Find Academic Supervisors

Ada DiaconescuView profile

Associate Professor

• Autonomic Computing
• Complex Systems
• Software Engineering
• +12 more

Ada Diaconescu is a Tenured Associate Professor at Télécom Paris (Institut Polytechnique de Paris) since October 2009. She is a member of the Autonomous Critical Embedded Systems (ACES) team within the Information Processing and Communication Laboratory (LTCI) , affiliated with the Computer Science and Networks (Infres) department. Research Focus: Autonomic Computing, Organic Computing, Complex Adaptive Systems, Self-Organising Systems, Smart Grids/Cities/Homes, Nature-Inspired Computing, Socio-Technical Systems Key Methodologies: Decentralised Control, Multi-Agent Systems, Component/Service-Oriented Architectures, Multi-Scale Feedback Loops Her recent publications (2021-2023) explore topics like holonic architectures for self-integration, multi-scale feedback systems in smart environments, and decentralised explanatory frameworks for AI transparency. She co-authored the textbook "Autonomic Computing: Principles, Design and Implementation" (Springer, 2013) and contributed chapters to books on Organic Computing (Springer, 2018) and Self-Aware Systems (Springer, 2017). Scientific Leadership: General Chair of IEEE SASO 2017 Steering Committee Co-Chair of IEEE ACSOS (2019-2021) Co-Organizer of Dagstuhl Seminars (2015, 2018) Doctoral Symposium Co-Chair at FAS* 2019 Teaching: Delivered courses on software engineering, autonomic systems, and complex system modeling at Télécom Paris and other institutions. Collaborations: Active in interdisciplinary research with institutions including Leibniz University, ETH Zurich, Arizona State University, and Lancaster University.

Shyam GollakotaView profile

Professor

• Artificial Intelligence
• Computing + Biology
• Human-Centered Computing
• +6 more

Shyam Gollakota is a Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington. His research spans Artificial Intelligence, Human-Centered Computing, and Wireless Systems , with interdisciplinary collaborations in Biology and Medicine. He leads the Mobile Intelligence Lab , co-founded Sound Life Sciences (acquired by Google), and developed battery-free computing technologies. Education: Ph.D. from MIT (2013), B.Tech from IIT Madras Awards: Infosys Prize 2024, Moore Inventor Fellow, NSF CAREER, ACM Grace Murray Hopper Award His research focuses on machine learning for mobile systems, battery-free sensing, and human auditory augmentation . Recent work includes proactive in-ear assistants (ACL 2025), spatial speech translation (CHI 2025), and sound bubbles for personalized audio (Nature Electronics 2024) . Publications span Human-Computer Interaction, Wireless Networking, and Medical AI . Scientific achievements include: Best Paper Awards: MOBICOM, SIGCOMM, UbiComp, SenSys, NSDI, CHI Breakthroughs: Battery-free phones, ambient backscatter, smartphone-based medical diagnostics His team co-developed underwater GPS (SIGCOMM 2023), AI-enabled ClearBuds (MobiSys 2022), and wireless blood clot testing (Nature Communications 2022) . Collaborations include the Washington Department of Agriculture for monitoring invasive hornets.

• Combinatorial optimization
• Statistical physics
• Epidemic inference
• +7 more

Alfredo Braunstein is an Associate Professor at the Department of Applied Science and Technology (DISAT), Politecnico di Torino, and a member of SmartData@PoliTO laboratory. His research spans combinatorial optimization, statistical physics of complex systems, epidemic inference, and computational biology . He leads the Simultaneous Inference from Multiscale Biological Data (SIMBAD) project (2023-2027) and previously led Statistical Inference via Belief Propagation for Epidemics (SIBYL) (2015-2017). He has supervised multiple PhD students in Physics of Complex Systems . Research Interests : Statistical physics, machine learning, network science, epidemic modeling, and optimization. Key Collaborations : Collegio Carlo Alberto, Human Genetics Foundation, and interdisciplinary teams. His recent publications focus on epidemic risk estimation , network dismantling , and message-passing algorithms . He received the La Ricerca dei Talenti award from Fondazione CRT (2015).

