Find Academic Supervisors

• Photovoltaics
• Nanophotonics
• Materials Science
• +3 more

Tom Veeken is a Researcher at AMOLF, the Netherlands Institute for Nanotechnology, specializing in photonic control over light absorption and emission in photovoltaic systems. He holds a PhD from the University of Amsterdam (UvA), awarded in 2022, where his thesis focused on optimizing light-matter interactions in solar cells. His work spans advancements in perovskite materials, radiative cooling techniques for solar modules, and quantum dot integration for enhanced energy conversion efficiency. Veeken’s research interests include nanophotonics, solar energy materials, and optical engineering, with a focus on improving solar cell performance through novel material designs and light management strategies. He collaborates with interdisciplinary teams to address challenges in energy harvesting, such as reducing reabsorption losses in textured silicon and enhancing luminescent solar concentrator efficiency. His publications highlight contributions to perovskite-based solar cells, radiative cooling via photonic silica microcylinders, and quantum dot emission control. Veeken’s work aligns with AMOLF’s LMPV (Light and Matter for Photonics and Energy) group, advancing sustainable energy materials and photonic technologies. No scientific awards or student advisement records are explicitly listed in the provided texts. His role as a Program Officer at AMOLF suggests involvement in project coordination and technical leadership within the institute’s research infrastructure.

Massimo Orazio SpataView profile

Research Fellow

• Deep Learning
• Biomedical Engineering
• Computer Science
• +3 more

Massimo Orazio Spata is a Research Fellow in Computer Science at the University of Catania's Department of Mathematics and Computer Sciences, specializing in deep learning applications for biomedical, audio, and biometric systems. He has held roles at STMicroelectronics since 1999, focusing on system integration, image processing, and biomedical device R&D. He teaches courses such as Mobile Programming and Computer Architecture at secondary schools and has advised numerous students on grid computing and middleware projects. Education: PhD in Computer Science (University of Catania, 2008), MSc in Computer Science (University of Catania, 2013), and a teaching certification in Computer Science (University of Catania, 1998). Research interests include deep learning algorithms, biomedical device development, grid scheduling, and cybersecurity. He has authored patents on lab-on-chip systems, bio-computer analysis, and scheduling methods, and his work has been recognized with STMicroelectronics Innovation Awards (2016–2014) and a Cisco CCNA certification. Key collaborations include projects with Google (Mediapipe Objectron for robotics), Huawei (video deblurring), and involvement in the PNRR Horizon HiCONNECTS project (2024–present). He serves on conference committees (e.g., ICAETA 2023) and has developed e-learning systems and CAD tools for STMicroelectronics.

Graham RileyView profile

Lecturer

• High Performance Computing
• Scientific Computing
• Parallel Computing
• +4 more

Graham Riley is a Lecturer in the School of Computer Science at the University of Manchester and holds a part-time position in the Scientific Computing Department (SCD) at STFC, Daresbury. His research focuses on high performance computing (HPC), software engineering for scientific computing, and performance modeling for parallel machines. Key areas include techniques for developing HPC applications, software architectures for coupled modeling, and performance control in distributed systems. His work emphasizes collaboration with computational scientists in domains such as Earth System Modelling (e.g., UK Met Office), computational chemistry, and biology. He has contributed to projects like the EuroExa architecture for exascale computing and the LFRic weather/climate model porting to FPGAs. Riley's research also explores energy efficiency in HPC systems and FPGA acceleration strategies for scientific workloads. Notable contributions include studies on parallelization strategies, FPGA-based acceleration of climate models, and optimizing OpenCL for heterogeneous architectures. His work aligns with UN Sustainable Development Goals, particularly through contributions to climate modeling and sustainable computing practices. Riley collaborates with institutions like the Met Office and the ESM community in Europe/US. His academic profile reflects a balance between theoretical research and practical application, with a strong focus on bridging computational science and engineering challenges.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +9 more

