Find Academic Supervisors

Aakriti UpadhyayView profile

Research Fellow

• Robotics
• Motion Planning
• Topological Data Analysis
• +3 more

Aakriti Upadhyay is a Research Fellow at the Colorado School of Mines (CSM), working in the Dynamic Automata Lab (DyALab) under Dr. Neil T. Dantam. She holds a Ph.D. in Computer Science from the University at Albany, SUNY, where she was part of the Robotics Algorithms and Computable Systems (RACS) lab under Dr. Chinwe P. Ekenna. Her research integrates Computational Geometry, Applied Mathematics, Machine Learning, and Topological Data Analysis (TDA) to advance motion planning for robotics and computational biology applications. Her work focuses on optimizing robot motion paths using topological methods, with extensions to identifying protein interaction sites. Key areas include robust path planning, fault tolerance in robotic systems, and topological modeling of biomolecular surfaces. Her research trends emphasize interdisciplinary applications of TDA, bridging robotics and computational biology. Collaborations include developing tools for Intrinsically Disordered Proteins (IDPs) analysis and enhancing path planning algorithms through geometric and topological insights. Aakriti has contributed to academic initiatives like the Student Activities Committee and advocated for women in engineering during the pandemic. Her work reflects a commitment to advancing both technical and community-oriented academic goals.

• Artificial Intelligence
• Geospatial Data Science
• Knowledge Graphs
• +5 more

Professor Manolis Koubarakis is a faculty member at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. His research focuses on geospatial data science, knowledge graphs, entity resolution, and AI applications in Earth observation. He leads projects like ExtremeEarth and Plato, advancing semantic data cube systems and geospatial question answering engines. His work bridges AI and geospatial technologies, with contributions to frameworks like pyJedAI and Strabo 2 for managing big geospatial data. Research Interests include: Geospatial Question Answering Systems Entity Resolution and Entity Linking Ontology-Based Data Access (OBDA) Knowledge Graph Construction and Applications Earth Observation Data Analytics AI for Geoinformatics Notable Projects: ExtremeEarth: Combines Copernicus satellite data with machine learning for environmental analytics. Plato: Semantic data cube system enabling advanced querying of multidimensional datasets. GeoQA2: A geospatial question answering engine evaluated on large benchmarks. JedAI Family: Tools for scalable entity resolution in structured/semi-structured data. His publications span 20+ years, with recent emphasis on: AI-driven Earth observation systems Geospatial RDF benchmarking 3D geospatial interlinking Large language models for legal and health content delivery

Jonathan SprinkleView profile

Professor

• Cyber-Physical Systems
• Autonomous Systems
• Traffic Control
• +3 more

Jonathan Sprinkle is Chair and Professor of Computer Science at Vanderbilt University's School of Engineering, with additional roles as Professor in Civil and Environmental Engineering and Electrical and Computer Engineering. Previously, he was Litton Industries John M. Leonis Distinguished Associate Professor at the University of Arizona and Interim Director of the Transportation Research Institute. His research focuses on model-based cyber-physical systems, autonomous systems, and traffic control, emphasizing industry impact. He holds a Ph.D., M.S., and B.S. in Electrical Engineering from Vanderbilt University and Tennessee Technological University. Key awards include the NSF CAREER Award (2013), UA Distinguished Scholar (2020), and NSF I-Corps Best Team (2012). His work spans traffic wave dissipation, autonomous vehicle safety, and large-scale field experiments like the CIRCLES consortium's 100-AV test. He leads initiatives in data-driven control systems and smart infrastructure through collaborations with NSF and industry. Research highlights include developing control barrier functions for automated vehicles, integrating LLMs for traffic analysis, and deploying ROS-based frameworks for vehicle communication. His contributions aim to bridge theoretical models with real-world applications, addressing challenges in energy efficiency, safety, and scalability.

Evimaria TerziView profile

Professor

• algorithmic data mining
• network analysis
• recommendation systems
• +3 more

Evimaria Terzi is a Professor and Department Vice Chair at Boston University (BU), affiliated with the Data Management Lab@BU. Her research focuses on algorithmic data mining with applications in network analysis, recommendation systems, ranking, and clustering. She holds a PhD from the University of Helsinki and has held prior roles at IBM Almaden Research Center (2007–2009) and the Helsinki Institute for Information Technology (HIIT) before 2007. Her work spans theoretical and applied domains, including team formation algorithms, fairness in AI, and large language model evaluation. Notable contributions include studies on LSM tree optimization, counterfactual explanations for auditing fairness, and the dynamics of memorization in LLMs. Her recent publications emphasize flexibility in database systems and ethical AI practices. Evimaria’s research has been recognized through her contributions to conferences like WSDM and KDD, where she has served in organizing roles. The themes of her work consistently bridge algorithmic innovation with real-world applications in social networks, healthcare, and collaborative systems.

