Gradient Models Professors and Supervisors | ApplySharif

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Aniket AmbekarView profile

Research Fellow

Eindhoven University of TechnologyNetherlands

Aniket Ambekar is a Research Fellow at the Department of Chemical Engineering and Chemistry, Eindhoven University of Technology. His research focuses on multiphase flow dynamics in porous media, with expertise in computational fluid dynamics (CFD) and experimental validation techniques. He holds a PhD in Chemical Engineering from the Indian Institute of Technology Delhi (2022), an MSc in Computational Fluid Dynamics from National Institute of Technology (2016), and a BSc in Chemical Technology from the University of Pune (2013). Research interests include packed bed hydrodynamics, gas-liquid flow mechanisms, and the role of wettability in two-phase systems. His work combines high-resolution simulations (e.g., volume-of-fluid method) with experimental measurements to study flow regimes, interfacial dynamics, and phase distribution. Notable contributions address perforation effects in structured packings, particle aspect ratio impacts, and monolith gas-liquid interactions. He has received prestigious awards including the Marie Skłodowska-Curie postdoctoral fellowship (2022) and the Outstanding Ph.D. Thesis Award (2024). Collaborations span European institutions, focusing on energy-efficient separation processes and reactor design optimization.

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Howard RundleView profile

Professor

University of OttawaCanada

Howard Rundle is a Full Professor in the Department of Biology within the Faculty of Science at the University of Ottawa. His research laboratory focuses on empirical studies in evolutionary ecology and evolutionary genetics, utilizing both laboratory and field approaches with model systems including various Drosophila species and the antler fly ( Protopiophila litigata ). His work is conducted both in controlled laboratory settings and in natural environments, particularly in Algonquin Park's Wildlife Research Station. Dr. Rundle's primary research interests span evolutionary ecology and evolutionary genetics, with particular emphasis on: The role of sexual selection in adaptation and purging of deleterious mutations Sexual conflict and its ecological context The evolutionary divergence of mate preferences and its contribution to speciation Quantitative genetics of sexual displays, particularly cuticular hydrocarbons in insects The impact of ecological complexity on evolutionary processes His recent publications reveal a consistent focus on how mating environments influence the dynamics of sexual conflict, adaptation, and speciation. Much of his work examines whether sexual selection helps or hinders adaptation, with growing emphasis on genomic approaches to understand the genetic basis of evolutionary responses. His research program demonstrates how ecological context fundamentally shapes evolutionary outcomes, particularly in the interface between natural and sexual selection. Dr. Rundle collaborates extensively with researchers at other institutions including Aneil Agrawal (University of Toronto), Russell Bonduriansky (University of New South Wales), Vincent Careau (University of Ottawa), Kelly Dyer (University of Georgia), Amanda Moehring (Western University), and Locke Rowe (University of Toronto). These collaborations enhance the scope of his research across multiple dimensions of evolutionary biology. The Rundle Lab maintains a strong commitment to equity, diversity, and inclusion, explicitly welcoming and supporting members from all racial, religious, and cultural backgrounds, as well as those from the LGBTQ+ community, operating under the University of Ottawa's principles in these areas.

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Alexander RodríguezView profile

Assistant Professor

University of Michigan-Ann ArborUnited States

Alexander Rodríguez is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan. His research focuses on advancing AI methods for modeling complex spatiotemporal dynamics, particularly in applications related to population health and community resilience. He specializes in machine learning, time series analysis, uncertainty quantification, and multi-agent systems, with an emphasis on scientific modeling and data-driven decision-making. Recent contributions include keynote talks at AAMAS 2025 (Autonomous Agents for Social Good workshop), presentations at the US National Academies Symposium, and invited talks at AAAI 2025 on topics like knowledge-guided machine learning and public health prediction. He co-organizes AAMAS 2025 as sponsorship co-chair and leads initiatives in AI for science and epidemic forecasting. His publications emphasize neural networks for time series forecasting, biomedical foundation models, and epidemic surveillance systems. Notable work includes 'Neural Conformal Control for Time Series Forecasting' (AAAI 2025) and 'Deepcovid: An operational deep learning-driven framework for explainable real-time forecasting' (2021). No scientific awards explicitly listed in available texts. His research group actively collaborates on grants related to AI applications in public health and infrastructure resilience, with a focus on data-centric methodologies and multi-agent systems.

