Find Academic Supervisors

• Bioinformatics
• Computer Science
• Algorithms
• +3 more

Dr. Juha Kärkkäinen is a University Lecturer and Principal Investigator at the Department of Computer Science, University of Helsinki, specializing in Algorithmic Bioinformatics. He supervises PhD students in the Doctoral Programme in Computer Science and actively contributes to research outputs and academic events. Institution: University of Helsinki Department: Department of Computer Science Rank: Lecturer His research focuses on string processing , data structures , and combinatorial pattern matching , with recent work on bijective Burrows-Wheeler transforms, LCP arrays, and efficient string indexing. Google Scholar lists publications spanning 2024 to 1999, highlighting his expertise in algorithm design and compression techniques . Notably, he has participated in and organized events like the International Workshop on Combinatorial Algorithms and delivered a keynote speech at the 1st Summer School on Bioinformatics Algorithms. While no explicit awards are detailed, his editorial roles in scientific collections and conferences underscore his academic influence.

Saugata GhoseView profile

Assistant Professor

• Data-centric computing
• Processing-in-memory
• Memory and storage systems
• +3 more

Saugata Ghose is an Assistant Professor in the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign (UIUC), with affiliate appointments in the Coordinated Science Laboratory and the Department of Electrical and Computer Engineering. His research focuses on data-centric computing, processing-in-memory architectures, memory systems, and hardware-software co-design. He holds a Ph.D. and M.S. in Computer Engineering from Cornell University and dual B.S. degrees in Computer Engineering and Computer Science from SUNY Binghamton. His academic positions include roles at Carnegie Mellon University (2016–2020) and postdoctoral research at CMU (2014–2016). Ghose has received notable awards such as the 2024 HPCA Hall of Fame, 2023 Intel Rising Star Faculty Award, and the 2019 CMU Wimmer Faculty Fellowship. His work has been supported by grants from NSF, Samsung, and Sandia National Laboratories. Research Interests: His group (ARCANA) explores data-centric architectures, processing-in-memory (PIM), and emerging memory technologies. Key areas include architectures for smart cities, autonomous systems, and genomics. He teaches courses on computer architecture and systems organization. Awards: HPCA Hall of Fame (2024) Intel Rising Star Faculty Award (2023) CMU Wimmer Faculty Fellow (2019) Cornell ECE Teaching Assistant Award (2013) Grants & Projects: NSF $2M for semiconductor advancements Samsung/Sandia grants for PIM programming models UIUC/ZJU DREMES collaboration on neuromorphic PIM Labs/Teams: Leads the ARCANA Research Group, focusing on reimagining computing around new applications. Collaborates with ASAP and HYBRID centers for co-design tools and neuromorphic architectures.

Vipul GoyalView profile

Associate Professor

• Cryptography
• Quantum Cryptography
• Security & Privacy
• +1 more

Vipul Goyal is an Associate Professor at the Computer Science Department of Carnegie Mellon University and a Senior Scientist at NTT Research in California. He is on leave from CMU since joining NTT Research in June 2020 after a 7-year tenure at Microsoft Research. His research focuses on Cryptography , Quantum Cryptography , Security & Privacy , and Theoretical Computer Science , supported by grants from NSF, DARPA, Department of Energy NETL, JP Morgan, Cisco, PNC, Ripple, and CyLab initiatives.

Xiaowen ZhangView profile

Professor

• Cryptography
• Information Security
• Cybersecurity
• +5 more

Xiaowen Zhang is a Professor of Computer Science at the College of Staten Island (CSI), City University of New York (CUNY), and a Doctoral Faculty Member at the CUNY Graduate Center. His academic work bridges theoretical and applied research in cybersecurity, information systems, and network technologies. Dr. Zhang holds a Ph.D. in Computer Science from the CUNY Graduate Center (2007) and a Ph.D. in Electrical Engineering from Northern Jiaotong University (1999), along with an M.A. from CUNY Queens College, an M.S. from Northern Jiaotong University, and a B.S. from Shanxi University. His research focuses on Cryptography, Information Security, Cybersecurity, Secure Biometrics, RFID Security & Privacy, Information Retrieval, and Wireless Sensor Networks . He explores both foundational cryptographic methods—such as secret sharing schemes and hash functions—and their practical implementations in secure systems, including RFID authentication protocols and data visualization platforms for sensor networks. The analysis of his recent publications reveals a consistent focus on security mechanisms in distributed and wireless environments . His work frequently combines cryptographic theory with system-level implementations, particularly in RFID and sensor networks. There is a strong trend toward privacy-preserving protocols, efficient data retrieval, and secure information sharing , often leveraging mathematical structures like Latin squares and Bloom filters. Dr. Zhang has been actively involved in mentoring students, as evidenced by numerous co-authored publications with graduate and undergraduate researchers. His contributions span journals such as Security and Communication Networks , Journal of Applied Security Research , and International Journal of Security and Networks , as well as major conferences including IEEE LISAT, ACM CODASPY, and IEEE Sarnoff Symposium.

