Find Academic Supervisors

Dianne P. O'LearyView profile

Professor

• Scientific Computing
• Numerical Analysis
• Computational Science
• +6 more

Dianne P. O'Leary is a Professor affiliated with the Department of Computer Science at the University of Maryland. Her work focuses on scientific computing, numerical analysis, and computational science, with a particular emphasis on matrix factorizations, differential equations, and Monte Carlo methods. She authored the textbook *Scientific Computing with Case Studies* (SIAM Press, 2009), which integrates theoretical concepts with practical case studies across physics, engineering, epidemiology, and biology. Her research explores topics such as image deblurring, blind deconvolution, elastoplastic torsion, and iterative methods for solving large linear systems. The book includes nineteen case studies that illustrate algorithm design, mathematical modeling, and software development principles. Notably, the text emphasizes stability, error analysis, and MATLAB implementation grounded in machine arithmetic and memory management. While no explicit scientific awards or student advisees are mentioned in the provided texts, her contributions to computational education and algorithm development are central to her work. Supplementary materials include solution manuals, MATLAB code repositories, and detailed notes on topics like sensitivity analysis, computer memory management, and finite element methods. Her case studies often bridge theory and application, such as modeling epidemics, robot control systems, and structural engineering problems. The book’s structure reflects a commitment to hands-on learning, with units dedicated to dense matrix computations, optimization, Monte Carlo simulations, and sparse matrix techniques for partial differential equations.

• Developmental Psychology
• Cognitive Development
• Psychology of Language
• +12 more

Pierre Largy is a Professor of Developmental Psychology at Toulouse Jean Jaurès University, affiliated with the NeuroPsychoLinguistics Laboratory (LNPL). He teaches in the UFR of Psychology and UFR SES, and has also taught at ESPE Midi-Pyrénées, the University of Geneva (Switzerland), and the University of Social and Human Sciences of Hanoi (Vietnam). His research focuses on cognitive development, language acquisition, and learning processes, particularly in written language. His research interests center on how children and students acquire written language skills, especially spelling and grammar, and the cognitive mechanisms underlying learning difficulties such as dyslexia. He investigates the interplay between orthography, reading, and writing, and has expanded his work to include digital writing (e.g., SMS and instant messaging), exploring how these forms reflect or affect linguistic competence. More recently, he has studied the impact of emotion on orthographic performance, using methods such as emotional induction via music or color. His experimental approaches include chronometric analysis, dual-task paradigms, and corpus studies, focusing on behavioral indicators like error types, reaction times, and emotional responses. The trends in his recent publications reveal a consistent focus on spelling acquisition, the cognitive load in writing, emotional influences on performance, and the linguistic characteristics of digital communication. His work bridges developmental psychology, psycholinguistics, and educational research, with a strong emphasis on empirical methods and real-world applications. Among his scientific contributions, he has organized major conferences such as “Written language disorders: from childhood to adulthood” (2012) and “Written language disorders in children: from theory to practice” (2008), and has served as an expert for the European Commission and research agencies in Quebec. He has reviewed for journals including l'Année Psychologique , British Journal of Educational Psychology , and Language and Cognitive Processes . Largy has supervised several doctoral students, including Lucille Soulier and Aurélie Simoës-Perlant. He has held significant administrative roles, including Director of the UFR of Psychology, Director of the Department of Developmental Psychology, and member of the University's Board of Directors. His research has been supported through collaborations and institutional responsibilities, though specific grants are not detailed in the text. He leads a research team within the LNPL laboratory, focusing on written language development and disorders. He is actively involved in research teams and laboratories, particularly the NeuroPsychoLinguistics Laboratory (LNPL), where he conducts experimental studies on writing, reading, and cognitive development. His ongoing work includes exploring emotional effects on learning and the evolving nature of written language in digital contexts.