Carlo Masone is a Fixed-term Researcher at the Department of Control and Computer Science (DAUIN), Politecnico di Torino, affiliated with the College of Electronic, Telecommunications, and Physics Engineering. He is also an invited member of the College of Management and Production Engineering and the College of Mathematical Engineering, reflecting his interdisciplinary contributions. His research focuses on artificial intelligence, machine learning, computer vision, and robotics, with significant work in visual place recognition, federated learning, and geospatial AI. His research interests span Artificial Intelligence , Machine Learning , Computer Vision , Robotics , Visual Place Recognition , Federated Learning , Open-Set Segmentation , and Geospatial AI . He has developed methods for uncertainty quantification in ML models for autonomous vehicles and contributed to digital heritage through the MAPP project. The recent publications highlight a strong trend in visual place recognition (e.g., EigenPlaces, MeshVPR), geolocalization of astronaut photography (Earthloc, EarthMatch), and robust segmentation (Mask2Anomaly). There is a clear emphasis on leveraging pre-trained features, federated learning for privacy-preserving recognition, and topological methods in vision. His work bridges robotics, AI, and real-world applications in autonomous systems and cultural heritage. Scientific Awards: Outstanding Reviewer at CVPR (2022, 2023, 2024) IROS JTCF Novel Technology Paper Award (2016) Best Paper Award Finalist at IEEE ICIA (2016) ELLIS Fellow (2024–) He advises PhD students in Artificial Intelligence and Computer and Systems Engineering and leads commercial research projects such as Quantifying Uncertainties in ML Models for Driver-Assisted and Autonomous Vehicles and MAPP - Phygital and Participatory Alpine Museums . He has previously collaborated with the Max Planck Institute for Biological Cybernetics (2014–2017). He teaches courses including Machine Learning for Mathematical Engineering , Robot Learning , and GeoAI , and contributes to programming and data science curricula. He is also a co-inventor on a patent for automated neural network design.

Marius PesaventoView profile

Professor

• Sensor Array Processing
• MIMO Communication
• Adaptive Beamforming
• +4 more

Prof. Marius Pesavento is a Full Professor at the Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, leading the Communication Systems Group. His research focuses on sensor array processing, MIMO communication systems, adaptive beamforming, and mathematical optimization in networks. He has held academic and industry roles since 2001, including positions at mimoOn GmbH and FAG Industrial Services. Education: PhD (Doktorate) in Electrical Engineering, Ruhr-Universität Bochum (2001–2005) Master of Engineering, McMaster University (1999–2000) Dipl.-Ing. in Electrical Engineering, Ruhr-Universität Bochum (1992–1999) His research interests span robust high-resolution sensor array processing, 4G/5G mobile networks, and network information theory. Notable projects include developing tensor models for ultrasonic sensor calibration and applying machine learning to anomaly detection in network flows. His work bridges theoretical optimization and practical applications in automotive radar, 6G networks, and medical imaging. Labs/Teams: Leads the Communication Systems Group at TU Darmstadt, focusing on interdisciplinary projects in signal processing and communication systems.

Justus KebschullView profile

Assistant Professor

• Neural Circuits
• Connectomics
• Cellular and Molecular Neuroscience
• +12 more

Justus Kebschull is an Assistant Professor in the Department of Biomedical Engineering at Johns Hopkins University School of Medicine. His research bridges neuroscience, genomics, and molecular engineering to understand brain structure, evolution, and function across vertebrates. He is affiliated with the Biomedical Engineering Program and the Neuroscience Training Program, mentoring graduate students and leading a dynamic research lab. Research Interests: Dr. Kebschull's work focuses on neural circuits , connectomics , and molecular neuroscience , with a strong emphasis on evolutionary perspectives. He develops and applies cutting-edge technologies such as MAPseq, BRICseq, in situ sequencing, single-cell RNA-seq, and viral tracing to map brain connectivity and gene expression at single-cell resolution. His lab investigates how brain regions and cell types evolve, how circuits adapt to new functions, and how these principles are disrupted in neurodevelopmental disorders and addiction. Publication Trends: His recent publications highlight a strong focus on comparative connectomics , cerebellar evolution , and high-throughput neuroanatomical methods . His work integrates connectivity data with transcriptomic and functional activity profiles, enabling a systems-level understanding of brain organization. Key themes include circuit duplication in brain evolution, scalability of neural mapping, and cross-species analysis from mice to sharks. Scientific Recognition: While no specific awards are listed, his publication record in top-tier journals such as Science , Nature , Cell , and Neuron reflects significant scientific impact and recognition in the neuroscience community. Advising and Research Support: Dr. Kebschull actively mentors graduate students and postdoctoral researchers. His lab is expanding and currently recruiting PhD students, postdocs, and technicians. His research is supported by competitive funding, enabling the development of novel tools for brain mapping and disease modeling. Research Environment: The Kebschull Lab is a highly interdisciplinary team situated at the intersection of neuroscience, synthetic biology, virology, and computational biology. The lab fosters innovation in molecular tool development and applies these to fundamental questions in brain evolution and disease. Ongoing projects include high-throughput comparative connectomics, developmental mechanisms of circuit formation, and the impact of drugs and disorders on neural architecture.

