Find Academic Supervisors

• Randomized algorithms
• Machine learning
• Big Data
• +3 more

Devdatt Dubhashi is a Professor at the Data Science and AI 3 department at Chalmers University of Technology. His research spans multiple domains within computer science and data science, with a focus on theoretical foundations and practical applications of algorithms and machine learning. Professor Dubhashi's primary research interests include the design and analysis of randomized algorithms, machine learning for Big Data, and computational biology. His work demonstrates a strong interdisciplinary approach, connecting theoretical computer science with practical applications in diverse fields such as quantum computing, transportation modeling, genomics, and social sciences. His research often bridges the gap between theoretical foundations and real-world implementations, as evidenced by his numerous collaborations across different domains. Dubhashi's recent publications reveal a consistent research trajectory focusing on algorithmic foundations of machine learning, with increasing emphasis on interdisciplinary applications. His work shows significant contributions to bandit algorithms, kernel methods, graph-based learning, and the theoretical understanding of deep learning models. There's also a growing focus on societal implications of AI, as seen in his publications addressing responsible AI development and policy considerations. Among his notable scientific contributions are publications in prestigious venues including ACM, IEEE, and Nature journals, covering topics from fundamental algorithm design to applications in computational biology and social sciences. His work has been cited extensively across multiple disciplines, reflecting its broad impact. Professor Dubhashi actively supervises PhD students and collaborates with researchers across Chalmers and internationally. His research group appears to focus on the intersection of theoretical computer science and practical machine learning applications, with projects spanning quantum computing, transportation modeling, and biological applications.

• Statistics
• Machine Learning
• Artificial Intelligence
• +6 more

Erik Kristiansson is a Professor in the Department of Applied Mathematics and Statistics at Chalmers University of Technology's School of Engineering Sciences. His academic work bridges computational methods with biological applications, focusing on the analysis of complex biological datasets using advanced statistical and machine learning approaches. Professor Kristiansson's research centers on applying statistical methods, machine learning, and artificial intelligence to analyze complex datasets from molecular biology and medicine. His primary applications focus on infectious diseases, antibiotic resistance mechanisms, and environmental science challenges. His work demonstrates a strong interdisciplinary approach, connecting mathematical modeling with real-world biological problems, particularly in understanding the spread and evolution of antibiotic resistance through environmental pathways and clinical settings. His publication record shows a consistent focus on antibiotic resistance gene identification, environmental contamination with pharmaceuticals, and the development of computational methods for biological data analysis. Recent work demonstrates increasing application of advanced machine learning techniques, including transformer models, to predict chemical toxicity and antibiotic resistance patterns. His research often involves large-scale genomic and metagenomic data analysis to track resistance gene dissemination across different environments. Professor Kristiansson actively collaborates across disciplines, working with environmental scientists, microbiologists, and medical researchers to address complex problems at the intersection of computational science and biological systems. His work on optical DNA mapping for bacterial typing represents an innovative approach to pathogen identification directly from clinical samples.

