Find Academic Supervisors

Clio AndrisView profile

Associate Professor

• Information Visualization
• Geovisualization
• GIS
• +4 more

Clio Andris is an Associate Professor at Georgia Tech, jointly appointed in the School of City and Regional Planning and the School of Interactive Computing. She directs the Friendly Cities Lab, focusing on mathematical models of social networks applied to urban planning, transportation, and geography. Her work integrates spatial analysis with visualization, emphasizing interdisciplinary collaboration. Education and Career: Andris earned a PhD in Urban Information Systems from MIT (2011), where she was an NDSEG Fellow. She held postdoctoral positions at Singapore-MIT Alliance for Research and Technology and the Santa Fe Institute. Prior to Georgia Tech, she was a faculty member at Penn State’s Department of Geography, affiliated with the GeoVISTA Center. Research Focus: Her research bridges social networks, geovisualization, and urban informatics. Key areas include spatial social network analysis, GIS applications for urban policy, and the impact of digital tools on civic engagement. She has developed innovative visual analytics tools like SNoMaN and ROBIN to democratize spatial data exploration. Awards: She received the NSF CAREER Award (2021) and NDSEG Fellowship (2011). Her lab is affiliated with the Center for Spatial Planning Analytics and Visualization (CSPAV) and the Information Visualization Lab. Labs and Collaborations: The Friendly Cities Lab focuses on socially just urban design through computational methods. Her work addresses issues like food security networks, pandemic impacts on biodiversity, and community mapping for activism. Grants and Outreach: Her NSF-funded projects emphasize public good applications, such as real-time pandemic risk communication and educational tools for migration data. She actively collaborates with non-profits and policymakers to translate research into actionable urban strategies.

Levent Burak KaraView profile

Professor

• Machine Learning
• Artificial Intelligence
• Computational Design
• +12 more

Levent Burak Kara is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in the Robotics Institute. He is a leading researcher in AI-driven computational design, additive manufacturing, and intelligent engineering systems, leading the Visual Design and Engineering Lab (VDEL) at CMU. Education: B.S., Mechanical Engineering, Middle East Technical University (1998) M.S., Mechanical Engineering, Carnegie Mellon University (2000) Ph.D., Mechanical Engineering, Carnegie Mellon University (2005) His research focuses on integrating machine learning, optimization, and geometric modeling to revolutionize engineering design and manufacturing. Key areas include topology optimization, CAD intelligence, digital twins, generative design, bioengineering, and electronic design automation. His work enables automation of traditionally labor-intensive design processes using deep learning and reinforcement learning. His recent publications reveal a strong trend toward physics-informed surrogate modeling, real-time simulation, manufacturability prediction, and AI-driven automation in mechanical, biomedical, and electronic systems. These works frequently appear in top journals such as Journal of Mechanical Design and Journal of Applied Mechanics , and at premier conferences like NeurIPS and DAC. Scientific Awards: National Science Foundation CAREER Award ASME Design Automation Society Young Investigator Award Google AI for Social Good Impact Scholar Kara advises several Ph.D. students and has secured significant funding from federal agencies such as the NSF and the U.S. Army Research Laboratory, as well as collaborations with industrial leaders including Cadence Design Systems and NVIDIA. His research is also supported by CMU’s NextManufacturing Center and the Critical Technology Initiative. He is actively involved in developing intelligent design systems that leverage AI to automate product design, optimize manufacturing processes, and improve medical diagnostics, particularly in oral cancer screening and organ preservation. His lab, VDEL, is a hub for innovation in AI-enabled engineering.

