Find Academic Supervisors

• Software Engineering
• Software Testing
• Program Analysis
• +4 more

Alessandro (Alex) Orso is the Dean and Professor at the University of Georgia College of Engineering , starting in 2025. Previously, he spent 25 years at Georgia Institute of Technology , where he served as a Professor in the College of Computing , Director of the Scientific Software Engineering Center , and Associate Dean for Off-Campus and Special Initiatives . Education: Ph.D. in Computer Science – Politecnico di Milano, Italy M.S. in Electrical Engineering – Politecnico di Milano, Italy B.S. in Electrical Engineering – Politecnico di Milano, Italy Research Focus: Alex Orso is a leading expert in software engineering , with a focus on software testing , program analysis , and debugging . His work aims to improve software reliability, security, and trustworthiness , and has been validated on real-world systems. He has led initiatives to integrate AI and large language models into testing workflows, particularly for REST APIs and mobile applications . Scientific Recognition: Distinguished Member of the Association for Computing Machinery (ACM) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) ISSTA Impact Paper Award (2021) Research Funding & Leadership: Orso has secured major funding from Google, IBM, Microsoft , and government agencies. In 2021, he led the acquisition of $11 million from Schmidt Futures to establish the Scientific Software Engineering Center at Georgia Tech, where he served as inaugural director. Teaching & Innovation: He was an early advocate of Georgia Tech’s Online Master of Science in Computer Science (OMSCS) and has taught a virtual software engineering course since its launch in 2014.

• Education Law
• Civil Rights Law
• Algorithmic Justice
• +6 more

Professor Maurice R. Dyson is a scholar, attorney, and policy expert at Suffolk University Law School , with affiliations spanning Columbia University, Southern Methodist University, and international institutions. He specializes in Education Law , Artificial Intelligence , and Civil Rights , focusing on systemic inequality, algorithmic bias, and legal reform. Dyson's career bridges mergers and acquisitions practice with pro bono litigation , including a landmark $14 billion school finance judgment in New York City. Harlan Fiske Stone Scholar, Columbia Law School King's Crown Scholar, Columbia College His research explores AI's racial justice implications , constitutional law , and criminal justice reform , particularly in pre-sentencing algorithms and bias amplification. Articles highlight intersections between technology , equity , and legal frameworks . Scientific awards include the King's Crown Award , Kluge Award , and C. Clyde Ferguson Jr. Keynote . Dyson co-founded the Crawford Legal Institute Mentorship Bond (CLIMB) program, recognized by the White House and California State Bar , addressing the school-to-prison pipeline through mentorship and equity initiatives.

Kegang LingView profile

Associate Professor

• Reservoir Engineering
• Rock Geomechanics
• Natural Gas Engineering
• +5 more

Kegang Ling is an Associate Professor in the Department of Energy and Petroleum Engineering at the University of North Dakota (UND) College of Engineering & Mines. He is also the Undergraduate Program Director for Petroleum Engineering, teaching courses like Reservoir Rock Properties and Natural Gas Engineering. With over 25 years of industry experience, his work bridges academia and practical applications in unconventional reservoirs. Ph.D., Petroleum Engineering, Texas A&M University (2010) M.S., Petroleum Engineering, University of Louisiana at Lafayette (2006) B.S., Geology, China University of Petroleum at Beijing (1995) His research focuses on CO2/H2 storage, natural gas engineering, and production optimization. Recent work applies discrete element modeling to rock properties and machine learning to EOR screening. He leads projects on fly ash utilization for drilling fluids and leakage detection in pipelines, emphasizing sustainability and efficiency. The last five years of publications show expertise in unconventional reservoir geomechanics, CO2 storage, and solvent-based EOR. Collaborations with SPE conferences and NSF-funded projects highlight interdisciplinary and applied trends. Keywords span Petroleum Engineering , Rock Mechanics , and Machine Learning , with subfields like huff-n-puff EOR and wellbore flow assurance . Scientific Awards & Funding: SPE Faculty Enhancement Travel Grant (2013, 2014) NSF Grant ($585,405): Nano-scale hydrocarbon thermodynamics NDIC Grants ($6.7M total): CO2 EOR, geomechanics, drilling fluids Research North Dakota Grant ($178,000): Cell mechanics for cancer diagnosis He has advised 5 graduate students (Ph.D./M.S.) and contributed to 130+ publications. Current funding includes projects on produced water treatment and molecular thermodynamics in nano-scale pores.

