Find Academic Supervisors

Andreas Milias ArgeitisView profile

Associate Professor

• Systems Biology
• Biochemistry & Molecular Biology
• Mathematics, Interdisciplinary Applications
• +6 more

Andreas Milias Argeitis is an Associate Professor in the Faculty of Science and Engineering at the University of Groningen. He leads the Milias-Argeitis Lab within the Molecular Systems Biology research unit at the Groningen Biomolecular Sciences & Biotechnology Institute (GBB). His research focuses on integrating experimental and computational approaches to understand cellular processes, particularly the coordination between cell growth, division, and metabolic dynamics in budding yeast. Key research areas include systems biology, TOR signaling pathways, cell cycle regulation, and the application of machine learning and mathematical modeling. His lab develops advanced tools like optogenetic control systems and deep learning algorithms for cell segmentation and tracking. Recent work has revealed metabolic oscillations linked to the cell cycle and explored the role of proteins like Sch9 in TORC1-dependent signaling. Notable achievements include an NWO Vidi Grant (2018) and an ENW Science-M Grant (2023) for studying how growth drives the cell division cycle. He has over 40 peer-reviewed publications, including work in Nature Communications , Nature Metabolism , and Journal of Cell Science . His research bridges fundamental biology with technological innovations in single-cell analysis and synthetic biology. Lab activities include CRISPR/Cas9 genome editing protocols, fluorescent protein maturation studies, and the development of photo-switchable enzymes. Collaborations span biochemistry, mathematics, and engineering, reflecting his interdisciplinary approach to unraveling cellular mechanisms.

• Human-AI Value Alignment
• Ethical Machine Learning
• Algorithmic Fairness
• +5 more

Neil S. Gaikwad is an Assistant Professor at the University of North Carolina, Chapel Hill, in the School of Data Science and Society and Department of Computer Science. He holds a PhD in Society-Centered AI from MIT and has affiliations with the MIT Dalai Lama Center for Ethics and UNC’s Parr Center for Ethics. His research focuses on Human-AI Alignment, ethical machine learning, and policy design for global equity, particularly in underserved populations. His work integrates ethical principles into AI systems to address societal disparities, climate change, and algorithmic fairness. Dr. Gaikwad has led projects such as the AI and Policy research program, developed systems like Daemo and EteRNA, and contributed to high-impact publications in AAAI, ACM CSCW, and PNAS. He has mentored over 30 students, many of whom have secured fellowships and advanced AI fairness research. Awards include MIT’s Karl Taylor Compton Prize and Facebook Research Fellowship. His teaching spans courses on AI ethics, policy, and societal impact at MIT, CMU, and UNC, with student evaluations praising his mentorship and course design. He has advised global institutions like the UN and EU on AI policy, and his work bridges academia, industry, and policy through initiatives like Data-driven Humanitarian Mapping. His art installations, such as Beyond Boundaries , explore environmental and ethical themes, reflecting his interdisciplinary approach to science and society.

Susanna RöblitzView profile

Professor

• Systems Biology
• Computational Pharmacology
• Mathematical Modeling
• +4 more

Susanna Röblitz is a Professor at the Department of Informatics within the Computational Biology Unit at the University of Bergen. Her research focuses on mathematical modeling of high-dimensional dynamical systems in life sciences, including molecular conformation dynamics, systems biology, and pharmacology. She specializes in rare event sampling, meshfree methods, clustering algorithms, and uncertainty quantification, with a particular emphasis on oscillatory systems such as hormonal rhythms. Her teaching includes courses such as BINF100 Introduction to Bioinformatics and BINF305 Systems Biology . Her research has been supported by grants from the Research Council of Norway and Nordforsk, including projects like Markov State Models for Cellular Phenotype Switching and RAS-TOOLS . Key research areas include hormonal regulation (e.g., menstrual cycle modeling), gene regulatory networks, and applications in pharmacokinetics and aquaculture. Recent work highlights include the development of Markov state models for gene networks and spectral clustering techniques for analyzing complex biological systems. She has supervised students such as Anna Kristina Fredheim Grandma, whose master’s thesis focused on pharmacokinetic modeling in personalized dosing. Her interdisciplinary collaborations span computational biology, veterinary medicine, and clinical pharmacology.