Thejasvi BeleyurView profile

Research Professor

• Animal Behavior
• Sensory Biology
• Collective Behavior
• +4 more

Dr. Thejasvi Beleyur is a Group Leader of the Active Sensing Collectives Lab at the Centre for the Advanced Study of Collective Behaviour, University of Konstanz, and holds an affiliated position at the Max Planck Institute of Animal Behavior. She also serves as IMPRS Faculty, contributing to interdisciplinary research at the intersection of animal behavior, sensory biology, and collective systems. Current: Group Leader, Active Sensing Collectives Lab (2025-present) Previous: Postdoc at Centre for the Advanced Study of Collective Behaviour (2021-2025) PhD: Max Planck Institute for Ornithology (2015-2021) Education: BS-MS in Biological Sciences, IISER-TVM (2008-2013) Dr. Beleyur's research program investigates how active-sensing agents like echolocating bats navigate complex sensory environments when operating in groups. Her work combines field observations, computational modeling, and swarm robotics to understand the sensorimotor strategies animals employ in information-limited settings. She has pioneered the development of the Ushichka dataset—a multichannel audio-video system for recording echolocating bats in natural habitats—which provides unprecedented insights into how bats modify flight and echolocation behaviors as group sizes change. Her publication record reveals a consistent trajectory examining sensory challenges in collective animal systems, with emphasis on echolocating bats. The research spans experimental field work, computational modeling, and methodological innovations in acoustic and video tracking. Recent work has expanded into developing computational tools like the beamshapes Python package for sound source modeling and exploring robot platforms to simulate bat collective behavior. Carl-Zeiss Nexus grant for inter-disciplinary research (2025) DFG Walter Benjamin postdoc grant (2021-2023) Early Career Researcher Award at International Bioacoustics Congress Google Cloud Platform Research Credits award ($1000) DAAD-GSSP Stipend for doctoral studies (2015-2020) Dr. Beleyur actively mentors students in her lab, currently supervising PhD student Frithjof and Master's student Aditya, following the completion of Gabriele's Master's thesis on the active-sensing Ro-BAT platform. Her research program is supported by competitive grants including the Carl-Zeiss Nexus grant and previously the DFG Walter Benjamin fellowship, which funded her work on 'The How and What of Active Sensing Collectives.' The Active Sensing Collectives Lab brings together an interdisciplinary team working at the interface of sensory biology, robotics, and collective behavior. The lab develops novel computational methods for analyzing complex datasets from multi-sensor field recordings, with emphasis on creating tools for long-term community use. Current projects include characterizing echolocating groups in the field and studying sensorimotor strategies using computational modeling.

Sara RivaView profile

Associate Professor

• Discrete Dynamical Systems
• Cellular Automata
• Boolean Networks

Sara Riva is an Associate Professor (Maître de Conférences) in Computer Science at Université de Lille, affiliated with the CRIStAL laboratory (UMR 9189). She is a member of the BioComputing research group and the MSV thematic group. Her academic journey includes a PhD jointly supervised by Université Côte d'Azur and Università degli Studi di Milano-Bicocca (2019-2022) and postdoctoral research at Université de Bordeaux (2022-2023). Her research explores Discrete Dynamical Systems , Cellular Automata , and Boolean Networks , with emphasis on equation solving, factorization methods, and dynamics modeling. She develops algorithmic approaches to analyze complex behaviors in computational and biological systems. Publications (2019-2023) demonstrate consistent focus on theoretical foundations of discrete systems, with applications in systems biology and complex modeling. Key themes include Boolean network dynamics, computational pipelines for equation solving, and sensitivity analysis in cellular automata. Awards: First prize for PhD students (Computer Science), STIC doctoral school Teaching: Extensive instructional experience at Université de Lille and Université Côte d'Azur covering: Algorithms & Programming (72+ lab hours) Databases (39+ lab hours) Logic, Graph Theory, Web Technologies (18+ lab hours each) IT Security and Information Coding (18 hours each) Academic Service: Member of CRIStAL's parity commission; Program Committee for AUTOMATA 2024; President of ADSTIC PhD association (2021-2022); Organized summer schools (EJCIM 2022).

Rosalyn AbbottView profile

Associate Professor

• biological engineering
• biomaterials
• biomedical engineering
• +15 more

Rosalyn Abbott is an Associate Professor in the Department of Biomedical Engineering at Carnegie Mellon University's College of Engineering. She leads the Abbott Lab which focuses on adipose tissue engineering, silk biomaterials, and non-invasive tissue assessments to study obesity and its link to type II diabetes. Her educational background includes: Ph.D. in Bioengineering from the University of Vermont (2012) MS in Biomedical Engineering from Rensselaer Polytechnic Institute (2008) BS in Biomedical Engineering from Rensselaer Polytechnic Institute (2008) Prof. Abbott's research centers on developing human adipose microenvironments that respond to stimuli hypothesized to alter disease mechanisms, particularly the transition from obese tissues to insulin-resistant type II diabetic tissues. Her lab integrates systems-based modeling with tissue engineering, perfusion bioreactors, and mechanical studies, using silk as a natural biomaterial to support long-term culture of adipose micro-environments in vitro. She is also actively involved in cellular agriculture research for sustainable meat production. Analysis of her recent publications reveals a strong focus on adipose tissue modeling, with particular emphasis on developing fat-on-a-chip systems, studying patient variability in adipose tissue, and exploring vascularized adipose constructs using decellularized matrices. Her work bridges fundamental tissue engineering with translational applications for metabolic disease research and sustainable food technologies. Among her notable scientific achievements is receiving an NSF CAREER Award for her research in biomedical engineering. Her work has been featured in various media outlets including Technology Networks' 'Teach Me in 10' episode, PCN Capital news, BuiltIn, and Tech Times. Prof. Abbott leads an active research group that includes undergraduate and graduate students. Recent lab news highlights include securing a $42M ARPA-H award for an implantable bioelectric medicine project, student successes such as Khushi graduating with her Master's and continuing as a PhD student, and presentations at various conferences. She actively mentors students who present their research at events like Meeting of the Minds. The Abbott Lab works at the interface of materials science and regenerative medicine, investigating how the 3D microenvironment affects tissue development and disease. Current research focuses on adipose tissue engineering strategies, silk biomaterials, and non-invasive tissue assessments to study obesity and its link to type II diabetes.

