At Susquehanna, we approach quantitative finance with a deep commitment to scientific rigor and innovation. Our research leverages vast and diverse datasets, applying cutting-edge machine learning to uncover actionable insights—driving data-informed decisions from predictive modeling to strategic execution. Whether adapting state-of-the-art techniques from academic literature or developing entirely novel approaches, our machine learning algorithms are built to tackle the unique challenges of global financial markets— where textbook approaches are rarely sufficient.
Our culture is intellectually driven and highly collaborative, shaped by top-tier researchers with backgrounds in mathematics, physics, and computer science—including a diverse group of PhD and postdoctoral alumni from the world’s leading programs. Our experienced engineering teams work closely with researchers to deliver robust solutions grounded in industry-tested principles and best practices. By investing heavily in both talent and infrastructure, we empower our teams to push the boundaries of applied research and deploy impactful innovations in real-world trading systems. We’re excited to contribute to the ICML community and to connect with researchers and engineers who share our passion for solving complex, high-stakes problems through machine learning.
David is an Associate Director in Quantitative Research at Susquehanna International Group. He earned his Ph.D. in Theoretical Physics from the University of Pennsylvania. David collaborates with machine learning and quantitative researchers to identify high-impact problems where deep learning methods can be effectively applied, helping bridge theoretical insights with practical modeling challenges.
Ali is the Head of Deep Learning Research at Susquehanna International Group, where he leads a team focused on developing advanced neural network models to generate predictive signals for both market making and market taking strategies. His group applies cutting-edge deep learning techniques to financial data, with particular emphasis on time series modeling and scalable inference. Ali holds a Ph.D. in Information Theory and master’s degrees in Mathematics and Financial Engineering, from the University of Michigan. In addition to his work at Susquehanna, he serves as an adjunct faculty member in the Mathematics Department at the University of Michigan, where he teaches on the application of artificial intelligence in finance.
Chris is a Quantitative Strategist and Associate Director at Susquehanna International Group. He holds a Ph.D. in Mathematics from the University of Michigan, where he specialized in several complex variables and geometry. Chris develops machine learning models to forecast asset prices over very short time horizons, providing predictive signals used in high-frequency trading strategies. His academic research has been published in mathematical journals, including studies in invariants in CR geometry.
Edo is the Millard E. Gladfelter Professor of Statistics, Data Science, and Finance at Temple University’s Fox School of Business, where he also serves as Co-Director of the Temple Data Science Institute. He earned his Ph.D. in Computer Science from Carnegie Mellon University and has held faculty and visiting positions at Harvard, MIT, Princeton, Google, and Microsoft Research. His research spans statistical machine learning, network analysis, experimental design, and causal inference, with applications in technology and systematic trading. Edo has published over 150 papers, is a Fellow of both the Institute of Mathematical Statistics and the American Statistical Association, and serves as an academic adviser to Susquehanna.
Wei leads a team of quantitative researchers, traders, and technologists managing proprietary equity strategies at Susquehanna International Group. She earned her Ph.D. in Operations Research and a master’s in Financial Mathematics from Stanford University, after completing her undergraduate studies in Mathematics and Computer Science at Peking University. Wei’s team builds machine learning models using tree-based and neural network architectures, with particular interest in integrating structural insights from fundamental data. She has also worked closely with Susquehanna’s core ML group to refine and scale the firm’s ML signal computation pipeline.
Rob is a Quantitative Researcher at Susquehanna International Group, where he leads cross-functional efforts to design systematic trading strategies and scalable research infrastructure. Rob completed his Ph.D. in Number Theory in just three years, supported by NSF fellowships, and went on to hold postdoctoral positions at EPFL and Stanford University. He has published in venues such as the Proceedings of the National Academy of Sciences. His work focuses on applying modern machine learning methods—particularly time series modeling and scalable inference—to high-frequency financial data.
Lyubo is the Quantitative Hiring Manager at Susquehanna International Group. He received his Ph.D. in Mathematics from MIT, specializing in algebraic topology. In addition to talent evaluation, Lyubo plays a central role in developing educational curricula for new quants, including deep learning training programs that span classical and modern ML techniques. He is a former International Mathematical Olympiad (IMO) gold medalist.