• Intelligent Agents
• Probabilistic Graphical Models
• Deep Learning
• +3 more

Prof. Yifeng Zeng is a Professor and Head of Research & Knowledge Exchange&UoA 11 Lead in Northumbria University's Department of Computer and Information Sciences. He leads the Autonomous Systems research group, focusing on intelligent agents, deep learning, computational intelligence, and digital education. With a PhD in Engineering (2005), he has secured the EPSRC New Investigator Award (2019) and led Innovate UK projects. His work spans multiagent systems, healthcare AI applications, and data-driven decision models. Affiliations: Northumbria University, Autonomous Systems Group Research Interests: His primary areas include probabilistic graphical models, reinforcement learning in multiagent systems, and AI-driven healthcare solutions like optical coherence tomography (OCT) for cancer diagnosis. He explores ethical AI through policy legibility and interpretable decision-making frameworks. Publications: Recent work emphasizes multiagent learning strategies, spiking neural networks, and deep learning applications in medical imaging. His 2025 articles highlight advancements in legible agent policies and cooperative exploration methods. Awards: EPSRC New Investigator Award (2019) Grants & Supervision: Co-Investigator on the Turing Network Development Award (2022). Supervises 2 researchers, focusing on autonomous systems and AI ethics. Leads projects integrating machine learning with healthcare diagnostics and educational technology. Labs & Teams: Directs the Autonomous Systems group, collaborating on typhoon response systems and medical imaging AI. Active in interdisciplinary projects blending robotics, computer vision, and decision theory.

Vignesh NarayananView profile

Assistant Professor

• Dynamical Systems
• Artificial Intelligence
• Computational Neuroscience
• +4 more

Vignesh Narayanan is an Assistant Professor in the Artificial Intelligence Institute (AII) and Department of Computer Science and Engineering at the University of South Carolina (USC), with affiliations to the Carolina Autism and Neurodevelopment (CAN) Research Center. He bridges AI with dynamical systems theory, computational neuroscience, and cyber-physical systems (CPS) to enhance safety and efficiency in human-AI collaboration. Education: B.Tech (SASTRA University, India, 2012), M.Tech (National Institute of Technology, India, 2014), Ph.D. (Missouri University of Science and Technology, 2017), Postdoc (Washington University in St. Louis, 2017-2021) His research spans dynamic modeling , AI integration , and neuroscience-inspired systems . Key projects include: Developing safety-constrained virtual health assistants using knowledge graphs to align with medical protocols Advancing collaborative autonomy for drones/vehicles through event-triggered control mechanisms Creating digital twins for opinion dynamics to analyze information propagation in social systems Designing quantum-echo state networks for chaotic system prediction on NISQ hardware Recent publications demonstrate expertise in reinforcement learning for battery health monitoring ( 2024 ), reservoir computing for time series reconstruction ( 2025 ), and knowledge-infused AI frameworks for explainable decision-making. His lab team includes researchers like Bharath Muppasani and Erik Connerty, while recent grants from NSF , AFOSR , and DoD fund projects on collaborative autonomy and secure cyber-physical control.

• Artificial Intelligence
• Data Science
• Optimization
• +8 more

Richard Allmendinger is a Professor of Applied Artificial Intelligence at Alliance Manchester Business School, The University of Manchester, and serves as Associate Dean for Business Engagement, Civic & Cultural Partnerships in the Faculty of Humanities. He holds qualifications including a PhD in Computer Science from the University of Manchester and advanced degrees from Karlsruhe Institute of Technology and RMIT. His research focuses on applying optimization and machine learning to real-world challenges in healthcare, manufacturing, sports, forensics, and AI-driven decision-making. He leads the North West Productivity Forum and advises organizations like Guru AI and River Capital. His work spans interdisciplinary collaborations with industry and academia, addressing problems such as cybersecurity investment optimization, extreme weather forecasting, and personalized medicine supply chain design. Notable achievements include winning the Test-Comp 2025 gold medal for FuSeBMC research and contributions to synthetic data generation and healthcare anomaly detection. Current projects include the Centre for Digital Trust and Society and EDASIDA (Enhancing Data Accessibility and Security), funded by EPSRC and Innovate UK. He actively supervises PhD research in optimization, AI ethics, and sustainable technologies, emphasizing practical applications and societal impact.