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Don ButlerView profile

Professor

Australian National University (ANU)Australia

Don Butler is a Professor at the ANU College of Law, Governance and Policy within the ANU Law School. He holds a BSc (hons 1) and PhD from the University of Queensland. His research focuses on climate change mitigation, carbon sequestration, biodiversity conservation, and environmental policy. He leads projects addressing carbon offset integrity, forest regeneration, and policy reform. His work spans savanna ecosystems, forest management, and the impacts of climate policy on land-use practices. Education: BSc (hons 1) and PhD (University of Queensland). Research Interests: Climate Change, Carbon Offsets, Biodiversity Conservation, Environmental Policy, Vegetation Dynamics, and Sustainable Agriculture. Collaborations include analyses of carbon projects in Australia, policy evaluations for the Clean Energy Regulator, and studies on groundwater-vegetation interactions. Projects: Includes developing improved carbon accounting methods for native regrowth, analyzing the National Stewardship Trading Platform, and investigating maladaptation risks in savanna carbon projects. His work emphasizes policy integrity and ecological outcomes. Advocacy and Grants: Focuses on policy reforms to enhance carbon market integrity and biodiversity co-benefits. Engages with government agencies and NGOs to shape climate and land management policies. Labs/Teams: Collaborates with the ANU Climate Change Institute and interdisciplinary teams on carbon offset methodologies and environmental governance.

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Liqiang WangView profile

Professor

University of Central FloridaUnited States

Liqiang Wang is a Professor in the Department of Computer Science at the University of Central Florida (UCF), where he directs the Big Data Lab. Previously, he served as faculty at the University of Wyoming (2006-2015). He holds a Ph.D. in Computer Science from Stony Brook University (2006) and spent a visiting research period at IBM T.J. Watson Research Center (2012-2013). His research focuses on big data analytics, high-performance computing, parallel systems optimization, and applying deep learning to detect programming errors and enhance model robustness. Education: Ph.D., Computer Science, Stony Brook University (2006); Visiting Researcher, IBM Watson (2012-2013). Research Interests: Improving accuracy and security of big data models, optimizing parallel computing systems (HPC, Cloud, GPUs), program analysis for concurrency errors, and deep learning applications in anomaly detection and adversarial robustness. Notable projects include scalable LSQR algorithms for seismic tomography and the OpenMP Analysis Toolkit (OAT) for concurrency error detection. Key Awards: NSF CAREER Award (2011), Castagne Faculty Fellowship (2013-2015), UCF Mid-Career Refresh Award (2020), and grants including a $50K NSF CIVIC-PG grant (2022) and Google/Meta donations. Advising and Grants: Supervises over 20 Ph.D./M.S. students and has secured grants totaling over $100K. Notable collaborations include seismic tomography with NCAR and cloud computing optimization. Labs/Teams: Director of UCF’s Big Data Lab, collaborating on projects like Parallel LSQR and Anti-Neuron Watermarking.

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Lesley W. ChowView profile

Associate Professor

Lehigh UniversityUnited States

Lesley W. Chow is an Associate Professor in Bioengineering and Materials Science & Engineering at Lehigh University. She leads the Chow Lab, focusing on designing biomaterials for regenerative medicine and tissue engineering, particularly musculoskeletal interfaces like the osteochondral junction. Her work integrates 3D printing, peptide-polymer conjugates, and self-assembly techniques to create hierarchical scaffolds mimicking native tissues. Chow holds a Ph.D. in Materials Science and Engineering from Northwestern University and a B.S. in Materials Science and Engineering from the University of Florida. Her research emphasizes understanding how tissue organization influences cell behavior and improving clinical translation of biomaterials. Key areas include osteochondral interface regeneration, immunomodulatory biomaterials, and spatially functionalized scaffolds using additive manufacturing. Her lab’s innovations address challenges in musculoskeletal repair, such as creating gradient scaffolds to replicate native tissue properties. Collaborations span biomaterials science, engineering, and clinical translation. She also advocates for diversity in engineering through frameworks promoting institutional accountability. Major grants include the NSF CAREER Award for spatially organized biomaterials. Her work is published in journals across biomaterials science and tissue engineering, with a focus on interdisciplinary solutions for complex tissue regeneration.