Jignesh M. PatelView profile

Professor

• Database Systems
• Query Optimization
• Data Analytics
• +3 more

Jignesh M. Patel is a Professor in the Computer Science Department at Carnegie Mellon University, where he leads research on efficient data analysis methods. His work focuses on improving both system efficiency (e.g., high-performance data algorithms) and human efficiency (e.g., user productivity with data systems). Research Focus: Patel's group specializes in database systems, query optimization, hardware acceleration, and human-data interaction. Their interdisciplinary work spans: Transactional processing and real-time analytics Query optimization techniques Hardware-algorithm co-design Natural language interfaces for data systems Memory-efficient data processing Professional Activities: Co-founded four technology companies (Paradise, Locomatix, Quickstep, DataChat). Serves on program committees for premier conferences including SIGMOD and CIDR (as co-chair). Teaches database systems courses at CMU. Awards: Received Best Paper Award at DaMoN 2010 for work on cluster efficiency.

Ana LucicView profile

Assistant Professor

• Interpretable Machine Learning
• Artificial Intelligence
• Earth System Science
• +3 more

Ana Lucic is an Assistant Professor in Artificial Intelligence at the University of Amsterdam , with a joint appointment between the Institute for Logic, Language and Computation and the Informatics Institute . Her research focuses on interpretable machine learning applications for scientific discovery and societal impact. Formerly at Microsoft Research AI for Science and Partnership on AI PhD in Explainable Machine Learning from University of Amsterdam (2022) BSc/MSc in Mathematics from McMaster University Research Highlights: Develops mechanistic interpretability methods for deep learning architectures. Created Aurora , a foundation model for Earth system forecasting outperforming traditional operational models in air quality prediction and tropical cyclone tracking. Pioneers Clifford-Steerable CNNs for geophysical data analysis. Actively hiring PhD students for AI transparency research . Collaborative Networks: Contributions to ELLIS Summer School and ICML workshops . Collaborates with Microsoft Research AI for Science team on climate-related ML projects. Involved in organizing TerraBytes workshop at ICML 2025. Recent Advancements: Key role in publishing Aurora model in Nature (2025), demonstrating superior performance in Earth system forecasting. Supervises Ege Erdogan , new PhD student focused on mechanistic interpretability. Actively contributes to open-source AI development through GitHub repositories and technical discussions.

Jared TannerView profile

Professor

• Compressed Sensing
• Matrix Completion
• Signal and Image Processing
• +7 more

Professor Jared Tanner is Professor of the Mathematics of Information at the University of Oxford's Mathematics Institute and a Fellow of Exeter College. Previously, he held positions at the University of Edinburgh (2007-2012) as Professor, Reader, and Lecturer in Mathematics, University of Utah (2006-2007) as Assistant Professor, and Stanford University (2004-2006) as an NSF Postdoctoral Fellow. His research focuses on extracting models from high-dimensional data to reveal essential information, with specific contributions including sampling theorems in compressed sensing using stochastic geometry, efficient algorithms for matrix completion, and theoretical understanding of deep neural networks. Recent interests include neural network initialization techniques to preserve geometric and information-theoretic properties, as well as network pruning methods. Professor Tanner has supervised numerous doctoral students at Oxford and Edinburgh, including Alireza Naderi, Thiziri Nait Saada, Ilan Price, Giuseppe Ughi, Charles Millard, Michael Murray, Simon Vary, Bernadette Stolz, Bogdan Toader, Rodrigo Mendoza-Smith, Ke Wei, Bubacarr Bah, and Andrew Thompson, many of whom have gone on to prestigious positions in academia and industry. His publication record spans over two decades with significant contributions to compressed sensing, matrix completion, and more recently deep learning theory. His work demonstrates a consistent progression from foundational theoretical work to practical applications in signal processing and machine learning. As an academic leader, Professor Tanner serves as Founding Editor-in-Chief of Information and Inference: A Journal of the IMA and has held editorial positions at several prestigious journals including Applied and Computational Harmonic Analysis and IEEE Signal Processing Letters . He has organized numerous conferences and workshops including Prospects in Mathematics and the FoCM Computational Harmonic Analysis workshop.

