Find Academic Supervisors

Bineet GhoshView profile

Assistant Professor

• Artificial Intelligence
• Autonomous Vehicles
• Deep Learning
• +5 more

Bineet Ghosh is an Assistant Professor in the Department of Computer Science at The University of Alabama's College of Engineering . He leads the Trustworthy Autonomy Lab , focusing on safety verification for autonomous systems with timing uncertainties. His educational background includes a PhD from UNC Chapel Hill (2023), an M.Sc. from Chennai Mathematical Institute, and a B.Sc. (First Class First with Gold Medal) from Ramakrishna Mission Vidyamandira. PhD in Computer Science (UNC Chapel Hill, 2023) M.Sc. in Computer Science (Chennai Mathematical Institute, 2016) B.Sc. (First Class First with Gold Medal) in Computer Science (Ramakrishna Mission Vidyamandira, 2014) His research spans Artificial Intelligence, Autonomous Vehicles, Deep Learning, Embedded Systems, Robotics, and Cyber-Physical Systems , with a focus on statistical verification methods and safety-aware scheduling. Recent publications analyze neural architecture sizing, GPU partitioning, and uncertainty-aware monitoring in autonomous systems. Scientific awards include the Chateaubriand Fellowship (2021), Best Presentation Award at ACM SIGBED (2022), and Best Paper Candidate at RTCSA (2022). He previously worked as a research intern at Tata Research Labs and software developer at Oracle.

• Database Information Management
• Big Data Management Systems
• Data Structures
• +2 more

Ioannis Tsaknakis is an Associate Professor at the Department of Electrical & Computer Engineering, School of Engineering, University of Peloponnese. He holds a PhD in computational geometry and multidimensional data structures from the University of Patras (2004) and has been actively involved in software systems research since 2004. His work spans Database Information Management , Big Data Systems , and Knowledge Mining , with a focus on data structures and computational geometry. Research Interests : Information Management in Databases Big Data Management Systems Computational Geometry Knowledge Mining in Databases/Web Publications highlight his contributions to IoT-driven educational frameworks, machine learning applications, and cryptographic systems for data security. He has taught courses on software design and data management since joining the University of Peloponnese in 2019. Contact : jtsaknakis@uop.gr . Office hours are in Building K (Monday & Tuesday, 8:00-9:00).

• Flexible spacecraft
• Flight control systems
• Robotic systems
• +7 more

Elisa Capello is a Full Professor of Flight Mechanics at Politecnico di Torino, Department of Mechanical and Aerospace Engineering (DIMEAS). She serves as Contact person for internationalization of innovation and technology transfer, Member of the Interdepartmental Center PIC4SeR (PoliTO Interdepartmental Center for Service Robotics), and Deputy Coordinator of the Doctoral College in Aerospace Engineering. With over 100 publications, her work spans aerospace engineering, control systems, and robotics. Her research focuses on flexible spacecraft, flight control systems, robotic systems, and unmanned aerial vehicles. She designs guidance, control and navigation systems for aircraft and spacecraft, develops control systems for wind turbines and wind farms, studies flight mechanics of fixed and rotary wing aircraft, tests unmanned aerial systems, and plans mission control for autonomous systems. Her work bridges theoretical control systems with practical aerospace applications, with strong emphasis on experimental validation. Her recent publications demonstrate a strong focus on advanced control techniques for aerospace applications, particularly in UAV control, spacecraft formation flying, and robotic systems. There's a clear trend toward integrating machine learning with traditional control methods, as seen in transformer-based MPC and multimodal learning approaches. Her work spans theoretical development, simulation, and experimental validation across multiple platforms. Member of the Editorial Board of IEEE Control Systems Society (2019-) Member of the Scientific Committee - IEEE Technical Committee Aerospace Control (2016-) International FAI Judge for Helicopter Championship (2009-2015) Professor Capello supervises numerous PhD students working on topics including autonomous aerial vehicles, path planning, risk analysis, spacecraft dynamics, and control. She leads multiple research projects including CREATEFORUAS (2019-2022), Assessment of drag free control systems for L3 gravity wave observatory (2018-2019), and Guidance, Navigation and Control algorithms for in-Orbit servicing (2020-2021). Her international collaborations include institutions in the USA, Japan, and Germany. She is actively involved with the Flight Dynamics, Control and Simulation research group at DIMEAS and the DRAFT (DRones Autonomous Flight Team) at PoliTo, where she mentors students in developing autonomous flight capabilities for various applications.

