Find Academic Supervisors

Julija ZavadlavView profile

Assistant Professor

• Multiscale Modeling
• Machine Learning
• Computational Chemistry
• +3 more

Prof. Julija Zavadlav is an Assistant Professor of Multiscale Modeling of Liquid Materials at the Technische Universität München (TUM), affiliated with the TUM School of Engineering and Design. Her research integrates physical modeling with machine learning and Bayesian techniques to develop multi-scale simulation frameworks for diverse applications in bioinformatics and engineering. Education: She earned her PhD in Physics from the University of Ljubljana (2015) and conducted postdoctoral research at ETH Zurich (2016–2019), where she received an ETH Postdoctoral Fellowship. Since 2019, she has held her current position at TUM. Research Interests: Her work focuses on advancing machine learning potentials, Bayesian uncertainty quantification, and multi-scale modeling for complex systems like ionic liquids, metal-organic frameworks, and biomolecules. Her ERC Starting Grant (2022) supports the SupraModel project, emphasizing scalable and interpretable models. Awards: Golden Teaching Award 2022 (Best Lecture), ERC Starting Grant 2022, and ETH Postdoctoral Fellowship. Her recent publications emphasize neural network potentials, transfer learning, and computational tools like JaxSGMC for Bayesian analysis. Grants and Labs: While specific lab names are not mentioned, her ERC grant underscores active funding. No formal student advisee list is provided, but her collaborative work suggests involvement in training next-generation computational scientists.

Wei-keng LiaoView profile

Research Professor

• Parallel and distributed file I/O
• Storage system design
• Data mining algorithms
• +5 more

Wei-keng Liao is a Research Professor in the Department of Electrical Engineering and Computer Science at Northwestern University's McCormick School of Engineering. His research spans high-performance computing with a focus on parallel and distributed systems. Dr. Liao's research interests include parallel and distributed file I/O and storage system design, data mining algorithm design and their parallelization, data management for large-scale scientific applications, and computational model design for large-scale applications on parallel and distributed environments. He is a key contributor to the Parallel netCDF project, which provides parallel I/O capabilities for scientific applications. His recent work shows strong trends in high-performance computing infrastructure, particularly in optimizing I/O systems for scientific applications, parallel data clustering algorithms, and machine learning acceleration in distributed environments. His publications span computational science, parallel computing, and data-intensive applications across various scientific domains including materials science, astrophysics, and healthcare. Best Paper Award at IEEE International Conference on Cluster Computing (2016) for Parallel DTFE Surface Density Field Reconstruction Dr. Liao has supervised numerous research projects funded by DOE, NSF, NASA, and Argonne National Laboratory, with recent work focusing on data libraries for exascale science, machine learning-driven resilience for extreme-scale systems, and scalable data clustering for scientific computing. He leads research on the Parallel K-means Data Clustering software package and is a principal developer of Parallel netCDF. His work connects multiple research groups through the Center for Ultra-scale Computing and Information Security (CUCIS) at Northwestern University, where he collaborates with scientists across disciplines to develop scalable computing solutions for complex scientific problems.

Yu BaiView profile

Associate Professor

• Artificial Intelligence (AI)
• Computer Architecture
• Neuromorphic Computing
• +8 more

Yu Bai is an Associate Professor of Computer Engineering at California State University Fullerton and a Research Associate Professor at the Institute of Mathematical Sciences since 2023. His research spans artificial intelligence (AI), neuromorphic computing, low-power hardware design, and nano-scale computing systems. He holds a PhD in Electrical Engineering from the University of Central Florida (2016), an MS in Electrical and Computer Engineering from the University of Texas-Pan American, and a BS in Electrical Engineering from Ukraine National Aviation University. Research interests include AI applications in robotics, STEM education, and embedded hardware, alongside design automation and CMOS circuit design. His work has been supported by grants from the Army Research Office, National Science Foundation, and National Academies of Sciences. Notable contributions focus on neural network acceleration frameworks, energy-efficient architectures, and spin-based neuromorphic fabrics. He previously worked at Siemens Energy Inc. on data analysis before transitioning to academia.

