Find Academic Supervisors

• Musculoskeletal Hand and Upper Extremity Injuries
• Orthosis Fabrication
• Emotional Intelligence
• +8 more

Erika Lewis is an Associate Professor in the Department of Physical Therapy and Kinesiology at the Zuckerberg College of Health Sciences, University of Massachusetts Lowell . She has been a full-time faculty member since 2004, teaching musculoskeletal evaluation courses and co-developing the interprofessional course Adaptive Devices for Better Life . Her clinical work as a Certified Hand Therapist spans orthopedic, plastic surgery, and neurological rehabilitation at Rehab Health 360 in Acton, MA. Ed.D in Leadership and Schooling (2004), University of Massachusetts Lowell MS in Physical Therapy (1992), University of Massachusetts Lowell BS in Pre-Physical Therapy (1987), University of Massachusetts Amherst Her research interests include musculoskeletal hand therapy, virtual reality rehabilitation, 3D-printed adaptive devices, and emotional intelligence in clinical performance. Google Scholar articles highlight her work on measurement reliability, therapeutic outcomes, and educational strategies. Scientific awards include the 2023 APTA Volunteer Service Award, 2017 Outstanding Reviewer for the Journal of Hand Therapy, and multiple ASHT Best Poster Awards (2014, 2013). She has led grants for WheelUp! , 3D orthotics, and geriatric exercise programs. As a lab collaborator , she works with the UMOVE UMass MOVEment Research Center and co-developed the Adaptive Devices for Better Life course. She served as department chair and DPT Program Director, and currently coordinates online education for APTA Hand and Upper Extremity Academy.

• Low-latency communication
• Medium access protocols
• Digital Twin
• +6 more

Yash Deshpande is a researcher at the Chair of Communication Networks at Technical University of Munich (TUM), focusing on reliable low-latency communication systems for AI-enabled robotics and industrial applications. He joined the chair in January 2021 after completing his MSCE at TUM. B.Sc./M.Sc. in Electronics, University of Pune (2016-2018) MSCE in Communications Engineering, TUM (2021) His research spans medium access protocols , digital twin latency modeling, and networked control systems . Current projects include 6G Future Lab Bavaria, AI.NET ANTILLAS, and 5G Testbed Bayern extensions. Recent publications address tree algorithms with interference cancellation , deterministic networking integration with 5G , and reproducible wireless channel emulation . Collaborative work includes industrial digital twins (AI.Fabrik) and network-aware timing bounds in precision protocols. He contributes to IEEE conferences (INFOCOM, GLOBECOM, CASE) and journals (IEEE Transactions on Communications, Information Theory), with emphasis on stochastic network models and learning-based MAC protocols .

Shayantan ChaudhuriView profile

Research Fellow

• Computational Chemistry
• Quantum Chemistry
• Covalent Drug Design
• +3 more

Shayantan Chaudhuri is a Research Fellow at the School of Chemistry, University of Nottingham, joining the Hirst Group in September 2023. His work focuses on quantum chemistry methods to characterize and accelerate covalent drug design, alongside prior research in computational simulation of metal nucleation on diamond electrodes and property-driven generative design of organic molecules and metal nanoparticles. Research Interests: Quantum Chemistry and DFT-based Methods Materials Science (metal nanoparticles, diamond surfaces) Machine Learning Applications Covalent Drug Design Scientific Contributions: Developed methods for quantum many-body dispersion calculations Investigated stability of gold atoms on diamond surfaces Studied electrochemical ozone generation using doped diamond electrodes Explored adiabatic vs. non-adiabatic electron transfer in 2D materials

Romain GrandchampView profile

Researcher

• Neuroscience
• Cognitive Science
• Brain-Computer Interface
• +3 more

Romain Grandchamp is a Researcher at Université Grenoble Alpes , affiliated with the Laboratoire de Psychologie et Neurosciences Cognitives (LPNC) and the Consciousness, Memory and MetaCognition (CoMMet) team. His work bridges neuroscience , brain-computer interfaces (BCI) , and cognitive modeling to study mental states , mind wandering , and inner speech . PhD in EEG and BCI (2012, Université Paul Sabatier) Developed tools like NeuroTRIP and Brainarium for BCI training and immersive neuroeducation Focus on electromyographic correlates of rumination, ICA decomposition , and neurofeedback applications Prior research includes neuroplasticity in meditation practitioners and verbal rumination mechanisms. Collaborations span institutions in France, Australia, Netherlands, and Morocco.