Javad FattahiView profile

Assistant Professor

• Energy Equity and Inclusion
• Distributed Energy Resources (DER)
• Software-Defined Nanogrids
• +6 more

Dr. Javad Fattahi is an Assistant Professor at the School of Electrical Engineering and Computer Science, University of Ottawa. He holds roles as an IEEE Senior Member, Professional Engineer (P.Eng.), and member of the Standards Council of Canada. He leads the IEEE 2030.5 smart-grid interoperability verification initiative as its VP and chief editor. Dr. Fattahi earned his BSc in Electrical Engineering from Iran University of Science and Technology (IUT), followed by graduate studies at Sharif University of Technology (SUT). He completed his PhD and postdoctoral research at the University of Ottawa, specializing in energy systems and optimization. His research focuses on energy equity, distributed energy resources (DERs), software-defined nanogrids, game theory, smart mobility, and nonlinear systems. His recent work emphasizes data-driven digital twins for grid stability, V2G integration in residential complexes, and federated Byzantine agreement models for EV infrastructure. Key contributions include transactive energy systems under IEEE standards and energy disaggregation in institutional buildings. Dr. Fattahi has received accolades including IEEE Senior Membership and P.Eng. certification. His lab develops solutions for grid-edge technologies, smart mobility, and resilient energy systems. Graduate opportunities are available via his UniWeb page.

Natasha FernandesView profile

Senior Lecturer

• Data Privacy
• Differential Privacy
• Privacy-Preserving Machine Learning
• +3 more

Natasha Fernandes is a Senior Lecturer in the School of Computing at Macquarie University. She holds a PhD in Computing from Macquarie University and École Polytechnique (France), and an undergraduate degree in Pure Mathematics and Computer Science from the University of Sydney. Her roles include involvement in the Data Horizons Research Centre and Future Communications Research Centre, alongside professional casual appointments in academic computing. Her research focuses on the mathematical foundations of data privacy, particularly differential privacy and its applications in natural language processing and machine learning. She develops privacy-preserving systems and tools using quantitative information flow techniques rooted in information theory. Key areas include privacy analyses for financial systems (e.g., Open Banking), API privacy, and optimizing utility in privacy pipelines. Fernandes has led or contributed to five research projects, including work on privacy analyses for financial transaction protocols and UAV-based machine learning systems. She received the 2021 John Makpeace Bennett Award for her doctoral research on differential privacy in metric spaces. Her academic journey combines industry experience as a backend software engineer with rigorous academic contributions, spanning over 21 peer-reviewed publications and collaborations across cybersecurity and privacy engineering domains.

Jürgen BernardView profile

Assistant Professor

• Visual Analytics
• Information Visualization
• Interactive Machine Learning
• +6 more

Jürgen Bernard is an Assistant Professor of Computer Science at the University of Zurich (UZH), affiliated with the Digital Society Initiative (DSI). He leads the Interactive Visual Data Analysis (IVDA) Group and holds a joint position in the Department of Informatics within the Faculty of Business, Economics, and Informatics. His research focuses on interactive visual data analysis, explainable machine learning, and human-centered AI interfaces. Bernard completed his PhD at TU Darmstadt in 2015, followed by postdoctoral roles at TU Darmstadt and the University of British Columbia. He has received prestigious awards, including the EuroGraphics Young Researcher Award (2022) and EuroVis Young Researcher Award (2021). His work emphasizes combining human expertise with algorithms in domains like healthcare, climate science, and digital humanities. Key research interests include visual analytics for time-oriented data, interactive machine learning, and applications in healthcare. His projects address challenges such as medical data interpretation, sensor data analysis, and decision-making support systems. Bernard actively contributes to conferences like IEEE VIS and chairs workshops such as VAHC 2023. Education: PhD in Computer Science (2015, TU Darmstadt); Diploma in Computer Science (2009, TU Darmstadt) Grants: SNF Grant on Personalized Visual Analytics (2024), BMW collaboration on manufacturing analytics Labs/Teams: IVDA Group, DSI Health Community

• Distributed/Parallel Computing
• Information Visualization
• Fault-Tolerant Communication Mechanisms
• +4 more

