Find Academic Supervisors

Mark Y. LibermanView profile

Professor

• Corpus Linguistics
• Speech and Language Technology
• Clinical Linguistics
• +4 more

Mark Y. Liberman is the Christopher H. Browne Distinguished Professor of Linguistics and Trustee Professor at the University of Pennsylvania. He holds a joint appointment in the Department of Linguistics and the Department of Computer and Information Science. His roles include Director of the Linguistic Data Consortium (LDC), Faculty Director of Ware College House, and former Director of the Institute for Research in Cognitive Science. Education: A.B. in Linguistics and Applied Mathematics from Harvard University (1965–1969), M.S. (1972) and Ph.D. (1975) in Linguistics from MIT. Research focuses on corpus-based phonetics, clinical linguistics applications, tonal phonology, formal models for linguistic annotation, and computational linguistics. He explores speech production, prosody, and interdisciplinary topics like language evolution and neurobiology of speech. Recent articles highlight advancements in speech biomarkers for neurodegenerative diseases, autism analysis, and computational linguistics. Awards include Fellowships from the AAAS and Linguistic Society of America. He advises graduate students and leads large-scale language resource initiatives like LDC, contributing to open-access linguistic datasets. Labs/Teams: Linguistic Data Consortium (LDC), Institute for Research in Cognitive Science (IRCS), and collaborations in computational linguistics and neuroscience.

Amin KeramatiView profile

Assistant Professor

• Transportation & Logistics
• Supply Chain Management
• Data Analytics
• +4 more

Dr. Amin Keramati is an Assistant Professor of Supply Chain Management at Widener University’s School of Business Administration. He holds a PhD in Transportation & Logistics from North Dakota State University and previously served as a graduate research assistant at the Upper Great Plains Transportation Institute. His work includes federally funded projects like the Mountain-Plains Consortium’s MPC-550 project, focusing on highway-rail grade crossing safety. Dr. Keramati teaches courses in enterprise resource planning, decision analytics, database management, transportation/logistics, project management, and data analytics. Education: PhD in Transportation & Logistics, North Dakota State University Graduate Research Assistant at Upper Great Plains Transportation Institute Research Focus: Dr. Keramati develops mathematical, statistical, and machine learning approaches to solve complex problems in transportation, supply chain, and logistics. Key areas include data mining, big data decision support, transportation network analytics, project scheduling, smart manufacturing, and accident analysis. His interdisciplinary work bridges supply chain optimization with emerging technologies like blockchain for healthcare and clinical trials. Awards & Recognition: Student Paper Award, American Association of State Highway and Transportation Officials (2017) Grants & Projects: Lead researcher on the Mountain-Plains Consortium’s MPC-550 project, which expanded his dissertation work on safety systems for highway-rail crossings. Collaborates on federally funded transportation safety initiatives and supply chain optimization for bioethanol and pharmaceutical industries. Labs/Teams: Active in Widener’s School of Business research groups focusing on supply chain innovation and interdisciplinary projects combining logistics with emerging technologies.

Jimeng SunView profile

Professor

• Artificial Intelligence
• Healthcare Analytics
• Drug Discovery
• +5 more

Dr. Jimeng Sun is a Health Innovation Professor at the Siebel School of Computing and Data Science and Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign. Co-founder of Keiji AI , he leads groundbreaking research at the intersection of artificial intelligence and healthcare, actively deploying clinical AI systems and developing frameworks like PyHealth and Therapeutics Data Commons . His research spans four major areas: Clinical AI Systems : Developing interpretable models (e.g., RETAIN) for patient similarity, temporal event prediction, medication recommendation, and clinical outcome forecasting Drug Discovery : Creating molecular optimization frameworks, drug-target interaction models, and AI-driven platforms Clinical Trials : Pioneering patient-trial matching, outcome prediction, and optimization frameworks using deep learning and graph neural networks Biosignal Analysis : Advancing sleep staging, seizure classification, and automated EEG/Cardiac monitoring systems With over 500 top-tier publications (including in Nature , NEJM AI , and leading AI conferences) and an h-index of 99, his work has been recognized with the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare award. He maintains active collaborations with institutions like Massachusetts General Hospital , Medidata Solutions , and OSF Healthcare . His recent publications reveal a strong focus on: Reinforcement learning applications in medical data analysis Large language model adaptation for clinical tasks Knowledge graph integration with AI systems Synthetic data generation for healthcare Multi-modal learning in clinical contexts Explainable AI for medical applications Dr. Sun's lab ( Sunlab ) emphasizes practical impact over theoretical work, actively collaborating with hospitals and healthtech companies. He welcomes contributions from clinicians, researchers, and industry partners through initiatives like his AI for Health webinar series .