Pierre FlenerView profile

Professor

• Artificial Intelligence
• Constraint Programming
• Discrete Optimization
• +5 more

Pierre Flener is a Professor at the Department of Information Technology, Division of Computing Science at Uppsala University. He leads the Optimisation Group and is a member of the Centre for Interdisciplinary Mathematics. His work focuses on constraint programming and discrete optimization, addressing complex scheduling, routing, and resource allocation challenges. Flener is an Officer of the Order of Merit of Luxembourg and co-founder of NordConsNet, the Nordic Network for Constraint Programming researchers. Research Interests: Flener’s research spans constraint programming, combinatorial optimization, and algorithm design. He develops models and tools for automated decision-making in domains like air traffic management, sensor networks, and industrial robotics. His work emphasizes practical applications, leveraging constraint satisfaction techniques to solve real-world puzzles such as vehicle routing and personnel allocation. Key Contributions: Flener has authored over 100 publications on constraint solving, symmetry breaking, and CP-based approaches to industrial problems. Notable projects include airspace sectorization optimization, energy-efficient sensor networks, and financial portfolio design. He has led initiatives like Auto-Tabling for MiniZinc and collaborated on CP applications in bioinformatics and image processing. Labs & Teams: He heads the Optimisation Group at Uppsala, fostering research in CP and its applications. NordConsNet, co-founded by Flener, connects Nordic researchers and practitioners in constraint technology.

Nima LashkariView profile

Assistant Professor

• Quantum Field Theory
• Quantum Gravity
• Black Hole Physics
• +3 more

Nima Lashkari is an Assistant Professor of Physics and Astronomy at Purdue University, affiliated with the College of Science . His research focuses on quantum field theory (QFT), quantum gravity, black hole physics, and quantum information theory. He holds a Ph.D. in Theoretical Physics from McGill University (2012) and a B.Sc. in Physics from Sharif University of Technology (2006). Prior to Purdue, he held postdoctoral positions at MIT, the University of British Columbia, and Stanford University, and was a member of the School of Natural Sciences at the Institute for Advanced Study (2018–2019). His research explores operator algebras in quantum gravity, local S-matrix formalisms, and multipartite entanglement. Notable interests include renormalization group flows as quantum error correction, eigenstate thermalization in QFT, and holographic principles. He is a member of the It from Qubit collaboration , focusing on non-perturbative quantum field theory and gravity through quantum information lenses. Lashkari’s work has contributed to understanding gravitational dynamics via entanglement, modular theory applications in QFT, and the interplay between quantum information and spacetime geometry. His recent talks include discussions on modular intersections, time interval algebras, and gravitational energy theorems derived from information inequalities.

• Robotics
• Control Systems
• Multi-Agent Systems
• +6 more

Herman Bruyninckx is a Part-Time Full Professor at Eindhoven University of Technology (TU/e) in the Mechanical Engineering department, specifically within the Control Systems Technology group and EAISI High Tech Systems initiative. He also serves as a professor (Hoogleraar) at KU Leuven in Belgium. Academic focus on robotics, control systems, and multi-agent coordination Active research in model predictive control , semantic mapping , and dynamic constraint algorithms Recent publications address industrial automation , agro-food robotics , and haptic technology Research Highlights : Developed hybrid decision-making frameworks for multi-agent navigation Innovated swing-free control methods for robotic pick-and-place operations Formulated constrained dynamics algorithms with LQR-Gauss principle integration Created ExoTen-Glove for haptic feedback in virtual environments Collaborative Projects : Coordinated with researchers like René van de Molengraft , Elena Torta , and Koen de Vos Contributed to NWO/TTW FlexCRAFT project for cognitive robotics in agro-food technology