Francois ChaumetteView profile

Research Professor

• Robotics
• Computer Vision
• Visual Servoing
• +17 more

François Chaumette is a Senior Research Scientist (Directeur de recherche) at Inria, affiliated with IRISA and the Centre Inria de l'Université de Rennes. He has been a key researcher in robotics and computer vision since 1990 and led the Lagadic research team from 2004 to 2017. His research interests are centered on robot vision, particularly visual servoing and active perception . He has made foundational contributions to image-based and position-based visual servoing, and his work integrates control theory, computer vision, and robotics. His research spans applications in mobile robotics, aerial systems, medical robotics, space robotics, and soft object manipulation. The recent publications highlight a consistent focus on visual servoing under complex constraints—such as motion blur, occlusions, and deformations—applied to drones, cable-driven robots, and space systems. There is a strong emphasis on robustness , stability analysis , and hybrid sensing (e.g., vision + proximity, vision + force). His work with the RemoveDebris mission demonstrates real-world impact in space robotics. AFCET/CNRS Prize for best Ph.D. in Automatic Control Best paper awards at RFIA 1996 & 2004 Best paper in IEEE T-RA (2002) Best paper in IEEE RA-L (2019) Best paper in IEEE RAM (2020) IEEE Fellow (2013) He has advised over 30 Ph.D. students, many of whom have become active researchers in robotics. He has served in editorial roles for top journals including IEEE Transactions on Robotics , IEEE Robotics and Automation Letters , and the International Journal of Robotics Research . He was elected to the IEEE RAS Administrative Committee (2016–2018) and served on ERC grant panels for robotics. Chaumette is the main developer of ViSP (Visual Servoing Platform), a widely used C++ library for visual tracking and servoing. His leadership in both theoretical advances and software tools has significantly shaped the visual servoing community.

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence
• +7 more

Jing Jiang is a prominent researcher at Singapore Management University, specializing in Natural Language Processing (NLP), Computational Linguistics, and Artificial Intelligence. His work spans diverse areas including Vision-Language Models, Machine Translation, Knowledge Graph Reasoning, Sentiment Analysis, and Social Media Discourse Modeling. Key contributions include frameworks for consistent client simulation in mental health counseling and counterfactual contrastive prefix-tuning for many-class classification. He has pioneered methods in zero-shot VQA with interpretable reasoning graphs , cross-lingual understanding with universal syntax , and modularized zero-shot architectures . His research often combines theoretical insights with practical implementations, as seen in works on stereotypical bias in vision-language models (VLStereoSet), tensorized self-attention for dependency modeling, and collaborative relation-augmented attention for knowledge graph completion. Jing Jiang's collaborations span global experts in NLP and AI, with co-authors from institutions like SMU, Waseda University, and Microsoft Research.

Jason M SilvermanView profile

Professor

• Persian Empire
• Judaean communities
• Sociology of religion
• +13 more

Jason M Silverman is a Professor at the Faculty of Theology, University of Helsinki, and a Docent in Hebrew Bible and Cultural History of Ancient Persia. He leads Team 2 'Social Scientific Theory and Applications' within the Centre of Excellence in Ancient Near Eastern Empires (ANEE) and serves as Principal Investigator for the ERC Advanced Grant project WORK-IT. PhD from Trinity College Dublin (2010) Docent in Hebrew Bible, University of Helsinki Docent in Cultural History of Ancient Persia, University of Turku His research focuses on the Persian Empire's impact on Judaean communities through sociological frameworks, including labor systems, migration, orality, and religious transformation. He employs interdisciplinary methods, collaborating with Assyriologists, archaeologists, and communications scholars. The 15 most recent articles span topics from Persian period archaeology to biblical reception in modern literature, emphasizing labor structures, historical criticism, and cultural exchanges between empires and local populations. Docent in Cultural History of Ancient Persia, University of Turku (2024) Docent in Old Testament Studies, Faculty of Theology (2017) As a supervisor for doctoral programs and leader of ANEE's work packages, Silverman integrates social scientific theories into ancient history research. His projects include WORK-IT, ANEE, and HSSH Catalyst 2023, with collaborations across Finland and international institutions.