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Nabil KahaleView profile

Associate Professor

ESCP Business SchoolFrance

Nabil Kahale is an Associate Professor of Finance at ESCP Business School in Paris. His research focuses on financial derivatives, Monte Carlo methods, optimization, and machine learning. He holds a PhD in theoretical computer science from MIT (1993) and an HDR (French habilitation) from Université Paris 1 Panthéon-Sorbonne (2020), enabling him to supervise PhD students. His academic career includes prior roles in theoretical computer science and consulting for banks. He has published widely in top journals such as Mathematical Finance , Management Science , and SIAM Journal on Computing . Education: Bachelor of Science in Engineering, École Polytechnique (1987) PhD in Theoretical Computer Science, MIT (1993) HDR in Economics, Université Paris 1 Panthéon-Sorbonne (2020) Research Interests: His work bridges finance and computational methods, emphasizing practical applications of stochastic models and algorithmic efficiency. Key areas include derivative pricing, risk management, and the integration of machine learning into financial systems. Professional Contributions: He has served as a consultant for banking institutions and a referee for the French Ministry of Economy and Finance. His research also intersects with social and economic policy analysis, such as evaluating the economic impact of public health measures. Labs/Teams: While no specific lab affiliation is mentioned, his collaborations span interdisciplinary teams in finance, computer science, and applied mathematics through his publications and consulting work.

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Kourosh DavoudiView profile

Associate Professor

Ontario Tech UniversityCanada

Kourosh Davoudi is an Associate Professor of Computer Science at Ontario Tech University's Faculty of Science. He holds a PhD in Computer Science from York University with a focus on Machine Learning and Data Mining. Prior to joining Ontario Tech in 2019, he was a postdoctoral research fellow at the University of Waterloo's Department of Management Sciences. His research interests span Natural Language Processing, Deep Learning, Reinforcement Learning, Graph Mining, and Machine Learning. He actively supervises graduate students in these areas and teaches courses such as Data Mining and Artificial Intelligence. His research emphasizes practical applications of AI techniques in areas like outbreak detection, sentiment analysis, and automated grading systems. Recent work includes innovations in neural document segmentation, vision-language models, and hybrid outbreak detection using social media data. His publications consistently address challenges in algorithm design, explainable AI, and domain-specific NLP applications. Dr. Davoudi has contributed to conferences such as COLING, EMNLP, and IEEE transactions, focusing on interdisciplinary applications of machine learning. His work bridges theoretical advancements with real-world problems in healthcare, education, and social media analysis.

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Amir A. ZadpoorView profile

Professor

Delft University of TechnologyNetherlands

Prof. Amir A. Zadpoor holds dual roles as Antoni van Leeuwenhoek Professor at TU Delft (Department of Biomechanical Engineering) and Professor of Orthopedics at Leiden University Medical Center. He leads the Additive Manufacturing Lab and specializes in biomaterials, tissue biomechanics, and orthopedic implants. His research focuses on 3D/4D printing, meta-biomaterials, and biodegradable metals for clinical applications. Key research interests include: designing function-tailored implants, antimicrobial biofunctionalized materials, and mechanically adaptive meta-implants. He has pioneered projects like 'Metallic clay' and 'Mechanobiology in-silico,' with applications in orthopedics and regenerative medicine. Notable awards include ERC grants, Vidi/Veni awards, and the Jean Leray Award. His lab develops deployable implants, self-folding origami lattices, and smart meta-implants. Ancillary roles include editorial positions at Springer Nature and directorships at Sylvanity/Zagres. Teaching includes courses on biomaterials, regenerative medicine, and computational biomechanics. Research outputs span over 150 peer-reviewed articles. Current priorities include sustainable biomaterials, AI-driven design optimization, and translating additive manufacturing innovations into clinical practice.

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Javier Arístegui RuizView profile

Professor

University of Las Palmas de Gran CanariaSpain

Javier Arístegui Ruiz is a Full Professor of Ecology at the University of Las Palmas de Gran Canaria (ULPGC), affiliated with the Faculty of Marine Sciences and the Department of Ecology. He has directed key institutions like the Institute of Oceanography and Global Change (2011-2012) and the Marine Technological Service (2015-2018). Presently, he leads the Canaries Observatory of Harmful Algae (OC-HABs) and the UNESCO Chair on Coastal and Marine Sustainability (since 2021). His research focuses on biological oceanography, climate change impacts, biogeochemical cycles, and pelagic ecosystem dynamics. Notable contributions include studies on plankton ecology, microbial oceanography, and nature-based solutions for carbon dioxide removal. He has published over 160 peer-reviewed articles, coordinated 35+ projects, and advised 17 PhD students in Biological Oceanography. Key awards include the Helmholtz International Fellow Award (2015). He serves as Co-Editor-in-Chief of Deep Sea Research II and Associate Editor of Frontiers in Marine Science , with extensive editorial and review roles in major journals. His work has influenced global initiatives like the IPCC Special Report on the Ocean and Cryosphere (2017-2019). Research highlights include climate-change stressors on plankton, carbon fluxes in the dark ocean, and mesoscale/submesoscale oceanographic processes. His fieldwork spans 35 oceanographic cruises across polar to tropical seas, emphasizing interdisciplinary collaboration with physical oceanographers.