Ziad IbbiniView profile

Research Fellow

• Developmental Biology
• Phenomics
• Marine Biology
• +3 more

Ziad Ibbini is a Research Fellow at the University of Plymouth's School of Biological and Marine Sciences, affiliated with the Embryo Phenomics research group. His work focuses on advancing bioimaging and computational methodologies to study developmental physiology in aquatic organisms. University: University of Plymouth School: School of Biological and Marine Sciences Position: Research Fellow Email: ziad.ibbini@plymouth.ac.uk His research integrates cutting-edge technology with developmental biology, emphasizing environmental adaptation and automated phenotyping systems. Key contributions include open-source tools like LabEmbryoCam and HeartCV for cardiovascular and behavioral analysis in embryos. Recent work applies deep learning (Dev-ResNet) and computer vision to detect developmental events and predict thermal responses in aquatic embryos, advancing understanding of physiological resilience under climate change stressors. The Embryo Phenomics research group develops spectral and imaging techniques to quantify thermodynamic and environmental responses across species, supporting comparative studies in developmental physiology.

• Biomechanics
• Control Systems
• Robotics and Automation
• +4 more

Dr. Huai-Ti Lin is an Associate Professor in the Department of Bioengineering at Imperial College London's Faculty of Engineering. His affiliations include the Centre for Neurotechnology and Robotics Forum. His research focuses on biomechanics, control systems, robotics, and neurosciences, with a particular interest in translating biological principles into engineering solutions. His lab develops bio-inspired sensors, neural devices, and robots by studying insect locomotion and sensory systems. Key projects include motion capture and neural recording techniques in insects like dragonflies. Dr. Lin holds a PhD from Tufts University, USA. His work integrates interdisciplinary approaches to understand how neural signals and physical bodies coordinate to enable sophisticated motor control in animals. The lab's innovations include the 'GoQBot' soft robot and passive aerial righting mechanisms. His research spans robotics, aerospace engineering, and artificial intelligence, with applications in micro aerial systems and obstacle negotiation. His articles highlight advancements in dragonfly flight mechanics, insect sensory systems, and bio-inspired algorithms. The lab's efforts aim to bridge biology and engineering for next-generation technologies. For more details, visit htlinlab.com .

Monty EscabiView profile

Professor

• Auditory Neuroscience
• Computational Neuroscience
• Speech Recognition
• +3 more

Monty Escabi is a Professor in the Department of Biomedical Engineering at the University of Connecticut (Storrs), where he leads the Physiological Acoustics Laboratory. His research bridges neurophysiology and computational modeling to understand auditory processing, focusing on neural coding of complex sounds in the inferior colliculus, thalamus, and cortex. He develops biologically inspired algorithms for noise-robust speech recognition systems. Education: B.S., Electrical Engineering, Florida International University (1993) M.S., Electrical Engineering: Signal Processing and Stochastic Modeling, Columbia University (1995) Ph.D., Bioengineering, University of California at Berkeley and San Francisco (2000) Research Interests: Dr. Escabi investigates how the central nervous system processes speech and natural sounds in noisy environments. His work combines neurophysiological recordings, computational modeling, and psychoacoustics to study: (1) spectrotemporal encoding in auditory pathways, (2) neural mechanisms for sound-source segregation, and (3) biologically inspired hearing technologies. Key areas include neural ensemble coding, auditory hierarchy optimization, and translating neurobiological principles into machine learning models for speech recognition. Publication Trends: Recent articles focus on neural mechanisms of auditory perception, computational models of speech recognition, and sound-statistics-based approaches to hearing science. Predominant themes include noise-robust auditory processing, hierarchical neural coding, and applications in neuroprosthetics, reflecting interdisciplinary integration of neuroscience, engineering, and machine learning. Laboratory: The Physiological Acoustics Laboratory employs large-scale neural recordings and computational techniques to study natural hearing principles and develop advanced auditory technologies.