Luis N AmaralView profile

Professor

• Computational Biology
• Machine Learning
• Biomedical Informatics
• +5 more

Luis N Amaral is a Professor at both the Department of Medicine (Pulmonary and Critical Care) and the McCormick School of Engineering at Northwestern University . His interdisciplinary research bridges computational sciences with biomedical applications. PhD in computational biology from Boston University (1996) Key research areas include machine learning , biomedical informatics , scientific integrity , and network science . Recent publications focus on clinical state extraction using AI, reproducibility in materials science, and foundational issues in artificial intelligence. Publications from 2025-2024 reveal trends in AI ethics , medical data analysis , and scientific methodology . His work combines complex systems research with healthcare applications , including pneumonia subtyping algorithms and tools for critical care diagnostics. Affiliated with the Institute for Artificial Intelligence in Medicine and Center for Computational and Social Sciences in Health , Amaral contributes to interdisciplinary research initiatives like NUCATS. His studies explore social network effects on health outcomes and system-level scientific challenges.

• Materials Science
• Shape Memory Materials
• Biomaterials
• +4 more

Ken Gall is a Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University with cross-appointments in Orthopaedic Surgery and Biomedical Engineering. His career integrates fundamental materials science with biomedical device commercialization, focusing on processing-structure-property relationships in metals and polymers for orthopedic applications. Education: B.S., University of Illinois at Urbana-Champaign (1995) M.S., University of Illinois at Urbana-Champaign (1996) Ph.D., University of Illinois at Urbana-Champaign (1998) Gall specializes in shape memory materials (metals and polymers), biomaterials, and 3D printing for medical devices. His current work targets 3D printed metals/polymers with engineered porous networks, soft synthetic biomaterials for tissue integration, and biopolymer surface structures. This bridges materials mechanics with clinical orthopedics and pelvic organ prolapse repair. Recent publications (2023-2025) show a clear shift toward computational methods (neural networks, finite element modeling) for predicting mechanical behavior of porous 3D printed structures. Key trends include clinical translation of gyroid lattices, in vivo validation of titanium implants, and material optimization for pelvic tissue applications—reflecting his entrepreneurial focus on commercializing university research. Scientific awards: ASEE Curtis McGraw Award (2012) TMS Robert Lansing Hardy Award (2008) ASM Bradley Stoughton Award (2005) ASME Gold Medal (2004) Presidential Early Career Award for Scientists and Engineering (PECASE) (2002) Gall actively consults for industry, the US Military, and US Intelligence Community while serving as an expert witness in patent litigation. His entrepreneurship includes co-founding MedShape and Vertera to commercialize orthopedic technologies. He teaches mechanical engineering courses (ME 592, 591, 555, 492, 491, 392) and engineering projects (EGR 393). The Gall Group research team develops porous metallic/polymeric implants using additive manufacturing, with emphasis on osseointegration, load-sharing, and fatigue behavior—particularly for foot/ankle and spinal applications.

Pierre-François GimenezView profile

Researcher

• Cybersecurity
• Machine Learning
• Network Security
• +3 more

Pierre-François Gimenez is a cybersecurity and machine learning researcher affiliated with the Laboratory Confidentiality, Integrity, Availability, Distribution . His work focuses on intrusion detection systems, malware analysis, and secure software design. Research Interests : Secure programming languages resistant to injection attacks Anomaly detection in network environments Machine learning applications for cybersecurity Malware dataset validation and bias mitigation Recent Publication Trends : His articles from 2025 demonstrate expertise in graph-based detection of advanced threats, synthetic network traffic generation, and robust malware detection frameworks. He also explores preference learning for recommendation systems in interactive configuration.