Philippas Tsigas is a Professor at the Department of Computer Science and Engineering at Chalmers University of Technology. He leads the Distributed Computing and Systems Research Group and has held roles as co-leader of research initiatives such as the PEPPHER project. His research spans distributed/parallel computing, information visualization, and fault-tolerant communication mechanisms. He has supervised numerous PhD students, including Yi Zhang, Håkan Sundell, and Farnaz Moradi. Research interests include lock-free data structures, multicore algorithms, secure network services, and visualization tools like Lydian and DataMeadow. Notable awards include Best Paper Awards at IPDPS 2003 and SNS 2012. His work has been published in top venues like IEEE Transactions on Parallel and Distributed Systems and ACM Journal of Experimental Algorithmics. Awards highlight contributions to lock-free algorithms and network modeling. Students have contributed to projects like NBmalloc (memory reclamation) and GPU Quicksort. Collaborations with institutions like SSF and VR have supported his research. Tsigas is also involved in teaching distributed systems and mentoring early-career researchers.

Rakesh V. VohraView profile

Professor

• Economics
• Computer Science
• Game Theory
• +5 more

Rakesh V. Vohra is the George A. Weiss and Lydia Bravo Weiss University Professor at the University of Pennsylvania, holding primary appointments in the departments of Computer and Information Science, Economics, and Electrical and Systems Engineering. He co-directs the Warren Center for Network and Data Sciences. His research spans game theory, market design, auction theory, and computational economics, with a focus on mechanism design, spectrum markets, and systemic risk in financial networks. He explores interdisciplinary topics such as NFTs, blockchain applications, and algorithmic fairness in healthcare equity. His work integrates economic theory with computational methods, addressing challenges in resource allocation, strategic behavior, and network dynamics. Recent studies analyze optimal bailouts in financial systems, sequential information acquisition, and the implications of unlicensed spectrum policies on wireless competition. He also investigates behavioral aspects of decision-making under uncertainty and the design of robust economic systems. Notable contributions include foundational work on combinatorial auctions, stable matching mechanisms under constraints, and the role of network effects in market stability. Though no formal awards are listed, his prolific publication record reflects significant academic influence across multiple disciplines.

Antoine CullyView profile

Researcher

• Robotics
• Artificial Intelligence
• Evolutionary Robotics
• +3 more

Dr. Antoine Cully is the director of the Adaptive and Intelligent Robotics Lab at Imperial College London. He previously served as a Research Associate in the Personal Robotics Lab (2016–present) and earned his PhD in Robotics and Artificial Intelligence from UPMC (Paris), focusing on algorithms enabling robots to adapt to mechanical damage swiftly. His work has been internationally recognized, including a Nature cover publication and awards for his PhD thesis. His research interests span evolutionary robotics, stochastic optimization, and quality-diversity algorithms, with current projects involving the EU H2020 'PAL' initiative to develop adaptive robotics for user preferences. Education: M.Sc. in Intelligent Systems and Robotics (UPMC, 2012), Engineer degree in Robotics (Polytech-Paris UPMC, 2012), PhD in Robotics and AI (UPMC, 2015). Research emphasizes adaptive robotics, damage recovery, and autonomous learning. Notable contributions include the T-Resilience algorithm and MAP-Elites optimization framework. Awards include the 'Outstanding Paper 2015' and 'Best Thesis' accolades. His lab focuses on advancing AI-driven robotics for real-world applications.

Panayiotis TsaparasView profile

Associate Professor

• Algorithmic Bias and Fairness
• Social Network Analysis
• Social Media Analysis
• +7 more