Congyi ZhangView profile

Research Fellow

• Computer Science
• Neural Shape Representation
• 3D Reconstruction
• +3 more

Congyi Zhang is a postdoctoral research and teaching fellow in the Department of Computer Science at the University of British Columbia. He earned his Ph.D. in Computer Science from Peking University and a B.Sc. in Mathematics from Fudan University. Prior to his current role, he served as a research associate at the Department of Computer Science, Hong Kong University, and as a visiting researcher at the Max Planck Institute for Informatics. His research focuses on neural shape representation, AI-driven 3D content generation, human-computer interaction, and 3D reconstruction techniques. He has contributed to methods that improve geometric accuracy, optimize sampling strategies, and enable efficient local editing of 3D models. Recent publications highlight advancements in neural implicit fields, multiview diffusion models, and efficient 3D mesh creation from 2D inputs. His work balances theoretical innovation with practical applications in digital media, orthodontics, and archaeology. He is currently transitioning to an Assistant Professor position at the Computer Science Department, University of Texas at Dallas, where he seeks motivated Ph.D. students for Spring/Fall 2026 cohorts.

Benoit ValironView profile

Researcher

• Formal Methods
• Logic and Programming Languages
• Quantum Computing
• +4 more

Benoit Valiron is a Researcher affiliated with the Formal Methods Laboratory, focusing on quantum computing, formal methods, and programming languages. His work integrates theoretical foundations with practical applications in quantum software development and formal verification. Research interests include semantics of quantum programming languages, reversible computing, and categorical logic. He explores quantum algorithms' operational and denotational semantics while developing tools like Qbricks for formal verification of quantum systems. Recent publications emphasize quantum circuit optimization, concurrency models in quantum systems, and applications of ZX-calculus. His work bridges theoretical advancements with real-world quantum technologies, addressing challenges in NISQ-era hardware and algorithm design. Key contributions include foundational papers on quantum control structures, time synchronization protocols for real-time systems, and vectorial λ-calculus extensions. His research often involves collaborations with institutions advancing quantum software ecosystems and formal methods in safety-critical systems.

• Deep Neural Networks
• Convolutional Neural Networks
• Social Robotics
• +4 more

Francisco Fernandes Junior is a Postdoctoral Researcher in the Department of Information and Communications Engineering at Aalto University, specializing in AI-driven systems. His work intersects computer science, human-computer interaction, and neural network optimization. His research focuses on Deep Neural Networks (100% fingerprint match), Convolutional Neural Networks (92%), and Social Robotics (90%), with significant contributions to neural architecture design and evolutionary strategies. Current projects explore generative AI in software development and real-time resource allocation frameworks. Recent publications reveal trends toward human-AI collaboration (2025 touchscreen error simulation) and low-code AI platforms (AgentForge), emphasizing practical applications in medical imaging and software engineering. His work contributes to UN Sustainable Development Goals through computational sustainability. ACM CHI 2025 Best Paper Honorable Mention Award (2025) Google Cloud for Researchers Grant (2025) As an active academic contributor, he serves as reviewer for IEEE Transactions on Artificial Intelligence (2024–present) and Neural Computing & Applications (2023), while participating in conference organization including ICAART 2025. His research integrates industry partnerships through cloud computing resources and collaborative AI frameworks.