• Molecular Biology
• Neurobiology
• Machine Learning
• +2 more

Prof. Frank Müller is a Universitätsprofessor at Forschungszentrum Jülich GmbH , leading the Molecular Sensory and Neurobiology research group. His work bridges computational methods and biological systems. Research focuses on: Graph-based machine learning for biological networks Transformer architectures for molecular data Computational approaches to sensory neuroscience Parameter-efficient models for multi-task learning Key article trends show expertise in graph transformers , molecular foundation models , and multi-agent reasoning systems . No awards explicitly mentioned. Current affiliation: IBI (Institute of Biological Information Processing), Forschungszentrum Jülich

Joseph MakinView profile

Assistant Professor

• Mathematical Neuroscience
• Neural Computation
• Brain-Machine Interfaces
• +7 more

Joseph Makin is an Assistant Professor of Electrical and Computer Engineering at Purdue University, affiliated with the Elmore Family School of Electrical and Computer Engineering. His research focuses on mathematical neuroscience, neural computation, and brain-machine interfaces (BMIs), with a strong emphasis on machine learning applications in neuroscience. He leads the Makin Lab, which develops algorithms for BMI/BCI systems, investigates theoretical aspects of neural computation, and explores pure machine learning principles inspired by biological systems. Education: Not explicitly listed in provided texts. His work bridges computational neuroscience and engineering, particularly in decoding speech and movement from neural activity using ECoG arrays. Notable projects include real-time speech decoding in paralyzed individuals and developing encoder-decoder frameworks for cortical activity translation. The lab also focuses on advancing recurrent neural network architectures and probabilistic graphical models, as evidenced by their textbook An Introduction to Modern Statistical Learning . Recent research trends emphasize self-supervised learning for speech decoding, hybrid system modeling of biological processes, and translational applications in neuroprosthetics. His team collaborates with neurosurgeons and biomedical engineers to translate theoretical insights into clinical tools. Scientific Awards: No explicit awards listed, though his BCI project reached finalist status in a major competition. Makin advises graduate students in machine learning and BMI projects, prioritizing strong backgrounds in neural networks and statistical methods. The Makin Lab is part of Purdue's broader efforts in computational neuroscience and biomedical engineering.

• Data Science
• Digital Agriculture
• Artificial Intelligence
• +3 more

Chen-Yi Lu is a Graduate Research Assistant at Purdue University's Department of Agricultural & Biological Engineering under Major Professor Chaterji. His research bridges digital agriculture and data science. University: Purdue University Department: Agricultural & Biological Engineering Advisor: Professor Somali Chaterji His work focuses on applying artificial intelligence, machine learning, and Internet-of-Things (IoT) to agricultural systems. Key areas include automated pest monitoring, anomaly detection in animal behavior, and AI-driven decision-support tools for sustainable food production. Recent publications highlight his expertise in deep learning for real-time agricultural monitoring systems, generative adversarial networks (GANs) for data augmentation, and AIoT-based solutions for pest management. His research spans interdisciplinary domains like computational modeling, sensor networks, and digital ecosystem design. Chen-Yi Lu's contributions emphasize integrating scalable databases and machine learning with agricultural safety, automation, and environmental stewardship. Current projects involve optimizing data flow for precision agriculture and developing cloud/edge computing platforms to enhance rural disaster response systems.

Andreas AndreouView profile

Professor

• Neuromorphic Engineering
• CMOS Microsystems
• Sensory Information Processing
• +6 more

