If you missed the deadline for submitting your talk for The Fifth Elephant 2019 – to be held in Bangalore on 25 and 26 July – you can propose a talk here.
We are accepting talks on:
- Data engineering – engineering and architecture approaches; problems that teams were attempting to solve (and therefore the solutions that they built).
- ML engineering – engineering and architecture approaches; problems that teams were attempting to solve (and therefore the solutions that they built).
- Data science – and its applications in diverse domains.
- Open source algorithms
- Data privacy and its solutions in technology; engineering implementations of HIPPA compliance, GDPR and other data protection frameworks.
- Data security – standards, approaches to solving data security, challenges and problems to solve for data security at scale.
- Business intelligence – how non-technical teams are accessing data in companies to mine intelligence; approaches to BI; real-life case studies and applications of BI; what counts as business intelligence for businesses.
- Decision science.
Perks for submitting proposals:
Submitting a proposal, especially with our process, is hard work. We appreciate your effort.
We offer one conference ticket at discounted price to each proposer.
We only accept one speaker per talk. This is non-negotiable. Workshops may have more than one instructor.
In case of proposals where more than one person has been mentioned as collaborator, we offer the discounted ticket and t-shirt only to the person with who the editorial team corresponded directly during the evaluation process.
The first filter for a proposal is whether the technology or solution you are referring to is open source or not. The following criteria apply for closed source talks:
- If the technology or solution is proprietary, and you want to speak about your proprietary solution to make a pitch to the audience, you should pick up a sponsored session. This involves paying for the speaking slot. Write to email@example.com
- If the technology or solution is in the process of being open sourced, we will consider the talk only if the solution is open sourced at least three months before the conference.
- If your solution is closed source, you should consider proposing a talk explaining why you built it in the first place; what options did you consider (business-wise and technology-wise) before making the decision to develop the solution; or, what is your specific use case that left you without existing options and necessitated creating the in-house solution.
The criteria for selecting proposals, in the order of importance, are:
- Key insight or takeaway: what can you share with participants that will help them in their work and in thinking about the ML, big data and data science problem space?
- Structure of the talk and flow of content: a detailed outline – either as mindmap or draft slides or textual description – will help us understand the focus of the talk, and the clarity of your thought process.
- Ability to communicate succinctly, and how you engage with the audience. You must submit link to a two-minute preview video explaining what your talk is about, and what is the key takeaway for the audience.
No one submits the perfect proposal in the first instance. We therefore encourage you to:
- Submit your proposal early so that we have more time to iterate if the proposal has potential.
- Talk to us on our community Slack channel: https://friends.hasgeek.com if you want to discuss an idea for your proposal, and need help / advice on how to structure it. Head over to the link to request an invite and join #fifthel.
Our editorial team helps potential speakers in honing their speaking skills, fine tuning and rehearsing content at least twice - before the main conference - and sharpening the focus of talks.
How to submit a proposal (and increase your chances of getting selected):
The following guidelines will help you in submitting a proposal:
- Focus on why, not how. Explain to participants why you made a business or engineering decision, or why you chose a particular approach to solving your problem.
- The journey is more important than the solution you may want to explain. We are interested in the journey, not the outcome alone. Share as much detail as possible about how you solved the problem. Glossing over details does not help participants grasp real insights.
- Focus on what participants from other domains can learn/abstract from your journey / solution. Refer to these talks from The Fifth Elephant 2017, which participants liked most: http://hsgk.in/2uvYKI9 and http://hsgk.in/2ufhbWb
- We do not accept how-to talks unless they demonstrate latest technology. If you are demonstrating new tech, show enough to motivate participants to explore the technology later. Refer to talks such as this: http://hsgk.in/2vDpag4 and http://hsgk.in/2varOqt to structure your proposal.
- Similarly, we don’t accept talks on topics that have already been covered in the previous editions. If you are unsure about whether your proposal falls in this category, drop an email to: firstname.lastname@example.org
- Content that can be read off the internet does not interest us. Our participants are keen to listen to use cases and experience stories that will help them in their practice.
To summarize, we do not accept talks that gloss over details or try to deliver high-level knowledge without covering depth. Talks have to be backed with real insights and experiences for the content to be useful to participants.