Xubo YueView profile

Assistant Professor

• Federated Data Analytics
• Bayesian Optimization
• Continuous Optimization
• +5 more

Xubo Yue is an Assistant Professor in the Department of Mechanical and Industrial Engineering at Northeastern University. His research focuses on federated data analytics, Bayesian optimization, continuous optimization, Gaussian processes, and deep learning. He holds a PhD in Industrial & Operations Engineering from the University of Michigan, Ann Arbor (2023). His work bridges theoretical advancements with practical applications in advanced manufacturing, predictive maintenance, and sustainable materials discovery. Key affiliations include the Institute of Industrial and Systems Engineers (IISE), INFORMS, and the American Statistical Association (ASA). Recent research emphasizes scalable federated learning frameworks for distributed systems, causal inference in sensor networks, and sharpness-aware optimization techniques to enhance generalization. His methodologies are applied to interdisciplinary domains such as materials science, IoT systems, and renewable energy simulations. Research trends reveal a focus on: Federated learning architectures for privacy-preserving analytics Bayesian optimization for high-dimensional design spaces Integration of causal reasoning with machine learning systems Autonomous experimentation for accelerated materials discovery No scientific awards are explicitly listed in the provided information. His academic advising and grant activities are not detailed in the current data.

Anurag PurwarView profile

Associate Professor

• Robotics Design
• Kinematic Analysis
• Machine Learning in Engineering
• +3 more

Dr. Anurag Purwar is an Associate Professor of Mechanical Engineering at Stony Brook University, directing the Computer-Aided Design and Innovation Lab and serving as PI for NSF I-Corps. He holds a Ph.D. from Stony Brook (2005) and a B.Tech. from IIT Kanpur (1995). His research merges rigid body kinematics with machine learning for mechanism and robot design, yielding 107+ peer-reviewed publications and patents licensed to industry. Key projects include the sit-to-stand walker assistive device (SAE Top 100 Award) and MotionGen software tools. Education: Ph.D., Stony Brook University (2005); B.Tech., IIT Kanpur (1995) Awards: A.T. Yang Award (2017), SAE Top 100 Award (2016), ASEE Distinguished Teaching Award (2021) Leadership: Program Chair (ASME 2014), Conference Co-Chair (IDETC 2016), NAI Senior Member Research focuses on machine learning-driven design of mechanisms, robotics, and assistive technologies. His labs develop algorithms for path synthesis, generative design, and kinematic analysis. He actively commercializes innovations through industry partnerships and has spun off startups like MotionGen.io.

Gabriel SosaView profile

Academic

• Art Education
• Illustration
• Public Art
• +5 more

Gabriel Sosa is the Saturday Studios Director in Art Education and a Visiting Lecturer in Illustration at Massachusetts College of Art and Design (MassArt). He is also Deputy Director of Essex Art Center in Lawrence, Massachusetts, demonstrating strong ties to community-based arts education and public engagement. Education: M.F.A., School of the Museum of Fine Arts at Tufts University, 2016 B.A., Boston University, 2007 Universidad Autónoma de Madrid, Spain, 2007–2008 His research and creative interests center on public art, language, identity, and community engagement . As a Cuban-American artist and former court interpreter, his work interrogates linguistic accessibility, cultural hybridity, and institutional critique. His projects often involve collaboration with urban communities, museums, and public spaces, reflecting a deep commitment to socially engaged art practices. Themes of visibility, communication, and spatial politics recur across his exhibitions and curatorial work. The 15 most recent articles reflect a consistent engagement with socially responsive art , blending curatorial innovation, performance, and public intervention. They span disciplines including public art, disability studies, digital culture, and political aesthetics , demonstrating a transdisciplinary approach rooted in community dialogue and critical theory. Scientific and Artistic Honors: Materia Abierta Residency, Mexico City (2021) Art & Law Program Residency, New York (2021) Urbano Project Residency, Boston (2021) Now + There Accelerator Program, Boston (2020) Lugar a dudas Residency, Colombia (2019) Gabriel Sosa actively mentors youth and emerging artists through programs like Saturday Studios and the Urbano Project. While no formal grants are listed, his residencies and public art projects suggest funding from cultural and community organizations. His curatorial and educational leadership at Essex Art Center and MassArt highlights a sustained commitment to expanding access to the arts. He leads initiatives at Saturday Studios and Essex Art Center , both focused on community-driven art education and exhibition. These labs and teams emphasize inclusivity, youth development, and cross-cultural dialogue, functioning as incubators for socially engaged artistic practice.