Dan KnightsView profile

Associate Professor

• Microbiome Research
• Computational Genomics
• Data Mining
• +4 more

Dan Knights is an Associate Professor in the Department of Computer Science and Engineering and the Biotechnology Institute at the University of Minnesota. His research bridges computational science and microbiome studies, developing advanced data mining methods for high-dimensional biological data analysis. He leads the Knights Lab, which focuses on computational genomics applications in human health and sustainability modeling. Dr. Knights' research interests center on microbiome analysis, computational genomics, and data science applications in biological systems. His work has significantly advanced our understanding of how the human gut microbiome changes with industrialization, immigration, and dietary patterns. He has developed innovative computational approaches including contributions to the widely used QIIME platform for microbiome analysis. His recent work extends into sustainability modeling, applying data science to identify practices that promote long-term civilizational resilience. Analysis of Dr. Knights' publication record reveals a strong focus on microbiome research with emphasis on human health applications. His work spans methodological development, clinical studies, and population-level investigations of microbiome dynamics. Key themes include the impact of Westernization on gut microbial communities, personalized diet-microbiome relationships, and computational approaches for analyzing complex microbial ecosystems. His research bridges computer science, genomics, and clinical medicine. 2023 Charles E. Bowers Faculty Teaching Award 2020 Highly Cited Researchers designation Dr. Knights has successfully mentored multiple PhD students to completion, including Benjamin Hillmann (2022), Pajau Vangay (2018), and Gabe Al-Ghalith (2018). His research has attracted significant funding, leading to the creation of CoreBiome, a University of Minnesota startup that was acquired by OraSure Technologies. His work has been recognized with multiple innovation awards, including the OVPR Innovation Award. Dr. Knights' research program demonstrates strong translational impact, moving from basic computational methods to commercial applications that accelerate microbiome discovery across industries. The Knights Lab at the University of Minnesota is a multidisciplinary research group focused on data science applications in microbiome research and sustainability modeling. The lab has developed widely used computational tools like QIIME 2 for microbiome analysis and has conducted groundbreaking research on how human migration and lifestyle changes affect gut microbial communities. Current projects include AI applications for predicting environmental impacts and developing methods for analyzing incomplete and high-dimensional biological datasets. Although not currently accepting new students, the lab has established itself as a leader in computational approaches to microbiome research.

• Data Science Systems
• Machine Learning Systems
• Database Systems
• +7 more

Yongjoo Park is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign, affiliated with the Siebel School of Computing and Data Science and the Data and Information Systems (DAIS) Research Lab. He co-founded and serves as Chief Scientist at Keebo, Inc., a startup developing automated data warehouse optimization platforms. Ph.D. in Computer Science and Engineering, University of Michigan, Ann Arbor (2017) M.S. in Computer Science, University of Michigan, Ann Arbor (2013) B.S. in Electrical Engineering, Seoul National University (2009) His research focuses on building intelligent data-intensive systems that integrate statistical and AI techniques for improved reliability, scalability, and usability in data science workflows. Key projects include Kishu (undoable Jupyter notebooks), AirIndex (automated index optimization), and CARE (causal-reasoning data systems). His work bridges database systems and machine learning, emphasizing end-to-end optimization for structured/unstructured data processing. Recent publications highlight trends in computational notebook checkpointing, index tuning through data-aware storage, and AI-driven database learning. His projects have been recognized at top venues including SIGMOD, VLDB, and CHI, with a focus on practical implementations for real-world data challenges. Scientific Awards: NSF CAREER Award (2025) Best Demo Award at SIGMOD 2025 ACM SIGMOD Jim Gray Dissertation Award Runner-up (2018) Engineering Council Outstanding Advising Award (2021) Teaching Excellence Awards (2023, 2024) Yongjoo advises multiple PhD and MS students, including Supawit Chockchowwat (joining CMKL University in Thailand) and Nikhil Sheoran (now at Databricks). His research group maintains strong industry collaborations and open-sources systems like Kishu and VerdictDB through their GitHub organization .

Fabrizio FornariView profile

Assistant Professor

• Internet of Things (IoT)
• Digital Twins
• Business Process Management (BPMN)
• +6 more

Fabrizio Fornari is an Assistant Professor in Computer Science at the University of Camerino (Italy), focusing on cutting-edge technologies like Internet of Things (IoT), Digital Twins, and Business Process Management (BPMN). His work bridges theoretical modeling with practical implementations, emphasizing formal verification and modern software development practices. Research Pillars: IoT programming paradigms (Fluidware, FloWare), Digital Twin applications for business processes, and formal BPMN verification. Technical Contributions: Development of model-driven frameworks (X-IoT, FloBP), 3D simulation tools for IoT platforms, and BPMN analysis utilities (BPMN Inspector, BProVe). Domain Applications: Earthquake response systems (SAFE), intelligent transportation data flows, and educational IoT test environments. Methodological Focus: Community detection algorithms for routine extraction, CI/CD optimization for databases, and systematic literature reviews on IoT-aware BPMN modeling. Collaborative Efforts: Active involvement in MIDas4CS workshops and repository platforms for process model sharing (RePROSitory).

Stefano ZacchiroliView profile

Professor

• Digital Commons
• Open Source Software Engineering
• Computer Security
• +5 more

Stefano Zacchiroli is a full professor at Télécom Paris , part of the Polytechnic Institute of Paris , affiliated with the Computer Sciences and Networks (Infres) department and the Autonomous Critical Embedded Systems (ACES) research team within the Information Processing and Communication Laboratory (LTCI) . His research focuses on digital commons , open source software engineering , computer security , and software supply chain integrity .