Mohammad is a Quantitative Researcher who works on developing machine learning and deep learning models to predict the prices of financial instruments across a range of time horizons. He earned his Ph.D. in Statistics from the University of Chicago, where his research focused on probability theory problems arising in statistical mechanics. He is also a former silver medalist in the International Mathematical Olympiad (IMO).
Tony is a Machine Learning Researcher at Susquehanna International Group, where he develops deep learning models to uncover structure in complex, irregular time series data. He holds a Ph.D. in Computer Science and Neuroscience from Princeton University. His academic work has been featured on multiple Nature covers and contributed to major milestones in the BRAIN Initiative. Tony’s research sits at the intersection of artificial intelligence, natural language processing, and neuroscience, reflecting a broad interest in how intelligent systems—both artificial and biological—perceive, learn, and reason.
Yue is a Machine Learning Researcher at Susquehanna International Group. She earned her undergraduate and master’s degrees in Mathematics and Statistics from Oxford University, followed by a Ph.D. in Mathematics from Stanford University. Her research interests span applied and theoretical aspects of ML, particularly in the context of financial prediction.
Jay has been with Susquehanna International Group since 2006 and currently leads several front-office technology teams supporting the firm’s trading strategies. He holds a combined B.S./M.S. in Computer Science and Information Systems, and holds FINRA certifications including Series 57 and the Securities Industry Essentials (SIE) exam. Jay has contributed to the development of low-latency research platforms and currently oversees teams applying advanced ML techniques to high-velocity, unstructured data for signal generation and trading decisions.
Jacob is a Deep Learning Engineer at Susquehanna International Group, where he supports the full lifecycle of training and deploying deep learning models. He contributes to infrastructure and experimentation efforts across various ML applications, collaborating with researchers and traders.
Nick is an Associate Director at Susquehanna International Group, where he co-leads technology for the U.S. equity options trading business and firmwide research computing. He holds a Ph.D. from Caltech and an A.B. from Harvard. Nick has a long-standing interest in machine learning, dating back to early research collaborations with NASA JPL in the 1990s. He later helped pioneer the application of ML to behavioral targeting in digital media and now supports ML-driven signal and strategy development at Susquehanna.
David is a natural language processing researcher at Susquehanna International Group. He holds a master’s degree in computer science and bachelor’s degrees in computer engineering and electrical engineering. His work focuses on language-model fine-tuning and alignment, dense representations, and production-grade NLP systems for financial applications. His technical interests also include embedded, real-time, and reliable systems, as well as robotics and computer graphics.
Linge is an AI Research Scientist at Susquehanna International Group. She holds a Ph.D. in Computer Science and specializes in transforming cutting-edge ML research into robust, production-ready systems. Prior to Susquehanna, Linge served as Senior Staff ML Tech Lead at Glassdoor, where she led efforts in search ranking, NLP infrastructure, salary modeling, and recommender systems. She also launched the company’s first generative AI product. A multilingual technologist, she has lived and worked across North America, Asia, and Europe.
Drew is a Quantitative Strategist at Susquehanna International Group. He holds a Ph.D. in Number Theory from the University of Chicago, graduating in 2019. Since joining Susquehanna, Drew has spent six years in the equity options group, where he has helped build trading signals and backtest infrastructure. His work now lies in leveraging large high-frequency data as a source of trading signals, with modern deep learning techniques a primary line of inquiry.
Ross is a Quantitative Researcher on the equities desk at Susquehanna International Group. He earned his Ph.D. from Rutgers University, where he studied combinatorics and probability, followed by a postdoctoral appointment at Yale. His academic work involved probabilistic techniques and limit theorems for combinatorial random variables. Ross applies machine learning to model complex and otherwise analytically intractable phenomena in financial markets, using techniques ranging from linear models to deep neural networks.
Yuejiao is a Quantitative Researcher at Susquehanna International Group, where she focuses on extracting signals from high-dimensional, noisy data using statistical learning and principled modeling. She received her Ph.D. in Applied Mathematics from UCLA and a bachelor’s degree in Mathematical Science from Peking University. Her research in bilevel optimization and federated learning has been published in top venues including NeurIPS, AISTATS, ICASSP, and Mathematical Programming. She is also a recipient of the Best Student Paper Award at ICASSP.