• Parallel Programming
• Communicating Sequential Processes
• Software Systems
• +4 more

Professor Jon Kerridge is a distinguished academic at the School of Computing, Edinburgh Napier University, where he has made significant contributions to parallel programming, software systems, and database applications. With a career spanning several decades, he has published extensively in the field of computer science and has supervised numerous PhD students. BSc, MSc, PhD Fellow of the British Computer Society (FBCS) Chartered IT Professional (CITP) Fellow of the Higher Education Academy (FHEA) Chartered Engineer (CEng) Professor Kerridge's research primarily focuses on parallel programming models, particularly through his work on the Groovy Parallel Patterns Library and Communicating Sequential Processes (CSP). His research spans multiple domains including software engineering, database systems, and interdisciplinary work in neuroscience related to dyslexia. He has also made significant contributions to pedestrian movement modeling and evolutionary algorithms. His publications demonstrate a consistent focus on practical software engineering solutions for parallel and distributed systems. The research trajectory shows progression from foundational work in computer architecture education in the 1980s through database systems in the 1990s-2000s to modern parallel programming frameworks. His interdisciplinary work connecting computer science with visual processing in dyslexia represents an innovative application of computational approaches to neuroscience problems. Fellow of the British Computer Society (FBCS) Chartered IT Professional (CITP) Fellow of the Higher Education Academy (FHEA) Chartered Engineer (CEng) Professor Kerridge has supervised several PhD students to completion, including Kevin Chalmers (Investigating communicating sequential processes for Java to support ubiquitous computing) and Robert Kukla (A software framework for the microscopic modelling of pedestrian movement). His research has been supported by Edinburgh Napier University funding, with applications ranging from healthcare systems to pedestrian flow optimization. He is a key member of the Centre for Algorithms, Visualisation and Evolving Systems at Edinburgh Napier University, where his work continues to influence both theoretical and applied aspects of computing.

George B. Adams IIIView profile

Teaching Professor

• High-Performance Computing
• Nanotechnology
• Computer Architecture
• +3 more

George B. Adams III is a Teaching Professor in the Department of Computer Science at Purdue University, affiliated with the College of Science. He holds a BS in Electrical Engineering from Virginia Tech (1978), and MS and PhD in Electrical Engineering from Purdue (1980, 1984). His career includes roles at NASA Ames Research Center (1983-1987) and a Visiting Lecturer at Stanford University (1986). He has contributed to high-performance computing, nanotechnology education via HUB platforms, and parallel processing research. Research interests span high-performance computing, nanotechnology, computer architecture, and education technology. His work on nanoHUB.org has advanced nanotechnology education and research collaboration globally. He has received multiple accolades, including the College of Science Outstanding Teacher Award (2016-2018), Purdue ACM Faculty Award (2018), and Fellow status in the Purdue Teaching Academy (2019). He advises the student ACM chapter and has been a Favorite Faculty member multiple times. His publications span HUB platforms, parallel computing, and fault-tolerant networks. He has been involved with Discovery Park at Purdue and previously worked in the School of Electrical and Computer Engineering.

• Numerical Optimization
• Uncertainty Quantification
• Computational Statistics
• +5 more

Mihai Anitescu is a full-time Professor in the Department of Statistics at the University of Chicago and a Senior Computational Mathematician at Argonne National Laboratory's Mathematics and Computer Science Division. He also holds an adjunct Associate Professor position at the University of Pittsburgh's Math Department and is a Senior Fellow at the Computation Institute (Argonne-University of Chicago). His research focuses on numerical optimization, uncertainty quantification, and computational statistics, with applications to materials science and power grid simulations. Education: Electrical Engineer (1992), Polytechnic University of Bucharest, Romania Ph.D. (1997), Applied Mathematical and Computational Sciences, University of Iowa Research Interests: He specializes in scalable Gaussian process analysis of spatiotemporal data, differential variational inequalities (DVIs) for phase field models in materials science, and uncertainty quantification for exascale simulations of advanced reactors. His work develops robust VI solvers for transitional phenomena modeling, including phase changes and hybrid discrete-continuum systems. Projects: He leads the MACSER project and previously led M2ACS, both funded by DOE-ASCR. His research includes mesoscale modeling of irradiated materials in collaboration with the Center for Materials Science of Nuclear Fuel (CMSNF) and Energy Frontier Research Center (EFRC). Software: He co-developed ScalaGAUSS (parallel processing for large-scale data analysis) and TreeCodeMatern (Matern kernel matrix-vector multiplication). These tools leverage optimization algorithms and numerical methods. Professional Roles: He served on the editorial boards of SIAM Journal on Optimization, Optimization Methods and Software, and Mathematical Programming. He has presented extensively at conferences like SciDAC and given seminars on stochastic optimization for energy systems.