• Data Provenance
• Intelligent Systems
• Knowledge Graphs
• +4 more

Prof. Dr. Paul Groth is a faculty member at the Informatics Institute within the Faculty of Science at the University of Amsterdam . He leads the Intelligent Data Engineering Lab (INDElab) and collaborates extensively with institutions like the Vrije Universiteit Amsterdam and Elsevier Labs . His research focuses on data provenance , knowledge graphs , and machine learning systems with emphasis on reproducibility and semantic integration. Research Interests include Data Provenance and Traceability Knowledge Graph Construction & Evolution Neurosymbolic AI Systems Graph Learning & Semantic Web Technologies Digital Twins & Ontology Design Article Trends reveal a 2025 focus on LLM-knowledge graph intersections , emergent data patterns , and hyperbolic embedding architectures . His work addresses challenges in data distribution shifts , entity matching , and graph neural network explanations , while maintaining a strong foundation in FAIR data principles and provenance tracking . Secondary Activities include co-founding longform.ai , serving as co-editor for Morgan & Claypool , and contributing to international conferences like ESWC and ISWC . His research receives funding from European Union , NSF , NWO , and industry partners such as Weaviate and Ahold Delhaize .

Edward J. AuerbachView profile

Associate Professor

• MRI Physics
• Neuroimaging
• Ultra-high Field MRI
• +9 more

Edward J. Auerbach is an Associate Professor in the Department of Radiology at the University of Minnesota, affiliated with the Center for Magnetic Resonance Research (CMRR). He has been instrumental in advancing ultra-high field (UHF) MRI technology since joining CMRR in 1998. Ph.D. in Biomedical Sciences and Medical Physics (University of Minnesota, 2003) B.S. and B.A. in Chemistry and Criminology & Law (University of Florida, 1997) His research focuses on optimizing MRI hardware/software for improved image quality, resolution, and acquisition speed at ultrahigh magnetic fields (7T/10.5T). Current projects include: 10.5T deuterium-based MRS/I for brain metabolism Full-stack automation for reproducible MRI of brain cancer Informatics tools for MRS optimization Recent publications highlight advancements in: 10.5T brain imaging with enhanced SNR arrays Fourier aggregation for motion-robust MRI Ghost correction in 7T diffusion MRI He collaborates extensively with NIH-funded teams exploring human brain mapping and connectome projects.

Daniel FriedView profile

Assistant Professor

• Natural Language Processing
• Code Generation
• Grounded Language Interaction
• +3 more

Daniel Fried is an Assistant Professor at the Language Technologies Institute within the School of Computer Science at Carnegie Mellon University. His research bridges natural language processing, pragmatics, and human-computer interaction, with a focus on making programming more communicative through language models. He previously held postdoctoral positions at FAIR Seattle and the University of Washington, earned a PhD from UC Berkeley, M.Phil from Cambridge, and B.S from the University of Arizona. University: Carnegie Mellon University Department: Language Technologies Institute Ranks: Assistant Professor Research Interests: Daniel Fried's work centers on grounded interaction, where language models perform tasks in real-world contexts. His key domains include code generation (e.g., InCoder, StarCoder), multi-agent pragmatic modeling, and multimodal dialogue systems. He explores how agents can infer user intent through language, enhance code synthesis via reinforcement learning, and improve factuality in embeddings. Recent Article Trends: His 2025 publications emphasize multi-agent coordination, programmatic skill induction, and execution-based code benchmarks. Work like RepoST and Agent Workflow Memory highlights scalable repository-level environments and memory mechanisms for agentic tasks. Papers such as CRScore and Dynamic Coalition Structure Detection reflect his focus on pragmatic evaluation and strategic language modeling. Scientific Awards: Okawa Research Grant (2023), Best Paper Award at Deep Learning for Code Workshop (2023), Best Paper Award at CVPR (2012) Advising: He mentors PhD students at CMU, including Andre He, JY Koh, and Zora Wang. His alumni advisees, like Alex Xie, have contributed to machine learning and education projects.

• Medical Physics
• Breast Cancer Imaging
• Magnetic Resonance Imaging
• +3 more

Dibash Basukala is a Research Fellow at the Sloan Kettering Institute , Memorial Sloan Kettering Cancer Center, affiliated with the Medical Physics Department . His work focuses on advanced MRI techniques for breast cancer assessment. Research Focus : Functional MRI, hypoxia imaging, diffusion-weighted imaging, and multisite reproducibility studies Key Collaborations : Katja Pinker-Domenig, Sunitha Thakur, Arka Bhowmik, Panagiotis Kapetas Publications : 2 journal articles and 1 review (2024–2025) in Journal of Magnetic Resonance Imaging and Frontiers in Oncology His research addresses technical validation of MRI biomarkers and clinical translation of functional imaging techniques for breast tumor characterization.