• Information Visualization
• Visual Analytics
• Graph Drawing
• +3 more

Tamara Munzner is a Professor of Computer Science at the University of British Columbia, with a confirmed email at tamara@cs.ubc.ca . Her research focuses on information visualization , visual analytics , and graph drawing , emphasizing practical design frameworks and theoretical foundations. Recent work explores visitor engagement with science museum exhibits large-scale data visualization challenges health informatics applications for chronic pain management advanced graph neural network visualization His publications in IEEE Transactions on Visualization and Computer Graphics and Eurographics conferences demonstrate her expertise in visual analytics. Scientific awards include the Best Panel Award at Euro Vis 2009 for her work on visualization education. Her research spans visualization design principles, dimensionality reduction techniques, and applications in genomic epidemiology and environmental sustainability.

David EppsteinView profile

Professor

• Graph Algorithms
• Computational Geometry
• Graph Theory
• +3 more

David Eppstein is a Distinguished Professor of Computer Science at the University of California, Irvine (UCI), affiliated with the Donald Bren School of Information & Computer Sciences. He holds academic leadership roles as director of the Center for Algorithms and Theory of Computation and associate director of the Center for Algorithms, Combinatorics, and Optimization. His research focuses on graph algorithms, computational geometry, discrete mathematics, and geometric graph theory. Eppstein earned a B.S. in Mathematics from Stanford University (1984) and a Ph.D. in Computer Science from Columbia University (1989). Research Interests: Graph drawing, information visualization, dynamic graph algorithms, mesh generation, optimal triangulation, K-shortest paths, subgraph isomorphism, data depth, exponential-time algorithms for NP-hard problems. Awards: ACM Fellow (2012), AAAS Fellow (2017), Distinguished Professor (2020), Best Paper Awards (2023, 2022), and SIAM recognition. Grants: Co-PI on a $1.2M NSF grant (2022) for geometric graph research, and previous NSF grants for algorithm studies (2016). His work bridges theoretical computer science and practical applications, including contributions to graph visualization, geometric algorithms, and combinatorial optimization. Notable recent achievements include resolving open questions in graph biplanarity and authoring the book Forbidden Configurations in Discrete Geometry (2018).

• Finite Model Theory
• Graph Structure Theory
• Computational Complexity
• +3 more

Kord Eickmeyer is a Lecturer at Technische Universität Darmstadt in the Department of Mathematics, specializing in the mathematical logic group. He holds a PhD in mathematics from Humboldt University Berlin and has held postdoctoral positions at TU Darmstadt (2011–2017) and the National Institute of Informatics in Tokyo (2011–2013). His research focuses on finite model theory, graph structure theory, and computational complexity, particularly in descriptive and parameterized complexity, as well as randomization and derandomization techniques. Research interests include exploring the boundaries of computational complexity through logical frameworks, analyzing graph structures for efficient algorithm design, and investigating the role of randomness in computation. His work bridges theoretical computer science and mathematical logic, with applications in algorithm design and formal methods. Publications span topics from model-checking on ordered structures to gap-planar graphs and randomized logics. Collaborations include prominent institutions like the National Institute of Informatics and Humboldt University Berlin. No scientific awards are explicitly listed, but his extensive academic contributions reflect a strong research trajectory. Advising and grants are not detailed in the provided text, though his academic career includes supervision roles during his PhD and postdoctoral phases. His involvement with the mathematical logic group at TU Darmstadt highlights collaborative research efforts in foundational areas of computer science and mathematics.