Ken OnoView profile

Professor

• Number Theory
• Combinatorics
• Algebra
• +6 more

Ken Ono is the STEM Advisor to the Provost and the Marvin Rosenblum Professor of Mathematics at the University of Virginia. He also holds courtesy appointments as Professor of Electrical and Computer Engineering, Professor of Data Science, and Professor of Statistics. Additionally, he is a Fellow of the Shannon Center for Advanced Studies and serves on the Institute for Advanced Study board of trustees and the National Security Agency Advisory Board. His educational background includes a Ph.D. in Mathematics from UCLA (1993) and a B.A. in Mathematics from the University of Chicago (1989). Throughout his distinguished career, Ono has held leadership roles including Vice President of the American Mathematical Society, Chair of the Mathematics Section of the American Association for the Advancement of Science, and Chairman of the Department of Mathematics at UVa. Ken Ono specializes in Number Theory, Combinatorics, Algebra, and Arithmetic Geometry, with recent work expanding into sports analytics and swimming optimization. His research has produced numerous publications on partition theory, modular forms, and related areas, continuing the mathematical legacy of Srinivasa Ramanujan. His recent publications demonstrate a continued focus on partition theory, modular forms, and their connections to prime numbers and combinatorial structures. The work shows sophisticated connections between classical number theory and modern mathematical physics, with applications in cryptography and data science. Guggenheim Fellowship David and Lucile Packard Fellowship Alfred P. Sloan Foundation Fellowship NSF CAREER Award Presidential Early Career Award (2000) National Science Foundation Director's Distinguished Teaching Scholar (2005) University of Chicago Alumni Medal for Professional Achievement (2023) Fellow of the American Mathematical Society Ono has advised 34 PhD students and 17 postdocs, mentoring numerous award winners including 2 Breakthrough Mirzakhani Prize winners, 9 Morgan Prize winners, and 9 Schafer Prize winners. He founded and directs the Spirit of Ramanujan Global STEM Talent Search, which has awarded 125 budding scientists from 23 countries. His research grants have supported extensive REU programs, with continuous undergraduate research opportunities for 25 years. Beyond academia, Ono serves as a technical consultant for elite swimmers, has worked as an Associate Producer on the film The Man Who Knew Infinity , and is actively involved with the Infinity Arts Foundation. His interdisciplinary work spans from pure mathematics to practical applications in sports science, where he has developed data-driven approaches to optimize swimming performance and served as a consultant for Olympic medalists. He co-founded the Velocity Swim Camp with Olympic coach Todd DeSorbo, combining mathematical analytics with elite swimming training.

James GrayView profile

Associate Professor

• String Theory
• Mathematical Physics
• Calabi-Yau Manifolds
• +7 more

James Gray is an Associate Professor of Physics and Affiliate Professor of Mathematics at Virginia Tech, affiliated with the Department of Physics within the College of Science. His research focuses on string compactifications, particularly exploring the intersection of String Theory with particle physics phenomena. He holds a Ph.D. from the University of Sussex and has been supported by grants such as NSF PHY-2310588. Gray’s research interests include mathematical string theory, algebraic geometry applications to string phenomenology, and computational methods like the STRINGVACUA Mathematica package. His work involves classifying Calabi-Yau manifolds, studying fibrations, and analyzing heterotic and F-theory compactifications to derive realistic particle physics models. His recent articles emphasize geometric structures (e.g., Calabi-Yau fibrations, moduli spaces) and computational approaches for metric approximations using machine learning. He collaborates on datasets for Calabi-Yau fourfolds and line bundle cohomology, contributing to string phenomenology’s algorithmic tools. Gray leads efforts in theoretical particle physics at Virginia Tech and is involved with the Center for Neutrino Physics. His work bridges pure mathematics (e.g., algebraic geometry) with high-energy physics, aiming to connect abstract geometric constructions to observable particle physics parameters.