Jarek NabrzyskiView profile

Professor

• Quantum Computing
• Blockchain Technology
• Distributed Ledger Technologies
• +4 more

Jarek Nabrzyski is the founding and current director of the Center for Research Computing (CRC) at the University of Notre Dame and a concurrent Professor in the Department of Computer Science and Engineering. He co-directs the Blockchain Research Lab with Ian Taylor and leads the Quantum Computing Lab. His research focuses on distributed ledger technologies, quantum computing, and resource management in distributed, cloud, and exascale systems. He has overseen over 50 national and international research projects developing cyberinfrastructure for science and industry, emphasizing collaboration and team-building in complex projects. Key areas of research include blockchain applications in decentralized systems, quantum algorithm optimization for near-term hardware, and cybersecurity protocols for data integrity. He actively engages in interdisciplinary initiatives like the Center for Social Science Research and has pioneered frameworks for verifiable credentials and reproducible scientific data. His work bridges theoretical advancements with practical industry partnerships. Notable contributions include hybrid cross-chain protocols, quantum Poisson solvers, and frameworks for blockchain trust analytics. His recent publications highlight advancements in AI-driven blockchain stabilization, suspicious transaction detection, and scalable quantum computing solutions. Despite no explicit mentions of awards, his leadership roles and project count underscore significant contributions to computational science and infrastructure. As a mentor, he fosters student engagement in cyberinfrastructure through initiatives like the Cyberinfrastructure Center of Excellence. His labs and collaborations drive innovation in quantum computing, blockchain interoperability, and high-performance computing systems. Current efforts include transparent data tracking for aerospace supply chains and secure scientific data reproducibility.

Joe D. WarrenView profile

Professor

• Computer Graphics
• Geometric Modeling
• Computational Geometry
• +3 more

Joe D. Warren is a Professor of Computer Science at Rice University, where he has served since 1986. His research focuses on computer graphics, geometric modeling, and computational geometry, with notable contributions to subdivision surfaces, as detailed in his book Subdivision Methods for Geometric Design . He also explores bioinformatics applications, including 3D modeling of mouse brain gene expression and lung motion analysis from 4D CT scans. Additionally, Warren teaches courses in computer game design and introductory programming, co-developing award-winning Coursera specializations. He earned his Ph.D. from Cornell University and previously served as Department Chair (2008-2013). Education: B.Sc. (Rice University, 1983), Ph.D. (Cornell University, 1986) Research Collaborators: Baylor College of Medicine, MD Anderson Cancer Center, Texas A&M Teaching: COMP460 (Game Design), COMP110 (Introductory Computing) His work on the GRACE tool (Graphical Ruler and Compass Editor) won the 1998 Quest for Java award. He has advised PhD students now at Washington University and Texas A&M. Current projects include game prototyping with Pi Studios and lung motion modeling for medical diagnostics.

David Ryan KoesView profile

Associate Professor

• Computational Drug Discovery
• Deep Learning
• Discrete Algorithms
• +3 more

David Ryan Koes is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh, affiliated with the Joint CMU-Pitt PhD Program in Computational Biology. His research focuses on computational drug discovery, deep learning, and discrete algorithms, aiming to develop novel methods for rapid drug development and open-source software tools. He holds an office at 3064 Biomedical Science Tower 3 and 748 Murdoch Building. Key roles include Associate Director of the CPCB program and leadership in initiatives like CompBio Academy. His software contributions include libmolgrid, gnina, and 3Dmol.js, which advance molecular modeling and visualization. Research interests emphasize AI-driven drug discovery, including molecular docking, pharmacophore modeling, and generative models for molecule design. Recent work involves deep learning for protein structure prediction and pharmacophore elucidation. He has secured NIH grants (e.g., R35GM140753) and collaborations with institutions like CMU and industry partners. Advising over 25 students in computational biology, biotech, and data science programs, Koes bridges academia and industry through projects like Pharmit and the Teach-Discover-Treat initiative. His lab's work spans from foundational ML research to applied drug discovery, with a focus on open science and reproducibility.

Zhiwei FengView profile

Assistant Professor

• Drug Discovery
• Computational Chemistry
• Machine Learning in Biology
• +6 more

Zhiwei Feng is an Assistant Professor at the School of Pharmacy, University of Pittsburgh , serving as Assistant Director of the Computational Chemical Genomics Screening Center (CCGS). He holds a PhD in Computational Chemistry/Materials from China and completed postdoctoral training at the University of Pittsburgh. With over 20 years of experience in drug discovery tools, his expertise includes computer-aided drug design (13 years), antibody drug discovery (6 years), and virtual reality apps for scientific visualization (7 years). Education: PhD in Computational Chemistry/Materials (2013), China MS and BS in Computer Sciences (2010 and 2007, respectively) Research Focus: AI-driven drug design (antibodies, small molecules) ML/DL-based chemogenomics platforms (e.g., Pain-CKB, Virus-CKB) Targeted therapies for pain, cancer, and stem cell expansion Systems pharmacology and PK/PD modeling His work emphasizes computational tools for drug repurposing, GPCR modulation, and anti-SARS-CoV-2 drug discovery. Key Achievements: Developed IsAb computational antibody design protocol Co-inventor of U.S. Patent 10,899,702 (TRPV1-based pain treatment) Over 65 publications in top journals like Cell , Nature Communications , and ACS Chemical Neuroscience . Lab & Collaborations: CCGS integrates computational and experimental methods for drug discovery Active collaborations in structural biology, systems pharmacology, and clinical data analysis