Christina LeslieView profile

Research Professor

• Computational Biology
• Systems Biology
• Machine Learning
• +7 more

Dr. Christina Leslie is a Research Professor and Member of the Computational & Systems Biology Program at Memorial Sloan Kettering Cancer Center (MSK). She leads an active research laboratory focused on developing computational approaches to understand complex biological systems. Dr. Leslie earned her PhD from the University of California, Berkeley and has established herself as a leading computational biologist in cancer research and immunology. Computational & Systems Biology Program, Memorial Sloan Kettering Cancer Center Gerstner Sloan Kettering Graduate School of Biomedical Sciences Dr. Leslie's research focuses on developing novel computational methods to study cellular biological systems from a global and data-driven perspective. Her lab exploits diverse high-throughput functional and genomic data to understand molecular networks underlying fundamental cellular processes, including transcription regulation, pre-mRNA processing, signaling, and post-transcriptional gene silencing. Her algorithmic methods draw heavily on machine learning to build accurate predictive models from noisy and high-dimensional biological data. Key areas of interest include modeling cell-type specific transcriptional programs and dissecting co- and post-transcriptional regulation, particularly microRNA-mediated gene regulation. Analysis of Dr. Leslie's publication record over the last five years reveals a strong focus on computational approaches to cancer genomics, immunology, and epigenetics. Her work bridges multiple disciplines, with a particular emphasis on developing machine learning methods to interpret complex biological data. The publications demonstrate increasing sophistication in integrating multiple data types (genomic, transcriptomic, epigenomic) to understand cancer biology and immune responses. Recent work shows a growing emphasis on single-cell technologies and spatial analysis of tumor microenvironments. Introduction of string kernel methodology for SVM classification of biological sequences Development of algorithms for predictive modeling of gene regulation First systems-level analyses of competition between microRNAs and between target transcripts Dr. Leslie actively mentors numerous graduate students and research associates, with current lab members including Vianne Gao, Alireza Karbalaghareh, Erik Ladewig, and several others. Her lab has received significant research funding to support their work on computational approaches to cancer biology and immunology. The Leslie Lab maintains close collaborations with multiple experimental groups at MSK, facilitating the translation of computational insights into biological understanding. The Leslie Lab operates within the Computational & Systems Biology Program at MSK, with strong ties to both the research and clinical missions of the institution. The lab maintains state-of-the-art computational infrastructure for analyzing large-scale genomic and proteomic datasets and collaborates extensively with wet-lab researchers to validate computational predictions experimentally.

Roberto TamassiaView profile

Professor

• Cryptography
• Information Security
• Algorithm Design
• +4 more

Roberto Tamassia is the James A. and Julie N. Brown Professor of Computer Science and Chair of the Computer Science Department at Brown University. He is also Director of Brown's Center for Geometric Computing. His research focuses on information security, cryptography, algorithms, graph drawing, and computational geometry. He has authored six textbooks and over 250 publications, and his work has been funded by ARO, DARPA, NATO, NSF, and industry sponsors. Education: PhD in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. Research Interests: Cryptography and Secure Systems Algorithm Design and Optimization Graph Drawing and Geometric Computing Encrypted Database Security Awards and Honors: IEEE Fellow Technical Achievement Award from IEEE Computer Society Listed among the 360 most cited computer science authors by ISI Grants and Funding: His research has been supported by major agencies and organizations including ARO, DARPA, and NSF, as well as industry partnerships. Labs and Affiliations: Directs Brown's Center for Geometric Computing, a hub for interdisciplinary research in computational geometry and graph algorithms.

• Embedded Systems
• Cyber-Physical Systems
• Computer Architecture
• +5 more

Aviral Shrivastava is a Professor at the School of Computing and Augmented Intelligence, Arizona State University, leading the Make Programming Simple Lab. He holds a Ph.D. and M.S. from the University of California-Irvine (2006, 2002) and a Bachelor’s from IIT Delhi (1999). His research focuses on making programming simple for embedded and cyber-physical systems, with a particular interest in manycore and accelerated computing, software for CPS, and resilient/fault-tolerant computing. He has co-authored over 120 publications in top venues like DAC, ESWEEK, and ACM TECS, with more than 3000 citations and 5 granted patents. His work has been recognized with multiple awards, including the 2010 NSF CAREER award and best paper nominations. Research Areas: Embedded and Cyber-Physical Systems Compiler Design for Modern Architectures Resilient and Fault-Tolerant Computing Scientific Awards: 2010 NSF CAREER award DAC 2017 Best Paper Award Candidate VLSI 2016 Best Student Paper Award LCTES 2010 Second Highest Ranked Paper ASPDAC 2008 Best Paper Candidate Advising & Grants: He has mentored 9 Ph.D. and over 20 Masters students. His research has been funded by NSF, DOE, NIST, SFAZ, and industry partners, totaling $3.5M. He teaches courses on computer organization, architecture, and embedded systems, with student evaluations averaging over 4/5. He also serves as General Chair of Embedded Systems Week (ESWEEK) and holds editorial roles in IEEE ESL, ACM TCPS, and ACM TECS.