• Reconfigurable Computing
• FPGA Accelerators
• Parallel and Distributed Computing
• +3 more

Viktor Prasanna is the Charles Lee Powell Chair in Engineering and Professor of Electrical and Computer Engineering and Computer Science at the University of Southern California. He holds courtesy appointments in Computer Science and leads the Center for Energy Informatics, focusing on interdisciplinary research linking energy technologies, computer science, and engineering. Education: BE in Electronics (Bangalore University), ME (Indian Institute of Science), PhD in Computer Science (Pennsylvania State University) His research spans reconfigurable computing, FPGA accelerators, parallel and distributed systems, and big data applications. He has pioneered high-performance architectures and algorithms using FPGAs, impacting domains like networking, security, HPC, and machine learning. Prasanna has published over 600 papers, received 22 best paper awards, and secured >$50M in grants. His work emphasizes energy-efficient computing, with recent grants totaling $12.9M (2016–2021). His h-index is 73, with 23,454 total citations. Scientific Awards: IEEE Fellow, ACM Fellow, AAAS Fellow, W. Wallace McDowell Award, multiple Distinguished Alumnus Awards He has advised over 70 doctoral students and led major centers including CiSoft (Big Data in oilfield tech) and CAST. His editorial roles include Editor-in-Chief of IEEE Transactions on Computers and Journal of Parallel and Distributed Computing.

• Photovoltaic Power Systems
• Energy Storage Systems
• Renewable Energy
• +3 more

Kari Lappalainen is an Assistant Professor in the Department of Electrical Engineering at Tampere University, affiliated with the Faculty of Information Technology and Communication Sciences. His research focuses on photovoltaic power systems, energy storage technologies, and renewable energy integration. He leads studies on photovoltaic module aging, parameter identification, and energy storage system optimization for power smoothing and ramp rate control. Key research interests include: Photovoltaic module diagnostics and performance analysis Energy storage system design for hybrid renewable plants Impact of environmental factors (e.g., temperature, cloud cover) on PV efficiency Advanced modeling techniques for photovoltaic systems Recent work emphasizes real-time monitoring of PV degradation via current-voltage curve analysis and optimization of energy storage configurations to mitigate power fluctuations. Over 50 peer-reviewed publications demonstrate sustained contributions to renewable energy systems research. Notably absent are awards or formal advisee listings, though collaboration with institutions like EU PVSEC and frequent conference participation indicate active academic engagement.

Dmitri TymoczkoView profile

Professor

• Composition
• Music Theory
• Geometry of Music
• +5 more

Dmitri Tymoczko is a Professor of Music at Princeton University, where he has taught composition and theory since 2002. His work bridges the gap between academic music theory and contemporary composition, creating a unique interdisciplinary approach that has earned him recognition as a leading figure in modern music scholarship and creation. Tymoczko's research interests focus on the geometric modeling of musical structures, polystylistic composition that draws from genres spanning Renaissance to rock, and the mathematical foundations of tonality. His theoretical work explores how harmonic structures can be represented geometrically, revealing fundamental patterns that connect diverse musical traditions. He approaches composition as 'a logical deduction from absurd premises,' blending rigorous structural thinking with playful musical exploration. His scholarly publications and compositions reveal a consistent interest in the intersection of mathematical precision and expressive freedom. The articles demonstrate expertise across music theory, composition, and the cognitive aspects of musical perception, with recurring themes of geometric modeling, tonal structure, and genre hybridization. His work often challenges traditional boundaries between academic and popular music forms. Guggenheim fellowship Rhodes Scholarship Leonard Bernstein fellowship from Tanglewood Fellowship from the Radcliffe Institute for Advanced Study Block lectureship from the Society for Industrial and Applied Mathematics First music-theory article published in Science magazine Tymoczko maintains an active performance career alongside his academic work, with his compositions performed by leading ensembles including the Brentano Quartet, Newspeak, and Roomful of Teeth. His teaching integrates theoretical insights with practical composition, fostering a generation of musicians who understand music's structural foundations while embracing creative freedom. He has developed innovative tools like the musical programming language 'arca' to explore new compositional possibilities.