Guoqing LuView profile

Professor

• Bioinformatics
• Genomics
• Viral Evolution
• +5 more

Guoqing Lu is an Isaacson Professor of Bioinformatics and Genomics at the University of Nebraska at Omaha (UNO), affiliated with the Department of Biology and School of Interdisciplinary Informatics. He holds a Ph.D. in Biology from Laval University, with additional master's degrees in Biology and Computer Science, and completed postdoctoral research at McGill University. His research focuses on computational methods for genomic data analysis, viral evolution, and applications in aquaculture and conservation biology. Dr. Lu has directed the Bioinformatics Research Core Facility at UNL and received major grants from NIH, NSF, and USDA. Awards include UNO’s Excellence in Research Award and Distinguished Research Award. He teaches courses such as Bioinformatics Algorithms and Comparative Genomics, and chairs committees including Bioinformatics UPC and Biology GPC. His lab explores topics like fish microbiomes, pathogen-host interactions, and CRISPR applications in aquaculture. Education: Ph.D. Biology (Laval University), M.Sc. Biology & Computer Science, Postdoc (McGill University). Research Interests: Bioinformatics tools development, genomic data mining, viral evolution modeling, fish pigmentation genetics, and eDNA-based ecological monitoring. His work bridges computational methods with biological problems like invasive species control and disease dynamics. Grants & Awards: Multi-million grants from NIH/NSF/USDA; awards for research excellence in UNO’s College of Arts & Sciences. Serves on editorial boards and reviews for agencies like NSF-China and UK MRC. Advising & Service: Advisor to students, leader in curriculum development for BIOT programs. Consults on sustainability and bioinformatics initiatives. Labs & Teams: Directs Dr. Lu’s Lab, focusing on bioinformatics applications in ecology, aquaculture, and conservation genetics. Collaborates with institutions globally on genomic projects.

Liat ShenhavView profile

Assistant Professor

• Computational Biology
• Machine Learning
• Deep Learning
• +7 more

Liat Shenhav is an Assistant Professor at the Institute for Systems Genetics and the Department of Microbiology at New York University School of Medicine, with affiliations in the Department of Computer Science at the Courant Institute and the Center for Data Science. She leads the Shenhav Lab, which focuses on computational biology and AI-driven solutions for women’s and children’s health, particularly fertility, pregnancy, and lactation. Her work integrates systems biology, machine learning, and multi-omics data to address challenges in maternal and infant health. Education: PhD in Computational Biology from UCLA (2020). Research Interests: Microbiome dynamics, human milk composition, AI in healthcare, trajectory analysis, and biomarker discovery. Her lab develops cutting-edge computational methods like FEAST, CTF, and DVT-Net to analyze microbiome and medical imaging data. Current projects include studying the role of breastfeeding in shaping infant microbiomes, early detection of preeclampsia via retinal imaging, and understanding reproductive tract microbiota’s impact on fertility. Awards include the NIH New Innovator Award (DP2, 2024) for breakthrough research on breastfeeding and respiratory health. Collaborations span obstetrics, pediatrics, ophthalmology, and computational biology. Labs/Teams: Shenhav Lab at NYU Langone, collaborations with Courant Institute, and global health partners like the CHILD Cohort Study.