• Computer Graphics
• Machine Learning
• Deep Learning
• +5 more

Dr. Frederick Li is an Associate Professor in the Department of Computer Science at Durham University, UK. He holds editorial roles as Associate Editor of Frontiers in Education (Digital Education) and Editorial Board Member of Virtual Reality & Intelligent Hardware. His research focuses on Computer Graphics, Machine Learning, Geometric Modelling, Collaborative Virtual Environments, Visual Aesthetics, and Educational Technologies. He earned his B.A. (Hons) and M.Phil. from The Hong Kong Polytechnic University and his Ph.D. in Computer Graphics from City University of Hong Kong. Prior roles include Assistant Professor at HK PolyU and project manager of a Hong Kong Government ITF-funded project. **Education**: B.A. (Computing Studies) and M.Phil. from HK PolyU; Ph.D. in Computer Graphics (CityU Hong Kong). **Research Interests**: His work spans mesh saliency detection, human-object interaction recognition, cloud modeling, face beautification, and educational technology. Recent achievements include awards for papers (e.g., Best Paper at ITiCSE 2014) and recognition such as EPSRC Peer Review College membership. He leads Durham's Undergraduate Board of Examiners and has been an external examiner at Northumbria University. **Awards**: Best Paper (ACM ITiCSE 2014), Outstanding Paper (ICALT 2013), EPSRC Peer Review College (2024), Outstanding BMVC 2024 Reviewer. **Grants & Labs**: His research is supported by grants from EPSRC and others. He collaborates with the Centre for Vision and Visual Cognition, VIViD, and AIHS group at Durham.

• Databases
• Programming Languages
• Compilers
• +7 more

Amir Shaikhha is an Associate Professor (Reader) in the School of Informatics at the University of Edinburgh. He was previously an Assistant Professor (Lecturer) at the same institution from 2020 to 2024 and a Departmental Lecturer at the University of Oxford until August 2020. He is also a Junior Research Fellow at University College, Oxford. His academic journey began with a Ph.D. from EPFL in 2018, where he was awarded the Google Ph.D. Fellowship in structured data analysis and a Ph.D. thesis distinction. His research centers on the design and implementation of data-analytics systems, drawing upon techniques from databases, programming languages, compilers, and machine learning. He develops high-performance systems such as SDQL.py, StructTensor, and VecHT, focusing on the compilation of data science workloads and optimization of tensor operations. His work bridges the gap between high-level abstractions and efficient execution, particularly in sparse and probabilistic computing domains. The recent publications highlight a strong trend in compiler-driven optimizations for data-intensive applications, including automatic differentiation, loop fusion, probabilistic programming, and domain-specific language (DSL) restaging. His research integrates machine learning for systems decisions and emphasizes reproducibility and performance. He has published consistently in top venues like PLDI, OOPSLA, SIGMOD, and CGO, reflecting sustained impact in programming languages and database systems. Dahl-Nygaard Junior Prize, 2025 Google Research Scholar Award, 2025 Most Influential Paper Award, GPCE 2024 Best Paper Award, GPCE 2017 Most Reproducible Paper Award, SIGMOD 2017 Google Ph.D. Fellowship, 2017 Amir Shaikhha has advised PhD students including Hesam Shahrokhi and has been nominated for Best Supervisor of the Year at the University of Edinburgh. He leads research projects that have received recognition and support through awards and grants, including the Google Research Scholar Award. He actively serves the community through program committees (e.g., GPCE, DBPL, DRAGSTERS), editorial roles, and peer review for premier journals. His leadership in organizing workshops and conferences underscores his role as a central figure in the programming languages and databases research communities. He leads a research group focused on compiler and database systems, with recent open-source releases such as StructTensor and VecHT. His team collaborates with researchers from institutions like MIT, EPFL, and TU Berlin, and he co-chairs workshops like Sparse@PLDI and DRAGSTERS. His lab emphasizes innovation in how data-intensive programs are compiled and executed efficiently across modern hardware.