Peter ZollerView profile

Professor

• Theoretical Physics
• Quantum Optics
• Many-Body Quantum Physics
• +3 more

Peter Zoller is a Professor of theoretical physics at the University of Innsbruck and Scientific Director at IQOQI Innsbruck (Austrian Academy of Sciences). His research focuses on quantum optics, many-body quantum physics, and quantum information science, with a strong emphasis on quantum simulation of gauge theories and atomic systems. He has trained 34 PhD students and hosted 57 postdoctoral researchers, fostering collaborations between theory and experiment. His group, the Zoller Group, explores quantum phenomena such as lattice gauge theories, entanglement dynamics, and topological order using advanced quantum simulation techniques. Key research interests include atomic physics, quantum gases, and applications of quantum technologies to high-energy physics problems. Recent work addresses string breaking in quantum simulators, entanglement Hamiltonians, and scalable architectures for fermionic quantum processors. Collaborations span institutions like Harvard, MIT, and the University of Innsbruck’s experimental teams. His contributions bridge foundational physics with cutting-edge quantum technologies, aiming to solve problems inaccessible to classical methods.

• Control Theory
• Distributed Control
• Networked Systems
• +12 more

Emma Tegling is a Senior Lecturer (Associate Professor) at the Department of Automatic Control, Faculty of Engineering (LTH), Lund University, Sweden. She joined the department in January 2021 and holds a prestigious WASP (Wallenberg AI, Autonomous Systems and Software Program) professorship. Her research focuses on the analysis and control of large-scale networked systems, with applications in distributed electric power networks and socio-epidemiological networks. She is actively involved in multiple research projects, supervises several PhD students, and contributes to major academic events in control theory. Education: Ph.D. in Electrical Engineering, KTH Royal Institute of Technology (2019) M.Sc. in Engineering Physics, KTH Royal Institute of Technology (2013) B.Sc. in Engineering Physics, KTH Royal Institute of Technology (2011) Emma Tegling's research centers on the fundamental limitations of distributed control, particularly in large-scale and non-normal network systems. Her work addresses critical challenges in vehicular formations, power grids, and social networks. She develops scalable control designs, consensus protocols, and optimal control strategies for complex networked environments. Her recent publications highlight breakthroughs in string stability, transient performance, and distributed optimization. The trend in her articles shows a strong focus on mathematical control theory, network dynamics, and real-world applications in socio-technical systems. Scientific Awards: WASP professorship (Wallenberg AI, Autonomous Systems and Software Program) Emma Tegling leads and co-leads several significant research grants, including WASP NEST: Learning in Networks and Dynamics of Complex Socio-Technological Network Systems. She actively supervises PhD students such as Jonas Hansson and David Ohlin, whose work has led to novel consensus protocols and optimal control formulations. Her academic leadership extends to organizing the European Control Conference and co-organizing interdisciplinary workshops on power and democracy in modern societies. She is also involved in public engagement and academic service through supervision and project coordination. Emma Tegling is a key member of the Department of Automatic Control at Lund University, contributing to research teams focused on networked systems, control theory, and AI integration. She collaborates extensively within ELLIIT (the Linköping-Lund initiative on IT and mobile communication) and participates in cross-disciplinary labs working on AI, digitalization, and natural/artificial cognition. Her work is aligned with UN Sustainable Development Goals related to sustainable energy and resilient infrastructure.

Philip BilleView profile

Professor

• Algorithms
• Data Structures
• String Processing
• +9 more

Philip Bille is a Professor and Head of the Algorithms, Logic and Graphs section at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), College of Engineering. His research centers on the design and analysis of efficient algorithms, particularly for string processing, compressed data, and data structures. His research interests lie at the intersection of theoretical computer science and practical applications. He focuses on algorithms , data structures , string indexing , pattern matching , and compressed computation . His work enables efficient querying and processing of large-scale, repetitive data, with applications in bioinformatics, intrusion detection, and green computing. The recent publications reflect a strong trend in developing space-efficient and fast algorithms for modern computational challenges. Key themes include compressed data structures , sliding window indexing , finite automata compression , and energy-aware matrix operations . These works demonstrate expertise in balancing theoretical rigor with practical performance. Philip Bille actively supervises multiple PhD students and leads several research projects. He contributes to advancing sustainable computing aligned with UN SDGs. His work integrates algorithmic theory with real-world efficiency. Supervises PhD projects on hierarchical compression, adaptive computation, and vector processor algorithms. Involved in research on green computing, compressed formats, and efficient data models. He is affiliated with the Algorithms, Logic and Graphs group at DTU, a hub for theoretical and applied algorithmic research. The team explores fundamental problems in data representation and processing, pushing the boundaries of what is computationally feasible in terms of time and space.