Ramana V DavuluriView profile

Professor

• Machine Learning
• Cancer Data Science
• Gene Regulation
• +4 more

Ramana V Davuluri serves as Professor in the Department of Biomedical Informatics at Stony Brook University's Renaissance School of Medicine. With over 20 years of experience in bioinformatics and computational genomics, he leads research at the intersection of machine learning and cancer genomics, focusing on translating high-dimensional -omic data into clinically actionable insights through statistically rigorous methodologies. Dr. Davuluri's research spans Machine Learning applications in Cancer Data Science , isoform-level gene regulation , and precision-medicine development. His lab pioneers bioinformatics solutions for genomic data interpretation, with emphasis on developing machine learning algorithms that convert NextGen sequencing outputs into experimentally testable discovery models. A core focus involves creating rapid biomarker identification systems from human tissue and blood samples through integrated computational-experimental approaches in systems biology. Analysis of his 2023-2025 publications reveals a dominant trend toward genomic foundation models (e.g., DNABERT variants), multi-omic cancer subtyping , and time-dependent therapeutic strategies for pediatric brain tumors and ovarian cancer. His work consistently bridges computational innovation with biological validation across diverse cancer types including glioma, lung adenocarcinoma, and high-grade serous carcinoma. As Principal Investigator for multiple multi-investigator and multi-site projects, Dr. Davuluri directs research integrating high-throughput experimental procedures with advanced data-mining techniques. His laboratory maintains strong collaborations across oncology, neuroscience, and immunology domains while developing genomics-based decision support systems for clinical translation. The Davuluri Lab employs a systems biology framework to develop novel informatics tools for precision oncology, with particular emphasis on translating genomic discoveries into clinical applications through biomarker discovery and therapeutic strategy optimization.

• Business Intelligence
• Cloud Computing
• Software Engineering
• +7 more

Professor Farookh Hussain is a distinguished academic at the School of Computer Science , University of Technology Sydney , specializing in Artificial Intelligence , Cloud Computing , and Software Engineering . His research spans diverse sectors including agriculture, manufacturing, healthcare, and transportation. Affiliated with the Australian Artificial Intelligence Institute (AAII) , he leads impactful work in business intelligence and carbon credit systems. Key research areas: AI applications, blockchain for provenance, carbon credit analytics Active in Masters/PhD supervision and cloud computing education Research Highlights : Developed KACINO framework for carbon dynamics modeling Created hybrid cybersecurity frameworks for supply chain risk management Advanced chatbot dialogue breakdown solutions through systematic reviews Proposed hypercomplex knowledge graph recommenders Published extensively on carbon credit price prediction and blockchain storage methods Contributions to water demand forecasting and collaborative robotics adoption Grant Activities : Secured funding from Hampton Capital Asset Management , Innovation Connections , and Science and Industry Endowment Fund Projects include LLM-driven text-to-SQL conversion , blockchain for melanoma data , and AI for storm water management

• Real-Time Control Systems
• Cloud Computing
• Automation of Large-Scale Systems
• +7 more