Jack SnoeyinkView profile

Professor

• Computational geometry
• Algorithms for geographical information systems (GIS)
• Structural biology
• +3 more

Jack Snoeyink is a Professor at the University of North Carolina at Chapel Hill, holding joint appointments in the Department of Computer Science (College of Arts & Sciences) and the School of Data Science and Society. His research focuses on computational geometry, with applications in molecular biology, geographic information systems (GIS), and geometric modeling. His work in computational geometry explores algorithmic design and analysis for problems in solid modeling, computer graphics, and robotics. Key application areas include terrain modeling in GIS, molecular structure validation in biochemistry, and computational topology. He has contributed to output-sensitive algorithms for convex hulls and Voronoi diagrams, and geometric search problems. Articles highlight his expertise in computational geometry, with trends spanning 1999-2000. Topics include contour tree algorithms (SODA'00), watershed extraction (ASPRS'99), and skeleton generation (Crust.pdf). His work bridges theoretical advancements with practical implementations in GIS and structural biology. Jack Snoeyink has collaborated with researchers like Marc van Kreveld, Christopher Gold, and Bettina Speckmann on projects related to Delaunay triangulation, regression depth computation, and geometric assembly problems. He previously served as a program director at the National Science Foundation's CISE division (2015-2018) and co-founded the TRIPODS program for data science foundations.

• Theoretical Computer Science
• Distributed Algorithms
• Parallel Algorithms
• +3 more

Yi-Jun Chang is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS). He previously held a Junior Fellow position at the Institute for Theoretical Studies, ETH Zurich (2019–2021), and earned his Ph.D. in Computer Science and Engineering from the University of Michigan (2019). His research focuses on theoretical computer science, particularly distributed, parallel, and sublinear graph algorithms. Ph.D., University of Michigan (2019) M.S., National Taiwan University (2015) B.S., National Taiwan University (2013) Chang’s research explores the complexity and optimization of algorithms in distributed systems, including leader election, graph shattering, expander decomposition, and subgraph detection. His work addresses fundamental challenges in time-energy trade-offs, communication efficiency, and deterministic vs. randomized approaches in models like LOCAL and CONGEST. Recent publications highlight advancements in distributed triangle enumeration, optimal coloring, shortest path computation, and certification in bounded pathwidth graphs. Awards include the PODC 2019 Best Paper and Best Student Paper Awards, followed by the 2020 PODC Doctoral Dissertation Award. PODC 2019 Best Paper Award PODC 2019 Best Student Paper Award 2020 PODC Doctoral Dissertation Award Chang teaches courses such as CS3230 (Design and Analysis of Algorithms) and CS5275 (The Algorithm Designer's Toolkit). He advises Ph.D. students Hung Thuan Nguyen and Haoran Zhou, and has collaborated with postdoctoral researchers including Gopinath Mishra and Dean Leitersdorf.

• Artificial Intelligence
• Nonmonotonic Reasoning
• Argumentation Frameworks
• +4 more

Stefan Woltran is a Full Professor in the Databases and Artificial Intelligence department at TU Wien. He serves as Vice Dean of Academic Affairs for the Informatics Master program and leads the Research Unit for Databases and Artificial Intelligence. His research focuses on logic-based AI, including Propositional Logic, Nonmonotonic Reasoning, Argumentation frameworks, Knowledge Representation, and Logic Programming. He coordinates the Double-Degree Program Logic and Computation. His research projects include analyzing formal properties of logic-based AI approaches, complexity analysis, and developing algorithms via logic and dynamic programming. Notable projects include the HYPAR and REVEAL-AI initiatives exploring abstract argumentation and AI problem-solving. He has contributed to over 150 publications since 2001, focusing on argumentation frameworks, computational complexity, and formal methods. Woltran teaches courses such as Abstract Argumentation, Formal Methods in Computer Science, and Theoretical Computer Science. His work integrates theoretical advancements with practical solver development, such as the ASPARTIX system for argumentation tasks. He actively participates in international conferences and competitions in computational argumentation, emphasizing the application of formal methods to real-world problems.