Diogo Ribeiro FerreiraView profile

Associate Professor

• Data Science
• Applied Machine Learning
• Deep Learning
• +6 more

Diogo Ribeiro Ferreira is an Associate Professor of Information Systems at Instituto Superior Técnico (IST) , part of the University of Lisbon . He is also a researcher at the Institute of Plasmas and Nuclear Fusion (IPFN) . His career spans teaching, research, and collaboration with institutions like JET (Joint European Torus) in nuclear fusion. His research focuses on Data Science , Applied Machine Learning , Deep Learning , and Process Mining , with applications in Nuclear Fusion (plasma diagnostics, disruption prediction) and Information Systems (workflow management, healthcare process analysis). He has taught courses like Data Administration in Information Systems and Data Analysis and Integration since 2003. His work bridges Computer Science and Physics , particularly in using Graph Neural Networks for plasma modeling and GPU Acceleration for high-performance computing. He has supervised over 20 PhD and MSc students, including André C. Santos and Fernando Szimanski, and co-authored 15+ recent publications in journals like Journal of Plasma Physics and Fusion Engineering and Design . Scientific Awards: 2012 ICGA Journal Award Best Paper Award at 9th IEEE International EDOC Conference 2005 Collaborations: Affiliated with JET Contributors and IPFN, focusing on real-time tomography and fusion reactor optimization.

• Causal Inference
• Machine Learning
• Natural Language Processing
• +3 more

Dhanya Sridhar is an Assistant Professor at the Department of Computer Science and Operations Research (DIRO) of the University of Montreal and a core academic member of Mila - Quebec Artificial Intelligence Institute. She holds a Canada CIFAR AI Chair and co-leads the IVADO R3AI working group on safe and aligned AI. PhD from University of California Santa Cruz Postdoctoral research at Columbia University Data Science Institute Her research focuses on integrating causality and machine learning to build AI systems that are robust to distribution shifts, capable of efficient task adaptation, and aligned with human knowledge. Recent work includes causal effects of social interactions on US election participation and causal modeling in geriatric-oncology patient outcomes. Current research themes: Causal representation learning Temporal causal inference Counterfactual modeling Causal abstraction in large models Responsible AI development Scientific awards: Canada CIFAR AI Chair Major grants: NSERC Discovery Grant (PVX20965-RGP) 2023-2029 NSERC DGECR Grant 2023-2025 Multiple MITACS Acceleration Quebec grants Advising: PhD students: Philippe Brouillard, Shruti Joshi, Mizu Nishikawa-Toomey, Tom Marty, Cristian Manta

R. Iris BaharView profile

Professor

• Computer Architecture
• Low Power Design
• Machine Learning
• +5 more

R. Iris Bahar is a Professor of Computer Science and Engineering at Colorado School of Mines, where she serves as Department Head of Computer Science since 2022. Previously, she held dual appointments as Professor of Engineering and Computer Science at Brown University for 26 years. Her research focuses on energy-efficient and reliable computing across system-level to device-level domains, with applications in near-data processing, robust machine learning for robotics, and noise-immune nanoscale circuit design. Research Interests include computer architecture, low-power design, and robotic system optimization. She pioneers hybrid generative-discriminative inference techniques for robot scene perception, achieving 40% accuracy improvements in occluded environments through FPGA-based Monte-Carlo sampling implementations. Her work on subthreshold CMOS noise modeling provides 1000x simulation speedups over SPICE-based methods. Near-Data Processing Architectures Approximate Computing Hardware/Software Co-design Nanoscale Noise Modeling Robust AI for Robotics Scientific Awards include the 2024 IEEE Undergraduate Teaching Award, 2019 Marie R. Pistilli Women in Engineering Achievement Award, and NSF CAREER award. She is an IEEE Fellow and ACM Distinguished Scientist. Funded Research spans NSF grants on thermal noise effects ($360k), durable data structures ($500k), and Brown SEED grants for autonomous robotics. Her lab trains 12+ students in hardware acceleration and probabilistic computing.