Atila AlvandpourView profile

Professor

• Integrated Circuits and Systems
• VLSI Design
• Analog and Mixed-Signal Circuits
• +6 more

Atila Alvandpour serves as Professor and Head of the Integrated Circuits and Systems Division at Linköping University's Department of Electrical Engineering (ISY), concurrently holding the position of Vice Head of the Department. He joined the university in 2003 following senior research scientist roles at Intel Corporation's Circuit Research Lab (1999-2003). His educational foundation includes M.S. and Ph.D. degrees earned from Linköping University in 1995 and 1999 respectively. Alvandpour's research centers on advanced nano-scale integrated circuit design , with pioneering work in data converters (ADCs/DACs) , RF transceivers , and ultra-low-power systems . His expertise spans sensor interfaces, energy-harvesting architectures, and multi-GHz digital circuits, driving innovations for IoT and biomedical applications through novel analog/mixed-signal techniques. Recent publications (2024-2025) reveal a strategic focus on system-level integration for emerging technologies, particularly energy-efficient SoCs for wireless optical sensing, RF energy harvesting, and bio-implantable devices. This trend emphasizes circuit miniaturization, power optimization, and multi-functional integration in cutting-edge CMOS processes. No formal scientific awards are documented in the source materials. As Division Head and active researcher with 24 U.S. patents, Alvandpour leads a significant research group within the Division of Electronics and Computer Engineering (ELDA). His IEEE senior membership and editorial roles for flagship journals like IEEE Journal of Solid-State Circuits demonstrate substantial professional influence, though specific grant details remain unspecified.

Stefan BöttcherView profile

Professor

• Databases
• Data Compression
• Graph Algorithms
• +3 more

Prof. Dr. Stefan Böttcher serves as Professor and Section Owner of Databases and Electronic Commerce within the Department of Computer Science at the University of Paderborn's Faculty of Computer Science, Electrical Engineering and Mathematics. His office (F2.217, Fürstenallee 11) operates by email appointment, and he teaches courses including Betriebssysteme (Operating Systems), maintaining active academic engagement through current publications and institutional roles. His research centers on advanced data compression methodologies for complex structures, specializing in grammar-based techniques for graphs, strings, and trees. Key focus areas include enabling efficient query processing directly on compressed representations—critical for semantic web triple stores and big data systems—while optimizing storage-performance tradeoffs through novel algorithmic approaches in Burrows-Wheeler transforms and tree recompression. Analysis of his 2018-2025 publications reveals consistent innovation in compressing non-linear data structures: from RECUT's tree recompression to 2025's string partitioning for BWTs, his work bridges theoretical algorithm design with practical database applications, particularly targeting high-performance querying in grammar-compressed graph frameworks for RDF triple stores. Scientific Awards No awards documented in source materials Prof. Böttcher mentors graduate researchers in database compression technologies through his Databases and Electronic Commerce section, with research outcomes published in premier venues like IEEE DCC and Big Data conferences. His projects demonstrate sustained funding through continuous high-impact outputs without explicit grant documentation in the provided texts. He leads the Databases and Electronic Commerce research group, driving innovations in compressed data structures that reduce storage overhead while maintaining query efficiency for semantic web and large-scale graph database implementations.

Dandan LiuView profile

Associate Professor

• Biostatistics
• Epidemiology
• Alzheimer's Disease
• +3 more

Dandan Liu serves as Associate Professor of Biostatistics at Vanderbilt University Medical Center, holding dual leadership roles as Executive Director of the Vanderbilt Biostatistics Data Coordinating Center (VBDCC) and Director of the Vanderbilt Institute for Clinical and Translational Research (VICTR) Methods Program. Her work bridges statistical methodology development with clinical and translational research across multiple disciplines. Her educational foundation includes a PhD in Biostatistics from the University of Michigan, providing the theoretical basis for her methodological innovations. Dr. Liu's research program demonstrates exceptional breadth, with core expertise in longitudinal data analysis for neurodegenerative diseases—particularly Alzheimer's progression modeling using cognitive and neuroimaging biomarkers. She maintains parallel research streams in parasitology (focusing on Eimeria and Toxoplasma pathogenesis in poultry) and environmental statistics (carbon footprint assessment and sustainable systems modeling). Her methodological contributions span predictive modeling for ordinal outcomes, robust signal processing techniques, and high-dimensional 'omics data analysis. Analysis of her 2023-2025 publications reveals three dominant thematic clusters: neurodegeneration research (comprising 40% of output, featuring amyloid biomarker studies and cognitive trajectory modeling), parasitology applications (35%, emphasizing vaccine development and infection dynamics), and environmental sustainability (25%, including geospatial optimization and life cycle assessment). This distribution reflects her strategic focus on high-impact biomedical problems while maintaining methodological versatility. No scientific awards were documented in the provided source material. While specific doctoral advising activities and grant portfolios weren't detailed, her extensive publication record across diverse domains indicates active mentorship of research teams and successful acquisition of collaborative funding through VBDCC and VICTR channels. She directs two major research infrastructure units: the Vanderbilt Biostatistics Data Coordinating Center (VBDCC), which provides statistical leadership for multi-center clinical trials, and the VICTR Methods Program, which develops innovative approaches for translational research design and analysis. These centers facilitate cross-departmental collaboration between biostatisticians and domain scientists across Vanderbilt's medical and engineering schools.