Johan Eker is a Professor in Real-Time Control Systems at Lund University's Faculty of Engineering and a Principal Researcher in Cloud Technology at Ericsson Research. He holds a dual affiliation, actively contributing to both academic and industrial research in the domain of control systems and cloud automation. His research interests lie at the intersection of computer science and control theory, with a focus on the automation of large-scale software systems such as cloud services and telecommunications networks. He leverages machine learning and advanced control methodologies to develop intelligent, adaptive, and scalable systems. His work benefits from close collaboration with Ericsson Research, enabling access to real-world data and operational environments for validation and innovation. Johan Eker is involved in cutting-edge research areas including autonomous cloud systems, event-based control, and learning-based control architectures. His contributions support the evolution of resilient, efficient, and self-managing infrastructures in modern computing and networking environments. He is actively engaged in research collaboration between academia and industry, bridging theoretical advancements with practical deployment. His work is supported through partnerships with Ericsson, allowing for real-world testing and implementation of control solutions in cloud and telecom systems. Johan Eker is affiliated with the Department of Control Engineering at Lund University, where his expertise contributes to education and research in systems, control, and real-time computing. He plays a key role in advancing the integration of control theory into modern software and networked systems.

• Machine Learning
• Augmented Intelligence
• Data Science
• +12 more

Simon Ruffieux is a Senior Researcher and Lecturer at the Department of Computer Science, University of Fribourg, and a member of the Human-IST Institute. He currently leads the HIP-Initiative (Human-IST x SwissPost Initiative) and coordinates academic projects related to Swiss Post. His academic roles include Lecturer and Senior Assistant , reflecting his active engagement in teaching and research. His research focuses on leveraging advanced technologies to support individuals, particularly those with special needs. Key areas include: Machine Learning and Data Science for urban systems (e.g., bike-sharing optimization) Human-Computer Interaction (HCI), especially gesture recognition and multimodal interfaces Augmented and Virtual Reality applications in rehabilitation and assistance Development of smart glasses for visually impaired users Physiological signal analysis for workload classification The 15 most recent publications reveal a strong trend in applying AI and data science to real-world challenges, particularly in assistive technologies and urban mobility. His work often involves interdisciplinary collaboration, integrating computer science with psychology, rehabilitation, and industrial applications. There is a consistent emphasis on user-centered design and real-world usability. Simon Ruffieux has not been mentioned as receiving specific scientific awards in the provided text. He has advised or collaborated with several researchers, including Nicolas Spycher, Samuel Torche, and Nicolas Ruffieux, on projects related to forecasting, AR, and gesture recognition. While no formal grant details are listed, his leadership of the HIP-Initiative suggests involvement in externally funded academic projects. His work is closely tied to the Human-IST Institute, where he contributes to interdisciplinary research in human-centered computing. He is actively involved in research teams focused on assistive technologies, gesture interaction, and data-driven urban solutions. The Human-IST Institute serves as the primary hub for his collaborative efforts, particularly through the HIP-Initiative with Swiss Post.

• Social Psychology
• Social Identity
• Group Processes
• +18 more

Professor Mark Levine is a leading figure in social psychology at Lancaster University , affiliated with the Department of Psychology and multiple interdisciplinary research centers including Security Lancaster (Behavioural Science) , Cyber Security Research Centre (Psychology) , Data Science Institute , and Social Processes . His research lies at the intersection of psychology and technology, focusing on social identity, group dynamics, and prosocial behavior in public and digital environments. His research interests include bystander intervention, violence prevention, urban resilience, cybersecurity, emergency response, public safety, and the role of technology in shaping human behavior. He employs diverse methodologies such as virtual reality, CCTV analysis, smartphone data collection, and computational text analysis. His work is highly applied, informing policy and practice in policing, emergency services, and community safety. The trends in his recent publications reflect a strong interdisciplinary focus, combining social psychology with computer science, criminology, and urban studies. His articles frequently analyze real-world behavioral data from public spaces and digital environments, emphasizing the protective role of group identity in emergencies and the feasibility of technological interventions for societal challenges. Scientific grants and projects he has led include: REASON: Resilient Autonomous Socio-cyber-physical agents (£3.3M, EPSRC) RBOC Network+: Urban resilience in 2050 (£2.25M, EPSRC) Challenging the Bystander Effect via Documentary Film (A$356K, ARC) STRETCH: Technology-enhanced support for older adults (£1.3M, EPSRC) “Being There”: Humans and Robots in Public Space (£2.4M, EPSRC) Advising and grants : He supervises 7 postgraduate research students and has secured over £15 million in research funding from EPSRC, ESRC, DSTL, NCSC, Home Office, and international bodies. He advises government departments, police forces, and city councils on public safety and prosocial behavior. His collaborative work extends to creative industries and third-sector organizations like AGE-UK Exeter. Research groups and collaborations : He is deeply embedded in interdisciplinary networks, contributing to projects involving computer scientists, roboticists, software engineers, and HCI researchers. His affiliations with Security Lancaster and the Data Science Institute reflect his central role in socio-technical research at Lancaster.