Fatih BayView profile

Professor

• Neutrino Physics
• Particle Physics
• Detector Development
• +3 more

Fatih Bay serves as Full Professor and Chair of the Department of Mechanical Engineering at Antalya Bilim University since 2020, leveraging his physics expertise in an engineering leadership role. With a Ph.D. in Physics from the University of Bern (2012), he has driven major contributions to neutrino physics through international collaborations at CERN, Fermilab, and KEK, currently focusing on the DUNE experiment's quest to explain matter-antimatter asymmetry. His academic journey features: B.Sc. in Physics, Kocaeli University (2004) M.Sc. in Physics, Middle East Technical University (2008) Ph.D. in Physics, University of Bern, Switzerland (2012) Dr. Bay's research centers on experimental neutrino physics and advanced detector development, specializing in liquid argon time projection chambers, emulsion detectors, and iron-scintillator calorimeters. His current work with DUNE integrates deep learning for neutrino interaction reconstruction while exploring supernova neutrino detection capabilities. This builds upon foundational contributions to OPERA, T2K, and MicroBooNE experiments. Analysis of his 2023-2025 publications reveals dominant focus on DUNE detector systems, with emerging trends in AI-driven event reconstruction, liquid argon purity optimization, and multi-detector synergies for supernova neutrino studies. His work bridges particle physics methodology with cutting-edge computing techniques to enhance detection precision in large-scale experiments. His exceptional contributions earned recognition through: Breakthrough Prize in Fundamental Physics (2016) for T2K experiment leadership Dr. Bay secured significant research funding including a TÜBİTAK 'Returning Researchers' fellowship (2015) and leads international collaborations requiring substantial project resources. While student advising details aren't specified, his department chair role implies graduate mentorship in mechanical engineering contexts. His laboratory work spans CERN (Switzerland), KEK (Japan), and Fermilab (USA) facilities, with current focus on DUNE's vertical drift technology development and ProtoDUNE-SP calibration systems.

Octavian Mihai MachidonView profile

Assistant Professor

• Mobile Computing
• IoT-based Systems
• Smart Public Governance
• +8 more

Octavian Mihai Machidon is an Assistant Professor at the Faculty of Computer and Information Science (FRI), specializing in mobile computing, IoT systems, and approximate computing. His research bridges technological innovation with diverse applications in agriculture, cultural heritage, and smart governance. Current research focuses on energy-efficient mobile systems, UAV-based agricultural monitoring, and smart governance frameworks Previously led H2020 Smart4All AgriAdapt project Recipient of multiple awards for research excellence and innovation His work demonstrates cross-disciplinary impact through projects like Mobiprox (IEEE IoT Journal) and SqueezeSlimU-Net (IEEE Journal of Selected Topics in Applied Earth Observations). Key trends include adaptive algorithms, real-time processing, and sustainable technology integration. 2024 - FRI Special Award for Exceptional Research Achievement 2023 - Agrobiznis 'Best Idea' award for AgriAdapt project As member of the Computer Communications Laboratory , he contributes to digital transformation initiatives and teaches courses in process automation and mobile sensing platform development.

• Electronic Design Automation (EDA)
• Analog/RF Circuit Simulation
• Multi-physics Analysis
• +2 more

Dr. QUAN Chen is an Associate Professor at the School of Microelectronics, Southern University of Science and Technology (SUSTech), holding this position since May 2025 after serving as Assistant Professor (2019-2025) and Research Assistant Professor at the University of Hong Kong (2012-2018). A Shenzhen high-level overseas talent, he earned his PhD from the University of Hong Kong and conducts cutting-edge research in electronic design automation. His academic credentials include: Ph.D. from The University of Hong Kong (2010) Master's degree from The University of Hong Kong (2007) Bachelor's degree from Sun Yat-Sen University (2005) Dr. Chen's research pioneers advanced EDA algorithms for large-scale analog/RF circuit simulation, post-Moore multi-physics analysis, and AI-assisted design technologies. His work addresses critical challenges in nanodevice modeling and quantum computing circuits, resulting in over 50 publications in top venues like IEEE TCAD and DAC, plus four Chinese patents. Analysis of his recent publications reveals dominant trends in exponential integrator methods for transient simulation, model order reduction techniques, and physics-informed machine learning for reliability analysis. His work bridges numerical mathematics with practical EDA applications across analog circuits, quantum hardware, and emerging memory technologies. Key recognitions include: Wu Wenjun Artificial Intelligence Science and Technology Award, Second Prize (2020) ICCAD Best Paper Award Nomination (2012) Dr. Chen actively recruits Postdoctoral Fellows, Research Assistants, and Graduate Students while leading major funded projects including NSFC key/general programs and Guangdong Provincial R&D initiatives. His industry partnerships with Huawei, Empyrean, and Guowei Group translate theoretical advances into real-world EDA solutions. He directs a specialized research group at SUSTech focused on computational methods for next-generation circuit design, fostering innovation in simulation algorithms and multi-physics analysis through academic-industry collaboration.