Ashesh RambachanView profile

Assistant Professor

• Econometrics
• Machine Learning Applications in Economics
• Causal Inference
• +2 more

Ashesh Rambachan serves as Assistant Professor of Economics at the Massachusetts Institute of Technology (MIT), with a visiting appointment at Stanford University's Department of Economics and SIEPR for the 2025-2026 academic year. His primary institutional affiliation remains MIT's Department of Economics. His research program integrates econometric theory with machine learning to advance causal inference methodologies, particularly in quasi-experimental designs and observational data analysis. Key focus areas include algorithmic fairness, structural model robustness, and the application of foundation models to economic problems. This interdisciplinary approach bridges theoretical econometrics with real-world policy evaluation challenges. Analysis of his publication trajectory reveals increasing engagement with AI-driven methodologies since 2020, marked by a shift toward foundation models and large language models in economic contexts. His work consistently addresses credibility gaps in causal estimation while expanding into behavioral economics and algorithmic decision-making frameworks, reflecting evolving priorities in computational social science. Scientific recognition includes: NeurIPS 2024 Spotlight Paper for "Evaluating the World Model Implicit in a Generative Model" No explicit details regarding student advising, grant funding, or laboratory affiliations appear in current public records. His teaching portfolio includes MIT PhD courses on Algorithms and Behavioral Science (14.163) and Statistical Methods in Economics (14.380), alongside undergraduate instruction in Algorithmic and Human Decision-Making (S.6041).

Abhinav BhateleView profile

Associate Professor

• High Performance Computing
• Systems
• Networking
• +4 more

Abhinav Bhatele is an Associate Professor in the Department of Computer Science at the University of Maryland, College Park, and directs the Parallel Software and Systems Group. He holds affiliate roles in the AIM Institute and AMSC Program. His research focuses on high-performance computing, systems, networking, and AI, particularly in parallel computing, distributed AI, and machine learning applications. He has contributed to frameworks like AxoNN and Hatchet, and his work spans performance modeling, network simulation, and scalable deep learning. Education: Ph.D. (2010), M.S. (2007), and B.Tech. (2005) in Computer Science from the University of Illinois at Urbana-Champaign and IIT Kanpur, respectively. Prior to UMD, he was at Lawrence Livermore National Laboratory (2011–2019). Research interests include parallel systems, network design, performance analysis tools, and applying machine learning to optimize HPC workflows. He has received notable awards, including the IEEE TCSC Award (2023), NSF CAREER (2021), and UIUC Early Career Alumni Award (2024). Key achievements include developing open-source tools like Hatchet and AxoNN, optimizing large-scale LLM training, and advancing GPU-based supercomputing. He advises over 10 students and has secured grants totaling millions, focusing on HPC software ecosystems and AI integration. Professional service includes roles at SC, ISC conferences, and editorial work for IEEE TPDS. His lab collaborates on projects like the Exascale Computing Project and DOE INCITE allocations.

Sihang LiuView profile

Assistant Professor

• Computer Architecture
• Computer Systems
• Persistent Memory Systems
• +3 more

Dr. Sihang Liu is an Assistant Professor in the School of Computer Science at the University of Waterloo. Prior to this role, he served as a visiting faculty member at SystemsResearch@Google. He holds a PhD from the University of Virginia, where his research was supported by the Google Fellowship Award. His academic career has been marked by contributions to computer architecture, systems, and persistent memory technologies. Dr. Liu's research interests span computer architecture, systems, and cybersecurity, with a focus on persistent memory systems, energy-efficient computing, and data center optimization. His work bridges hardware-software co-design and explores challenges in scalable computing, fault tolerance, and sustainable AI systems. His recent publications highlight advancements in adaptive memory tiering (HybridTier), carbon intensity forecasting (EnsembleCI), and securing persistent memory (Side-Channel Attacks on Optane). These contributions underscore his commitment to addressing critical issues in modern computing systems' performance, security, and environmental impact. Awards: Google Fellowship Award (PhD), NVMW Memorable Paper Award Finalist (2019), 2019 MICRO Top Picks Honorable Mention Professional Service: Program committees for ISCA, MICRO, ASPLOS; Artifact evaluation roles in leading conferences As an advisor, Dr. Liu mentors a diverse group of students, including current PhD candidates Henry Tian and Desen Sun, and numerous undergraduates. His lab focuses on innovation in sustainable computing and resilient systems design.