• Recommender Systems
• Personalization
• Data Mining
• +6 more

Alexander Tuzhilin is a prominent academic researcher in the fields of recommender systems, personalization, and data mining. His work spans over two decades, focusing on theoretical foundations and practical applications of context-aware recommendations, optimization in data validation, and temporal database systems. He has collaborated extensively with scholars like Gediminas Adomavicius, Konstantin Bauman, and Balaji Padmanabhan. Research Interests: Recommender systems, context-aware computing, temporal database design, and optimization techniques in data mining. Publications: 10+ peer-reviewed articles in journals such as Information Systems Research , Management Science , and INFORMS Journal on Computing , with a focus on algorithmic innovation and business impact. Collaborations: Worked with leading researchers in information systems and operations research, contributing to interdisciplinary advancements in eCRM and data-driven decision-making.

Navid Rekab-sazView profile

Assistant Professor

• Natural Language Processing
• Information Retrieval
• Fairness and Bias in AI
• +7 more

Navid Rekab-saz is an Assistant Professor at the Institute of Computational Perception, Johannes Kepler University Linz (JKU), Austria. He is actively involved in research and teaching, offering courses such as Natural Language Processing and Natural Language Processing with Deep Learning . He maintains regular office hours and is accessible via email and a dedicated booking system for meetings. His research focuses on natural language processing , information retrieval , fairness and bias in AI , and recommender systems , with applications in humanitarian action and ethical AI. He employs deep learning and machine learning techniques to address challenges in bias mitigation, explainability, and domain adaptation. His work often bridges technical innovation with societal impact, especially in developing inclusive and fair AI systems. The recent publications of Navid Rekab-saz reflect a strong trend in debiasing strategies , parameter-efficient learning , and evaluation of societal biases in search and recommendation systems. His research spans from foundational work on word embeddings and retrieval models to applied studies in humanitarian NLP and gender bias in user queries. He frequently collaborates with a broad network of researchers and contributes to the development of datasets and benchmarks. Scientific Awards: Best Student Paper Award at ISMIR 2022 for 'Traces of Globalization in Online Music Consumption Patterns and Results of Recommendation Algorithms' Advising and Grants: Navid Rekab-saz has advised and collaborated with numerous students and researchers, many of whom are co-authors on his publications. While specific grant details are not listed in the provided text, his extensive publication record in top-tier venues suggests active involvement in funded research projects, likely supported by national or European funding bodies. He is also engaged in interdisciplinary research, particularly at the intersection of technical AI and legal or social implications. Labs and Teams: He is a core member of the Institute of Computational Perception at JKU, where he contributes to research projects in computational linguistics and AI. He collaborates closely with the team led by Prof. Markus Schedl and participates in initiatives related to music information retrieval, fairness in AI, and humanitarian applications of NLP.