Andreas Andreou is a Professor of Electrical and Computer Engineering at Johns Hopkins University (JHU), with secondary appointments in Computer Science and the Whitaker Biomedical Engineering Institute. He co-founded the JHU Center for Language and Speech Processing (CLSP) and co-directs the Andreou Lab, focusing on brain-inspired microsystems, neuromorphic engineering, and biomedical sensors. His research spans CMOS-based neuromorphic processors, event-based vision systems, and wearable health monitoring devices like the StethoVest. Key contributions include silicon retinas, polarization-sensitive imagers, and algorithms for pattern analysis. Research Interests: Neuromorphic Computing: Designing energy-efficient brain-inspired chips using 3D CMOS, FETs, and memristive technologies. Biomedical Microsystems: Wearable acoustic sensors for cardiac monitoring and vestibular prosthetics. AI Hardware: Neuromorphic accelerators for edge computing, leveraging LLMs for automated circuit design. Notable Achievements: IEEE Fellow (since 2020) Recipient of the 3rd Best Paper Award at IEEE BioCAS 2018 $2M DARPA grant for bio-inspired event cameras Labs/Teams: The Andreou Lab collaborates with the Kavli Neuroscience Discovery Institute and NSF-funded neuromorphic projects. Ongoing work includes neuromorphic Ising machines, LLM-driven chip design, and quantum sensing for medical applications.

Nina MilosavljevicView profile

Lecturer

• Neuroscience
• Vision Systems
• Optogenetics
• +3 more

Nina Milosavljevic is a Lecturer in the Division of Neuroscience, focusing on retinal physiology, optogenetics, and the role of light in biological rhythms. Her work contributes to UN Sustainable Development Goals related to health and well-being. Research Interests: Vision systems, optogenetics, retinal ganglion cells, circadian rhythms, and light effects on behavior. Projects: Biological rhythms, sensory systems, and circadian timing mechanisms. Publications: Over 30 peer-reviewed articles, including studies on retinal cell types, ipRGC signaling, and optogenetic tools. Contributions: Dataset on G-protein signaling mechanisms and collaborative work on retinal degeneration. Labs/Teams: Involved in the Centre for Biological Timing and interdisciplinary projects on sensory systems and brain disorders.

Mudassar IqbalView profile

Senior Lecturer

• Gene Regulatory Networks
• Machine Learning
• Bioinformatics
• +4 more

Mudassar Iqbal holds the position of Senior Lecturer in Health Data Sciences within the Division of Informatics, Imaging & Data Sciences. His research focuses on integrating computational methods with biological data to study gene regulatory networks, circadian rhythms, and cancer biology. He earned a Doctor of Philosophy from the University of Kent in 2009, specializing in machine learning applications for protein-protein interaction prediction. His work contributes to UN Sustainable Development Goals related to health and innovation. Key research interests include spatial transcriptomics, single-cell multiomics analysis, and computational modeling of biological systems. His recent studies explore age-related gene networks in AML, chronotype associations with psoriasis, and algorithm development for integrating imaging and genomic data. He collaborates internationally on projects analyzing gene expression, circadian biology, and cancer genomics. His methods like CellPie and TriTan advance scalable tools for spatial and multi-omics data analysis. Research affiliations include the Digital Futures, Lydia Becker Institute, and Christabel Pankhurst Institute. He has supervised three academic works and published widely in journals like Nucleic Acids Research and eLife , focusing on bioinformatics-driven discoveries in health and disease.

• In-Memory Databases
• Data Stream Processing
• Database Systems
• +12 more

Matthias Uflacker is a Professor at the Hasso Plattner Institute (HPI), University of Potsdam, Germany, where he conducts research in database systems, in-memory computing, and data-driven enterprise applications. His work bridges systems research and practical applications in Industry 4.0, education, and bioinformatics. University: University of Potsdam School: Hasso Plattner Institute Department: Department of Digital Engineering Research Focus: In-Memory Databases, Stream Processing, Causal AI, and Database Education His research interests span in-memory database systems , real-time data stream processing , GPU-accelerated analytics , and causal structure learning . He also contributes to computer science education , particularly in agile software engineering and interactive learning tools for K-12. The recent publications reflect a strong trend in enterprise data management , with a focus on benchmarking (e.g., ESPBench), performance optimization (e.g., Kafka, Hyrise), and intelligent decision support using causal models in industrial contexts. His work increasingly integrates AI for monitoring and automation in manufacturing and e-commerce. He has co-authored numerous publications in top-tier venues such as VLDB, ICDE, EDBT, and KDD, often in collaboration with Hasso Plattner and other HPI researchers. Matthias Uflacker leads and contributes to research projects on autonomous database systems , dynamic pricing simulations , and educational technology platforms for programming exercises. His team develops tools for real-time data integration, causal analysis, and classroom software engineering assessment.