• Network Coding
• Information Theory
• Wireless Networks
• +3 more

Muriel Medard is the NEC Professor of Software Science and Engineering in the Electrical Engineering and Computer Science (EECS) Department at MIT. She leads the Network Coding and Reliable Communications Group in the Research Laboratory for Electronics. Affiliated with MIT since her undergraduate studies (B.S. EECS 1989, Mathematics 1989, Humanities 1991), she also holds an M.S. (1991) and Sc.D. (1995) from MIT. She is a member of the U.S. National Academy of Engineering (2020), German National Academy of Sciences Leopoldina (2022), Fellow of the National Academy of Inventors (2018), and multiple other prestigious societies. Her research focuses on network coding, information theory, wireless networks, and optical networking. Notable contributions include foundational work in network coding protocols, error correction, and secure communication systems. Medard has pioneered algorithms like GRAND (Guessing Random Additive Noise Decoding) and ORBGRAND, which optimize decoding efficiency and energy consumption in communication systems. Medard’s honors include the IEEE Kobayashi Computers and Communications Award (2022), IEEE Armstrong Achievement Award (2017), and MIT’s Egerton Award (2004). She has over 60 patents, co-founded CodeOn and Steinwurf (as Chief Scientist), and supervised over 40 master’s, 20 doctoral students, and 25 postdocs. She chairs the IEEE Transactions on Information Theory and has held leadership roles in the IEEE Information Theory Society. Her service contributions include founding the Women in Information Theory Society (WithITS), mentoring initiatives, and roles on MIT’s Faculty Committees on Student Life and Campus Planning. Medard’s work bridges theoretical advancements with practical applications in 5G/6G systems, cybersecurity, and energy-efficient hardware.

Kevin TangView profile

Professor

• Networks
• Control and Optimization of Computer Networks
• Control Theory
• +8 more

Kevin Tang is a Professor in the School of Electrical and Computer Engineering at Cornell University, where he has been a faculty member since 2007. His research spans networks, control, optimization, and game theory, with a strong focus on the control and optimization of computer networks. He is affiliated with the Department of Electrical and Computer Engineering and leads a research group focused on information networks and systems. Ph.D., Electrical Engineering, California Institute of Technology, 2006 M.S., Electrical Engineering, California Institute of Technology, 2002 M.S., Electronics Engineering, Tsinghua University, 2001 B.E., Electronics Engineering, Tsinghua University, 1999 Professor Tang's research interests include networks, control theory, optimization theory, and game theory , with applications in computation systems, information theory, and decision systems. His work bridges theoretical foundations with practical implementations in networking and distributed systems. His recent publications show a strong trend in network optimization, privacy-preserving systems, and control of networked systems , appearing in top venues such as IEEE/ACM Transactions on Networking, SIAM Journal on Optimization, NeurIPS, and ICML. Themes include network coding, multipath transport, synchronization, and mathematical optimization for network performance. Notable scientific awards include: Presidential Early Career Award for Scientists and Engineers (PECASE), 2012 IBM Faculty Award, 2018 ACM Senior Member, 2019 First Place in AT&T SDN Network Design Challenge, 2016 Michael Tien '72 Excellence in Teaching Award, 2011 He has advised students such as J. Cheng and S. Tseng, and has been actively involved in research grants and projects related to networked systems and control. From 2016 to 2019, he served as Director of Graduate Studies for the ECE department at Cornell. His research group, accessible at networks.ece.cornell.edu , investigates the interplay between economics, control, and networking in modern distributed systems.

Bjarne StroustrupView profile

Professor

• Computer Science
• Programming Languages
• Software Development
• +3 more

Bjarne Stroustrup is a Professor of Computer Science at Columbia University in New York City. He is renowned as the designer and original implementor of the C++ programming language. His work has profoundly influenced software development, emphasizing object-oriented and generic programming paradigms. Stroustrup authored influential books such as The C++ Programming Language , A Tour of C++ , and The Design and Evolution of C++ . He actively contributes to the ISO C++ Standards Committee, leading efforts to evolve the language through technical papers and proposals. His research focuses on improving C++'s safety, performance, and expressiveness, including work on concepts, move semantics, and concurrency features. He has collaborated with industry and academic partners to shape the language's future direction while maintaining its efficiency and flexibility. Stroustrup's technical contributions span compiler design, template metaprogramming, and standard library development. He advocates for programming practices that balance abstraction with low-level control, making C++ a cornerstone for systems programming and high-performance applications.