Pierre MonninView profile

Researcher

• Knowledge Graphs
• Semantic Web
• Graph Embedding
• +4 more

Pierre Monnin is a Junior Fellow in AI at Université Côte d'Azur , conducting research within the Wimmics team at the I3S Laboratory . He also teaches within the EFELIA Côte d'Azur program. His work spans multiple institutions through funded projects like SHACKLE (EU Horizon), ECLADATTA and AT2TA (ANR), with collaborations at Télécom Paris , Università di Bari , and INESC-ID in Lisbon. Previous roles include temporary lecturer at TELECOM Nancy (2023-2024) and researcher at Orange (2020-2023). Research Interests focus on the knowledge graph lifecycle (construction, matching, refinement, mining, discovery) from neurosymbolic AI and analogical reasoning perspectives. He explores Domain knowledge injection into ML models Symbolic-semantics for graph embeddings Zero-shot bootstrapping techniques Context-aware semantic annotation Link prediction with constraint enrichment Life sciences applications Recent scientific awards include: Best Paper Award at ESWC 2024 (Student & Resource Papers) Best Thesis Award from French Association EGC (2022) 1st Prize (Accuracy Track) at Semantic Web Challenge (2021) His teaching portfolio covers AI fundamentals for foreign languages, marketing, and adult education programs, with specialized courses in Semantic Web technologies NoSQL databases XML tools Compiler implementation He supervises multiple PhD students and interns on topics involving neurosymbolic refinement , knowledge reconciliation , and analogical reasoning . Key software contributions include: KGPrune - Web application for thematic Wikidata subgraph extraction PyGraft - Synthetic knowledge graph generation tool DAGOBAH UI - Semantic table interpretation interface He also maintains datasets like PGxLOD and YAGO4-LP for pharmacogenomics and link prediction.

• Reliable and Fault-Tolerant Computing
• RISC-V Architecture
• FPGA-Based Systems
• +12 more

Sarah Azimi is a fixed-term researcher at the Department of Control and Computer Science (DAUIN) within the College of Computer, Film and Mechatronics Engineering at Politecnico di Torino. She actively contributes to research and teaching in the domains of reliable computing, reconfigurable systems, and AI applications for space and smart city security. Research Interests: Reliability and fault tolerance in safety-critical and space systems RISC-V and FPGA-based architectures High-performance computing (HPC) and reconfigurable computing AI resilience and real-time gesture recognition for public safety Radiation effects and hardening techniques for aerospace applications Publication Trends: Her recent publications focus on RISC-V reliability, radiation effects in space missions, AI resilience in reconfigurable platforms, and smart city security through gesture recognition. Her work spans both journal and conference venues, emphasizing practical and mission-tailored solutions in embedded and aerospace computing. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: Sarah Azimi supervises multiple PhD students including Federico Buccellato, Aobo Cui, and Giorgio Cora. She leads the competitive research project Safe Smart City: Detecting Violence and Requests for Help in Real Time Through Video Surveillance Devices (2024). She is also a member of the RAMSES CubeSat-1 Development project (2025–2026) and led the commercial research project on the Rempro fault-tolerant processor (2022–2023). Labs and Teams: She is a key member of the CAD - Electronic CAD & Reliability Group (DAUIN) at Politecnico di Torino, contributing to cutting-edge research in electronic design automation and system reliability for aerospace and terrestrial applications.

• Theoretical Neuroscience
• Statistical Physics of Neural Networks
• Dynamics of Neuronal Networks
• +6 more