• Database systems
• Large scale analytics
• Distributed systems
• +3 more

Beng Chin Ooi is a Lee Kong Chian Centennial Professor at the National University of Singapore (NUS), School of Computing (SoC). He holds a B.Sc. (1st Class Honors) and Ph.D. in Computer Science from Monash University. His research focuses on database systems, large-scale analytics, and distributed computing. He has held leadership roles including Dean of School of Computing (2007–2013) and Director of Smart Systems Institute (2011–2021). Key achievements include the Singapore President’s Science Award (2011), ACM Fellow (2011), IEEE Fellow (2009), and multiple best paper awards. His work emphasizes scalable data management, blockchain systems, and healthcare data analytics. Education: Monash University (B.Sc., Ph.D.) Leadership: Dean (SoC), Director (Smart Systems Institute) Awards: Over 15 major honors including ACM SIGMOD E.F. Codd Innovations Award (2020) His research spans distributed databases, big data systems, and innovative applications of blockchain technology. Recent work includes NASI (neural architecture search) and Rafiki (ML-as-a-service).

Laura EloView profile

Professor

• Computational Biomedicine
• Medical Bioinformatics
• Data Science
• +7 more

Laura Elo serves as Professor of Computational Medicine and Head of the Medical Bioinformatics Centre at the University of Turku, Finland. She concurrently holds the position of Research Director at Turku Bioscience Centre and acts as InFLAMES Flagship Contact, driving interdisciplinary biomedical research initiatives. Her academic foundation includes a PhD in Applied Mathematics (2007) and Adjunct Professorship in Biomathematics (2011), establishing her quantitative expertise before transitioning into biomedical applications. Her research program focuses on transforming molecular and clinical datasets through statistical modeling and advanced machine learning . Key thrusts include robust computational tools for proteome/epigenome analysis, AI-driven digital health diagnostics, and computational systems immunology for immune-mediated diseases. This work directly addresses challenges in reproducibility and scalability of high-throughput biotechnology data. Analysis of her recent publications reveals dominant themes in type 1 diabetes biomarker discovery , multi-omics integration , and immune system modeling , with strong emphasis on clinical translation through collaborations with experimental and medical teams. Her scientific recognition includes: JDRF Career Development Award Professor Elo actively trains MSc/PhD students and postdoctoral fellows while leading major research initiatives including ERC grants. Her teaching portfolio spans Bioinformatics Journal Club, AI in Diagnostics, and Systems Biology courses. The Elo Lab (https://elolab.utu.fi) operates as a hub for computational biomedicine, developing open-source tools like CellRomeR while maintaining close ties with Turku Bioscience Centre's experimental facilities for validating computational predictions in immunology and metabolic disease contexts.

Dennis SäringView profile

Professor

• Deep Learning
• Medical Image Analysis
• Artificial Intelligence
• +5 more

Prof. Dr. Dennis Säring is a faculty member at the University of Applied Sciences Wedel , specifically affiliated with the School of Engineering. His academic and research activities focus on Deep Learning , Medical Image Analysis , and applications of Artificial Intelligence in healthcare and biomedical imaging. He has led seminars on Deep Learning topics and supervised student projects in Autonomous Driving at Audi's AADC 2018 competition. Research Highlights : Cardiovascular imaging, forensic age estimation via MRI, neural network-based bone segmentation, and cerebrovascular aneurysm analysis. Technical Expertise : Cardiac MRI, 3D/4D image processing, parametric mapping, and spatiotemporal data fusion. His recent publications (2018-2023) emphasize 3D MR segmentation for age assessment, CMR strain analysis in athletes, and T1/T2 mapping for myocarditis. Key collaborations include institutions like the University Medical Center Hamburg-Eppendorf and Wedler Hochschulbund, with funding for autonomous vehicle research. While no explicit scientific awards are listed, his work spans clinical cardiology, forensic radiology, and AI-driven medical diagnostics.