Ying LiView profile

Associate Professor

• Wireless Sensor Networks
• Drone Networks
• Ad Hoc Networks
• +4 more

Ying Li is an Associate Professor of Computer Science at Colby College, located in Waterville, Maine. She holds a Ph.D. in Computer Science from the University of New Hampshire, and M.Eng. and B.Eng. degrees from Hubei University of Technology in China. Her research focuses on wireless networks, particularly in energy-efficient communication strategies for resource-limited environments such as drone networks, ad hoc networks, and delay-tolerant networks. She has contributed to protocols for improving network connectivity, data dissemination, and mission-oriented systems. Professor Li teaches courses including Computer Organization, Programming Languages, and Advanced Computer Networks. Her work emphasizes practical solutions for challenges in network security, service availability, and computing efficiency. Education: Ph.D. in Computer Science, University of New Hampshire M.Eng. in Applied Technology, Hubei University of Technology B.Eng. in Computer Science and Technology, Hubei University of Technology Research Interests: Dr. Li’s research spans Wireless Sensor Networks , Drone Networks , Ad Hoc Networks , and Delay-Tolerant Networks . She explores energy-efficient trajectory planning, multi-agent reinforcement learning for collaborative drone operations, and protocols to enhance reliability in intermittently connected systems. Recent efforts include studies on erasure coding for IoT communication and containerized network performance evaluation. Her work often bridges theoretical algorithm design with real-world network challenges. Grants & Advising: Professor Li has advised numerous students on research projects, including work on energy-aware routing, multi-agent systems, and drone replacement strategies. She collaborates on grants related to network optimization and resilience, though specific funding details are not detailed in the provided texts. Labs & Teams: While no dedicated lab is explicitly named, her research involves experimental setups in Colby’s Computer Science department, focusing on simulations and prototyping for network protocols and drone systems.

Saksham AgarwalView profile

Assistant Professor

• Networking
• Systems
• Hardware
• +3 more

Saksham Agarwal is an Assistant Professor in the departments of Electrical and Computer Engineering and Computer Science at the University of Illinois at Urbana-Champaign (UIUC). His research focuses on the intersection of networking, systems, and hardware, with a particular emphasis on host congestion control, datacenter architectures, and network protocols. He received his Ph.D. in Computer Science from Cornell University under the supervision of Prof. Rachit Agarwal and completed his undergraduate studies at IIT Kanpur. Research Interests: Host congestion control, datacenter networks, hardware-software co-design, network protocols, and systems optimization. His work includes seminal contributions like hostCC (host congestion control architecture) and Harmony (congestion-free datacenter architecture). Awards: ACM SIGCOMM Doctoral Dissertation Award (2025), Cornell CS Dissertation Award (2025), IRTF Applied Networking Research Prize (2025), Google PhD Fellowship (2019), Best Student Paper Awards (SIGCOMM 2024 and SIGCOMM 2018). Teaching: CS 438/ ECE 438 Communication Networks, ECE 598MCI Modern Cloud Infrastructure. Advising: Current students include Midhul Vuppulapati (SIGCOMM'24 Best Paper recipient), Shreyas Kharbanda, and Omar Eqbal. Labs/Teams: Lead researcher on host congestion control and datacenter network projects. Collaborates on open-source implementations of hostCC and Harmony .