Chi-Ting is a Quantitative Researcher at Susquehanna International Group. He earned his Ph.D. in Astrophysics from Ludwig-Maximilians-Universität Munich and previously held postdoctoral positions at Stony Brook University and Brookhaven National Laboratory. Chi-Ting uses machine learning to model intraday equity price movements and search for new trading signals. His background in astrophysics involved combining theory, simulation, and observational data to understand the structure of the universe.
Mike is a senior strategic advisor at Susquehanna International Group whose career has spanned trading, risk management, mentorship, and firm-wide education. He earned his degree in Math and Finance from MIT and is a CFA Charterholder. Mike focuses on long-term value creation and the development of scalable ideas and talent across trading, research, and technology. He plays a central role in the continuing education of Susquehanna professionals, including those working in ML and systematic trading.
Jake is a Systems Engineer at Susquehanna International Group, where he focuses on optimizing performance across both computationally intensive workloads and low-latency trading systems. He studied Electrical Engineering at Drexel University, where he learned to break down complex hardware problems into practical, scalable solutions. Outside of work, Jake built a custom livestreaming system for endurance racing that runs entirely over cellular networks—operating reliably for 9 hours straight in all conditions. He’s always up for talking about storage systems, Jupyter setups, Factorio optimization, or the latest hardware bug that’s still haunting you.
Theo is a Software Engineer at Susquehanna International Group, where he designs trader interfaces and visualization tools for interacting with predictive models. He holds a B.A. in Computer Science and English Literature from Swarthmore College. Theo has built chess-playing AIs and enjoys exploring game AI in his spare time.
Kristy is a Quantitative Recruiter at Susquehanna International Group where she is focused on Machine Learning, Quantitative Research, and Systematic Trading hiring. She holds a B.S. in Linguistics from the Massachusetts Institute of Technology. Kristy is a past silver medalist in the Math Prize for Girls Olympiad and a U.S.A. Mathematical Olympiad (USAMO) qualifier.
Mike leads experienced hiring strategy at Susquehanna International Group for teams across Machine Learning, Quantitative Research, Systematic and Semi-Systematic Trading, and Front Office Development. He focuses on identifying and recruiting top technical talent to support Susquehanna’s dynamic and evolving trading strategies. Mike’s work is centered on building strong partnerships with candidates and hiring managers to shape high-impact teams at the forefront of quantitative finance and technology.
Kelly leads campus hiring strategy for teams across Machine Learning, Quantitative Research, and Systematic Trading at Susquehanna International Group. She holds a Bachelor of Science in Psychology from the University of Pennsylvania. Kelly’s work focuses on aligning talent acquisition with the firm’s technical evolution, helping shape high-impact teams working on cutting-edge research and model-driven trading.
David is an Associate Director in Quantitative Research at Susquehanna International Group. He earned his Ph.D. in Theoretical Physics from the University of Pennsylvania. David collaborates with machine learning and quantitative researchers to identify high-impact problems where deep learning methods can be effectively applied, helping bridge theoretical insights with practical modeling challenges.
Ali is the Head of Deep Learning Research at Susquehanna International Group, where he leads a team focused on developing advanced neural network models to generate predictive signals for both market making and market taking strategies. His group applies cutting-edge deep learning techniques to financial data, with particular emphasis on time series modeling and scalable inference. Ali holds a Ph.D. in Information Theory and master’s degrees in Mathematics and Financial Engineering, from the University of Michigan. In addition to his work at Susquehanna, he serves as an adjunct faculty member in the Mathematics Department at the University of Michigan, where he teaches on the application of artificial intelligence in finance.
Chris is a Quantitative Strategist and Associate Director at Susquehanna International Group. He holds a Ph.D. in Mathematics from the University of Michigan, where he specialized in several complex variables and geometry. Chris develops machine learning models to forecast asset prices over very short time horizons, providing predictive signals used in high-frequency trading strategies. His academic research has been published in mathematical journals, including studies in invariants in CR geometry.
Edo is the Millard E. Gladfelter Professor of Statistics, Data Science, and Finance at Temple University’s Fox School of Business, where he also serves as Co-Director of the Temple Data Science Institute. He earned his Ph.D. in Computer Science from Carnegie Mellon University and has held faculty and visiting positions at Harvard, MIT, Princeton, Google, and Microsoft Research. His research spans statistical machine learning, network analysis, experimental design, and causal inference, with applications in technology and systematic trading. Edo has published over 150 papers, is a Fellow of both the Institute of Mathematical Statistics and the American Statistical Association, and serves as an academic adviser to Susquehanna.