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Tjisse van der HeideView profile

Professor

University of GroningenNetherlands

Tjisse van der Heide is a Professor of Coastal Ecology at the Groningen Institute for Evolutionary Life Sciences (GELIFES) at the University of Groningen and a researcher for the Royal Netherlands Institute for Marine Research (NIOZ). He also serves as Vice Chair of the Dutch OBN Knowledge Network for Nature Restoration and Management's team of experts on dune and coastal landscapes. Van der Heide's research focuses on coastal ecosystems and the role of ecosystem engineers—organisms that create suitable living circumstances for themselves and other species through environmental modification. His work examines how ecosystems function and how dissipated ecosystems can be restored, combining field and lab experiments with computer models. In 2024, he received a prestigious Vici-grant for his research on restoration of coastal ecosystems by temporarily mimicking habitat-forming species, developing a framework that integrates ecology, industrial design, and engineering principles. Analyzing his recent publication record reveals strong emphasis on ecosystem engineering processes, habitat restoration techniques, and climate change impacts on coastal systems. His research spans seagrass beds, mussel and oyster reefs, salt marshes, and dune ecosystems, with particular focus on how biotic interactions shape coastal landscapes and influence restoration success. His interdisciplinary approach frequently bridges theoretical ecology with practical conservation applications. Van der Heide has received significant recognition including the Vici-grant (2024) and the Heineken Young Scientist Award. His research has direct policy implications, as evidenced by his media commentary on coastal development projects like power cables through the Wadden Sea. His work involves extensive collaboration with researchers across multiple institutions, focusing on both fundamental ecological processes and applied restoration techniques. Van der Heide's research group conducts field studies throughout the Wadden Sea region and other coastal areas, contributing to our understanding of ecosystem dynamics and developing practical solutions for coastal conservation challenges.

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Oksana BuzhdyganView profile

Researcher

Free University of BerlinGermany

Dr. Oksana Buzhdygan is a Senior Researcher in Theoretical Ecology at the Department of Biology, Faculty of Biology, Chemistry, Pharmacy at Freie Universität Berlin. She holds a PhD in Ecology from Chernivtsi National University and has held postdoctoral positions at institutions including the University of Georgia (USA) and Freie Universität Berlin. Her research focuses on biodiversity-ecosystem functioning links, multitrophic interactions, and ecological network analysis. Education: BSc, MSc, and PhD in Ecology (2000–2008), Chernivtsi National University, Ukraine Postdoctoral Fellowships: University of Georgia (2010–2012), Freie Universität Berlin (2013–2021) Research Interests: Environmental change impacts on biodiversity and ecosystem functions Ecological network analysis of multitrophic systems Food web dynamics in grasslands and freshwater ecosystems Publications reflect her expertise in biodiversity effects on energy flow, human impact on ecosystems, and plant diversity drivers. Her work bridges theoretical ecology with applied conservation challenges, particularly in grasslands and agroecosystems. Labs/Teams: Member of the Tietjen Group (Theoretical Ecology at Freie Universität Berlin), collaborating widely in international projects on biodiversity and ecosystem services.

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Bhavin J. ShastriView profile

