI/ML, and Query Optimization. He leads the DBIS Lab, which explores database internals, vectorized data processing, and open-source projects like Apache AsterixDB. Education: Ph.D. in Computer Science from the University of California, Irvine; M.S. in Computer Science (Data Science) from the University of Southern California. Current courses taught include Advanced Programming, Advanced Database Systems, and Introduction to Database Systems. He advises Ph.D. and Master’s students on topics like Vector Databases, Query Scheduling, and Resource Management. Recent research trends involve optimizing group-by/aggregation operators, schema inference for semi-structured data, and memory management in complex join queries. His work bridges theoretical advancements with practical implementations in open-source systems. DBIS Lab activities include student participation in senior design projects, directed research, and volunteer roles. The lab emphasizes industry collaboration for hands-on experience in database systems development.

Philip HackneyView profile

Associate Professor

• Algebraic Topology
• Higher Category Theory
• Operad Theory
• +1 more

Philip Hackney is Associate Professor and Devon Endowed Professor of Mathematics at the University of Louisiana at Lafayette, specializing in algebraic topology and higher category theory. He holds a Ph.D. and M.S. from Purdue University and B.S. from Central Michigan University. Prior to joining UL Lafayette, he was a research fellow at the Centre of Australian Category Theory in Sydney. Research Focus: His work employs abstract homotopy theory to study higher structures including operads and their generalizations, with applications in mathematical physics and geometry. Publication Trends: Recent articles focus on advanced category theory, operadic structures, and homotopical methods. Research demonstrates consistent exploration of higher-dimensional algebraic structures through combinatorial and topological approaches, with frequent applications in mathematical physics contexts.