Pengzhou (Abel) WuView profile

Research Fellow

• Causality
• Machine Learning
• Generative Models
• +3 more

Pengzhou (Abel) Wu serves as a Postdoctoral Researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the Technical University of Berlin since July 2023, focusing on the intersection of causality and machine learning methodologies. His academic foundation includes: Ph.D. in Statistical Science (2022) from The Graduate University of Advanced Studies M.E. in Information and Communication Engineering (2013) from Zhejiang University B.E. in Electronic and Informational Engineering (2010) from Xiamen University Dr. Wu's research spans causal inference and discovery, generative deep learning models, and nonlinear independent component analysis (ICA) with emphasis on identifiability. His work bridges machine learning with physical sciences and cognitive science, developing science-inspired ML frameworks and advancing ML applications for scientific discovery. Key methodologies include probabilistic modeling and causal structure learning. He operates within BIFOLD's research ecosystem, a premier Berlin-based institute dedicated to data science foundations and machine learning innovation.

Tadamoto IsogaiView profile

Assistant Professor

• Cancer Biology
• Cell Biology
• Biomedical Engineering
• +3 more

Tadamoto Isogai is an Assistant Professor at the Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center since 2023. His research integrates high-resolution 3D single-cell imaging with computational methods to study cytoskeletal architecture's role in cancer cell survival. He applies mathematical models inspired by financial economics to analyze gene pathway dynamics without experimental perturbation, focusing on actin-binding proteins in membrane protrusions. Dr. Isogai earned his PhD from The Netherlands Cancer Institute in Amsterdam. His work bridges cell biology, biophysics, and bioinformatics, emphasizing systems-level approaches to understand mechanotransduction and cell migration. Notable contributions include uncovering CLIC4's role in RhoA signaling and developing algorithms for 3D microscopy analysis. His recent studies explore actin-membrane interactions during cell protrusion initiation, mechanical regulation of glycolysis, and systems biology approaches to lamellipodial actin networks. Research outputs span Nature , Cell Systems , and Philosophical Transactions of the Royal Society , reflecting interdisciplinary impact.

Ruimin ChenView profile

Assistant Professor

• Data Analytics
• Statistical Learning
• Uncertainty Quantification
• +4 more

Ruimin Chen is an Assistant Professor in the Department of Mechanical Engineering at the University of Connecticut (UConn), joining in August 2022. She holds a Ph.D. in Industrial Engineering from The Pennsylvania State University (2021), dual M.S. degrees in Industrial Engineering and Operations Research from the same institution (2018), and a B.S. in Industrial Engineering from Southeast University, China (2016). Her research focuses on data analytics, statistical learning, system identification, and uncertainty quantification, with applications in advanced manufacturing systems, privacy/security, and human-machine teaming. Key areas include quality monitoring in smart manufacturing, additive manufacturing process optimization, and interpretable AI for safety compliance. Her recent work explores federated hyperdimensional computing for distributed quality monitoring, cognitive data fusion in hybrid manufacturing, and brain-inspired computing for melt pool characterization. She also investigates educational strategies integrating Six Sigma principles with hands-on engineering practice. Notable contributions include methodologies for heterogeneous quality characterization in additive manufacturing, causal inference frameworks using Bayesian networks, and ontology-driven process analytics. She emphasizes translational research bridging theoretical computer science with industrial applications.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +6 more

Shwai He is a PhD student and Affiliate Assistant Professor at the University of Maryland, advised by Ang Li. Their research focuses on advancing AI systems through innovative approaches in large language models (LLMs), fairness in machine learning, and efficient neural network architectures. Key areas include multi-agent systems, causal modeling for bias mitigation, and optimization techniques for mixture-of-experts models. Research interests span artificial intelligence, machine learning, and natural language processing with emphasis on practical applications like healthcare diagnostics and social pairing systems. Notable work includes developing GNWT-based multi-agent digital twins for social platforms and improving LLM transparency through token analysis. Publications highlight contributions to counterfactual fairness, dynamic-depth transformers, and parameter-efficient methods. Current efforts explore computational efficiency in vision-language models and bio-inspired antibody prediction systems. No scientific awards have been mentioned. Advising and grants: Currently a PhD student under Ang Li's supervision. Research involves collaborations across computer science and bioinformatics domains.