Carolina RuizView profile

Professor

• Machine Learning
• Artificial Intelligence
• Data Mining
• +5 more

Carolina Ruiz is the Associate Dean of Arts and Sciences and Harold L. Jurist Dean’s Professor of Computer Science at Worcester Polytechnic Institute (WPI). She holds a PhD in Computer Science from the University of Maryland College Park (1996) and has been at WPI since 1997, progressing from Assistant Professor to Full Professor. Her research focuses on Machine Learning, Artificial Intelligence, and Data Mining applied to medicine, health, and education. Notable projects include developing AI-driven methods for sleep analysis, behavioral health interventions like the SlipBuddy app, and interdisciplinary programs in Bioinformatics and Neuroscience. She leads the Knowledge Discovery and Data Mining Research Group and serves on WPI’s Academic Planning Committee. Ruiz has advised over 35 graduate students, 150 undergraduates, and 12 high school researchers, emphasizing vertical integration of research teams. She co-led a $1.2M NSF grant (2017-2021) bridging Biology and Computer Science education through transdisciplinary curricula. Key service roles include Associate Department Head of Computer Science and governance committees at WPI. Education: PhD Computer Science, University of Maryland (1996) MS Computer Science, Universidad de Los Andes (1990) BS Computer Science & Mathematics, Universidad de Los Andes (1988-1989) Ruiz’s research spans medical AI applications (stroke prediction, sleep modeling), educational technology (computational biology curricula), and wearable sensor analytics. Her work has been featured in media including Medical News Today and NSF-funded initiatives. She emphasizes translational research bridging academia and real-world societal challenges.

Hani GoodarziView profile

Associate Professor

• Computational Biology
• RNA Biology
• Cancer Genomics
• +3 more

Hani Goodarzi, PhD , is an Associate Professor in the Department of Biochemistry and Biophysics at the University of California, San Francisco (UCSF), with dual affiliation to the Arc Institute and Helen Diller Cancer Center. His laboratory employs a systems biological framework integrating computational and experimental approaches to investigate metastatic progression in cancer and neurodegenerative diseases. Princeton University PhD in Quantitative & Computational Biology Postdoctoral Fellow, Rockefeller University (Cancer Systems Biology) NIH R01 Grants (2016–2026) Research Themes : Machine learning for in silico functional genomics Evolutionary dynamics of oncRNA regulatory modules in cancer Post-transcriptional control via tiRNA fragments and RNA methylation RNA structural element discovery using pyPAGE algorithms Scientific Recognition : Vilcek Prize for Creative Promise (2022) NIH K99/R00 Award (2015) Tri-Institutional Breakout Prize (2014) Blavatnik Regional Award (2015) Laboratory Impact : Developed Deep Generative AI for early-stage lung cancer detection Identified ENPP1 as innate immune checkpoint in breast cancer Created pyPAGE framework for gene-set enrichment analysis

Ramya Korlakai VinayakView profile

Assistant Professor

• Machine Learning
• Statistical Inference
• Crowdsourcing
• +2 more

Ramya Korlakai Vinayak is the Dugald C. Jackson Assistant Professor in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison. She also holds affiliations with the Department of Computer Science and the Department of Statistics at UW-Madison. Her research program bridges theoretical machine learning with practical applications in data science and crowdsourcing. Education: PhD in Electrical Engineering from California Institute of Technology (Caltech), advised by Prof. Babak Hassibi B.Tech in Electrical Engineering with minor in Physics from Indian Institute of Technology Madras (IIT Madras) Dr. Vinayak's research focuses on developing theoretically grounded machine learning tools for reliable inference using data from human sources. Her work spans machine learning theory, statistical inference, and crowdsourcing systems, with particular emphasis on preference learning, metric learning, and robust dataset construction. She has pioneered methods for learning from limited pairwise comparisons, auto-labeling systems, and human-in-the-loop out-of-distribution detection. Her recent publications reveal a consistent trajectory toward building practical machine learning systems that incorporate human feedback while maintaining theoretical guarantees. The pattern shows increasing focus on ethical considerations in AI, particularly regarding bias in generative models and reliable human-AI collaboration frameworks. Scientific Awards: Faculty for the Future fellowship (2013-2015) from Schlumberger Foundation NSF CAREER Award American Family Funding Initiative Award with Fred Sala Dr. Vinayak leads an active research group with multiple PhD students across ECE and CS departments. She has secured significant research funding including an NSF grant for "Uncovering the cognitive and neural fingerprints that make each of us unique" in collaboration with Tim Rogers, Rob Nowak and Brad Postle. She is also co-organizing the MidWest Machine Learning Symposium and NeurIPS tutorials on dataset construction. Her research group operates at the intersection of theory and practice, developing mathematically grounded frameworks that address real-world challenges in dataset construction, human-AI collaboration, and reliable machine learning systems.