Assistant Professor

Queen's University at KingstonCanada

Bhavin J. Shastri is an Assistant Professor in the Department of Physics, Engineering Physics and Astronomy at Queen's University in Canada. His research explores the physics of light for computing , pushing frontiers in information and signal processing through photonic computing and quantum/neuromorphic photonics . He is affiliated with the Centre for Nanophotonics and NUCLEUS , a pan-Canadian photonic computing program funded by NSERC CREATE, bridging artificial intelligence and quantum information . Canada Research Chair & Principal Investigator Faculty Affiliate at Vector Institute (2020-) Editorial Board Member of JPhys Photonics (2019-) Member of IEEE Photonics Society Technical Affairs Council (2019-) Visiting Researcher Scholar at Princeton University (2018-) Shastri Lab members have access to world-class shared facilities, including the Centre for Nanophotonics (CFI-Innovation Fund), Nanofabrication Kingston , the Centre for Advanced Computing , and the Digital Research Alliance of Canada . The lab takes an interdisciplinary approach combining nanophotonics with complex systems on emerging substrates. His research focuses on silicon photonics , nanophonic processors , and photonic integrated circuits with applications to deep learning , nonlinear programming , and quantum information science . His articles show consistent exploration of quantum photonic neural networks , photonic memory systems , and optical signal processing for machine learning and quantum technologies . 2020 IUPAP Young Scientist Prize in Optics 2014 Banting Postdoctoral Fellowship 2012 D. W. Ambridge Prize 2011 IEEE Photonics Society Graduate Student Fellowship 2011 NSERC Postdoctoral Fellowship Multiple Best Student Paper Awards Shastri's lab supervises Ph.D. candidates and postdoctoral fellows working on quantum photonics , neuromorphic computing , and photonic AI systems . His recent work includes photonic tensor cores for scientific computing , quantum photonic neural networks , and all-optical memory systems. Shastri Lab designs programmable nanophotonic processors with potential to outperform microelectronic processors in energy efficiency and computational speeds by seven and four orders of magnitude respectively. Their work spans from device design to system-level implementations in optical computing for machine learning and quantum information processing .

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Dimitri BertsekasView profile

Professor

Arizona State UniversityUnited States

Dimitri Bertsekas is the Fulton Professor of Computational Decision Making at the School of Computing and Augmented Intelligence , Arizona State University (ASU), where he has been a full-time faculty member since 2019. Previously, he held the position of McAfee Professor of Engineering at MIT (1979–2019). He is renowned for his contributions to optimization, control theory, and artificial intelligence. Education: Bachelor's degree in Engineering from the National Technical University of Athens (Greece). MS in Electrical Engineering from George Washington University (1969). PhD in System Science from MIT (1971). Research Interests: Bertsekas' work bridges theory and application in optimization, control systems, and reinforcement learning. His research emphasizes large-scale computational methods, dynamic programming, and stochastic systems, with significant impacts on machine learning and AI. Publications & Awards: He has authored over 20 books and numerous papers, including seminal works like Neuro-Dynamic Programming and Dynamic Programming and Optimal Control . His accolades include the INFORMS John von Neumann Theory Prize (2018), the IEEE Control Systems Award (2022), and membership in the U.S. National Academy of Engineering. Affiliations: Bertsekas founded Athena Scientific (1995), a publisher of technical books, and serves as Chief Scientific Advisor at Bayforest Technologies (London). He has advised numerous students and held editorial roles for scientific journals.

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Yi-Chin TohView profile

Professor

Queensland University of TechnologyAustralia

Yi-Chin Toh is a Professor in the Faculty of Engineering at Queensland University of Technology (QUT), leading the Micro Tissue Engineering Lab. She holds an ARC Future Fellowship and has held prior roles as an Assistant Professor at the National University of Singapore. Her expertise lies in microfluidic tissue models for drug testing and organ-on-a-chip technologies. She earned her B.Eng. (2001) and Ph.D. (2008) in Bioengineering from National University of Singapore, followed by postdoctoral training at MIT and A*STAR. Her research focuses on advancing alternative animal-free technologies, with notable contributions to multi-organ microfluidic platforms and 3D bioprinted tissue models. She has published over 70 peer-reviewed articles, secured eight patents, and received accolades like the 2022 Lab on a Chip Pioneers in Miniaturization Award and 2019 Global 3R Award. She holds editorial roles in journals like Biomicrofluidics and contributes to conferences such as MicroTAS. Key research areas include mechanobiology modeling, hepatic-microbiome interactions, and AI-driven single-cell analysis. Her lab’s work bridges academia and industry, emphasizing translational applications in drug safety and toxicology. Current projects include modular microfluidic platforms for multi-organ interactions and FDA-regulatory compliant non-animal testing systems. Education: B.Eng in Chemical Engineering, National University of Singapore (2001) Ph.D. in Bioengineering, National University of Singapore (2008) Awards & Leadership: Lab on a Chip Lectureship (2022), Global 3R Award (2019), ARC College of Experts member, and leadership roles in MicroTAS conferences. She also leads the ARC Training Centre for Cell and Tissue Engineering Technologies.

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