• Data Mining
• Database Systems
• Explainable AI
• +5 more

Elena Maria Baralis is a Full Professor at the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin. She serves as Pro-Rector, member of the Board of Directors (without voting rights), member of the Academic Senate (without voting rights), and coordinator of the University's Permanent Observatory for Monitoring the Academic Sector. She chairs the Control and Computer Engineering Department and previously chaired the Computer Engineering School from October 2012 to October 2018. Her research interests focus on database systems and data mining, specifically explainable AI, bias detection in data analytics, and machine learning algorithms for big data. Her work spans various application domains including predictive maintenance, Industry 4.0, and healthcare. Recent publications demonstrate her expertise in speech processing, bias mitigation, and innovative neural network architectures like Kolmogorov-Arnold Networks. Her research output shows a clear trend toward addressing fairness and explainability in AI systems while exploring novel approaches to speech and language understanding. Professor Baralis has received significant recognition including becoming a Fellow of the Academy of Sciences of Turin in 2017. She has served as Editor-in-Chief for IEEE Internet of Things Journal (2016-2019) and Knowledge and Information Systems (2014-present). She actively mentors doctoral students including Claudio Savelli (researching Machine Unlearning), Eleonora Poeta, Giuseppe Gallipoli, Alkis Koudounas, and others. Her research is supported by numerous projects including AI4CTI (Artificial Intelligence for Cyber Threat Intelligence, 2025-2028), Smart manufacturing driven by Machine Learning in Industry 4.0 (2019-2020), and I-REACT (2016-2019).

Mihai PopView profile

Professor

• Bioinformatics
• Genomics
• Computational Biology
• +3 more

Dr. Mihai Pop is a Professor of Computer Science and Director of the University of Maryland Institute for Advanced Computer Studies (UMIACS). He holds appointments in the Department of Computer Science, UMIACS, and the Center for Bioinformatics and Computational Biology (CBCB). His research focuses on computational biology, metagenomics, and algorithm development for genomic data analysis. He received a Ph.D. in Computer Science from Johns Hopkins University (2000), followed by work at The Institute for Genomic Research (TIGR) developing genome assembly algorithms. Education: Ph.D., Computer Science, Johns Hopkins University, 2000. Research Interests: Bioinformatics, genomics, metagenomics, computational geometry, software testing. His lab develops tools for analyzing microbial communities and has pioneered methods for metagenomic assembly and analysis. Notable tools include the AMOS genome assembly toolkit. Recent Article Trends: Recent work emphasizes long-read sequencing, metagenomic profiling (e.g., TIPP3), and strain-level analysis (e.g., Strainy). He addresses challenges in scaling sequence-based searches and improving taxonomic resolution in large datasets. Awards: ACM Fellow (2019), ISCB Fellow (2022), UMD Excellence in Teaching Award (2015). Grants & Leadership: Co-leader of the Human Microbiome Project data analysis group. Active in diversity initiatives to promote inclusivity in computational fields. Labs/Teams: Pop Lab (pop-lab.org) focuses on computational methods for microbial genomics and metagenomics.

Massimo PiccardiView profile

Professor

• Natural Language Processing
• Computer Vision
• Machine Learning
• +3 more

Massimo Piccardi is a Professor of Natural Language Processing (NLP), Computer Vision, and Machine Learning at the University of Technology Sydney (UTS) , where he has been since 2002. He currently serves as the Head of the School of Electrical and Data Engineering and leads the Big Data Analytics program at the Global Big Data Technologies Centre. His research focuses on advancing NLP, machine learning applications in healthcare, and cybersecurity in IoT systems. He has authored over 200 journal papers and conference proceedings, secured significant ARC and CRC grants, and holds the IEEE Computer Society Distinguished Contributor Award (2022). Education & Professional Roles: Joined UTS in 2002, progressing from Associate Professor (2002–2007) to Professor (2008–present). Serves as Associate Editor for IEEE Transactions on Big Data and Editor for Artificial Intelligence in Medicine. Active in professional societies including IEEE, ACL, and ALTA (President, 2023–2024). Research Interests: Core areas include NLP (translation, summarization, adversarial attacks), healthcare informatics (clinical NLP, health service analysis), and cybersecurity (IoT security, privacy-preserving systems). Cross-cutting themes include generative models, cross-lingual systems, and ethical AI. Grants & Projects: Principal Investigator on ARC Discovery/Linkage projects and CRC grants. Recent projects include controllable machine translation (Amazon), privacy-preserving digital agriculture, and STEM innovation (ASTRID project with NBN Co). Labs & Collaborations: Leads the UTS Global Big Data Technologies Centre, collaborating on projects like adversarial NLP attacks, medical machine translation, and secure IoT frameworks.