Wei leads a team of quantitative researchers, traders, and technologists managing proprietary equity strategies at Susquehanna International Group. She earned her Ph.D. in Operations Research and a master’s in Financial Mathematics from Stanford University, after completing her undergraduate studies in Mathematics and Computer Science at Peking University. Wei’s team builds machine learning models using tree-based and neural network architectures, with particular interest in integrating structural insights from fundamental data. She has also worked closely with Susquehanna’s core ML group to refine and scale the firm’s ML signal computation pipeline.
Rob is a Quantitative Researcher at Susquehanna International Group, where he leads cross-functional efforts to design systematic trading strategies and scalable research infrastructure. Rob completed his Ph.D. in Number Theory in just three years, supported by NSF fellowships, and went on to hold postdoctoral positions at EPFL and Stanford University. He has published in venues such as the Proceedings of the National Academy of Sciences. His work focuses on applying modern machine learning methods—particularly time series modeling and scalable inference—to high-frequency financial data.
Lyubo is the Quantitative Hiring Manager at Susquehanna International Group. He received his Ph.D. in Mathematics from MIT, specializing in algebraic topology. In addition to talent evaluation, Lyubo plays a central role in developing educational curricula for new quants, including deep learning training programs that span classical and modern ML techniques. He is a former International Mathematical Olympiad (IMO) gold medalist.
Mohammad is a Quantitative Researcher who works on developing machine learning and deep learning models to predict the prices of financial instruments across a range of time horizons. He earned his Ph.D. in Statistics from the University of Chicago, where his research focused on probability theory problems arising in statistical mechanics. He is also a former silver medalist in the International Mathematical Olympiad (IMO).
Tony is a Machine Learning Researcher at Susquehanna International Group, where he develops deep learning models to uncover structure in complex, irregular time series data. He holds a Ph.D. in Computer Science and Neuroscience from Princeton University. His academic work has been featured on multiple Nature covers and contributed to major milestones in the BRAIN Initiative. Tony’s research sits at the intersection of artificial intelligence, natural language processing, and neuroscience, reflecting a broad interest in how intelligent systems—both artificial and biological—perceive, learn, and reason.
Yue is a Machine Learning Researcher at Susquehanna International Group. She earned her undergraduate and master’s degrees in Mathematics and Statistics from Oxford University, followed by a Ph.D. in Mathematics from Stanford University. Her research interests span applied and theoretical aspects of ML, particularly in the context of financial prediction.
Jay has been with Susquehanna International Group since 2006 and currently leads several front-office technology teams supporting the firm’s trading strategies. He holds a combined B.S./M.S. in Computer Science and Information Systems, and holds FINRA certifications including Series 57 and the Securities Industry Essentials (SIE) exam. Jay has contributed to the development of low-latency research platforms and currently oversees teams applying advanced ML techniques to high-velocity, unstructured data for signal generation and trading decisions.
Jacob is a Deep Learning Engineer at Susquehanna International Group, where he supports the full lifecycle of training and deploying deep learning models. He contributes to infrastructure and experimentation efforts across various ML applications, collaborating with researchers and traders.
Nick is an Associate Director at Susquehanna International Group, where he co-leads technology for the U.S. equity options trading business and firmwide research computing. He holds a Ph.D. from Caltech and an A.B. from Harvard. Nick has a long-standing interest in machine learning, dating back to early research collaborations with NASA JPL in the 1990s. He later helped pioneer the application of ML to behavioral targeting in digital media and now supports ML-driven signal and strategy development at Susquehanna.
David is a natural language processing researcher at Susquehanna International Group. He holds a master’s degree in computer science and bachelor’s degrees in computer engineering and electrical engineering. His work focuses on language-model fine-tuning and alignment, dense representations, and production-grade NLP systems for financial applications. His technical interests also include embedded, real-time, and reliable systems, as well as robotics and computer graphics.
Linge is an AI Research Scientist at Susquehanna International Group. She holds a Ph.D. in Computer Science and specializes in transforming cutting-edge ML research into robust, production-ready systems. Prior to Susquehanna, Linge served as Senior Staff ML Tech Lead at Glassdoor, where she led efforts in search ranking, NLP infrastructure, salary modeling, and recommender systems. She also launched the company’s first generative AI product. A multilingual technologist, she has lived and worked across North America, Asia, and Europe.