Liqun LuoView profile

Professor

• Neuroscience
• Neural Circuit Assembly
• Olfactory System
• +5 more

Liqun Luo is the Ann and Bill Swindells Professor at Stanford University's School of Humanities and Sciences, holding primary appointments in the Department of Biology and courtesy in Neurobiology. As a Howard Hughes Medical Institute Investigator and member of multiple Stanford institutes, he has pioneered research on neural circuit assembly and function. B.S., University of Science and Technology of China (1986) Ph.D., Brandeis University (1992) His research spans neural circuit development using Drosophila and mouse models, synaptic specificity mechanisms through cell-surface proteins, and motivated behavior neurobiology including thirst/hunger drives. Recent work focuses on circuit rewiring and single-cell manipulation tools . His 15 most recent publications demonstrate expertise in dimensionality reduction in circuit assembly , repulsive synaptic interactions , and computational approaches to neuroendocrinology . Articles highlight cross-species comparisons and innovative methodologies. Scientific honors include: 2025 National Academy of Sciences Award in Neurosciences 2020 Society for Neuroscience Education Award 2019 National Academy of Sciences Pradel Award 2012 National Academy of Sciences membership Dr. Luo teaches BIO 102: Introduction to Neuroscience and has advised numerous doctoral students including Zhuoran Li and Alina Xiao. His lab develops cutting-edge tools like the TRAP and TRIO methods for circuit mapping, while maintaining active collaborations across multiple disciplines.

• Machine Learning
• Deep Learning
• Error Control Coding
• +4 more

Malihe Aliasgari, Ph.D., is an Assistant Professor at Kean University since 2021, affiliated with the School of Integrative Science and Technology (IST) in the College of Science, Mathematics and Technology. She holds a Ph.D. in Communication Engineering from NJIT (2020) and a Ph.D. in Pure Mathematics from Amirkabir University (2014). Her office is located in STEM 502, and she can be contacted at (908) 737-7224. Education: Ph.D., Communication Engineering, New Jersey Institute of Technology (NJIT), 2020 Ph.D., Pure Mathematics, Amirkabir University, Tehran, Iran, 2014 Research Interests: Dr. Aliasgari focuses on theoretical and applied research in Machine Learning, Deep Learning, and coding theory. Her work bridges mathematical rigor with engineering applications, particularly in Error Control Coding, Information Theory, Algebraic Coding, and Distributed Computing. Academic Affiliation: She is part of the Department of Computer Science and Technology under the IST school, which offers programs in interdisciplinary science and technology fields. Teaching: Dr. Aliasgari teaches courses in Machine Learning, Coding Theory, Programming, and Calculus, emphasizing the mathematical foundations of engineering solutions.

Sos S. AgaianView profile

Professor

• Big and Small Data Analytics
• Computational Vision and Sensing
• Machine Learning
• +14 more

Sos S. Agaian is a Distinguished Professor of Computer Science at the College of Staten Island and the Graduate Center, City University of New York (CUNY). Previously, he was the Peter T. Flawn Professor of Electrical and Computer Engineering at the University of Texas at San Antonio (UTSA), where he also served in the Graduate School of Biomedical Sciences and led the Multimedia and Mobile Signal Processing Laboratory. He has held visiting positions at Tufts University and Tampere Institute of Technology and was a Leading Scientist at AWARE, Inc., Massachusetts. Dr. Agaian holds a PhD in Mathematics and Physics from the Steklov Institute of Mathematics, Russian Academy of Sciences, a Doctor of Engineering Sciences from the Institute of Control Systems, RAS, and an MS in Mathematics and Mechanics (summa cum laude) from Yerevan State University, Armenia. His research spans Big and Small Data Analytics, Computational Vision, Machine Learning, Digital Forensics, Information Fusion, and Fast Algorithms . His work emphasizes extracting meaning from visual content and developing intelligent systems for applications in healthcare, biomedical data mining, multimedia security, and urban computing. He has contributed foundational theories such as Agaian's Theorem , Agaian's Family , Agaian's Method , and Agaian’s Matrix , which are widely recognized in signal processing and combinatorics. His publications include over 650 peer-reviewed papers, ten books, and nineteen edited proceedings. His research has been cited extensively, reflecting broad impact across engineering and computer science. Trends in his recent work focus on real-time data analytics, neurocomputing, and secure multimedia systems, particularly in defense and medical imaging. SPIE Fellow (2005) AAAS Fellow (2010) IS&T Fellow (2013) IEEE Fellow (2017) Entrepreneurship Award (UTSA, 2016, 2013) Innovator of the Year (UTSA, 2014) Tech Flash Titans - Top Researcher (2014) Distinguished Research Award (UTSA, 2006) Dr. Agaian has mentored over 30 PhD and 60 Master’s students, many of whom now work at top institutions and companies including Intel, Raytheon, MIT-LL, Siemens, and Cisco. He has secured over $7 million in research funding from NSF, DARPA, U.S. Army, DOE, and AFOSR. He co-founded BA Logix, Inc. and the Center for Simulation, Visualization, and Real Time Prediction at UTSA, which received a $5M NSF grant. He holds over 44 U.S. and international patents, several of which are licensed commercially, including to Latakoo for use in media transmission (e.g., NBC’s Sochi Olympics coverage). He leads the Computational Vision, Machine Learning, and Data Analytics (CSMAD) Laboratory , which focuses on real-time intelligent systems, multimodal biometrics, cancer imaging, and urban computing. The lab trains students in cutting-edge research and collaborates with industry and government sponsors.