Radu JianuView profile

Lecturer

• Data Visualisation
• Visual Analytics
• Human Computer Interaction
• +7 more

Dr. Radu Jianu is a Lecturer in the Department of Computer Science at City, University of London , where he has been a faculty member since 2016. He is affiliated with the giCentre , a leading research group in information visualization. He earned his PhD and MSc in Computer Science from Brown University, USA, and a Diploma in Engineering from the Polytechnic University of Timisoara, Romania. His academic career includes a previous role as Assistant Professor at Florida International University (2012–2016). His research focuses on Data Visualisation, Visual Analytics, and Human-Computer Interaction . He conducts interdisciplinary collaborations with domains such as biology, food policy, and energy decarbonisation, aiming to develop interactive visual tools that enhance data understanding and decision-making. His methodological approach includes user studies, eye-tracking, and the design of novel visualization techniques. Dr. Jianu teaches Programming in Java and Cognition and Technologies , and he coordinates the Programming Bootcamp. He also holds administrative responsibilities as the Progression and Support Director in the Computer Science Department and is a member of its Executive Committee (ExCo). His recent publications reflect a growing interest in LLM-assisted visual analytics, gaze-aware systems, and collaborative human-AI analytical frameworks . He has published in top venues such as IEEE TVCG, CHI, EuroVis, and Nature Immunology, with several best paper awards. His work on the RAMPVIS project highlights his contributions to visualization in public health emergencies. Scientific Awards: Best Paper Award, Symposium on Graph Drawing (2018) Best Short Paper Award, EuroVis (2020) Advising and Grants: Dr. Jianu supervises multiple PhD and MSc students, including Dany Laksono (Energy Decarbonisation) and Maeve Hutchinson (NLP-mediated Visualization). His students have co-authored high-impact, award-winning papers. He has been involved in funded research initiatives such as RAMPVIS, which received support from UKRI/EPSRC for developing visual analytics infrastructure during the COVID-19 pandemic. Labs and Teams: He is an active member of the giCentre at City, University of London, a hub for visualization research. He also collaborates with interdisciplinary teams in epidemiology, immunology, and computer science, contributing to large-scale projects like the Immunological Genome Project and RAMPVIS.

Kay C. WieseView profile

Professor

• Bioinformatics
• Computational Biology
• Evolutionary Computation
• +4 more

Kay C. Wiese is a Professor and Software Systems Chair at the School of Computing Science, Simon Fraser University. His research focuses on computational intelligence and bioinformatics, particularly RNA secondary structure prediction and visualization. He leads the Bioinformatics Research Lab and has contributed to RNA design and gene finding. Wiese holds a PhD in Computer Science from the University of Regina (1999) and degrees in Computer Science and Mathematics from the Universität des Saarlandes (Germany). He has extensive editorial roles, including Associate Editor for the IEEE/ACM Transactions on Computational Biology and Bioinformatics, and has organized major conferences like the IEEE Symposium on Computational Intelligence in Bioinformatics. His teaching interests include Bioinformatics, Computational Biology, and Discrete Mathematics. Wiese has supervised numerous graduate students, including Boris Shabash, Wenbo Jiang, and Andrew Hendriks. His research group developed tools like jViz.RNA for RNA visualization and SARNA-Predict for structure prediction. His work bridges computational methods with biological applications, emphasizing algorithmic innovation and practical software solutions.