Ambuj K. SinghView profile

Professor

• Machine Learning
• Social Networks
• Network Science
• +7 more

Ambuj K. Singh is a Distinguished Professor of Computer Science at the University of California, Santa Barbara (UCSB), with a part-time appointment in the Biomolecular Science and Engineering Program. He holds a PhD from the University of Texas at Austin (1989), an MS from Iowa State University (1984), and a BTech from the Indian Institute of Technology, Kharagpur (1982). His campus affiliations include the Center for Bio-Image Informatics, Information Network Academic Research Center, and IGERT on Network Science. PhD, University of Texas at Austin, 1989 M.S., Iowa State University, 1984 B.Tech., Indian Institute of Technology, Kharagpur, 1982 Research interests span network science, machine learning, and bioinformatics, with a focus on graph-based methodologies. His work addresses: Data-centric modeling of dynamic networks Representation learning and explainability in graph neural networks Network analysis in social systems and biological networks Applications in drug discovery and brain sciences Geometry-preserving distance metrics for data integrity Recent publications highlight advancements in counterfactual explanations, GNN benchmarking, molecular graph pretraining, and self-attention for event detection. Scientific contributions include: Founding Acelot, Inc., an in silico drug discovery company Editorial roles at IEEE Transactions on Knowledge & Data Engineering and BMC Journal of Clinical Bioinformatics NSF-IGERT (2013-2018), ARL-funded Information Networks Academic Research Center (2009-2014), and US Army MURI grants Advising has involved mentoring over 50 graduate/postdoctoral students, including 30+ PhD candidates. He leads a multidisciplinary research group at UCSB and collaborates with off-campus entities like Acelot, Inc.

Katja HoseView profile

Professor

• Knowledge Graphs
• Semantic Web
• Data Management
• +7 more

Katja Hose is a Full Professor of Data Management at TU Wien's DBAI research unit, heading the Data Management and Knowledge-Driven AI Lab. She previously held a Poul Due Jensen Foundation Professorship at Aalborg University. Her research focuses on data and knowledge engineering, including graph databases, knowledge graphs, querying, analytics, and machine learning, with interdisciplinary applications in bioscience, healthcare, and environmental assessment. Education: PhD in Computer Science (Ilmenau University of Technology, 2009), Postdoc at Max Planck Institute for Informatics (2009–2012). Academic roles include Program Co-Chair for ISWC 2024 and EDBT 2023, and editorial board membership at VLDBJ and TGDK. She leads projects like TARGET (health virtual twins) and ARMADA (data management). Research Interests: Knowledge Graphs, Semantic Web, Big Data, Machine Learning, Data Integration, and Provenance Systems. Key contributions include SHACL shape extraction, conversational data analytics, and environmental knowledge graphs. Awards include the 2025 Distinguished Meta-Reviewer Award and 2024 Manfred Paul Award. Advising and Grants: Supervised students including E. Pürmayr (Diploma Thesis 2025). Active in EU projects (TARGET, ARMADA) and grant coordination. Labs/Teams: DMKI Lab at TU Wien, collaborating with interdisciplinary teams in healthcare and environmental science.

• Pharmacology
• Traditional Chinese Medicine
• Cancer Therapy
• +7 more

Wing Lam is an Associate Research Scientist in the Department of Pharmacology at the Yale School of Medicine. He holds a BSc in Molecular Biology and a PhD in Biochemical Pharmacology from City University of Hong Kong, followed by postdoctoral training at Yale. His research focuses on developing traditional Chinese medicine (TCM) formulations as adjuvants for cancer therapy, notably YIV-906, which enhances chemotherapy efficacy and mitigates intestinal toxicity. Lam also pioneered the STAR database for herbal drug discovery and the Mechanism-Based Quality Control (MBQC) platform for botanical drug standardization. Education: BSc (Hons) Molecular Biology, City University of Hong Kong, 1995 PhD Biochemical Pharmacology, City University of Hong Kong, 1999 Postdoc, Pharmacology, Yale University, 1999-2002 His research interests span cancer pharmacology, TCM modernization, and mitochondrial toxicity mechanisms. Key projects include YIV-906’s role in enhancing anti-PD1 and CAR T-cell therapies, developing L-nucleoside analogs like troxacitabine, and investigating tylophorine analogs’ antitumor effects. Lam has co-chaired sessions at multiple Consortium for Globalization of Chinese Medicine (CGCM) meetings and contributed to patents on herbal drug formulations and quality control methods. Recent work explores YIV-906’s potential for inflammatory bowel disease (IBD) and phase II clinical trials for colon and liver cancers. Lam’s publications highlight synergistic drug interactions, mitochondrial DNA depletion mechanisms, and TCM’s evidence-based application in chronic diseases. His grants include studies on PHY906 as an adjuvant in rectal cancer therapy and collaborations with Yiviva, Inc. He maintains active roles in editorial boards, including a special issue on herbal drug quality control in Frontiers in Pharmacology . Lam’s lab is embedded within Dr. Yung-Chi Cheng’s group, focusing on translational pharmacology and botanical drug innovation.