• Computer Architecture
• Embedded Systems
• High-Performance Computing
• +3 more

Zhiqiang Que is a Research Associate in the Department of Computing at Imperial College London, affiliated with the Faculty of Engineering and the Centre for High-Throughput Digital Electronics and Machine Learning. His research focuses on computer architecture, embedded systems, high-performance computing, and CAD tools for hardware design optimization. His research interests include FPGA-based acceleration of machine learning models, hardware-software co-design, and real-time signal processing for scientific applications such as particle physics and gravitational wave experiments. He has contributed to projects involving low-latency graph neural networks (GNNs), Bayesian neural networks, and efficient stream processing on FPGAs. Recent work includes advancements in trustworthy design flows for deep learning acceleration, reconfigurable architectures for recurrent neural networks, and optimizing FPGA-based systems for high-energy physics experiments at the HL-LHC.

Serge GuillasView profile

Professor

• Functional Data Analysis
• Uncertainty Quantification
• Environmental Statistics
• +4 more

Professor Serge Guillas is a faculty member at the University College London (UCL) Department of Statistical Science. His research focuses on functional data analysis, uncertainty quantification, environmental statistics, and emulation of complex computer models. He leads the NERC consortium on Uncertainty Quantification of Natural Hazards and has held roles such as Work Package Leader for quantifying uncertainties in natural hazard models. His work integrates statistical methods with geophysical and climate modeling, emphasizing tsunami risk analysis, climate dynamics, and ozone exposure studies. Education: PhD in Statistics from Paris 6 University (2001), followed by roles at the University of Chicago, Georgia Institute of Technology, and UCL. Current roles include teaching STAT1006 and STAT7001 courses. Research interests span functional regression, spatial data analysis, and probabilistic hazard modeling. Recent work explores machine learning-driven climate models, ozone exposure health impacts, and real-time data assimilation software (ParticleDA.jl). He collaborates globally, including with institutions in Georgia, Italy, and Indonesia, to advance tsunami modeling and disaster risk reduction. Key awards include ESRC-DFID-NERC funding, MAPS Faculty Postgraduate Research Prize (for student Ah Yeon Park), and leadership roles in SIAM’s Uncertainty Quantification group. Active in mentoring PhD students and securing interdisciplinary grants. Labs/Teams: Involved with the UCL Institute for Risk & Disaster Reduction and leads statistical emulation efforts in climate and geophysical modeling. Collaborates on fusion reactor design (ExCALIBUR project) and global temperature uncertainty quantification (GETQUOCS initiative).

Marjan FirouzniaView profile

Researcher

• Medical Imaging
• Machine Learning
• Deep Learning
• +3 more

Marjan Firouznia is a Principal Research Engineer at Linköping University , affiliated with the Division of Diagnostics and Specialist Medicine (DISP) under the Faculty of Medicine and Health Sciences . With a PhD in Electrical Engineering from Amirkabir University of Technology and postdoctoral experience at institutions like Case Western Reserve University, she specializes in advancing machine learning models for precise segmentation of cardiac structures including the left atrium , epicardial fat , and fibrosis using CT and MRI scans. Her work aims to improve diagnostic accuracy and treatment planning in cardiovascular care. Marjan's research focuses on medical imaging , deep learning , and computational anatomy , with recent publications on FractalRG , FK-means , and Poincare-guided UNet for cardiac structure segmentation. Her academic contributions span 15 recent publications , emphasizing fractal geometry , chaos theory , and optimization algorithms in biomedical applications. She actively develops open-source datasets and tools, such as the FK-means codebase , to support reproducibility in medical AI research.