Panayiotis Tsaparas is an Associate Professor in the Department of Computer Science & Engineering at the University of Ioannina, Greece. He is also a Collaborating Senior Researcher at the Archimedes Research Center since 2023. His academic journey includes a Ph.D. from the University of Toronto, postdoctoral work at the University of Rome (La Sapienza) and the University of Helsinki, and research experience at Microsoft Research, Search Labs. Education: B.Sc., University of Crete, Greece Ph.D., University of Toronto, Canada, supervised by Allan Borodin His research focuses on algorithmic fairness, social network and media analysis, and data mining . He investigates how opinions form and spread in networks, how bias manifests in algorithms like PageRank, and how to design fair recommendation systems. His work bridges theoretical algorithm development with practical applications in social computing. His recent publications, appearing in top venues like WWW, KDD, WSDM, and SDM, reveal strong trends in fairness-aware algorithms, opinion dynamics, polarization modeling, and temporal network analysis . He has pioneered work on fairness in PageRank and link recommendations, and on measuring and moderating polarization in online communities. Scientific Awards: Best paper award at ACM SIGMOD Workshop on Data Bases and Social Networks (DBSocial), 2013 Best paper award runner-up at ACM KDD, 2006 He leads a research group and has advised students on topics including internet review analysis and election prediction using Twitter. He has secured significant funding, notably a Marie Curie Reintegration Grant (JMUGCS) , which supported research on jointly mining user-generated content across reviews, social networks, and behavioral data. His teaching includes undergraduate and graduate courses such as Data Mining and Online Social Networks and Media . He collaborates extensively with researchers like Evaggelia Pitoura, Nikos Mamoulis, Aristides Gionis, and others, forming a strong network in the data mining and social network analysis community.

Ga WuView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Recommender Systems
• +15 more

Ga Wu is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, where he leads the Applied Machine Learning Research Lab (DAMLR). His research spans machine learning, artificial intelligence, recommender systems, and large-scale data analytics, with strong industry and interdisciplinary collaborations. Research Interests: Machine Learning and Large-scale Data Analysis Recommender Systems and Personalization AI Safety, Fairness, Accountability, and Transparency Generative AI and Large Language Models Computer Vision (Object Detection on Low-power Devices) AI for Healthcare and Mental Health Physical Dynamic Modeling with ML Bioactive Peptides and Protein Property Understanding His recent publications have been accepted at premier venues such as SIGIR and ICLR, reflecting impactful contributions in information retrieval and deep learning. His work is increasingly focused on ethical, safe, and interpretable AI systems with real-world applications. Scientific Awards & Grants: NSERC RTI Award (2025) Mitacs Elevate Grant (awarded multiple times in 2024–2025) Transforming Climate Action Research Program (TCA) Support (2025) Advising and Funding: Dr. Wu actively supervises graduate students, including PhD candidates and Master’s students like Naihe Feng and Mahtab Sarvmaili. His lab is fully funded and currently recruiting PhD students for projects in computer vision and big data infrastructure in collaboration with industry partners. Labs & Teams: He founded and directs the Applied Machine Learning Research Lab (DAMLR) , which fosters collaboration between academia and industry to advance theoretical and applied AI research.

Shrideep PallickaraView profile

Professor

• Machine Learning
• Deep Learning
• Large-Scale Systems
• +12 more

Shrideep Pallickara is a Professor in the Department of Computer Science at Colorado State University, where he also directs the Center for eXascale Spatial Data Analytics and Computing (XSD) . His research is funded by the National Science Foundation, Department of Homeland Security, Environmental Protection Agency, Department of Agriculture, and the UK's e-Science program. Research Interests: His research lies at the intersection of machine learning and large-scale systems, focusing on: Spatiotemporal data management and analytics Extreme-scale storage systems Stream processing for IoT and cyber-physical systems Deep learning over petabyte-scale, high-dimensional datasets Model construction for forecasting natural and urban phenomena His work addresses challenges in computational tractability, resource utilization, and convergence in distributed environments. Systems developed in his lab are deployed in domains such as urban sustainability, agriculture, epidemiology, environmental monitoring, healthcare, and defense. Research Trends in Publications: His recent publications demonstrate a strong focus on scalable analytics for geospatial and environmental data. Key themes include deep learning for soil moisture and salinity prediction, efficient visualization of massive satellite datasets, spatiotemporal search and summarization, and model performance profiling across spatial domains. The work integrates scientific domain knowledge with machine learning and systems innovation. Scientific Awards: NSF CAREER Award Board of Governors Award for Excellence in Undergraduate Teaching OLIE Award N. Preston Davis Award Monfort Professorship Best Paper Award at IEEE/ACM CCGrid 2019 Best Paper Award at BDCAT 2023 Best Paper Award at IEEE Cluster 2012 Best Student Paper Award at IEEE CloudCom 2010 Shortlisted for ACM DEBS-2015 Grand Challenge Award One of the Six Best Papers at ACM/IEEE GRID 2005 Advising and Grants: He advises numerous graduate students, many of whom are co-authors on his publications. His research is supported by major grants from NSF, DHS, EPA, USDA, and UK e-Science, enabling the development of open-source systems such as Granules, NaradaBrokering, Galileo, Funnel, and Spindle. Labs and Teams: He leads the XSD Center, which develops and maintains large-scale open-source software systems involving over 2500 classes and a million lines of code. These systems are used in academic, commercial, and defense applications.