Geoffrey BartonView profile

Professor

• Bioinformatics
• Computational Biology
• Protein Structure Prediction
• +4 more

Professor Geoffrey Barton is the Chair of Bioinformatics at the Computational Biology department within the School of Life Sciences at the University of Dundee . With a career spanning decades, he has been pivotal in advancing bioinformatics tools and methodologies for analyzing biological data, particularly in protein structure prediction and RNA sequencing. Core Research Interests : Protein structure and function prediction, computational methods for large biological datasets, small RNA analysis, and integration with wet-lab collaborations across plants, model organisms, and human disease. Awards : Fellow of the Royal Society of Edinburgh (2019) Fellow of the Royal Society of Biology (2011) Brian Cox Prize for Excellence in Public Engagement with Research (2023) Publications and Software : He leads the development of widely used tools like JPred (25,000+ predictions/month) and Jalview (55,000+ installations). His group's work spans proteomics, transcriptomics, and structural genomics, addressing questions in basic science and clinical applications. Media and Collaboration : Available for media commentary on bioinformatics and computational biology, he collaborates extensively with international institutions, including the University of Cambridge and Oxford.

Susann MüllerView profile

Associate Professor

• Microbial Ecology
• Flow Cytometry
• Environmental Biotechnology
• +5 more

Prof. Dr. Susann Müller is Senior Scientist and Group Leader of the Flow Cytometry Working Group at the Department of Applied Microbial Ecology, Helmholtz Center for Environmental Research (UFZ) in Leipzig, Germany. Since 2011, she has held an Associate Professor position for Microbiology at Leipzig University’s Faculty of Life Sciences, bridging fundamental microbial ecology with environmental biotechnology applications through single-cell analytics. Education: 1985: Diploma in Biochemistry, Martin Luther University Halle-Wittenberg 1992: PhD, University of Halle-Wittenberg (Population dynamics of S. cerevisiae) 2003: Habilitation, Technical University Dresden (Multiparametric Cytometry) Her research pioneers microbial community flow cytometry to extract single-cell high-dimensional data, applying macroecological concepts to quantify stability metrics (resistance, resilience, displacement speed, elasticity) in engineered systems. Current focus includes bio-based circular economy initiatives: developing the carboxylate platform for sustainable chemical production and biological phosphate recovery from wastewater streams for resource valorization. Recent publications (2021-2025) reveal consistent innovation in flow cytometry applications, with emphasis on stability assessment in bioreactors, predator-prey dynamics in complex communities, and real-time monitoring of wastewater systems. She integrates ecological theory with multi-omics and data science to decode microbial assembly principles across environmental, agricultural, and industrial contexts. Professional roles: President, German Society of Cytometry (DGfZ, 2008-2010) Associate Editor, Microbiology for Cytometry Part A ISAC Educational Committee (2011-2012) and Scholars Program Committee (2013-2015) Current grants: PHOM project (SMWK InfraProNet 2024-2027): €449,160 for wastewater phosphorus recovery Z-PROJECT (DFG 2022-2025): €556,550 for bacterial biofilm analysis PROMICON (EU H2020 2021-2025): €200,000 for industrial microbiome consortia Moore Foundation (2020-2024): $23,000 for archaeal evolutionary tools Chinese Scholarship Council (2022-2026): Artificial community construction The Flow Cytometry Working Group under her leadership at UFZ develops standardized mock communities (Nature Protocols 2020), automated analysis tools (flowEMMi), and cytometric barcoding methods. It collaborates with Leipzig University, Technical University Dresden, and international partners including UC Santa Barbara, driving innovations in real-time environmental monitoring and wastewater treatment optimization.

• artificial intelligence
• image analysis
• deep learning
• +8 more

Natasa Sladoje is a Professor in Computerized Image Analysis at the Department of Information Technology, Uppsala University. She is affiliated with the Vi3 and Image Analysis research group and leads the MIDA research group. Her work spans artificial intelligence, biomedical image analysis, deep learning, and algorithm development, with applications in medical imaging and life sciences. Her research focuses on developing advanced image analysis methods, particularly using machine and deep learning, to enable automated analysis of image data in science and everyday life. Key areas include medical image analysis, image registration, segmentation, pattern recognition, and discrete geometry. She applies these techniques to critical domains such as oral cancer detection, cytology, and multimodal imaging. The recent publications highlight a strong trend in AI-driven medical diagnostics, particularly in cancer detection using whole slide images, self-supervised learning for sparse instance detection, and contrastive learning for multimodal image registration. Her work also emphasizes reproducibility and benchmarking in bioimage analysis through frameworks like BIAFLOWS and public datasets like HISTOBREAST. She has no listed scientific awards in the provided text. Natasa Sladoje supervises research within the MIDA group and collaborates extensively on projects involving bioimage analysis, deep learning, and medical applications. While specific grant details are not mentioned, her leadership in collaborative frameworks and publication output suggests active involvement in funded research initiatives. She leads the MIDA (Medical Image Analysis) research group, which focuses on developing and applying novel image analysis tools for biomedical applications, particularly in cancer diagnostics and multimodal imaging.