• Material Science
• Biohybrid Materials
• Environmental Applications
• +3 more

Christophe Meunier is a researcher specializing in hybrid materials, particularly focusing on biohybrid systems that integrate biological components with inorganic matrices. His work emphasizes environmental applications and biomedical innovations through advanced material design. Key Collaborations: Su, B. L., Michiels, C., Wang, L. Research Themes: Photosynthesis mimicry, cell therapy microcapsules, hybrid alginate-TiO₂ systems Research Focus: Meunier has pioneered the biomimicry of photosynthesis via biosystem immobilization in silica matrices, aiming to create 'living materials' with functional biological-inorganic interfaces. His recent projects explore alginate@TiO₂ hybrid microcapsules for controlled insulin delivery and cell therapy applications, demonstrating high biocompatibility and stability. Academic Contributions: With 34 research outputs spanning material science, biomedical engineering, and environmental applications, Meunier's work aligns with UN Sustainable Development Goals through innovative hybrid material design. His collaboration network includes experts in chemistry, physics, and medical fields.

Farnoush Banaei-KashaniView profile

Associate Professor

• Data Management
• Big Data
• Decision Support Systems
• +4 more

Farnoush Banaei-Kashani is an Associate Professor (Tenured) in the Department of Computer Science and Engineering at the University of Colorado Denver. She also holds an Adjunct Associate Professor position in the Department of Mathematical and Statistical Sciences. As the founder and director of the Big Data Management and Mining Lab (BDLab), she leads multiple GAANN Fellowship Programs, including BDSE (Big Data Science and Engineering), DDC (Data-Driven Cybersecurity), and II (Infrastructure Informatics). She directs the 'Data Science in Biomedicine' MS Track and focuses on data-driven decision systems (DDSs), integrating machine learning and big data analytics into healthcare, energy, transportation, and environmental applications. Education: Details not explicitly provided in the text. Her research spans data management cycles for DDSs, addressing challenges like big data volume, velocity, and variety. Key projects include iWatch (crime surveillance), POCM (mobility monitoring), and GeoSIM (urban texture documentation). She teaches courses such as Machine Learning Systems, Big Data Science, and Data Mining. Publications highlight advancements in sea ice classification, federated learning, proteomic networks, and privacy-preserving AI. Her work is funded by NSF, NIH, DOT, and industry partners like Google and IBM. She has advised numerous students and contributes to academic leadership as editor, conference chair (ACM SIGSPATIAL 2018/2019), and program committee member for venues like SIGMOD and KDD.

• Pharmacoepidemiology
• Drug Safety
• Diabetes Mellitus
• +3 more

Björn Pasternak is a Research Professor leading the Pharmacoepidemiology group at the Department of Medicine, Solna, Karolinska Institutet. His team specializes in large-scale registry studies focused on adverse drug effects, with research programs in pediatric pharmacoepidemiology, safety of diabetes medications, and rapid assessment of drug safety concerns. The group leverages high-quality epidemiological methods to inform evidence-based clinical decisions. Pasternak's research interests span pharmacoepidemiology, drug safety, diabetes outcomes, cardiovascular risk, and perinatal health. His work utilizes nationwide registries to evaluate real-world drug effects, socioeconomic disparities in treatment, and long-term outcomes of chronic therapies. Key focuses include GLP-1 receptor agonists, SGLT2 inhibitors, antibiotic use in pregnancy, and opioid safety. Recent publications demonstrate strong emphasis on Scandinavian cohort studies of diabetes medications (GLP-1 agonists, SGLT2 inhibitors) and their associations with thyroid cancer, liver events, intestinal obstruction, and cardiovascular/renal outcomes. Other trends include perinatal pharmacovigilance (antibiotics, acid-suppressive drugs) and neurodegenerative risks in athletes.