Boris SedlakView profile

Researcher

• Distributed Systems
• Edge Computing
• Service-Oriented Computing
• +5 more

Boris Sedlak is a PostDoc Researcher and Ph.D. student in the Distributed Systems Group (DSG) at TU Wien, Austria. His research focuses on ensuring runtime requirements in large-scale computing systems through causal inference and active inference concepts, with applications in edge computing, vehicular networks, and distributed systems governance. Education: Ph.D. in Computer Science (Ongoing) at TU Wien, funded by the TEADAL EU Horizon project. M.Sc. in Software Engineering & Internet Computing (Distinction) at TU Wien (2018–2022). B.Sc. in Media Informatics at FH St. Pölten, Austria (2015–2018). Research Interests: Boris specializes in distributed computing continuum systems, active inference for system equilibrium, causal inference in distributed environments, edge computing elasticity, and privacy-preserving data stream processing. He explores applying neuroscience-inspired concepts to computing systems for adaptive and self-organizing solutions. Key Projects: TEADAL (2022–2025): Focuses on federated and trustworthy data lakes in distributed systems. INTEND (2024–2026): Investigates intelligent service adaptations through active inference. AloTwin (2023–2025) and FogProtect (2020–2022): Address edge computing security and data governance. Recent Contributions: Boris has published extensively on adaptive stream processing, SLO-aware task offloading in vehicular platoons, and equilibrium models using active inference. His work bridges causal reasoning and system design, emphasizing scalable solutions for heterogeneous edge infrastructures. Advisory & Collaboration: Supervises bachelor/master theses and actively collaborates on topics involving service-level objectives, causal models, and edge intelligence. He has presented at ICSOC, IEEE Edge Computing, and AIoTwin Summer School.

M. SchumannView profile

Assistant Professor

• Econometrics
• Nonlinear Panel Data Models
• Fixed Effects Estimation
• +4 more

M. Schumann is an Assistant Professor of Econometrics at Maastricht University's School of Business and Economics , specializing in quantitative economics. His research bridges statistical theory and applied economic modeling. Key research areas include Nonlinear panel data models with fixed effects Second-order bias reduction techniques Integrated likelihood inference Difference-in-differences methodologies Applications in innovation systems and flood statistics Recent publications demonstrate interdisciplinary trends, combining econometric theory with hydrology-inspired statistical approaches. His work appears in top-tier journals like Journal of Econometrics and Journal of Business & Economic Statistics . Collaborative efforts with researchers like T.A. Severini, G. Tripathi, and M.F. Arroyabe highlight his methodological and applied contributions to both economic and environmental sciences.

Rupak ChatterjeeView profile

Assistant Professor

• Quantum Information and Computation
• Quantum Machine Learning
• Quantum Optimization
• +4 more

Rupak Chatterjee is an Industry Assistant Professor in the Department of Applied Physics at New York University's Tandon School of Engineering. His research bridges quantum information/computation and mathematical physics, with a focus on quantum algorithms for machine learning, optimization, operator algebras, and quantum mechanical systems. Education: Postdoctoral Scholar, Physics (James Franck Institute, University of Chicago) Ph.D. & M.S., Physics (Stony Brook University) M.Math., Mathematics (University of Waterloo) B.Sc., Physics (University of Calgary) Chatterjee's research explores quantum systems for machine learning, quantum optimization protocols, and mathematical frameworks like C∗-algebras and supersymmetric quantum mechanics. His work integrates theoretical rigor with applications in quantum computing and complex physical systems. His recent publications (2019–2024) demonstrate a strong emphasis on quantum entanglement, chaos in optomechanical systems, adiabatic quantum optimization, and relativistic quantum mechanics. Several publications also apply quantum methods to finance and diffusion modeling. No awards, student advising, or grant information is documented in the provided text.