Christos AntonopoulosView profile

Associate Professor

• Wireless Networks
• Network Communication Protocols
• Embedded Software
• +6 more

Dr. Christos Antonopoulos is an Associate Professor at the Department of Electrical and Computer Engineering, University of Patras. He holds a Diploma and PhD in Electrical Engineering from the University of Patras (2002, 2008) and has participated in over 16 European research projects (FP5, FP6, FP7, Horizon 2020) and 6 national projects. Research Interests: Wireless Networks Cyberphysical Systems Embedded Software Architecture Internet of Things Cross-Layer Protocols Sensor Networks Technical Expertise: His work involves network simulation, power optimization, and reconfigurable computing. He has published >100 journal/conference papers and 13 book chapters with over 1000 citations.

Dewar FinlayView profile

Professor

• Healthcare Technology
• Artificial Intelligence
• Digital Signal Processing
• +5 more

Dewar Finlay is a Professor of Electronic Systems and Head of the School of Engineering at Ulster University . He previously served as Research Director for the School of Engineering and Interim Associate Dean for Research & Impact within the Faculty of Computing, Engineering and the Built Environment. His work bridges healthcare technology and computational engineering. Education: BEng in Electronic Systems, Ulster University PhD in Computing, Ulster University Research Interests: His research focuses on healthcare technology with emphasis on computerised ECG analysis and deep learning applications in cardiology . He explores AI-driven diagnostics , signal processing , and medical device validation through projects like DTNet+ Digital Twin Network and IoT-Driven Cybersecurity Framework for Intrusion Detection in Drones . Scientific Awards: Best Poster (2024) - Calibrated Uncertainty AI in ECG Analysis Early Career Investigators Award (2022) - British Society for Heart Failure Grants & Collaborations: He has secured funding from EU Horizon 2020 , RCUK , DEL , and InvestNI . Current projects include AI-assisted echocardiography for congenital heart defects in Sub-Saharan Africa and federated learning frameworks for cardiac healthcare.

Reyan AhmedView profile

Assistant Professor

• Graph Theory
• Algorithms
• Network Visualization
• +3 more

Reyan Ahmed serves as an Assistant Professor of Practice at The University of Arizona, holding a Ph.D. from the institution with office GS 831. His academic profile bridges theoretical computer science and practical visualization systems. Ph.D., The University of Arizona Dr. Ahmed's research centers on graph-theoretic foundations with emphasis on sparsification techniques, spanner constructions, and geometric optimization. His work advances computational geometry through polygon-based facility assignments and stress-invariant metrics, while pioneering neural network integrations for Steiner tree approximations and vertex-splitting algorithms. Key contributions include multi-level graph sketching frameworks and scalable layout methodologies addressing NP-hard combinatorial problems. Analysis of his 2021-2025 publications reveals three dominant threads: (1) Algorithmic innovations in graph sparsification using machine learning-assisted search, (2) Theoretical advancements in additive spanners with lightness guarantees, and (3) Practical visualization systems for evolving networks. His methodology consistently merges discrete mathematics with gradient-based optimization for real-world network applications. Scientific recognition: No awards documented in source materials Research leadership includes development of Monte Carlo tree search frameworks for graph optimization and semantic zooming architectures for massive network visualization. Current projects focus on scale-invariant stress metrics and multi-priority sparsification, with ongoing exploration of neural network accelerators for classical combinatorial problems. No student advising or grant details were provided. Lab infrastructure supports large-scale graph processing through custom visualization pipelines, though specific team compositions remain undocumented.