Renata Borovica-GajicView profile

Associate Professor

• Database Systems
• Machine Learning
• Artificial Intelligence
• +12 more

Renata Borovica-Gajic is an Associate Professor in Data Analytics and an ARC DECRA Fellow at the School of Computing and Information Systems (CIS), University of Melbourne. She also serves as Associate Dean (Diversity and Inclusion) for the Faculty of Engineering and IT, demonstrating leadership in both research and academic community development. Her research lies at the intersection of database systems, machine learning, and artificial intelligence, with a vision of creating adaptive, self-driving database engines that optimize query execution in real-time. Her work spans learned indexes, query optimization, data quality, and data-driven traffic optimization, aiming to reduce costs and improve performance in data analytics. The recent publications reflect a strong trend toward integrating machine learning into core database operations—particularly through learned indexes, bandit-based tuning, and reinforcement learning for traffic systems. These works emphasize automation, provable guarantees, and real-time adaptation, showcasing a cohesive research agenda focused on intelligent, self-optimizing data systems. Her scientific excellence is recognized by numerous awards, including: L'Oréal-UNESCO for Women in Science Fellowship (2023) Victorian Young Tall Poppy (2024) Test of Time Award at SIGMOD 2022 Multiple Research and Teaching Excellence Awards from the University of Melbourne Google Research Inclusion Award (2021) She actively mentors PhD students and leads significant research projects funded by the Australian Research Council, Google, and Telstra. Her service includes roles as Associate Editor for SIGMOD Record, conference organization (e.g., aiDM, ADC, VLDB), and leadership in diversity and inclusion initiatives. She has also contributed to influential publications such as a chapter in the 7th edition of Database System Concepts . Her research lab focuses on AI-powered databases, traffic optimization via reinforcement learning, and self-healing data systems, positioning her at the forefront of next-generation data management.

Jesse ThalerView profile

Professor

• Theoretical Particle Physics
• Quantum Field Theory
• Machine Learning
• +7 more

Jesse Thaler is a Professor of Physics at the Massachusetts Institute of Technology (MIT), where he has been a faculty member since 2010. He is affiliated with the MIT Center for Theoretical Physics - a Leinweber Institute (CTP-LI), the Laboratory for Nuclear Science (LNS), the Statistics and Data Science Center (SDSC), and the Institute for Data, Systems, and Society (IDSS). Since 2020, he has served as the inaugural Director of the NSF Institute for Artificial Intelligence and Fundamental Interactions (IAIFI). Thaler received his Sc.B. in Math/Physics from Brown University in 2002, followed by a Ph.D. in Physics from Harvard University in 2006. From 2006 to 2009, he was a Miller Research Fellow at the University of California, Berkeley. His research focuses on theoretical particle physics, particularly on fusing quantum field theory with machine learning techniques to address fundamental physics questions. His work spans three main areas: Data Science and AI/ML applications in particle physics, Collider Physics and QCD, and Beyond the Standard Model physics. He is an expert in jet physics, which involves studying collimated sprays of particles produced at the Large Hadron Collider (LHC), and he investigates jet substructure to enhance searches for new phenomena and understand gauge theory dynamics. His research also explores strategies for probing dark matter at the LHC and theoretical structures of supersymmetry. Data Science and AI/ML: Merging deep learning with physics principles for improved particle physics analysis Collider Physics and QCD: Developing new theoretical frameworks for analyzing collider data Beyond Standard Model: Exploring dark matter detection strategies and theoretical extensions to the Standard Model Thaler has received numerous prestigious awards including being named an American Physical Society (APS) Fellow in 2022 and receiving a Simons Investigator Award in the same year. Other notable honors include the Fermilab Distinguished Scholar (2018-2020), Simons Fellowship in Theoretical Physics (2018), Frank E. Perkins Award for Excellence in Graduate Advising (2017), and the Presidential Early Career Award for Scientists and Engineers (2012). As an advisor, Thaler has mentored numerous Ph.D. students, postdoctoral researchers, and undergraduate students. His research group has produced significant work in jet physics, machine learning applications for particle physics, and beyond the standard model physics. He has also secured substantial research funding through grants from the Department of Energy and other agencies. Thaler leads the Thaler Research Group at MIT and serves as Director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions, which brings together researchers from MIT, Northeastern University, Harvard University, and Tufts University to advance AI research with applications to fundamental physics.