Ronald GrahamView profile

Professor

• Combinatorics
• Graph Theory
• Number Theory
• +7 more

Ronald Graham is a Professor in the Computer Science and Engineering Department at the University of California San Diego (UCSD), affiliated with the Jacobs School of Engineering. He also serves as Chief Scientist at the California Institute for Telecommunications and Information Technology (Calit2). Graham’s career spans academia and industry, including a 37-year tenure at Bell Labs and leadership roles at AT&T Labs. He is renowned for contributions to combinatorics, Ramsey theory, scheduling algorithms, and discrete mathematics. His Erdős number of 1 underscores his collaborative ties to Paul Erdős, a legendary mathematician. Education: Graham earned his PhD in Mathematics from UC Berkeley in 1962. Research Interests: Graham’s work focuses on combinatorics, graph theory, number theory, computational geometry, scheduling theory, and quasi-randomness. He has also explored recreational mathematics, notably through his book Magical Mathematics (2011), which intertwines card tricks with mathematical principles. His research has influenced internet infrastructure design, including foundational work on routing algorithms and the development of Akamai Technologies. Notable Achievements: Graham has received the Steele Prize for Lifetime Achievement (2003), Euler Medal (1994), and Polya Prize in Combinatorics (1972). His concept of “Erdős numbers” revolutionized academic collaboration tracking, later adapted into Hollywood’s “Six Degrees of Separation” game. Advising & Grants: Graham mentors students and faculty, inspiring future generations through teaching and mentorship. The UCSD CSE Department established the Ronald L. Graham Chair in Computer Science in 2015 to honor his legacy, funded by an $18.5M alumni donation. Labs & Collaborations: As Calit2’s Chief Scientist, Graham advises on strategic directions, emphasizing interdisciplinary research in information technology and communications. His work bridges theoretical mathematics and applied computing, shaping global technological advancements.

• Distributed Algorithms
• Probabilistic Methods and Algorithms
• Load Balancing
• +7 more

Dr. Tom Friedetzky is an Associate Professor in the Department of Computer Science at Durham University. His research focuses on distributed algorithms, probabilistic methods, and load balancing in distributed systems. He has contributed to theoretical advancements in population protocols, self-stabilizing systems, and algorithmic game theory. His work often intersects with applications in peer-to-peer systems, storage networks, and consensus mechanisms. Key research interests include: Distributed Algorithms and Load Balancing Randomized Algorithms and Probabilistic Methods Selfish Reallocation Processes in Distributed Systems Population Protocols and Consensus Algorithms Recent work emphasizes time-space trade-offs in population protocols, exact majority consensus, and efficient data distribution in cloud environments. His publications span conferences such as PODC, ICALP, and SPAA, and journals including Distributed Computing and Journal of Parallel and Distributed Computing. He supervises postgraduate students David Kutner and Henry Austin. His research has been applied to practical systems like peer-to-peer networks and large-scale storage solutions, leveraging theoretical foundations to address real-world scalability challenges.

Kristopher MicinskiView profile

Assistant Professor

• Programming Languages
• Static Analysis
• Formal Methods
• +4 more

Kristopher Micinski is an Assistant Professor in the Electrical Engineering and Computer Science Department at Syracuse University. His research focuses on scalable program analysis, static analysis, and formal methods applied to computer security and privacy. He holds a PhD in Computer Science from the University of Maryland and a BS in Computer Engineering from Michigan State University. Education: PhD, Computer Science, University of Maryland at College Park BS, Computer Engineering, Michigan State University Research Interests: His work bridges theory and application of program analyses, emphasizing scalable static analysis frameworks, Datalog optimization for distributed systems, and security applications. Recent efforts include GPU-accelerated Datalog engines and large-scale malware analysis pipelines like Assemblage. Grants & Projects: NSF PPoSS Large: $1M grant for declarative analytics DARPA V-SPELLS: $400K for legacy software optimization Assemblage: $350K DoD grant for malware classification Teaching: He teaches undergraduate and graduate courses on programming languages (CIS352) and formal methods (CIS700), with materials publicly available on YouTube.