Yasutaka KameiView profile

Professor

• Empirical Software Engineering
• Software Reliability
• Software Testing and Debugging
• +7 more

Yasutaka Kamei is a Full Professor at the Graduate School and Faculty of Information Science and Electrical Engineering , Kyushu University since 2024. He holds a Doctorate in Information Science from Nara Institute of Science and Technology and has been a InaRIS Fellow since 2023. His career spans roles as Associate Professor (2015-2023), Visiting Researcher at Queen's University (2015-2017), and Assistant Professor (2011-2015). Research Fields: Specializing in Empirical Software Engineering , he investigates software reliability, defect prediction, code review practices, and mining software repositories. His work on fuzz testing , technical debt , and automated program repair has produced influential frameworks like TraceJIT and PAFL . He pioneered studies on developer behavior in GitHub and federated learning for cross-project defect prediction. Scientific Trends: Recent publications focus on LLM applications in mutation testing , visual bug reporting , and CI/CD inefficiencies . His team explores developer-centric challenges in modern practices, including token-based micro commits and fuzzing build failures . Awards & Grants: InaRIS Fellowship (2023): 10-year, 100M yen grant for human-machine interaction in software development IPSJ/ACM Early Career Award (2013) Leadership: He chairs PC for MSR2018 , co-organizes NII Shonan Seminars , and serves on committees for ICSE , ASE , and SANER conferences. His editorial roles include Empirical Software Engineering and Automated Software Engineering journals.

Maikel Lázaro PÉREZ GORTView profile

Researcher

• Computer Science
• Data Security
• Database Systems
• +3 more

Maikel Lázaro PÉREZ GORT is a Researcher at the Department of Environmental Sciences, Computer Science and Statistics (DAIS) at Ca' Foscari University of Venice. He is affiliated with the Research Institute for Complexity Communications and teaches advanced courses on relational data watermarking in doctoral programs. His primary research focuses on database security, specifically developing robust watermarking techniques for relational data integrity protection against attacks in untrusted environments. Teaching Activities: Doctoral Course (DM226/2021): 'Relational Data Watermarking: Fundamentals' and 'Robust Schemes' Past courses include 'Programming and Laboratory-2' for undergraduate Computer Science students and 'Lab of Human-Centered Design' for Digital Management programs. Research Interests: His work addresses challenges in data watermarking resilience, including topology optimization for watermark sources, query impact analysis, and prevention of additive attacks. Recent efforts emphasize earth observation data integrity and quantile-based distortion minimization techniques. Publications: Over 13 peer-reviewed articles from 2017-2025, focusing on watermarking methodologies for relational databases, earth observation systems, and forensic image analysis. Key themes include brute-force attack resilience, semantic-driven marking, and high-quality key generation schemes. Awards/Grants: No specific awards or grants mentioned in the provided materials. Labs/Teams: Affiliated with the DAIS department's research groups working on information systems security and complexity science.