Prof. Dr. Moritz Helias is a University Professor and leads the Theory of Multi-Scale Neuronal Networks group at the Institute for Advanced Simulation (IAS-6), Computational and Systems Neuroscience, Forschungszentrum Jülich. His research bridges biological and artificial neural networks, focusing on dynamics, information processing, and the physics of AI. The group is part of a larger interdisciplinary institute that integrates theory, simulation, and data analysis to understand the brain. Institution: Forschungszentrum Jülich School: Institute for Advanced Simulation Department: IAS-6, Computational and Systems Neuroscience Position: Professor and Group Leader Email: m.helias@fz-juelich.de His research interests lie at the intersection of statistical physics and neuroscience. He investigates how structure shapes dynamics in both biological and artificial networks, aiming to uncover general principles of information processing. Using methods from statistical physics, his work enables a unified framework for understanding collective phenomena, learning, and generalization. Key areas include spiking neural networks, renormalized field theory, and the theoretical foundations of AI. The recent publications reflect a strong trend toward multi-scale modeling of neural systems, integrating statistical physics with neuroscience. Topics include spiking network dynamics, mean-field theory, renormalization, and applications of machine learning in physics. The work spans biological realism and artificial intelligence, with implications for neuromorphic computing and brain-inspired AI architectures. While no scientific awards are listed in the provided texts, his group actively contributes to open science through tools like NEST and theoretical frameworks that influence both neuroscience and AI. Prof. Helias supervises a research group focused on theoretical and computational approaches, contributing to collaborative projects involving large-scale simulations and data analysis. His team works closely with experimentalists and theorists to validate models and advance understanding of brain function. The group is also involved in developing simulation technologies and theoretical tools that support reproducible neuroscience. The Theory of Multi-Scale Neuronal Networks group is embedded within a vibrant research environment at IAS-6, collaborating with teams in statistical neuroscience, computational neurophysics, and future simulation architectures. This fosters a loop between data, theory, and simulation, enabling cutting-edge research on brain function and artificial intelligence.

Tony Jun HuangView profile

Professor

• Acoustofluidics
• Microfluidics
• Lab-on-a-chip
• +3 more

Tony Jun Huang is the William Bevan Distinguished Professor of Mechanical Engineering and Materials Science at Duke University, with additional professorships in Electrical and Computer Engineering and Biomedical Engineering. His research focuses on acoustofluidics, optofluidics, and micro/nano systems for biomedical diagnostics and therapeutics. Ph.D. in Mechanical and Aerospace Engineering (UCLA, 2005) Huang's research has revolutionized biomedical microsystems through acoustofluidic technologies, enabling contactless particle manipulation, exosome isolation, and advanced diagnostic platforms. His work has been cited over 36,000 times (h-index: 102) with 30 issued/pending patents. Recent publications highlight his innovations in acoustic tweezers, extracellular vesicle analysis, topological acoustofluidics, and AI-assisted biomimetic imaging. His lab develops technologies for single-cell analysis, non-invasive diagnostics, and programmable material systems. 2023 Highly Cited Researcher (Web of Science) 2020 Fellow of the National Academy of Inventors (NAI) 2019 Van C. Mow Medal (ASME) 2017 Analytical Chemistry Young Innovator Award (ACS) 2010 NIH Director's New Innovator Award Huang has taught courses including ME 535: Biomedical Microsystems and mentored numerous graduate students through his Duke Acoustofluidics Lab. His lab's technologies are applied in cancer biomarker detection, Alzheimer's diagnostics, and wound healing hydrogels.

Ninghao LiuView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Explainable AI (XAI)
• +6 more

Dr. Ninghao Liu is an Assistant Professor of Computer Science in the School of Computing at the University of Georgia, part of the Franklin College of Arts & Sciences - Division of Physical & Mathematical Sciences. He holds a Ph.D. in Computer Science from Texas A&M University (2021) and an M.S. in Electrical and Computer Engineering from Georgia Institute of Technology (2015). His research focuses on Explainable AI (XAI), Graph Mining, Model Fairness, Recommender Systems, and Outlier Detection, with notable contributions to foundational AI techniques and their applications in education, healthcare, and environmental sciences. Dr. Liu has secured significant funding, including a three-year NSF grant (2022–2025) for 'Graph-Oriented Usable Interpretation' and a five-year $10 million grant from the U.S. Department of Education (2024–2029) for the GenAI Empowered National Initiative for STEM+C Education. He has also been honored with the Outstanding Paper Award at ICML 2022, Best Paper Award Shortlist at WWW 2019, and other distinctions. His work emphasizes interpretable machine learning, graph neural networks, and addressing algorithmic bias. He collaborates across disciplines, contributing to radiology AI, climate-smart forestry, and pandemic prediction through knowledge-enhanced deep learning. His lab is based at the Boyd Research and Education Center, where he advances research in trustworthy AI systems and data-centric solutions.