Ranadeep DawView profile

Assistant Professor

• Spatial Statistics
• Spatiotemporal Modeling
• Computational Statistics
• +11 more

Ranadeep Daw is an Assistant Professor in the Department of Mathematics and Statistics at the University of West Florida, part of the Hal Marcus College of Science and Engineering. He joined UWF in Fall 2024 after completing a postdoctoral fellowship at the National Institute of Environmental Health Sciences. He holds a Ph.D. in Statistics from the University of Missouri Columbia and degrees in Statistics from the Indian Statistical Institute, Kolkata. Ph.D. in Statistics, University of Missouri Columbia, 2023 M. Stat., Indian Statistical Institute, Kolkata, 2016 B. Stat., Indian Statistical Institute, Kolkata, 2014 Dr. Daw's research lies at the intersection of spatial statistics, computational methods, and machine learning. He develops low-rank spatial models, regionalization techniques using Karhunen-Loève expansions, and novel algorithms like REDS and SBoost for large-scale spatial prediction. His work applies to environmental risk modeling, exposure assessment, and biostatistics. He is actively developing computational tools in Julia, including the CAGE.jl package. His recent publications demonstrate a strong trend in scalable spatial methodologies, blending statistical theory with machine learning for environmental and public health applications. Key themes include data reduction, uncertainty quantification, and high-resolution imputation of pollutants like NO2. He has published in top journals such as Journal of Geographical Systems , Environmetrics , and Journal of Data Science . Scientific Awards: INSPIRE Scholarship, Department of Science and Technology, Government of India (2011–2016) Qualified for National Level, Regional Mathematical Olympiad (2010) Dr. Daw has served as a reviewer for journals like Spatial Statistics and Signal Processing . He has mentored at the graduate level as a Teaching Assistant and Instructor at the University of Missouri. He is actively seeking collaborations, particularly in biostatistics and Julia-based tool development. His research has been supported through academic appointments and fellowships, including at NIEHS and Deloitte. He is involved in developing open-source tools and has presented his work at major conferences including JSM and ICSA. His current projects include SBoost and MVCAGE, focusing on supervised dimension reduction and boosting for spatial data.

• Differential Geometry
• Geometric Analysis
• Mathematical Relativity
• +1 more

Dr. Armando Cabrera Pacheco is a Research Fellow at the University of Tübingen, affiliated with the Tübingen AI Center and Department of Computer Science within the Faculty of Mathematics and Natural Sciences. His interdisciplinary work bridges geometric analysis, relativity theory, and machine learning foundations. Education: Ph.D. in Mathematics, University of Miami, USA (2016) M.Sc. in Mathematics, University of Miami, USA (2012) Bachelor's degree in Mathematics, Universidad Autónoma de Yucatán, México (2009) Research Focus: His research explores the geometric structure of spacetime in general relativity, stability theorems for black holes, and Riemannian manifold extensions. Recently, he investigates foundational machine learning concepts like loss aggregation geometries and axiomatic learning frameworks, creating novel intersections between differential geometry and algorithmic theory. Publication Trends: Early work (2016-2021) centered on geometric analysis of relativistic spacetimes, positive mass theorems, and asymptotically hyperbolic manifolds. Recent publications (2023-2024) demonstrate a pivot toward machine learning theory, developing geometric frameworks for loss functions and aggregation algorithms while retaining physics-inspired mathematical rigor. Awards: Teaching Award for Mathematics for Machine Learning, University of Tübingen (Winter 2022-2023) Teaching & Leadership: He has taught graduate courses in Riemannian Geometry and Mathematics for Machine Learning, receiving recognition for pedagogical excellence. Actively organizes international conferences including "Mathematical Relativity: A Riemannian Approach" (CIMAT-Guanajuato, 2019) and "Encuentro Iberoamericano de Geometría Diferencial" (Granada, 2019).