Drew is a Quantitative Strategist at Susquehanna International Group. He holds a Ph.D. in Number Theory from the University of Chicago, graduating in 2019. Since joining Susquehanna, Drew has spent six years in the equity options group, where he has helped build trading signals and backtest infrastructure. His work now lies in leveraging large high-frequency data as a source of trading signals, with modern deep learning techniques a primary line of inquiry.
Ross is a Quantitative Researcher on the equities desk at Susquehanna International Group. He earned his Ph.D. from Rutgers University, where he studied combinatorics and probability, followed by a postdoctoral appointment at Yale. His academic work involved probabilistic techniques and limit theorems for combinatorial random variables. Ross applies machine learning to model complex and otherwise analytically intractable phenomena in financial markets, using techniques ranging from linear models to deep neural networks.
Yuejiao is a Quantitative Researcher at Susquehanna International Group, where she focuses on extracting signals from high-dimensional, noisy data using statistical learning and principled modeling. She received her Ph.D. in Applied Mathematics from UCLA and a bachelor’s degree in Mathematical Science from Peking University. Her research in bilevel optimization and federated learning has been published in top venues including NeurIPS, AISTATS, ICASSP, and Mathematical Programming. She is also a recipient of the Best Student Paper Award at ICASSP.
Chi-Ting is a Quantitative Researcher at Susquehanna International Group. He earned his Ph.D. in Astrophysics from Ludwig-Maximilians-Universität Munich and previously held postdoctoral positions at Stony Brook University and Brookhaven National Laboratory. Chi-Ting uses machine learning to model intraday equity price movements and search for new trading signals. His background in astrophysics involved combining theory, simulation, and observational data to understand the structure of the universe.
Mike is a senior strategic advisor at Susquehanna International Group whose career has spanned trading, risk management, mentorship, and firm-wide education. He earned his degree in Math and Finance from MIT and is a CFA Charterholder. Mike focuses on long-term value creation and the development of scalable ideas and talent across trading, research, and technology. He plays a central role in the continuing education of Susquehanna professionals, including those working in ML and systematic trading.
Jake is a Systems Engineer at Susquehanna International Group, where he focuses on optimizing performance across both computationally intensive workloads and low-latency trading systems. He studied Electrical Engineering at Drexel University, where he learned to break down complex hardware problems into practical, scalable solutions. Outside of work, Jake built a custom livestreaming system for endurance racing that runs entirely over cellular networks—operating reliably for 9 hours straight in all conditions. He’s always up for talking about storage systems, Jupyter setups, Factorio optimization, or the latest hardware bug that’s still haunting you.
Theo is a Software Engineer at Susquehanna International Group, where he designs trader interfaces and visualization tools for interacting with predictive models. He holds a B.A. in Computer Science and English Literature from Swarthmore College. Theo has built chess-playing AIs and enjoys exploring game AI in his spare time.
Kristy is a Quantitative Recruiter at Susquehanna International Group where she is focused on Machine Learning, Quantitative Research, and Systematic Trading hiring. She holds a B.S. in Linguistics from the Massachusetts Institute of Technology. Kristy is a past silver medalist in the Math Prize for Girls Olympiad and a U.S.A. Mathematical Olympiad (USAMO) qualifier.
Mike leads experienced hiring strategy at Susquehanna International Group for teams across Machine Learning, Quantitative Research, Systematic and Semi-Systematic Trading, and Front Office Development. He focuses on identifying and recruiting top technical talent to support Susquehanna’s dynamic and evolving trading strategies. Mike’s work is centered on building strong partnerships with candidates and hiring managers to shape high-impact teams at the forefront of quantitative finance and technology.
Kelly leads campus hiring strategy for teams across Machine Learning, Quantitative Research, and Systematic Trading at Susquehanna International Group. She holds a Bachelor of Science in Psychology from the University of Pennsylvania. Kelly’s work focuses on aligning talent acquisition with the firm’s technical evolution, helping shape high-impact teams working on cutting-edge research and model-driven trading.
Stop by Booth #325 to meet our machine learning + systematic trading experts. While you’re there, play our trading game, pick up giveaways, and grab a cup of coffee on us.
Apply to be considered for an expedited interview process with Susquehanna