Passes and honorarium for speakers:
We pay an honorarium of Rs. 3,000 to each speaker and workshop instructor at the end of their talk/workshop. Confirmed speakers and instructors also get a pass to the conference and networking dinner. We do not provide free passes for speakers’ colleagues and spouses.
Travel grants for outstation speakers:
Travel grants are available for international and domestic speakers. We evaluate each case on its merits, giving preference to women, people of non-binary gender, and Africans. If you require a grant, request it when you submit your proposal in the field where you add your location. The Fifth Elephant is funded through ticket purchases and sponsorships; travel grant budgets vary.
You must submit the following details along with your proposal, or within 10 days of submission:
- Draft slides, mind map or a textual description detailing the structure and content of your talk.
- Link to a self-recorded, two-minute preview video, where you explain what your talk is about, and the key takeaways for participants. This preview video helps conference editors understand the lucidity of your thoughts and how invested you are in presenting insights beyond the solution you have built, or your use case. Please note that the preview video should be submitted irrespective of whether you have spoken at past editions of The Fifth Elephant.
- If you submit a workshop proposal, you must specify the target audience for your workshop; duration; number of participants you can accommodate; pre-requisites for the workshop; link to GitHub repositories and a document showing the full workshop plan.
Data Science for the discretionary managers: Lessons from a 60 trillion$ traditional industry resistant to change and facing the quant threat
Investment management is a 60 Trillion$ industry, and despite the recent advancements in data science and machine learning, still remains fairly discretionary. Untill recently, less 20% of the funds called themselves quantitative.
However, there is an absolutely massive transformation taking place right now within the discretionary investment management industry. Quantitative and systematic strategies have produced far more consistent returns over the last few years and investment assets are flowing out of discretionary funds at an alarming rate.
Discretionary managers have finally woken up, and are now scrambling to understand what’s taking place and how they must change in relation to it. Many will not survive the shift. Others, who move quickly and efficiently towards building quantitative processes will take advantage and be better off for it. The key to make success will lie in building the right infrastructure, hiring for the correct roles and have cross functional support.
We will dive into the following themes and how institutional managers can begin to effectively redirect themselves:
- Investors are finally aware of asymmetric risk they were taking on with active discretionary mutual funds and hedge funds.
- Most classic systematic strategies based on price; volume and fundamentals have been arbitraged out and there is now an arms race to build new strategies with new data sets.
- Discretionary managers are scrambling to incorporate new data sets, but lack the understanding of how to analyze their efficacy and more importantly, how to incorporate them into their discretionary trading processes.
- The organizational structure of discretionary management teams along with the type of people they hire is broken and outdated for today’s challenges.
- Companies cannot just hire a bunch of data scientists, tell them to work with 50-year-old fund managers with MBAs and hope that magic will ensue. The two sides simple do not speak the same language
- Fund managers aren’t educated as to how all of this works and often distrust the data and signals coming out of the process
- Building the right infrastructure will remain pertinent to surviving this shift
- Firms should invest in a centralized infrastructure capable of acquiring new data sets, doing basic descriptive work and making it available and reliable enough to have PMs use across the firms.
- In addition, firms need to build cross functional teams on the PM’s desk, customized to their style and support them with the data and infrastructure team at the top. Fighting over centralized quantitative research capacity with other PMs will lead to a disaster.
- Explainability is the key! Firms need to ensure there is a role/process in place that has a deep understanding of the PMs process so that they can work in coordination and also a strong understanding of quant processes to bridge the gap.
- Finally, PMs need to upskill themselves with a basic understanding, in order to effectively communicate and run their teams. If they aren’t educated as to how all of this works, they are never going to trust the signals coming out of the process when the time comes to make buy and sell decisions?
I am the CEO/founder of Auquan. I have 8+ years of global experience in finance with Deutsche Bank in Mumbai/New York and as a derivatives trader with Optiver in Chicago, where I was the first (and only) female trader in the company! At Optiver, I traded volatility arbitrage strategies and was involved first hand in making the shift from discretionary to automated trading. Since 2017, I have been employing new and cutting edge ML and Deep Learning techniques at Auquan to solve financial prediction problems for hedge funds and asset managers.